At a glance

• Current cloud backlog of 鈧�22.9 billion, up 27% and up 26% at constant currencies
• Cloud revenue up 22% and up 24% at constant currencies
• Cloud ERP Suite revenue up 25% and up 27% at constant currencies
• Total revenue up 9% and up 11% at constant currencies
• IFRS operating profit up 8%, non-IFRS operating profit up 7% and up 9% at constant currencies
• 2026 non-IFRS operating profit outlook updated to reflect dilutive impact from Dremio and Prior Labs acquisitions

Christian Klein, CEO:

鈥淲e delivered another quarter of strong current cloud backlog growth, up 26% at constant currencies. This performance is underpinned by our Autonomous Enterprise strategy with strong momentum across our Autonomous Suite as well as our Business AI Platform. Customers are choosing 麻豆原创 to enable accurate and compliant AI outcomes grounded in their most critical business processes and data.鈥�

Dominik Asam, CFO:

鈥淨2 was another strong quarter, highlighted by sustained current cloud backlog and free cash flow growth against a volatile macroeconomic backdrop. These results reflect our disciplined execution and our ability to deliver against our operating objectives. As part of that execution, we aggressively drive our own transformation into an Autonomous Enterprise, leveraging AI to boost both effectiveness and efficiency at the same time.鈥�

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 to 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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Marcus Winkler, +46 (6227) 7-67497, marcus.winkler@sap.com, CEST
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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.
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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.

Headquartered in Itabera铆, Goi谩s, S茫o Salvador Alimentos S.A. (SSA) is one of Brazil鈥檚 leading food companies. Its brands such as SuperFrango and Boua offer a wide portfolio of fresh and processed poultry as well as other meat, egg, and dairy products, fish, and frozen foods. The company’s products are exported to dozens of countries worldwide.

While SSA has built a reputation for quality and innovation in the food industry, one of its internal processes,聽the management of PPE delivery,聽still relied on manual paperwork. After successfully implementing 麻豆原创 S/4HANA in 2024, the company continued its journey with 麻豆原创. The company started to collaborate with the 麻豆原创 AppHaus team to explore opportunities with 麻豆原创 Build and Cloud Foundry.

The point of departure was that every day around 150 physical signatures had to be collected to confirm that PPE had been delivered to employees. These signed documents were then handed over to the archive team for manual scanning and storage. There was no digital record of which equipment was delivered, to whom, or when. As a result, it was impossible to track usage or send timely reminders for mandatory PPE replacements every six months.

Innovation along the human-centered approach

In dedicated workshops, SSA stakeholders and users analyzed the situation along 麻豆原创鈥檚 human-centered approach to innovation. They explored new ways to improve and redesign this process with 麻豆原创 Build and Cloud Foundry. After some iterations and testing, the new solution was implemented in January 2026.

SSA employees can now use digital signature capture linked to an automatic tracking of what was delivered and when. Via automated WhatsApp reminders, they learn聽when it鈥檚 time to replace their equipment, thus ensuring compliance,聽efficiency, and more visibility to the warehouse.

This new process has already shown the following benefits:

• From 150 manual signatures to electronic signatures using digital authentication
• 100% elimination of paper in the process
• Simplified service and an easy-to-understand and intuitive app
• Higher visibility to the warehouse
• Reporting capabilities available
• Better cost management
• Regulated issuance and compliant PPE distribution

Implementation

Although the implementation also had to integrate external applications via specific application programming interfaces (APIs), the new solution went live in January 2026. Based on previous innovation projects and its experience with 麻豆原创, the customer team was able to act mostly independently. Typical questions mostly referred to best practices for integration.

Innovation journey and outlook

鈥淲hat truly stood out was the SSA team鈥檚 remarkable maturity, fast learning mindset, and determination,鈥� Mirela Viersa, customer innovation principal at 麻豆原创 AppHaus, said. 鈥淔ollowing our exploration workshop, where we identified the key challenge, we partnered closely to design their future journey鈥攖urning vision into a structured and actionable path forward.鈥�

After this latest successful implementation project with 麻豆原创, SSA plans to use 麻豆原创 Build for future developments as their single source for development, especially now with 麻豆原创 Build Code. And based on this confidence and proven enablement, the SSA team plans to look into opportunities that 麻豆原创 Business AI and other intelligent solutions might hold in stock for them.

Top image via S茫o Salvador Alimentos

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.

AI as a key element

In the AI category, the Austrian energy company OMV prevailed with its transformation project. Building on an existing 麻豆原创 S/4HANA landscape, the transformation focused on intelligent automation, advanced analytics, and AI-driven decision-making in core business processes. A key factor for success was a step-by-step approach: instead of a hasty implementation, OMV first laid a solid foundation and introduced AI in a targeted manner into the user experience and business processes. The focus was on regulation, security, and continuous evaluation. The clear principle was to standardize, integrate, and establish a stable data foundation before it scaled more broadly. Early in the process, the company relied on Joule in 麻豆原创 SuccessFactors solutions and 麻豆原创 S/4HANA. In the next step, the transformation will continue so OMV can make even greater use of AI.

Intelligent water supply

With a data-driven platform for monitoring its water network, the Belgian water utility Farys was recognized by the jury in the Innovation category. With the help of 麻豆原创 technology and AI, the utility can now deploy resources more efficiently and optimize its processes in a targeted way. Water leaks can be detected early before greater damage occurs, water quality issues can be predicted, and energy consumption can be reduced. The result: less downtime, lower repair costs, and a noticeably better quality of service. Having a unified solution also fosters cross-departmental collaboration by breaking down existing data silos. In the future, risk assessments for pipelines and facilities will be integrated, and data-driven decisions will help determine where repairs will have the greatest impact. With this strategy, Farys is preparing its infrastructure for the demands of climate change.

Achieving goals through teamwork

E.ON UK won the Best Team category with its 麻豆原创 S/4HANA transformation project. With its group-wide project running from 2021 to 2027, the company aims to standardize processes and build a future-proof, scalable IT landscape. The central challenge was to consolidate various legacy systems in a highly regulated market. What sets this project apart is the close collaboration between the company, IT, and external partners. The company used workshops, road shows, and a targeted key user network to focus on team culture and cohesion from the very beginning. The result is not only a successful technical transformation, but above all a lived team culture鈥攁 key factor to the project鈥檚 success.

A new data foundation

In the Transformation category, Electrica Furnizare came out on top. The starting point was a fragmented system in which data was scattered and an overarching overview was missing. The company鈥檚 existing infrastructure was neither scalable nor able to respond flexibly to growing regulatory and customer requirements. With the introduction of 麻豆原创 S/4HANA combined with 麻豆原创 Business Technology Platform (麻豆原创 BTP), 麻豆原创 Customer Experience solutions, and 麻豆原创 Business AI, Electrica Furnizare achieved one of the largest transformations in the utility sector. The result: a single, reliable data source for all departments, end-to-end optimized processes, and a solid foundation that paves the way for future innovations and the full deployment of AI capabilities.

Better customer service through comprehensive modernization

Loudoun Water鈥檚 far-reaching modernization project won the Customer Experience category, with the company using 麻豆原创 S/4HANA, 麻豆原创 Service Cloud 2.0, and 麻豆原创 SuccessFactors solutions to optimize its processes. With the introduction of 麻豆原创 Service Cloud 2.0, Loudoun Water is the first utility company worldwide to take this step. The effort paid off: customer service is faster and more modern, work orders are better organized, billing and payments are managed more efficiently, and time tracking for employees has been simplified. The migration was carried out in a single, coordinated step, creating a future-proof, scalable foundation on which Loudoun Water can further expand its position as a technology leader in the industry.

Innovation as a driver of the energy transition

The winners of the first Energy and Utilities Innovation Award demonstrate the wide range of possibilities through which companies in the energy and utilities sector are actively shaping digital transformation. Whether AI-driven decision-making processes, intelligent infrastructure monitoring, or a consistent realignment of the IT landscape, all the projects have one thing in common: they rely on solid foundations, step-by-step implementation, and close collaboration among all stakeholders. Melanie Fiolka, go-to-market & community engagement lead for Utilities at 麻豆原创, summarizes it as follows: 鈥淭he energy and utilities industry is undergoing profound change. The winners of the first Energy and Utilities Innovation Award demonstrate how vision is turned into tangible value through clear strategies, solid data foundations, and the adoption of AI.鈥�

The award makes it clear that innovation in the industry is no longer the exception, but has become a strategic necessity.

The acquisition will accelerate 麻豆原创鈥檚 success in TFMs that started with 麻豆原创-RPT-1 and bring one of the world鈥檚 leading TFM research teams into the 麻豆原创 family.聽Prior Labs聽will continue to聽operate聽as an independent entity, with 麻豆原创 committing to聽investing聽more than 鈧�1 billion over the next four years to scale it into a globally leading frontier AI lab for the structured data that聽underpins聽the world鈥檚 businesses.

For聽additional聽information about the acquisition, see the聽press release聽from May 2026.聽

Visit the . Get 麻豆原创 news via  and .

Media Contacts:
Alex Vaught, +1 (206) 678-5712, 聽alex.vaught@sap.com, PST
Ilaina Jonas, +1 (646) 923-2834, 聽ilaina.jonas@sap.com, EST
Daniel Reinhardt, +49 151 168 10157, 聽daniel.reinhardt@sap.com, CET
麻豆原创 麻豆原创 Room;听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.  

As customer journeys become more connected and complex, gaps in execution can lead to inconsistent experiences, slower response times, and missed opportunities. The next frontier of customer experience is not generating more insights, but turning insight into coordinated action at scale.

This latest release of the solution portfolio helps organizations strengthen that foundation by connecting workflows across marketing, commerce, sales, and service鈥攅nabling more consistent, scalable execution across every customer interaction.

Explore the highlights of the Q2 2026 release. For full sub-solution details, see our recaps for the , , , , and solutions.

Turning customer intent into action

Customer interactions are becoming more conversational, connected, and immediate across channels and touchpoints. At the same time, organizations need faster access to information and simpler ways to take action鈥攚hether engaging customers, managing campaigns, or responding to changing business needs.

• Conversational AI shopping through a model context protocol (MCP) server: Enable secure integration between and AI agents that can guide or act on behalf of customers. AI assistants can query real-time product information, provide inventory updates, manage shopping carts, and complete transactions directly within chat or voice interfaces鈥攃reating more intelligent, conversational buying experiences beyond the traditional storefront. 

• Joule in 麻豆原创 Engagement Cloud (麻豆原创 Early Adopter Care): Bring 麻豆原创’s conversational AI directly into campaign workflows. Teams can ask product or campaign questions in natural language and get accurate answers without searching across multiple systems. They can also duplicate successful campaigns without starting from scratch, freeing more time for strategic thinking, creativity, and customer engagement.

• Rich communication services (RCS) in 麻豆原创 Engagement Cloud: Engage customers with rich, interactive messages supported by Google and featuring media, carousels, and action buttons within native mobile messaging experiences. Branded, verified messages help build trust and guide customers smoothly from discovery to purchase without requiring an additional application.

Scaling personalized engagement

Recognizing customer intent is only the beginning. As engagement channels expand, marketing teams need to respond quickly while delivering relevant, personalized experiences at scale. This requires frictionless campaign execution, timely insights, and the ability to tailor every interaction to each customer’s needs and preferences.

• AI-assisted content composer (pilot): Generate high-quality, on-brand campaign content in . Using Gemini models informed by audience, product, and campaign context, teams can quickly create and refine content variations so they can launch personalized campaigns faster and spend less time on manual content creation.

• Embedded audience builder: Enable marketers to access and activate rich data from directly within 麻豆原创 Engagement Cloud. With this capability, they can build advanced segments themselves without switching systems or waiting on data analysts. The precision and relevancy of omnichannel campaigns can be improved by combining behavioral, transactional, account, and profile data with operational data across the business.

Enabling consistent sales execution at scale

Success depends on turning insight into disciplined, repeatable actions that drive predictable revenue outcomes. As sales environments grow more complex, even small inconsistencies in data, priorities, or execution can undermine forecasts and cause opportunities to slip away. Acting with greater consistency and confidence calls for stronger data integrity, aligned behaviors, and clearer guidance.

• Agentic opportunity summary overview: Give sales teams the tools they need to quickly assess deal health. This capability in aggregates engagement signals, activity levels, and progress indicators into a real-time view, allowing teams to identify risks early, prioritize effectively, and maintain deal momentum.
• : Optimize sales velocity and help ensure the right product placement with retail execution enabled by intelligent, AI-enhanced processes that maximize revenue. Teams can improve visit planning and execution, harness insights to improve sales performance, and optimize interactions. For consumer products companies, this helps drive shelf availability, promotion compliance, and merchandising effectiveness across retail locations. Field teams gain greater visibility into store-level execution, enabling more consistent brand presence and stronger sell-through performance.

• 麻豆原创 Incentive Management: Improve sales team effectiveness by using the solution, which is part of solutions. It helps drive profitable behaviors that increase revenue and support business growth while providing real-time performance insights, dispute management, and motivating rewards. Teams can use flexible tools to streamline incentive compensation and quickly design, test, and launch sales plans. AI-supported recommendations are also available to guide organizations in optimizing plans, maximizing outcomes, and uncovering actionable insights.

• Consumer Products Intelligence (麻豆原创 Early Adopter Care program): Enable consumer product companies to turn the enormous amount of sales and trade data they generate into better decisions. It uses analytics and AI to help improve trade spend performance, increase sales revenue and margins, and reduce manual effort.
   

Standardizing service execution across the enterprise

Service teams are increasingly expected to deliver faster, more reliable support while managing growing complexity across channels and requests. Achieving this objective requires simplifying how services are accessed and helping ensure consistent processes across the organization.

• Self-service catalog: Allow employees to quickly find what they need without understanding backend processes. Through this guided, intuitive catalog for requests are automatically routed with the right context, reducing delays and improving resolution times.

• Content package framework: Leverage the framework for 麻豆原创 Enterprise Service Management to deliver rapid, scalable value across lines of business. Prebuilt, reusable configurations for case types, workflows, and catalogs help organizations deploy services more quickly while simplifying implementation across the business. With this approach, organizations can eliminate complexity, empower partners, and speed adoption. Content packages for HR service delivery will be coming soon. 
• Email editor in 麻豆原创 Service Cloud and 麻豆原创 Enterprise Service Management: Compose, edit, and manage customer communications more efficiently while maintaining high-quality service interactions. The modern, user-friendly email editor is built for an AI-first world.

• Creation of sales objects from customer hub: Let agents fully manage leads, opportunities, appointments, and sales orders directly from the service agent workspace of 麻豆原创 Service Cloud. This capability helps turn each customer interaction into an opportunity to deliver more value.

Accelerating connected order management

Turning insight into action requires connected systems that can adapt as the business evolves. As organizations expand order channels, fulfillment networks, and technology landscapes, they need integration and order management that can keep pace so teams can respond faster to change.

• Flow connector: Enables smooth data flow between the solution, other 麻豆原创 solutions, and third-party products. This predefined capability allows business users to configure custom business flows and integrations with minimal IT involvement, creating connected order management processes across the enterprise.

Execution at scale: the next customer experience advantage

As AI becomes embedded in daily operations, the differentiator shifts from insight generation to execution.

Our recently announced strategic partnerships with and Google Cloud help extend this execution-first approach by connecting AI-powered service, commerce, and engagement experiences directly to operational systems and business data. As a result, organizations can move from isolated interactions and insights to coordinated actions that drive faster resolutions, better customer experiences, and greater business impact.

Learn more about 麻豆原创 CX in Q2鈥�2026 

Read the 麻豆原创 Help documentation to get started with these new capabilities:

Balaji Balasubramanian is president and chief product officer for 麻豆原创 Customer Experience and Consumer Industries at 麻豆原创.

Those investments are critical. But they weren鈥檛 the finish line. They were the foundation. And today, they鈥檙e paying off in new ways as organizations adopt AI to drive measurable business outcomes.

But there鈥檚 another part of that foundation which is becoming just as important: data. The organizations best positioned to compound their value from AI are those that have invested in data foundations that give AI the trusted business context it needs to reason, recommend, and act.

Now, in conversations with customers across the Americas and around the world, I鈥檓 seeing a new pattern emerge. The discussion is shifting from where AI can be applied to what happens when intelligence becomes embedded into the core of how the business operates.

While every transformation is different, three common shifts keep coming up.

1. From AI use cases to intelligent business processes

The first wave of AI adoption focused on proving value. Organizations identified high-impact use cases, delivered measurable results, and built confidence that AI could make a difference.

That work isn鈥檛 finished. But increasingly, customers are asking how intelligence can become part of the business processes employees use every day. We鈥檙e already beginning to see what this looks like in practice.

For example, HR Path Brazil, a Brazilian company specializing in recruiting and managing talent for international firms, is using Joule embedded in 麻豆原创 SuccessFactors HCM to automate routine HR interactions. It is helping employees find the information they need faster while allowing HR teams to focus on more strategic work. The company has reported a seven percent reduction in standard HR support cases and two hours of聽HR support workload eliminated each week, which quickly adds up.聽It鈥檚 one example of how embedded and connected AI is becoming part of how work gets done.

2. From measuring AI use to measuring the business outcomes it creates

One of the biggest changes I鈥檓 seeing is how organizations define success. ROI is becoming a given and that is reflected in the data, especially in Oxford Economics research out just this week. It showed that organizations investing in AI expect to see an average return of 21% this year but increasing to 38% in two years. And as agentic AI continues scaling, it is projected to deliver $17.6 million in returns, more than quadrupling last year鈥檚 estimates (US$4.3 million).

This is allowing organizations to focus more on business outcomes from their AI. They are asking questions like can we shorten cycle times? Can we improve decision-making? Can we free employees to spend more time creating value? Can we become more resilient and responsive as a business?

This is an important shift because it changes the conversation from implementing technology to improving how the business performs.

3. From systems of record to the new operating system for the enterprise

The third shift is the one I believe will have the greatest long-term impact. For decades, enterprise software primarily captured transactions, standardized processes, and automated routine work. Now it鈥檚 beginning to help organizations anticipate change, recommend actions, coordinate work across functions, and increasingly execute routine decisions with human oversight.

That鈥檚 why I believe the Autonomous Enterprise represents more than the next phase of automation. It represents a new operating model for business.

Instead of people spending time connecting information across finance, supply chain, procurement, HR, and customer operations before deciding what to do next, intelligent systems can increasingly provide context, surface recommendations, orchestrate work, and help teams execute.

People remain firmly in control. But they鈥檙e supported by enterprise software that is becoming an active participant in how the business operates and executes, not simply a system that records what already happened.

Where we go from here

The organizations creating the greatest long-term advantage won鈥檛 just be the ones deploying the largest number of AI use cases. They鈥檒l be the ones that use those early successes to rethink how work gets done across the enterprise.

The journey to the Autonomous Enterprise won鈥檛 happen overnight, and it won鈥檛 replace the need for strong leadership, governance, or talented people. If anything, those become even more important.

We鈥檒l likely look back on today鈥檚 AI projects much the same way we now look back on the early days of cloud transformation; not as the destination, but as the foundation for a fundamentally new way of operating and innovating continuously.

The true winners from this shift will be the ones who continuously become more intelligent, more adaptive, and ultimately more autonomous.

Jan Gilg is a member of the Extended Board of 麻豆原创 SE and global president of Customer Success & Americas.

But what if more or better software is not necessarily the answer? Each solution solves a real problem for a specific team. But collectively, they create a consequence no one planned for: every new tool builds its own data world, without a common language or context across them.

In a 2026 study by Oxford Economics, . Twenty-nine percent remain highly fragmented. Organizations with siloed CX tech are more likely to face an inability to connect customer needs to actionable data insights. Fifty-eight percent reported this challenge, compared to 47% among those with harmonized environments.

More than what tools businesses choose to add to their CX landscape, how they connect and interact with each other becomes even more important.

A unified data strategy sounds straightforward in principle. In practice, most businesses find that the obstacle is not ambition but rather execution. Every integration decision made without a clear data architecture becomes a future campaign mired in manual reconciliation, a customer journey that breaks at the handoff, or a personalization promise the disparate data sources cannot support. Implementation without a validated strategy creates new fragmentation inside the solution meant to eliminate the old. And without a structured way to pressure-test decisions before any commitment is made, even well-resourced organizations find themselves repeating the same cycle: invest, integrate, fragment, repeat.

The real barrier is not budget or technology

A Forrester study of more than 1,000 senior executives found that , ahead of budget, technology maturity, and talent. The same study found that 56% of respondents struggle with poor data quality; 55% face persistent data silos. This is not for lack of investment in technology, but because the connections between systems were not designed or maintained effectively.

What bridges that gap is not another platform, it is the expertise to think through data connectivity decisions before they are made and the ongoing guidance to ensure those decisions compound into measurable gains over time. The for solutions provides that guidance along every step of the journey.

Define what success actually looks like

The most common reason data unification projects fall short of expectations is not technical failure, it is a failure to define and measure what success looks like for the entire business before the work begins.

The value management session from the Advanced Success Plan for 麻豆原创 Customer Experience establishes that definition at the outset: What does a fully unified data strategy actually enable? It means running the next marketing campaign without manual data reconciliation, and presenting an AI readiness road map without caveats. Stakeholders will know, at every checkpoint, whether the investment is moving the business forward, not just moving the project forward.

Design the strategy before building the integrations

A robust data strategy starts with good design. Product guidance from the Advanced Success Plan for 麻豆原创 Customer Experience covers available out-of-the-box integrations, common usage scenarios, and pitfalls and how to avoid them鈥攁ll delivered in a live remote session by an 麻豆原创 expert who can answer questions in real time. The data strategy can be conceptualized and pressure-tested before any budget or technical commitments are made.

Validate every critical decision with expert guidance

With the technical assistance and functional assistance from the Advanced Success Plan for 麻豆原创 Customer Experience, businesses have continuous access to expert guidance at every critical decision point. Beyond resolving immediate questions, the ongoing access also shares insight on how 麻豆原创 thinks through problems, strengthening in-house expertise with every interaction. The result is an organization that makes better decisions not just now but for the future.

Measure whether the strategy is delivering

Adoption and innovation checkpoints conducted on a quarterly or semi-annual basis bring the measurement back to where it started: the business outcomes defined at the outset. Do campaigns run without manual reconciliation? Is the AI use case performing against its stated goals? A clear throughline from the value management success KPIs to the adoption and innovation checkpoints proves the benefits of the investment.

Where cycles and silos break

The cycle of invest, integrate, fragment, repeat is not inevitable. It is the predictable result of making technical decisions without an anchoring business imperative, and integration decisions without strategic expert guidance. Organizations that break the cycle do not necessarily have better technology than their competitors鈥攖hey have better judgment about how to use it.

The Advanced Success Plan for 麻豆原创 Customer Experience exists for exactly that reason: to put proactive and prescriptive guidance at every decision point where that judgment matters most. When data strategy is designed before it is built, validated before it is committed, and measured against real business outcomes, the technology investment already made starts working harder.

The stack was never the problem. When the thinking behind the technology finally matches its ambition, the personalized, seamless experience becomes a reality.

Tara Tracey, global product owner for the Advanced Success Plan for 麻豆原创 Customer Experience.
Ella De Torres, product manager for the Advanced Success Plan for 麻豆原创 Customer Experience.

While the amount the average global business spends on AI increased slightly to US$28 million this year, the level of ROI from that investment has spiked. Globally, companies expect to drive ROI of 21% this year (US$6.3 million), up from 16% last year. That ROI is expected to grow to 38% in two years鈥� time (US$15.9 million).

Agentic AI is central to those ROI expectations. In the next two years, average ROI from agentic AI is expected to reach US$17.6 million, more than quadrupling from last year鈥檚 estimates (US$4.3 million).

These insights have been revealed in new global research, , which surveyed 2,600 business leaders across 13 countries.*

Commenting on the research, 麻豆原创 Chief AI Strategy Officer Sean Kask noted, 鈥淎I has moved from experiment to execution, and that鈥檚 beginning to show real returns. But there鈥檚 still a long way to go. Because AI that lacks context鈥攚hether that鈥檚 processes, data, or governance鈥攁t best creates activity without outcomes and at worst creates risk.鈥�

AI inching closer to enterprise maturity

While global investment in AI increased slightly from US$26.7 milion in 2025, there were significant changes in key markets. Investment increased significantly in Brazil, UK, Australia, and Germany, while leading markets like China and India saw funding decreases.

Today, almost a third of all tasks (30%) in the average business are supported by AI, a figure expected to increase to 48% in two years. Yet, while strategic investment in AI has almost doubled year-on-year to 17%, piecemeal approaches remain by far the most prevalent (41%).

Some of this may be a leadership problem. Under a half of companies have a dedicated AI leader responsible for AI adoption (46%), clear frameworks about AI development (52%), or even training on AI capabilities and risks (41%).

Yet, despite those challenges, 69% of businesses are satisfied with their current AI ROI, even though more than two-thirds are not convinced AI is achieving its full potential.

Some of this optimism is due to agentic AI, since over eight in 10 (83%) businesses say agentic AI has moderate to very high potential to transform their organization. Yet, it is still early days for the technology, with only three percent of businesses saying they are fully prepared for agentic AI, while the majority say they are either partially prepared or not prepared at all.

Global businesses meeting key AI challenges

Organizations are facing a range of challenges achieving ROI from AI, including data, workforce, and governance issues.

Data quality remains the biggest challenge for global organizations. The number of businesses that say they are data ready for AI dropped from last year, with 73% of companies revealing challenges with incomplete data. And that is impacting day-to-day work, with 79% of businesses experiencing rework, delays, or backlogs due to low quality AI outputs.

Similarly, businesses are managing the workforce impacts of AI. Almost eight in 10 businesses (78%) are either unsure or agree their company upskilling is not keeping up with the evolution of AI tools. And just one percent of respondents said AI will have no impact on their workforce planning. Meanwhile shadow AI use is increasing year-on-year, with 69% saying it happens at least occasionally.

鈥淭he next step in achieving value will be to integrate AI deeply with contextual data and processes,” Kask said. “But businesses across the world must understand AI often provides value that is harder to measure than expected, and risk that moves faster than most governance can keep up with. Businesses are quickly discovering that AI governance plays a foundational role in unlocking the value from AI.鈥�

Governance is a critical obstacle in the way of enterprise AI value. Just 12% of businesses say either their skills or their processes and frameworks are fully ready to govern AI effectively.

These issues may be exacerbated in an agentic future. Today, 38% of companies do not have a human-in-the-loop process for agentic workflows, 37% don鈥檛 have permission and access controls for agents, and only 44% have a registry of the agents in their business. This is critical, given more than two-thirds of businesses (69%) either agree or are unconvinced if they are deploying agents quicker than they can govern them.

Future of value from AI is the Autonomous Enterprise

鈥淩ealizing real value from AI is not going to be easy because it demands a new approach,鈥� Kask concluded. 鈥淏usinesses large and small will need to connect AI to the data and processes that run their organizations, and make sure it has the context and governance to drive trusted results. That鈥檚 what we call the Autonomous Enterprise. This isn鈥檛 a technical change; it鈥檚 a human one. Because you can only achieve real value if agents, processes, and people work as one.鈥�

*Australia, Brazil, Canada, China, France, Germany, Italy, India, Japan, Singapore, Thailand, United Kingdom, and United States.

External talent, including contractors, consultants, and project-based specialists, is becoming a core component of how work gets done rather than a stopgap solution.

Leading organizations are responding by treating external talent less as a short-term fix and more as a standing part of workforce strategy. With 74% of employers worldwide , workforce planning has become less about filling roles in sequence and more about maintaining access to critical capabilities. This shift moves organizations to a workforce model that can respond quickly, scale efficiently, and align with long-term business priorities.

End of 鈥渢emporary鈥� talent

External workers have historically been brought in to meet short-term needs, helping fill gaps during peak periods or support one-off projects. While that approach still exists, ongoing volatility has proven that this is no longer sufficient.

Demand signals change quickly, transformation is continuous, and new skill requirements emerge faster than internal teams can adapt.

In this context, external talent provides a clear advantage. It gives companies access to specialized expertise on demand, helps accelerate innovation, and supports operations without overextending internal resources. It also allows leaders to rethink workforce composition to better balance stability with adaptability.

This shift mirrors the recent shifts seen in procurement and supply chain functions, where visibility and cross-functional integration have become drivers of long-term success. Workforce strategy is moving in a similar direction.

You can鈥檛 manage what you can鈥檛 see

As organizations expand their use of external talent, visibility remains essential. Many companies still manage contingent labor in disconnected ways, which makes it harder to understand where talent is deployed, what it costs, and how effectively it is being used. Without that visibility, workforce decisions remain reactive.

When organizations can see how external talent is deployed across business units, geographies, and projects, they can plan with greater confidence. This level of insight also supports stronger governance by improving compliance, supplier performance, and consistency from sourcing to offboarding.

In practice, organizations that invest in visibility often see measurable improvements in efficiency, productivity, and decision-making speed. More importantly, they begin to treat external labor as a strategic lever rather than a cost center.

From reactive hiring to predictive planning

Visibility is essential, but the real opportunity lies in turning workforce data into actionable insight.

AI is playing an increasingly important role in this transformation. By analyzing hiring patterns, project pipelines, and market signals, AI can help organizations anticipate future talent needs instead of reacting to them. This is especially valuable in environments where workforce decisions need to balance cost, speed, and quality. For example, organizations can use AI to:

• Anticipate external talent needs tied to major initiatives, such as ERP rollouts or expansion projects, before staffing gaps affect delivery
• Identify where external specialists can help address immediate skill gaps while longer-term hiring continues
• Analyze market signals and workforce composition to help guide insourcing vs outsourcing strategies
• Flag bottlenecks and make corrections in onboarding, approvals, or assignment start times that delay productivity and increase costs

As organizations look to make external talent a more strategic part of workforce planning, technology becomes increasingly important. helps organizations gain greater visibility into their external workforce, connect talent data across the enterprise, and use AI-driven insights to make more informed staffing decisions.

By bringing together workforce planning, services procurement, and external talent management, organizations can better anticipate skill needs, improve agility, and align workforce investments with business priorities.

More connected approach to talent

One of the most important shifts underway is how organizations think about workforce composition. Rather than treating external and internal talent as separate categories, forward-looking companies are managing both as part of a single ecosystem.

This integrated approach offers several advantages. First, it more closely aligns with business goals. Leaders can allocate resources based on outcomes rather than employment type, ensuring the right skills are applied where they create the most value. Second, it improves agility. When workforce models are designed to flex continuously, organizations can quickly respond to changing conditions without disrupting operations. Third, it enhances the employee experience for both internal teams and external contributors by streamlining processes and making them more efficient.

Technology plays a key role in enabling this shift and provides organizations with the tools to manage external talent alongside internal workforce data, improving visibility and supporting more data-driven decision-making. Solutions such as 麻豆原创 Fieldglass help organizations bring greater transparency, consistency, and insight to how external talent is sourced, managed, and aligned to business needs. While no single solution defines success, the ability to connect data, processes, and insights is increasingly important.

Building resilience in an always-on economy

Business no longer moves in predictable cycles. Demand shifts quickly, priorities evolve in real time, and skills gaps can emerge faster than traditional hiring models can address. In that environment, resilience depends on staying adaptable while keeping work moving.

That is why external talent is becoming a more strategic part of workforce planning. With finding skilled talent becoming increasing more difficult, many organizations are looking for ways to maintain access to specialized capabilities as business needs shift. External talent can help teams move faster, bring in targeted expertise, and sustain progress on critical initiatives without overextending the core workforce.

For many organizations, this reflects a broader change in mindset. External talent has moved closer to the center of workforce strategy, especially in areas where speed, specialization, and adaptability matter most. How well organizations plan for and manage that talent will shape their ability to execute, compete, and grow.

Amber Roth is vice president of GTM for 麻豆原创 Fieldglass.

That was the takeaway from a between 麻豆原创 CFO Dominik Asam and 麻豆原创 Fioneer CEO Matthias Tomann. Their conversation touched on topics like the future of , the role of AI, and the growing importance of integrated enterprise platforms.

For decades, banks and insurers have built operating models around regulatory fragmentation, country-specific requirements, layered systems, and continuous workaround solutions. As a result, the industry is running on patchwork architecture that is expensive to maintain, slow to change, and fundamentally misaligned with how AI works.

Partnership built for financial services innovation

Since joining forces in 2021, 麻豆原创 and 麻豆原创 Fioneer have significantly expanded their joint capabilities for the financial services sector. As Tomann highlighted in the conversation, the partnership has already delivered substantial momentum for 麻豆原创 Fioneer:

• R&D investment increased by 120%
• Annual software sales more than doubled
• Major customers successfully transitioned to 麻豆原创 Cloud ERP
• The platform evolved into a richer, more scalable, and highly capable ecosystem

Together, the companies are combining 麻豆原创鈥檚 trusted cloud and data infrastructure with 麻豆原创 Fioneer鈥檚 deep financial services expertise to help institutions simplify operations, modernize core systems, and prepare for the AI-driven future.聽

Executives from both companies will be exploring these critical topics further at their annual which is now open for registration.

AI is not the starting point, data integration is

Everyone wants AI, but AI can only create value from integrated data, real-time access, and standardized processes. But most financial institutions still operate on the opposite: fragmented foundations. That reality will define the winners over the next five years.

The organizations that succeed will not be the ones experimenting with the most models. They will be the ones that establish unified, trusted, real-time enterprise data with strong governance. That is the real competitive advantage.

But even that is only part of the story. The next phase is not just about using AI to analyze better; it is about AI executing work.

We are now seeing a fundamental shift: from systems that store and report information to systems that act on that information in real time, orchestrating end-to-end processes across the business. This marks the transition to the Autonomous Enterprise, 麻豆原创鈥檚 vision for the future of business where AI does not just support decisions but increasingly drives execution, within clearly defined guardrails.

Financial services can no longer afford 鈥減atchwork architecture鈥�

This shift makes one thing clear: The traditional approach to building IT landscapes is no longer viable.

For years, many financial institutions solved problems incrementally鈥攁nother point solution, another integration layer, another workaround. But eventually every workaround becomes technical debt and integration is the single largest IT cost category.

Tomann made clear during the conversation that the emphasis must be on simplification rather than adding more complexity.

What 麻豆原创 and 麻豆原创 Fioneer are driving is not another modernization cycle. It is a structural shift toward comprehensive, integrated platforms and AI driven processes that replace fragmentation, not sit on top of it.

The result is a scalable financial services platform where core banking, lending, reporting, insurance, and analytics operate within an integrated architecture instead of disconnected silos.

Real-time finance is becoming a strategic requirement

Real-time capability is becoming foundational to competitiveness鈥攚hether it鈥檚 risk management, regulatory reporting, customer experience, fraud prevention, treasury operations, or AI-driven decision making.

Institutions that can act on integrated data instantly will have a major advantage over those still moving information between disconnected systems overnight. With integrated data and AI embedded in core processes, finance is moving toward continuous financial intelligence:

• Forecasting becomes dynamic and always up to date
• Risk is detected and assessed in real time
• Closing processes become increasingly automated
• Decisions are guided by AI based on live business context

Increasingly, AI assistants and agents take over execution of finance processes, from planning and risk management to invoicing and financial close, under strict governance. The role of finance shifts from reporting on the business to steering the business in real time.

AI will reward those who simplify

One of the most striking statements from Asam during the discussion is that 麻豆原创 is already seeing 10x performance improvements from AI-driven process improvements. But it also highlights something many organizations still underestimate: just how much AI rewards those who standardize.

The more fragmented the processes and data structures are, the harder it becomes to operationalize AI at scale. In contrast, organizations with standardized platforms, harmonized data, and integrated workflows will accelerate much faster.

That is why modernization conversations today are no longer simply 鈥淚T projects.鈥� They are business strategy discussions.

Future of financial services will be built on trust, scale, and intelligence

Financial services organizations are operating in an increasingly complex geopolitical and regulatory environment. Infrastructure decisions are no longer just about performance and cost, they are about compliance, security, operational resilience, and national requirements.

This is why scalable, enterprise-grade cloud platforms are becoming so critical.

The institutions that thrive in the next era of financial services will be the ones that can combine trusted data, integrated operations, AI-enabled processes, scalable infrastructure, and regulatory resilience into a single operating model.

The future of financial services will not be defined by isolated AI experiments. It will be defined by who can build the most intelligent, connected, and adaptable enterprise foundation for what comes next.

.

Kris Kowal, Banking Industry Leader at 麻豆原创.
Falk Rieker, Financial Services Industry Leader at 麻豆原创.

Anyone who is looking for a prime example of how migration to the cloud can do far more than just simplification and standardization should take a closer look at Swarovski. The legendary manufacturer of precision-cut crystals, jewelry, and watches鈥攚ith its origins in Wattens, in the Austrian region of Tyrol鈥攈as transformed its IT landscape from a cost factor into a strategic tool for a digital future.

The transformation was guided by Lea Sonderegger, serving in a dual role as CDO and CIO at Swarovski, with such great success that she was awarded the special 鈥淐loud Excellence鈥� prize in the large enterprise category at the CIO of the Year ceremony held by CIO Magazine last October.

The judging panel found her brownfield approach to be especially praiseworthy: Swarovski employees use but continue to use the familiar processes and databases. A complete redesign of these processes in parallel to the migration would have been too risky and cost-intensive. It would have also resulted in a much longer project duration, Sonderegger is convinced.

25,000 tests with 600 participants

The brownfield implementation was carefully executed. Preparations took two years and involved more than 600 participants performing around 25,000 tests. Two dress rehearsals with strict governance ensured that every function and every data point was ready for the migration.

Sonderegger and her colleagues reserved a 66-hour conversion window for the go-live on April 20, 2026. During this period, all global IT processes at Swarovski were paused. During the subsequent sensitive hypercare phase, 24×7 support ensured that any issues that arose could be dealt with quickly. Thanks to these measures, the transition was seamless. After the conversion window closed, all processes resumed without problems.聽

Simplification and standardization ensure consistent data

Despite the large effort involved, this migration was merely the first step. While the switch to 麻豆原创 Cloud ERP Private created the technical foundation, it鈥檚 the subsequent investments that deliver additional added value. These investments concentrate on the incremental reduction of complexity through consolidation of fragmented solutions, the reassessment of user-specific code, and the harmonization of data鈥攁ll with the overall goal of creating a more coherent, easier-to-handle ERP landscape.

To achieve this, Sonderegger and her team are replacing user-specific applications with 麻豆原创 standard solutions step by step and only leaving custom developments in place where they offer clear advantages. 鈥淭he combination of simplification and a return-to-standard solutions improves data consistency, provides for robust, reliable processes, and, ultimately, makes our entire organization more agile,鈥� Sonderegger says.

Cloud technology is not an end in itself

By integrating key functions such as finance, supply chain management, retail, and e-commerce鈥攁nd enabling their combined use in the cloud鈥斅槎乖� Cloud ERP Private provides for reliable processes and consistent data quality all while enabling customer experiences on a wide variety of front-end solutions on this side of the ERP system.

麻豆原创 Cloud ERP Private manages a diverse product range at Swarovski across different regions and price points and integrates with the planning results provided by other 麻豆原创 and non-麻豆原创 systems.

鈥淚n all of these activities, cloud technology is never an end in itself, but rather a lever for improving efficiency, resilience, and innovative capabilities,鈥� Sonderegger says. This determination is especially important to her.

It鈥檚 not an IT project, it鈥檚 a business transformation

Ultimately, Sonderegger and her team succeeded in executing the project on time and on budget because its scope was clearly defined, and strict discipline in change management prevented mission creep. In addition, the company benefited from the experience of its implementation partner, 麻豆原创 Consulting, and its unrestricted access to 麻豆原创 expertise.

The example of Swarovski proves that even an essential, unavoidable migration can and should do much more than just avoid risks and cut maintenance costs. The implementation of 麻豆原创 Cloud ERP Private was imperative here, because 麻豆原创 ERP Central Component (麻豆原创 ECC) had reached the end of its lifecycle.

And the implementation is showing the luxury goods manufacturer the way to the future because everyone involved in the process didn鈥檛 just consider it to be an IT project but, above all, a business transformation from day one. One that involved hundreds of experts from different fields and that enjoyed full management support from the beginning.

AI-driven demand forecasts optimize warehouse stocks

Artificial intelligence is also playing a key role in this implementation, with 麻豆原创 Cloud ERP Private as the operational backbone of an AI ecosystem that can deliver reliable, real-time data and robust, standardized transaction processes.

Swarovski doesn鈥檛 use artificial intelligence as a standalone technology, but instead as an integrated capability that complements business processes across all functions. The company is already using AI for demand forecasting, for example, and then uses the results to optimize warehouse stock levels across regions, with the aim of improving the customer experience.

And the AI agent factory initiative enables the development of AI agents that link 麻豆原创 Cloud ERP Private data with data from non-麻豆原创 systems, always with the objective of 鈥渁utomating repetitive tasks, supporting decision-making, and boosting productivity along the entire value chain,鈥� Sonderegger emphasizes.

Top image courtesy of Swarovski

With updates to and , partners now get a self-service purchasing experience that cuts out that complexity, with pricing transparency comparable to what partners get on AWS and Azure marketplaces.

Smarter path from opportunity to purchase

The updated experience connects the full purchasing journey in one place, 麻豆原创 for Me: from finding the right solution, to submitting a quote, to completing the transaction.

Start with the right catalog

Partners start with a personalized catalog for their specific opportunity. Partner authorization, customer eligibility, product prerequisites, and related products are shown upfront 鈥� searchable, sortable, and packaged in an intuitive way, so there is no need to cross-reference multiple systems or wait for someone else to confirm what can be sold.

Know your pricing before you ask

Price transparency is built into the experience. With shopping carts powered by 麻豆原创鈥檚 own commerce solutions, partners can see what features are included in the product and can explore options and simulate quotes with full pricing visibility from the start, without waiting on internal teams to pull numbers together. , and this experience is built around that.

Guided selling: No guesswork and easier to learn

麻豆原创’s portfolio is broad, and pricing can get complex, especially for partners newer to selling 麻豆原创, that previously had to track price lists, prerequisites, and product relationships on their own before building a quote. The experience now surfaces eligibility requirements, dependencies, and configuration options as partners work, so nobody starts from scratch.

Test scenarios before you submit

Before requesting approval, partners can run simulations to validate pricing and test different quote configurations before quote approval. Fewer surprises going in means fewer revisions coming back.

Faster approvals, less waiting

Internal approvals and administrative complexity have , and for many deals, that friction sits in the quote and approval stage. Partners now submit pre-validated, perfectly priced quotes only when they have tried and tested different pricing possibilities in the cart, and even complex scenarios move through a streamlined approval process with fewer delays.

End-to-end, fully digital

Once a quote is approved, the purchase completes digitally from start to finish: no chasing signatures, no manual uploads, no last-mile handoffs. Self-service e-commerce has become the , and the move to 麻豆原创 for Me and 麻豆原创 Store reflects that shift.

See it in action

The experience looks different depending on your partner model. For CC Flex partners, the journey includes commission visibility at every step, from the configurator through to the approved quote. For VAR partners, the flow moves from cart configuration to fully automated quote generation and straight through to checkout, with a confirmation email from 麻豆原创 once the order is placed.

What’s next

Upcoming enhancements will introduce embedded AI assistance through Joule, guiding partners through the purchasing journey with in-context support at every step. We will also include full partner cloud pricelist coverage and the ability to manage post sales via this kind of guided digital buying journey soon.

Whether you are managing a straightforward opportunity or a complex deal, the updated experience in 麻豆原创 Store and 麻豆原创 for Me gives partners clearer pricing, fewer roadblocks, and more control over how they close.

Start your next quote: Visit .

Shreya Datta is senior director of 麻豆原创 Marketplace Direct Business at 麻豆原创.

Under the official banner of Climate Cooperation in a Fractured World, delegates spread across the city and the tone was noticeably different from previous years. Fewer pledges, more blueprints. Less “what should we aim for,” more “who is going to finance and build it.”

Sustainability is a driver of growth

If there was a single reframing that ran through the week, it was this: sustainability is not a cost of growth, it is a driver of it.

That shift was visible in how decarbonization was discussed. Conversations that once centered on targets now centered on operations: Scope 3 emissions, value-chain engagement, procurement and logistics decisions, energy demand reduction. Practitioners repeatedly pointed to an “execution gap”鈥攖he distance between climate strategies on paper and projects that are actually permitted, financed, and built鈥攁nd to the unglamorous work of unblocking infrastructure and untangling supply-chain bottlenecks as the real frontier.

Electrification gave the growth argument its clearest expression. The launch of the Electrify Now initiative, which aims to lift electricity’s share of final energy demand from roughly 20% today to 35% by 2035, was framed as an industrial strategy.聽Nearly doubling electricity’s share of energy demand in under a decade is an acceleration, and the week’s energy-transition summits were clear about what it demands: scaling renewables at pace, doubling down on energy efficiency, and, above all, building out the grid infrastructure to carry it. Speeding up permitting and resolving supply-chain constraints were named repeatedly as the bottlenecks that will decide whether the target is met.聽

The heatwave outside made that case tangible. As cooling demand surges and extreme weather stresses networks, a clean, resilient electricity system is fast becoming a precondition for business continuity and not just decarbonization.聽UK-focused sessions highlighted the substantial clean energy investment flowing into the country since 2024 as evidence that the in its own right.聽

The same logic ran through the finance agenda. Sessions on moving from risk to resilience and from risk to opportunity focused on mobilizing capital for adaptation and climate-resilient infrastructure, and on the practical challenge of connecting available capital with investable projects through better data, governance, and pipeline development.

Nature is now on the agenda

Perhaps the most striking development of the week was where nature sat on the agenda, and where it is headed. Speakers were blunt about the underlying problem: our economic system is very good at valuing what we take from nature and very poor at valuing nature itself. Worse, we actively pay to destroy it. Figures cited during the week put global investment flows that harm nature at around US$7.5 trillion a year, against roughly $250 billion flowing into activities that help it. As one speaker put it, the task is not to lament that imbalance, but to get the economics right and to start treating nature as something that can be measured, managed, and steered with the same discipline as carbon or financial risk.

That 鈥済etting the economics right鈥� is fast becoming a data challenge for business. Work such as the on the economics of landscape restoration suggests that investing in nature can generate returns comparable to investing in factories, railways, or other conventional infrastructure. As nature-related risks and opportunities are codified into emerging frameworks and regulation, companies will have to treat nature as a set of measurable data points: impacts and dependencies per site, per supplier, and per product line, rather than a one鈥憃ff narrative in a sustainability report.

Governments have levers too, from requiring companies to stress test for nature-related risk, to shaping incentives so that capital flows toward restoration rather than degradation. For corporate leaders, that translates directly into new categories of information that need to be captured and governed: nature鈥憆elated financial exposure, land use and biodiversity metrics, and nature鈥憄ositive investment pipelines. What was once an externality is quickly becoming a set of operational KPIs.

, professor at the London School of Economics, noted that this was the first year nature was represented at the event, but also how far it still has to travel: “Today here in the outdoor tent, next year in the big room.” The implication for businesses is that the organizations that move nature into their core data models and decision frameworks now are better positioned when this topic inevitably moves from the tent to the board agenda.

The AI warning: get sustainability data in now

Underpinning nearly every theme was data. Location-specific climate analytics were described as “the new lens” for understanding financial risk, and AI featured in almost every discussion of how organizations can gain visibility and control over complex energy, water, and supply chain systems.

But the sharpest point made during the week was a warning. As Stephen Jamieson, chief marketing officer of , put it: “If we don’t get sustainability data into AI right now, AI will optimize around it. AI works within the systems, the data, and the constraints you give it. If your sustainability priorities live only in documents and presentations rather than in your data and processes, AI will optimize confidently in entirely the wrong direction.”

The logic is uncomfortable, but hard to argue with. Sustainability now plays out at the transaction level鈥攕uch as carbon cost per shipment, Scope 3 exposure per supplier, packaging compliance per SKU鈥攁nd the volume, granularity, and pace of those requirements exceed what manual processes and fragmented tools can manage. An organization whose cannot see its financial constraints, or whose supply chain system cannot see supplier regulations, hands its AI a broken map.

麻豆原创鈥檚 answer to this is the Autonomous Enterprise: a maturity journey that starts with intelligence based on trusted, transparent data; moves to optimization where AI is weighing trade-offs across cost, risk, and sustainability impact in real time; and progresses toward autonomy, where sustainability rules are embedded directly into enterprise workflows and executed within defined guardrails. Sustainability stops being a reporting activity and becomes a governing factor in how decisions are made. The architecture choices organizations make now will determine whether that automation can scale safely later.

From the tent to the big room

London Climate Action Week 2026 closed with an uncomfortable message delivered in 35-degree heat: the climate is not waiting for business strategies to mature. But a hopeful signal surfaced, too: the growth case, the nature case, and the technology case for climate action are converging, and each is being made in the language of returns, resilience, and competitive advantage.

The task for business leaders is to bring those cases inside capital allocation, procurement, and the data and systems where decisions are actually made. The organizations that thrive will be the ones that move the sustainability agenda into the big room, before the next heatwave makes the argument for them.

For more information on scaling sustainability for your business, visit .

Monica Molesag is global head of Sustainability Communications at 麻豆原创.

Equally important to our customers are the on-premise maintenance and support practices that help safeguard the software鈥檚 ongoing performance and resilience, with regular updates and technical assistance that support business continuity.

As we continue to innovate across our portfolio, we also continue our efforts to let our commercial practices reflect the flexibility and transparency our customers need, and on engaging constructively with regulators around the world.

In a constructive dialogue with the European Commission, 麻豆原创 has now agreed to a set of adaptations to these practices that will further improve this flexibility, transparency, and predictability. These measures reflect our broad commitment to continuously evolving our practices to serve customers better as their businesses and technology landscapes change.

Our updated policies will apply to all current and future 麻豆原创 customers worldwide for all of 麻豆原创’s on-premise products. Taken together, they represent one of the most customer-friendly maintenance and support approaches in the business software industry, and they set a leading example of what customers can expect from 麻豆原创. This further flexibility will not come at cost of business continuity, reliability, and scale, of course.

Specifically, 麻豆原创 is committed to the following adaptations:

Greater choice in maintenance and support

麻豆原创 understands that customers that run 麻豆原创 on-premise software want the choice and flexibility to tailor their maintenance arrangements to match the way their business operates. In response, 麻豆原创 is further enhancing how customers can organize their 麻豆原创 system landscape by providing a clear, streamlined framework for splitting it into separate parts, known as commercial installations, for which customers can select different levels of 麻豆原创 support, elect no support at all ,or make other choices outside of 麻豆原创 Support for that particular installation. This gives organizations even greater ability to tailor their on-premise support arrangements across different parts of their 麻豆原创 environment in a way that best fits their operational and commercial priorities, allowing them to scope their maintenance and support strategy to match their strategic positioning and unique business outlook.

Providing more flexibility on unused licenses

麻豆原创 already offers attractive programs to leverage unused licenses by reallocating on-premise licenses to cloud subscriptions, to other on-premise licenses, or simply to terminate them.

With additional commitments, 麻豆原创 is offering even more flexibility to help customers terminate licenses, in objectively justified cases. This concerns severe workforce reductions, software products in customer specific maintenance, bankruptcy, divestiture, and implementation failure cases.

麻豆原创 is also expanding access to single-metric contracts, which provide an alternative and often simpler way of calculating license fees on which maintenance and support fees are based. Broader availability of these contracts will give customers an even more transparent and predictable basis for managing their ongoing costs. The maintenance and support fees for the single metric contract are scaling with the single metric, which allows better adjustment to changing business conditions.

Simpler contract terms and policies

Clarity in contractual terms and policies is essential for customers making long-term technology decisions. As part of these commitments, 麻豆原创 will further clarify some of its key contractual provisions and applicable policies. This provides even greater predictability, ensuring that customers can plan their support obligations with full confidence as they expand their 麻豆原创 deployments.

Easier terms for returning customers

When a customer returns to 麻豆原创 maintenance and support, it benefits from the innovation and corrections that were deployed during that time. Our commitments introduce meaningful improvements to the terms for customers that resume 麻豆原创 maintenance and support after a period of absence.

麻豆原创 will not charge any administrative fees for customers coming back and limit the back-maintenance fee to the minimum of six months or 50% of the fees for the time off. In addition, a defined set of outdated products will not trigger any back-maintenance at all. These improvements provide further confidence that returning to 麻豆原创 maintenance and support will be straightforward and cost-effective.

All these commitments were developed in a close and constructive discussion with the European Commission, but also with 麻豆原创’s customer representatives, like the German-Speaking User Group (DSAG).

鈥淔rom the perspective of DSAG member companies, this is an important step in the right direction. The provided flexibility will help more customers to make the right decisions regarding their 麻豆原创 system architecture. Even with 麻豆原创’s cloud-based strategy, it is important to decide on your own how to proceed with systems that still have a positive impact on the company and there’s no need to shut them down,鈥� said Jens Hungershausen, Chairman of the Executive Board of DSAG. 鈥淲e see this development as the result of our effort to drive an ongoing dialogue and partnership between 麻豆原创 and the customer community on such improvements. These commitments will deliver tangible benefits for customers and strengthen trust while keeping customer choice and flexibility at the center.鈥�

Our teams are ready to help

To support a seamless experience for every customer, 麻豆原创 account executives and customer-facing teams will be fully briefed on all the changes outlined above. They are equipped to walk customers through the details, answer questions, and apply these commitments in a fair, transparent, and predictable way. Whether a customer is looking to restructure their system landscape, explore single-metric contracts, terminate unused licences, or understand the improved terms for returning to 麻豆原创 support, 麻豆原创 teams stand ready to guide them through every step of the process. There will be a clearing structure set up by 麻豆原创 in case a customer may contend the application of these new rules.

Looking ahead

At 麻豆原创, we are committed to empowering organizations with the enterprise software and services they need to thrive as they modernize toward an AI-enabled autonomous enterprise at their own pace. We champion customer choice and continuously work to maintain an open, vibrant ecosystem for our partners and customers alike. These commitments are the product of constructive and collaborative engagement with the European Commission, and they are designed to deliver real, meaningful benefits to our global customer base.

As noted above, these commitments relate to our on-premises maintenance and support services. Our cloud offerings continue to evolve through ongoing innovation in areas like 麻豆原创 S/4HANA Cloud, RISE with 麻豆原创, and our broader cloud portfolio and are unaffected by these changes. However, the added clarity and flexibility support customers as they modernize toward an AI-enabled autonomous enterprise at their own pace

We believe these commitments establish a new benchmark for customer-friendly practices in the enterprise software industry.

For full details on the commitments, including the conditions for their application, please visit .

The full text of the commitments as adopted by the European Commission is also available on the Commission’s competition website under case number AT.40823.

We look forward to continuing to support the success of our customers’ businesses in the future!

Stefan Steinle is executive vice president and head of Global Customer Support at 麻豆原创.

麻豆原创 remains committed to open competition, customer choice and innovation. The commitments provide greater clarity, choice and safeguards for customers managing complex on-premise environments, while supporting flexible IT strategies aligned with business priorities.

As the only Fortune 50 technology company headquartered in Europe, 麻豆原创鈥檚 maintenance practices are aligned with industry standards and offer customers a broad range of deployment, licensing and maintenance options across on-premise and cloud environments.

The commitments strengthen customer choice and predictability by making policies more transparent, introducing targeted flexibility for exceptional shelfware situations and reinforcing consistent execution through improved guidance, training and independent oversight.

The decision relates solely to on-premise maintenance policies and does not concern 麻豆原创鈥檚 cloud offerings. However, the added clarity and flexibility support customers as they modernize toward an AI-enabled autonomous enterprise at their own pace. In closing this matter, 麻豆原创 is able to move forward with a clear framework for customers, partners and investors.

Learn more here.

At the center of that effort sits a deceptively complex challenge: understanding, in real time, which revenue and cost factors are driving or eroding gross margin across a highly diversified business. For years, answering that question meant manual reporting, delayed insights, and finance teams spending valuable time on data gathering rather than analysis.

That鈥檚 now changing, thanks to a co-innovation initiative developed together with 麻豆原创 and Numen, a global 麻豆原创 partner specializing in digital transformation and enterprise software implementation.

From manual reporting to proactive decision intelligence

The project鈥檚 goal was to replace a labor-intensive gross margin analysis process with a generative AI application embedded directly into Natura &Co’s financial workflows. Built on 麻豆原创 Business AI Platform, 麻豆原创’s unified foundation integrating business technology, data, and AI capabilities, the application connects directly to data in 麻豆原创 S/4HANA to provide finance teams with automated insights and narrative recommendations in real time, without the need for manual data pulls or offline reporting.

The application enables users to explore revenue, cost, and margin drivers interactively, identifying at a glance which elements are protecting or eroding margin performance across markets and product lines. Crucially, human oversight remains central to the design: the AI application generates insights, while finance professionals retain full control over interpretation and decisions.

鈥淭he implementation of gross margin analysis using AI in 麻豆原创 S/4HANA marked an inflection point in the analytical capability of our finance area,” said Rog茅rio Dias Garcia, tech manager, ERP Latam, Natura &Co. “We overcame delays and raised the standard of insights by integrating margin analysis from 麻豆原创 S/4HANA with a large language model connected via the 麻豆原创 AI Core layer. This architecture allowed us to provide, in an agile, secure, and completely anonymous manner, a stratified and precise view of gross margin offenders and protectors鈥攄iscriminating exactly which revenue or cost elements were driving market performance.鈥�

A collaborative architecture for scalable AI adoption

Natura &Co鈥檚 application derived from a prototype 麻豆原创 partner Numen created in early 2024 at 麻豆原创鈥檚 global on business AI, leveraging the generative AI capabilities of聽麻豆原创 Business AI Platform. The solution was designed and developed through close collaboration between Natura &Co, Numen, and 麻豆原创. From the outset, the approach was to align AI adoption with concrete business priorities, ensuring the application would be scalable and production-ready rather than a standalone prototype.

Numen brought deep 麻豆原创 implementation expertise to the project, combining knowledge of 麻豆原创 S/4HANA architecture with hands-on experience in building solutions on 麻豆原创 Business AI Platform. The technology stack鈥斅槎乖� S/4HANA, 麻豆原创 AI Core, 麻豆原创 Fiori, and 麻豆原创 Business Technology Platform鈥攑rovided the secure, integrated foundation needed to connect financial data with generative AI capabilities in an enterprise context.

鈥溌槎乖� enabled the transformation by providing the technological foundation and expert support,鈥� said Carlos Aravechia, head of Data Design & Intelligence at Numen.

The success of the project has validated a broader conviction at Natura &Co: that generative AI, embedded directly in ERP workflows, can fundamentally reposition finance from a transactional function to a strategic business partner.

A blueprint for other businesses

The Natura &Co project demonstrates a pattern that other organizations can replicate, particularly those running 麻豆原创 S/4HANA. The combination of structured ERP data with the contextual reasoning capabilities of large language models creates a foundation for decision intelligence that goes well beyond traditional business intelligence tools.

The project was built within a six-month co-innovation sprint and went live in August 2025. It is currently in use across Natura &Co鈥檚 Equador operations.

Looking ahead, Natura &Co is already planning the next phase: integrating Joule Agents to further automate the extraction of standard analytical content and deepen the AI-driven optimization of financial processes.

鈥淭he success of this initiative validates the transformative potential of embedded AI within our ERP,鈥� Dias Garcia noted. 鈥淲e are now ready to move forward鈥攄eepening these insights and integrating the capability of Joule Agents to maximize the extraction of standard content and further optimize our business decisions.鈥�

For 麻豆原创 customers evaluating how to move from AI experimentation to AI in production, the Natura &Co project offers a concrete, replicable model: start with a high-value, well-defined business process, embed AI directly into existing workflows, and build in human oversight from the start.

Operating within a complex landscape that spans all aspects of ERP, including HR with 麻豆原创 SuccessFactors HCM, 麻豆原创’s People Analytics team faced growing demand for HR data and insights鈥攆rom HR, as well as Sales, Finance, and Operations. What started as a challenge soon became an opportunity for a more governed, self-service approach.

Centralized dashboards hit their ceiling

For years, 麻豆原创’s People Analytics function operated from a centralized model focused on the consumption layer: building, maintaining, and optimizing dashboards tailored to specific business requirements. The MyTeam Dashboard鈥攁 360-degree view of workforce data available to every people manager at 麻豆原创, covering upcoming birthdays, salary, and performance information鈥攂ecame the company’s single most used report. That success was a testament to how much the business valued easy access and visibility into key HR data.

But it also revealed the limits of the model. As demand grew, the analytics team found itself permanently on the defensive, saying 鈥渘o鈥� far more than 鈥測es,鈥� managing backlogs of individual KPI additions, and negotiating timelines for incremental changes. Furthermore, data management, maintenance, and governance proved to be a challenge. The centralized dashboard approach could not scale to meet the breadth of data needs across an organization of 麻豆原创’s size and complexity.

The solution: using data products with People Intelligence in 麻豆原创 Business Data Cloud

麻豆原创 IT made a strategic decision to shift the center of gravity in its analytics architecture, moving to govern and open up the data layer beneath the consumption layer. At the heart of this change is the data product, a managed asset that ingests data from systems, transforms it, and exposes it in a governed, reusable form so downstream analytics can rely on consistent, trusted building blocks.

Data products fall into two categories: primary data products, which are sourced directly from transactional systems, such as a job structure data product from 麻豆原创 SuccessFactors HCM, and derived data products, which combine these primaries to answer broader questions. An example of this is a total employee and external workforce data product that fuses multiple sources into a single, harmonized view.

This is where in 麻豆原创 Business Data Cloud became transformative for 麻豆原创鈥檚 People Analytics team. Rather than building all foundational HR data products from scratch, People Intelligence delivers a catalog of pre-built, 麻豆原创-tested data products and derived insights directly on top of 麻豆原创 SuccessFactors HCM. Workforce composition insights alone include , encoding hundreds of joins, tested and documented by 麻豆原创 product teams, which is complexity that even AI-assisted modeling tools cannot yet reliably replicate without extensive testing and governance work.

麻豆原创 IT’s approach is deliberate: adopt 麻豆原创-delivered data products out of the box, build differentiating derived products on top, and free IT capacity for what actually differentiates and optimizes 麻豆原创’s HR processes.

Data sensitivity is also top of mind for the 麻豆原创 team. With People Intelligence, the same data product is made available in multiple “flavors”鈥攁 full PII (personally identifiable information) view and a mini view with common company-visible information鈥攈elping to ensure the right data reaches the right consumer in the right format. This is especially useful with regards to Works Council鈥檚 sensitivity requirements of PII data. This data governance can help humans and agents work with the data in a compliant way.

鈥淭here is a meaningful difference between data you can trust and data sourced informally,鈥� Oliver Huth, head of Platform, Corporate Functions, & Analytics at 麻豆原创, states. 鈥淏uilding that trust at scale is what the shift to data products鈥攑owered by People Intelligence in 麻豆原创 Business Data Cloud鈥攊s making possible for our teams at 麻豆原创.鈥�

What鈥檚 changed and what鈥檚 coming

The outcome for both IT and the business is clear. 麻豆原创 IT populated its internal data product catalog rapidly, reaching the critical mass needed for broad adoption. HR data that was previously locked behind dashboard requests now powers use cases across functions. For 麻豆原创鈥檚 business teams, the outcome is faster time to insight. Pre-built intelligent content in People Intelligence serves as an 80% starting point for business conversations, replacing blank-sheet requirement gathering with focused discussions. Leaders and managers can also access personalized KPI views through MyMetrics, choosing a KPI, seeing an overview and AI summary, and jumping to the dashboard if additional information is needed. Users can also turn to Joule to ask questions in natural language and get replies with visualized charts. This pre-built, self-service approach has significantly reduced the volume of HR data inquiries and dashboard requests

麻豆原创鈥檚 next steps for People Intelligence include recreating the MyTeam Dashboard by composing it from the readily available data products.

In addition, 麻豆原创 IT is very excited to adopt AI agents that can operate directly on top of governed data products, querying a variety of data including employee, salary, and skills. As Huth notes, “Investing in a data product strategy is the essential first step. It is what enables governed data access and produces AI-ready models as a result.” 麻豆原创’s standard development teams are currently building Joule Assistants and Joule Agents, including a People Intelligence Assistant to be released in November 2026

Learning from 麻豆原创鈥檚 experience

For HR and people analytics teams facing growing data demand, fragmented access, and the pressure to deliver more with less, 麻豆原创 IT’s experience offers a clear road map: adopt People Intelligence in 麻豆原创 Business Data Cloud, use 麻豆原创-delivered data products out of the box, invest now in a governed data product architecture, and treat intelligent content as a starting point. This can improve analytics delivery today and is the infrastructure that will make AI agents trustworthy tomorrow.

.

The acquisition accelerates agentic AI and expands customers鈥� ability to聽combine 麻豆原创 and non-麻豆原创 data to run analytical and AI workloads in real time, with no data movement or conversion necessary, and with vastly improved economics for enterprise analytics.

For additional information about the acquisition, see the press release from May 2026.

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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.  

While cash is losing importance, contactless payments and mobile wallets are becoming dominant, with many providers enabling customers to make payments directly from their smartphones. These shifts put constant pressure on retailers to update their systems. The demand for mobile, contactless, and secure payment solutions keeps growing.

Market trends determine success

As a globally operating company, 麻豆原创 keeps a close eye on the latest trends, looking not just to adapt its solutions but to rethink them. 麻豆原创 Customer Checkout, the intelligent integrated point of sale (POS) solution for retail, merchandising, and catering, relies on strong partnerships to stay ahead and keep pace with those trends. Speed and the right collaborations make the difference.

“The POS market is in constant flux. Competitors are alert and customers arrive every day with new demands and ideas,” said Harald Tebbe, development lead for 麻豆原创 Customer Checkout, speaking to the scale of change in the market. “Ultimately, market trends decide who stays successful and who doesn’t. Even though payment processes aren’t our direct business, our focus is on offering customers a wide range of payment options to make the end customer’s shopping experience as simple and convenient as possible. Payment infrastructure is a critical component of that experience. Our trusted technology partnerships are what make it possible, which is why we’re continuously looking for new collaborations.”

End-to-end solution through 麻豆原创 Customer Checkout and Viva.com

As the first tech bank in Europe for businesses and being active in 29 countries, Viva.com offers customers an integrated omnichannel payments, banking, and financing platform.

Viva.com’s leading Tap on Any Device payment technology takes centre stage. It turns any Android device鈥攆rom fixed and mobile payment terminals to self-checkout tills and handheld devices鈥攐r iPhone into a secure card terminal, optimizing the customer experience and helping customers pay wherever they are. Tap on Any Device by Viva.com supports over 40 payment methods, DCC, surcharge, offline payments, while the tech bank delivers real-time settlement 365 days a year and a cashback scheme reducing transaction fees to zero percent.

Since 麻豆原创 Customer Checkout was built to be hardware-independent, works across a variety of devices, and integrates with different payment terminals, the partnership creates an ideal end-to-end solution for customers in retail, consumer goods, food service, and other sectors. Open API interfaces in 麻豆原创 Customer Checkout and Viva.com’s Terminal Integration Hub made the integration straightforward and quick to implement.

鈥淲ith Viva.com, 麻豆原创 Customer Checkout customers in 29 European countries get integrated payments and banking in one platform: From omnichannel payments and our leading Tap on Any Device technology to fast, seamless access to tailored financing options -no need to switch providers,” Harry Xenophontos, Chief Business Officer at Viva.com, said. “That’s the unique proposition we’ve built with 麻豆原创.”

Secure data communication for customers

Integrating 麻豆原创 Customer Checkout with Viva.com requires a dedicated plug-in, which simplifies the connection between the POS system and the payment terminal by ensuring data is transferred via the HTTPS protocol, keeping communication secure. This matters in a period where both innovation and the protection of sensitive data are front of mind.

Customers also benefit from a particularly fast setup process and the freedom to choose from a wide range of devices to use as payment terminals. With three possible integration options, the system offers flexibility so merchants can find the right fit for their specific needs.

The joint project supports the development and delivery of forward-looking POS solutions that work well for both merchants and end customers.

Elena Vavitsa is a senior solution specialist for 麻豆原创 Customer Checkout.

Top image courtesy of Viva.com

West made a point that stayed with me. Using AI simply to generate more output is only the beginning. The more powerful use case is to treat AI as a collaborator in the thinking process: something that can challenge assumptions, ask questions we might not have asked, and help us see a problem from a different angle. In that role, AI is not replacing judgment, it is creating useful friction around it.

That shift matters because the work of building technology is changing. Developers are no longer just writing deterministic logic and controlling every outcome in advance. Increasingly, they are working with systems that are creative, probabilistic, and less predictable by design. That opens up huge possibilities, but it also raises the bar. Quality, guardrails, metrics, and trust become even more important when software starts to reason in ways that are not always fully scripted.

The advice West offered to developers was simple and probably more durable than any specific tool or framework: fall in love with the problem, not the solution. The technologies will change. The models will change. The implementations will change. But the ability to understand a problem deeply, stay curious, and apply AI with judgment will remain valuable.

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At a time when so much of the conversation around AI feels breathless, that feels like a good way to approach this moment: not with blind enthusiasm, and not with fear, but with curiosity and discipline. What are we trying to understand? What are we trying to improve? Where would better questions, better feedback, or better judgment make the biggest difference?

That is where AI starts to feel less like a wave we have to chase, and more like a capability we can shape with purpose. AI gives us new ways to build, learn, and decide. The question is not just how much more we can produce with it, but how much better our thinking can become when we use it well.

WALLDORF 鈥� (NYSE: 麻豆原创) and IBM today announced that global enterprises JYSK, GBM, DIFARE Group and Plastilene Group have selected 麻豆原创 Cloud ERP Private solutions on to modernize enterprise resource planning (ERP) workloads in secure, reliable and scalable cloud environments.

Spanning retail, technology services, pharmaceutical and manufacturing, these clients are among the tens of thousands of businesses that run 麻豆原创 landscapes on IBM Power servers.

According to a from the IBM Institute for Business Value (IBV), modernizing ERP workloads is essential for driving AI adoption and business growth, with companies embedding AI into ERP systems achieving up to 27% higher ROI. Leveraging 麻豆原创 Cloud ERP Private on IBM technology can support customers as they scale on-premises ERP environments to the cloud and accelerate AI-enabled business workflows.

The platform delivers a flexible hybrid cloud environment, which can help organizations:

• Reduce the total cost of ownership (TCO) of cloud ERP operations, supported by the ability to scale granularly to match business demand and leverage IBM鈥檚 global cloud infrastructure, designed to be highly resilient.
• Migrate to 麻豆原创 Cloud ERP Private and to the 麻豆原创 Business Warehouse (麻豆原创 BW) application as part of the 麻豆原创 Business Data Cloud (麻豆原创 BDC) solution, securely, quickly and with minimal disruption, including support for hybrid cloud and multicloud deployments.
• Mitigate operational and security risk, through IBM Power鈥檚 enterprise-grade resilience and integrated IBM Cloud security and compliance protection.

Modernizing ERP Workloads in More Secure, Scalable Cloud Infrastructure Across Industries

Known for its high security, scalability and reliability, IBM Power servers are ranked as one of the top servers for uptime and availability among 麻豆原创-certified infrastructure, engineered for fewer disruptions and faster migration, supported by the highly resilient and secured IBM Cloud platform. These clients are rapidly migrating on-premises 麻豆原创 software systems to the cloud, modernizing business processes and becoming more agile:

• JYSK, the international home furnishing retailer based in Denmark, is advancing its global modernization journey with IBM and 麻豆原创. With more than 3,600 stores in 50 countries, the retailer needs its 麻豆原创 software landscape to be more secure, scalable and future-ready to enable it to keep up with the demands of its global business. JYSK has a long history with IBM technologies and continues to work with IBM to advance in their RISE with 麻豆原创 journey.
• DIFARE Group, a leading pharmaceutical manufacturing company based in Ecuador, required a robust, secure and scalable infrastructure to modernize its 麻豆原创 software landscape and support critical business operations. As long-term users of IBM Power servers, DIFARE Group continues to place its confidence in IBM technology and has expanded into RISE with 麻豆原创 on IBM Power Virtual Server to help move to the cloud faster and more cost effectively.
• Plastilene Group, an innovator, developer and manufacturer of flexible film solutions in Colombia, chose IBM technologies to modernize its 麻豆原创 software landscape. With the ability for the solution to deliver better TCO, Plastilene can continue its focus on growth and regional diversification.
• GBM, a leading IT services company in Central America and the Caribbean, is focused on improving agility, scalability and real-time insight to better support its customers. By leveraging IBM technology curated for 麻豆原创 Cloud ERP Private to help gain reliability, security and high performance, GBM is creating a strong foundation to adopt 麻豆原创 software innovations and drive continuous transformation across the organization.

Industry-Leading 麻豆原创-Certified Infrastructure Enables Cloud Modernization

鈥淎s enterprises modernize, the journey to 麻豆原创 Cloud ERP Private is dedicated to helping on-premises customers of 麻豆原创 ERP tailor their transformation and bring business applications, data and AI together with 麻豆原创 Business AI Platform. Some of these customers are now modernizing their cloud ERP landscapes and advancing their cloud ERP digital transformation strategies with 麻豆原创 solutions on IBM Power Virtual Server,鈥� said Lalit Patil, CTO for RISE with 麻豆原创 and Head of Cloud Lifecycle Engineering and Operations, 麻豆原创 SE.

鈥淥rganizations across industries are accelerating their move to 麻豆原创 Cloud ERP Private and require a trusted cloud platform designed for mission鈥慶ritical workloads,鈥� said Hillery Hunter, General Manager for IBM Power, CTO, IBM Infrastructure. 鈥淏y combining the security, scalability and resiliency of IBM Power and IBM Cloud with the transformation capabilities of 麻豆原创 Cloud ERP Private, we are committed to helping clients move forward with confidence on their modernization journeys.鈥�

IBM is a full lifecycle strategic partner of 麻豆原创, providing end-to-end consulting and technology solutions for 麻豆原创 customers including hybrid cloud, automation and agentic AI. IBM and 麻豆原创 recently progress across AI and agentic capabilities to help accelerate enterprise transformation, including an expanded collaboration through the Agent2Agent (A2A) interoperability standard to perform complex multi-agent services for clients. IBM Consulting Advantage can now manage Joule Agents, which work directly with IBM鈥檚 watsonx Orchestrate agents.

For more information about 麻豆原创 Cloud ERP Private on IBM Power Virtual Server, visit: .

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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.

The Advanced Success Plan version for 麻豆原创 Customer Experience solutions is designed precisely for this moment. It offers utilities customers a structured, continuously guided approach to maximizing value from 麻豆原创 Customer Experience (麻豆原创 CX) investments, from first adoption through to AI-enabled optimization.

A sector in motion: why customer experience has become strategic for utilities

Four converging forces are making customer experience a boardroom priority for utilities and increasing the demand for structured, expert-led adoption support.

• Energy transition and service complexity: Renewables, EV charging, solar feed-in tariffs, and demand response programs are adding new service dimensions. Customers expect their energy provider to manage this complexity seamlessly.
• Deregulation and customer switching: In liberalized energy markets, customers can, and do, switch providers. The cost of acquisition consistently exceeds the cost of retention. Superior service experience is a measurable retention lever.
• Smart metering and data volume: Smart meter rollouts generate billions of interval readings daily. This data can fuel proactive outreach and personalized billing, but only if the customer experience platform is correctly configured to act on it.
• Regulatory intensity: Billing accuracy mandates, complaint resolution timelines, and outage notification requirements are intensifying. The 麻豆原创 CX portfolio can support compliance when features are correctly activated and configured.

麻豆原创 in utilities: a significant and growing market

Utilities are not a niche segment for 麻豆原创. Understanding the scale of the 麻豆原创 utilities community frames why the Advanced Success Plan for 麻豆原创 Customer Experience solutions matters for this industry in particular. 麻豆原创 is trusted by hundreds of utilities customers globally and is . The investment in the relationship has been made. The question is whether customers are fully using what they have.

With the Advanced Success Plan, customers can turn the 麻豆原创 Customer Experience portfolio into a driver of growth and innovation. They can gain the confidence to act decisively, supported by unlimited expert guidance and intelligent insights that help ensure every feature can deliver measurable business value.

The business case: customer retention over acquisition

For utilities operating in competitive markets, retaining customers has become as strategically important as acquiring new ones. Customers can switch suppliers in minutes via a digital platform, complaint escalation processes are visible on social media, and billing errors in a smart meter world are harder to excuse and faster to escalate.

The 麻豆原创 CX portfolio can address these challenges across the full customer lifecycle. 麻豆原创 Engagement Cloud enables targeted, segmented communications. 麻豆原创 Service Cloud can centralize complaint management and agent interactions. 麻豆原创 Sales Cloud helps manage accounts, contacts, leads, and opportunities. 麻豆原创 Commerce Cloud is the聽digital sales and self-service storefront layer and can handle how customers discover, compare, purchase, and manage energy products online. What the Advanced Success Plan for 麻豆原创 Customer Experience solutions does is help to ensure these capabilities are not just purchased but adopted, optimized, and continuously improved.

How the Advanced Success Plan structures the adoption journey

The Advanced Success Plan for 麻豆原创 Customer Experience solutions helps organize the adoption journey across four distinct phases, each with a defined purpose and a set of targeted services:

1. Introducing innovation: Identify and prioritize the right innovations for your business. Services here include product guidance sessions covering areas such as utilities session and agent desktop, intelligent selling, campaigns and segmentation, and AI foundation and use case navigator.
2. Adopting innovation: Plan and prepare for structured adoption with minimal risk. Services include the AI process fit-gap analysis, key feature advisory, release guidance, success expert engagement, service planning, value management, innovation checkpoints, and adoption checkpoints.
3. Activation and optimization: Enable hands-on activation of AI and customer experience capabilities in your environment. Services include activation sessions across 麻豆原创 Sales Cloud, 麻豆原创 Service Cloud, 麻豆原创 Marketing Cloud, and 麻豆原创 Engagement Cloud; AI agent activation; embedded AI activation; Joule activation; and technical and functional assistance.
4. Success measurement and continuous improvement: Measure outcomes and sustain momentum through ongoing engagement. Services include a continuous engagement model, release guidance, success expert, value management, innovation checkpoints, and adoption checkpoints.

8 services available to utilities customers鈥攁nd how each one helps

The Advanced Success Plan for 麻豆原创 Customer Experience solutions comprises eight distinct services. Each has a defined scope, a specific business need it addresses, and measurable benefits. For utilities customers, each service connects to a characteristic operational or strategic challenge.

Product guidance
Structured sessions introduce utilities teams to the capabilities most relevant to their context, from meter-read-driven billing notifications in 麻豆原创 Service Cloud to segmented communications in 麻豆原创 Engagement Cloud. The goal is to accelerate time-to-awareness, so teams know what exists before they have to find it.

Key feature advisory
This is curated, customer-specific guidance on which features to activate and in what sequence. For utilities, this means filtering a broad 麻豆原创 CX road map down to the capabilities that matter for smart metering, complaints management, and outage communications then discarding what does not apply.

Release guidance
Every 麻豆原创 CX quarterly release brings dozens of updates. Release guidance helps ensure utilities teams receive a focused brief on what is relevant to their industry context, not a generic list of all product changes, so adoption decisions can be faster and better informed.

Solution review
This is a structured review of the current solution configuration against best practice. For utilities, this commonly surfaces configuration gaps in complaint workflows, billing notification templates, or service agent desktop layouts that erode the customer experience over time if left unaddressed.

Transformation advisory
Strategic guidance connects 麻豆原创 CX capabilities to the outcomes utilities care about most: cost-to-serve reduction, complaint resolution improvement, and regulatory compliance. Transformation advisory helps move the conversation from features to business impact.

Activation
This is hands-on, expert-assisted activation of specific capabilities in the customer’s live environment. For utilities, this includes activating Joule for service agents and enabling AI-driven case categorization in 麻豆原创 Service Cloud. Many autonomous agents or assistants announced at 麻豆原创 Sapphire, once available, can be activated to help guide service agents to process cases.

Technical assistance
Direct technical support across all phases of the engagement covers integration architecture, performance guidance, load testing guidance for mass billing cycles, and resolution of complex system behavior. This service is especially critical for utilities given the high-volume, time-sensitive nature of meter-read processing and month-end billing runs.

Functional assistance
This comprises business process and functional configuration support throughout the engagement. Utilities-specific coverage includes complaint handling workflows, outage notification sequences, and the configuration of billing determinant displays in the service agent desktop, helping to ensure what is built serves the way utilities actually operate.

A practitioner’s perspective

Working directly with utilities customers shows first-hand how the Advanced Success Plan for 麻豆原创 Customer Experience solutions can drive meaningful business value, especially for organizations without deep technical or functional 麻豆原创 CX expertise in-house. Utility companies face a specific set of challenges: complex billing architectures, regulatory requirements, and high customer expectations for responsive service. The right services delivered at the right moment can be genuinely transformational. Four patterns stand out consistently:

1. Bridging the knowledge gap for business users: Unlike system integrators or technical consultants, many utility business teams may not fully grasp the functional nuances required to maximize their 麻豆原创 investment. Value-driven customer success manager engagement becomes critical here by translating technical possibilities into business outcomes, advising on feature adoption, and ensuring cross-team alignment across operations, IT, and customer management. The customer success manager acts as connective tissue between what 麻豆原创 CX can do and what the business needs to achieve.
2. Go-live checks are the foundation for seamless launches: Utilities cannot rely on generic go-live checklists. Are meter reads flowing end-to-end? Are billing determinants and rate structures properly mapped? Are customer service orders integrated with billing and meter management systems? Comprehensive go-live checks built for the utilities context eliminate the guesswork, giving teams confidence that key processes are functioning correctly from day one, not discovered to be broken three months into the first billing cycle.
3. Road map guidance enabling better design decisions: Each 麻豆原创 CX release brings dozens of new features, but only subsets are relevant for a given customer in a given market. Regular, curated road map guidance helps utilities focus on the features and design decisions that will most efficiently drive their specific outcomes, whether that is improving customer satisfaction, streamlining operations, or enriching digital self-service. This targeted approach prevents costly missteps and empowers business stakeholders to make confident decisions without requiring deep product expertise of their own.
4. Early-phase support building for reliability and performance: The early project phase is often when performance issues and integration risks are best addressed, but it is also where internal teams feel least certain. Proactive support that includes performance reviews spanning interconnected billing and service systems, high-level architecture guidance, and utilities-specific load testing helps ensure reliable operation under real-world peak conditions. Utilities processing millions of daily meter reads, running mass billing cycles at month-end, and handling seasonal demand spikes need to know their system will hold before the peak arrives, not after it has passed.

What stronger 麻豆原创 CX adoption looks like in practice

When utilities customers engage with the full Advanced Success Plan for 麻豆原创 Customer experience solutions at the right moments, the outcomes can be concrete and measurable:

• Adoption confidence: Teams understand the 麻豆原创 CX capabilities that matter for their industry and know how to use them. Adoption is driven by guided enablement, not trial and error.
• Configuration quality: Solution reviews and functional assistance can ensure complaint workflows, billing notifications, and service processes are correctly configured, reducing workarounds and support volume.
• Release relevance: Every quarterly 麻豆原创 CX update is assessed for utilities relevance. Teams receive a focused brief on what to adopt, not a generic list of all product changes.
• AI activation at pace: Joule, embedded AI agents, and 麻豆原创 Engagement Cloud intelligence features are activated with expert assistance, helping to move from available-in-catalogue to live-in-production for utilities use cases.
• Integration reliability: Standard integrations between 麻豆原创 Service Cloud and 麻豆原创 S/4HANA Utilities are correctly configured, giving agents real-time access to meter history, billing data, and service orders.
• Transformation clarity: Transformation advisory connects the 麻豆原创 CX road map to the outcomes utilities care about most: cost-to-serve reduction, complaint resolution improvement, and regulatory compliance.

A service portfolio built for the complexity of utilities

The 麻豆原创 investment in the utilities sector is substantial and well-established. Utilities organizations around the world have already deployed the 麻豆原创 CX portfolio and are running their customer operations on it. The question is not whether they have access to world-class customer experience capabilities, but whether they are fully using what they have.

The eight services described above, combined with customer success manager-led perspectives on go-live quality, road map relevance, and performance assurance, represent a comprehensive framework for utilities organizations to realize the full potential of their 麻豆原创 CX investment.

The Advanced Success Plan for 麻豆原创 Customer Experience solutions is not a generic offering. When applied with industry focus and customer success manager expertise, it becomes a strategic asset, one that helps utilities deliver on the promise of a superior customer experience in an increasingly competitive, regulated, and technically complex market.

Rajeev Ranjan is product manager for the Advanced Success Plan for 麻豆原创 Customer Experience.
Tara Tracey is global product owner for the Advanced Success Plan for 麻豆原创 Customer Experience.

The agreement, concluded at the end of 2025, marks a significant step in migrating Nokia鈥檚 麻豆原创 landscape to the RISE with 麻豆原创 journey. By adopting RISE with 麻豆原创 Methodology, Nokia will follow a structured, end-to-end approach to migrating its ERP landscape covering processes, data, applications and operating models while gaining continuous access to innovation and embedded AI capabilities delivered through 麻豆原创鈥檚 cloud ERP portfolio.

Nokia has selected Microsoft Azure as the cloud platform underpinning the transformation, providing the global scale, security and performance required to support the company鈥檚 most business-critical enterprise workloads. 鈥淣okia鈥檚 decision reflects a clear commitment to business-led transformation,鈥� said , Global President Customer Success Europe, APAC, Middle East and Africa and Member of the Extended Board, 麻豆原创 SE. 鈥淩ISE with 麻豆原创 Methodology provides Nokia with a structured road map, integrated toolchain and continuous access to innovation. It enables the company to modernize its ERP landscape while keeping a clean core and building a strong foundation for enterprise AI.鈥�

A Structured Approach to ERP Transformation

RISE with 麻豆原创 is designed as a comprehensive business transformation framework rather than a point solution. It combines a standardized transformation methodology, integrated tools and expert guidance to help organizations move from legacy ERP environments to RISE with 麻豆原创.

麻豆原创 will operate and manage the 麻豆原创 S/4HANA software environment in the cloud, allowing Nokia to shift focus from infrastructure management to business outcomes. The approach supports process standardization, operational simplification and ongoing innovation, rather than a one-time system migration.

Nokia has been on a business and technical transformation journey with its next-generation 麻豆原创 S/4HANA software environment, covering finance and key logistics capabilities, supported by 麻豆原创 solutions and applications. These include 麻豆原创 S/4HANA for central finance, 麻豆原创 Master Data Governance, 麻豆原创 Extended Warehouse Management, 麻豆原创 Global Trade Services and 麻豆原创 S/4HANA Cloud for advanced ATP. AI-enabled functionality embedded in 麻豆原创鈥檚 cloud applications will be progressively adopted as part of the journey.

鈥淭his agreement builds on our existing work with 麻豆原创 and Microsoft and supports Nokia鈥檚 ambition to secure how we run our core business operations,鈥� said Marek O膷kay, VP, Global Head of IT Procurement & Vendor Management, Nokia. 鈥淏y applying RISE with 麻豆原创 Methodology on Microsoft Azure, we are strengthening a structured and future ready path for business growth 鈥� one that simplifies our ERP landscape, enables continuous innovation and strengthens our commitment for AI driven processes.鈥�

Microsoft Azure as the Cloud Foundation

Microsoft Azure will serve as the cloud foundation for Nokia鈥檚 RISE with 麻豆原创 journey, aligning with Nokia鈥檚 broader cloud and data strategy. Nokia already operates parts of its 麻豆原创 landscape on Azure, and consolidating workloads on a single hyperscale platform is expected to deliver benefits in performance, security latency and operational resilience.

As part of the agreement, Microsoft will collaborate closely with 麻豆原创 and Nokia throughout the transformation, supporting migration activities and ongoing optimization.

鈥淭his collaboration demonstrates how cloud platforms, enterprise applications and AI can come together to support complex, global business transformations,鈥� said Joacim Damgard, CVP, Europe North Microsoft. 鈥淏y running 麻豆原创 S/4HANA on Azure within the RISE with 麻豆原创 journey, Nokia is creating a scalable and secure foundation for continuous innovation.鈥�

Building on a Longstanding 麻豆原创 Relationship

Nokia has been an 麻豆原创 customer for decades. In recent years, the company has been consolidating multiple ERP systems into a unified 麻豆原创 S/4HANA software landscape as part of its next-generation ERP program.

The move to RISE with 麻豆原创 helps secure that journey, providing a structured methodology to help accelerate transformation, reduce complexity and unlock cloud native capabilities.

Visit the聽. Get 麻豆原创 news via聽 and .

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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is a multinational oil, gas, and chemical company headquartered in Vienna, Austria. With operations spanning Europe, the Middle East, Africa, New Zealand, and Norway, OMV is a truly global enterprise.

Like much of the energy sector, OMV is navigating a significant strategic pivot. The company is investing heavily in sustainability initiatives: transforming plastic waste back into oil, building one of Europe’s largest waste-sorting facilities to produce feedstock for refineries, and recycling plastic cups collected from aircrafts into sustainable kerosene. This shift in the business model has triggered a corresponding transformation inside the business鈥攁nd nowhere more so than in HR.

OMV’s People and Culture (P&C) function launched a strategic program to place people at the center of the company’s transformation. The ambition was clear: become a global HR center of excellence, increase service quality, standardize and harmonize processes, and move decisively towards digitization, automation, and self-service.

The challenge: fragmented data and a manual reporting cycle

Before 麻豆原创 BDC entered the picture, OMV’s HR data landscape was fragmented across a patchwork of systems that were never designed to work together for workforce reporting and analytics. Employee data lived in two on-premise 麻豆原创 HCM systems and 麻豆原创 SuccessFactors HCM, alongside Microsoft Excel, SharePoint, and a system originally built for financial consolidation that P&C used for headcount reporting and planning.

The day-to-day consequences were significant. When a business unit head or department manager wanted a workforce KPI鈥攈eadcount figures, turnover rates, or anything beyond a basic report available in the system鈥攖hey would raise a request with their HR business partner. From there, the HR business partner would spend considerable time navigating multiple systems, manually pulling data, compiling it into spreadsheets, and formatting it into a presentation before handing it back to the manager. It was time-consuming, error-prone, and consumed HR capacity that should have been spent on strategic work. Managers had no direct, self-service access to their own workforce data.

Choosing 麻豆原创 Business Data Cloud and People Intelligence

“Normally, our strategy is not to be the first with a new solution. With 麻豆原创 BDC it was different,” Bernhard Graser, head of 麻豆原创 Finance, HR, and Reporting at OMV, said. “We saw the potential immediately and wanted to stop the outbound migration of our HR and 麻豆原创 data and keep it firmly in the 麻豆原创 ecosystem.”

The timing was fortuitous. OMV had already completed a substantial 麻豆原创 SuccessFactors HCM implementation, having deployed , , and and going live in 2023 with , 麻豆原创 SuccessFactors Compensation, and 麻豆原创 SuccessFactors Recruiting. With all core employee data now sitting in a cloud-based SaaS system, the foundation for 麻豆原创 BDC connectivity was already in place.

OMV鈥檚 implementation of 麻豆原创 BDC and People Intelligence

OMV structured its 麻豆原创 BDC journey in three steps.

The first step鈥攖urning People Intelligence live鈥攚as connecting 麻豆原创 SuccessFactors HCM to 麻豆原创 BDC. This was not entirely without friction: OMV discovered that its on-premise HCM systems sat on a different Identity Authentication service than 麻豆原创 SuccessFactors HCM, which required alignment before integration could proceed.

A more substantive challenge was data governance. As an Austrian company with a Works Council, it was not possible for OMV to simply push all HR data into 麻豆原创 BDC. The team implemented data masking, configured Read Access Logging, and established permission controls that mirror 麻豆原创 SuccessFactors HCM exactly, meaning a user can only see data in 麻豆原创 BDC that they are already authorized to view in 麻豆原创 SuccessFactors HCM. This level of governance was a prerequisite before any business users could interact with the system.

The second step, currently in progress, involves migrating both HCM systems to . Once complete, 麻豆原创 S/4HANA will connect directly to 麻豆原创 BDC, enabling a fully unified data feed from both 麻豆原创 SuccessFactors HCM and 麻豆原创 S/4HANA into a single platform.

The third step, planned for the near future, is the retirement of the legacy reporting stack entirely, eliminating the spreadsheets and replacing the current workaround in the financial consolidation system with 麻豆原创 BDC as the single reporting and planning environment for HR.

麻豆原创 BDC’s architecture played a key role in the decision. 麻豆原创-managed data products鈥攑re-built data models maintained and updated by 麻豆原创鈥攚ere particularly attractive, especially because OMV had kept its 麻豆原创 SuccessFactors HCM configuration close to standard. That near-standard posture meant a larger share of OMV’s HR data could be served through 麻豆原创-managed products, reducing the internal maintenance burden. When something changes in a source system or a data definition, it is 麻豆原创’s responsibility to update the model, not OMV’s.

Current and future use cases

After evaluating the intelligent content available in People Intelligence, OMV decided to start with workforce composition insights, now live and providing out-of-the-box dashboards on headcount, workforce structure, and composition, fully configurable and filterable by business users.

With the foundation in place, OMV’s P&C team has been actively collecting ideas for what to build next on 麻豆原创 BDC. On the operational side, the team wants to track accident-related data as a workforce KPI, monitor open positions across the business, and measure time-to-hire. Diversity is another priority鈥攄ata that currently sits fragmented across systems. Through its participation in 麻豆原创’s forward deployed engineering program, OMV is co-building use cases around learning and certification compliance鈥攁 business-critical need in a refinery environment where workers must hold current safety certifications to enter operational sites鈥攁s well as skills and headcount. Looking further ahead, OMV intends to move into machine learning and predictive modelling, using the capability in 麻豆原创 Business Data Cloud to forecast workforce demand and identify gaps in skills and FTEs before they materialize.

The bottom line

The direction is clear: a single source of truth for HR data, self-service access for every manager and business unit head, and a platform capable of growing from descriptive reporting today into predictive workforce intelligence tomorrow. As Graser encouraged his audience at the end of his session at Madrid: “We see great potential in 麻豆原创 BDC鈥攏ot only in HR, but also in finance. You should try it.”

Learn more about People Intelligence .

Who owns the procurement motion? How does pricing get negotiated? Who handles the contract? What about tax, invoicing, and payment? Which team tracks approvals? And once it’s signed, how does it fit into existing contracts and renewal cycles?

Most of these previously mentioned challenges occur in e-mail chains, spreadsheets, and conversations that no one fully documents. actively avoid suppliers who send irrelevant outreach鈥攖hey’ve already done their research by the time they engage. But the internal process of actually completing a purchase remains as manual as it was a decade ago. Research, shortlist, evaluate, and then hand it off to a process that moves at a completely different speed.

This is especially true for organizations with existing 麻豆原创 investments. Expanding a software landscape that’s already complex鈥攁dding new capabilities, aligning contracts, managing co-term timing across multiple products鈥攁dds layers of coordination that can slow even straightforward decisions to a crawl. Every new solution that doesn’t co-term with an existing contract means another renewal date to track, another negotiation cycle to manage, and another piece of the landscape that runs on its own timeline.

麻豆原创 Store exists to help remove those layers and barriers. For 麻豆原创 customers, it’s the single place to discover, trial, and purchase both 麻豆原创 and partner solutions鈥攐ver 3,600 of them鈥攚ithin an environment that’s already connected to their existing 麻豆原创 landscape. When a customer modifies an existing contract through 麻豆原创 Store, new purchases automatically co-term with the original order. Pricing and discounts, if applicable, are inherited from the previous contract. There are no separate renewal cycles to manage, no renegotiations from scratch.

The buying process itself is also built to help remove the common points of friction. Automated entitlement checks confirm compatibility before a purchase is completed. Pre-verified product dependencies prevent deployment issues after the fact. For customers that want to move quickly, many solutions offer a 鈥淏uy Now鈥� path that goes from selection to provisioned access in under 15 minutes. No forms to fill out, no calls to schedule.

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For deals that require more than a standard checkout鈥攏egotiated pricing, custom terms, specific payment methods鈥攑rivate offers on 麻豆原创 Store can keep the entire transaction inside the platform rather than pushing it back into offline coordination. on an e-commerce transaction has grown 83% in recent years; the appetite for completing complex deals digitally is there.

. For 麻豆原创 customers, the shift is already underway, inside a marketplace built specifically for their environment.

to discover 麻豆原创 and partner solutions, request quotes, start trials, and manage purchases through a more centralized experience.

Brent Eason is senior director for 麻豆原创 Marketplace Direct Business.

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.

AI infrastructure as strategic asset 

Although infrastructure ownership is just one element of digital sovereignty strategy, European politicians and policymakers have argued that without domestic data centers, Europe risks dependence on U.S. and Chinese providers for critical AI capabilities.

This concern is echoed by some industry leaders鈥攑articularly those in finance and regulated sectors鈥攚ho increasingly view AI infrastructure as a foundation of economic security. Specifically, they argue that sovereign data centers enable companies to comply with stringent European regulations on data protection and AI governance. They say that locally operated infrastructure ensures that data remains under European jurisdiction, reducing exposure to foreign legal regimes and enhancing trust among customers and regulators.

Security and compliance imperatives 

European leaders also frame AI infrastructure as a hedge against geopolitical risk. They argue that dependence on external providers introduces vulnerabilities, whether through legal exposure, supply chain disruptions, or political tensions.

As Christian Klein, CEO of 麻豆原创 SE, noted at the 麻豆原创 Sapphire Madrid event last month, many European customers operate in the public sector or other highly regulated industries. 鈥淕eopolitical risk is a growing concern,鈥� he said. 鈥淲hat if sanctions suddenly block data flows across borders? Or if the latest LLMs can鈥檛 be deployed in certain regions?鈥�

Christine Lagarde, president of the European Central Bank, also highlighted this concern in her November 2025 speech titled noting that Europe must 鈥渁void single points of failure鈥� in critical areas such as data centers and compute capacity.聽聽

Proponents of sovereign AI infrastructure also argue that it can stimulate broader economic growth. Data centers often anchor the ecosystems of startups, research institutions, and industrial applications, enabling Europe to capture more value from the AI stack.

From a technical standpoint, proximity also matters. Locally sited data centers reduce latency and improve performance for AI applications, particularly those requiring real-time processing or integration with industrial systems.

But despite these perceived advantages, many European business leaders have urged policymakers to take a more moderate, nuanced approach towards sovereign data. Their concerns are not about the need for data sovereignty itself, but about how it is implemented鈥攑articularly the push to rapidly build new, domestically controlled AI data centers. They emphasize that that data residency (location) is only one element of the four standard pillars of a sovereign data strategy, which also include legal sovereignty (jurisdictional control), operational sovereignty (independent operations), and technical sovereignty (data control).

In discussions with policymakers, European business leaders from diverse sectors have been warning that reducing reliance on U.S. technology too quickly is unrealistic. This reflects a structural reality: Europe remains deeply dependent on non-European providers for cloud infrastructure, chips, and AI platforms.

Research from Swiss cloud provider Proton suggests that around 75% of publicly listed European companies rely on U.S. tech services, (primarily Microsoft and Google) for critical infrastructure, including e-mail, cloud, and software. Therefore, attempting rapid substitution risks disrupting operations without delivering viable alternatives.

Barriers and concerns

Even the most ardent proponents of sovereign AI infrastructure acknowledge that there are major practical barriers to building massive AI data centers in Europe, including energy. AI data centers are extremely power-intensive, and Europe already faces grid constraints, high electricity prices, and long permitting timelines.

Without significant investment in energy systems, some European business leaders warn that new data center projects risk delays, cost overruns, or cancellation.

Another concern is that infrastructure-focused, sovereignty-driven policies may distort markets. Critics warn that infrastructure subsidies could flow to less competitive domestic providers resulting in slower innovation and the misallocation of capital resources to politically driven projects rather than economically viable ones.

In this view, sovereignty risks becoming industrial policy for its own sake, rather than a driver of efficiency or innovation. But perhaps the most significant critique is that the focus on infrastructure may distract from a more pressing issue: AI adoption.

Europe has historically lagged in deploying digital technologies. Some business leaders, including 麻豆原创鈥檚 Klein, argue that the priority should be accelerating AI use across industries and point out that infrastructure alone will not drive productivity gains. Over-emphasis on the infrastructure component of sovereignty could slow deployment through added complexity and cost. As Klein has noted, focusing primarily on infrastructure is a mistake if it is at the expense of developing AI applications and software.

Europe, he said recently, should prioritize 鈥渃ode over concrete.鈥� At the World Economic Forum in Davos earlier this year, senior executives from major European firms, including Capgemini and Ericsson, also warned against an overly protectionist approach. They argued that excluding or limiting global providers would raise prices, slow tech adoption, and reduce competitiveness.  

The business view 

From a business standpoint, AI is rapidly becoming a general-purpose technology, and the costs of AI infrastructure directly impacts productivity. If European AI infrastructure is more expensive, European companies risk falling behind global peers.

While data residency and the other elements of digital sovereignty are essential for some businesses operating in sensitive and highly regulated sectors, the sovereignty debate in Europe risks oversimplifying a fundamentally global industry. As Henna Virkkunen, the European Commission鈥檚 technology chief, noted: 鈥淣obody can be competitive alone.鈥�  

Indeed, since AI development depends on globally integrated supply chains, including semiconductors, software, and talent, fully localized infrastructure may be neither feasible nor desirable.

Rather than building duplicative infrastructure to support AI development, Europe鈥檚 real competitive advantage may lie in its treasure trove of operational data鈥攁 resource that is often difficult to access because of overly restrictive regulation and data access rules, prompting growing calls for reform from business leaders across Europe.

Easing and standardizing data access rules would help European businesses tap into this resource and compete more effectively with international rivals as they move into the next phase of AI enablement鈥攖he .