Regulatory frameworks are shifting across multiple jurisdictions simultaneously. Energy costs, labor markets, and customer expectations are each moving in their own direction, often in direct conflict.

The question I am most often asked, across industries and geographies, is a version of the same thing: how do we build an organization that can absorb continuous turbulence without losing operational coherence?

My answer is increasingly the same: you cannot manage permanent disruption reactively. You build systems that can anticipate, adapt, and act鈥攁utonomously, at scale, and in real time. That is what business AI, properly embedded into an organization’s digital core, now makes possible. And the clearest evidence I can offer comes from our customers, many of whom shared their incredible AI innovation journeys at our recent flagship 麻豆原创 Sapphire events.

Data is the foundation, not an afterthought

Ericsson’s journey is instructive precisely because the company confronted a truth that many organizations are still resisting. As Esra Kocat眉rk Norell, vice president of Customer Experience and Enterprise IT at Ericsson, put it directly: “Once you scale AI, it stops being an AI problem and becomes a data problem.”

That insight drove a deliberate investment in a unified business data fabric built with 麻豆原创 Business Data Cloud, a governed architecture that allows data to remain in place while centrally managing business semantics, governance, and lifecycle policies.

More than 85,000 users are now live on the unified AI platform Joule, with Ericsson moving confidently from experimentation to enterprise-wide execution. The company is advancing on two parallel fronts: modernizing its ERP backbone through RISE with 麻豆原创 while simultaneously unlocking AI-driven value in decision-making, efficiency, and new business models.

What Ericsson demonstrates is that the path to trusted, repeatable AI runs through data governance, and that building that foundation early is a strategic advantage, not a cost.

From the digital core to the physical world

If Ericsson illustrates what AI transformation looks like at the level of data architecture, Martur Fompak International, a global leader in automotive seating and interior systems, shows what it looks like on the shop floor. The company has deployed an autonomous intralogistics model enabled by Joule and embodied AI capabilities from 麻豆原创, working with robotics partner Humanoid to integrate AI-powered robots directly into live manufacturing operations.

The system connects production signals and business context to autonomous physical execution. Guided by material data, storage locations, sequencing, and production priorities, humanoid robots now execute material flows across the manufacturing environment鈥攊dentifying, transporting, and delivering materials to the line while continuously confirming back into 麻豆原创 systems.

The logic is about “combining cognitive autonomy with physical automation,” 脰zlem Alt谋n谋艧谋k, Group Intelligent Technologies director at Martur Fompak International, described it, to “transform execution, accelerate decisions, and scale intelligent enterprise capabilities across the organization.”

Early results show increased throughput and fewer errors, with a future target of up to five times greater work efficiency set for mass production. Martur Fompak International was the sole winner in the AI Excellence category at the 2026 麻豆原创 Innovation Awards, recognition not just of the technology, but of the willingness to reimagine factory environments.

Speed, scale, and the intelligent platform

Prysmian, the global cable solutions leader with 鈧�20 billion in revenue and operations spanning more than 50 countries, took a different but equally decisive path. The company completed its evolution to an AI-ready cloud platform through RISE with 麻豆原创 in just four months, then used that foundation to pursue more than 100 AI use cases across its business. The results are measurable: 70% automation of repetitive activities, an 80% reduction in implementation time for new solutions, and 50% acceleration in time-to-market for new products.

What strikes me about Prysmian’s journey is how it reframes the role of enterprise technology. Giovanni Cauteruccio, group CIO and digital officer at Prysmian, described embedded AI as “a key differentiator, enabling us to accelerate solution deployment and strengthen AI skills and culture across the organization.”

In other words, the platform is not simply a system of record, but a capability-building engine that makes the organization smarter over time.

Architecture of agility

Viewed together, these three stories point toward something larger than the sum of their parts. The Autonomous Enterprise is not a distant aspiration. It is being built now, by organizations that have made a deliberate commitment to embedding AI into their operational core as a fundamental design principle instead of a feature.

What makes this possible is the convergence of a governed data foundation, an intelligent ERP platform, and AI capabilities that extend from the desktop to the production line to the supply chain. When these elements are properly integrated, organizations gain something that no amount of reactive management can provide: the ability to sense, decide, and act faster than disruption can mount.

Uncertainty is not going away. The organizations that will navigate it best are the ones building systems today that turn volatility into signal and signal into advantage.

Manos Raptopoulos is global president of Customer Success Europe, APAC, Middle East & Africa and a member of the Extended Board of 麻豆原创 SE.

The Netherlands-based, family-owned maritime group has been building vessels since 1927 and today operates internationally with more than 12,500 people and over 35 yards across six continents. With an ambition to become the world鈥檚 most sustainable maritime solution provider, Damen is combining its long-standing craftsmanship with innovation, digitalization, and operational excellence.

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After investing heavily in 麻豆原创 S/4HANA as its core ERP foundation, Damen is now focused on applying 麻豆原创 Business AI to extend the impact of that platform.

鈥淲e really would like to harvest the business value of that initial investment,鈥� said Han Coenraad, product manager ERP at Damen. 鈥淲e use AI, especially in our operational processes, to get things running more smoothly and support more data-driven decision-making.鈥�

Damen has enabled 麻豆原创 Joule for Consultants and 麻豆原创 Joule for Developers and is exploring conversational capabilities that allow employees to interact with 麻豆原创 systems and processes using natural language.

Looking ahead, Damen sees opportunities to use embedded and custom AI use cases to improve data quality, enhance operational processes, and help teams work faster and closer to customers.

One example is using AI to support parts sales engineers by reading customer documents from email and automatically transferring the right information into sales orders. The goal, Coenraad explained, is to 鈥渂uild vessels sooner, with lower costs and more customer satisfaction.鈥�

Inspired by 麻豆原创 Sapphire and the vision of the Autonomous Enterprise, Damen is ready for the next step: 鈥淭he strategy shift from 麻豆原创鈥攚e are really looking forward to it and we really embrace it. Let鈥檚 make that happen.鈥�

This year at 麻豆原创 Sapphire, we shared our vision for the Autonomous Enterprise — the next evolution of how businesses run, where AI agents execute critical workflows so people can focus on innovation, customer value, and business growth.

This vision comes to life through a reimagined Joule Work, evolving from an AI assistant into the central workspace for enterprise AI. We also introduced the 麻豆原创 Autonomous Suite, bringing AI agents and assistants across core business functions to execute complex workflows with human oversight. With 麻豆原创 Business AI Platform, customers and partners can build, manage, and govern AI agents. And by expanding Industry AI, we’re delivering AI grounded in deep business context and domain expertise to solve industry-specific challenges.

Customers are already benefiting. IT division, Bosch Digital, integrated 麻豆原创 Joule for Developers directly into their coding workflows. Developers saw a 20% increase in productivity using Joule to automate routine coding tasks and optimize code. Joule also generates test cases, speeding up unit testing by 15% to 20% and freeing senior developers for high-value tasks. , the country鈥檚 leading airport operator, defines safety thresholds, service levels, and playbooks, and its agent, Smart Network for Operative Winter (SNOW), executes them. The SNOW agent is a winter operations system that integrates real-time weather, runway, and operations/maintenance data to automatically orchestrate work at Patagonian airports. The agent has improved runway safety, cut direct costs by 16%, and reduced administrative effort by 90%.

built a tool using  so its clients can better handle international tax rules by developing and managing their own custom AI agents and solutions. This way, PwC鈥檚 clients can focus on strategy while AI handles tax. PwC鈥檚 tool helped one pharmaceutical company handle VAT on international transfers 60% more efficiently.

Another customer, , a global fashion retailer, used an AI agent, built on 麻豆原创 Joule, to cut HR process cycle times by 40% to 60%. The agent helps employees quickly handle HR transactions, such as leave requests and payroll queries, through natural language conversations. Reducing time spent on administrative tasks allows HR teams to focus on strategic talent management. These are just some of the customers getting value. There are many more.

Now let鈥檚 dive into the releases from Q2 2026.

Please note that this article covers only AI offerings released from April 1, 2026, to June 30, 2026.

Joule

Joule Work
麻豆原创 Early Adopter Care program (registrations closed)

redefines how people interact with and execute end-to-end business processes. As the user engagement component of the Joule solution, it moves the user experience beyond fragmented, transactional interfaces toward a unified, intelligent way of working across 麻豆原创 and non-麻豆原创 systems. Its dynamic workspace adapts to users’ intent, helping them focus on outcomes rather than spending time finding information. And because it can delegate execution to AI, users will no longer need to coordinate work across multiple application interfaces manually.

Joule Work will allow users to express in natural language what they want to accomplish, triggering to coordinate teams of Joule Agents that will surface the right insights and automate routine work across business domains and systems to achieve the goal. This happens in intent-driven, adaptive workspaces built in real time that keep teams focused on driving decisions and impact. Joule Work can help reduce manual handoffs, shorten cycle times, and enable teams to turn decisions into actions faster. A key function of Joule Work is to connect users with Joule Assistants, which are like smart teammates organized by function. These assistants use context to intuit people鈥檚 intent and act by coordinating the appropriate Joule Agents across the business. Joule Assistants understand organizations deeply and can automate complex tasks within and across functions, freeing employees to address more strategic work.

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Joule Work mobile app
General availability

Employees who use 麻豆原创 on the go can rely on the Joule Work mobile app to interact with 麻豆原创 applications in natural language on their smartphones or tablets. Joule is integrated directly into the app, so a simple chat can surface the latest figures, help complete approvals or maintenance tasks, and support work across areas such as sales, HR, and supply chain processes without having to navigate multiple mobile apps. On iPhone and iPad, users can even start by saying 鈥淗ey Siri, ask Joule in Joule Work,鈥� then speak their question, which is passed straight to Joule for a response. This gives organizations a single, mobile-enabled entry point to Joule capabilities and lets employees gain insights and act on tasks across their 麻豆原创 solutions using everyday language.

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Voice for Joule
麻豆原创 Early Adopter Care program (registrations closed)

A new partnership with LiveKit delivers intelligent voice for Joule, extending the experience beyond the keyboard and into settings where work happens. This partnership helps advance 麻豆原创’s vision for the Autonomous Enterprise. With LiveKit, 麻豆原创 customers can use real-time voice capabilities in Joule and access reliable, always-on conversational AI. This brings voice AI to a full range of roles, devices, and environments, putting Joule within reach of employees whose work is done away from a keyboard.

Enhancements for Joule
Multi-system support for 麻豆原创 S/4HANA Cloud Editions

Joule now supports connecting multiple 麻豆原创 S/4HANA Cloud Private Edition systems or clients and multiple 麻豆原创 S/4HANA Cloud Public Edition systems within a single Joule formation.

Work seamlessly across different 麻豆原创 S/4HANA environments through one unified Joule interface, increasing flexibility and efficiency for organizations operating multiple systems. Administrators enable this feature by configuring system-specific destinations with naming conventions, including additional systems in the Joule formation via System Landscape, and mapping system identifiers in the Joule Admin Center.

Developers can build custom capabilities that leverage data and functionality from multiple back-end systems. Business users access and execute processes across all connected systems naturally within their workflow.

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Autonomous SCM

麻豆原创 Digital Manufacturing, AI-assisted production engineering
General availability

Production engineers can analyze error logs to identify root causes and generate resolution instructions for production processes using 麻豆原创 Digital Manufacturing. The feature also enables engineers to extend production processes via script tasks generated based on natural language input.

Organizations can reduce error analysis time for production process errors by 20%, reduce error analysis time for connectivity errors by 20%, and cut the time to handle a production process or connectivity error from 4.5 to 3.6 hours — while improving operating time from 92% to 92.92%.

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麻豆原创 Digital Manufacturing, AI-assisted description enhancement
General availability

Quality managers can generate clearer and more structured initial descriptions of complex issues using 麻豆原创 Digital Manufacturing. By reducing bias and subjective language, a more balanced and factual representation of the problem at hand is created. Users can also refine and rephrase initial rough descriptions, facilitating more effective follow-up and thorough investigation, and translate descriptions into different languages.

This offers organizations an up to five percent improvement in the efficiency of quality engineers during issue handling and resolution, and an up to 10% reduction in errors during problem handling.

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Autonomous Finance

Project Billing Price Verification Agent
Beta release

Billing specialists can quickly identify mismatches between agreed prices and billing amounts using the Project Billing Price Verification Agent in the manage project billing application of 麻豆原创 S/4HANA Cloud Public Edition.

The agent identifies the relevant contracts and statements of work for the related customer project, extracts key pricing data, and compares them with the values in the project billing request. It highlights discrepancies, provides context, and suggests corrective actions.

Organizations can reduce time spent resolving price discrepancies by 75%, cut revenue leakage from undetected incorrect billing by 75%, and improve cash flow while reducing days sales outstanding by fewer billing cycle delays.

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麻豆原创 S/4HANA Cloud Public Edition, AI-assisted localized business data management
General availability

Accounts receivable managers can access and explore localized financial and logistics data through Joule using natural language, without leaving their daily workflows. The capability enables users to run complex reports using natural language instead of manual selection screens, and instantly filter, navigate, and explore results with AI-supported context awareness. This way, finance teams can reduce the amount of training effort required and increase productivity and confidence across the organization.

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Autonomous Spend

Expense Automation Agent
General availability

Expense Automation Agent helps employees who submit business trip expenses by creating a first draft of their expense reports. It automatically collects and adds transactions, fills in relevant fields using contextual information and past behavior, and lets employees quickly review and adjust before submission. Customers can reduce manual data entry, shorten report completion time by up to 30%, and allow employees to focus more on their core work.

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麻豆原创 Ariba Contracts, AI-assisted contract creation from documents and prompts
General availability

Contract managers who create and manage large volumes of agreements can use AI鈥慳ssisted contract creation from prompts to start new contracts more efficiently. The feature lets users enter a simple natural-language prompt directly in the contract creation flow, then proposes contract header fields in seconds for review and confirmation before finalization. Organizations can reduce the effort required to initiate contracts, provide a guided in鈥慶ontext experience, and build a scalable foundation for future AI capabilities while maintaining clear human oversight of each contract.

麻豆原创 Fieldglass, AI-assisted SOW worker role recommendations
General availability

Procurement specialists who manage statements of work can now define suitable worker roles more quickly. This feature applies generative AI to the SOW context, including scope, outcomes, and timelines, to propose relevant roles that users can review and refine. Organizations benefit from faster, more consistent SOW authoring, improved fit鈥憈o鈥憇cope, and clearer, better-governed worker role definitions.

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麻豆原创 Ariba Invoicing, AI-assisted multi-model data extraction
General availability

Procurement and accounts payable teams working in 麻豆原创 Ariba Invoicing can rely on multi鈥憁odel data extraction to capture invoice information more accurately. The feature leverages the latest large language models in the content extraction service to interpret and extract key invoice data, enabling a smoother capture process. Organizations gain a more reliable and efficient invoice processing experience, with improved data quality that helps reduce manual corrections and downstream errors.

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Concur Travel, AI-assisted policy rule generator
麻豆原创 Early Adopter Care

Travel program administrators who manage Concur Travel policies can set up and adjust travel rules more efficiently with the policy rule generator. By pasting existing policy text into an AI-based rule generator, they can automatically produce multiple rule classes and rules in a single step, then apply them via a guided wizard. Organizations save time on policy implementation, reduce configuration errors, and promote more consistent, compliant travel policies across their programs.

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Autonomous CX

Order Reliability Agent
Beta release

The Order Reliability Agent helps customer service and order management teams stay on top of order issues consistently. The agent continuously monitors orders in 麻豆原创 Order Management Services, detects risks such as failures or delays, and either takes automated corrective action or presents clear recommendations and root-cause insights for staff to review. Companies can cut the time spent analyzing and handling exceptional orders by around half. The agent can also reduce customer churn related to fulfillment problems by about 20%, helping create a more reliable order experience.

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麻豆原创 Revenue Growth Management, AI-assisted trade promotion creation
General availability

Key account managers who plan trade promotions in 麻豆原创 Revenue Growth Management can set up promotions more quickly. When they enter a promotion name in the relevant account context, the system proposes key details such as dates, promotion type, duration, and sell鈥慽n timing based on master data, historical promotions, and past user edits. Organizations can shorten promotion setup time by up to 75% and reduce data鈥慹ntry errors and rework by around 30%, improving both efficiency and consistency in promotion planning.

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麻豆原创 Revenue Growth Management, AI-assisted deal sheet generation
General availability

Key account managers can quickly turn promotion data into retailer-ready deal sheets. Starting from a single promotion, the feature fills in system-of-record fields, applies appropriate PDF or Excel templates, and checks that required information is present before the document is created. This helps organizations produce consistent, audit-ready deal sheets in seconds, reduce formatting and data-entry errors, and give account teams more time to focus on customer negotiations rather than document preparation.

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Joule with 麻豆原创 Order Management Services
Beta release

Operations managers and order management teams using 麻豆原创 Order Management Services can rely on Joule to handle everyday operational questions and tasks through simple natural language. By enabling conversational access to key data and actions across areas such as order processing, orchestration, sourcing, availability, returns, and flows, Joule provides real-time, role-aware guidance directly in the flow of work. Organizations benefit from faster access to relevant transactions and insights, can act earlier to prevent issues from escalating, and support smarter, more timely decisions that save both time and operational cost.

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麻豆原创 Engagement Cloud, email campaign duplication
麻豆原创 Early Adopter Care program

Marketing teams can duplicate existing email campaigns to speed up everyday execution. When a marketer copies a previous campaign, email campaign duplication carries over layout, branding, and technical settings, so they only need to update content such as copy or offers. This helps organizations reduce campaign setup time, keep branding and formatting consistent, and limit repetitive configuration work and related errors across channels.

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麻豆原创 Business AI Platform

Build

Joule Studio
麻豆原创 Early Adopter Care

The new Joule Studio gives product teams, architects, and developers a single place to extend, build, and integrate AI experiences from business intent through to production-ready solutions. It starts from the outcomes you want to achieve, uses your own processes and data for context, and connects out of the box across your application landscape. At the same time, it can generate product requirements and technical specifications from your company-specific context, apply eval-based, data-driven guardrails to AI coding assistants under 麻豆原创-managed enterprise controls, and remain open so you can work with third-party or 麻豆原创 models in the development environment that fits your needs.

There will be a migration path from the original Joule Studio to the new version to help customers transition without disruption.

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麻豆原创 S/4HANA custom code migration agent
General availability

ABAP developers and migration teams moving from 麻豆原创 ECC to 麻豆原创 S/4HANA can use the 麻豆原创 S/4HANA custom code migration agent to automate the complex, time-consuming process of migrating custom ABAP code from 麻豆原创 ECC to 麻豆原创 S/4HANA. Delivered as an agentic AI capability in 麻豆原创 Joule for Developers, ABAP AI, the agent runs 麻豆原创 S/4HANA readiness checks via ABAP test cockpit across entire custom code packages, interprets the findings, categorizes issues, and applies a mix of deterministic quick fixes and AI-based code changes with confidence scores, while recording all updates in transport requests for full traceability. High-confidence fixes are applied automatically, and lower-confidence proposals are added as comments for developer review, so teams retain control over final code quality while spending far less time on object-by-object analysis, freeing capacity for higher-value design decisions and overall migration governance.

and .

Contextualize and Reason

Generative AI hub, enhancements

The generative AI hub in the 麻豆原创 AI Core infrastructure integrates with hyperscaler-agnostic operations to improve accuracy and support enterprise-wide adoption of business AI.

Batch API enabling processing of high鈥憊olume foundational model (FM) workloads
Developers and platform teams working with 麻豆原创 AI Core can use the batch API to process high-volume foundational model workloads more efficiently. By submitting large collections of non-urgent AI requests as a single input file, they can run jobs asynchronously in the background. At the same time, 麻豆原创 AI Core writes results to an object store, ensuring real-time, fast, and responsive user experiences. This improves scalability for high-volume processing, simplifies the developer experience across different models and providers, and ensures fair, predictable throughput so that large jobs do not block smaller ones.

Inference observability service: centralized logging and feedback for generative AI workloads
Teams running generative AI workloads on 麻豆原创 AI Core can use the inference observability service to monitor, analyze, and systematically improve model outputs. The service centrally records prompts, responses, and key context with controlled overhead, lets developers and users rate the quality of each response, and attaches lightweight feedback. It also supports labels and filters so interactions can be easily discovered and exported as datasets for fine鈥憈uning, prompt engineering, and benchmarking. Organizations gain standardized transparency into prompt and response quality, reduce costs and effort by replacing ad hoc logging with a unified, compliant feedback channel, and accelerate iterative improvement using structured inference data stored in S3 or metadata-only mode, and managed via REST APIs for labels, feedback, and record retrieval.

Speech-to-speech
The availability of speech鈥憈o鈥憇peech (S2S) recognition helps agent and app developers build natural, end鈥憈o鈥慹nd voice experiences into 麻豆原创 applications.

New models available
New models are supported, including Gemini 3.1 Flash Lite, Claude Opus 4.7, GPT Realtime and Mistral Small, GPT 5.4, GPT 5.4-nano, and GPT 5.3-Codex.

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麻豆原创 Joule for Consultants, enhancements

Custom knowledge grounding connects 麻豆原创 Joule for Consultants to a separate grounding service that indexes organizational content via 麻豆原创 AI Core. It enables Joule to securely index and reference an organization鈥檚 methodologies, policies, templates, and delivery standards alongside 麻豆原创鈥慶urated knowledge. By grounding responses in company鈥憇pecific documentation, consultants can receive more accurate guidance that aligns with established ways of working across projects and engagements.

Expert workspace introduces personalized 鈥渆xperts鈥� that help tailor guidance for specific projects, domains, or workstreams. Context is retained across conversations, so users can switch between initiatives while preserving project-specific knowledge.

and .

麻豆原创 Document AI enhancements

Model selection
This new feature allows you to choose the large language model (LLM) used for document processing. The Default LLM reflects the best-performing model at any given time, while additional models such as Gemini 2.5 Flash and GPT-5 are also available. The list of supported models is updated frequently to ensure access to the latest advancements.

New standard document types
麻豆原创 Document AI workspace and OData V4 APIs now support three additional standard document types: learning certificate, order confirmation, and traffic violation notice. This expands the range of business documents that can be processed out of the box, reducing the need for custom configurations.

Configuration of document-level confidence ranges
麻豆原创 Document AI now supports configurable document-level confidence thresholds, making it easier to assess extraction quality at a glance. Custom confidence ranges 鈥� low, medium, and high 鈥� can be defined on the configurations tab of a schema version. When documents are processed, the overall confidence score is displayed in the document header with color-coding: red for low, orange for medium, and green for high confidence.

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麻豆原创 Domain Models

are AI models trained on 麻豆原创 domain knowledge, including code, data, metadata, business processes, architectural knowledge, and documentation. When customers initiate a query or want to create code, the models are designed to provide results firmly grounded in the 麻豆原创 context instead of relying on generic internet knowledge. Combined with context graphs and agents, the models bring deep 麻豆原创 knowledge to Joule, Joule Studio, Joule Agents, and 麻豆原创 applications.

麻豆原创 Domain Models will help:

• Create custom extensions in 麻豆原创 S/4HANA Cloud Public Edition and 麻豆原创 Ariba: Developers in Joule Studio can use specialized models for 麻豆原创 S/4HANA and 麻豆原创 Ariba to understand and generate clean core-compliant code from natural language.
• Query information in 麻豆原创 S/4HANA Cloud Public Edition and 麻豆原创 Ariba: Customers can use natural-language prompts in Joule to access customer data that is grounded in the underlying data models and the business context.

These capabilities will help create clean core extensions while preserving 麻豆原创 standards and governance. 麻豆原创 Domain Models are running under the hood of Joule and Joule Studio and are not directly exposed to customers.

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Govern

麻豆原创 AI Agent Hub enhancements

麻豆原创 AI Agent Hub gives organizations a single control pane for all AI agents, LLMs, and MCP servers across the enterprise. Featuring automated AI asset discovery across major platforms, including Microsoft, Google, AWS, and now ServiceNow and 麻豆原创 AI Core, alongside structured governance assessments and a verification badge that integrates directly with runtime solutions to control which agents and MCP servers are approved for use.

Looking ahead, we will expand into runtime observability and governance, identity and access control, agent-in-process mining, and workforce impact mapping to make the AI Agent Hub the central command center for AI governance at scale.

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Process Consulting Agent
General availability

Process owners, analysts, and operational leaders can turn process data into clear, practical insights without needing specialist analytics skills with the Process Consulting Agent. Users can ask questions in natural language, and the agent retrieves and analyzes relevant process information through a multi鈥慳gent system, returning structured findings along with suggested next steps. Organizations can cut the time spent searching complex data per artifact by up to 90% and reduce the effort to analyze, design, model, and monitor processes by around five percent, helping teams move more quickly from insight to action.

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Enterprise Content Research Agent
General availability

Enterprise architects and portfolio managers can quickly find and understand architectural information through the Enterprise Content Research Agent in 麻豆原创 LeanIX. By querying inventory data and related documentation across sources such as 麻豆原创 LeanIX, Confluence, and SharePoint, the agent highlights missing fields, supports gap analysis, and helps keep records complete and consistent, while leveraging MCP Server tools as needed. This reduces the time spent on informational searches and navigation, simplifies data management tasks, and supports stronger governance over architecture data across the landscape.

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WalkMe solutions, AI knowledge indexing
General availability

Digital adoption and enablement teams can use AI knowledge indexing to make internal documentation easily available to WalkMe鈥檚 AI services in a controlled way. The feature processes connected knowledge sources, such as web pages and files, extracts text content, and converts it into secure vector embeddings, enabling WalkMe鈥檚 contextual AI assistance to ground guidance in company policies, wikis, and procedures rather than generic models. Organizations can improve real-time compliance outcomes and see a 21% increase in procurement policy adherence, while giving employees faster, policy-aligned answers directly in their workflows.

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WalkMe solutions, AI knowledge referencing
General availability

Digital adoption teams and application owners receive app guidance that aligns with their company鈥檚 policies and standards with WalkMe鈥檚 AI knowledge referencing. When certain conditions are met, such as editing a specific field or completing a form, the feature retrieves relevant content from connected internal documentation so tools like AI SmartTips or chat can compare user input with best practices and provide tailored feedback. This allows organizations to anchor AI assistance in trusted company information and help employees access the right policy or governance details when needed.

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WalkMe solutions, pinned AI
General availability

Operations, HR, finance, and other business teams now benefit from on-screen assistance exactly where they enter data or complete tasks, using WalkMe鈥檚 pinned AI. By attaching AI smart tips to input fields and AI Launchers to specific elements, the feature provides contextual guidance in place, grounded in company knowledge sources, so users can continue their work without switching applications. Organizations can improve data quality across key forms and workflows, reduce errors and rework, and see measurable gains such as a 41% improvement in data quality.

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WalkMe solutions, on-demand AI
麻豆原创 Early Adopter Care program

Employees working across line-of-business applications can turn to WalkMe鈥檚 on-demand AI for quick answers or step-by-step support without leaving their current screen. Through a conversational in-app menu that travels with users across applications, they can ask questions, retrieve company knowledge, and trigger automations or Smart Walk-Thrus, keeping guidance and execution closely connected. Organizations benefit from faster access to trusted information and a measurable impact on quality, including up to a 41% reduction in time spent correcting ERP-related business tasks.

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麻豆原创 LeanIX solutions, AI-assisted enterprise architecture decision management
General availability

Enterprise architects and architecture review boards get faster, more consistent decisions with enterprise architecture decision management in 麻豆原创 LeanIX solutions. By providing context, such as transformation diagrams or landscape changes, they can ask the AI to generate a draft architecture decision entry that includes the relevant background, decision, and implications for stakeholders to review and approve. This reduces manual data extraction and authoring effort, streamlines collaboration on approvals, and helps ensure architecture decisions are documented and concluded in a timely, traceable way.

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麻豆原创 LeanIX solutions, AI-assisted fact sheet calculations
General availability

Enterprise architects and workspace admins using AI-assisted fact sheet field calculations in 麻豆原创 LeanIX solutions can quickly turn plain-language business rules into working calculations. When they describe the rule they need, the feature generates readable, commented code that is aware of their fact sheet types, fields, relations, and enums. Hence, calculations align with the actual workspace configuration. This helps teams move from a business question to a usable metric in minutes, increase self-service configuration, reduce reliance on JavaScript skills, and speed up the delivery and maintenance of calculated fields that downstream reports and views depend on.

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麻豆原创 LeanIX solutions, AI-assisted automation creation
General availability

Enterprise architects and workspace admins can transform plain-English workflow descriptions into working automations with AI-assisted automation creation in 麻豆原创 LeanIX solutions. When they describe the review, update, or governance flow they need, the feature generates the appropriate triggers, conditions, and actions with field mappings aligned to the current workspace configuration. This lets teams build and scale automations themselves, increasing EA productivity, reducing reliance on technical experts, and making it easier to keep key processes such as onboarding workflows and lifecycle checkpoints consistently automated.

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麻豆原创 Cloud ALM, AI-assisted document summary
General availability

Document summary helps project teams and engineers understand long documents more quickly. Within the documents application, users can trigger an AI-generated summary, review and edit it in a separate window, and then apply it as a persistent summary section in the document. This shortens the time spent manually reading and extracting key points, supports faster comprehension of complex engineering content, and enables quicker decisions without leaving the document workflow.

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麻豆原创 Signavio solutions, AI-assisted context analyzer, text-to-event matching, and sentiment analysis
General availability

The context analyzer helps process owners and analysts match free text with process objects, such as sales orders or purchase requisitions, to the corresponding process events in event logs. The feature links free-text records such as survey responses, feedback, comments, and tickets to the corresponding process events, so qualitative experience data appears alongside operational logs. This enriches process mining with unstructured text, reduces manual mapping work, and improves process analysis accuracy by around 30%, helping teams pinpoint bottlenecks and experience issues more effectively.

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Jonathan von Rueden is chief AI officer for 麻豆原创 SE.

*Disclaimer: This article provides estimated benefits. All calculations are estimates based on 麻豆原创 customer case studies, 麻豆原创 benchmarks, and other research. Actual benefits may vary and may be affected by additional factors not considered by this article. The information is provided 鈥渁s is鈥� without warranty of any kind, expressor implied, and in no event shall 麻豆原创 be liable for any damages whatsoever in relation with the use of this article. See Legal Notice on for use terms, disclaimers, disclosures, or restrictions related to this material.

When suppliers send order confirmations as PDF files, even minor discrepancies in price, quantity, or delivery dates can trigger significant manual effort within procurement. For Lemvigh鈥慚眉ller, one of Denmark鈥檚 largest wholesalers within steel, plumbing, heating and electrical products, this has long been a familiar challenge, consuming substantial time and resources.

The company has now tackled the very point where earlier automation initiatives often stalled. With a new solution based on several specialized AI agents, developed on 麻豆原创 technology and implemented in close collaboration with NTT DATA Business Solutions, supplier PDF order confirmations can now be read, interpreted, compared, and processed automatically鈥攄irectly against 麻豆原创 systems.

鈥淲e have previously tried both RPA and traditional automation approaches without really achieving the desired effect. The key difference this time is that we broke the task down into multiple independent AI agents, each responsible for a specific part of the process. Together, they now handle what previously required manual review,鈥� says Frederik Aakerlund, IT director at Lemvigh鈥慚眉ller.

10 weeks from idea to AI agents in production

The project originated with an e-mail from Jess Frederiksen, an AI鈥憇avvy project manager in Lemvigh鈥慚眉ller鈥檚 Market and Procurement organization. After successfully matching an order confirmation with a purchase order using ChatGPT as an experiment, he approached the IT director to explore whether this could be turned into a fully integrated system solution.

From the initial tests to production deployment, the entire project took just 10 weeks. According to Lemvigh鈥慚眉ller, this short implementation timeline was critical in allowing the solution to demonstrate tangible business value quickly and build internal support.

鈥淭his was not a long-running project. In 10 weeks, we moved from idea to AI agents in production, already delivering measurable value to our procurement officers,鈥� Aakerlund says.

Over time, Lemvigh鈥慚眉ller expects the solution to free up resources equivalent to three to four full-time employees. These resources will instead be redeployed to higher-value activities, including handling the most complex and exception鈥慸riven orders.

鈥淭he objective is not to reduce headcount, but to use our expertise more effectively. The AI agents take care of routine tasks, enabling procurement officers to focus on cases where their experience genuinely matters,鈥� Aakerlund adds.

More than 100,000 order confirmations automated

Each year, Lemvigh鈥慚眉ller sends approximately 175,000 purchase orders to more than 2,000 suppliers. While part of this volume is handled in a structured manner via EDI, around 60% of supplier order confirmations are still received as unstructured documents.

With the coordinated AI agents in place, the company can now automatically identify delays, quantity changes, and price discrepancies鈥攁nd respond significantly faster.

鈥淧reviously, when order confirmations were handled manually, it could take hours or even days before changes were reflected across the organization. Today, the AI agents update the data almost immediately, allowing customers to receive a much more accurate picture of deliveries far sooner,鈥� says Klaus Heinemann, head of 麻豆原创 ERP at Lemvigh鈥慚眉ller, who led the development together with the project team. 鈥淚n addition, we now identify price discrepancies before the final invoice is issued, saving time both for us and for our suppliers.鈥�

Multiple AI agents orchestrated in a single workflow

The solution is built around three cooperating AI agents, each with a clearly defined role in the process. One agent handles incoming e-mails and attachments, a second extracts and structures data from PDF documents, and a third compares the extracted information against purchase orders in 麻豆原创 to determine whether there is a match or a deviation.

As a result, complex and unstructured supplier data can be processed in a unified, automated workflow without requiring procurement officers to open and manually review lengthy PDF files.

鈥淲hat makes this solution robust is the interaction between the agents. Each agent is highly specialized, but they are orchestrated in a way that ensures the process flows seamlessly from start to finish,鈥� Heinemann explains.

During the project, the importance of master data quality also became increasingly clear.

鈥淚n areas such as Incoterms and other master data, we identified improvements that need to be addressed. This has been an important learning not just for this initiative, but for our broader work with AI,鈥� he says.

While it is still too early to measure the full impact on customer experience, error rates, or claims, expectations are that faster and more precise handling of supplier confirmations will, over time, lead to fewer surprises and significantly improved delivery transparency. Internally, the solution has been met with strong interest and curiosity among employees.

鈥淧rocurement officers clearly recognize the value of being relieved from the most tedious routine work. This has sparked a constructive dialogue about how technology can best support their day鈥憈o鈥慸ay responsibilities,鈥� Heinemann says.

Business AI with a clear business outcome

According to Lemvigh鈥慚眉ller, the investment is expected to deliver a return within a relatively short timeframe.

鈥淲e are talking about quarters rather than years when it comes to ROI. That is why it was essential for us to get the solution into production quickly and focus on processes with a clear and measurable impact,鈥� Aakerlund says.

For 麻豆原创, the project serves as a concrete example of how artificial intelligence can be embedded directly into core business processes rather than remaining a disconnected experiment.

鈥淢any companies talk about AI agents primarily in terms of automation. Lemvigh鈥慚眉ller demonstrates that the real challenge鈥攁nd the real opportunity鈥攍ies in coordination,鈥� says David Pontoppidan, head of AI at 麻豆原创 for the Nordics and Baltics. 鈥淚t is the orchestration of three specialized agents directly within the core process that makes this solution robust. This is also where many multi鈥慳gent initiatives fail, not due to limitations of individual agents but because of insufficient coordination. Lemvigh鈥慚眉ller has succeeded by anchoring the solution in its 麻豆原创 landscape, where data, business rules, and governance frameworks are already firmly established.鈥�

He continues: 鈥淚nnovation is not about company size. Lemvigh鈥慚眉ller shows that a Danish organization with short decision paths and a pragmatic approach to technology can move faster than many large global enterprises that are still in the planning stage. Ten weeks from idea to production is far from the norm, but perhaps it should be.鈥�

Designed for operations and scalability

The solution was implemented in close collaboration with NTT DATA Business Solutions, which was responsible for making the solution production鈥憆eady and fully integrated into Lemvigh鈥慚眉ller鈥檚 麻豆原创 landscape.

鈥淏y distributing responsibilities across multiple AI agents, Lemvigh鈥慚眉ller has been able to automate a complex process without losing transparency or control. This has enabled a fast and secure transition from pilot to production and ensures a more robust solution that can easily be expanded as new requirements emerge,鈥� says Kristian Dahl, 麻豆原创 UX manager at NTT DATA Business Solutions.

According to Dahl, the modular, agent鈥慴ased architecture was a key enabler in moving efficiently from proof of concept to live operation.

First step in a broader AI agent strategy

Initially, the AI agents have been deployed for selected supplier inboxes and business areas. However, Lemvigh鈥慚眉ller already sees significant potential in applying the same agent鈥慴ased approach across additional administrative processes.

鈥淭his is the first AI agent solution we have put into production. The experience has given us the confidence to consider similar approaches across other areas, including invoice processing and order management,鈥� Aakerlund concludes.

Ellen Vig Nelausen is a Nordic Integrated Communications Expert at 麻豆原创.

is a site where potential and existing 麻豆原创 customers can explore 麻豆原创 Business AI solutions, access pre-built applications and content, and discover use cases, services, reference architectures, and best practices鈥攁ll free of charge.

The self-service portal offers opportunities to not only explore solutions but get a feel for how 麻豆原创 technology could work in your organization. Cost and ROI estimator tools prompt users to input information unique to their organization鈥攃ustomizations, hyperscalers, and number of users, for example鈥攁nd get tangible, personalized use cases to bring back to the business. Maturity assessments use details about an organization鈥檚 current platform strategy and architecture landscape to guide users on next steps to achieve the Autonomous Enterprise.

The site also includes missions that provide step-by-step guidance on how to materialize 麻豆原创 solutions, including what solutions and services may be needed, what the architecture can look like, how other customers are deriving value, and more. For AI specifically, there are almost 400 features and agents available for customers to explore in the 麻豆原创 Business AI Catalog.

At 麻豆原创 Sapphire Orlando, two customers using 麻豆原创 Discovery Center shared the value they鈥檙e getting from the site and how it鈥檚 guiding their approach to digital transformation.

Agilent

is a global leader in life sciences, diagnostics, and applied chemical markets, producing and supplying analytical laboratory instruments, software, consumables, and services. Looking to avoid manual analysis and late detection in tariff and compliance changes, Agilent turned to 麻豆原创 Discovery Center. 鈥淲e have one core principle: do not solve the problem that has already been solved,鈥� Manthan Peshne, chief enterprise architect at Agilent Technologies, Inc., said. 麻豆原创 Discovery Center allowed Agilent to stay true to this principle.

鈥淲e started exploring 麻豆原创 Discovery Center and we found quite a few good building blocks, essentially in the form of missions,鈥� Peshne said. 鈥淲e also looked at some composable services which we could assemble together鈥t鈥檚 not a complete solution鈥ut we found a completely unrelated industry and use case that we could use.鈥� Using a mission set in the context of the oil and gas industry, the Agilent team was able to explore how an AI agent could interpret unstructured regulatory signals, extract the tariff context, determine its relevance, and convert that fragmented information into actionable alerts. Not only did Agilent find a solution to its problem, but the 麻豆原创 Discovery Center mission accelerated design thinking and development for the project.

鈥淲e also ended up with an enterprise pattern by building this solution, which gives us a platform where I could replicate this to other scenarios where I have external inputs or signals that we need to capture and put in the context of Agilent data,鈥� Peshne added.

Sutherland  

 is an AI-driven business transformation company that designs, runs, and automates enterprise operations at scale to help its clients achieve real, measurable business outcomes. A large part of this includes building and delivering production-grade agentic AI solutions on top of leading foundational models.

For Sutherland, 麻豆原创 Discovery Center has kick-started many projects. 鈥淚nstead of starting an MVP to see what the problem is and where to start, we have a ready-made solution from which we can pick up from,鈥� Amar Busireddy, 麻豆原创 enterprise architect at Sutherland Services, said. 鈥淚t gives us a start somewhere around 20%-30% depending on the scenario.鈥�

Having a starting point boosts confidence, Busireddy said, and enables Sutherland鈥檚 consultants to create solutions faster, which means faster time-to-value for its customers. Specifically, the missions available in 麻豆原创 Discovery Center have helped: 鈥淲e are using missions to kick-start educating our consultants and with implementing and helping our customers,鈥� Busireddy said. Missions around Joule are plentiful, including how to integrate the AI solution with 麻豆原创 SuccessFactors solutions, 麻豆原创 Ariba solutions, and 麻豆原创 S/4HANA.

鈥淭here are many reference architectures given for many problems, which might not be sufficient for us or might not fit our requirements 100%, but that definitely give us the direction on what to use and what not to use. From there, we can plan budget and time,鈥� he said.

麻豆原创 Discovery Center: The starting point

The experiences of Agilent and Sutherland show that 麻豆原创 Discovery Center is more than a resource hub鈥攊t is a catalyst for action. By offering practical guidance, reusable missions, reference architectures, and planning tools that help evaluate fit, cost, and value, the site enables organizations to accelerate innovation without starting from scratch. For organizations looking to turn AI ambition into actionable business outcomes, 麻豆原创 Discovery Center is the place to begin.

Partnership opens new pathways for enterprise data migration with 麻豆原创 AI-supported tooling complemented by Palantir鈥檚 AIP for data migration scenarios to simplify an expedite digital transformation for 麻豆原创 customers, with Accenture as a co-innovation partner for joint customers

This year at 麻豆原创 Sapphire, 麻豆原创 and Palantir announced an expansion of their strategic partnership focused on delivering new data migration capabilities designed to help enterprise customers succeed in the AI era and realize 麻豆原创鈥檚 vision of the Autonomous Enterprise.

The enhanced partnership is designed to facilitate joint customers鈥� cloud migrations, with the most complex data migration scenarios moving quickly, securely, and confidently through their business transformation journeys.

AI embedded across the migration life cycle

The expanded partnership builds on 麻豆原创鈥檚 broader agentic migration strategy, uniting 麻豆原创鈥檚 deep expertise in enterprise applications and 麻豆原创 Business AI with Palantir鈥檚 AIP to deliver AI-driven data migration capabilities that accelerate timelines, secure cost efficiencies, and fundamentally transform how organizations operate. 麻豆原创 customers can now leverage Palantir鈥檚 AIP for data migration scenarios alongside the agent-led toolchain from 麻豆原创, which includes business transformation tools and the new migration and modernization assistants to accelerate their transformations to 麻豆原创 Cloud ERP.

鈥淭o turn the vision of the Autonomous Enterprise into reality, organizations need trusted partners to help them transform their core operations and unlock the power of business data and AI,鈥� said Christian Klein, CEO of 麻豆原创 SE. 鈥淭ogether with Palantir, we are enabling customers to move to the cloud with speed and confidence through complementary capabilities that accelerate innovation across the enterprise.鈥� 

鈥淲e are proud to partner with 麻豆原创 and Accenture to bring the power of advanced AI and data migration to the world鈥檚 most important operations. This partnership is designed to help customers realize the full value of their data, accelerate cloud migrations and AI adoption, and build more resilient and efficient operations,鈥� said Alex Karp, co-founder and CEO of Palantir Technologies. 

Accenture as global strategic services partner

Accenture plays a key role in bringing this joint effort to life as the first global strategic services partner for this initiative, helping their clients translate these capabilities into large-scale business-led programs. Together, 麻豆原创, Palantir, and Accenture can help joint customers identify acceleration opportunities across 麻豆原创 and non-麻豆原创 systems sooner, achieve faster time-to-value, and drive continuous innovation by fundamentally redefining the approach to 麻豆原创 Cloud ERP migrations.

By embracing AI from day one, organizations can automate migration analysis, planning, remediation, testing, and impact assessment鈥攎oving from tracking project timelines to creating measurable business value at every stage. 

鈥淚n today鈥檚 world, speed to value is critical for our clients,鈥� said Julie Sweet, chair and CEO of Accenture. 鈥淲e are excited to co-innovate with 麻豆原创 and Palantir to help our clients accelerate their journey to ERP modernization with 麻豆原创, which is the foundation for reinventing core operations and using AI to achieve new performance frontiers.鈥� 

New 麻豆原创-validated deployment options

麻豆原创 is making Palantir AIP for data migrations scenarios available as an 麻豆原创 Endorsed App on the 麻豆原创 Store, and soon as an 麻豆原创 Solution Extension, establishing a trusted, 麻豆原创-validated path for accelerating complex data migrations, including migrations to 麻豆原创 Cloud ERP. The new 麻豆原创 Solution Extension unites 麻豆原创鈥檚 deep expertise in mission-critical business processes and semantically rich data with Palantir AIP to securely accelerate data migrations for 麻豆原创 customers. With the new offering, customers can gain faster insights and more intelligent, data-driven business outcomes.

Together, are redefining 麻豆原创 data migrations, including ERP modernization through complementary solutions, transforming a traditionally complex migration process into an effort that enables faster value realization. 

Availability 

Palantir AIP for data migration scenarios is now available as an 麻豆原创 Endorsed App on the 麻豆原创 Store. The 麻豆原创 Solution Extension is planned to be generally available to 麻豆原创 customers in Q3 2026.

Jan Gilg is a member of the Extended Board of 麻豆原创 SE.

The acquisition helps customers make their 麻豆原创 and non-麻豆原创 enterprise data AI-ready and will provide customers with the tools they need to unify, cleanse and harmonize data across sources for superior enterprise-wide agentic AI.

Visit the . Get 麻豆原创 news via  and .

Media Contacts:
Aim茅e Leabon, +1 (646) 799-3277, aimee.leabon@sap.com, EST 
Daniel Reinhardt, +49 151 168 10 157,鈥�daniel.reinhardt@sap.com, CEST  
麻豆原创 麻豆原创 Room; press@sap.com

This document contains forward-looking statements, which are predictions, projections, or other statements about future events. These statements are based on current expectations, forecasts, and assumptions that are subject to risks and uncertainties that could cause actual results and outcomes to materially differ.鈥� Additional information regarding these risks and uncertainties may be found in our filings with the Securities and Exchange Commission, including but not limited to the risk factors section of 麻豆原创鈥檚 2025 Annual Report on Form 20-F. 
漏 2026 麻豆原创 SE. All rights reserved.  
麻豆原创 and other 麻豆原创 products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of 麻豆原创 SE in Germany and other countries. Please see  for additional trademark information and notices.  

In a blog post, that’s tolerable. In a financial disclosure, a regulatory filing, or a supply chain commitment, it is simply unacceptable.

is statistical. Answers to enterprise level problems are a lot more deterministic. The distance between 90% and 100% accuracy is not incremental. In our world, it is existential.

In 2026, AI is no longer evaluated on novelty. It is evaluated on precision, governance, scalability, and business impact. As organizations move from pilots to scaled programs, five moments will define whether they capture lasting value or expose themselves to avoidable risk. I have seen these moments play out across every major market I oversee.

1. The governance moment: when agents become digital coworkers

The first moment arrives when stops being a tool and starts being an actor.

Agentic AI systems plan, reason, orchestrate with other agents, and execute workflows autonomously. They touch sensitive data and influence decisions at scale. If you are not already governing them as you govern your human workforce, you are exposing your organization to risk.

Agent sprawl will mirror the shadow IT crises of the past decade, but the stakes are categorically higher. Enterprises must establish agent lifecycle management, clear autonomy boundaries, policy enforcement, and continuous performance monitoring. Every board needs to answer three questions: Who is accountable when an agent makes the wrong call? How are decisions audited? When does the machine escalate to a human?

Geopolitical fragmentation compounds this urgency. Sovereign cloud, sovereign AI, and data localization are no longer theoretical concerns. They are regulatory realities in markets from New York to Frankfurt to Riyadh to Singapore. Governance in the age of AI is less about controlling risk at the edge and more about embedding deterministic control into probabilistic intelligence. That is a C-suite mandate, not an IT project.

2. The data foundation moment: when the last mile is the only mile that matters

The second moment is quieter, but it is where most enterprises will ultimately win or lose.

AI is only as reliable as the data and processes it operates on. Fragmented master data, siloed systems, and over-customized ERP landscapes introduce unpredictability at the worst possible moment: when AI provides a recommendation that affects your customers, your cash flow, or your compliance position.

Enterprise AI value will not come from generic large language models trained on internet-scale text. It will come from intelligence grounded in your enterprise data鈥攐rders, invoices, supply chain records, financial postings鈥攅mbedded directly in your processes. Relational foundation models optimized for structured business data will outperform generic LLMs in forecasting, anomaly detection, and operational optimization.

The question every board should be asking is not only “What AI can we add?”, but also, “Is our data estate ready, or are we layering probabilistic intelligence onto fragmented foundations?”

3. The employee interaction moment: when the interface disappears

The third moment happens in your employees’ daily workflows, and it will accelerate faster than most organizations expect.

In 2026, we are moving from static application interfaces to generative user interfaces. Instead of navigating between systems, employees express intent: “Prepare a briefing for my highest-revenue customer visit this week.” AI agents orchestrate the workflows, assemble the context, and surface recommended actions.

But adoption is not automatic, and trust is not given. Employees will embrace AI teammates only when they are confident that outputs respect governance boundaries, reflect real business rules, and deliver measurable gains. Role-specific AI personas tailored for the CFO, the CHRO, the head of supply chain, built on trusted data and embedded in familiar workflows, are what will close the adoption gap.

Organizations that invest in AI-native architecture will accelerate ROI. Those that bolt AI onto legacy interfaces will struggle with trust, usability, and scale. This is a design decision with strategic consequences.

4. The customer moment: when intelligence becomes a competitive moat

AI proves its enterprise value most visibly at the customer edge.

Trained on your own data, your own policies, and your own interaction history, customer-specific intelligence compounds in ways that competitors cannot easily replicate. This is especially powerful in exception-heavy environments: dispute resolution, claims handling, returns management, service routing. AI that can classify cases, surface relevant documentation, recommend policy-aligned resolutions, and learn continuously from outcomes transforms these high-cost, high-friction processes into sources of competitive differentiation.

In 2026, your customers will not reward novelty. They will reward reliability, relevance, and responsiveness. Organizations that use AI to absorb complexity, without losing control over outcomes, will build moats that generalist tools cannot breach.

5. The strategy moment: when you decide how far to go

The final moment is the one that falls squarely on leaders.

AI adoption is not a single journey. It requires leaders to orchestrate three layers in parallel:

• Embedded AI: Persona-driven productivity gains built into core applications for immediate returns
• Agentic AI: Multi-agent orchestration of complex, cross-system workflows
• Industry AI: Deeply specialized applications co-developed to address the highest-value challenges specific to your sector

The trap is false sequencing: focusing only on embedded AI leaves value on the table and jumping to deep industry transformation without governance and data maturity multiplies risk. The organizations that will lead are those that align ambition with readiness and invest in clean core architecture, modern data foundations, and cross-functional AI ownership, while moving decisively from pilots to programs.

The leadership test

In 2026, the winners will not be those with the most AI features. They will be those who treat AI as a core operating layer, governed like a workforce, grounded in trusted data, tailored to employees and customers, and calibrated to the realities of their industry.

The gap between 90% and 100% is precisely where enterprise value lives. It is also where leadership is tested. The decisions you make in the coming months will determine whether AI becomes your most powerful source of durable advantage or your most expensive lesson in misplaced confidence.

This is the moment to move with precision.

Manos Raptopoulos is global president of Customer Success Europe, APAC, Middle East & Africa, and a member of the Extended Board 麻豆原创 SE.

The software itself wasn’t the barrier; the operational weight around it was: dedicated data centers, expensive hardware, annual upgrade cycles that consumed months of IT resources, and the specialist teams to keep it all alive.

麻豆原创 Business AI and 麻豆原创 Cloud ERP have completely changed the economics of enterprise software. The heavy infrastructure disappeared into a subscription. A 200-person company can now run its core business processes as efficiently as a global enterprise, on a predictable monthly cost, without an army of IT staff.

AI accelerates this further. What took months of configuration and specialist knowledge can now be activated through natural language and intelligent automation. The deep industry expertise 麻豆原创 spent 50 years encoding into its software is now accessible to businesses of all sizes.

“John Boos is a 137-year-old company, with 137 years of tech debt,” said Britt East, CIO at John Boos & Co. “To make matters more complex, we are growing incredibly fast. Every quarter is a record quarter! 麻豆原创 Cloud ERP will be the backbone of our business in perpetuity, giving us a standard and scalable foundation to support growth while also unleashing our workforce with real AI use cases that make their lives a lot easier and the company as a whole more successful.”

The real value of 麻豆原创 Business AI is that a midsize manufacturer in Stuttgart or a growing logistics company in Dallas could access intelligent business operations at speed and price point they can afford.

Won’t AI then replace software altogether?

Think of it this way: GPS system is genuinely intelligent. It calculates optimal routes, adapts to real-time traffic, and reroutes dynamically. But it is only as good as what backs it鈥攖he data underneath it, like accurate roads, turn restrictions, and governance for local speed limits, timeframes for live incident feeds and so on. Without the structured, maintained, trusted data layer, the intelligence has nothing to work with鈥攊t would confidently lead you off a cliff.

Software is not being replaced by AI. Software is becoming AI’s superpower.

With deep process and industry knowledge, semantically rich business data and enterprise-grade governance built in,聽 AI gets what it lacks on its own to deliver reliable, battle-proven, trustworthy, repeatable, and auditable results鈥攅very time. Agents are probabilistic. They predict, they infer, they move fast, and that is powerful. But it means that the more AI agents you deploy, the more valuable your underlying software systems become.

And the cost? Running a stack of AI tools adds up to significant infrastructure investment, fast. However, serious software companies, including 麻豆原创, have already embedded their AI directly into their platforms, and they often co-develop with leading AI providers, so you are not choosing between AI and 麻豆原创. You’re choosing 麻豆原创 with AI already inside it.

“Many companies used to delay decisions because ERP felt too complex,” shared Tobias Siebler, CEO of FULCRUM Consulting Germany. “That has changed. With 麻豆原创 Cloud ERP, you can start small, get live quickly, and still have a setup that grows with the business, including the current and new AI capabilities as they become available.”

The new stack: What this actually looks like

Imagine a shipping company that processes 10,000 orders a day. Traditionally, humans monitored exceptions, chased suppliers, and rerouted freight when things went wrong. Today, AI agents can scan the full order pipeline in real time, flag anomalies, draft supplier communications, and propose rerouting options鈥攁ll within the governed environment of 麻豆原创’s supply chain data. Humans are irreplaceable in making the final call, but the agents do the legwork.

With Joule, work starts with what needs to be accomplished, not which system to open. Teams move from intent to execution in real time. Decisions are shaped by data, operational capacity, financial constraints, and customer demand.聽 AI agents handle coordination across workflows. People make the calls that matter. The whole process runs on the unmatched human ability to make decisions based on multifaceted considerations, supported by auditable, structured data.

That is the model. AI can鈥檛 replace the system. AI operates inside the system, supervised by humans and connected to real business data, constrained by real business rules and governance, delivering real business outcomes.

AI needs rich, structured, semantically meaningful business data to perform. 麻豆原创 has 50 years of exactly that.

For fast-growing companies: 麻豆原创 GROW Fast

Markets shift. Expectations evolve. Technology accelerates change.聽Naturally, our customers demand quicker and better results. 麻豆原创 GROW Fast services are designed to help customers go live with AI-ready 麻豆原创 Cloud ERP with speed and predictability. The deployment of finance and spend core capabilities for 麻豆原创 Cloud ERP, as well as other 麻豆原创 solutions on the way, can be done in months, not quarters. And from there, the business can expand into the rest of 麻豆原创 Business Suite fast, all activated with AI from day one.

Companies taking advantage of 麻豆原创 GROW Fast are gaining compound advantages with a platform that becomes more capable with every AI advancement that 麻豆原创 and its partners embed into it. The companies that are waiting? They will be implementing what the leaders deployed today鈥攖hree years from now.

The human element is not going away, it’s going up the stack

As we disrupt everything we do and work with AI to achieve better, faster business outcomes, 麻豆原创 partners become key change agents. All around the globe, 麻豆原创 partners are being enabled to extract business value quickly for our customers with the AI-ready 麻豆原创 GROW Fast services. This is a step-by-step change into a world of AI-first business value adoption and should be leveraged by all our partners.

“Many organizations still assume that 麻豆原创 is designed exclusively for large enterprises,” explained David Bay贸n Esporr铆n, go-to-market director of the Global 麻豆原创 Practice at INETUM. “In reality, that perception no longer reflects today鈥檚 market. With 麻豆原创 Cloud ERP, and especially with 麻豆原创 GROW Fast, companies of almost any size can optimize core business processes and harness the power of AI to accelerate growth in a simple and cost-effective way.” (.)

We are living through a platform shift, not unlike the one the internet created. The businesses that thrive will be the ones that move with intention, combining the intelligence of AI with the governed, structured, operationally rich foundation that enterprise software provides.

The great equalizer is here. The only question is: How fast do you want to use it to your advantage?

Santina Franchi is president of the Corporate Segment at 麻豆原创.
Guido Beuningen head of AI and Public Cloud for the Corporate Segment at 麻豆原创.

The announcement was made at HANNOVER MESSE 2026, where they showcased PacXplorer, a high-tech packaging machine from Uhlmann that serves as both an industrial demonstrator and a development platform.

PacXplorer: Connected Production in Industrial Practice

Developed through collaboration within Factory鈥慩, the PacXplorer brings together digital twins, condition monitoring, smart services and interoperable production solutions within a collaborative data ecosystem. Factory鈥慩 is a lighthouse project funded by the German Federal Ministry for Economic Affairs and Energy as part of the Manufacturing鈥慩 initiative. Its objective is to establish a decentralized data space for the capital goods industry, enabling secure and interoperable data exchange across companies and industries for equipment manufacturers and operators alike.

The machine is integrated into 麻豆原创 system landscapes and operated live. It demonstrates how industrial data can be used in a sovereign, interoperable and cross鈥慶ompany manner not as a theoretical model, but in real production operations. This creates transparency regarding asset condition, utilization and performance while laying the foundation for new data鈥慸riven services.

Service as a Key to Resilience

The value of this approach becomes particularly clear in service operations. Where production, data and operations are tightly interconnected, service plays a decisive role in ensuring asset availability, productivity and stable customer relationships. One often underestimated lever is spare parts service: delays lead directly to downtime and economic losses, especially in volatile market and supply situations. At the same time, these processes remain heavily manual in many industrial companies.

麻豆原创 and Uhlmann are deliberately advancing the further development of this area. An AI鈥憇upported process assists throughout the entire workflow from handling incoming inquiries and clarifying missing information to identifying the correct spare part and generating quotations. The approach integrates into existing 麻豆原创 service and sales processes and is closely aligned with real鈥憌orld business operations. The objective is fast, reliable and scalable customer service.

鈥淭oday, industry is less concerned with cost optimization than with decision鈥憁aking under uncertainty,鈥� says Dominik Metzger, President and Chief Product Officer, 麻豆原创 Supply Chain Management, 麻豆原创 SE. 鈥淲ith 麻豆原创 Business AI and integrated production and service solutions, we move decision鈥憁aking directly into business processes. This allows companies to identify risks early, respond with greater flexibility and remain operational even under unstable conditions.鈥�

Rethinking Value Creation Together

The collaboration between 麻豆原创 and Uhlmann illustrates a fundamental shift in industry: resilience is not achieved through additional buffers, but through better, faster and more-connected decisions across the entire value chain. Companies that manage their production and service processes in a data鈥慸riven way can respond more flexibly to change and secure their competitiveness over the long term.

Beyond HANNOVER MESSE, 麻豆原创 and Uhlmann will continue their innovation partnership. Following the event, the PacXplorer will be operated at the S.Factory in 麻豆原创 Experience Center Walldorf, serving as a platform for customers, partners and co鈥慽nnovation to continuously advancing industrial transformation.

Visit the . Get 麻豆原创 news via  and .

Media Contact:
Dana Roesiger, +49 6227 7 63900, dana.roesiger@sap.com, CET
麻豆原创 麻豆原创 Room; press@sap.com

This document contains forward-looking statements, which are predictions, projections, or other statements about future events. These statements are based on current expectations, forecasts, and assumptions that are subject to risks and uncertainties that could cause actual results and outcomes to materially differ.  Additional information regarding these risks and uncertainties may be found in our filings with the Securities and Exchange Commission, including but not limited to the risk factors section of 麻豆原创鈥檚 2025 Annual Report on Form 20-F.
漏 2026 麻豆原创 SE. All rights reserved.
麻豆原创 and other 麻豆原创 products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of 麻豆原创 SE in Germany and other countries. Please see for additional trademark information and notices.

Image copyright: 漏Uhlmann Group

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, our new user experience, is gaining momentum and driving significant impact for our customers. Customers are already efficiency, enhancing processes, improving , and .

Joule is now live across 35 solutions and will continue to meet our customers where they are: across the applications they use, with a firm understanding of their business context and data. That鈥檚 why in Q1 we are embedding Joule into more applications鈥攆rom 麻豆原创 Datasphere, where it can now execute tasks or explain specific functionalities, to 麻豆原创 Intelligent Clinical Supply Management, where users can use natural language to retrieve critical data and navigate to relevant applications.

Joule Agents, such as the Tender Analysis Agent, are boosting customer revenue growth by extracting critical requirements and flagging risks in complex documents. While project managers in 麻豆原创 S/4HANA Cloud Public Edition are saving time setting up projects with the new Project Setup Agent. Plus, there are many more agents to discover below.

Agents are becoming a key new user鈥攁nd enabler鈥攐f enterprise software, joining humans as the only other non-deterministic operators while simultaneously expanding enterprise software鈥檚 scope and usefulness. Our agents will continue to deliver trustworthy, repeatable, and auditable results every time.

We now have over 30 specialized agents and more than 2,500 Joule Skills. The agent-to-agent protocol means our agents work across 麻豆原创 and non-麻豆原创 systems. As the number of agents grows across both, 麻豆原创 AI Agent Hub already today provides customers with the essential infrastructure and guardrails to manage, govern, and discover agents in this new ecosystem.

Some highlights from Q1 2026:

• 麻豆原创 Joule for Consultants is a conversational AI solution that provides expert guidance on cloud transformations, drawing on 麻豆原创鈥檚 knowledge base. To improve trust and traceability, citations are now displayed in a dedicated side panel and can be grouped for clarity. Administrators can enable web search, allowing Joule to draw from public content while maintaining clear source attribution. For tailored answers to problems where the system may not have customer-specific documentation, consultants can now upload up to 10 PDF or text files directly into the chat. This is further enhanced by the inclusion of content from the 麻豆原创 Enterprise Architecture Reference Library, which provides more complete and accurate answers to complex queries. Get started here.
• 麻豆原创 Business AI for supply chain minimizes disruptions and simplifies planning. The Project Setup Agent allows project managers to rapidly establish new projects by drawing on data from past initiatives. 麻豆原创 Integrated Business Planning users can now generate complex formulas in Microsoft Excel with natural language. 麻豆原创 Digital Manufacturing can distill complex manufacturing issues into clear descriptions. Joule is also helping 麻豆原创 Integrated Product Development users create problem reports and requirement models with simple, natural-language commands. Explore more below.
• 麻豆原创 Business AI for finance offers greater efficiency and insight across critical processes. Joule now translates complex e-invoicing errors into plain language. The Dispute Resolution Agent automates root-cause analysis for invoice disputes, while payment advice processing significantly reduces document processing time. Unstructured data, such as PDFs, can now be automatically transformed into sales orders, and accountants can access natural language explanations for complex fixed asset calculations. Users can personalize their home page and easily understand system errors using natural language across 麻豆原创 S/4HANA Cloud Public Edition. Learn more below.
• 麻豆原创 Business AI for procurement and customer experience enhances the entire commercial journey with new capabilities. In procurement, automated statement of work (SOW) creation in 麻豆原创 Fieldglass reduces the time to define deliverables. The Catalog Optimization Agent means e-commerce managers can continuously improve product data quality. In retail, managers can get instant, conversational answers from Joule on order management data. There’s so much more to learn below.
• 麻豆原创 Business AI for IT and developers puts the latest tools and greater control directly into the hands of developers and data professionals. Joule is now generally available in 麻豆原创 Datasphere, enabling users to navigate the platform, get answers, and execute tasks using simple conversational language. The generative AI hub in AI Foundation continues to expand, offering developers access to the newest models, including OpenAI GPT 5.2, Gemini 3.0 Pro, Anthropic Claude Opus 4.6, and Claude Sonnet 4.6. Developers also gain greater power through enhancements such as advanced prompt optimization, metadata filtering, and declarative orchestration configurations in the prompt registry. Additionally, 麻豆原创 Document AI now offers more granular control with custom confidence thresholds and expanded document support. Dive into everything below.
• 麻豆原创 Business AI for industries delivers specialized intelligence to solve unique business challenges. Sales teams can accelerate their response process with the new Tender Analysis Agent, which automates the review of complex RFQ documents to improve win rates. Joule now works with 麻豆原创 Commodity Management to turn verbal or written negotiations directly into detailed draft deals. In life sciences, clinical supply professionals can use predictive analytics to reduce inventory waste costs, and Joule dramatically cuts information search time. 麻豆原创 Self-Billing Cockpit automates invoice data extraction from any format, significantly reducing manual processing time. Discover more for industries below.
• 麻豆原创 Business AI for business transformation management provides the critical insights needed to navigate and accelerate organizational change. Joule is now in 麻豆原创 Signavio, enabling natural-language searches that cut information discovery time. Business process model and notation simulations in 麻豆原创 Signavio provide clear, actionable summaries directly within process diagrams. Meanwhile, enterprise architects can leverage guidance in 麻豆原创 LeanIX to surface actionable insights directly from their architecture inventory, accelerating transformation execution and reducing the time to uncover them. Read more about transformation management below.

Joule

Joule, enhancements

User experience is improved by streamlining startup times and introducing cross-thread search functionality that lets end users find information across all conversation threads without manually checking individual histories. The document grounding capability has also seen a substantial upgrade, now supporting seamless integration with Google Drive.

To set up, see: , , and .

Furthermore, scalability has been greatly improved, as the system now supports up to 8,000 documents per pipeline, enabling large-scale data repositories to be processed and utilized efficiently.

For more information, see .

麻豆原创 Joule for Consultants, enhancements

Enhanced Citation Visibility
麻豆原创 Joule for Consultants has improved how citations are displayed for all identified sources returned by the product. Citations have been relocated to the right side in a dedicated panel for clearer visibility, and now also include public web search results when applicable (see below).

A new grouping feature has also been added, allowing citations to be grouped. This update provides users with a more transparent view of where information originates, strengthens trust, and improves traceability across all responses.

To see the sources and panel, click the sources button below each message; the panel will open on the right, showing all grouped sources.

Enable Web Search
Administrators can now enable/disable web search via the control panel for all assigned end users in 麻豆原创 Joule for Consultants.

When enabled, 麻豆原创 Joule for Consultants will consider public web content in its reasoning and cite relevant public sources in responses when they contribute to the answer. This enhancement gives organizations greater flexibility and transparency by enabling broader coverage of information while maintaining clear source citations for all sources used.

File Uploads in the Joule Message Input
End-users can now upload up to 10 files directly from the conversational message input box and reference them throughout the entire conversation.

Supported file types include PDF and TXT. Each file should be no more than 10 MB/600K characters; for PDFs, an approximation. A 100-page limit applies; if your file is larger, split it into multiple documents. Image files are currently not processed and will be ignored. We are working diligently to make this feature even more useful to end users. This enhancement enables richer, context-aware interactions by allowing you to incorporate your uploaded documents into its conversational responses throughout the session. Please be aware that the standard data privacy terms apply. See also the help documentation for additional information on the free user quota.

Content: 麻豆原创 Enterprise Architecture Reference Library
麻豆原创 Enterprise Architecture Reference Library data has been ingested and is now available for use in conversations. As more data is added, relevant portions may be included in 麻豆原创 Joule for Consultants鈥� responses, enabling more complete, accurate, and context-rich answers to user queries. Since 麻豆原创 Enterprise Architecture Reference Library content cannot be link-referenced, you won鈥檛 see the additional content listed under sources, even though it will be referenced.

Get started .

SECTION

麻豆原创 Business AI for supply chain

Project Setup Agent
Beta release

Project managers can now rapidly establish new projects by drawing on data from similar past initiatives. The agent bypasses complex interfaces and reduces reliance on the project management office (PMO) to facilitate the swift allocation of key resources needed to launch projects effectively. With a 10% reduction in project creation time, 16% faster resource allocation, and 30% less time spent reworking projects due to incorrect templates, teams can shift focus from operational coordination to improving project profitability and driving efficiency.

Get started .

麻豆原创 S/4HANA Cloud Private Edition, AI-assisted retrieval of equipment information in service management
General availability

Service managers using the AI-assisted retrieval feature in 麻豆原创 S/4HANA Cloud Private Edition gain a complete 360-degree view of customer equipment. The feature provides instant access to warranty information and a full history of service transactions, complemented by an AI summary and actionable recommendations. This allows service managers to more efficiently oversee service schedules, reduce potential downtime, and ensure customer equipment operates at peak performance.

Get started .

麻豆原创 S/4HANA Cloud Public Edition, AI-assisted input recommendations for returns order creation
General availability

Returns clerks can accelerate the creation of customer returns with data field recommendations powered by historical data. This feature analyzes past return documents with similar process variants to automatically suggest the most common input values and return reasons, minimizing manual data entry and reducing errors. Organizations benefit from a one percent reduction in data management costs and a five percent decrease in business and operations analysis expenses, enabling returns teams to process orders more efficiently while maintaining accuracy.

Get started .

麻豆原创 Integrated Business Planning, AI-assisted MRO inventory analysis
General availability

Inventory planners get a new analytical assistant in the MRO inventory analysis feature for 麻豆原创 Integrated Business Planning. The feature accelerates root cause analysis by generating clear, natural-language summaries that explain the key drivers behind recommended safety stock and reorder points. By translating complex calculations into understandable insights, this capability enables planners to reduce time spent analyzing inventory runs by 30%, leading to faster adoption of outputs and ensuring that inventory parameters align with strategic business goals.

Get started .

麻豆原创 Integrated Business Planning, add-in for Microsoft Excel, AI-assisted planning
General availability

Supply chain planners can now simplify their work with a new AI-assisted planning add-in for Microsoft Excel. Instead of manually creating complex formulas or formatting rules, which often require technical expertise, they can simply describe their needs in natural language, and the system automatically generates the correct syntax. This intuitive way of interacting with the system removes technical barriers and improves a planner鈥檚 efficiency by 10%, freeing them to focus on strategic analysis rather than implementation details.

Get started .

麻豆原创 Integrated Business Planning, AI-assisted system security check
General availability

Supply chain planners and security analysts gain a robust way to assess system configurations against established security recommendations. The feature evaluates compliance states and provides clear guidance on required adjustments, helping administrators identify and address potential gaps while aligning configurations with 麻豆原创 best practices. Organizations can expect a 27% increase in compliance with hardening guidelines and a 32% reduction in the effort required to meet security recommendations. This feature strengthens the protection of sensitive data and reduces the risk of security breaches.

Get started .

麻豆原创 Integrated Product Development, AI-assisted problem report creation
General availability

Maintenance engineers can simplify the creation of formal problem reports by leveraging AI capabilities in 麻豆原创 Integrated Product Development. By describing an issue in their own words to Joule, it intelligently extracts key details like the problem name, tags, and priority, and then automatically generates a structured report. This streamlined process dramatically reduces manual data entry and ensures all reports are consistent and compliant with organizational standards, improving overall efficiency.

and get started .

麻豆原创 Integrated Product Development, AI-assisted requirements model creation
General availability

Requirements managers now have a more direct path to creating requirement models within 麻豆原创 Integrated Product Development by using natural language commands with Joule. This feature allows them to initiate new models, specify names, and apply templates in a single step, completely bypassing the need to navigate through complex folder structures. This streamlined approach provides a much faster starting point for new projects and empowers users to begin their work immediately without requiring deep knowledge of the repository layout.

Get started .

麻豆原创 Field Service Management, AI-assisted automated scheduling analytics
General availability

Field service dispatchers and consultants can now access clear, on-demand explanations of auto-scheduling results that demystify complex system logic. The new feature interprets scheduling reports and translates technical scoring details into business-friendly insights, explaining why specific technicians were assigned, why alternatives were passed over, and why certain activities remained unscheduled. This transparency drives a 12.5% increase in dispatcher productivity and a five percent reduction in erroneous resource allocations, strengthening trust in automated decisions while significantly reducing analysis time.

Get started .

麻豆原创 Digital Manufacturing, AI-assisted description enhancement
General availability

Quality managers documenting complex manufacturing issues can now generate clear, objective, and structured descriptions with minimal effort. 麻豆原创 Digital Manufacturing for issue resolution offers description generation that refines rough initial inputs, removes bias and subjective language, and produces balanced, factual problem statements. With support for multilingual translation and enhanced clarity, organizations can achieve up to five percent improvement in quality engineer efficiency during issue handling and up to 10% reduction in errors throughout the problem resolution process.

Get started .

麻豆原创 Business AI for finance

Dispute Resolution Agent (for 麻豆原创 S/4HANA Cloud Public Edition)
Beta release

When invoice disputes arise, accounts receivable specialists need to act quickly without sacrificing accuracy. 麻豆原创 S/4HANA Cloud Public Edition introduces an agent that automates root-cause analysis, scanning invoices, sales orders, delivery records, pricing agreements, and tax rules to identify the source of discrepancies. The agent detects incorrect charges and recommends compliant solutions, such as credit memo creation, enabling finance teams to resolve disputes faster, minimize manual investigation, and cultivate stronger vendor relationships through transparent, efficient processes.

Get started .

麻豆原创 S/4HANA Cloud Public Edition, AI-assisted smart personalization of my home for applications
General availability

麻豆原创 S/4HANA Cloud Public Edition users can easily configure their home page with the most relevant applications through AI-assisted smart personalization. By describing their task in natural language, the system identifies the appropriate app, which can then be added to their home screen with a single click. This intuitive capability reduces the cost of personalizing the home page by 33%, shortens the learning curve for new users, and improves satisfaction by keeping frequently needed tools readily accessible.

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麻豆原创 S/4HANA Cloud Public Edition, AI-assisted error explanation
General availability

When encountering system errors, 麻豆原创 S/4HANA Cloud Public Edition users can turn to a new feature that generates clear, natural language explanations and resolution recommendations. This capability transforms cryptic error messages into easy-to-understand guidance, helping users of all experience levels quickly rectify issues and continue with their work. By reducing error resolution time by five percent, organizations benefit from increased productivity, improved data quality, and shorter training cycles for new team members.

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麻豆原创 S/4HANA Cloud Public Edition, AI-assisted sales order creation from unstructured data
General availability

Sales representatives benefit from a streamlined order creation process in 麻豆原创 S/4HANA Cloud Public Edition that handles unstructured data like PDF or image-based purchase orders. After uploading a file, 麻豆原创 Document AI automatically extracts the relevant information and proposes the data for a corresponding sales order request. This automation significantly reduces manual data entry, minimizes errors, and improves overall operational efficiency, allowing teams to process orders faster and enhance customer satisfaction.

Get started .

麻豆原创 S/4HANA Cloud Public Edition, AI-assisted processing of payment advices with 麻豆原创 Document AI
General availability

Accounts receivable clerks can accelerate their workflow using the 麻豆原创 Document AI-powered payment advice processing feature in 麻豆原创 S/4HANA Cloud Public Edition. The system automatically extracts payment amounts, references, and currencies from diverse invoice formats across multiple languages, with a self-learning capability that continuously improves recognition accuracy. Organizations implementing this feature can reduce document processing time by 70%, cut template maintenance time by 83%, and decrease value loss from manual processing delays by 40%.

Get started .

麻豆原创 S/4HANA Cloud Private Edition, AI-assisted fixed asset key figures explanation
General availability

Asset accountants gain clarity on complex fixed asset calculations through a new AI feature in 麻豆原创 S/4HANA Cloud Private Edition. The feature generates natural-language explanations that detail the origins of displayed values and how figures such as depreciation are calculated; for example, illustrating the impact of mid-year acquisitions with specific depreciation keys. This transparency reduces the effort required to analyze asset values, enables faster responses to asset-related questions, and helps mitigate compliance risks.

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麻豆原创 S/4HANA Cloud Private Edition, AI-assisted settlement rule proposal for asset capitalization
General availability

Overhead and asset accountants can now streamline the complex process of creating settlement rules for investment measures, eliminating the traditionally time-consuming, error-prone manual configuration. The solution automatically determines receivers, calculates percentages, and proposes feasible rules based on contextual data and user-defined instruction profiles. Organizations reduce the effort required to create full settlement rules by 50% while simultaneously improving accuracy in asset capitalization and enhancing overall operational efficiency across their financial processes.

Get started .

麻豆原创 Document and Reporting Compliance for 麻豆原创 S/4HANA Cloud Private Edition, AI-assisted electronic document error handling
General availability

Tax accountants navigating the growing complexity of e-invoicing mandates across multiple countries gain an easy way to decode technical errors without wading through intricate XML or JSON formats. Joule, integrated with 麻豆原创 Document and Reporting Compliance, delivers plain-language explanations of electronic document errors, enabling faster root-cause identification and more efficient resolution. Organizations get an 80% reduction in time spent understanding and resolving errors, dropping from 150 minutes to approximately 30 minutes. This results in faster processing cycles, reduced penalty risks, and improved cash flow.

Get started .

麻豆原创 S/4HANA Cloud Public Edition, AI-assisted error resolution for cost accounting
General availability

Operations managers in retail organizations can now access Joule via 麻豆原创 Order Management Services, enabling them to query order data and receive real-time, role-specific operational guidance across order processing, orchestration, sourcing, availability, returns, and fulfillment flows. Joule surfaces instant insights and recommended actions directly in the workflow, reducing the need to navigate multiple systems. This enables proactive intervention before issues escalate. The feature offers faster transaction access, improved responsiveness and accuracy, and lower operational risk, which support smarter, quicker decisions across the order lifecycle.

Get started .

麻豆原创 Business AI for spend management

Expense Report Validation Agent
General availability

Business travelers can enjoy a smarter, guided approach to expense report completion with an agent that proactively identifies missing items, prompts for necessary details, and clarifies confusing alerts throughout the submission process. By simplifying how users understand and resolve issues, the agent ensures accurate, policy-compliant reports with minimal effort required. This means a 30% reduction in time spent preparing and submitting reports, a 24% increase in first-pass approvals, and a noticeably improved employee experience that removes friction from the expense management process.

Get started .

Expense Pre-Submit Audit Agent
麻豆原创 Early Adopter Care

Expense report submitters can now catch receipt accuracy issues and policy breaches before hitting the submit button, avoiding the frustration of rejected reports and delayed reimbursements. This agent automatically reviews expenses during creation, surfacing compliance problems and offering smart suggestions for quick corrections. The agent uses a non-blocking design that keeps users in control of final decisions. Organizations benefit from a 10% decrease in sent-back expense reports, reduced rework for travelers, managers, and auditors alike, and a noticeably smoother reimbursement process that enhances the overall employee experience.

Get started .

Expense Automation Agent
麻豆原创 Early Adopter Care

Employees burdened by the administrative chore of creating expense reports can now delegate the heavy lifting to a Joule Agent. This agent automatically builds expense reports by aggregating transactions, populating custom fields based on contextual details and user history, and preparing everything for a quick review before submission. The outcome is up to 30%鈥� reduction in time on task for auto-generated expense reports. This offers a modern expense management experience that slashes manual data entry, accelerates the submission process, and frees employees to focus on high-value work rather than paperwork.

See the demo .

Concur Expense, AI-assisted configuration for audit rules
General availability

Expense administrators responsible for managing complex audit rule setups can now interact with their configuration environment in plain language, eliminating the need for deep technical expertise or tedious manual adjustments. This AI-assisted feature enables admins to search existing rules, create new ones, and receive real-time explanations simply by asking questions like “What rules apply to meals in France?”, delivering clear, actionable guidance instantly. The outcome is a 40% reduction in audit rule configuration effort, fewer support tickets, and empowered administrators who work with greater independence, accuracy, and confidence in maintaining compliance logic.

Get started .

Policy Navigator
麻豆原创 Early Adopter Care

Business travelers seeking quick answers to company travel and expense policies no longer need to sift through lengthy documents or wait for admin responses. Policy navigator in Joule allows employees to ask questions in natural language and receive clear, contextual guidance grounded in approved policies, whether planning a trip, in the middle of a journey, or completing an expense report. The result is in-the-moment policy clarity that prevents non-compliant spend before it happens, reduces support tickets, and empowers travelers to make confident, compliant decisions without disrupting their workflow.

Get started .

麻豆原创 Business AI for procurement

麻豆原创 Fieldglass Services Procurement, AI-assisted SOW deliverables creation
General availability

Procurement specialists can accelerate the development of their statements of work using the deliverables feature in 麻豆原创 Fieldglass Services Procurement. The feature analyzes the defined project scope and automatically generates precise, relevant deliverables that ensure tight alignment between buyer expectations and supplier commitments. By adopting this capability, organizations can reduce the time required to manually create SOW deliverables by 70% and cut the risk of poor outcomes by 50%, while fostering stronger collaboration during the negotiation process.

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麻豆原创 Business AI for customer experience

Catalog Optimization Agent
General availability

E-commerce product managers tasked with maintaining large 麻豆原创 Commerce Cloud catalogs gain an always-on agent that continuously reviews product descriptions, attributes, and translations against company quality standards. This agent pinpoints merchandising gaps and delivers actionable recommendations to enhance catalog accuracy, ensure consistency across languages, and improve product discoverability. The business impact is a 70% reduction in time to translate catalog data, 65% less time spent adding descriptions per asset, and a five percent reduction in data quality costs, all of which contribute to higher conversion rates and a more agile merchandising operation.

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麻豆原创 Revenue Growth Management, AI-assisted trade promotion creation
General availability

Key account managers in consumer industries can benefit from a streamlined, single-view promotion-creation experience in which simply naming a promotion automatically populates key fields. Drawing on master data, historical promotions, and learned preferences specific to each retailer, the system suggests dates, types, durations, and sell-in periods, then continuously refines its recommendations based on user edits over time. The impact is a 75% reduction in promotion setup time, 30% fewer data-entry errors and rework, and increasingly personalized suggestions that eliminate repetitive manual effort across promotion cycles.

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麻豆原创 Business AI for IT and developers

Joule Studio code editor and Joule Studio CLI

Building on the transformative capabilities of Joule studio low-code, 麻豆原创 is expanding the Joule studio family with two powerful new offerings designed to meet developers exactly where they work: Joule Studio code editor, a Visual Studio Code IDE extension, and Joule Studio CLI, a versatile command-line interface. Together, these tools deliver a unified, AI-assisted development experience that spans the full spectrum of development personas and preferences on Joule.

• Joule Studio code editor brings the intelligence of Joule directly into Visual Studio Code, the world’s most popular development environment, empowering pro-code developers with AI-guided scaffolding, contextual code generation, intelligent recommendations, and seamless integration with Joule, all without leaving their preferred IDE. 
• Joule Studio CLI extends this same power to the terminal, enabling developers and DevOps teams to automate project creation, manage configurations, execute deployments, and orchestrate CI/CD workflows through scriptable, command-line commands鈥攊deal for headless environments, automation pipelines, and teams that value speed and precision at the command line.

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Joule with 麻豆原创 Datasphere
General availability

Data professionals working within 麻豆原创 Datasphere can now accomplish informational, navigational, and transactional tasks through natural conversation with Joule. Whether asking how to use specific functionalities, retrieving details about a 麻豆原创 Datasphere instance, or switching system settings like language preferences, users receive instant answers with direct references to product documentation. Joule can even execute tasks directly from the conversation without requiring interaction with the standard interface. This direct execution reduces reliance on internal IT support and enables faster, more intuitive navigation throughout the platform.

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麻豆原创 Document AI, enhancements

Document level confidence
Customers now set confidence ranges for fields in the Schemas feature. When customers edit field settings, they can define their own thresholds for low, medium, and high confidence. These custom settings are reflected in the extraction results displayed for the relevant fields on the document details screen. See and .

Expanded Transportation Management
Customers can now use the Transports feature to export and import channels and workflows. See .

New schemas: business partner + delivery note for WM
The service plans embedded edition and premium edition now also support the standard document type, business partner document. See the list of supported document types in . Get started with 麻豆原创 Document AI, and .

Generative AI Hub in AI Foundation, enhancements

Metadata
Customers can now manage metadata for documents, collections, and chunks created with the Vector API to enable advanced filtering and organization of their content. For more information, see .

Retrieval API
Customers can merge and rank search results across multiple data repositories using the Retrieval API’s post-processing capabilities. For more information, see .

Prompt optimizations
Custom metrics are supported in prompt optimizations, enabling customers to define and optimize prompts based on their specific evaluation criteria. Only LLM-as-a-judge metrics with numerical or Boolean output types can be used in optimization tasks.For more information, see and . Customers can provide separate test and train datasets for prompt optimization. For more information, see .

Prompt registry
The prompt registry now enables customers to create and manage orchestration configurations declaratively, allowing them to version and track complex AI workflows alongside their prompts for better governance and reproducibility.For more information, see .

Secrets
Customers can now enter generic secrets using a form instead of JSON. The form appears in the Add Generic Secret dialog when you activate document grounding. A dropdown menu lets them choose the type of document repository. Depending on their selection, the remaining fields adjust dynamically, allowing them to complete the data. Some fields are already prefilled.If they prefer working directly with JSON, switch to the code view by clicking the 顒� icon. For more information, see .

New models available
New models are supported, including OpenAI GPT 5.2, Gemini 3.0 Pro, Perplexity Deep Research, and Anthropic Claude Opus 4.6.For more information on new and deprecated models, .

麻豆原创 Joule for Developers, ABAP AI capabilities, enhancements

New ABAP AI capabilities mean developers can expect a 20% reduction in time and effort to write ABAP/JAVA code, 25% reduction in time and effort to test ABAP/JAVA code, and 4.4% faster time to realized value.

This quarter, developers can now easily generate ABAP Unit tests for:

• Public, protected, and private methods of global ABAP classes
• Public methods of local classes within global class pools

See .

In addition, the documentation chat allows developers to interact with documentation on the 麻豆原创 Help Portal, providing context-aware answers and links to relevant documentation. This capability enhances productivity by offering quick access to related documentation directly within the development environment. See .

Finally, developers can now get AI-powered explanations of their ATC findings and code in the Custom Code Analysis/Custom Code Migration app. See and .

Get started .

麻豆原创 Business AI for industries

Tender Analysis Agent
General availability

Sales teams can elevate their tender response process with the Tender Analysis Agent, which automates the review of complex RFQ documents. The agent extracts critical product requirements, flags potential risks and policy gaps, and suggests optimized configurations tailored to customer needs. By reducing the effort to process incoming tenders by five percent and improving win rates, organizations can achieve measurable revenue growth while accelerating sales cycles and uncovering valuable cross-sell and up-sell opportunities.

Get started .

麻豆原创 Commodity Management, AI-assisted commodity work center
General availability

Commodity traders can transform how they capture and manage complex deals using the commodity work center in 麻豆原创 Commodity Management. Working alongside Joule, the feature converts verbal or written negotiations into detailed draft deals, automatically populating the numerous fields that traditionally require extensive manual entry. This enables traders to redirect their focus toward negotiating better commercial outcomes, while improving data accuracy and driving greater operational efficiency across their trading activities.

Get started .

麻豆原创 Intelligent Clinical Supply Management, AI-assisted predictive subject dynamics
General availability

Clinical trial coordinators seeking to boost their supply planning capabilities will find a powerful ally in 麻豆原创 Intelligent Clinical Supply Management. The predictive subject dynamics feature analyzes historical and real-time data to forecast patient enrollment trends and dropout rates, automatically generating insights that would otherwise require extensive manual analysis. This enables supply chain teams to redirect their focus to strategic decision-making, while reducing clinical inventory waste costs by up to two percent and improving demand forecasting accuracy across their trial operations.

Get started .

Joule with 麻豆原创 Intelligent Clinical Supply Management
General availability

Clinical supply professionals juggling multiple tasks and complex systems need quick access to information without disrupting their workflow. Together with Joule, 麻豆原创 Intelligent Clinical Supply Management delivers an intuitive, conversational interface that understands natural-language requests, enabling users to retrieve critical data and navigate to relevant applications effortlessly. This streamlined experience results in an 83% reduction in time spent on information searches, freeing teams to concentrate on higher-value activities and significantly boosting overall productivity.

Get started .

麻豆原创 Self-Billing Cockpit, AI-assisted document processing
General availability

Billing clerks managing self-billing workflows frequently encounter invoices arriving in a mix of formats鈥擡xcel, PDF, CSV, or text files鈥攐ften unstructured and spanning multiple languages. 麻豆原创 Self-Billing Cockpit addresses this challenge by leveraging intelligent document processing to parse and extract invoice data from virtually any format, converting it into structured payloads ready for automated billing. The result is significantly reduced time spent processing invoice line items, fewer customer-specific interfaces for integration specialists to build and maintain, and improved extraction accuracy through minimized manual intervention.

Get started .

麻豆原创 Business AI for business transformation management

Joule with 麻豆原创 Signavio solutions
General availability

Process analysts and optimization specialists working across complex organizational workflows require rapid access to diagrams, documentation, and performance metrics. 麻豆原创 Signavio solutions integrate with Joule to enable natural-language keyword searches across process diagrams, dictionary items, and help resources. At the same time, best-practice KPI recommenders guide users to the most relevant success measures. This intuitive approach delivers 50% faster information searches and navigation, ensuring teams make data-driven decisions with improved search quality and an enhanced overall user experience.

Get started .

麻豆原创 Signavio solutions, AI-assisted business process model and notation simulation insights
General availability

Process analysts leveraging 麻豆原创 Signavio can now access embedded business process model and notation simulations directly within their process diagrams, eliminating the need for fragmented tools and manual interpretation. Key metrics such as costs, cycle times, and resource utilization are automatically translated into clear, actionable summaries that highlight bottlenecks and opportunities for improvement. This streamlined approach reduces time to access process modeling insights by 50%, empowering teams to compare scenarios effortlessly and communicate findings to stakeholders with greater confidence and clarity.

Get started .

麻豆原创 LeanIX solutions, AI-assisted architecture guidance
General availability

Enterprise architects seeking to accelerate transformation initiatives can leverage 麻豆原创 LeanIX to surface actionable insights directly from their architecture inventory. The feature analyzes enterprise architecture data to identify opportunities and guides users through the workflows and tasks needed to efficiently act on recommendations. Organizations benefit from a 95% reduction in time to discover insights, 80% faster transformation execution, and a five percent reduction in value erosion from delayed action. Overall, this feature drives greater architectural productivity and more agile decision-making.

Get started .

Jonathan von Rueden is chief AI officer of 麻豆原创 SE.

*Disclaimer: This article provides estimated benefits. All calculations are estimates based on 麻豆原创 customer case studies, 麻豆原创 benchmarks, and other research. Actual benefits may vary and may be affected by additional factors not considered by this article. The information is provided 鈥渁s is鈥� without warranty of any kind, expressor implied, and in no event shall 麻豆原创 be liable for any damages whatsoever in relation with the use of this article. See Legal Notice on for use terms, disclaimers, disclosures, or restrictions related to this material.

A great way to break through that roadblock is to listen to leaders who jumped in early to use AI to transform outcomes. , a managing director of Finance in the Global IT division at Accenture, is one of those people.

The professional solutions and services company employs nearly 780,000 employees across 52 countries, who work with 350 partners to serve over 9,000 clients. The idea of transformation at Accenture鈥檚 scale might be intimidating to some, but not Lambert. He鈥檚 leading an聽ongoing transformation聽of Accenture鈥檚 finance function, which he calls 鈥渢he heartbeat鈥� of the company.

The results he鈥檚 achieved鈥� including saving the finance team a combined 57,000 hours annually by having AI generate narrative summaries for reporting鈥攕hine a spotlight on what鈥檚 possible. And he鈥檚 just getting started.

I had a chance to speak with him about how he became a leader in AI-driven transformation, and what others can learn from his achievements. This is a lightly edited version of our conversation

Q: As you know, innovating with AI is about reshaping how a business delivers value. But not every business leader is leading the charge. Some are watching and waiting. Why did you roll up your sleeves and decide to be on the forefront?

A: Taking a leadership position on AI is important to keep moving forward and shaping new services and capabilities. For example, across a company our size, even though we鈥檙e hyper focused on emerging technologies, we can find small problems across our technology landscape. There are processes and data living in different places and silos develop over time. Most large companies have this challenge. But those are valuable processes, and the business data we have is especially valuable. AI opens up new opportunities to bridge those gaps and deliver more end-to-end outcomes, so that our finance function can meet the growing business expectations of our stakeholders.

For many companies, the key to getting impactful results from business AI is to start with one function that鈥檚 central to business performance. Why was finance the right place for you to begin, and what did you want to achieve?

I always say finance is the heartbeat of our organization. I heard one of our global IT leaders use that phrase, and while it was inspirational, it also made me think, 鈥淟et鈥檚 not accidentally cause a heart attack for the organization.鈥�

Jokes aside; he was right. Your transactional and operational data flows through finance, and management decisions sit on top of it. Starting there gave us the ability to make end-to-end impact across processes that touch procurement, liquidity, forecasting, receivables, and more. And 麻豆原创 gives us a digital core where all that transactional data is harmonized.

The bottom line is that finance is the natural starting point if you want to move from reactive reporting toward more proactive, AI-driven insights that you can use to help move the business forward. So, we set out to unify data and transform finance processes in a way that scales across the whole value chain.

Cash and liquidity are so important in the finance function, and to an entire company. But managing it requires bringing together data, forecasting, and decision-making across many teams. How did AI help?

If finance is the heartbeat of a company, cash and liquidity are the lifeblood of your systems. Here鈥檚 a great example: Accenture engages in a lot of acquisitions, and we run operational cash in 50-plus countries, so it鈥檚 easy for decisions to default to historical, manual reviews. That鈥檚 what was happening at Accenture before a forward-thinking leader stopped by and asked if we could apply machine learning to the problem. Great leaders often ask great questions, and that one really got us thinking.

[AI] freed up 20% of our idle cash, which we could then move into global operations to fund acquisitions and strategic growth.

Eli Lambert

We took inspiration from retail: how stores treat inventory based on discounts and sales. If you treat cash like stock, you can apply those same learning models to figure out how much you really need to hold onto at any point in time. That鈥檚 how we built what we call 鈥淚ntelligent Cash.鈥� It brings all the business data together into a single data mart, a repository for structured data for a specific department or line of business, and uses machine learning to generate recommendations that our teams can act on.

AI is so good at this, and here鈥檚 what鈥檚 incredible: It freed up 20% of our idle cash, which we could then move into global operations to fund acquisitions and strategic growth. Now what used to take months, or even more than a year to build, we can now do it in days or weeks because 麻豆原创鈥檚 data cloud brings [麻豆原创] Datasphere, Databricks, and our machine-learning workloads into one place. The result is faster decision-making, better visibility, and much more accurate forecasting.

I love hearing about how you were able to use gains, delivered through strategic AI innovation, and then channel those gains into a high-value activity for the organization.聽 I know you also worked on receivables, something that impacts cash flow and customer relationships. What pain points did you face, and how did automation and machine learning transform the process?

Receivables were highly manual compared to payables. Clearing was inconsistent, and reconciliation took a lot of time because payments often come incomplete or with partial data. Anyone who works in or near finance knows exactly what I鈥檓 talking about. So, we co-developed on the 麻豆原创 platform a machine-learning-based receivables solution. It more than doubled the automation rate for receivables processing and tripled automatic reconciliation, about a 300% improvement.

As part of that, we introduced high-confidence, one-click matching recommendations that reduce errors and cut down the manual work. We saw a seven percent uplift in auto-clearing with a cash application scheduler built on the 麻豆原创 platform that delivers matches about 77% faster. All of that adds up to a more efficient receivables process, improved cash-flow visibility, and better productivity for the team.

In a global organization like Accenture, reconciling financial data and surfacing meaningful insights can be a huge amount of work. You turned to generative AI to help, which is really smart. What led you to that approach, and how is it changing your team鈥檚 day-to-day experiences?

We were dealing with balance sheet reconciliations across 50-plus countries, and the process was decentralized. I know a lot of companies face this problem. So, first, we moved everything online. Then we brought in machine learning and generative AI to analyze cost categories, summarize data, and surface important shifts.

[Our] Intelligent Financial Advisor, built on the 麻豆原创 platform, can generate narrative commentaries that are so accurate that over 90% are simply approved with little or no revision. That鈥檚 saved about 57,000 hours globally. Our teams can focus on higher-value analysis instead of manual reconciliation.

Eli Lambert

We then deployed an Intelligent Financial Advisor built on the 麻豆原创 platform that can generate narrative commentaries that are so accurate that over 90% are simply approved with little or no revision. That鈥檚 saved about 57,000 hours globally, just in controllership work, and helped us move to a three-day global close instead of five. The insights come faster and clearer, and the teams can focus on higher-value analysis instead of manual reconciliation. It鈥檚 also helping create more consistent roll-ups across regions and letting us use our talent more strategically.

I鈥檓 hearing this theme of not only measurable business gains from outputs, but the ability to better allocate time from manual, rote tasks to ones that deliver far more value for the business. That also applies to planning and forecasting. How did you bring AI into that part of the finance function?

Our planning work had grown too complex. Remember, we鈥檙e a large-scale, multifaceted global business. So, we replaced old models with 麻豆原创 Analytics Cloud, which gives us multi-year planning models enhanced by AI.

We applied it first to merger and acquisition modeling, where accuracy really matters. It lets us model very complex data sets and helps our finance team collaborate more easily across the business. The results have been more accurate forecasts, reduced risk of errors, and much better collaboration between executives and practitioners. Early results were strong, and that encouraged us to expand AI use in planning more broadly.

What advice do you have for leaders who are not as far along in using AI to supercharge business results?

First, start with a high-impact function tied to real outcomes. Then focus early on data quality and harmonization; it鈥檚 the foundation for everything that comes after. Then get your cadence right and your team working together. Hone in on the use cases that really matter to you鈥攖he best vendors can help you identify those鈥攁nd make sure to get the help you need from those vendors and their partners.

Use AI to spur growth. At Accenture, we鈥檝e been able to use AI to save significant cash in one area, which we then invest in another, high-growth process鈥攁cquisitions in our case. That鈥檚 how you use AI to really rethink your business and move it to the next level.

Eli Lambert, on advice to other enterprises

As you go, take a crawl-walk-run approach: start slow then increase the pace of scale and adoption over time. Be sure to invest in change management and upskilling as you go to spur learning and adoption. And partner closely with technology providers and system integrators who鈥檝e been there before. That accelerates everything.

The final suggestion I have is to use AI to spur growth. At Accenture, we鈥檝e been able to use AI to save significant cash in one area, which we then invest in another, high-growth process鈥攁cquisitions in our case. That鈥檚 how you use AI to really rethink your business and move it to the next level. And that鈥檚 possible today in ways that were not, even five years ago. Seize that opportunity.

I couldn鈥檛 agree more with Lambert. AI really does provide an opportunity to re-imagine entire business processes for greater impact.

To keep exploring what鈥檚 possible, at Accenture. Then see more AI use cases in and across all your , including procurement, supply chain, manufacturing, and more.

Brenda Bown is chief marketing officer for 麻豆原创 Business AI.

ANYbotics, a Swiss robotics company, has stepped into this space with a vision to shape a safer future for industrial inspection, one where robots operate as autonomous members of the inspection team, running inspection operations integrated into plant maintenance workflows.聽

This vision is embodied in the company鈥檚 鈥淎NYmal鈥�: a four-legged inspection robot designed specifically for heavy industry.

Unlike general-purpose robotics platforms, ANYmal is engineered to operate in 鈥渂ig, dirty, dusty, and dangerous鈥� environments, says Nicole Zingg, director of Technology Partnerships at ANYbotics. Places where stairs, corrosion, heat, and unreliable connectivity are the norm, not the exception.

But hardware, Zingg says, is only one part of the puzzle that makes ANYmal indispensable to customers.

Inspection robotics is about data

鈥淲e build a hardware platform,鈥� Zingg explains, 鈥渂ut inspection robotics is really about data that is consistent and trustworthy.鈥�

ANYmal autonomously navigates industrial sites to collect data that goes beyond what a human can collect alone. Beyond just visual inspection, its sensors also collect multi-modal data, including thermal imaging, ultrasonic leak detection, gas concentration detection, acoustic anomaly detection, and more. The observations are fed into what ANYbotics calls 鈥渋nspection intelligence,鈥� which transforms the collected data into actionable operational insights. The result is higher uptime, longer asset lifecycles, and, most importantly, safer working conditions for humans.

ANYmal can make a huge impact on operations. One offshore wind customer, Zingg says, has used ANYmal to manage all inspections and has eliminated the need to send personnel to a remote platform for months. When human intervention was eventually required, ANYmal鈥檚 data from prior inspections made all the difference. The customer already knew exactly what was wrong, which expert to send, and what equipment to bring鈥攁voiding costly and risky trial-and-error site visits.

Yet for ANYbotics, delivering insights is not enough if those insights are not integrated in the software systems customers use.

鈥溌槎乖� is where ANYbotics needs to be native鈥�

Through extensive user research, ANYbotics discovered that many plant operators, maintenance managers, and field service teams already run their daily operations in 麻豆原创. Work orders, asset histories, performance trends, and decisions all flow through 麻豆原创 systems. 鈥淚f customers are using 麻豆原创, 麻豆原创 is where ANYbotics needs to be native,鈥� Zingg says.

Meanwhile, 麻豆原创鈥檚 Project Embodied AI was looking for robotics companies to partner with. The project focuses on extending the impact of 麻豆原创 Business AI into physical operations by enabling robots to autonomously perform complex tasks with an understanding of the broader business context.

It was clearly a perfect fit and has delivered advantages for both companies.

On the system side, a continuous, unbroken digital thread connects ANYbotics insights from industrial inspections to data in 麻豆原创 systems, helping inform key business and operational decisions across the organization.

For end users, embedding ANYmal directly into familiar 麻豆原创 workflows can also help ease adoption, since introducing robotics into already stretched industrial workforces can trigger anxiety. Concerns about job security, workflow disruption, and complexity are common, but embedding ANYmal directly into familiar 麻豆原创 workflows can help reduce that friction, Zingg explains.

Treating robots as part of the workforce

The first major integration point was聽. Rather than sending only human technicians, customers can now dispatch work orders directly to ANYmal as they would to any other field team member. The robot then autonomously executes inspection tasks, gathers data, and reports the results directly back into a company鈥檚 麻豆原创 system.

From there, the integration expanded into asset-related scenarios and is now moving toward broader enablement via 麻豆原创 Business Technology Platform (麻豆原创 BTP), with the goal of allowing robot-generated data to land wherever customers need it in their 麻豆原创 landscape.

The ambition is not to force humans to adapt to robots, but for robots to adapt to human workflows. 鈥淎NYmal has to put data in the 麻豆原创 system, just like human team members,鈥� Zingg notes. ANYmal becomes another worker in the same operational system of record.

Project Embodied AI in practice

This combination of ANYbotics robotic technology with 麻豆原创 bridges the gap between physical operations and enterprise applications and tangibly reflects the goal of Project Embodied AI.

On the 麻豆原创 side, AI agents operate on ANYmal鈥檚 robotic systems to execute physical tasks, such as safety inspections.

On the ANYbotics side, ANYmal is a physical object that moves through space, perceives its environment, and acts within real-world constraints. ANYmal uses 麻豆原创 historic and time-series data to inform decisions while at the same time remaining fully autonomous even in environments with no connectivity.

It鈥檚 important to note, Zingg stresses, that ANYbotics has control over ANYmal鈥檚 behavior and inspection execution, while 麻豆原创 has control over the business context such as work orders, asset data, or operational priorities. It is the 麻豆原创 business context that informs how ANYmal鈥檚 insights are consumed and acted upon while ANYbotics controls ANYmal鈥檚 physical interactions.

Scaling safely and responsibly

Today, more than 200 ANYmal robots are already in productive use worldwide, with inspection deployments in heavy-industry environments that would otherwise require constant human exposure.

Safety remains central to ANYbotics. Each deployment includes extensive testing and an on-site field engineer who helps ANYmal learn and validate its environment and trains customer teams on safe operational procedures. While ANYmal is built to work independently, humans remain firmly in the loop.

A glimpse into the future

As industries face labor shortages and aging workforces, undocumented expertise can all too often be lost. With autonomous inspection robots such as ANYmal, this knowledge is captured and turned into programs that can run day in and day out across multiple sites. The captured data flows into 麻豆原创 to become organizational intelligence that survives any workforce turnover.  

ANYbotics鈥� partnership with 麻豆原创 shows that this combination of robotics and enterprise software is moving swiftly from the experimental stage to real-world implementation.

In the future, industrial inspection will be powered by AI, not as disembodied dashboards or isolated machines, but as an integrated intelligent system where physical robots and digital workflows in 麻豆原创 systems operate as one.

In that future, robots like ANYmal are no longer novelties. They are coworkers, albeit mechanical four-legged ones, quietly extending human capability into places humans were never meant to go. These robots, together with 麻豆原创, are shaping for a future where dirty, dangerous, and dusty industrial inspections are being transformed into business insights.

Top image courtesy of ANYbotics

Once closed, the acquisition will strengthen 麻豆原创 Business Data Cloud (麻豆原创 BDC)鈥攊ntegral for 麻豆原创’s AI-First and Suite-First strategy鈥攁nd accelerate the evolution of 麻豆原创 BDC to a fully interoperable enterprise data platform for enterprise-wide agentic AI. It will provide customers with the tools they need to unify, cleanse and harmonize data across sources for superior enterprise-wide agentic AI.

“Reltio is a natural fit with 麻豆原创,鈥� said Muhammad Alam, member of the Executive Board of 麻豆原创 SE, 麻豆原创 Product & Engineering. 鈥淎cquiring them will further improve our position as a leading business AI provider, combining 麻豆原创 and non-麻豆原创 data to deliver data context that business AI requires. AI cannot reach its full potential when data is fragmented across business units, platforms and domains without connection or context.鈥�

By integrating Reltio after closing the acquisition, 麻豆原创 will make customers’ enterprise data fully AI-ready. Customers will be able to rely on trusted, high-quality data across 麻豆原创 and non-麻豆原创 sources that Joule and Joule Agents use to deliver faster time-to-value for business AI.

Reltio鈥檚 platform helps organizations manage and govern structured and unstructured enterprise data from start to finish. Its AI-based entity resolution identifies and merges related records from different formats and applications into one reliable 鈥済olden record鈥� system of context. Its cloud-native, AI-first design supports a single, consistent view of customers, products, suppliers, locations and employees across both 麻豆原创 and non-麻豆原创 applications. Customers running AI tasks will benefit from increased reliability and consistency of data, bundled in a single source of truth, improving business AI. With that, customers can trust that AI results are correct, and AI-interactions are resolved fast.

鈥淛oining forces with 麻豆原创 presents a tremendous opportunity for us to accelerate our mission,鈥� Reltio Founder and CEO Manish Sood said. 鈥淓nterprise AI needs trusted context that is open and interoperable across the heterogeneous IT landscapes our customers run. This combination accelerates our ability to deliver Reltio as the system of context across 麻豆原创 and non-麻豆原创 environments, while maintaining continuity for our customers and our partner ecosystem.鈥�

Reltio’s data cleansing, unification capabilities and agent-driven workflows will work alongside 麻豆原创 Business Suite applications to improve decisions, reduce integration complexity and deliver trusted, consistent data critical for successful business processes and AI use cases. Low latency delivery and support for the Model Context Protocol (MCP) enable real-time, multiagent workflows across 麻豆原创 and non-麻豆原创 environments, allowing AI agents, such as a procurement agent, to assess supplier risk and trigger actions almost instantly using trusted, real-time data. Reltio offers prebuilt, industry-specific 鈥渧elocity packs鈥� that include data models, rules, matching logic and integrations, and solutions tailored to sectors like life sciences, healthcare and financial services.

By integrating Reltio after closing the acquisition, 麻豆原创 intends to accelerate its customers’ ability to govern and expose master data as trusted and context-rich data products across multiple sources that serve both traditional analytics workloads and AI agents. Reltio will become a core capability within 麻豆原创 BDC, with a flexible commercial model where customers can purchase Reltio as a separate solution or with other 麻豆原创 products. The Reltio portfolio will also remain available as a standalone offering for the foreseeable future.

The transaction is expected to close in Q2 or Q3 of 2026, subject to customary closing conditions, including regulatory approvals.

Visit the . Get 麻豆原创 news via  and .

About Reltio

Reltio is a leader in data unification and management, delivering cloud-native, AI-native master data management (MDM) to help enterprises create trusted data and unlock context intelligence for analytics, automation, and agentic AI. Designed for complex, multi-vendor environments, Reltio helps organizations unify, cleanse, harmonize, govern, and activate core data from multiple sources in real time鈥攁cross 麻豆原创 and non-麻豆原创 systems. The Reltio Data Cloud uses advanced entity resolution, continuous data quality, and relationship intelligence within an intelligent data graph to connect data across systems and reveal the full context behind customers, products, suppliers, and other key business entities. This enables organizations to reduce data friction, improve operational execution, and accelerate time to trusted decisions. For more information, visit .

About 麻豆原创

As鈥痑 global leader in enterprise applications and business AI, 麻豆原创 (NYSE:麻豆原创)鈥痵tands at the鈥痭exus鈥痮f business and technology. For over 50 years, organizations have trusted 麻豆原创鈥痶o bring out their best by uniting business-critical鈥痮perations spanning finance, procurement, HR, supply chain, and customer experience. For more information, visit鈥�.

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鈥淭here鈥檚 so much data; I would say in esports, too much data,鈥� said Thom Valks, partnerships manager at Team Liquid, referring to the 1 trillion points of data his company deals with. 鈥淗ow do you figure out what the right questions are to ask? And then how do you get quick answers to those questions? That was the main problem.鈥�

Founded more than two decades ago, Team Liquid has become a global powerhouse in professional gaming.

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鈥淲e鈥檙e the biggest esports organization in the world,鈥� Valks said. 鈥淕aming is a huge industry, a billion-dollar industry nowadays. And esports is at the tip of the pyramid. People come to watch with thousands in stadiums like normal sports. And we are the best team in the world at it.鈥�

Before partnering with 麻豆原创, Team Liquid relied on spreadsheets and manual analysis.

鈥淲e were doing everything in Excel and manually combing through the data, which turned out to be really not doable,鈥� Valks explained. In 2018, the team began working with 麻豆原创 Business Technology Platform (麻豆原创 BTP), connecting directly to game publishers鈥� APIs for League of Legends and Dota. This allowed analysts to build dashboards and streamline data processing.

The impact was immediate: 鈥淏efore we partnered with 麻豆原创, I think we had something like four or five analysts per game. If we can off source that to a tool and focus on really important data questions, that鈥檚 way more beneficial. And I would say the last year or so with AI, it鈥檚 really taken a next step.鈥�

Now, Team Liquid is taking its relationship with 麻豆原创 one step further by turning to Joule to sort through the data and make decisions even faster.

鈥淛oule has taken the data that we have in our database and you can now ask it: 鈥楩ind me the best hero to play against this team over the last six months.鈥� It will turn out an answer that actually makes sense. That’s revolutionary for us.鈥�

Looking ahead, Team Liquid hopes to expand access to Joule across the organization. 鈥淚f we can get Joule to everyone, it really innovates their gameplay,鈥� Valks said. 鈥淭hat鈥檚 something our competitors should be really afraid of.鈥�

Matt Dillman is a senior videographer at 麻豆原创.

Now, however, many economists and policymakers view AI as a potential catalyst for reversing that slowdown. AI鈥攅specially the rise of generative AI and AI agents鈥攊s widely expected to shape the next phase of productivity growth in advanced economies, including those in the U.S. and Europe.

The key question for business leaders is not whether AI will matter, but how large the productivity gains will be, how quickly they will materialize, and which region will benefit most.

Productivity growth

The (OECD) estimates that AI could raise annual labor productivity growth in advanced economies by roughly 0.4 to 1.3 percentage points, depending on adoption intensity and sector exposure. These gains would be meaningful because even an additional half percentage point of annual productivity growth compounds significantly over a decade.

However, the OECD and other economists stress that outcomes depend heavily on complementary investments in digital infrastructure, workforce training, and organizational change, rather than on technology alone.

Between 1995 and 2019, U.S. labor productivity grew at 2.1% annually compared to one percent in Europe. This disparity arose in part because companies in the U.S. invested more aggressively in information, communications, and technology while those in Europe were constrained more by regulatory and other factors.

Expectations for AI-driven productivity gains remain generally stronger in the U.S. than in Europe. suggests that widespread adoption of generative AI could raise U.S. labor productivity growth by around one to 1.5 percentage points per year.

Several structural factors support this view. The U.S. has a deep technology ecosystem, global leadership in AI research and venture capital, and a large, digitally intensive services sector, including finance, professional services, and IT, where generative AI tools can be rapidly deployed.

Agentic AI

In both Europe and the U.S., AI agents represent a particularly important development. Unlike earlier automation tools that handled isolated tasks, AI agents鈥攍ike Joule Agents from 麻豆原创鈥攁re designed to plan, reason, and execute multi-step workflows. For example, an agent might manage customer service tickets, draft responses, query databases, escalate issues, and update systems鈥攁ll with limited intervention.

In knowledge-based industries, this kind of workflow automation could significantly raise output per worker. But rather than replacing entire occupations, AI agents may reduce time spent on repetitive administrative and 鈥渓ong-tail鈥� tasks, enabling workers to focus on higher-value analysis, strategy, and interpersonal activities.

Despite stories about failed corporate AI projects, which can typically involve bolt-on or stand-alone AI pilots rather than a more integrated, holistic approach, recent evidence from the U.S. suggests that productivity gains are already emerging in some sectors. For example, financial institutions have reported significant efficiency improvements in back-office operations through AI deployment.

Similarly, experimental studies in professional services show that generative AI can increase output quality and speed, particularly for less experienced workers, effectively narrowing skill gaps within teams.

European outlook

The outlook for productivity gains in Europe from AI is more mixed. According to a recent the medium-term gain in productivity from the AI alone would vary considerably across countries, and for Europe as a whole would be rather modest: about 1.1 percent cumulatively over five years.

But with pro-growth reforms, the IMF suggests that much bigger gains are possible over the longer run. Like the OECD, the IMF emphasizes that regulatory frameworks, labor market structures, and the pace of technology diffusion will strongly influence outcomes.

Several structural differences shape Europe鈥檚 trajectory and the size of what has been called the 鈥淎I growth dividend.鈥� First, AI adoption among small and midsize enterprises (SMEs), which form a larger share of the European economy than in the U.S., tends to be slower. Second, Europe鈥檚 digital market remains more fragmented across national boundaries, languages, and regulatory systems, which can complicate scaling technology platforms. Third, the European Union has taken a more precautionary regulatory approach to AI governance. While this may reduce certain risks, it could also dampen short-term productivity gains if compliance burdens slow deployment.

Europe鈥檚 strengths

That said, Europe has strengths. It leads in advanced manufacturing and industrial engineering, sectors where AI-driven optimization, robotics, and predictive maintenance can raise capital productivity. In these areas, AI agents embedded in industrial systems could significantly enhance supply chain efficiency and reduce downtime.

In addition, as 麻豆原创 executives have pointed out, Europe has an enormous repository of structured business and manufacturing data, which is essential for reliable and effective AI systems as well as trust in AI Agents.

If AI adoption accelerates in manufacturing and energy systems and if European companies seize the opportunity to build advanced AI agents and apps using their business data, Europe could see much more robust medium-term productivity gains. As an example, 麻豆原创’s internal use of AI tools has already significantly improved its own developer productivity.

Labor flexibility

A critical factor in both the U.S. and Europe is labor market adjustment. Historically, the U.S. labor market has demonstrated greater flexibility, with higher rates of job switching and occupational mobility. This flexibility may facilitate faster reallocation of workers into AI-complementary roles, amplifying productivity gains, though this could be offset by more effective existing workforce retraining.

As the (BIS) has noted, AI鈥檚 productivity effects are unlikely to be automatic. Productivity gains from AI depend on complementary investments in skills, management practices, and digital infrastructure. The BIS warns that without these, AI tools may produce only marginal efficiency improvements.

The historical lesson from past general-purpose technologies, such as electricity and IT, is that productivity surges occur only after organizations redesign processes to exploit new capabilities and take a holistic rather than piecemeal approach toward implementation.

No AI bubble

While some investors have expressed concerns about an AI bubble, total AI spending in the U.S. is still below one percent of GDP. Joseph Briggs, senior global economist at Goldman Sachs, notes that this is well below historical infrastructure cycles. For comparison historical infrastructure investments such as IT spending, railroads and canals typically represented between two and five percent of GDP.

Like these previous investment waves AI, particularly agentic AI, is likely to generate significant productivity growth and a corresponding boost to GDP in those regions and sectors that seize the AI opportunity.

Over decades, our customers have built powerful digital foundations on 麻豆原创 to run end-to-end business processes across their enterprises鈥攐ften extending and customizing these systems to support their unique business needs. Now, many are entering the next phase of transformation: modernizing their 麻豆原创 landscapes to unlock the full potential of AI.

As companies move to cloud-based 麻豆原创 environments and clean-core architectures, they are preparing to embed intelligence directly into business processes. This enables new forms of automation, with AI agents that operate across enterprise systems and execute increasingly complex tasks.

Modernizing these systems while introducing AI at scale is a significant undertaking. It requires technologies that integrate with existing applications, operate reliably within mission-critical workflows, and meet the governance standards enterprises demand.

That鈥檚 why, over the past few years, we have partnered with to combine advanced AI technology with deep business context. Our goal is to help organizations accelerate modernization and apply AI across the applications and processes key to their success. This collaboration will be showcased at NVIDIA GTC.

Building the foundation for enterprise-grade AI

Through our collaboration with NVIDIA, we are accelerating the entire life cycle of enterprise AI鈥攆rom model development to high-performance runtime execution鈥攁nd powering AI scenarios across our portfolio. ,  which consists of open libraries such as and , helps accelerate large-scale model training across distributed RL environments. It enables teams to build and refine enterprise-grade AI models faster.

Models are hosted through  and , where our customers and partners leverage those best suited to their use cases. microservices optimize inference performance, and we have observed up to a 20% improvement compared to another popular open source serving engine. Enabled by NVIDIA GPUs and NVIDIA NIM, the increased performance allows organizations to combine advanced AI models with trusted 麻豆原创 business data and processes to ensure that AI operates within the workflows that drive business operations.

Modernizing the business logic that runs the enterprise

AI models trained on 麻豆原创 knowledge and accelerated using NVIDIA technologies are already helping customers tackle some of their most pressing modernization challenges. For example, evolving business logic embedded in the 麻豆原创 systems that run their operations.

For decades, organizations have extended 麻豆原创 applications with custom ABAP code that reflects how their businesses operate. That logic captures years of operational knowledge across finance, supply chain, service processes, and more. But modernizing these environments for the cloud and preparing them for the next generation of AI-driven innovation can be complex.

To help accelerate this journey, 麻豆原创 developed . a foundation model trained exclusively on real-world ABAP code and the business logic used across 麻豆原创 environments. The solution incorporates specialized models for code-related tasks, including StarCoder2 for code completion, and Codestral for deeper code understanding and explanations. These models are served through NVIDIA NIM microservices to deliver high-performance inference.

brings these capabilities into the developer experience, helping teams analyze existing ABAP code, understand how customizations interact with core business processes, and generate new code when needed. By making decades of embedded business logic easier to interpret and update, we help organizations accelerate modernization and preserves the knowledge that makes their operations unique.

Connecting AI to business operations

The collaboration between 麻豆原创 and NVIDIA also explores how AI can operate within enterprise workflows to help organizations apply intelligence across both physical operations and complex planning environments. One emerging area is embodied AI, in which intelligence extends beyond software systems into the physical world. By combining AI reasoning with sensors, robotics, and enterprise data, organizations can connect real-world observations directly with digital business processes.

For example, predictive maintenance alerts from can trigger robotic inspections that analyze equipment using thermal, visual, and acoustic signals. These signals are evaluated alongside asset histories and maintenance records to identify potential issues. then orchestrates follow-up actions through , prioritizing work orders and guiding technicians with the right operational context. By linking physical-world insights with enterprise workflows, organizations can turn physical-world signals into coordinated enterprise actions.

The same principle applies to complex planning environments. Supply chains today must manage a constantly shifting web of constraints, from supplier availability and transportation disruptions to evolving customer demands. With NVIDIA, we are exploring technologies, such as NVIDIA Metropolis and NVIDIA Cosmos, to bring the latest AI advancements into warehouse management, safety, and asset inspection.

Together, we are also bringing new capabilities to  that combine agent-based reasoning with the  GPU-accelerated optimization engine. This enables planners to simulate complex supply chain scenarios and evaluate alternatives with more speed and accuracy. By integrating advanced optimization with 麻豆原创鈥檚 supply chain planning capabilities, organizations can dynamically model constraints, adapt plans as conditions change, and make more confident decisions in increasingly complex environments.

Collaboration with large-scale 麻豆原创 customers helps identify real operational bottlenecks, paving the way for AI-driven solutions. . The Taiwan-based global electronics manufacturer and manufacturing solutions provider will work with 麻豆原创 to develop AI-powered innovations for manufacturing and supply chain operations.

By combining 麻豆原创鈥檚 enterprise applications and business context and Foxconn鈥檚 manufacturing expertise, organizations can enhance operational efficiency, increase resilience, and advance decision-making across complex production and supply networks.

Experience agentic AI at NVIDIA GTC

麻豆原创 is enabling Joule Agents across its application portfolio, helping organizations automate tasks and coordinate complex workflows within business processes. At NVIDIA GTC, visitors will see how these capabilities are extended using  on to build agents tailored to specific enterprise scenarios.

And because 麻豆原创鈥檚 AI architecture is model-agnostic, organizations can bring their own models into these workflows, in addition to those deployed through 麻豆原创 AI Core. The hands-on experience at NVIDIA GTC will demonstrate how organizations can build AI-driven workflows that operate directly within the enterprise systems that run their business.

It all happens at , taking place March 16-19, 2026. Join us to see how 麻豆原创 and NVIDIA are helping organizations modernize enterprise systems, accelerate AI adoption, and move toward the AI-native enterprise:

• Attend our session: on Tuesday, March 17, from 2:00-2:40 p.m.
• Visit the 麻豆原创 booth, #2001, to explore agentic AI in action, participate in hands-on vibe-coding with Joule Studio, and witness next-generation enterprise automation

.

Brenda Bown is chief marketing officer for 麻豆原创 Business AI.

This has led to a 鈥淭erminal Renaissance,鈥� a return to text-in, text-out interaction.

For many applications, text works, letting users express intent naturally with no onboarding. However, text struggles to convey structured data that is common in business, and without real-time updates, static text results lose relevance the moment they鈥檙e generated.

Structured data is easier to digest when users can filter, sort, and visualize it鈥攖hat is why graphical user interfaces (GUIs) excel at presenting structured data and guiding users through complex workflows. But GUIs are expensive to build and rigid, forcing generic, one-size-fits-all solutions that struggle to provide the fluid, tailored experiences users now demand.

Text is flexible but limited; GUIs are robust but rigid. Generative UI is the unmet need between them and the new frontier for business software.

From static dashboards to dynamic workspaces

Imagine a procurement manager investigating a supply chain disruption. Instead of navigating five different applications and manually cross-referencing data, she asks: 鈥淪how me the suppliers at risk in Southeast Asia and model alternative sourcing scenarios.鈥�

This request sets agents to work behind the scenes. They gather and analyze live data, simulate outcomes, and calculate the projected impact of every alternative. Execution agents are also pre-positioned and ready to act on command.

The user doesn鈥檛 have to deal with any of this complexity. For them, a dynamic interface materializes in seconds鈥攏ot a generic dashboard, but a purpose-built mission control center. Interactive maps highlight affected regions and supply chain graphs update in real time. As the user tweaks parameters, risk scores adjust instantly. Embedded controls stand ready to trigger purchase orders or notify suppliers, enabling the user to decide and execute. Collaboration is simplified; colleagues can join a living workspace: no briefing decks, no context-setting calls.

This is the future: a business suite where a user鈥檚 intent defines their interface and their decisions drive action. To get there, we are combining Joule and Joule Agents with our vision for generative UI. This is not just about on-demand dashboards; it鈥檚 about steering a business with interfaces that adapt to each user’s role, context, and tasks. This is 鈥渧ibe coding鈥� for enterprise operations: shifting focus from syntax to intent.

We are entering an era where AI constructs UIs on the fly, allowing users to engage with them immediately. Generative UI marks the transition from static software suites to 鈥渂atch size 1鈥� applications that act like ephemeral control centers tailored to a specific problem.

Challenges and 麻豆原创鈥檚 answers

Delivering an intent-driven business suite at enterprise scale requires addressing complex realities. We are building generative UI because we understand its promise and its perils鈥攁nd we have unique assets to bridge that gap.

Accuracy

Large language models (LLMs) can produce plausible but incorrect outputs, or 鈥渉allucinate.鈥� A consumer chatbot that hallucinates a movie plot is tolerable; a procurement system that misrepresents supplier terms has real consequences. Our generative UI approach addresses this by visualizing data directly from systems of record with transparent lineage. Grounding the UI in real-time, trusted data is our first defense against inaccuracy.

Trust

If every interface is generated on the fly, how do users know it is reliable? Trust is built on consistency and predictability. Our generative UI is built on the familiar and proven architectural grammar of 麻豆原创 Fiori for lists, dashboards, and workflows. The content is bespoke and the structure is consistent and familiar, so users can always judge and adjust with confidence.

Complexity

Enterprise systems are sophisticated and unique. They are built over decades, encoding massive domain knowledge and business logic. Generative UI builds on Joule鈥檚 existing integration and orchestration capabilities, which already connect to systems across a landscape and coordinate agents to execute complex workflows. Generative UI leverages this foundation, letting users interact with deeply integrated processes through simple interfaces while Joule handles the orchestration underneath.

Why this matters now

The expectations set by consumer AI are real, and the gap between what employees experience at home and what they use at work is widening.

The future of enterprise software isn’t chatbots bolted onto legacy screens. It’s bespoke mission control鈥攊nterfaces that materialize around a user鈥檚 intent, grounded in live data, executed by agents, and governed by the user.

With that, we鈥檙e reimagining how work gets done.

Jonathan von Rueden is chief AI officer of 麻豆原创 SE.

Headquartered in Nuuk and 100% owned by the Government of Greenland, Royal Greenland is modernizing its 麻豆原创 platform and moving from on premise to cloud ERP in order to future鈥憄roof core processes and unlock embedded AI across its 麻豆原创 business applications.

鈥淲e are moving from our existing setup to 麻豆原创 Cloud ERP and 麻豆原创 Business Data Cloud because we want access to the capabilities you can consume on a cloud platform,鈥� said Lars Bo Hassinggaard, CIO at Royal Greenland for more than 25 years.

The company brings high鈥憅uality wild鈥慶aught fish and shellfish from the North Atlantic and Arctic Ocean to consumers worldwide. It has been running 麻豆原创 since 1998 but is now embarking on its most significant transition to date: migrating 麻豆原创 ERP Central Component to 麻豆原创 Cloud ERP while simultaneously elevating its business intelligence (BI) landscape into 麻豆原创 Business Data Cloud and later transforming BI into 麻豆原创 Datasphere.

The project follows the structured RISE with 麻豆原创 framework, which consolidates platform transformation, operations, and the innovation cycle into one contract.

Lean, selective data transition: 90% fewer data to move

As part of the migration, Royal Greenland is reducing its data volume significantly using the 鈥淟ean Selective Data Transition鈥� method.

鈥淲e are keeping 10 years of data and cleaning up, so we avoid outdated company codes and historical data that no longer create value,鈥� Hassinggaard explained. 鈥淲e鈥檝e achieved a 90% reduction in what needs to be stored and migrated. The method combines data analysis, scoping, and standardized mapping objects in a guided process, ensuring that Royal Greenland only carries forward what is truly necessary, making the financials of the transformation more predictable and avoiding unnecessary complexity.鈥�

Technology first, innovation next

Go鈥憀ive is planned for March 1, 2027. The year 2026 is dedicated to the platform lift itself. From 2027, Royal Greenland will begin building business鈥慸riven improvements on top of the standardized core鈥攆or example, new user interfaces and process optimization using small AI agents within finance and administration.

鈥淩oyal Greenland and 麻豆原创 have worked together since 1998, and we look forward to getting started on the technical part of the platform uplift this January,鈥� Hassinggaard shared. 鈥淲e鈥檙e keeping the transformation as simple as possible for now and will use 2027 to activate the benefits, such as improved data analysis, better user experience, and more efficient work processes.鈥�

Royal Greenland is following a classic waterfall approach and has already established a 鈥済olden shell鈥� as the basis for further configuration and retrofitting.

麻豆原创 is responsible for implementing the cloud solution, which will run on Microsoft Azure, initially in Sweden, with the option to move later to a Danish data center. External advisor Spektra Analytics has supported contract validation.

From in鈥慼ouse experiments to standardized, 鈥渃onsumed鈥� AI

Although Royal Greenland has already successfully experimented with its own AI solutions, including vision鈥慴ased projects in production, the strategic direction ahead is to leverage embedded, standardized AI data products from 麻豆原创 and models built on the 麻豆原创 Business Data Cloud and its semantic data layer.

鈥淲e are a company that prefers to tap into existing AI solutions rather than invent them ourselves,鈥� Hassinggaard said. 鈥淚t鈥檚 far more efficient for us. There is no reason for us to spend resources reinventing what 麻豆原创 already provides. The initial focus will be on process optimization within administrative functions such as finance鈥攕mall AI agents that can streamline daily work.鈥�

Advice to others: Allocate more time, and understand your method

Hassinggaard is clear that the RISE with 麻豆原创 contract, methodology, and preparation work require time and organizational maturity. His advice to other companies facing a similar cloud ERP decision: 鈥淒o it thoroughly鈥攁nd allocate more time than you think. Study the methodology, pricing, and contracts. And bring a competent advisor on board.鈥�

Ellen Vig Nelausen is an integrated communications expert for 麻豆原创 Regional Communications.

Together, the companies plan to create the digital backbone for a sovereign, interoperable and AI-supported healthcare system. The solutions will combine the expertise of Fresenius, one of the world鈥檚 largest healthcare companies, with future-oriented 麻豆原创 technologies and meet high requirements for data sovereignty, security and regulatory compliance. The plan is to provide an open, integrated and data鈥慸riven digital health ecosystem that enables hospitals and medical facilities worldwide to use AI securely and to handle health data responsibly.

Digital sovereignty for healthcare

麻豆原创 and Fresenius plan to jointly build an individual, scalable healthcare platform that enables connected, data-driven healthcare processes. Based on this, the companies will develop joint, future-oriented and AI-supported healthcare solutions to sustainably increase quality, transparency and efficiency across the entire care chain and set new standards for digital innovation in the healthcare sector. The foundation will be proven 麻豆原创 technologies and products such as 麻豆原创 Business Suite, 麻豆原创 Business Data Cloud (麻豆原创 BDC), 麻豆原创 Business Technology Platform (麻豆原创 BTP) and 麻豆原创 Business AI. These core elements help create a unified, compliant, open and expandable base for the more-secure exchange and use of data as well as for operating AI models in a controlled environment.

Together, the companies also plan to build a sovereign, European solution for an integrated healthcare ecosystem that supports the integration of modern hospital information systems (HIS) based on 麻豆原创鈥檚 鈥淎nyEMR鈥� strategy. Interfaces based on open industry standards such as HL7 FHIR will enable the more-seamless connection of HIS, electronic medical records (EMRs) and other medical applications.

鈥淲ith 麻豆原创鈥檚 leading technology and Fresenius鈥� deep healthcare expertise, we aim to create a sovereign, interoperable healthcare platform for Fresenius worldwide. Together, we want to set new standards for data sovereignty, security and innovation in healthcare. Thanks to 麻豆原创, Fresenius can harness the full potential of digital and AI-supported processes and sustainably improve patient care,鈥� says Christian Klein, CEO and Member of the Executive Board of 麻豆原创 SE.

鈥淭ogether with 麻豆原创, we can accelerate the digital transformation of the German and European healthcare systems and enable a sovereign European solution that is so important in today鈥檚 global landscape. We are making data and AI everyday companions that are secure, simple and scalable for doctors and hospital teams. This creates more room for what truly matters: caring for patients,鈥� adds Michael Sen, CEO of Fresenius.

As part of the joint transformation project, both companies plan to invest a mid three-digit million euro amount in the medium term to consistently drive the digital transformation of the German and European healthcare system through the use of digital and AI-supported solutions.

The partnership is implemented through various forms of collaboration. These include joint investments in startups and scaleups, joint technological developments and close cooperation within coordinated governance structures between the two companies.

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Media contact:
Dana Roesiger, +49 62277 7 63900, dana.roesiger@sap.com, CET
麻豆原创 麻豆原创 Room; press@sap.com

This document contains forward-looking statements, which are predictions, projections, or other statements about future events. These statements are based on current expectations, forecasts, and assumptions that are subject to risks and uncertainties that could cause actual results and outcomes to materially differ.聽 Additional information regarding these risks and uncertainties may be found in our filings with the Securities and Exchange Commission, including but not limited to the risk factors section of 麻豆原创鈥檚 2024 Annual Report on Form 20-F.
漏 2026 麻豆原创 SE. All rights reserved.
麻豆原创 and other 麻豆原创 products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of 麻豆原创 SE in Germany and other countries. Please see for additional trademark information and notices.

That鈥檚 why, in Q4 2025, we significantly enhanced the way customers work with AI through new models, sovereign cloud offerings, and partnerships, alongside numerous product updates. Let鈥檚 dive straight in.

麻豆原创-RPT-1 is a novel AI model that is optimized explicitly for predictions on tabular data. While LLMs predict the next word in a text sequence, 麻豆原创-RPT-1 forecasts the next field in a table row; it can interpret relational business data and handle virtually any predictive task. Additionally, as our single, universal AI engine, 麻豆原创-RPT-1 enables customers to simplify their approach to working with AI by eliminating the need for a myriad of narrow AI specialist models, each requiring arduous training, maintenance, and investments. 麻豆原创-RPT-1 also requires 50,000 times less energy, 100,000 fewer GPU FLOPs, and offers up to 3.5 times better predictions and 50 times more speed than state-of-the-art LLMs. Announced at 麻豆原创 TechEd and now available in our generative AI hub, customers can leverage the .

EU AI Cloud is our new full-stack sovereign cloud offering that supports EU data residency and full sovereignty. It makes meeting regulatory and operational requirements easier by giving customers complete control over their infrastructure, platform, and software. Customers can deploy it on 麻豆原创鈥檚 own data centers, on trusted European infrastructure, or as a fully managed solution on-site. Now, European enterprises and public sector organizations can benefit from the latest AI innovations securely, in full compliance with European standards and with the sovereignty and flexibility they need.

We also took steps to simplify our customers鈥� data landscape and preserve the business context of all data. 麻豆原创 Snowflake combines and (麻豆原创 BDC). This partnership enables zero-copy data sharing across Snowflake and 麻豆原创 BDC Connect. Customers using Snowflake can integrate their existing instances with 麻豆原创 BDC for seamless, real-time access to combined, semantically rich 麻豆原创 and non-麻豆原创 data in 麻豆原创 BDC. 麻豆原创 Snowflake will be made generally available in Q1 2026, and 麻豆原创 BDC Connect for Snowflake will be available later in H1 2026.

Furthermore, 麻豆原创鈥檚 generative AI hub includes the latest frontier models from Mistral, OpenAI, Gemini, and Anthropic, allowing customers to implement the model that best suits their specific use cases. The 350 AI features, including Joule Agents, along with the over 2,400 Joule skills, are already delivering unparalleled value to customers鈥攂uilt on AI Foundation in  (麻豆原创 BTP).

Here are some of the highlights from Q4 2025:

• Joule was more integrated than ever in Q4. The bidirectional integration with Microsoft 365 Copilot offers a unified user experience, allowing users to access insights directly within their workflows. Joule for Consultants has enhanced citation visibility, while Joule deep research capability provides users with synthesized explanations for complex inquiries that draw on both internal and external data鈥攕tructured or unstructured鈥攗sing capabilities like Model Context Protocol, document grounding, and Perplexity. Joule analytics center offers customers granular insights into user adoption, and the Joule preview landscape provides a dedicated customer environment for testing and validating software updates before they are released to production. Explore all the new capabilities for Joule in the section below as well as within the specific products.
• 麻豆原创 Business AI for supply chain delivers unprecedented clarity. New analysis capabilities in 麻豆原创 Integrated Business Planning summarize complex optimization, inventory, and forecast results, translating intricate calculations into clear, natural language. The new Production Planning and Operations Agent automates prerequisite checks for releasing production orders by identifying material shortages and suggesting workarounds to prevent delays. There鈥檚 more to discover below.
• 麻豆原创 Business AI for human resources is transforming talent management and reducing administration. The Performance Preparation Agent automates data collection and generates talking points to ensure managers are fully prepared for more impactful one-on-one meetings. Employees can also boost internal mobility by identifying and surfacing hidden skills directly from their resumes. There is so much more to explore; dive into everything below.
• 麻豆原创 Business AI for finance is packed this quarter, with new agents automating more complex processes. The Accounting Accruals Agent helps expedite the period-end close. The International Trade Classification Agent ensures robust compliance for global shipping, and the Cash Management Agent provides unparalleled oversight of cash flow. Joule also now assists with master data governance, analyzes allocation run results, and simplifies risk management tasks. There is just the beginning in finance, so check out everything below.
• With 麻豆原创 Business AI for IT and developers, customers can build, automate, and analyze more quickly and easily than ever. Joule Studio agent builder is in GA and enables users to create custom AI agents that automate complex, end-to-end business processes. To manage this growing landscape, the new AI agent hub in 麻豆原创 LeanIX offers a central dashboard for governing agents. 麻豆原创 is also introducing its own foundational models: 麻豆原创-RPT-1, a new model for structured business data, and 麻豆原创-ABAP-1 to efficiently understand ABAP code. See more below.
• The latest 麻豆原创 Business AI innovations for spend management, procurement, and customer experience are simplifying complex processes and making them more personalized. In spend management, the new Booking Agent simplifies trip planning with tailored recommendations, while the Receipt Analysis Agent ensures accurate expense reports. Procurement customers can use natural language to route demands in 麻豆原创 Ariba and automate the creation of statements of work in 麻豆原创 Fieldglass. In customer experience, marketers can now instantly build reports in 麻豆原创 Emarsys using simple prompts, and service agents receive AI-generated summaries to resolve billing inquiries more efficiently.

Joule

Joule with Microsoft 365 Copilot
General availability

Bidirectional integration between Joule and Microsoft 365 Copilot has been completed. This integration helps deliver a unified user experience across enterprise systems. Users can now access Joule capabilities directly from within Microsoft 365 Copilot, bringing Microsoft-powered insights into the generative AI environment in Joule.

This tight interoperability will help strengthen how organizations work, collaborate, and make decisions within their 麻豆原创 and Microsoft landscapes.

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Joule Analytics Center
General availability

麻豆原创 customers, including IT administrators and development teams, can now utilize the Joule Analytics Center to gain granular, tenant-specific insights into user adoption and engagement. This interactive dashboard enables them to filter and visualize usage data by product, scenario, interaction type, and client, revealing precisely how end-users are leveraging Joule over time. By analyzing these trends and specific usage patterns, organizations can gain a clear understanding of the most impactful scenarios, identify opportunities for improvement, and make data-driven decisions to optimize the overall user experience.

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Joule Preview Landscape
General availability

麻豆原创 customers, including IT administrators and development teams, can also leverage the Joule Preview Landscape, a dedicated environment within 麻豆原创 BTP designed to provide greater visibility and control over software updates. Addressing the previous challenge of deploying new capabilities to all tenants simultaneously, this landscape introduces a crucial validation period. Customers can test and validate new Joule framework updates for two weeks and content updates for four weeks before they are released to production systems. This proactive approach allows teams to thoroughly assess the impact of changes, identify potential issues, and ensure a seamless transition, ultimately empowering them to adopt new features with confidence while avoiding disruptions to live business operations.

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麻豆原创 Joule for Consultants, product enhancements
General availability

麻豆原创 consultants can now benefit from enhanced features within 麻豆原创 Joule for Consultants, designed to improve trust and the quality of answers. The conversational solution offers greater transparency, with improved citation visibility that clearly displays all information sources, including public web searches. We have begun integrating more than 9 TB of 麻豆原创-exclusive, gated content, including the Implementation Guide (IMG), 麻豆原创 Simplification List, and the 麻豆原创 Enterprise Architecture Reference Library. The tool鈥檚 knowledge base is continually updated with the latest information from 麻豆原创 Learning, 麻豆原创 Help, and additional sources, including 麻豆原创 News, the AI Feature Catalogue, and other related public sources.

This provides consultants with a more trustworthy experience by showing exactly where information comes from, while the expanding knowledge base helps them deliver more complete, accurate, and context-rich answers to their queries, thanks to the increased input character count, which has expanded from 2000 to 10000.

We鈥檝e also enabled new functionality 鈥� Console, which provides access to the latest release notes, usage metrics (Admin-Only), an integrated Prompt Library, and system settings (Admin-Only) for both Standard and Administration-level users. Additionally, we have initiated limited pilot programs that enable the direct incorporation of customer-specific documents into 麻豆原创 Joule for Consultants, allowing for more personalized and tailored consulting experiences. These pilots are designed to test the integration of client proprietary information with the broader knowledge base, ensuring that consultants can access both general industry insights and customer-specific data within a single, secure platform.

With 麻豆原创 Joule for Consultants, consultants can save up to 1.5 hours per day through faster, more precise knowledge access, up to 50%* fewer design iterations and subsequent rework, and 14%* faster project execution (see this for details).

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Joule deep research capability
Beta release

麻豆原创 users across various functions can now unlock a profound understanding of their business challenges through Joule deep research capability. This advanced feature enables them to submit complex inquiries and receive not just data, but expertly synthesized explanations and contextual insights, intelligently drawing from both their internal 麻豆原创 data and comprehensive external web sources via Perplexity, all presented directly within their work environment.

This deep interpretive power significantly reduces the effort required for manual data reconciliation and analysis, fostering more confident decision-making and equipping users with a straightforward, actionable narrative for strategic initiatives.

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麻豆原创 Business AI for human resources

Performance Preparation Agent
General availability

The Performance Preparation Agent proactively prepares managers by automating data collection and generating personalized talking points, ensuring they arrive at every employee 1:1 with relevant insights and actionable next steps, such as scheduling follow-ups or requesting peer feedback.

This intelligent preparation significantly simplifies the performance review process, dramatically reducing manager administrative burden by up to 50%* in prep time and 80%* in follow-up efforts, ultimately fostering more impactful discussions that can lead to a 30%* reduction in voluntary turnover.

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麻豆原创 SuccessFactors solutions, AI-assisted skill identification from resume
General availability

Employees leveraging 麻豆原创 SuccessFactors solutions can now effortlessly surface their full capabilities, enriching their Growth Portfolio through an innovative AI-driven skill identification process. By simply uploading a resume, the system intelligently analyzes its content, identifies relevant skills against the universal taxonomy, and presents them for inclusion, revealing previously undocumented “hidden skills” to create a more comprehensive “Whole-Self” profile.

This not only reduces employee time spent on skills profile maintenance by up to 50%* but also significantly enhances internal talent mobility and succession planning, resulting in an up to 10%* increase in internal fill rates and substantial reductions in HR and manager effort for talent-related tasks.

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麻豆原创 SuccessFactors Succession and Development, AI-assisted successor recommendation
General availability

HR leaders and succession planners now gain an unparalleled advantage with this feature that intelligently recommends potential successors. By using generative AI to analyze a rich dataset encompassing skills, competencies, and work experience from Employee Central, Talent Intelligence Hub, and Job Profile Builder, this capability provides a meticulously curated list of candidates.

This not only slashes HR鈥檚 time spent on successor analysis and recommendations by up to 50%*, but critically, it also eliminates subjective biases and surfaces highly qualified nominees who might otherwise be overlooked, thereby reducing critical role vacancies by half and instilling greater confidence through explainable ranking results.

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麻豆原创 SuccessFactors solutions, 麻豆原创 Document AI, embedded edition
General availability

麻豆原创 Document AI is now generally available in 麻豆原创 SuccessFactors Onboarding to boost user efficiency and data precision. This intelligent solution seamlessly automates the critical step of extracting key data 鈥� such as ID type, number, and validity dates 鈥� directly from uploaded national ID documents, discreetly prompting new hires to validate the captured information before final submission.

The result is an up to 15%* acceleration in overall onboarding cycles and a significant 30%* improvement in validation accuracy, collectively delivering error-free data management and enhancing productivity across the entire talent acquisition process.

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Watch the full highlights of the 麻豆原创 SuccessFactors H2 2025 release:

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麻豆原创 Business AI for supply chain

Production Planning and Operations Agent
Beta release

Production planners can significantly accelerate order-to-delivery cycles with the Production Planning and Operations Agent. This agent automates crucial prerequisite checks for releasing production orders, covering material, capacity, and scheduling availability. It identifies material shortages and suggests workarounds, including alternative components or scheduling adjustments. Once all criteria are met, the human planner approves, and the agent releases the production order.

This capability reduces manual work, keeps production moving, and boosts throughput by cutting order processing delays, leading to up to 50%* higher productivity among production supervisors in locating release order information and a 2%* reduction in production downtime losses.

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麻豆原创 Integrated Business Planning, AI-assisted supply optimization analysis
General availability

Supply chain planners leveraging 麻豆原创 Integrated Business Planning gain unprecedented clarity into their complex optimization runs with a new feature for supply optimization analysis.

Powered by Joule, the explanation function helps planners understand issues that arise with optimizer runs by providing the reasons for unfulfilled requirements. Joule can provide explanations for the following types of unmet requirements: 鈥淒emand Not Fully Met,鈥� 鈥淢issed Inventory Targets,鈥� and 鈥淢issed Adjusted Values.鈥�

For instance, planners can ask Joule questions such as, 鈥淲hat is the status of the optimization run for planning area XYZ?鈥� 鈥淲hich products are affected by unfulfilled demand?鈥�, or 鈥淭ell me which locations have unfulfilled inventory targets.鈥�
As a result, planners achieve up to 25%* higher productivity in analyzing planning results, translating into quicker and more confident adjustments to the supply chain model and enhanced overall operational agility.

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麻豆原创 Integrated Business Planning, AI-assisted analysis of inventory optimization results
General availability

Inventory planners utilizing 麻豆原创 Integrated Business Planning are now empowered with profound clarity into their complex inventory optimization results through a new feature designed for detailed analysis of safety stock output. This advanced capability precisely summarizes the rationale behind recommended safety stock levels and any adjustments, translating intricate calculations into accessible human language by highlighting key influences such as demand variability, lead time fluctuations, and service levels, alongside any planner deviations.

This dramatically increases the speed of analysis and adoption of outputs, ensuring both inputs and outcomes align with strategic business goals for working capital management and customer service, ultimately leading to a reduction of up to 25%* in the time inventory planning FTEs spend deciphering optimizer run results.

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麻豆原创 Integration Business Planning, AI-assisted forecast results analysis
General availability

Supply chain planners managing 麻豆原创 Integrated Business Planning can now access advanced forecast results analysis, offering a generative AI summary of statistical forecast details directly within their planning UI. This capability clarifies complex information, such as the chosen algorithm鈥檚 rationale and time series considerations, while providing concrete recommendations for accuracy improvement.

This enhanced insight significantly improves planner visibility, usability, and efficiency, directly leading to an up to 25%* boost in productivity for analyzing forecasting runs and enabling more confident, strategically sound decisions across the supply chain.

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麻豆原创 Integrated Product Development, AI-assisted text generation
General availability

Product managers can utilize AI-assisted text generation capabilities within 麻豆原创 Integrated Product Development to enhance descriptions for new campaigns and ideas. The feature transforms simple text into more creative and compelling narratives, which users can then further enrich or simplify.

By improving the quality of these foundational descriptions, organizations can reduce campaign creation costs by up to 50% and drive a potential 1% increase in revenue from new products.

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Joule with 麻豆原创 Logistics Management
麻豆原创 Early Adopter Care release

Logistics clerks engaging with 麻豆原创 Logistics Management can now harness Joule to streamline their warehouse and transportation planning operations. Through natural language interactions, they can efficiently perform tasks such as querying and creating storage bins, managing freight tendering, and scheduling or inquiring about pickup and delivery documents.

This intuitive, conversational capability fundamentally improves decision-making and streamlines end-to-end logistics processes. It boosts the productivity of supply chain planners by delivering a reduction of up to 30%* in time spent on information search requests and a 20%* reduction in the effort required to navigate to relevant content.

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麻豆原创 Business AI for finance

Accounting Accruals Agent
Beta release

Finance teams can enhance precision and speed during period-end close using the Accounting Accruals Agent. This agent systematically processes accruals by analyzing historical financial data and relevant accounting policies, automatically generating journal entries ready for quick review and confirmation.

This capability not only boosts productivity by reducing the manual effort in calculations by up to 80%* and review/posting by up to 50%*, but it also ensures a timely month-end close, freeing staff for more strategic responsibilities.

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International Trade Classification Agent
Beta release

Global trade compliance teams and product classification specialists now gain a strategic advantage with the International Trade Classification Agent. This AI agent rigorously classifies goods for international shipping by intelligently applying product characteristics against trade regulations, recommending precise customs tariff numbers and commodity codes with transparent rationale for expedited review.

This capability ensures robust compliance, minimizes manual classification errors, and provides an audit-ready decision-making process, resulting in a reduction of up to 50%* in the effort required to manage international trade product classification.

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Cash Management Agent for 麻豆原创 S/4HANA Cloud Public Edition and 麻豆原创 S/4HANA Cloud Private Edition
Beta release

Cash managers across both 麻豆原创 S/4HANA Public Cloud and 麻豆原创 S/4HANA Private Cloud editions gain unparalleled oversight and optimized financial performance with the Cash Management Agent. This agent meticulously gathers opening balances and projected cash flows to forecast precise closing positions. It proactively identifies potential shortages or surpluses in alignment with treasury policies, and for 麻豆原创 S/4HANA Private Cloud Edition users, extends its capabilities to automate bank reconciliations with high accuracy.

The agent then generates and proposes efficient bank transfers and cash optimizations, enabling managers to fund operations effectively, capitalize on investment opportunities, and maximize interest yields. This integrated approach fundamentally streamlines data retrieval and decision-making, resulting in a substantial reduction of up to 70%* in overall cash management effort.

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Change Record Management Agent
Beta release

Product managers and design engineers can accelerate product development and manage engineering changes with greater precision using the Change Record Management Agent. The agent proactively identifies similar change records impacting the same product, suggesting the creation of new change record drafts and initiating the process with recommended next steps.

This capability not only eliminates delays caused by fragmented data and manual checks but also significantly enhances governance and traceability, resulting in an up to 20%* reduction in time to create change requests, a 1% *reduction in time to market new products, and a 2%* reduction in overall engineering change costs.

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麻豆原创 S/4HANA Cloud Private Edition, AI-assisted depreciation key explanation
General availability

Asset accountants operating within 麻豆原创 S/4HANA Cloud Private Edition now get enhanced clarity and efficiency in managing fixed assets. This specialized feature provides user-friendly, natural language explanations of depreciation keys and their underlying calculation procedures, making complex accounting concepts accessible to business users.

The result is increased productivity and satisfaction for accounting teams, enabling faster onboarding, more efficient period-end closing activities, and improved decision-making for future investment planning. Specifically, it reduces the effort required to specify depreciation keys during implementation by up to 75%* and to analyze and address fixed asset queries by up to 90%*.

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麻豆原创 S/4HANA Cloud Private Edition, Joule for Developers, ABAP AI capabilities
General availability

ABAP developers working within 麻豆原创 S/4HANA Cloud Private Edition find a specialized copilot in Joule that鈥檚 uniquely trained on 麻豆原创 data and processes. Joule accelerates development tasks by providing real-time explanations of ABAP objects, predicting and generating subsequent lines of code, and supporting full-stack ABAP Cloud scenarios directly within ABAP Development Tools for Eclipse.

This sophisticated assistance can significantly reduce the time and effort required for coding by up to 20%* and for testing by up to 25%*, ultimately boosting developer productivity, enhancing clean core implementations, and delivering a 6.6%* faster time to realized value.

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麻豆原创 Master Data Governance on 麻豆原创 S/4HANA Cloud Private Edition, AI-assisted central governance
General availability

Sales managers, procurement specialists, and other business users utilizing 麻豆原创 Master Data Governance on 麻豆原创 S/4HANA Cloud Private Edition can now streamline master data tasks with Joule. This capability enables them to interact with Master Data Governance functions using natural language processing, allowing for seamless search, display, submission of new business partners, modification of existing ones, and tracking of governance process status, without requiring extensive technical knowledge.

This approach significantly increases flexibility and ease of data entry, resulting in a reduction of up to 85%* in effort for managing master data and a decrease of up to 10%* in annual operating income loss due to delayed or incorrect updates.

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麻豆原创 S/4HANA Cloud Public Edition, AI-assisted allocation run results
General availability

Business analysts and cost accountants now gain immediate clarity into their financial data with a new Joule feature for allocation run results. This capability allows them to efficiently view amounts allocated across diverse objects, including cost centers, profitability objects, or profit centers, and quickly navigate to detailed run reports for in-depth review.

This streamlined access reduces the effort of synthesizing data from multiple sources, provides rapid insights into complex cost allocations, and enables swift assessment of potential impacts from organizational changes, resulting in an up to 70%* decrease in time spent on allocation result analysis and up to 40%* faster resolution of allocation issues.

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麻豆原创 S/4HANA Cloud Public Edition, AI-assisted hands-free production order management
General availability

Production supervisors working in 麻豆原创 S/4HANA Cloud Public Edition can now benefit from hands-free production order management. By leveraging natural language queries, supervisors can effortlessly retrieve order details and manage operations without physical interaction.

This advancement significantly enhances operational efficiency, facilitates rapid responsiveness to unplanned demands, and ensures more reliable production order processing, ultimately leading to an up to 50%* increase in supervisor productivity and a 2%* reduction in production downtime losses.

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Joule with 麻豆原创 Risk and Assurance Management
General availability

Compliance managers and risk specialists working with 麻豆原创 Risk and Assurance Management can significantly simplify tasks through Joule. This integration allows business users to intuitively navigate the system and access critical enablement content using natural language, enabling them to quickly find answers and perform work-related tasks without extensive prior knowledge.

This streamlined experience fosters greater user satisfaction and frees up valuable time for strategic activities, resulting in a reduction of up to 50%* in time spent on informational searches and a corresponding decrease of up to 50%* in time navigating and performing tasks within the system.

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麻豆原创 Business AI for spend management

Booking Agent
General availability

Business travelers enjoy a significantly streamlined and personalized booking experience through the Booking Agent. This Joule Agent proactively delivers tailored flight and hotel recommendations by analyzing individual traveler preferences, company travel policies, and budget constraints, all accessible via chat-based interaction.

This not only enhances user satisfaction and supports sustainable choices but also reduces the time spent booking a trip by up to 11.5%*, while simultaneously improving policy compliance and granting organizations superior oversight and control over travel expenditures.

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Receipt Analysis Agent
麻豆原创 Early Adopter Care release

Employees submitting business expenses now experience unprecedented accuracy and efficiency with ExpenseIt, powered by the Receipt Analysis Agent. This AI agent leverages a comprehensive suite of data, including maps, vendor databases, web searches, and Concur Travel itineraries, to itemize and categorize receipt data precisely.

By reasoning both the receipt content and external context, it creates highly accurate expense entries, dramatically reducing the time spent manually managing and editing them. This ensures ExpenseIt gets it right the first time, reducing the need for send-backs and resulting in a potential up to 19%* reduction in the time required to generate expense items.

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麻豆原创 Business AI for procurement

麻豆原创 Ariba Intake Management, AI-assisted demand intake
General availability

Employees creating procurement demands within 麻豆原创 Ariba Intake Management now experience a smarter, more efficient process with the Demand Intake feature. By simply using natural language to articulate their needs, employees can rely on the AI to intelligently assess their requests and route them to the most appropriate procurement or buying channel.

This innovative approach delivers up to 12%* productivity gain for casual users creating requisitions, while significantly reducing the risk of maverick spending with a 5%* improvement in non-compliant spend, and further streamlining operations with a 10%* reduction in purchase order processing time.

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麻豆原创 Fieldglass Services Procurement, AI-assisted SOW deliverables creation
General availability

Procurement professionals and buyers using 麻豆原创 Fieldglass can now streamline the creation of comprehensive statements of work (SOWs). This feature analyzes project scope and existing data to automate critical SOW components, automatically drafting structured event hierarchies, and generating precise, relevant deliverables.

By automating these time-consuming tasks, this integrated approach reduces manual effort, improves data consistency, and ensures deliverables are closely aligned with project goals, enabling businesses to achieve up to a 70%* reduction in manual creation time and an up to 50%* reduction in poor outcomes tied to inadequate SOWs.

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麻豆原创 Business AI for customer experience

麻豆原创 Emarsys Customer Engagement, AI-assisted report builder
General availability

Marketers have a powerful new way to create individualized reports with the AI-assisted report builder in 麻豆原创 Emarsys Customer Engagement. Using simple user prompts, they can query underlying datasets to instantly generate custom reports and visualizations, eliminating the need for specialized BI skills or technical support. This streamlined approach to flexible reporting enables marketing teams to reduce the time spent on campaign performance analysis by up to 67%*, allowing them to iterate quickly and communicate results more effectively throughout the organization.

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麻豆原创 Service Cloud Version 2, AI-assisted premise billed consumption summary
General availability

Customer service agents using 麻豆原创 Service Cloud Version 2 can more effectively resolve customer billing inquiries with the AI-assisted premise billed consumption summary. This feature automatically analyzes the latest 12 billing cycles, correlates consumption data with temperature trends, and generates a concise, human-readable summary.

By providing agents with immediate, actionable insights that eliminate the need for manual analysis, it helps increase the speed of issue resolution and reduces the average time to summarize business objects by up to 90%*.

麻豆原创 Business AI for IT and developers

Joule studio, agent builder
General availability

Business and IT professionals can now use Joule Studio鈥檚 agent builder to create powerful AI agents capable of automating highly complex business processes. This tool allows them to build agents that can plan, reason, and dynamically orchestrate multi-step workflows across both 麻豆原创 and non-麻豆原创 systems, effectively tackling ambiguity where standard automation falls short.

With Joule Studio agent builder, organizations have the potential to reduce the time spent on frequent business tasks by up to 40%* and cut the time needed to build and deploy custom agents by up to 35%*, significantly improving decision-making speed and operational efficiency.

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Generative AI Hub in AI Foundation, enhancements
General availability

麻豆原创-RPT-1, 麻豆原创鈥檚 first enterprise relational foundation model

麻豆原创 introduced its first enterprise relational foundation model, 麻豆原创-RPT-1, accompanied by a no-code testing playground environment.

Unlike large language models (LLMs), 麻豆原创-RPT-1 is a foundation model that establishes a new category of AI models specifically designed for relational and structured business data. 麻豆原创-RPT-1 comes pretrained, significantly reducing the need for customers to handle time-consuming and costly model training tasks that are typically required with narrow AI models. It delivers reliable, fact-based predictions by grounding responses in verified enterprise data, providing the accuracy and dependability that critical business operations require.

麻豆原创-RPT-1 addresses analytical and predictive tasks through in-context learning, enabling users to perform classification and regression on tabular data by providing example records directly within the API call. The model can be consumed as a ready-to-use endpoint and integrated into applications and business processes. The initial release supports common predictive scenarios, including binary and multiclass classification, as well as numerical regression.

麻豆原创 also offers an interactive, web-based testing environment, 麻豆原创-RPT playground, where customers can experience the in-context learning capabilities at no cost, utilizing their own data or 麻豆原创-provided example datasets, without any coding.

麻豆原创-RPT-1 is available on the generative AI hub in AI Foundation for productive use. It comes in two flavors: 麻豆原创-RPT-1-small for ultra-fast predictions and high throughput, and 麻豆原创-RPT-1-large for maximum accuracy.

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麻豆原创-ABAP-1 foundation model

To empower customers and partners to build custom, AI-driven developer productivity use cases, the 麻豆原创-ABAP-1 foundation model is now available on the generative AI hub. Trained on more than 250 million lines of ABAP code, 30 million lines of CDS code, and extensive technical documentation, 麻豆原创-ABAP-1 is purpose-built to efficiently understand, explain, and give immediate access to ABAP code knowledge, best practices, and latest innovations.

Customers can try the new model for free as part of the generative AI hub trial. Additional capabilities will be released in 2026.

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Prompt Optimizer

Developed in close collaboration with Not Diamond, the prompt optimizer helps automate and accelerate the creation of effective AI prompts across leading models. This frees users to adapt their prompts to any model for their use cases without the manual effort of rewriting prompts.

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Orchestration registry

Developers can now manage the lifecycle of orchestration workflow configurations, including saving, versioning, and deleting orchestration configurations.

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New models available

New models are supported, including Perplexity Sonar, Sonar Pro, Anthropic Claude 4.5 Sonnet, Anthropic Claude 4.5 Haiku, Cohere Command A Reasoning, and Gemini 2.5 Flash Lite.

For more information on new and deprecated models,

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麻豆原创 Document AI, enhancements
General availability

Vision-enabled information extraction

Schema administrators can now choose between . When enabled, documents are processed by a multimodal model that interprets visual elements, such as hazard pictograms, stamps, signatures, logos, charts, and labels, in conjunction with the text. This improves accuracy for visually rich documents, such as Safety Data Sheets (SDS) or Compliance Declarations. It improves data completeness and accuracy, reduces manual tagging and verification by automating the extraction of visual elements, and optimizes cost and performance with per-schema control.

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Processing of e-mail attachments

Users can now process e-mail attachments alongside or separately from the e-mail body, providing extensive flexibility and enhancing data extraction.

Get started with and .

Document workflows

Users can now quickly and easily define multistep workflows to process documents according to their specific needs. The new Workflows feature allows users to combine basic capabilities of 麻豆原创 Document AI to automate and streamline complex tasks.

Workflows can be triggered automatically via inbound channels, with no need for additional tools or integrations. Alternatively, users can upload a file and start the workflow manually. Workflows extend beyond extraction and classification, providing support for tasks such as e-mail processing, content-based routing, and automated processing.

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Built-in transport management

麻豆原创 Document AI provides an Integration with 麻豆原创 Cloud Transport Management, allowing users to leverage the Transports feature to export and import their schemas across their 麻豆原创 Document AI service instances 鈥� for example, development, quality, and production. It ensures that schemas and workflows are consistent across instances, facilitating better collaboration among teams and systems.

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麻豆原创 Cloud ALM, AI-assisted requirement generation
General availability

Consultants can now automatically generate high-quality business requirements directly from Fit-to-Standard workshop transcripts. By analyzing discussion content, this new feature in 麻豆原创 Cloud ALM populates a predefined template. It integrates 麻豆原创 Best Practices to suggest solution proposals, shifting the consultant鈥檚 focus from manual transcription to strategic review and refinement.

This automation reduces the time spent creating requirements by up to 50%* and the time needed for subsequent user story creation by up to 20%*, significantly accelerating project documentation.

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麻豆原创 Micro-App Hub, AI-assisted user learning, and change management
Early Adopter Care program

Learning specialists and content creators can now streamline their entire 麻豆原创 user-learning lifecycle, accelerate adoption, and reduce costs with 麻豆原创 Micro-App Hub. By connecting with 麻豆原创 Signavio and 麻豆原创 Cloud ALM, this feature analyzes project scope, identifies learning needs, and automatically generates tailored, business-aligned training content for every user role.

This not only produces up-to-date materials quickly for various authoring and learning tools but also dramatically cuts the time for initial learning needs assessment by up to 60%* and content development by up to 50%*, ensuring faster onboarding and higher accuracy by aligning learning with 麻豆原创 updates.

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麻豆原创 Business AI for industries

Utilities Customer Self-Service Agent
General availability

Utilities organizations can with the Utilities Customer Self-Service Agent. This AI agent provides fast, personalized answers in multiple languages. It provides a deep understanding of customer context, including contracts, tariffs, and consumption data, through its integration with 麻豆原创 S/4HANA Cloud Private Edition.

Designed to address industry shifts such as deregulation and prosumer growth, it efficiently handles complex customer interactions, resulting in a reduction of up to 90%* in the average cost of AI-handled contacts and a decrease of up to 60%* in overall customer service operational costs.

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Tender Analysis Agent
General availability

Sales and bid management teams can optimize their response process with the Tender Analysis Agent. This agent automates the evaluation of complex tender and RFQ documents by extracting critical product requirements, flagging potential risks or policy gaps, and suggesting optimized configurations based on predefined company standards.

This automation reduces manual effort and accelerates sales cycles, helping businesses achieve up to a 1%* improvement in operating margin from personalized products, a 0.5%* increase in cross-sell/up-sell revenue, and a 5% reduction in sales FTEs per billion in revenue.

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麻豆原创 Sports One, AI-assisted scouting
General availability

Scouts using 麻豆原创 Sports One now have access to unparalleled efficiency in player assessment, thanks to AI-assisted scouting. This capability allows them to rapidly digest complex scouting reports and match analyses through generated summaries in Joule, as well as pose specific questions using natural language to extract precise answers.

This significantly reduces the need for extensive documentation, liberating substantial time and resources, which translates to a decrease of up to 75%* in the effort and cost associated with summarizing player scouting reports, directly supporting sporting directors with enhanced decision-making.

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麻豆原创 Information Collaboration Hub for Life Sciences, AI-assisted error analysis
General availability

Supply chain planners can quickly resolve complex issues in serialization data exchange with the error analysis feature in 麻豆原创 Information Collaboration Hub for Life Sciences. The tool automatically classifies errors, provides easy-to-understand descriptions, and proposes resolution steps, helping planners identify root causes and manage exceptions without needing technical support.

Organizations can increase the productivity of their operational support teams by up to 25%* and lower distribution costs by up to 5%* through minimizing disruptions.

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麻豆原创 Batch Release Hub for Life Sciences, AI-assisted batch release processing
麻豆原创 Early Adopter Care program

For users of 麻豆原创 Batch Release Hub for Life Sciences, making swift, informed decisions about batch releases are becoming significantly more efficient with AI-assisted batch release processing. Through the Joule interface, this feature streamlines access to essential data and provides an organized view of worklist items, clearly highlighting releases that require detailed investigations due to blocked checks.

The conversational search capability further simplifies finding product documentation, ensuring critical issues are addressed promptly, and past insights are readily available for current decisions, ultimately reducing the time needed to access vital batch release information by up to 90%*.

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麻豆原创 Business AI for business transformation management

Dashboard Analyzer Agent for 麻豆原创 Signavio
Beta release

Business process professionals utilizing the Dashboard Analyzer Agent can transform 麻豆原创 Signavio dashboards into intelligent, prescriptive tools. This AI agent autonomously interprets complex event logs and KPIs to identify inefficiencies, explain root causes, and generate actionable recommendations in natural language.

By embedding these AI-driven insights directly into the user鈥檚 workflow, organizations can achieve a reduction of up to 80%* in the time required to access process mining insights and significantly reduce the value erosion caused by poor data interpretation.

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Screen Guide Agent for 麻豆原创 Signavio
Beta release

Business users and process analysts can accelerate their understanding and adoption of the 麻豆原创 Signavio platform with the Screen Guide Agent. This AI agent provides dynamic, on-screen guidance by explaining the purpose of different features, highlighting the most relevant data, and offering next-step recommendations in natural language.

By transforming complex screens into intuitive experiences, organizations can reduce new user onboarding costs by up to 50%* and cut the time needed to interpret a page by up to 30%*, empowering users of all levels to work more confidently and productively.

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Workspace Administration Agent for 麻豆原创 Signavio
Beta release

Administrators and workspace managers can now significantly simplify and expedite user onboarding in 麻豆原创 Signavio with the Workspace Administration Agent. This AI agent automates the process of creating users in bulk, assigning correct roles and licenses, and granting immediate access to necessary dashboards and collaborative workspaces.

By implementing this tool, organizations can achieve a reduction of up to 90%* in the time it takes to provide user access rights, ensuring new team members can contribute from day one.

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Value Case Creation Agent for 麻豆原创 Signavio
Beta release

Process improvement leaders and business analysts can translate raw process insights into compelling, data-driven business cases with the Value Case Creation Agent. This AI agent automatically identifies operational inefficiencies, quantifies their potential financial impact, and generates editable value case drafts that summarize the problem and expected benefits.

By streamlining the justification for transformation initiatives, organizations can reduce the time required to create a value case by up to 70%* and decrease the erosion of value from inaction, ensuring that improvement efforts are prioritized based on clear ROI.

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Process Content Recommender Agent for 麻豆原创 Signavio
Beta release

Enterprise architects and process managers can rely on the Process Content Recommender Agent for intelligent guidance on specific process questions within 麻豆原创 Signavio. By reasoning over thousands of best practices from both 麻豆原创 and internal custom models, the agent delivers a structured, prioritized list of tailored content, including relevant KPIs and value accelerators.

This capability enables organizations to reduce content search time by up to 50%* and improve the productivity of their business process management resources, allowing teams to make faster, data-driven decisions on their transformation initiatives.

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麻豆原创 Signavio Process Transformation Manager, AI-assisted insights description generator
General availability

Analysts working with 麻豆原创 Signavio Process Transformation Manager can collaborate more effectively and accelerate decision-making through this new integrated feature. It automatically generates clear, consistent, and business-user-friendly descriptions for insights derived from 麻豆原创 Signavio Process Intelligence.

Analysts can save significant manual effort by transforming complex meta-model terms into readily understandable language, which improves readability and stakeholder alignment. The result is an up to 80%* reduction in time spent translating meta-model terms and an up to 5%* improvement in overall business user productivity.

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麻豆原创 Signavio solutions, AI-assisted transformation advisory, initiative builder
General availability

Transformation leads can now translate high-level business documents into concrete, actionable projects using the initiative builder in 麻豆原创 Signavio solutions. By uploading strategic reports, operational reviews, or financial statements, users can automatically extract key challenges and instantly convert them into pre-defined initiatives within the 麻豆原创 Signavio Process Transformation Manager.

This ensures that transformation efforts are directly aligned with company goals, dramatically improving efficiency by reducing the manual effort required to find relevant insights by up to 75%* and accelerating overall execution.

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麻豆原创 LeanIX, AI Agent Hub
General availability

The AI Agent Hub enables CIOs and business leaders to view their entire AI agent landscape immediately. From a single dashboard, they can understand where agents are deployed, which processes they interact with, and how agents are performing.

This enables teams to evaluate effectiveness, identify redundancies, and manage AI as they would any other enterprise asset: aligned to outcomes, governed by policy, and continuously optimized for performance.

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WalkMe, 麻豆原创 Joule Action Bar
麻豆原创 Early Adopter Care release

Employees working across different enterprise systems can leverage the Joule action bar, a proactive AI assistant powered by WalkMe. This intelligent overlay operates seamlessly across both 麻豆原创 and non-麻豆原创 applications, interpreting on-screen context to understand user activities and deliver real-time insights or recommend the subsequent best actions directly within their workflow.

By offering a unified and intuitive AI experience that anticipates user needs, the action bar helps people work faster and more efficiently, reducing friction and harmonizing tasks across all systems.

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Philipp Herzig is CTO of 麻豆原创 SE.

*Disclaimer: This article provides estimated benefits. All calculations are estimates based on 麻豆原创 customer case studies, 麻豆原创 benchmarks, and other research. Actual benefits may vary and may be affected by additional factors not considered by this article. The information is provided 鈥渁s is鈥� without warranty of any kind, expressor implied, and in no event shall 麻豆原创 be liable for any damages whatsoever in relation with the use of this article. See Legal Notice on for use terms, disclaimers, disclosures, or restrictions related to this material.

Its advanced refrigeration, air conditioning, and heat pump technologies keep supermarket shelves, hotel rooms, and hospital operating theaters at the right temperatures, whatever the ambient temperature is. Its compressors are essential for storing medicines, preserving perishable goods in shipping containers, and processing frozen foods. And if that isn鈥檛 impressive enough, its technology keeps ice hockey players gliding across the ice and breweries fermenting yeast for your beer.

The company is a longstanding RISE with 麻豆原创 customer and, like 麻豆原创, is constantly innovating its products to stay ahead. Christian Stenzel, vice president of Organization and IT at BITZER, has a clear vision for an 麻豆原创 strategy that prioritizes integration and rapid adoption of AI: 鈥淥ptimizing business processes is as important as product innovation at BITZER.鈥�

The 麻豆原创 Research and Innovation team is equally committed to keeping 麻豆原创 ahead by exploring new technologies and one team is currently dedicated to Project Embodied AI. Embodied AI combines artificial intelligence with a physical form, such as robots, that can perceive and act in the real world. Embodied AI agents take this a step further: extending the impact of into physical operations by making robots cognitive.

To explore potential use cases where cognitive robots could bring value, the Project Embodied AI team invited a select group of forward-thinking leaders and innovation professionals from 麻豆原创 customers to join its Physical AI and Cognitive Robots Exploration Council. And BITZER was one of them.

鈥淒emand-driven production is key in our business,鈥� said BITZER’s Stenzel, who immediately saw the potential value in using robots to meet demand fluctuations.

Running on (麻豆原创 BTP) and , already in place, BITZER already had the ideal software landscape to serve as a proof-of-concept test ground.

Before deployment, NEURA鈥檚 , one of Europe鈥檚 most advanced humanoid robots, was virtually trained for the pick-task use case on NVIDIA Isaac Sim software.

A new benchmark for intelligent automation

This proof of concept for Project Embodied AI sets a new benchmark for intelligent automation in warehouses, Stenzel said. The results highlight:

• Seamless integration: 麻豆原创 EWM connected directly with physical warehouse operations, no costly middleware required.
• True autonomy: Robots performed pick-tasks independently, demonstrating advanced task-level autonomy.
• Agility and flexibility: Robots could enable demand-driven production, operating 24/7 to meet shifting needs.
• Reliable processes: Orders of materials were automatically created, demonstrating how operational mistakes could be minimized.

A decisive step forward

Dr. Lukasz Ostrowski, head of Embodied AI and Robotics at 麻豆原创, heralded this proof-of-concept as a decisive step forward: 鈥淭he proof of concept at BITZER is great first step for experiencing firsthand how the impact of 麻豆原创 Business AI can be extended into physical operations. Further proofs of concept are planned as Project Embodied AI continues to assess the business value of embodied AI for customers.鈥�

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To navigate AI鈥檚 evolution, organizations need to understand that it鈥檚 no longer just a question of “What can AI do?” but “How do we set our organization up for success with AI? How do we build for it? What problems do I solve with which models? How do we govern it?”

Looking ahead to five critical themes that will define enterprise AI in 2026, these present both opportunities and challenges for organizations. Let’s dive in.

1. New categories of AI foundation models unlock enterprise value

Advances in generative AI stem from breakthroughs in 鈥渇oundation models,鈥� massive neural networks trained on vast amounts of data that can be adapted to a wide range of tasks.

Large language models (LLMs) were the first wave of foundation models at scale. General-purpose LLMs, trained on the equivalent of all the text on the internet, opened the door to many value-adding use cases, including summarizing documents, writing code, and powering applications like ChatGPT and Claude. Over the last few years, we have already seen the foundation model approach applied to other domains, such as video creation and voice.

In 2026, specialized foundation models optimized for specific data types and domains will power the high-value enterprise AI use cases. Video generation models have already shown that models grounded in real-world physics data can reason about scenes and physical dynamics. Emerging world models demonstrate that simulating the physical world unlocks new possibilities in simulation, synthetic training data, and digital twins. Vision-language-action models demonstrate that robot-specific foundation models can generalize to new tasks and environments, enabling the transformation of web-scale knowledge into real-world actions in logistics and manufacturing.

In the enterprise domain, a similar shift is underway for structured data found in databases and transactional business software. While LLMs are impressive across many enterprise use cases, they cannot handle tasks like numerical predictions, such as inferring a delivery date or supplier risk score. However, work on relational foundation models shows that training on structured datasets鈥攆or example, data in tables, rather than generic text or images from the internet鈥攃an deliver high predictive accuracy without the tedious feature engineering and training required in classical machine learning. This means organizations can deploy predictive models in days, not months. Recent launches of relational foundation models, such as 麻豆原创-RPT-1, Kumo, and DistilLabs, highlight how new models can directly support use cases like forecasting, anomaly detection, and optimization across ERP, finance, manufacturing, and supply chain scenarios.

In 2026, these specialized models are expected to scale to deliver superior performance and economics for structured business tasks, surpassing general-purpose LLMs and state-of-the-art machine learning algorithms. These models will emerge as the workhorses behind high-value enterprise tasks.

2. Software evolves toward AI-native architecture

AI has seen various approaches create value over the decades, from the first rules-based expert systems to probabilistic deep learning and the recent explosion in generative AI. In 2026, organizations will shift from enhancing existing AI applications and processes to AI-native architectures, which will fully realize the promise of modern AI.

AI-native architecture adds a continuously learning, agentic intelligence layer on top of deterministic systems, enabling applications to become intent-driven, context-aware, and self-improving rather than being statically coded around fixed workflows. Agentic systems will still only be as good as the context layer they can reliably retrieve and ground on. Here, organizations should invest in truly comprehensive, semantically rich knowledge graphs that provide a scalable source of context, making AI-native software dependable and self-improving.

Enterprise applications will increasingly be built natively around AI capabilities, featuring user experiences designed for multi-model, natural language interaction; AI agents reasoning through complex processes; and a foundation managing foundation models, services, and a knowledge graph capturing semantically rich business data.聽AI-native architecture also enables more employees to create apps鈥攕uch as smaller, ad-hoc productivity applications鈥攊n a matter of minutes without straining IT.聽

AI-native architecture builds on, and even requires, established SaaS principles and investments in modern cloud applications. The technical term for combining probabilistic, adaptive AI models with deterministic systems of record is called neurosymbolic AI. It brings together AI鈥檚 best capabilities to adapt with reliable, governable, and deterministic processes. Next-gen applications will not just have AI bolted on; they鈥檒l be built around AI at their core. This means combining reasoning, business rules, and data to deliver insights and automation seamlessly. Imagine ERP systems that proactively flag anomalies, recommend actions, and even execute workflows autonomously鈥攁ll while staying aligned with company policies and regulations.

3. Agentic governance becomes mission-critical

Over the past two to three years, generative AI has introduced a wave of value-added use cases. These use cases were largely based on users sending a prompt to a model, receiving a response, and then interacting with the model again.

Last year saw the start of the next wave of innovation: AI agents capable of planning and iteratively reasoning through multi-step tasks, including selecting tools, self-reflecting on progress, and collaborating with other AI agents. These advanced AI agents promise to tackle complex business processes that were previously immune to automation, such as analyzing myriad documents, records, and policies to or .

However, the proliferation of AI agents, many of which handle critical tasks and sensitive data, demands the development of new capabilities. Agentic governance will emerge as a critical capability as organizations deploy hundreds of specialized AI agents. The “agent sprawl” challenge will mirror previous shadow IT crises, but with higher stakes given agents’ autonomous decision-making capabilities.

Forward-thinking enterprises will establish comprehensive governance frameworks addressing five dimensions: agent lifecycle management (version control, testing protocols, deployment approval, retirement procedures); observability and auditability (agent inventory, logging, reasoning paths, and action traces); policy enforcement (embedding business rules, regulatory constraints, and ethical guidelines into agent execution); human-agent collaboration models (defining autonomy boundaries, approval requirements, and escalation pathways); and performance monitoring (tracking accuracy, efficiency, cost, and business impact).

The organizational shift will prove profound鈥攆rom viewing AI as an independent tool to managing agents as digital coworkers requiring onboarding, performance reviews, and continuous improvement. HR and IT functions will collaborate on “digital workforce management” as organizations treat agentic governance as seriously as they do traditional workforce oversight.

4. Intent-driven ERP and generative UI emerge as a new user experience

Consumers are becoming increasingly familiar with computer interactions requiring prompts in natural language, voice, and even images and gestures. At the same time, generative AI鈥檚 ability to create text, graphs, code, and HTML on the fly is improving rapidly. In parallel, AI agents enable users to simply express their intentions, allowing the agent to determine how to work toward achieving that goal.

These advancements open the door to varied and entirely new modalities for users to work with enterprise software, as well as 鈥渘o-app ERP鈥� experiences. For example, to book a customer visit, a worker typically needs to open an analytics application to review the account, look in the CRM system to retrieve the customer鈥檚 address, and then navigate to another application to book travel, among other tasks. 

In 2026, we will see 鈥済en UI鈥� experiences increasingly surface via digital assistants, relieving users from the need to navigate between multiple applications and perform manual tasks. With time, AI will allow the user to simply express the intent: 鈥淧repare a trip to my customer with the most leads.鈥� From here, an AI agent will plan out the steps and required systems, interacting with the user to confirm travel details while dynamically generating analytical graphs and briefing material in the window. As AI agents develop stronger calculation and prediction tools, users will be able to “speak to their data” more naturally, with agents making data-based decisions in the background. To be clear, interactions with agents will extend far beyond a chat box; organizations will enjoy rich visualizations, complete workflows, and the ability to build hyper-personalized apps with just a few commands.

The user interface will not disappear. No-app ERP experiences and autonomous agents require the same foundational substrate that humans rely on for their daily work: structured workflows, security, governance, and business logic defined in business applications. The difference is that agents consume these primitives programmatically at scale, not only through a GUI, and humans can interact with these agents via natural language without ever needing to open the application.

These capabilities will usher in a new paradigm for human-AI collaboration and productivity in the workplace. Personalized experiences and adaptive workflows across applications and data sources will lower adoption barriers. This ability to focus solely on achieving a user鈥檚 intention, regardless of the interaction modality and underlying systems, will drive return on investment (ROI) in AI and enterprise software.

5. Deglobalization drives sovereign AI offerings

AI sparked debates about digital sovereignty among nations due to AI鈥檚 potential impact on everything from scientific discovery and national security to economic productivity and even culture. Events in geopolitics, such as supply chain disruptions caused by tariffs and war, have only intensified the urgency that many nations and organizations feel to become digitally sovereign.

Digital sovereignty has two broad definitions. First, digital sovereignty is an information security designation governing data storage and access, such as U.S. FedRAMP and German VSA, required to process sensitive governmental data in a 鈥渟overeign cloud.鈥� Second, and more broadly, sovereignty refers to the provenance of physical assets, intellectual property, legal jurisdiction, and services along the cloud stack. For example, does an application utilize an AI model created in Europe, the U.S., or China, and is the data center geographically isolated?聽

The high stakes, geopolitical uncertainty, and complexity of 鈥渟overeign AI鈥� will lead enterprises to increasingly demand AI and cloud solutions that are simultaneously cutting-edge, flexible, and fully sovereign. This intensifies the shift from globalized one-size-fits-all cloud to regionally compliant, AI-powered enterprise platforms. At the same time, governments will continue to refine their national AI strategies to invest in areas along the stack where they can compete and create value.

Executing on the 2026 AI themes

In 2026, AI is poised to move from a supporting tool to a fundamental pillar of the enterprise. This shift is driven by a convergence of defining trends鈥攊ncluding increasingly capable agents, generative UI, and AI-native architecture鈥攖hat push AI from the application layer and into the very core of business operations.

Organizations that thrive will be those that recognize this shift and build an enterprise that is purpose-built for AI: establishing robust governance to manage a new, collaborative workforce of humans and AI agents; embracing gen UI to lower adoption barriers and an intent-driven user experience that helps employees interact naturally; seeking out specialized foundation models that are precisely tuned for enterprise use cases to drive business value; and, finally, building applications natively around AI that combine reasoning, business rules, and data, delivering proactive insights and automation.

However, in 2026, organizations will still need high-quality, connected data. Data siloes severely limit the effectiveness of AI. As mentioned, AI-native architecture requires established investments in modern cloud applications that harmonize data across the entire business鈥攂ecause unified data means AI鈥檚 outcomes are more accurate and relevant.

Jonathan von Rueden is chief AI officer at 麻豆原创 SE.
Walter Sun is senior vice president and global head of AI for 麻豆原创 Business AI at 麻豆原创.
Sean Kask is vice president and head of AI Strategy for 麻豆原创 Business AI at 麻豆原创.

This is where 麻豆原创 and the 麻豆原创 partner ecosystem make a decisive difference.

Together, we help customers translate AI strategies into meaningful outcomes by pairing partner industry expertise with聽麻豆原创 Business AI, which brings hundreds of purpose-built, domain-rich capabilities embedded across 麻豆原创 applications. These capabilities help automate processes, elevate decision-making, and enhance employee productivity across the enterprise.

麻豆原创 Business Technology Platform (麻豆原创 BTP) amplifies this foundation by giving customers the ability to integrate, extend, and build AI-powered solutions in a scalable and secure environment.

Across industries, 麻豆原创 and our partners are helping customers unlock real value from AI. The examples below show how organizations are already achieving tangible impact today.

Driving efficiency with AI-powered process automation

Manual, repetitive processes remain one of the biggest barriers to operational excellence. The 麻豆原创 partner ecosystem plays a critical role in helping customers uncover these inefficiencies and redesign them with AI-driven automation.

For frozen-food manufacturer聽FRoSTA, 麻豆原创 partners聽sovanta AG,听补苍诲听Amista聽identified invoice processing as a major bottleneck. By orchestrating the workflow using聽麻豆原创 Build Process Automation聽and extracting, interpreting, and validating data through聽麻豆原创 Document AI, the partners were able to . Invoices that once required several minutes of manual effort now flow through the system in under a minute, with roughly 60 percent fully automated. Employees can redirect their attention to higher-value work, such as resolving exceptions and collaborating with suppliers.

This is the power of pairing partner expertise with 麻豆原创 Business AI and 麻豆原创 BTP solutions: Organizations quickly shift from isolated task automation to connected, intelligent workflows that scale across departments and regions. What begins as a single use case becomes the foundation for a broader automation strategy鈥攁ccelerating processes, reducing manual effort, and tightening the connection between data, people, and decisions.

Accelerating innovation by making AI accessible to every team

As demand for AI grows, many organizations face a familiar hurdle: the scarcity of specialized AI talent. Partners in the 麻豆原创 ecosystem help close this gap by combining their industry knowledge with tools in 麻豆原创 Business AI and 麻豆原创 BTP that make it easier for teams across the business to experiment, prototype, and deploy AI solutions at speed.

A strong example comes from Aspen Pumps, which partnered with NTT DATA聽to . Using low-code capabilities from聽麻豆原创 Build to design and orchestrate workflows and 麻豆原创 AI Core to power AI models, the team rapidly developed a series of automation bots鈥�12 in total. These now streamline activities such as invoice validation, order routing, and even interpreting CAD drawings to accelerate quote creation. Many proof-of-concept initiatives were completed in under a week, demonstrating how accessible innovation becomes when intelligent capabilities are built directly into the tools teams already use.

By lowering the barriers to experimentation, 麻豆原创 and partners help organizations innovate faster and more confidently. Teams can explore new ideas, test them safely, and scale what works鈥攚ithout waiting for scarce technical resources or lengthy development cycles. Innovation becomes a daily practice, not a specialized activity reserved for a few.

Building a future-ready foundation with scalable, extensible architecture

As AI becomes more deeply integrated into business operations, leaders are prioritizing platforms that will scale with them, not constrain them. This is where 麻豆原创 partners help customers design architectures that can evolve with changing market needs while preserving the stability of their core systems.

Steel manufacturer聽Al Ghurair Iron and Steel (AGIS)聽offers a powerful example. Working with聽Deloitte, the company using 麻豆原创 Business AI embedded in 麻豆原创 S/4HANA Cloud, private edition, combined with the integration and extension capabilities of 麻豆原创 BTP. A planning cycle that once required 15 minutes of manual coordination now takes less than five. The solution has been replicated across multiple locations, and more than 400 calculations are now automated, giving teams more time to analyze results and optimize operations rather than manage spreadsheets.

When 麻豆原创 Business AI and 麻豆原创 BTP come together with partner expertise, companies gain a foundation they can rely on as their AI ambitions grow. They can scale new capabilities across plants, regions, or business units; extend processes without disrupting mission-critical systems; and seamlessly connect 麻豆原创 and non-麻豆原创 environments into a cohesive, intelligent landscape.

Turning AI potential into business transformation

These stories demonstrate what becomes possible when customers, 麻豆原创, and our partners work together: faster processing, smarter decisions, empowered employees, and architectures built for long-term agility and growth.

With the combined strength of the 麻豆原创 partner ecosystem, the domain-rich intelligence of 麻豆原创 Business AI, and the extensibility of 麻豆原创 BTP, organizations can move beyond pilots and embed AI where it matters most: in the daily processes and decisions that run their businesses.

Learn more about what’s possible for your business with 麻豆原创 Business AI at .

From decades of customer support defined by reaction to calls, tickets, or queues, to the evolution of proactive support with pre-AI digital platforms, to the current AI-powered ecosystem that is redefining how support teams strategize, operate, and deliver resolutions. AI-enabled support anticipates needs, predicts failures, and delivers instant, seamless resolutions at scale.

And most importantly, this shift is as transformational as it is technological.

Keeping pace with transformation

As customers navigate complex and ambitious transformation projects, whether it鈥檚 moving to the cloud, scaling AI, or modernizing complex operations, there is always a quiet mandate: systems supporting critical business processes must run smoothly because the costs of downtimes have never been higher.

For businesses, uninterrupted operations are non-negotiable. 麻豆原创鈥檚 AI-driven support can anticipate issues before they arise, helping to ensure critical processes run smoothly, even during high-volume peak events. 麻豆原创 uses 麻豆原创 Business AI to help prevent issues proactively, working to ensure a smooth experience by avoiding system outages, platform scalability issues, data overloads, or service overloads. During the peak sales event of Cyber Week 2024, 麻豆原创 achieved 100% uptime for 麻豆原创 Commerce Cloud customers. As the Cyber Week 2025 numbers come in, we already have delivered 100% uptime and improved GMV for global sales events like Singles Day (GMV reached 鈧�7,108.72M, or +180.2% YoY, with 6,315.99K orders, or +46.4% YoY) and El Buen Fin (GMV hit 鈧�12,341.70M, or +13.18% YoY, and 10,385.74K orders, or +32.24% YoY).

Scaling self-service with AI

Structured knowledge and curated content enable 麻豆原创 to build AI and AI agents with high confidence levels. Today, over 82% of customer issues are addressed via self-service. This allows users to get instant resolution to issues or bridge knowledge gaps they face during the use, implementation, and continuous improvement of 麻豆原创’s solutions.

AI in instant response and resolution

When it comes to delivering instant response and resolution in customer support, the impact of AI-integrated services is remarkable. When 麻豆原创鈥檚 Auto Response Agent is highly confident of the solution, based on the underlying data and knowledge, it can deliver highly relevant solutions that can save customers significant time and effort. Additionally, the first contact resolution rate for cases answered automatically by the agent is at par with what human-human support interactions achieve.

Supporting 麻豆原创 Business AI

麻豆原创 Business AI supportability is all about making AI real for customers through the right systems that drive successful adoption. As 麻豆原创 delivers AI capabilities across its portfolio, we enable customers to have the right support when they encounter issues in early deployment.

As customers scale AI across their organizations, we have concrete processes and tools to help support them, so they can deploy new AI with the utmost confidence. For example, the Incident Solution Matching service is integrated with 麻豆原创 Joule for Consultants, allowing efficient support information retrieval and helping to eliminate the hassle of searching through vast amounts of 麻豆原创 documentation.

Empowering support engineers with AI

AI is not just transforming customer outcomes, it鈥檚 also transforming how our engineers and experts deliver precision and speed, freeing them from logistical tasks so they can focus on support requests that need specialized attention. Thanks to 麻豆原创鈥檚 AI-integrated self-service offerings, we鈥檙e able to instantly resolve customer issues four out of the five times they come to us.

AI-powered solution recommendations in self-service can eliminate the need for at least 10% of the cases being created. This is a big win for human-generated knowledge being delivered by AI-generated tools. Every third case gets submitted with an AI-recommended product component for optimal routing and faster processing.

In 麻豆原创鈥檚 multi-location, multilingual, global setup, standardized communication is key. Around 10% of responses by support engineers take advantage of 麻豆原创鈥檚 AI-assisted language optimization services.

There鈥檚 more. We have agentic case resolution, AI-assisted creation of 麻豆原创 Knowledge Base Articles, and automatic error categorization, covering use cases that help our engineers deliver their best work with greater accuracy and higher quality.

And, of course, 麻豆原创 runs its own products and solutions, serving as a first reference for our customers. As Dr. Benjamin Blau, 麻豆原创鈥檚 Chief Process and Information Officer, puts it: 鈥淭his is ‘麻豆原创 runs 麻豆原创’ in action. As customer zero, we validate every AI innovation in real-world complexity before it reaches you. We鈥檝e architected this multi-agent AI on our own 麻豆原创 Business Technology Platform, including the 麻豆原创 AI Core foundation, and our service and support data lake. Agentic case resolution is a blueprint for enterprise-grade, responsible AI, proving the power and maturity of the 麻豆原创 Business AI portfolio, empowering customers with faster resolutions for an elevated experience.鈥�

Will AI replace support teams?

Short answer: No.

To elaborate, let鈥檚 take the example of an AI agent that automatically responds to customers. 麻豆原创鈥檚 instant response and resolution are only activated when the system is very confident with its response. Our commitment to the relevant, reliable, and responsible use of AI helps ensure that there鈥檚 no experimentation with customer cases that deserve hands-on attention from engineers and experts. The legacy of trust that 麻豆原创 has earned over 50 years of industry leadership, which is also trusted by 90% of Fortune 500 companies, drives this rigor applied to AI.

What does this mean for our engineers? Any move to augment our work with AI is not about replacing people. It鈥檚 about freeing time, energy, and creative space to focus on high-impact tasks that need critical thinking and human insight. AI amplifies human expertise. Customers benefit from this blend of machine intelligence and human insight, ensuring every solution is relevant and responsible.

It鈥檚 also important to highlight that 麻豆原创 is a growth company. The use of technology helps us deliver what customers expect from support teams and build ongoing knowledge that feeds AI systems for intelligent decision-making, also meeting the future demands of AI-augmented support.

Yes, the world is witnessing role reductions across the industry with the adoption of AI in business workflows, but we also see the emergence of critical new roles that help us navigate the current reality. How many of us had heard of AI trainers or carbon accountants 15 years ago?

These are exciting times for innovation. 麻豆原创鈥檚 partnerships, such as our collaboration with Databricks and Snowflake, empower developers to turn business data and AI into real business outcomes.

We鈥檙e truly at the crossroads of innovation and transformative tools that can turn imagination into impact. 麻豆原创鈥榮 Chief Technology Officer, Philipp Herzig, summarizes it perfectly: 鈥淎I is transforming business at every level, but it鈥檚 people who turn transformation into progress. With 麻豆原创 Business AI, we鈥檙e combining the best of human ingenuity and machine intelligence to deliver impact that matters.鈥�

Stefan Steinle is executive vice president and head of Customer Support & Cloud Lifecycle Management at 麻豆原创.

The system was developed by 麻豆原创 Japan and INSPIRATION PLUS, a venture from Oita University focused on disaster prevention.

EDiSON, which runs on 麻豆原创 Business Technology Platform, exemplifies how intelligent enterprise technology can be harnessed to address global challenges such as cyclones (typhoons) and floods. It integrates diverse types of information, including real-time, visual meteorological data and historical data records, to drive predictive insights and smarter operations assisted by 麻豆原创 Business AI and machine learning.

These predictions offer authorities a vital tool for delivering faster response times and mitigating damage in the event of a natural disaster. This includes forecasting terrain damage, dispatching emergency services to affected areas and supporting decision-making in issuing evacuation advisories. The Solomon Islands initiative is envisioned as a blueprint for other small island developing states, showcasing how cutting-edge technology can democratize disaster resilience.

鈥淓DiSON represents a leap forward in how science and technology can empower vulnerable communities,鈥� UNESCO Chief of Disaster Risk Reduction Soichiro Yasukawa said. 鈥淏y integrating AI and real-time data, we are not only improving early warning capabilities but also building a foundation for long-term resilience and sustainable development.鈥�

The project, part of UNESCO鈥檚 Disaster Prevention Strengthening Program, will be operational in 2026. It aims to establish a scalable, data-driven model for disaster preparedness and responses of small island nations that face the increasing severity of natural disasters driven by climate change. The Solomon Islands, located in the South Pacific Ocean, face frequent threats from earthquakes, tsunamis, cyclones, droughts and flooding. EDiSON will serve as a transformative solution to enhance national preparedness and response capabilities.

EDiSON integrates static and real-time dynamic data from government, municipal and private sector sources. The system delivers predictive insights and real-time visibility into emerging disaster risks. This empowers authorities to issue timely evacuation orders and make informed decisions that protect lives and infrastructure.

Why EDiSON? Proven Performance and Scalable Sustainability

鈥淭his project exemplifies 麻豆原创鈥檚 commitment to using technology to empower resilient communities,鈥� said Sophia Mendelsohn, chief sustainability and commercial officer at 麻豆原创 SE. 鈥淓DiSON is a powerful example of how our cloud platform and AI capabilities can be tailored to meet the needs of communities facing real-world challenges. We鈥檙e proud to support UNESCO in bringing this innovation to the Solomon Islands and beyond.鈥�

UNESCO鈥檚 selection of EDiSON was driven by the system鈥檚 proven track record in Japan鈥攁 country renowned for its advanced disaster management systems. The system鈥檚 ability to overcome traditional barriers such as fragmented data, limited analytical capacity and underutilization in field operations makes it especially valuable for resource-constrained nations such as the Solomon Islands. EDiSON鈥檚 modular design helps ensure scalability and adaptability, enabling governments to deploy sophisticated disaster management tools without requiring extensive budgets or technical expertise.

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Lesa Plingen, +49 622 776 9000, lesa.plingen@sap.com, CET
麻豆原创 麻豆原创 Room; press@sap.com

This document contains forward-looking statements, which are predictions, projections, or other statements about future events. These statements are based on current expectations, forecasts, and assumptions that are subject to risks and uncertainties that could cause actual results and outcomes to materially differ. Additional information regarding these risks and uncertainties may be found in our filings with the Securities and Exchange Commission, including but not limited to the risk factors section of 麻豆原创鈥檚 2024 Annual Report on Form 20-F.
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This month at , we announced a wave of 麻豆原创 Business AI innovations all built on the same technology foundation that powers our that we are now delivering to our customers and partners, allowing them to add even more value in the future.

We showed how the future of enterprise software is built on an AI-native architecture, powered by 麻豆原创 app, data, and AI foundation. With this approach, we are enabling a platform shift across the tech stack in a non-disruptive fashion, empowering developers to work faster and smarter using the frameworks and tools of their choice.

麻豆原创 HANA Cloud and 麻豆原创 Business Data Cloud: powering our AI-native future

麻豆原创 HANA Cloud is the database for 麻豆原创鈥檚 AI-native software architecture and the foundation of our broader data fabric strategy. At 麻豆原创 TechEd, we announced new AI capabilities for 麻豆原创 HANA Cloud that spur AI innovation.  

For example, Model Context Protocol (MCP) support for 麻豆原创 HANA Cloud is now generally available. This provides direct access to rich multi-model engines. Agents can be grounded in full enterprise data context: navigating relationships across customers and suppliers, understanding geographic dependencies through spatial data, and performing semantic searches through vector embeddings — all within a single in-memory engine.  

We鈥檙e also expanding 麻豆原创 HANA Cloud knowledge graph engine capabilities (Q1 2026) so customers can automatically generate knowledge graphs from 麻豆原创 HANA Cloud metadata. What used to take weeks of manual modeling can now happen automatically in minutes. But that鈥檚 not all. We鈥檙e also enabling agentic memory in 麻豆原创 HANA Cloud. With long-term memory, AI agents can memorize past inputs and decisions — learning and remembering just like humans — and become continuously smarter.

These advances show that 麻豆原创 HANA Cloud is truly powering an AI-native future. .

Bringing together the power of 麻豆原创 BDC and Snowflake

We are bringing the power of Snowflake together with 麻豆原创 Business Data Cloud (麻豆原创 BDC), calling it 麻豆原创 Snowflake. This partnership enables zero copy data sharing with Snowflake via 麻豆原创 BDC Connect.

Enterprises already using Snowflake today can leverage 麻豆原创 BDC Connect to integrate their existing instances of Snowflake with 麻豆原创 BDC, giving them seamless, real-time access to combined, semantically rich 麻豆原创 with non-麻豆原创 data in 麻豆原创 BDC. 麻豆原创 Snowflake will be made generally available in Q1 2026, and 麻豆原创 BDC Connect for Snowflake in H1 2026. Find more information here.

麻豆原创-RPT-1: a new category of AI models

One of our most exciting announcements at 麻豆原创 TechEd was the launch of our first enterprise relational foundation model 麻豆原创-RPT-1, pronounced: 鈥渞apid one.鈥�

Businesses run on structured data. But large language models (LLMs) struggle with a general understanding of table structures and associated semantics. This requires the use of machine learning, or 鈥渘arrow AI,鈥� for tasks like classification, regression, and more. But classical machine learning necessitates training a model on each task, which easily can lead to hundreds of separate models.

麻豆原创-RPT-1 puts them all into one single, pre-trained model that understands relational business data and predicts business outcomes. Unlike language, image, or video models, 麻豆原创-RPT-1 accurately predicts business based on tabular data such as payment delays, supplier risks, upsell opportunities, customer churn risk, and more.

We believe that 麻豆原创-RPT-1 is a super capable foundation model today. It provides up to 2x better prediction quality compared to narrow models and 3.5x better prediction quality as compared to LLMs. .

麻豆原创-RPT-1 comes in three versions. 麻豆原创-RPT-1-small is for super-fast predictions and 麻豆原创-RPT-1-large is for highest accuracy. Both will be generally available in Q4 2025 in the generative AI hub in AI Foundation. 麻豆原创-RPT-1-OSS is the open-source version, available in Hugging Face and GitHub.

You can test 麻豆原创-RPT-1 today with your data or our use case data samples via no-code UI or via API in the new 麻豆原创-RPT-1 playground, an intuitive and interactive space to test for free and open to everyone and .

We are continuously adding new capabilities to AI Foundation and models to the generative AI hub, empowering developers to experiment with orchestration tools and leading models to scale AI development and productization across 麻豆原创 and non-麻豆原创 environments. For example, Perplexity is now generally available in the generative AI hub, so users can correlate business data with external data from the internet. Evaluation Services and Prompt Optimizer, in close collaboration with NotDiamond, are now also generally available in AI Foundation, freeing up users to adopt the most appropriate model for their use cases without the need for rewriting prompts. .

Digital sovereignty made in Germany, for Europe

Digital sovereignty is becoming increasingly important, reflecting the need for regional AI services that align with local regulations, standards, and values. As an example, Europe will benefit from its own strong, trustworthy infrastructure to support innovation, data protection, and ethical AI.

AI Foundation, including various models and all the services we offer, is already available on our own cloud infrastructure. As a next step, we are expanding our 麻豆原创 Cloud Infrastructure offering in our 麻豆原创 data center in Walldorf, Germany, to Deutsche Telekom through the Industrial AI Cloud project, providing secure, high-performance infrastructure for AI innovations across public institutions, defense, and society. 麻豆原创 delivers 麻豆原创 Cloud Infrastructure, 麻豆原创 Business Technology Platform, and applications 鈥� including our AI Foundation with frontier AI from Mistral, Cohere, and others 鈥� on Telekom鈥檚 Munich data center. Both companies uphold the highest standards of data protection, security, and reliability.

This marks a milestone as more European companies join the Industrial AI Cloud project, advancing applied AI across Europe with trusted, business-embedded solutions that unlock the full potential of industry data. See the announcement here.

Enabling customers to build, extend, share, and orchestrate AI agents

To help manage Joule Agents and Joule skills, we have introduced the concept of AI Assistants 鈥� role-based AI teammates, accessed through Joule 鈥� like a financial assistant that brings together agents for cash collection, treasury, and more. We will provide AI Assistants in Joule for every core business role, offering our users an agentic experience like never before.

Out-of-the-box Joule Agents are powerful, but we know that every company has unique requirements. We believe AI should adapt to users鈥� systems, not the other way around, so we are enabling them to use Joule Studio to extend 麻豆原创鈥檚 pre-built agents with custom fields, tools, and reasoning logic while retaining all the deeply grounded integration capabilities 麻豆原创 provides. Joule Studio also provides low-code tools to build custom agents that integrate with all other Joule Agents, Joule skills, and 麻豆原创 BDC.

Using a low-code approach, users can build Joule Agents visually with natural language and drag-and-drop. But we also want to meet the needs of developers who want ultimate flexibility. Our pro-code approach gives developers the freedom to build agents using the agentic framework of their choice 鈥� for example, LangGraph, CrewAI, Google鈥檚 Agent Development Kit, and more. 麻豆原创 Cloud SDK for AI now supports agentic development, ensuring these pro-code agents can be seamlessly integrated and giving developers the best of both worlds: deep integration and full flexibility.

No matter how you want to build agents, an important question is how to integrate them into the larger ecosystem beyond 麻豆原创. We鈥檙e making Joule Agents fully compatible with the agent-to-agent (A2A) protocol soon, so agents can discover and collaborate with each other.

A2A exposes rich semantics describing an agent鈥檚 capabilities, allowing both 麻豆原创 and third-party agents to work together seamlessly. We are collaborating with partners 鈥� AWS, Google, Microsoft, ServiceNow, and more 鈥� to standardize this protocol for full interoperability. This capability will allow Joule to orchestrate tasks across multiple agents, both 麻豆原创 and non-麻豆原创, increasing automation and productivity across the enterprise. Read more here.

To manage and govern agents across the enterprise, is now generally available, providing centralized control of 麻豆原创 and non-麻豆原创 agents. In addition, is available now for tracing agent actions, benchmarking against KPIs, and identifying bottlenecks or opportunities for agents to further improve business.

No 麻豆原创 TechEd without ABAP news

The ABAP journey continues with 麻豆原创-ABAP-1, which will be available in the generative AI hub in Q4 2025. Trained on ABAP code, it is designed to build ABAP AI use cases, enabling developers to build smarter, custom AI solutions in modern ABAP code. .

In addition, ABAP Cloud development is coming to Visual Studio (VS) Code. The new ABAP Cloud extension for VS Code delivers a streamlined, file-based development experience with built-in AI assistance. Powered by an ABAP language server, it will initially support 麻豆原创 Fiori UI service development and expand to additional ABAP Cloud scenarios over time. This brings ABAP development into the same environment where developers already build with UI5 and CAP. General availability is planned for Q2 2026. .

What鈥檚 next: embodied AI and quantum

麻豆原创 TechEd is always an opportunity to look to the future. This year, that future includes not just humans, but also autonomous devices, including humanoid robots.

By integrating Joule Agents natively with robots, 麻豆原创 is bringing business logic into the physical world, enabling a wide range of autonomous devices to operate with enterprise context. We highlighted our strategic partnerships with robotics companies and system integrators to serve customers like Sartorius, Bitzer, and Matur Fompak, demonstrating how our expanding physical AI ecosystem enables robots to understand business processes and execute complex tasks autonomously.

Early proof-of-concept deployments show Joule successfully integrated with 麻豆原创 business applications and autonomous systems across asset performance, logistics, field services, and warehouse operations. While still in the pioneering stage, these implementations illustrate how 麻豆原创 is extending Joule to serve both human users and autonomous devices, shaping the future of enterprise AI.

Read more about the partnerships and implementations here.

AI is a new compute paradigm that changes everything. But there is another compute paradigm on the horizon: quantum computing. It鈥檚 early days, but 麻豆原创 is driving the future of enterprise computing with a vision to help businesses get ready for quantum computing.

麻豆原创 is not building quantum hardware; instead, we are focusing on creating quantum algorithms for business applications. These solutions are simple to deploy 鈥� on when needed, off when not 鈥� and are designed to be hardware-agnostic, collaborating with partners such as IBM to ensure seamless integration without re-platforming. This approach will enable organizations to unlock operational efficiency and drive better business results at enterprise scale.

I couldn鈥檛 be more excited about what鈥檚 next for our customers鈥� future as we bring 麻豆原创鈥檚 AI-native architecture to life.

Philipp Herzig is CTO of 麻豆原创.

The IDC MarketScape vendor analysis model is designed to provide an overview of the competitive fitness of technology and suppliers in a given market. The research methodology utilizes a rigorous scoring methodology based on both qualitative and quantitative criteria that results in a single graphical illustration of each supplier鈥檚 position within a given market. The Capabilities score measures supplier product, go-to-market and business execution in the short-term. The Strategy score measures alignment of supplier strategies with customer requirements in a three to five year timeframe. Supplier market share is represented by the size of the icons.

According to the IDC MarketScape, 鈥淎I-enabled tools are revolutionizing field service management, transforming reactive operations into predictive excellence.鈥�

麻豆原创 was recognized for the following strengths:

• End-to-end field service management offering as part of the full enterprise suite: 麻豆原创 Field Service Management is a fully integrated component of the 麻豆原创 Business Suite, enabling end-to-end business process execution across planning, logistics, operations, finance, and customer service. It connects seamlessly with core 麻豆原创 solutions such as 麻豆原创 S/4HANA, customer experience, asset management, and supply chain management, ensuring that service delivery is fully aligned with enterprise-wide processes. This deep integration eliminates silos, enables real-time collaboration across departments, and supports consistent, efficient service execution across the entire value chain.
• AI innovations and generative AI capabilities: 麻豆原创 Field Service Management is infused with AI and generative AI to simplify and accelerate service delivery. 麻豆原创 is able to support generative summaries of equipment history, work orders, and past service activities. 麻豆原创 has established an embedded AI copilot for field service that enables users to execute commands, automate actions, and retrieve context-aware insights using conversational language with the benefit of boosting productivity and responsiveness across the service life cycle. 麻豆原创 also has a robust auto-scheduling engine designed for complex, high-volume service operations.

Commitment to continuous innovation

Field service organizations face growing complexity, workforce shortages, and rising customer expectations that demand smarter, faster, and more connected service delivery. 麻豆原创 continues to lead the market by integrating AI-driven insights, intelligent automation, and end-to-end connectivity across its portfolio.

麻豆原创 remains focused on enabling customers to:

• Boost technician and dispatcher productivity
• Drive customer-centric and revenue enabling operations
• Reduce operational costs and accelerate complex workflows via intelligent automation and AI
• Provide a connected and extensible platform for field service

麻豆原创 is proud to be recognized by the IDC MarketScape as a Leader in AI-enabled field service management. We remain committed to helping our customers run their service operations smarter, safer, and faster 鈥� combining data, applications, and AI to deliver measurable business outcomes and exceptional customer experiences.

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Ryan Jones is product marketing manager for Operate and Service at 麻豆原创.

*Doc #US52967825e, September 2025

Across industries, operational tasks often require real-time judgement and adaptation to constantly changing conditions. Traditional automation falls short here, as physical execution remains disconnected from business logic.

麻豆原创 addresses this gap with its , which extends the impact of into physical operations. By enabling robots to act with business awareness, they can autonomously execute complex tasks while interpreting real-time enterprise context. This allows organizations to adapt more quickly and reliably to dynamic operational environments.

麻豆原创 is uniquely positioned to deliver these innovations due to decades of experience building business applications deeply embedded in the processes that power the modern enterprise. This allows 麻豆原创 to integrate robotics into business functions seamlessly, and in a way no other company can.  As a result, autonomous robots operate as an extension of enterprise systems鈥攏ot as isolated automation鈥攅nabling consistent, context-aware execution in dynamic environments.

To explore how embodied AI can support your operations, contact us . Current capabilities available for productive use or testing span a range of warehouse and operational scenarios, including picking and placing, asset inspection, quality inspection, and safety inspection.

The following examples illustrate these capabilities across real-world use cases.

Enabling autonomous warehouse execution at Bosch

One of the many strategic initiatives and are working closely is in the area of , where Bosch and 麻豆原创 is collaborating with to explore how physical AI can transform warehouse automation. In a joint proof of concept, 麻豆原创鈥檚 embodied AI capabilities and Aimbo鈥檚 Industrial Embodied AI deployment middleware enabled Aimbo鈥檚 humanoid robot, Anigon, to execute warehouse tasks based on real-time operational context from 麻豆原创 Extended Warehouse Management (麻豆原创 EWM), including delivery data, order priorities, and storage locations.

This initiative combined 麻豆原创 S/4HANA-driven workflows directly with autonomous robotic execution at a European Bosch facility. Trained and deployed by Aimbo in 10 working days, a single Anigon unit equipped with the universal dual grippers provided by Bosch was rapidly integrated to handle intralogistics workflows across three distinct bin configurations and three workstation layouts, creating a bi-directional data loop with 麻豆原创’s embodied AI connected to 麻豆原创 EWM. It successfully demonstrated how enterprise software, AI, and humanoid robotics can work together to enable more adaptive, scalable, and resilient industrial operations.

While the initiative remains in an exploration phase and was validated in dedicated beta and test system environments, the collaboration highlights the strong potential of embodied AI for intelligent warehouse automation and future enterprise-scale industrial AI applications.

“This achievement demonstrates what鈥檚 possible when bold collaboration meets enterprise innovation鈥攃onnecting AI, robotics, and 麻豆原创 to shape the future of autonomous operations. Together, we are moving physical AI from experimentation to enterprise-scale industrial AI with real operational impact.”

Gamze 脰zt眉rk, Head of CoE ERP Product Excellence, Bosch Digital

Optimizing warehouse inspections at Vodafone Procure & Connect

麻豆原创, together with 听补苍诲听, is demonstrating how embodied AI and humanoid robotics can help address operational inefficiencies and safety risks in warehouse environments. The collaboration highlights聽麻豆原创鈥檚 role in connecting robots to end-to-end warehouse processes and business context, helping enable more intelligent, responsive and traceable operations.

In a scenario test at 鈥檚 warehouse in Duisburg, Germany, 麻豆原创鈥檚 embodied and Accenture鈥檚 physical聽AI capabilities enable humanoid robots to perform inspections and monitor operations using real-time business context from 麻豆原创 systems, including inventory data, task priorities, and storage locations.聽The聽pilot聽demonstrates how robots can identify damaged or misplaced items, detect aisle obstructions, and feed findings directly back into 麻豆原创 systems for closed-loop traceability聽and faster operational response.

By reducing reliance on manual inspections and improving operational consistency, the collaboration shows physical AI can enhance both efficiency and safety in dynamic warehouse operations. Learn more about this collaboration聽.

Scaling vehicle validation at Mahindra & Mahindra

, a global automotive and industrial group, collaborated with 麻豆原创 to target a high-volume, error-prone process: manual validation of vehicle identifiers.

Here, 麻豆原创’s embodied AI uses 鈥檚 visual AI, Amalgamation AI, to cross-check real world observations to 麻豆原创 records, enriched by application data and context engine numbers, VIN plate, and Engraved Chassis Number, enabling fast, scalable vehicle validation.

This approach has the potential to increase throughput by up to three times, reduce manual verification effort by about 67%, and achieve data extraction accuracy of 95%-99%.

Optimizing material handling at Martur Fompak

, a global supplier of automotive seating systems, teamed up with 麻豆原创 to address throughput bottlenecks and limited shop floor responsiveness caused by manual material handling.

For this scenario, 麻豆原创鈥檚 embodied AI orchestrates a robot to remove materials across a live automotive manufacturing environment, guided by real-time business context from , including physical attributes and warehouse logistics.

“By assigning repetitive, non-value-adding, and physically demanding tasks to robots, we reduce physical strain and potential injuries while enabling our people to focus on safer, more meaningful, and higher value work that drives productivity and innovation.鈥�

脰zge G眉l艧ah Geydirir, 麻豆原创 Logistics Modules & Operational Excellence Manager at Martur Fompak

Early findings indicate throughput and work order completion improvements of up to five times, along with improved inventory accuracy.

Read the full customer success story and see the 2026 麻豆原创 Innovation Award recognition .

Advancing spare parts inspection at Tetra Pak

, a global leader in food processing and packaging solutions, partnered with 麻豆原创 to address warehouse bottlenecks and risk of errors in spare parts inspection processes.

This proof of concept shows 麻豆原创’s embodied AI and replicating parts of Tetra Paks’ 麻豆原创 Extended Warehouse Management inbound process in a lab, where a humanoid robot verifies the received items and packs them into plastic boxes for further processing. These results can be automatically recorded back into 麻豆原创, closing the loop between execution and system updates.

An 麻豆原创 expert estimates that this end-to-end automation will improve stock and quality accuracy by 10%鈥�20% and build in-house expertise in advanced technologies for continued productivity gains.

Reducing costly storage errors with autonomous inbound processing at 础谤莽别濒颈办-尝骋

, a global manufacturing joint venture, partnered with 麻豆原创 to address a costly and persistent issue with incorrect storage placement in inbound logistics. At the 础谤莽别濒颈办-尝骋 facilities, such errors lead to millions of euros in annual losses through rework, inventory discrepancies, and production disruptions.

In this ongoing proof of concept, 麻豆原创鈥檚 embodied AI enables a humanoid robot to execute and record inbound storage tasks guided by real-time context from (麻豆原创 EWM). Context-enriched 麻豆原创 EWM tasks are routed through 鈥檚 fleet management system, allowing the robot to scan barcodes, transport materials, and provide confirmations with 麻豆原创-level traceability.

This approach has the potential to reduce manual intervention and eliminate placement errors at the source, improving inventory accuracy and end-to-end traceability. By preventing rework and downstream production disruptions, it can significantly lower inbound handling costs while improving overall process quality.

麻豆原创 runs 麻豆原创: scaling autonomous logistics execution in 麻豆原创鈥檚 own warehouse

麻豆原创 partnered with , an innovative robotics startup, to validate embodied AI in a live warehouse environment, demonstrating how AI-driven robotics can enhance operational efficiency.

In this proof of concept, 麻豆原创鈥檚 embodied AI uses real-time business context from to select, prioritize, and orchestrate warehouse tasks through Cyberwave鈥檚 platform. Cyberwave validates tasks in digital twin environments before live deployment, while robotic policies are trained and improved using real-world data, simulation, reinforcement learning, and vision-language-action models.

This demonstrates the readiness of 麻豆原创鈥檚 embodied AI to support autonomous execution in real warehouse operations while maintaining a closed loop between robotic execution and 麻豆原创 system updates.

Cutting-edge experiments at BITZER demonstrate measurable productivity gains

, a leading name in refrigeration, air conditioning, and heat pump technology, teamed up with 麻豆原创 and NEURA Robotics to revolutionize warehouse logistics. In a recent pilot proof-of-concept, BITZER鈥檚 warehouse became a testing ground for one of Europe鈥檚 most advanced humanoid robot, , which was able to perform pick-tasks on its own in real time.

The tasks are selected by embodied AI agents. The process also integrates 麻豆原创鈥檚 business logic from through (麻豆原创 BTP). Prior to its warehouse deployment, 4NE1 was trained virtually using NVIDIA’s Isaac Sim software. This ensured the robot was fully prepared for real-world operations. By integrating embodied AI into warehouse operations, BITZER could reach 24/7 utilization and a high level of responsiveness.

The technology can add to human expertise, stepping in during demand fluctuations and peak periods. It also complements regular shifts with flexible and scalable support. This ensures operations remain agile and efficient, even under varying workloads. Thanks to a single source of truth, orders can be expanded or cancelled in near real time, as robots execute changes almost instantly. This improves reaction times and enables BITZER to maintain high service levels while optimizing the use of resources.

鈥淲e are excited to join the Embodied AI initiative with 麻豆原创 and NEURA. We believe this collaboration will enhance our operational efficiency and drive innovation in our processes.鈥�

Christian Stenzel, Vice President of Corporate Organization and IT, BITZER

Transforming warehouse operations at Sartorius

Imagine stepping into a warehouse in which intelligent machines work side-by-side with humans. The first proof of concept for embodied AI at shows it is possible and marks a milestone in the journey to next-level logistics.

鈥淪o far, like many others, our focus was on fixed automation, in which specialized equipment handles only a single task. Now we鈥檙e making automation intelligent, and far more dynamic, to help us navigate a fast-moving world.鈥�

Steffen Dietz, Manager of Business Process Management in Operations and Supply Chain, Sartorius

The proof of concept, also delivered through the partnership with 麻豆原创 and NEURA Robotics, demonstrates how cognitive robots can support manual workstations in an advanced warehouse environment. Here, the humanoid robot 4NE1 was trained with Sartorius products in NEURA Robotics鈥� lab. The solution builds on an 麻豆原创 S/4HANA migration and 麻豆原创 Extended Warehouse Management (麻豆原创 EWM) rollout in May, which established the foundation for leveraging the latest capabilities from 麻豆原创.

The result boosts efficiency and enhances operational resilience. 鈥淲e鈥檙e really happy to help spearhead this new age together with 麻豆原创 and NEURA,鈥� Steffen Dietz, manager of Business Process Management in Operations and Supply Chain at Sartorius, shared.

Robotics company partners

Building upon these successes, 麻豆原创 announced the following additional partnerships:

AgiBot

Agibot creates general-purpose embodied robot products and an application ecosystem. The company delivers a complete product portfolio and deploy across all major application scenarios.

“Our 麻豆原创 partnership transforms industrial automation by combining humanoid capabilities with enterprise intelligence that understands business context,” said Peng Zhihui, founder of .

.

ANYbotics

ANYbotics provides a full-stack autonomous inspection solution that combines autonomous robotics with inspection intelligence.

“Integrating this continuous flow of inspection intelligence with 麻豆原创 makes operations not only autonomous but truly intelligent, where issues are predicted, understood, and prevented before they affect production,鈥� said Dr. P茅ter Fankhauser, CEO and co-founder of .

.

Apptronik

makes AI-powered humanoid robots designed to collaborate thoughtfully with humans, initially in critical industries such as manufacturing and logistics, with future applications in healthcare, the home, and beyond.

Today, Apptronik鈥檚 flagship robot, Apollo, excels at flexible work in dynamic environments, supporting industrial tasks like warehouse picking, placing, and material handling while adapting to changing demand on the floor. With 麻豆原创鈥檚 business context guiding every action, these humanoid robots can execute warehouse tasks with the right priorities at the right time so automation stays aligned with real operational needs.

Booster Robotics

Booster Robotics provides T1 humanoid robots for warehouse operations and field maintenance.

“Our humanoid platforms, with 麻豆原创’s intelligence, creates an adaptive automation foundation that understands business processes and operational context,” said Cheng Hao, CEO of .

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Boston Dynamics

delivers mobile robots for industrial inspection and safety monitoring, enabling autonomous patrols, anomaly detection, and rapid incident response across large-scale facilities.

By connecting robots鈥� real-time site awareness with 麻豆原创鈥檚 enterprise intelligence, organizations can turn inspection data into safer operations and faster, more informed decisions.

Galbot

Galbot’s fully autonomous, general-purpose humanoid robots have been deployed across a wide range of applications, including industrial, logistics, retail, and healthcare sectors. Powered by proprietary vision-language-action models, the Galbot G1 autonomously performs complex tasks such as precise parts sorting, industrial bin handling, and end-to-end pharmacy operations. These models enable Galbot robots to rapidly adapt to dynamic environments, ensuring high precision and efficiency even in challenging real-world conditions.

“Our collaboration with 麻豆原创 marks a key milestone in transforming how robots understand and operate within enterprise environments. By integrating business context awareness into our robots, we’re creating automation that seamlessly adapts to shifting operational priorities in real time,” said He Wang, founder and CEO of .

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Humanoid

Humanoid offers reliable HMND 01 humanoid robots that provide cost-effective industrial automation and warehouse solutions, with modular designs that enable configurations for logistics operations, asset monitoring, and scalable field service applications.

“麻豆原创’s AI platform gives our robots intelligence to adapt and scale with enterprise needs, which creates flexible automation,” said Artem Sokolov, founder and CEO of .

RobotEra

develops humanoid robots designed for enterprise operations and, together with 麻豆原创鈥檚 embodied AI, enables robots to execute end-to-end 麻豆原创 EWM workflows鈥攆rom material verification and visual inspection to exception handling鈥攇uided by real-time business context.

By integrating 麻豆原创鈥檚 operational intelligence with RobotEra鈥檚 humanoid robotics platform, customers can enable automation that understands warehouse priorities, handles exceptions, and completes 麻豆原创 EWM tasks with enterprise-grade reliability.

Unitree Robotics

Unitree Robotics provides advanced quadruped Go2 robots for warehouse navigation and asset inspection, plus G1 humanoids with human-like dexterity for logistics operations, alongside industrial B2 models for outdoor facility maintenance.

“麻豆原创 embodied AI agents will revolutionize enterprise autonomous operations from warehouse management to predictive maintenance across facilities,” said Wang Xingxing, CEO and founder of .

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Robotics enablement partners

麻豆原创 also introduced the following robotics enablement partners to connect humanoid and mobile robots, optimize intralogistics, streamline inspections, and orchestrate physical assets enabled by 麻豆原创 Business AI and automation technologies:

Accenture

partners with 麻豆原创 to advance physical AI for enterprise operations, helping organizations connect digital business processes with autonomous execution.

The collaboration brings 麻豆原创鈥檚 process and data intelligence together with Accenture鈥檚 expertise in robotics, digital twins, and operational transformation to support use cases such as inspection, risk detection, and workflow optimization.

Aimbo AG

delivers the industrial embodied AI deployment Middleware that connects enterprise software with robotic execution. The company鈥檚 vision is to build robust, closed-loop system capabilities spanning models, data, evaluation, and deployment.

True value for industrial leaders lies not in a powerful model alone, but in a reliable system that integrates deeply into the factory鈥檚 operations, continuously creating production value and improving with every cycle.

Capgemini

Capgemini explores the value that can be derived from the convergence of advanced technologies such as agentic and multi-agent AI systems, humanoid robotics, reinforcement learning, spatial computing, real-time 3D environments, and conversational AI. and 麻豆原创 are jointly exploring physical AI to help organizations gain a competitive edge.

Cyberwave 

Cyberwave聽connects 麻豆原创 systems to the physical world through its physical AI platform, which integrates robots, sensors, and digital twins into enterprise workflows.

鈥淭ogether with 麻豆原创, Cyberwave turns enterprise data into coordinated physical action鈥攂ridging the gap between digital intelligence and real-world operations through physical AI,鈥� said Simone Di Somma, founder of .

FairConsult 24|7

partners with 麻豆原创 to deliver embodied AI-enabled warehouse execution, combining 麻豆原创鈥檚 business context-driven task intelligence with FairConsult 24|7鈥檚 fleet management system to orchestrate enterprise-connected robotic operations.

Together, they unite 麻豆原创鈥檚 real-time operational priorities with fleet-level control, enabling robots to execute warehouse tasks with transparency, governance, and measurable performance.

Fujitsu

brings physical AI and visual intelligence capabilities through its Amalgamation AI offering, enabling intelligent image processing and data extraction that can be deployed to edge devices including cameras and robots.

Together with 麻豆原创, Fujitsu’s AI-driven visual capabilities integrate with 麻豆原创’s agent-based workflows to capture and validate real-world inputs鈥攕uch as vehicle identifiers in automotive inspection鈥攄irectly within 麻豆原创 systems for downstream business processing.

HCLTech

HCLTech provides automation expertise through its AI Force platform and 麻豆原创 integration capabilities, leveraging in collaboration with 麻豆原创 to accelerate generative AI-led robotics solutions.

“Our collaboration with 麻豆原创 enables cognitive robotics to seamlessly integrate with enterprise systems, transforming business operations through automation,” said Vijay Guntur, CTO and head of Ecosystems at .

KINEXON

KINEXON brings physical AI to day-to-day material flow management, helping customers scale mixed-fleet operations with a vendor-agnostic orchestration platform for autonomous mobile robots (AMRs), automated guided vehicles (AGVs), and manual vehicles.

“Our collaboration with 麻豆原创 infuses business-driven agentic reasoning into real-world material movement planning and execution, maximizing utilization and throughput,” said Dr. Alexander Huettenbrink, co-CEO of .

LG CNS

, a South Korea鈥揵ased IT and AI transformation company with presence across Asia, partners with 麻豆原创 to deliver AI-driven enterprise transformation.

Combining 麻豆原创鈥檚 business context and application intelligence with LG CNS鈥檚 PhysicalWorks, a robot training and multi-vendor robot orchestration and control platform, the collaboration, starting with PoC initiatives, enables AI-driven automation and optimization using real-time data across business processes.

Lighthouse

Lighthouse transforms business complexities into streamlined digital solutions, leveraging expertise across 麻豆原创 Intelligent Asset Management, 麻豆原创 Business AI, and 麻豆原创 BTP. 

鈥淓mbodied AI has huge potential for use cases, including asset and site inspection, health and safety, and quality inspection to deliver more resilient, flexible operations. We see major customer needs today, such as hazardous environments on offshore platforms in the oil and gas industry, utilities, and transportation,” said Urs Gehrig, managing director of Business Development at .

valantic

and 麻豆原创 are partnering to bring embodied AI to real-world operations. By merging 麻豆原创鈥檚 context-driven orchestration with valantic鈥檚 integration expertise, they enable high-precision, automated asset inspections. This collaboration transforms insights into immediate, coordinated action, ensuring safer operations, stronger compliance, and reliable asset performance. It’s embodied AI, made practical.

Through these strategic alliances, 麻豆原创 continues to lead the evolution from traditional robotic tools to those that empower autonomous operations, informed by deep business context.

To explore how 麻豆原创 technology makes proofs of concepts possible in robotics, explore the . To get involved in 麻豆原创’s Embodied AI initiative, .

Dr. 艁ukasz Ostrowski is head of Embodied AI and Robotics at 麻豆原创.

Enabled by 麻豆原创 Business AI and built on 麻豆原创 Business Technology Platform, the assistant is already delivering faster response times, enhanced supplier experiences and smooth multilingual automation.

With 38 shows across global cities and a workforce of almost 4,000 artists and staff from 80 countries, Cirque du Soleil鈥檚 operations are as dynamic as its performances. Managing more than 70,000 supplier invoices annually鈥攅specially from tour-supporting vendors at residency shows鈥攈as placed increasing pressure on the accounts payable team. Nearly 40 percent of inquiries are from vendors seeking invoice status updates, and these inquiries were often delayed due to limited visibility and manual processing.

To address this, Cirque du Soleil turned to 麻豆原创 Business AI. The company鈥檚 new AI-enabled invoice assistant automates the triage-and-response process for invoice-related emails. It analyzes incoming messages in all languages, identifies the request type, extracts invoice details, determines the status of each invoice and even captures the sentiment of the email, prioritizing those that need attention more urgently. The assistant then generates a proposed response in English and French, significantly reducing the average handling time from 30 minutes to just two minutes per inquiry.

鈥淭he time-consuming research required to identify the payment status of an invoice and its reason was overwhelming,鈥� said Philippe Lalumi猫re, vice president of information technology, Cirque du Soleil Entertainment Group. 鈥淲e were looking for a more efficient way to handle this, and 麻豆原创 Business AI provided us with a simply stunning answer.鈥�

The assistant leverages 麻豆原创 HANA Cloud to store and process structured data derived from incoming emails, along with analysis results and generated responses. 麻豆原创 AI Core foundation supports intelligent automation, enabling the assistant to detect urgency, sentiment and even root causes of payment delays. This has drastically reduced manual workload and improved supplier satisfaction.

“As we partner with Cirque du Soleil on this transformative journey, it鈥檚 inspiring to see how technology is streamlining operations and focusing organizations even more on what they do best鈥攄elivering unforgettable experiences,鈥� said Dr. Philipp Herzig, chief technology officer and chief AI officer at 麻豆原创 SE. 鈥淭his is a perfect example of how data and AI can unlock both creativity and efficiency.鈥�

Key benefits include:

• Efficiency: Automation frees up employee time and accelerates response rates.
• Accuracy: AI minimizes human error and helps ensure timely, contract-compliant payments.
• Multilingual support: Bilingual capabilities enable inclusivity across Cirque鈥檚 global supplier base.
• Scalability: The assistant handles high volumes without additional resources.
• Enhanced experience: Standardized, timely responses foster stronger supplier relationships.

With this innovation, Cirque du Soleil continues to lead not only in the world of live performance but also in operational excellence. 麻豆原创 remains a trusted partner in this journey, helping the company scale its global footprint while staying agile and responsive.

Visit the . Get 麻豆原创 news via  and .

Media Contact:
Lesa Plingen, +49 622 776 9000, lesa.plingen@sap.com, CET
麻豆原创 麻豆原创 Room; press@sap.com

Top image courtesy of Cirque du Soleil

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