Depending on the underlying architecture of your AI solution, that data can cross multiple applications, vendors, and regions before a response ever reaches you.

That data journey matters, especially for organizations in regulated industries who are entrusted with sensitive or regulated data. It is why sovereign capabilities have become critical to keeping data within approved geographic boundaries, limiting who can access it, and supporting compliance with data residency and regulatory requirements.

Today, to support customers with specific sovereignty and data residency needs, 麻豆原创 made Cohere鈥檚 agentic AI platform, North, generally available on 麻豆原创 Business AI Platform. North enables enterprises to securely deploy AI agents and workflow automations at scale within their own infrastructure, without compromising on control or compliance.

North: Cohere鈥檚 enterprise-grade AI agent platform

North sets the standard for business performance by helping teams automate work and accelerate decisions that drive results, all in one scalable, secure workspace. It enables organizations to create AI agents and workflow automations to streamline operations across finance, sales, HR, IT, and customer support. Use cases include forecasting, CRM management, meeting preparation, IT service automation, and enhanced customer service.

Deep customization across models, agents, and workflows ensures North adapts to specific organizational needs while maintaining robust control over deployments.

The 麻豆原创 and Cohere partnership is especially valuable in sovereignty-conscious markets globally where AI adoption needs to align with national expectations of security, privacy, transparency, and accessibility.

麻豆原创 and Cohere’s North platform: Compliant AI made even easier

The combination of 麻豆原创 Business AI Platform and North can deliver exponential value. 麻豆原创 can help reduce overhead by hosting, operating, and supporting the solution end to end. Our APIs, solutions, and knowledge sources make it easier for North users to build AI agents grounded in their unique business context, policies, and enterprise data.

麻豆原创鈥檚 managed delivery model connects North’s AI capabilities to 麻豆原创 environments, reducing the need for organizations to manage separate infrastructure, operations, support, and commercial relationships. In other words, 麻豆原创 Business AI Platform transforms North into a managed, enterprise-ready, and sovereign AI service.

This partnership expands 麻豆原创鈥檚 sovereign solutions that can be tailored to customer needs. For customers with the most advanced requirements, offers data, operational, technical, and legal sovereignty. Our sovereign AI offerings include and Industrial AI Cloud.

For regulated industries and the public sector, this is the foundation for adopting AI while keeping sovereignty, compliance, and business value aligned from day one.

Try it today: .

Brenda Bown is chief marketing officer for 麻豆原创 Business AI Platform and Joule at 麻豆原创.

See Legal Notice on  for use terms, disclaimers, disclosures, or restrictions related to this material.

• Build on proven business controls: You already have governance standards and practices in place. AI governance should connect to and extend these controls.
• Adopt innovation with confidence: The approach to governance we advocate is the same publicly available and documented approach we use to develop, deploy, and govern the solutions and services we deliver.
• Stay in control as your business grows: The controls and standards you have in place should not only promote AI adoption, but also enable you to assess costs, risks, and value as adoption increases.

Since the advent of this new world of autonomous technology, people have endeavored to identify the most impactful application of it. As the path to value realization has become clearer, a new challenge has emerged: ensuring both that AI usage does not lead to unintended and potentially harmful consequences and that this usage remains compliant with emerging regulatory regimes.

By adhering to the principles outlined above, 麻豆原创 makes it possible accomplish both ends. More importantly, as we showed at 麻豆原创 TechEd, 麻豆原创 also provides the solutions necessary to govern, control, and innovate now and into the future. 

AI governance announcements from 麻豆原创 TechEd

Here鈥檚 what we talked about at 麻豆原创 TechEd on the AI governance front.

True AI governance isn鈥檛 about figuring out if policies and rules have been followed after the fact; it should be about making sure governance is applied when it matters. 麻豆原创 Enterprise AI Gateway ensures this kind of proactive governance by consolidating authentication, policy enforcement, and routing at runtime, while also allowing for continuous monitoring, usage tracking, and auditing. The gateway thus controls traffic across MCPs, APIs, agent-to-agent connections and LLMs. Building on 麻豆原创 Integration Suite, the gateway consolidates and streamlines real-time oversight and control over AI in action.

As a compliment to the gateway, we also announced customer access to curated 麻豆原创 Sovereign Cloud LLMs directly through  . This allows customers to use these LLMs in critical processes while adhering to established governance standards. What鈥檚 more, by allowing direct access on , customers can reduce both cost and complexity in AI operations.

Being able to provide agents with the architecture context they need to operate effectively is also essential to the control of agent behavior. At 麻豆原创 TechEd, we described new features in the 麻豆原创 LeanIX portfolio enabling enterprise architects to control the architecture data Joule Agents use to make and execute decisions.

This architecture context feature relies on a knowledge intake agent to extract relevant information from security and architecture decision documents, as well as propose connections to the 麻豆原创 LeanIX knowledge graph, and submit all data for human approval before making it available to agents.

Finally, to support EU AI Act assessments directly in , we also introduced our new AI Governance Assistant. The assistant audits and flags incomplete documentation in the agent inventory, pre-fills fields from completed assessments, identifies systems that appear high-risk but lack classification, and triggers reassessment when a fact sheet changes materially.

And just as the architecture context feature depends on human approval, all outputs from the AI Governance Assistant appear as drafts for legal and compliance review.

麻豆原创 and AI governance in depth

The solutions and innovations around AI governance we discussed at 麻豆原创 TechEd are part of a much broader story. That story is, to a degree, historical. 麻豆原创 has long played a key role in the governance of core operational and financial functions. Our approach to AI governance is built on鈥攁nd a logical extension of鈥攖his legacy.

Of course, 麻豆原创 is hardly new to this, having long ago established clear internal standards for AI governance while continuously introducing features and solutions devoted to AI governance. Among these solutions is 麻豆原创 AI Agent Hub, itself an outgrowth of AI governance capabilities in 麻豆原创 LeanIX. By allowing companies to quickly create and maintain a reliable inventory of agents, regardless of platform, MCP servers, and LLMs, the hub鈥檚 powerful discovery features are essential to comprehensive AI governance.

The other part of the story is value. While governance of AI is crucial for myriad reasons, companies invest in AI to achieve measurable business goals. As it turns out,t he observability necessary for effective governance can be leveraged and augmented to serve the purpose of performance management and evaluation.

With 麻豆原创 Signavio agent mining capabilities, for example, you can monitor agent behavior within the context of your business processes. In this way, you can ensure agent compliance while capturing the business impact agents deliver.

Governance should not be an afterthought

The evolution of AI is not going to slow down, and neither will AI adoption. Unfortunately, while the focus here rightly falls on continuously discovering new ways to leverage AI for operational excellence and competitive advantage, this focus should not mean that governance takes a back seat.

Governance, on the contrary, is the key to successful AI adoption.

It is important to remember that governance in the era of AI is not simply a question of regulatory or corporate compliance. Because this technology involves varying degrees of autonomy, governance must be applied to the behavior of the technology itself, not just to the behavior of the humans managing it. This is one reason that we emphasize runtime enforcement of governance policies.

The challenge we see today, however, is that no one has yet established clear industry benchmarks and standard practices for AI governance. There also seems to be a general lack of established platforms focused on governance.

We say a 鈥済eneral lack鈥� because, in point of fact, 麻豆原创 offers precisely such a platform, one built on decades of governance expertise and experience.

Governance has never been an afterthought for us. And when it comes to AI governance today, as the many innovations outline here show, it is absolutely top of mind.

Dominik Rose is chief product officer for 麻豆原创 LeanIX at 麻豆原创.

As AI is deployed across business processes, effective decision-making now hinges on unifying fragmented data and business context. AI agents can generate results quickly, but good judgement depends on deep understanding of how an organization runs. They need access to data across the enterprise, trusted views of business entities, and an understanding of the relationships, processes, and rules that define how the business operates.

麻豆原创 Business Data Cloud (麻豆原创 BDC) is the trusted data foundation for 麻豆原创 Business AI Platform, providing a business data fabric that connects data, semantics, processes, and policies, while preserving business context that gives data meaning. The innovations announced this week extend these capabilities making enterprise data more accessible and providing agents with the trusted business context for successful AI.

Extend the business data fabric with an open lakehouse foundation

Enterprise data is distributed across sources, including databases, warehouses, and data lakes. Making that data accessible across environments often requires duplicating copies, increasing costs and making it harder to maintain consistent business context.

With the completed acquisition of Dremio, 麻豆原创 is enhancing the open lakehouse foundation for business users and AI agents to access, share, and work with distributed data across the landscape.

• Discover, govern, and share data across platforms: 麻豆原创 BDC now supports native Apache Iceberg and a universal catalog powered by Apache Polaris, providing an open and governed data foundation for users and Joule to consume data. Together with 麻豆原创 BDC Connect, this builds on 麻豆原创鈥檚 commitment to platform interoperability with zero-copy data sharing across partners including Databricks, Snowflake, Google, AWS, and Microsoft.
• Query distributed data with zero copies: Supported by Dremio鈥檚 high-performance SQL query engine, users can federate access across lakehouses and relational databases including SQL Server, MongoDB, Oracle, and Postgres without moving data. Through integration with Joule Work, business users can ask complex questions in natural language and get answers grounded in trusted 麻豆原创 and custom data products and the business context behind them, without needing to know where the data resides or how to query it.
• Accelerate analytics and AI autonomously: For developers, maintaining query performance requires building summary tables, updating materialized views, or creating separate BI extracts to keep analytical queries fast enough for analytics and AI. This engineering work is reduced with Dremio鈥檚 Reflection technology, which learns from how your data is being used and automatically precomputes and optimizes the results your workloads need. As workloads change, Reflections adapt with them, removing the need for manual performance tuning while delivering sub-second performance at scale for analyst queries and high-concurrency agentic workloads.

鈥淲ith 麻豆原创 Business Data Cloud, we are building an enterprise foundation that unifies, governs, and is ready for innovation, so analytics and AI can scale without compromising the quality, compliance, and trust.”

Ganesh Sivakumar, Global Chief Data Analytics & AI Officer, Mead Johnson Nutrition

Accelerate your journey to the cloud

We also announced an expansion of the partnership with Databricks, bringing the latest set of industry-leading data platform capabilities including Lakeflow, Agent Bricks, Unity Gateway, and Lakebase to 麻豆原创 Databricks in 麻豆原创 Business Data Cloud.

麻豆原创 Application Data Foundation makes data from 麻豆原创 applications available for consumption in your 麻豆原创 BDC engine of choice, including 麻豆原创 Databricks. This provides a cloud-native way to access data in 麻豆原创 Business AI Platform, helping to lower the total cost of ownership and reduce time to value. 麻豆原创 data products can subsequently be shared across the business data fabric using 麻豆原创 BDC Connect for zero-copy access, avoiding the creation of additional pipelines, data copies, and silos.

For example, a manufacturer could use historical sales, inventory, and supply chain data to develop AI models for demand forecasting. The data is accessible from 麻豆原创 BDC, where data professionals can enrich it with third-party data and create custom data products in 麻豆原创 Databricks. Data engineers can build models on a semantically rich foundation, helping produce forecasts grounded in logic that is already embedded in 麻豆原创 systems while accelerating model development.

Build a trusted system of context across domains

Unified master data is only as valuable as the governance applied to it. Across the enterprise, the same customer, supplier, or product can appear differently across systems with different identifiers, attributes, and relationships. For an AI agent, understanding that these records represent the same business entity is essential for making trusted decisions.

Earlier this year, we announced the acquisition of Reltio, adding capabilities to help customers cleanse, harmonize, and unify data across systems. With the acquisition now complete, is now generally available in 麻豆原创 BDC, bringing multi-domain master data management natively into the business data fabric.

麻豆原创 Reltio鈥檚 AI-based entity resolution matches and reconciles records across 麻豆原创 and third-party systems to create unified, 360-degree views of business entities, including customers, suppliers, products, and other critical entities. By bringing together trusted attributed, relationships and business interactions 麻豆原创 Reltio gives business users and agents a more complete understanding of these entities across sources. These trusted profiles can be made available as governed data products in 麻豆原创 BDC, giving Joule and Joule agents consistent, connected business context to support decisions and actions.

Turn enterprise knowledge into reusable business context for AI

Every organization operates according to a defined set of business rules, relationships, processes, and policies that determine how decisions are made. 麻豆原创 Knowledge Graph provides rich business context across your data, connecting thousands of entities, relationships, and rules, creating a comprehensive view of the enterprise. Agents working on specific tasks need access to the context most relevant to the business problem they are solving, rather than the full enterprise model.

At 麻豆原创 TechEd Berlin, we introduced 麻豆原创 Business Data Cloud Ontology Manager, a new AI-assisted experience that enables teams to create domain-specific views of 麻豆原创 Knowledge Graph for applications, agents, or defined business scenarios. These curated views bring together the entities and relationships most relevant to each use case, grounding agents in the right business context they need to solve a particular business problem.

For example, a collections agent needs to understand customers, invoices, payment terms, and credit risk. 麻豆原创 Business Data Cloud Ontology Manager lets teams create and publish a domain view for collections. This context helps Joule Agents connect information across these business entities, supporting answers and recommendations that reflect the relationships and rules relevant to the scenario. Because these views inherit definitions and governance from 麻豆原创 Knowledge Graph, teams can reuse the same business semantics in views for related use cases, such as cash flow or order management, without having to redefine those concepts.

Get started

Irfan Khan is president and chief product officer of 麻豆原创 Data & Analytics.

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At 麻豆原创 TechEd in 2026, 麻豆原创 announced a series of innovations in Joule Work, Joule Studio, and 麻豆原创 Business AI Platform.

From building and extending AI agents to connecting business data and governing AI experiences, see how 麻豆原创 helps customers move towards the Autonomous Enterprise. Hear 麻豆原创 leaders and industry voices share what鈥檚 new, their perspectives, and more.

In 1996 麻豆原创 held its first event for developers. Thirty years later in Berlin, 麻豆原创 CTO Philipp Herzig told the 麻豆原创 TechEd keynote audience, 鈥淲e are experiencing the biggest and fastest technological shift in our lifetime.鈥�

鈥淔or decades developers have been building systems in a structured, deterministic way. But the world for builders has changed tremendously with AI,鈥� he said. 鈥淚t changes what’s possible. This is what 麻豆原创 TechEd is all about: combining both classical and new technologies that unlock the magic for business and provide endless opportunities to innovate.鈥�

From vision to execution

Underscoring his point, Herzig noted that during the 麻豆原创 Sapphire conference earlier this year, the company 鈥減ut out a bold vision to enable the Autonomous Enterprise, re-imagining every business process end-to-end with AI. And just like 30 years ago, the whole tech stack is shifting.鈥�

For 麻豆原创, this is reflected in Joule Work and the new 麻豆原创 Business AI Platform. Herzig described Joule Work as a 鈥済roundbreaking new way to experience software鈥� that taps into 麻豆原创鈥檚 five autonomous domains and assistants that reason and act across 麻豆原创 and non-麻豆原创 data, and 麻豆原创 Business AI Platform as 鈥渢he foundation where you build and extend agents, manage all your data, and govern AI experiences.鈥�

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Designed for better business outcomes

Elaborating, he emphasized that 麻豆原创 Business AI Platform 鈥渋sn鈥檛 just another tech platform, it鈥檚 specifically designed to build better business outcomes, deliver faster time-to-value and better price performance.鈥� In addition, it is enterprise-readiness from day one: secure, governed, and compliant, 鈥渁ll made possible through our unique context layer built on 50 years of domain and industry expertise.鈥�

Together, Joule Work and 麻豆原创 Business AI Platform underpin 麻豆原创鈥檚 commitment to build the Autonomous Enterprise. 鈥淎nd it鈥檚 here today,鈥� Herzig told the audience. 鈥淲e are excited to show you our latest updates at 麻豆原创 TechEd.鈥�

For the rest of the keynote, Herzig and his 麻豆原创 colleagues鈥攊ncluding 麻豆原创 Chief AI Officer Jonathan von Rueden; Manoj Swaminathan, president and chief product officer for 麻豆原创 Autonomous Suite; Tina Berlin, head of AI Foundation EU at 麻豆原创; Michelle Niedernhuber, lead product manager for 麻豆原创 AI Agent Hub; and Soeren Ruder, chief product officer and global head of Customer Experience & Solutions鈥攄etailed these updates and demonstrated them on stage.

Joule Work

Beginning with Joule Work, Herzig said 麻豆原创 had re-designed the user experience from the ground up: 鈥淎ll to make it more delightful and intuitive to use than ever. One starting point. One experience.鈥�

Joule Work now connects all 麻豆原创 applications as well as Office365, Claude Cowork, Glean, and ServiceNow out of the box. It remembers preferences and context from past interactions and continuously adapts. In addition to the conversational interface, shareable spaces that dynamically assemble data into interactive applications are also available.

The keynote speakers described Joule Work as a redesigned, cross-application AI experience available everywhere, including web, desktop, mobile, voice, and even Apple CarPlay. And they noted that Joule Work has been tested for months by more than 110,000 麻豆原创 employees, along with more than 150 customers, who joined the 麻豆原创 Early Adopter Care program, 鈥渁ll so you get the best AI experience.鈥�

One early adopter, Robert Mueller from Bosch Digital, joined Herzig on stage and said his teams had been testing Joule Work, Joule Studio, and the first assistants and agents and that the first user feedback had been positive. 鈥淥ur next step is to bring Joule into daily use,鈥� he said.

麻豆原创 Autonomous Suite

Manoj Swaminathan then took the stage to demonstrate the 麻豆原创 Autonomous Suite. 鈥淔or over five decades, 麻豆原创 has built the systems that help you run your business in the best way possible. Continuing that focus, we now deliver out-of-the-box a suite of 麻豆原创-managed assistants and agents across five horizontal domains, with industry specialization.鈥�

鈥淚鈥檓 excited to announce today 40 assistants and 300 agents between 麻豆原创 and our partners for you to explore,鈥� he added. 鈥淭hese give you a baseline for systems of intelligence and systems of action, built around three priorities, delivering: automation, intelligence and insights, and optimizations, with value that can be realized and measured, across reduction in cost, increased productivity, increased speed, improved quality, and more.鈥�

The new assistants include a Financial Consolidation Assistant that can reduce the time to close the books by up to 68%, a Sourcing Assistant that can reduce total cost of ownership by 15%, and a Supplier Management Assistant that detects and responds to supplier risk 60% faster.

鈥淔or these tasks, an answer that is 鈥榩robable鈥� is just not good enough,鈥� Swaminathan said. 鈥淵ou need the right answer, every single time, and they need to be compliant and traceable. That is what these assistants and agents uniquely enable, because every single one of these agents are ISO and SOC compliant.鈥�

麻豆原创 Business AI Platform

He noted that every agent is natively an Agent to Agent (A2A) endpoint, connected through 麻豆原创鈥檚 A2A gateway, and the platform supports extensions by default. 鈥淔or you as a builder, we鈥檝e made available the 麻豆原创 Business AI Platform, the foundation the 麻豆原创 Autonomous Suite is built on, so you can build to whatever your requirements are in Joule Studio.鈥�

鈥淵ou might ask, 鈥業’m already doing this with support from public models, why build on top of 麻豆原创鈥檚 Business AI Platform?鈥� Because a public model does not know your processes, your master data or your company. 麻豆原创 Business AI Platform does,鈥� Swaminathan shared. 鈥淥ur AI agents are embedded in the deterministic workflows of our applications. They deliver the one true answer as they run on the same data, following the same rules, and leave the same audit trail as our applications and you decide the level of autonomy.鈥�

But he pointed out that the 麻豆原创 Autonomous Suite does not restrict itself to only back-office operations. 鈥淥ur biggest customers are losing 11% of revenue to unplanned downtime experiences with on-site operations. Customers understand the problem but there hasn’t been a solution that responds to these challenges until now.鈥�

Physical AI by 麻豆原创

To address this issue, Swaminathan announced physical AI by 麻豆原创, which extends the Autonomous Enterprise into the physical world, turning business decisions into physical action, and bringing verified outcomes back into 麻豆原创 in a secure and governed way.

During an on-stage appearance, Ricky Watts, who runs the Industrial and Robotics Division of Intel, said the company is already running physical AI at its operational site in Chengdu, where 麻豆原创 warehouse management is driving autonomous robots closing the loop, writing outcomes back into 麻豆原创 as live transactions.

Jonathan von Rueden then took over the keynote stage to talk about the more than 300 standard Joule agents across the suite and how Joule Work and skills can adapt and customize standard agents to get the most from AI. If you want to go further still, he said, 鈥淛oule Studio is just one click away.鈥�

Joule Studio: Extend, build, and integrate your agents

Joule Studio, 麻豆原创’s agent-building platform, lets anyone customize AI with a skill: a natural language description and a few lines of code defining what an agent should do, opening agent-building beyond developers.

If you want to go deeper, and need to know where to begin, the Process Consulting Agent identifies where and what to build before development begins. “Just say what you want to get it done,” said von Rueden. 麻豆原创 Knowledge Graph finds the relevant APIs, MCP servers, and data products, and Joule Studio generates the specifications, structure, and integration logic.

The result: Moving from intent to a working agent takes roughly 2.5 hours instead of 13. Because agents don’t need to guess at requests, they use fewer tokens. “In our testing, that adds up to 50% lower cost per task,” von Rueden told the audience.

“With Joule Studio on 麻豆原创 Business AI platform, we focus on four things: business outcomes, time to value, performance for price, and enterprise readiness with deep integration.”

That last point matters in complex landscapes. With dozens of platforms, data sources, and tools running alongside 麻豆原创 systems, agents need to work across those boundaries safely and with governance, without a custom integration for every new platform.

“You’d expect a certain level of out-of-the-box capabilities and we’ve delivered,” Tina Berlin said. Agent gateway is generally available today, connecting agents and endpoints across any landscape boundary.鈥�

For more complex scenarios requiring rate limiting, routing, protocol translation, and advanced access control, 麻豆原创 is announcing 麻豆原创 Enterprise AI Gateway, built on 麻豆原创 Integration Suite as a natural overlay to agent gateway.

Herzig then returned to the stage to underscore the importance of reliable data for business AI. “Any AI system is only as good as the data it reasons over,” he said, “data that lives in different physical systems, be it 麻豆原创 or non-麻豆原创, on clouds, on-premise, and the edge.”

麻豆原创 Business Data Cloud

鈥淪o how do you bring it together in a consistent, AI-ready way?鈥� The answer, Herzig said, is 麻豆原创 Business Data Cloud, which provides customers with universal business context across their entire multi-cloud landscape. 鈥淎nd thousands of customers are benefitting from the outcomes, time to value, and lower cost already today.鈥�

In addition, he announced that the full scope of the entire Databricks platform will now be available natively in 麻豆原创 Business Data Cloud. This means that data from different systems can now be combined into a 鈥済olden鈥� master record, providing customers with a unified, AI-ready data foundation.

麻豆原创 executives also introduced a new 麻豆原创 Business Data Cloud Ontology Manager, available as beta. With it, customers can enrich 麻豆原创 Knowledge Graph with their industry-specific concepts or build entire ontologies.

A new Governance Assistant ensures compliance with EU regulation, while agent observability, and 麻豆原创 Signavio agent mining close the loop between agent design and production behavior to deliver governance. Runtime verification enforcement ensures that agents not verified in 麻豆原创 AI Agent Hub cannot be invoked.

Governance

All of it is 麻豆原创-managed with governance built in: ISO and SOC compliant, runtime enforced, with every agent testable and traceable via 麻豆原创 AI Agent Hub. And the new 麻豆原创 Enterprise AI Gateway delivers policy enforcement, enhanced observability, and runtime controls all in one place.

鈥淔or AI governance we need to connect the design time layer where we define, assess and verify an agent with the runtime layer that enforces, monitors and optimizes agent behavior,鈥� Michelle Niedernhuber said.

With the EU AI Act now in force, 麻豆原创 is releasing the AI Governance Assistant for the 麻豆原创 Early Adopter Care program customers in November, she shared. The assistant uses existing information, guides the agent owner through the remaining questions, and produces an audit-ready AI risk classification.

Together, 麻豆原创 AI Agent Hub and 麻豆原创 Enterprise AI Gateway serve as the design and runtime integration layer that connects oversight with enforcement. By connecting oversight, runtime enforcement, and continuous improvement, 麻豆原创 gives enterprises confidence that their agents operate as intended.

鈥淕overnance is not what slows AI adoption,鈥� Niedernhuber noted. 鈥淚t is what makes AI adoption at scale possible.鈥�

AI proven under pressure: 麻豆原创 brings AI to space

Rounding out the keynote, Herzig announced that together with partner Hewlett Packard Enterprise (HPE), 鈥溌槎乖� is taking our tabular foundation models, 麻豆原创-RPT and TabPFN, for real-time predictions in space to put AI to work under extreme conditions.鈥�

In space, a delayed decision can mean the difference between system safety and total loss. 麻豆原创’s AI enables faster decision-making and earlier failure prediction, when the stakes are highest.

Herzig explained how the foundation models analyze live hardware telemetry, identify sensor degradation, and reconstruct missing readings in real time. The outcome is a structured report recommending actions to the spaceship crew or ground staff on what to do to prevent a problem from becoming a crisis.

鈥淣ow, if we can run predictions like this in one of the most unforgiving environments, think about what this means for your operations here on Earth. That鈥檚 AI proven under pressure,鈥� Herzig said, bringing the keynote to a close.

Supporting key workstreams, including data management, test management, custom code modernization, and business configuration, these assistants help organizations simplify transformation activities, accelerate project execution, improve decision-making, and increase readiness for cloud ERP adoption.

ERP transformation has become a business imperative for many organizations. Cloud modernization connects organizations to AI, automation, and analytics capabilities that legacy on-premises infrastructure could not support at scale. However, transformation programs can be complex, involving numerous stakeholders and requiring thousands of business and technical decisions.

This year at 麻豆原创 Sapphire, 麻豆原创 introduced new agent-led transformation tooling and outlined how migration and modernization assistants can reduce ERP migration efforts.

Today, four of these assistants are available to early adopter customers and are already supporting live cloud ERP transformation projects.

Migration and modernization assistants go live in customer transformation projects

ERP transformation projects require organizations to assess large volumes of business data, understand quality issues, evaluate migration implications, and prioritize remediation efforts across complex landscapes. The Data Management Assistant analyzes data quality metrics, identifies and prioritizes issues based on business impact, recommends remediation actions, and helps execute corrections through both automated and guided remediation approaches.

AkzoNobel, a global leader in paints and coatings, is currently on a RISE with 麻豆原创 journey, consolidating its ERP landscape as part of a large-scale transformation. Participating in the 麻豆原创 Early Adopter Care program, the company uses the Data Management Assistant to define data quality scope, evaluate issues across the landscape, and prioritize remediation efforts based on business impact. The assistant gives a structured, end-to-end approach to orchestrating workstreams and managing data quality throughout the company’s ongoing transformation.

“The Data Management Assistant gave us clear visibility into our most business-critical data quality issues and helped us prioritize remediation where it matters most,” said Thilak Selvam, Master Data Program Lead at AkzoNobel. “It reduced analysis effort, accelerated our planning, and provided a structured approach to maintaining data hygiene and quality controls as we prepare for future transformation initiatives.”

Defining test scope, preparing test cases, validating business processes, and ensuring representative data is available often requires substantial effort from both business and IT teams. The Test Management Assistant accelerates test preparation and improves testing effectiveness. By analyzing business processes, requirements, and user stories, it helps define test scope, identify critical business scenarios, generate test cases, and determine the test data needed to execute them.

Test cases are handed off to 麻豆原创 Test Automation by Tricentis for automated execution, enabling continuous validation at scale. The result is less manual effort, improved coverage of business-critical processes, and greater confidence in deployment readiness.

The Custom Code Assistant helps organizations modernize their custom developments as part of the transition to 麻豆原创 Cloud ERP Private and continues to support clean core compliance and cloud-ready transformation beyond initial migration.

It supports teams in assessing custom code, identifying migration and remediation opportunities, aligning extensions with clean core principles and 麻豆原创-recommended APIs, and adapting legacy applications for cloud-ready architectures. Acting autonomously, it classifies findings by complexity, applies available automated fixes, and generates AI-driven recommendations and code corrections for the remaining findings. The assistant helps customers determine where to adopt 麻豆原创 standard processes and where custom extensions provide genuine business differentiation. This reduces technical debt, accelerates cloud adoption, and enables organizations to preserve the capabilities that set their business apart while taking full advantage of 麻豆原创 innovation.

For customers adopting 麻豆原创 Cloud ERP, the Business Configuration Assistant supports organizations in accelerating business design and implementation planning. It recommends and autonomously applies configurations tailored to the customer’s industry, segment, and business goals, supporting teams in defining scope, setting up organizational structures, and translating requirements into implementation-ready configurations. It also extracts source configurations, flags deviations from 麻豆原创 target requirements, and maintains a full audit trail鈥攔educing manual effort, improving consistency, and accelerating the path to a production-ready 麻豆原创 Cloud ERP system.

Expanding across the full transformation life cycle

麻豆原创 plans to further expand the migration and modernization assistant portfolio across the transformation life cycle. Planned innovations include the System Analysis Assistant, which creates a customer-specific understanding of existing systems, configurations, and custom developments, providing the foundation for AI-supported guidance throughout the transformation journey and the basis for all other assistants, grounded in customer-specific system data.

In addition, the Project Management Assistant is planned to provide real-time project insights, proactively identify risks, and help orchestrate transformation activities across teams and stakeholders.

The portfolio will also be extended with the Rollout Assistant, which helps organizations align global templates with regional requirements, accelerating local rollouts while reducing effort for business and IT teams through AI-driven guidance.

Together with the Data Management, Test Management, Custom Code, and Business Configuration Assistants available today, these capabilities will offer customers a fully integrated, agent-led transformation experience that helps organizations simplify complexity, accelerate execution, and realize value from 麻豆原创 Cloud ERP faster.

As 麻豆原创 continues to expand its migration and modernization assistant portfolio, organizations will increasingly benefit from AI support across every phase of ERP transformation, from initial system assessment and project planning through implementation, go-live, and beyond.

Learn more at .

Maura Hameroff is chief marketing officer for 麻豆原创 Cloud ERP Private and RISE with 麻豆原创.
Sonja Li茅nard is head of ABAP Platform at 麻豆原创 SE.
Soeren Ruder is chief product officer and global head of Customer Experience & Solutions for 麻豆原创 SE.

Space is unforgiving. Vacuum, radiation, orbital debris, and solar storms can produce mission-critical crises at any moment. Adding to the complexity, onboard sensors and mission system telemetry produce too much time-sensitive data to be routed back to Earth for analysis. Latency has to be reduced to ensure the safety of the spacecraft. 

Enter 麻豆原创’s tabular AI foundation model, , which makes predictions directly from structured data鈥攅ven with some data missing鈥攁nd without requiring the usual model training or fine-tuning. Running directly onboard the ISS on HPE Spaceborne Computer-2, it supports anomaly triage in real-time telemetry data, predicts issues before failures escalate, reconstructs missing sensor values based on historical data, and generates explainable mission-health reports for astronauts and ground teams.

“This is proof that tabular AI on 麻豆原创 works at the edge of possibility,” said Mayank Shrivastava, vice president of Generative AI and Foundation Models at 麻豆原创 SE. “Space is the ultimate stress test for AI. If a foundation model can predict mission-critical failures in orbit, under radiation, energy, and connectivity constraints, imagine what it can do for a global enterprise. This is the same model that will predict supplier risk, cash flow anomalies, and equipment failures across your enterprise. What works in orbit works in your supply chain.” 

Imagine this: in orbit, data arrives in fragments, connectivity comes and goes, and you cannot send a technician when something starts to fail. You have to anticipate problems with a variety of signals. Enterprises face the same challenge on the ground: a supplier whose deliveries are quietly slipping, a payment pattern that鈥檚 drifting, a compressor that鈥檚 running hot. Predictive models that are able to spot early warning signs in a satellite are also built to find those same signals in your business data, so you can act before small problems become critical ones.

How 麻豆原创 strengthens business functions with models for structured data 

麻豆原创-RPT is part of 麻豆原创’s portfolio of tabular foundation models, or TFMs, that includes the model by Prior Labs, and , which orchestrates, manages, and governs TFMs, predictions, and workflows. Large language models (LLMs) excel at language and reasoning, but TFMs are built for the structured tabular data that underpin real-world operations, such as the sensor-driven data used in this project.  

麻豆原创-RPT is about 50,000 times more energy-efficient than state-of-the-art LLMs. This is especially important in space, where every watt is constrained. 麻豆原创-RPT is also highly adaptable. It requires no pre-training as conditions evolve. 

Outcomes that will be delivered onboard include real-time predictions for computer and mission health status, an overall mission risk score, an assessment of whether manual intervention is needed, human-readable explanations of AI-driven findings, and ad hoc reports for crew and ground teams. 

鈥淗PE Spaceborne Computer-2 continually demonstrates the advantages of having a high-performance compute solution aboard the ISS, supporting advanced technologies and on-going research in space鈥� said Scott Wiest, Vice President and Global Sales Chief Technologist, HPE Fellow. 鈥淒eploying 麻豆原创鈥檚 tabular AI on HPE Spaceborne Computer-2 shows what鈥檚 possible with real-time detection and prediction using AI in the ultimate edge environment.鈥� 

From orbit to enterprise 

The deployment of 麻豆原创-RPT on HPE鈥檚 Spaceborne Computer-2 on the ISS, by early 2027, will show how it strengthens business functions on earth. The constraints of space, including extreme energy limits, intermittent connectivity, radiation interference, and zero tolerance for failure are relevant to challenging use cases on earth. These include disaster relief operations, agriculture in remote areas, distributed manufacturing, and spread-out supply chains.  

Every insight aboard the ISS accelerates 麻豆原创’s road map for enterprise edge AI and key use cases that leverage structured data for the most important analytical and operational processes, even in the most challenging settings.  

Looking ahead, the mission lays the groundwork to support space-based data centers, deep space exploration, and other collaborations on space missions. Our long-term vision for space-based compute infrastructure is anchored in proven, efficient, and accurate AI. 

Explore how to deliver tabular AI on 麻豆原创, learn more about the specialized capabilities behind it, and try the 麻豆原创-RPT AI model with your own data on the . 

Brenda Bown is chief marketing officer for 麻豆原创 Business AI Platform and Joule at 麻豆原创.

AI has gone from insight to action and now, with physical AI, it comes into the physical world.

Enterprises have made substantial investments in both AI and physical AI. AI helps business systems deliver remarkable insights and recommendations on financials, inventory, and supply chains. Physical AI creates value every day with physical systems鈥攕uch as industrial robots, humanoids, sensors, or robot dogs鈥攄eployed across production lines, warehouses, field service teams, and logistics networks.

As automation, robotics, and sensing technologies have matured across industries, the volume and richness of the physical signals coming from physical execution has grown substantially; physical operations today generate continuous intelligence about factors such as asset health, task completion, quality, throughput, and exceptions.

Today, however, there is a gap between what the business knows and the intelligence generated by physical operations on the ground. This gap is one of the most common sources of operational friction across industries. It contributes to delayed responses, ill-informed decisions, and physical operations that never enter the business record. As a result, revenue and margins decrease while risk exposure increases.

From enterprise intelligence to physical action

This year at 麻豆原创 TechEd, 麻豆原创 revealed how our physical AI removes this operational friction: the system that runs your business is the same system that controls your physical operations.

The enterprise knowledge in your business system triggers the physical actions; physical signals arrive with the business context already attached;听physical operations in the field flow back into 麻豆原创 as structured data intelligence. Every physical action leaves a traceable, auditable record tied to the relevant business process.

The full context of your business is behind every physical action; verified outcomes are brought back into 麻豆原创 in a secure and governed way for follow-up actions.

When the system that runs your business is also the system that governs how, when, and why physical tasks are executed, the result is a fundamentally different kind of operational capability. For enterprises already running 麻豆原创, this means physical execution inherits the intelligence, context, and governance already in place.

This is how physical AI by 麻豆原创 transcends automation and extends the Autonomous Enterprise into the real world.

“Foxconn’s vision for intelligent manufacturing requires physical systems that understand business context, not just motion commands,” said Dr. Wang, head of Physical AI at Foxconn. “With physical AI by 麻豆原创, warehouse tasks from 麻豆原创 Extended Warehouse Management directly orchestrate our humanoid robots and autonomous transport systems. Asset inspections triggered by maintenance plans flow to our robotic platforms and return verified outcomes back to 麻豆原创. This closed loop between enterprise systems and physical execution is what sets this collaboration apart: the robot knows why it acts, and the business system knows what happened.”

How physical AI by 麻豆原创 capabilities are designed to transcend automation

1. Business-driven physical action

Physical AI is built to ground every physical execution in the business context: order priorities, safety rules, inventory status, and compliance constraints. Every action has a clear reason tied to business outcomes, and the business system remains in control of what gets executed, when, and why. The result is physical execution that is intelligent from the start, not just automated.

2. Physical evidence into digital workflows

Every physical task creates a complete audit trail and physical observations flow back into 麻豆原创 automatically, transforming field outcomes into business insights rather than isolated records in a separate system. Physical world events become inputs to the decisions and processes that follow.

3. Enterprise-governed execution

Built on the 麻豆原创 Business AI Platform, physical AI capabilities are designed to provide traceability, auditability, and human-in-the-loop control as first principles. Physical world data flows into 麻豆原创 Business Data Cloud as new structured data products, enriching planning, predictions, and operational strategy over time.

4. Vendor and hardware agnostic

Working across device platforms, physical systems, and AI models of the customer’s choosing, customers retain freedom over their physical layer. 麻豆原创 provides the business context, orchestration, and governance. Partners provide the physical execution.

Physical AI at work with customers and partners

础谤莽别濒颈办-尝骋: physical AI executes and records inbound storage tasks

础谤莽别濒颈办-尝骋 is a joint venture between Turkish durable consumer, Ar莽elik and South Korea鈥檚 LG Electronics, manufacturing air conditioners. Physical AI by 麻豆原创 enables a humanoid robot to execute and record inbound storage tasks. This has addressed the costly and persistent issue of incorrect storage placements in inbound logistics with potential savings of millions of euros.

• Associated 麻豆原创 solutions: 麻豆原创 Extended Warehouse Management
• Physical AI ecosystem partners:
• Video:

Intel: physical AI in autonomous warehouse operations

Intel is one of the world’s largest semiconductor manufacturers, operating a vast global warehousing and logistics network and serves as both a听 proof-of-concept environment and a technology collaborator. By integrating 麻豆原创鈥檚 enterprise workflows and warehouse processes with Intel-powered edge AI and robotics technologies, warehouse transport tasks generated in 麻豆原创 are automatically translated into robot missions and executed autonomously in the warehouse.

麻豆原创 provides the business context, workflow governance, and process orchestration, ensuring that every physical action aligns enterprise priorities and operational goals. Intel provides the physical AI hardware and software foundation at the edge, delivering the compute AI and real-time robotics capabilities that enable autonomous systems to perceive, decide, and act in dynamic environments. The combined capabilities of 麻豆原创 and Intel create a closed-loop system; business decisions become physical actions and operational outcomes are continuously fed back into business systems.

In a proof of concept demonstrated at Intel鈥檚 Chengdu site in China, material movement is automated end-to-end, reducing both transport distances and repetitive manual handling. Physical AI capabilities from 麻豆原创 and Intel demonstrate how enterprise decisions can directly drive physical execution in logistics workflows.

• Associated 麻豆原创 systems: 麻豆原创 Extended Warehouse Management
• Physical AI ecosystem partners:

Holcim: industrial asset inspections

Holcim is a Swiss multinational building materials and sustainable construction company, operating in heavy industry. The company has significant infrastructure assets with inspection, maintenance, and safety use cases. Since attending the Embodied AI Jam held in August 2026, Holcim has implemented physical AI capabilities for asset inspections with industrial drones and quadruped dogs.

On-site quadruped dogs inspect conveyor belts in dark, dusty areas and the status of kilns in extreme temperatures. Drones inspect assets in heights difficult for humans to reach. And end-to-end automation starts and finishes in a 麻豆原创 system. In 麻豆原创鈥檚 asset management solution, the maintenance plan triggers an action for the industrial drone/quadruped dog; once the action is executed, a maintenance notification is created back into 麻豆原创.

• Associated 麻豆原创 systems: 麻豆原创 Plant Maintenance
• Physical AI ecosystem partners: , ,
• Video:

Start your physical AI journey today

From warehouse operations to asset maintenance, from safety inspections to keeping your operations compliant, physical AI capabilities from 麻豆原创 have the potential to transform your business. It connects the intelligence of your enterprise with real-world execution; the 麻豆原创 system that you trust to run your business is the same system that informs the actions of your physical systems, be it a robot, an industrial drone, or a robodog.

As a part of Industry AI, you can begin your physical AI journey today with warehouse operations and asset inspection use cases or a physical AI use case, tailor-made to your requirements. Contact 麻豆原创 via email for more information.

Lukasz Ostrowski is global head of Physical AI and Robotics at 麻豆原创.

In Q3, we expanded our portfolio of Joule Assistants and introduced dozens of specialized agents that help customers move beyond isolated tasks toward connected, end-to-end execution. The value of AI has always been about what it accomplishes for our customers. Our recent releases help them streamline processes, increase productivity, and build more resilient operations.

Joule is 麻豆原创鈥檚 portfolio-wide AI engagement layer, combining agentic capabilities with 麻豆原创 business applications to help organizations become an Autonomous Enterprise. Through Joule Work, the new UI for AI, people describe what they want to achieve, get relevant insights, build app-less spaces personalized to their needs, and take action across connected business systems. When it鈥檚 time to execute, Joule Work selects the right Joule Assistant for the job. These assistants act as domain experts, bringing knowledge of roles and processes to the task at hand. They orchestrate specialized Joule Agents to execute tasks, using business context and data, respecting established authorizations, and maintaining audit trails. People set the direction, assistants coordinate agents, and agents carry out work with proper oversight and governance.

For example, billing teams can create invoices, identify anomalies, and resolve posting errors with the Billing Assistant and its agents. Supply chain planners can detect critical shortages earlier, assess trade-offs between service levels and inventory investment, and coordinate responses with the Supply Chain Planning Assistant. Maintenance teams can turn asset signals into coordinated maintenance action, helping address problems before they become larger operational disruptions. People retain oversight and control, while assistants and agents help reduce manual effort.

Customers are already putting these capabilities to work. PwC is using 麻豆原创鈥檚 Billing Assistant to help identify potential issues earlier, supporting better client outcomes. Novartis has launched a pilot of the Sourcing Assistant to help sourcing professionals focus on strategy, supplier engagement, and value creation.

鈥淎gents bring consistency and speed to work that previously required a lot of manual effort and coordination,鈥� , head of Procurement at Novartis. He expects improvements of 25% faster cycle times and a 35% uplift in FTE productivity.

As organizations adopt more agents, they need a clear view of their AI landscape. 麻豆原创 AI Agent Hub provides a central inventory of 麻豆原创 and non-麻豆原创 agents, helping teams keep track of and govern their growing agent portfolio. With 麻豆原创 Signavio agent mining and the Process Consulting Agent, we enable customers to identify their biggest opportunities for agentic AI and execute on them right away. This visibility supports a more coordinated approach to managing AI adoption across the business.

With Joule Studio, accessible through Joule Work, customers can create agents tailored to their business and extend 麻豆原创 standard agents with their own instructions. Browser-based tools and pro-code development options support different levels of technical expertise, while testing, versioning, and deployment turn ideas into agents that take action. This lets customers extend 麻豆原创鈥檚 capabilities to meet their business requirements.

Now, let鈥檚 dive into recent innovation.

Please note that this article covers only AI offerings released since July 1, 2026.

Joule

Joule Work

Employees can spend less time navigating applications and more time turning information into outcomes with Joule Work, which is available on desktop, in-app, web, and mobile. Joule Work, the new UI for AI, connects local and cloud environments, bringing conversational AI closer to the files, information, and business tools employees use every day. Employees can work with local files and folders in a secure sandbox environment, access current information through integrated web search, and connect to 麻豆原创 and non-麻豆原创 applications using Model Context Protocol (MCP) tools and Agent-to-Agent (a2a) integrations. Custom and corporate skills help streamline repetitive tasks, while local data and connected tools support faster creation of presentations, summaries, and other assets. And where a skill is not enough, the 麻豆原创 Knowledge Graph kicks in to search the entire space of business objects available to users for deeper transactional and analytical queries. Together, these capabilities reduce manual information analysis and help employees focus on higher-value work.

Dynamic workspaces, which we refer to as spaces, bring together insights, applications, Joule Assistants, and agents to support business workflows. Employees express what they want to accomplish, and Joule Assistants coordinate specialized agents to carry out the work. This effectively initiates the journey into a world where generative UI delivers an ultimate personalized experience to any end user. Joule Studio complements this experience with capabilities to build, deploy, and govern custom agents and workflows.

Early adopter feedback shows how this experience is changing the way employees interact with business information. Reinhausen, a global technology leader in electrical grid technologies, joined the Joule Work desktop early adopter program to support its AI journey. 鈥淲hat once required up to an hour of manual effort across our 麻豆原创 S/4HANA environment is now accessible in seconds through natural language, fundamentally changing how our teams interact with business data,鈥� said Peter Wied, director of Complexity Management at Reinhausen.

Customers are also using custom assistants to bring expertise closer to employees. Warehouse automation company KNAPP used Joule Studio to build an assistant that helps workers identify and resolve problems without waiting for an expert, reducing issue resolution times by about 90% and time spent on documentation and training by about 80%.

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The general availability for Joule Work is now underway in a phased approach.

Joule Studio

Business and IT teams can tailor Joule to their organization鈥檚 needs with Joule Studio, combining custom AI agents for complex business processes with reusable skills for everyday tasks. The agent builder enables teams to create agents that plan, reason, and dynamically address business challenges across 麻豆原创 and non-麻豆原创 systems. These agents can coordinate multi-step processes, invoke APIs, work with documents, and collaborate with users or other agents, helping organizations handle ambiguity, exceptions, and workflows that go beyond predefined automation.

The skill builder complements these capabilities with reliable, reusable operations accessed through Joule鈥檚 conversational interface. Teams can create skills to retrieve specific data, execute system transactions, and connect 麻豆原创 and third-party applications using predefined logic. Joule can select and trigger relevant skills based on context, helping employees complete frequent tasks more efficiently.

New intent-based development capabilities draw on business context from 麻豆原创 Signavio, 麻豆原创 Knowledge Graph, and 麻豆原创 Domain Models, helping teams translate business needs into tailored solutions. Pro-code capabilities let developers work in their preferred IDE, Git repository, and AI coding tools.

Together, the two builders help organizations combine flexible, goal-driven agents with predictable, task-specific automation. Grounding agents in business data and processes supports reliable outcomes, while centralized management provides visibility into skills, agent execution, and data privacy enforcement. Joule Studio helps customers build agents faster, improve accuracy while reducing costs, deploy with built-in governance, and ground AI in their business context. Estimated benefits include a 35% reduction in the time to build and deploy custom Joule Agents and a 10% reduction in the time to deploy custom Joule skills, alongside less time spent on frequent business tasks.

The agent builder is available for free until December 2026, after which it becomes a premium AI feature. The skill builder is a base AI feature.

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The general availability for Joule Studio is now underway in a phased approach.

麻豆原创 Joule for Consultants, AI-assisted custom knowledge benchmarking
Generally available

麻豆原创 consultants can use custom knowledge grounding in 麻豆原创 Joule for Consultants to receive expert guidance that combines 麻豆原创-curated content with system connectivity for an organization鈥檚 institutional knowledge. Through a grounding service that indexes organizational content with 麻豆原创 AI Core grounding, surfacing relevant information from internal documents into the Joule workflow. This way, responses reflect the firm鈥檚 methodologies, policies, delivery standards, and domain expertise. This reduces time spent searching and switching between tools while supporting growth in knowledge sources over time; it can improve productivity by up to 19%, reduce design iterations and subsequent rework by 50%, increase the share of profit by 1.5% through more optimized 麻豆原创 processes, and accelerate time to realized value by 13.7%.

Customers are already seeing the value of 麻豆原创 Joule for Consultants. reports a 40% reduction in development effort and a 25% decrease in software rework, helping its consultants focus more on innovation and client delivery.  expects to reduce consultant training time by 20 to 30% and save 93 hours each week on information searches, enabling consultants to build expertise faster and spend more time addressing clients鈥� business challenges.

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This capability is generally available.

Autonomous SCM

Product Design Assistant

• Change Record Management Agent
• Product Data Maintenance Agent
• Product Structure Optimization Agent

Design engineers and product managers can bring products to market faster and avoid costly rework with the Product Design Assistant. It connects data across design, engineering, compliance, and downstream processes, analyzing designs, recipes, and product information to surface insights. It also flags issues early and checks materials and requirements against internal rules and regulations. Three agents released in Q3 strengthen the foundations of product development. Engineers can keep product master data accurate with the Product Data Maintenance Agent, while the Product Structure Optimization Agent consistently optimizes bills of material across product views to reduce costs. When designs need to be changed, the Change Record Management Agent identifies similar change records affecting the same product and proposes a new draft version to initiate the change process. This reduces delays caused by fragmented change data and improves governance and traceability.

The result is faster, more confident design decisions and higher product quality, with estimated benefits including a 19% reduction in product design costs, a 10% increase in prototype success, a five percent reduction in cost of goods sold from design improvements, and a 12% reduction in tooling and material waste costs.

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The general availability for Product Design Assistant is now underway in a phased approach.

Logistics Assistant

• Batch Optimization Agent
• Warehouse Workload Optimization Agent
• Fixed Bin Maintenance Agent
• Material Reservation Processing Agent

Warehouse and logistics teams can keep operations running smoothly from inbound shipments to outbound deliveries with the Logistics Assistant. It senses disruptions as they happen and coordinates detection, planning, and execution across warehousing and transportation, managing decisions on labor, inventory, dispatch, and freight. Four agents released in Q3 focus on warehouse execution. Supervisors can match staff to demand and spot potential resource shortages early with the Warehouse Workload Optimization Agent. The Fixed Bin Maintenance Agent adjusts bins and replenishment as demand changes. On the inventory side, the Batch Optimization Agent monitors batch expiration and triggers actions to reduce waste. The Material Reservation Processing Agent lets users manage material reservations in natural language, with built-in human review. Together, these capabilities shorten the time from plan to execution, improve resilience to disruption, and lower the cost to serve by reducing expedites and inventory waste.

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The general availability for Logistics Assistant is now underway in a phased approach.

Manufacturing Assistant

• Production Planning and Operations Agent
• Production Master Data Readiness Agent
• Production Planning Order Progress Agent
• Workplace Safety Agent

Production planners and plant operators can keep manufacturing on track and respond to disruptions sooner with the Manufacturing Assistant. It detects issues in real time and runs corrective workflows for quality, scheduling, and workforce management. It also recommends next-best actions with clear pros and cons, so operators can focus on the exceptions that matter most.

Four agents released in Q3 support production from preparation through execution. The Production Master Data Readiness Agent makes sure materials are fully ready for production without manual setup, and the Production Planning Order Progress Agent identifies coverage issues at every order level. To speed up order-to-delivery, the Production Planning and Operations Agent helps planners release orders in as little as half the time. On the shop floor, the Workplace Safety Agent lets workers report safety concerns by describing what they observe, then assesses risks and suggests actions based on the situation and past data.

By coordinating people, systems, and workflows toward shared operational goals, the assistant helps reduce downtime, waste, and safety response times while supporting compliance through built-in traceability. Estimated benefits include a 14% reduction in production cycle time, a 19% reduction in scrap and defect rates, a six percent reduction in changeover time, a 10% reduction in operating income lost to unplanned downtime, and a 10% reduction in energy costs.

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The general availability for Manufacturing Assistant is now underway in a phased approach.

Supply Chain Planning Assistant

• Exception Management Agent
• Inventory Investment Allocation Agent
• Supply Chain Planning Orchestration Agent

Supply chain planners can detect issues earlier and respond to change with confidence using the Supply Chain Planning Assistant. It brings AI agents into everyday planning work, including exception management, demand fulfillment, inventory assessment, and capacity analysis, with one-click scenario simulation, root-cause analysis, and actionable recommendations. Three agents released in Q3 help planners turn analysis into action. The Exception Management Agent detects and resolves critical supply planning issues, giving planners the insights they need to protect service levels and avoid costly expedites. The Inventory Investment Allocation Agent weighs service levels against working capital to recommend optimal stock targets. The Supply Chain Planning Orchestration Agent coordinates these and other specialized agents across planning workflows. By speeding up decisions and surfacing capacity issues and persistent shortages in advance, the assistant helps planners keep schedules and inventory targets on track, with estimated benefits including a 20% reduction in planning coordination overhead and a 10% reduction in planning cycle time.

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The general availability for Supply Chain Planning Assistant is now underway in a phased approach.

Asset Operations Assistant

• Operational Maintenance Planning Agent
• Maintenance Event Planning Agent
• Maintenance Request Initiation Agent
• Maintenance Job Debrief Agent
• Equipment Insight Agent
• Task List Management Agent

Maintenance teams can identify critical asset conditions earlier and resolve them before they turn into larger operational disruptions with the Asset Operations Assistant. It turns asset signals into coordinated maintenance action and automates prioritization, planning, and scheduling, leaving teams more time for high-value work and exceptions.

Six agents released in Q3 cover the maintenance cycle from first request to final debrief. Technicians can create and route maintenance requests from natural-language input with the Maintenance Request Initiation Agent, while the Equipment Insights Agent provides a complete view of equipment to support better decision-making. The Operational Maintenance Planning Agent combines preventive, predictive, and corrective tasks into executable weekly and biweekly plans. When conditions change, the Maintenance Event Planning Agent analyzes real-time data to reprioritize tasks and adjust schedules. The Maintenance Task List Management Agent automates task list creation for greater consistency, and the Maintenance Job Debrief Agent streamlines job debriefs with AI-driven recommendations. By connecting asset health, risk, work outcomes, and financial impact, the assistant helps organizations increase planned work, reduce unplanned downtime, and improve uptime.

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The general availability for Asset Operations Assistant is now underway in a phased approach.

麻豆原创 Integration Business Planning, AI-assisted scenario planning

Supply chain planners can respond to disruptions with confidence using scenario planning in 麻豆原创 Integrated Business Planning. Within one workspace, they can assess impacts on demand and sales orders, identify alternative supply sources, and simulate mitigation options against KPIs such as service level and utilization before adopting the best-performing scenario. This streamlined approach helps planners make faster, data-driven decisions, build trust in their plans, and strengthen supply chain resilience.

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This capability is generally available.

Autonomous HCM

HR Service Assistant

• HR Knowledge Agent
• HR Workflow Accelerator Agent
• Payroll Explanation
• Time Off Service Agent
• Time Sheet Service Agent
• HR Service Orchestration Agent

HR service teams can resolve routine employee requests conversationally, without creating a case each time, using the HR Service Assistant. The assistant provides always-on support and draws on six agents released in Q3. Employees get fast, cited, policy-grounded answers from the HR Knowledge Agent, while the HR Service Orchestration Agent resolves requests through self-services or routes them to the right specialist agent. Common everyday questions are handled directly in the conversation: the Payroll Explanation Agent clarifies pay statements, and the Time Sheet Service Agent and Time Off Service Agent guide employees through submitting timesheets and creating compliant leave requests. When processes stall, the HR Workflow Accelerator Agent identifies delays and resolves them, allowing HR teams to reserve expert attention for complex cases. Estimated benefits include a 24% increase in case deflection to self-service, a 26% reduction in time to resolution, and a 40% reduction in queries that need Tier 2 or higher intervention.

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The general availability for HR Service Assistant is now underway in a phased approach.

HR Operations Assistant

• Employee Data Integration Agent
• Position Optimization Agent
• Job Change Request Agent
• Compensation Guidance Agent
• Job Architecture Maintenance Agent
• Skills Governance Agent

HR teams and managers can simplify core HR administration while keeping employee, job, and organizational data consistent and compliant with the HR Operations Assistant. Six agents released in Q3 help connect these activities: managers can find, edit, and create positions with the Position Optimization Agent and translate their intent into job changes with the Job Change Request Agent, while the Compensation Guidance Agent recommends policy-aligned salary increases based on performance, compensation, and skills data. To maintain the underlying data, the Employee Data Integration Agent resolves data inconsistencies, while the Job Architecture Maintenance Agent and Skills Governance Agent help keep role鈥搒kill mappings current and the skills ecosystem clean and AI-ready. Working alongside the assistant鈥檚 other specialized agents, they reduce administrative effort and support more reliable HR processes, with estimated benefits including a 39% reduction in data errors, a 20% reduction in HR transactional work, and a 25% reduction in manager time on core HR workflows.

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The general availability for HR Operations Assistant is now underway in a phased approach.

Performance and Goals Assistant

• Calibration Equity Agent
• Goal Creation Agent
• Goal Progress Agent

Managers and HR teams can align employee goals with business strategy and bring greater consistency to performance evaluations using the Performance and Goals Assistant. Three agents released in Q3 contribute to its broader capabilities: the Goal Creation Agent generates SMART performance goals grounded in strategy and role context; the Goal Progress Agent tracks completion in real time and flags risks before deadlines are missed; and the Calibration Equity Agent identifies potential inconsistencies in employees’ performance ratings before or during calibration. By combining clearer goals, proactive progress monitoring, and insights into rating inconsistencies, the assistant helps managers conduct more efficient reviews and make better-informed calibration decisions, with estimated benefits of a 21.4% reduction in review completion time and a 30% reduction in manual decision-making effort for managers.

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The general availability for Performance and Goals Assistant is now underway in a phased approach.

HR System Assistant

• Configuration Transport Agent
• HCM Audit Agent
• Role-based Permissions Agent

HR administrators can resolve access issues, manage configuration changes, and keep systems audit-ready with the HR System Assistant. It automates low-risk tasks and guides higher-risk changes, helping reduce errors and accelerate go-lives. Its Q3 releases address three key administrative needs: the Configuration Transport Agent discovers, bundles, and securely migrates configuration changes across tenants; the HCM Audit Agent enables natural language queries of 麻豆原创 SuccessFactors audit logs; and the Role-Based Permissions Agent queries user access, helps fix permission issues, and automates secure access resolution. As part of the assistant鈥檚 broader capabilities, these agents support faster troubleshooting, fewer configuration defects, and stronger traceability, with estimated benefits of a 70% reduction in configuration transport effort, a 90% reduction in time spent generating audit reports, and a 90% reduction in permission incident resolution costs.

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The general availability for HR System Assistant is now underway in a phased approach.

Autonomous Finance

Billing Assistant

• Billing Posting Agent
• Billing Creation Agent
• Billing Anomaly Agent
• Billing Adjustment Agent
• Error Explanation and Resolution Agent
• Billing Orchestration Agent

Billing teams can improve invoice accuracy and resolve issues faster with the Billing Assistant, which coordinates activities from document creation through adjustments and posting support. There are six agents being released in Q3: teams can prioritize due lists and create policy-aligned billing documents with the Billing Creation Agent, while the Billing Anomaly Agent flags unusual taxes and charges for review. When errors arise, the Billing Posting Agent and the Error Explanation and Resolution Agent help teams understand the issues and identify corrective steps, while the Billing Adjustment Agent cancels and reissues invoices to apply adjustments. The Billing Orchestration Agent coordinates these activities to keep billing processes moving. Together, these capabilities reduce manual effort and support faster invoice correction and dispute resolution, with estimated benefits including a 90% reduction in time spent analyzing and resolving posting issues, a 70% reduction in billing document creation effort, a 70% reduction in anomaly detection effort, and a 54% reduction in invoice reprocessing effort.

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The general availability for Billing Assistant is now underway in a phased approach.

Financial Closing Assistant

• Journal Entry Agent
• Accounting Accruals Agent
• Receivables and Payables Clearing Agent
• Analytical Business Insights Agent
• Asset Accounting Anomaly Detection Agent
• Financial Consistency Analysis Agent
• Financial Closing Orchestration Agent

Finance and accounting teams can accelerate period-end execution with the Financial Closing Assistant while controlling the extent to which agents participate in journal postings, clearing, and analysis. Seven agents released in Q3 support connected closing activities: teams can streamline audit-ready journal processing and accruals management with the Journal Entry Agent and Accounting Accruals Agent, and identify clearing opportunities through AI-based matching and aging analysis with the Receivables and Payables Clearing Agent. The Asset Accounting Anomaly Detection Agent and Financial Consistency Analysis Agent help improve accuracy by preventing invalid asset postings and resolving data errors, while the Analytical Business Insights Agent turns financial KPI analysis into executive summaries. Coordinating these workflows, the Financial Closing Orchestration Agent helps keep activities moving across teams and agents. This approach supports a faster, more reliable close, with an estimated 35% increase in productivity for staff performing reconciliations and adjustments.

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The general availability for Financial Closing Assistant is now underway in a phased approach.

Cash and Treasury Assistant

• Cash Positioning Agent
• Bank Relationship Agent
• Cash and Treasury Orchestration Agent

Treasury teams can manage liquidity more confidently and lower financing and banking costs with the Cash and Treasury Assistant. It monitors internal funding positions and needs, proposes cash transfers between bank accounts, and identifies ways to streamline bank wallet share and fees. Three agents released in Q3 support this work. The Cash Positioning Agent optimizes cash and performs cash-positioning tasks, such as checking balances, forecasting cash flows, and transferring funds. The Bank Relationship Agent gives treasurers a comprehensive view of the enterprise鈥檚 banking relationships, and the Cash and Treasury Orchestration Agent coordinates these activities with the assistant’s other specialized agents. Better interest yields and lower financing costs can free up funds for growth initiatives. Estimated benefits include a 50% increase in cash management and treasury staff productivity and a 10% reduction in bank commissions and fees. Productivity is also estimated to rise by 30% in the following areas: liquidity management and cash forecasting, FX risk and hedging, bank relationship management, and treasury reporting and compliance.

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The general availability for Cash and Treasury Assistant is now underway in a phased approach.

Financial Consolidation Assistant

• Financial Consolidation Data Submission Agent
• Financial Consolidation Orchestration Agent

Consolidation teams and local accountants can complete the financial close faster and with less manual effort using the Financial Consolidation Assistant. The assistant supports work across data preparation and consolidation. It guides users through issue resolution, simplifies master data maintenance, and speeds up simulations of mergers, FX changes, and other business scenarios. Two agents released in Q3 make the close cycle smoother. Local accountants can automate data submissions and resolve errors before data reaches group consolidation using the Financial Consolidation Data Submission Agent. The Financial Consolidation Orchestration Agent coordinates its agents. Teams spend less time collecting and correcting data and more time analyzing results, with an estimated 25% increase in productivity for staff performing reconciliations and adjustments.

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The general availability for Financial Consolidation Assistant is now underway in a phased approach.

Sales Operations Assistant

• Billing Block Resolution Agent
• Credit Block Resolution Agent
• Delivery Issue Resolution Agent
• Supply Issue Resolution Agent

Sales teams can keep orders moving and address fulfillment obstacles directly in their workflow with the Sales Operations Assistant. It automates sales order creation from unstructured data and supports order updates, shortening processing cycles and reducing errors. Four agents released in Q3 target common sources of delay: teams can address billing and credit blocks with the Billing Block Resolution Agent and Credit Block Resolution Agent, tackle delivery issues with the Delivery Issue Resolution Agent, and identify, analyze, and receive recommended solutions for supply problems with the Supply Issue Resolution Agent. By combining faster issue resolution with accurate, up-to-date sales data, the assistant helps improve customer satisfaction and support informed pricing decisions, with estimated benefits including a 15% reduction in revenue lost to fulfillment issues, a 10% reduction in financial losses from stockouts, and a 50% reduction in time spent resolving billing blocks and credit issues.

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The general availability for Sales Operations Assistant is now underway in a phased approach.

Recurring Receivables Assistant

• Collection Insights Agent
• Convergent Invoicing Execution Agent
• Subscription Lifecycle Agent

Finance teams can accelerate collections and manage high-volume recurring receivables more efficiently with the Recurring Receivables Assistant. Supporting recurring billing and subscription-based business models, it brings customer account context, risk insights, and recommended next steps into order-to-cash activities. Three agents released in Q3 help teams act on these insights: collectors get collection support from the Collection Insights Agent, while the Convergent Invoicing Execution Agent automates invoice corrections through geography-aware, regulation-compliant rebilling. The Subscription Lifecycle Agent complements these activities by automating subscription contract management from search through execution. Together, these capabilities help protect revenue, reduce downstream corrections, and free blocked cash, with estimated benefits including a 20% reduction in controllable days sales outstanding, a 20% increase in accounts receivable staff productivity, and a 10% reduction in controllable write-offs.

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The Recurring Receivables Assistant is available in the 麻豆原创 Early Adopter Care program.

Accounts Receivable Assistant

• Collection Account Preparation Agent
• Dispute Resolution Agent
• Dunning Insights Agent
• Collections Email Outreach Agent
• Dispute Creation Agent
• Receivable Account Analysis Agent
• Accounts Receivable Orchestration Agent

Accounts receivable teams can prepare more effective collection calls and release disputed cash sooner with the Accounts Receivable Assistant. Seven agents released in Q3 connect account analysis, customer outreach, and dispute handling: collectors can build a complete customer account picture and prioritize their next steps with the Receivable Account Analysis Agent, Collection Account Preparation Agent, and Dunning Insights Agent, while the Collections Email Outreach Agent automates correspondence for worklist items that do not require a call. The Dispute Creation Agent manages dispute cases using natural language processing and provides insights, while the Dispute Resolution Agent validates cases and suggests actionable solutions. The Accounts Receivable Orchestration Agent coordinates these activities to reduce manual effort and collection delays. Estimated benefits include a 35% increase in accounts receivable staff productivity, a 25% reduction in controllable days sales outstanding, a 15% reduction in uncollectable write-offs, and a five percent reduction in customer churn from disputes.

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The general availability for Accounts Receivable Assistant is now underway in a phased approach.

Tax Compliance Assistant

• Legal Change Agent
• Regulatory Document Mapping Explanation Agent
• Statutory Report Analysis Agent
• Statutory Report Extension Update Agent
• Tax and Compliance Orchestration Agent

Finance and IT teams can keep pace with regulatory changes and reduce manual compliance work with the Tax and Compliance Assistant. It supports continuous monitoring and automation across tax calculations, e-invoicing, statutory reporting, and payments, helping teams identify issues before they become costly disruptions. Five agents released in Q3 connect regulatory discovery with reporting maintenance: the Legal Change Agent helps teams discover relevant regulatory changes, while the Regulatory Document Mapping Explanation Agent clarifies regulated e-invoice field mappings and compliance rules through Joule. Teams can review statutory reports for anomalies and recommended fixes with the Statutory Report Analysis Agent, and merge report extensions when 麻豆原创 delivers new versions using the Statutory Report Extension Update Agent. The Tax and Compliance Orchestration Agent coordinates these activities, supporting traceable processes and faster error correction. Estimated benefits include a 75% reduction in compliance rework costs, a 30% reduction in tax compliance process costs, a 40% reduction in regulatory penalties and fines, and a 50% reduction in compliance monitoring effort.

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The general availability for Tax and Compliance Assistant is available in the 麻豆原创 Early Adopter Care program.

International Trade Assistant

• International Trade Product Profile Agent
• International Trade Classification Agent

International trade teams can streamline goods classification and manage changing trade requirements with the International Trade Assistant, bringing AI-powered support into enterprise import, export, compliance, and origin management processes. Two agents released in Q3 connect product understanding with classification: the International Trade Product Profile Agent collects and unifies product data to establish its semantic meaning, while the International Trade Classification Agent automatically classifies goods for shipping, helping teams save time and support compliance. Alongside capabilities such as sanction screening and EU Intrastat reporting, these agents help replace disconnected, reactive processes with a more governed and scalable approach to international trade.

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The general availability for International Trade Assistant is available in the 麻豆原创 Early Adopter Care program.

Accounts Payable Assistant

• Accounts Payable Orchestration Agent
• Payment Inquiry Handling Agent
• Receivables and Payables Clearing Agent

Accounts payable teams can reduce manual payment handling and reconcile vendor accounts more efficiently with the Accounts Payable Assistant. It combines payment automation, proactive validation, and guidance on exceptions with insights into cash flow and discount opportunities. Three agents released in Q3 support these workflows: teams can manage routine invoice-specific payment inquiries with the Payment Inquiry Handling Agent and generate balanced clearing proposals for open items in accounts payable and receivable with the Receivables and Payables Clearing Agent. The Accounts Payable Orchestration Agent coordinates the agents. By streamlining payment-related tasks and helping teams address discrepancies, the assistant offers estimated benefits of a 35% increase in accounts payable staff productivity and a 20% reduction in duplicate payments and exceptions.

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The general availability for Accounts Payable Assistant is now underway in a phased approach.

麻豆原创 S/4HANA Cloud Public Edition, AI-assisted KPI and report summary

Managers and executives can get a quicker view of business performance with KPI and report summaries in 麻豆原创 S/4HANA Cloud Public Edition. The feature turns Generic Drilldown reports into concise natural-language summaries that highlight key metrics, trends, anomalies, and threshold breaches directly within 麻豆原创 Fiori apps. This makes complex analytics easier to interpret and share, helping users make more informed decisions without manual report analysis.

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This capability is generally available.

麻豆原创 S/4HANA Cloud Public Edition, EHS environment management, AI-assisted compliance management

EHS compliance managers can set up regulatory obligations and permits more efficiently with environment management and compliance management in 麻豆原创 S/4HANA Cloud Public Edition. The feature scans PDF documents to capture header data, identify relevant requirements, and propose related tasks such as maintenance notifications or reporting activities, while keeping the source document and proposed actions visible side by side. This reduces data entry errors and setup time, strengthens auditability by linking requirements to their source, and can reduce compliance obligation management costs by up to 65% while lowering the risk of penalties and fines.

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This capability is generally available.

麻豆原创 S/4HANA Cloud Public Edition, AI-assisted aging work in progress analytics and management

Project financial controllers can track and manage aging work in process more effectively with Joule in 麻豆原创 S/4HANA Cloud Public Edition. The feature analyzes WIP for time-and-expense projects, identifies delayed billing, summarizes relevant insights in dynamic charts, and supports actions to help prevent revenue loss. This shortens the time needed to review WIP amounts, speeds billing decisions, and helps reduce potential cash flow risk.

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This capability is generally available.

麻豆原创 Cloud Identity Access Governance, AI-assisted access review

Access reviewers can make faster, more informed certification decisions with AI-assisted access review in 麻豆原创 Cloud Identity Access Governance. The feature provides context-aware recommendations based on historical review outcomes, peer-group analysis, and organizational access patterns, along with business-friendly explanations to help reviewers assess whether a user鈥檚 access remains appropriate. This reduces review effort and operational overhead while improving decision quality, strengthening access governance, and supporting compliance.

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This capability is generally available.

麻豆原创 S/4HANA Cloud Public Edition, AI-assisted document extraction for simplified business partner master data maintenance

Sales managers, purchasing managers, and business partner master data specialists can accelerate business partner onboarding with document extraction in 麻豆原创 S/4HANA Cloud Public Edition. Integrated into the Manage Business Partner app, 麻豆原创 Document AI extracts names, addresses, bank account details, and tax numbers from uploaded purchase orders, quotations, and invoices, and automatically populates the relevant fields. This reduces manual transcription and switching between files and screens, helping improve data accuracy, consistency, and operational efficiency.

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This capability is generally available.

麻豆原创 S/4HANA Cloud Public Edition, AI-assisted incoming bill of exchange posting

Finance teams can streamline incoming bill of exchange processing in 麻豆原创 S/4HANA Cloud Public Edition with automated data extraction and posting proposals. By uploading PDF or image files, they can capture bill details, identify missing or inconsistent data, and match open customer items to support posting and reconciliation. This reduces manual entry and errors, improves processing efficiency, and helps teams handle higher volumes across supported countries.

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This capability is generally available.

Autonomous Spend Management

Purchasing Assistant

• Purchase Order Lifecycle Management Agent

Purchasing teams can make faster, more consistent buying decisions and protect margins with the Purchasing Assistant. It identifies spend leakage, recommends the best-fit suppliers with clear, explainable reasoning, and automatically enriches and validates catalog data at upload. It also monitors frequently purchased materials for potential supply gaps and assigns new sources of supply. The Purchase Order Lifecycle Management Agent, released in Q3, extends this support beyond the point of purchase. It flags delivery risks for open orders and guides purchasers on the next steps. By automating analysis and reducing manual follow-up, the assistant improves catalog quality and keeps critical materials in stock, with estimated benefits including a one percent decrease in buying spend and a 30% increase in buying staff productivity.

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The general availability for Purchasing Assistant is now underway in a phased approach.

Supplier Management Assistant

• Supplier Management Identification and Optimization Agent

Supplier managers and procurement teams can build a more resilient supplier base and make better informed decisions with the Supplier Management Assistant. It supports the full supplier life cycle: it unifies supplier data across systems into a single, consistent view, assesses supplier performance to recommend targeted development plans, and continuously monitors financial, compliance, and ESG risk using real-time external data. The Supplier Management Identification and Optimization Agent, released in Q3, adds AI-powered supplier identification and optimization, helping teams find suitable suppliers and make sourcing decisions faster. With less manual effort spent gathering supplier information and assessing risk, organizations can respond to changes in their supply base sooner, with an estimated 20% increase in supplier management staff productivity.

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The general availability for Supplier Management Assistant is now underway in a phased approach.

Procurement Contract Assistant

• Procurement Contract Compliance Monitoring Agent
• Procurement Contract Analysis Agent
• Procurement Contract Renewal Optimization Agent

Procurement and contract management teams can protect contract value and reduce risk across the contract life cycle with the Procurement Contract Assistant. It produces legally compliant first drafts, continuously monitors terms, and supports decisions on renewal, renegotiation, and new sourcing. Three agents released in Q3 help bring existing and active agreements under control. Teams can speed up the ingestion of legacy contracts with the Procurement Contract Analysis Agent, which extracts metadata and obligations, while the Procurement Contract Compliance Monitoring Agent monitors those obligations and detects deviations in real time. As agreements near expiration, the Procurement Contract Renewal Optimization Agent evaluates them and recommends the next action. By surfacing risks to pricing, usage, SLAs, and obligations earlier, the assistant improves compliance and renewal outcomes, with estimated benefits including a 45% increase in contract management staff productivity and a 50% reduction in gaps in terms-and-conditions monitoring.

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The general availability for Procurement Contract Assistant is now underway in a phased approach.

Category Management Assistant

• Category Strategy Development Agent
• Market Insights Agent

Category managers can build, refine, and continuously optimize category strategies with the Category Management Assistant, providing procurement teams with a basis for more proactive decision-making. The assistant combines spend data, supplier context, and external market intelligence into a single view of each category, surfacing risks and opportunities such as market shifts, compliance gaps, and value leakage early. Two agents released in Q3 connect market research directly to strategy. The Market Insights Agent automates category research and delivers verified procurement intelligence, and the Category Strategy Development Agent turns market intelligence into data-driven strategy recommendations. Category managers spend less time gathering information and can adapt their strategies as conditions change, with estimated benefits including a 35% increase in category management staff productivity and a one percent reduction in maverick spend.

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The general availability for Category Management Assistant is now underway in a phased approach.

Invoicing Assistant

• Invoicing Orchestration Agent

Accounts payable teams can move toward touchless invoice processing with the Invoicing Assistant, which automates AP from invoice receipt to payment scheduling with minimal human input. It extracts invoice data, accelerates posting, and draws on integrated procurement and finance data to reduce errors, provide real-time insights, and detect potential fraud. The Invoicing Orchestration Agent, released in Q3, fully automates AP by extracting invoice data and orchestrating end-to-end processing with minimal human input. Less time spent on manual data entry and exception handling results in a more secure, compliant, and efficient process, and more room for higher-value work.

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The general availability for Invoicing Assistant is now underway in a phased approach.

Receiving Assistant

• Return Goods to Supplier Agent

Procurement and receiving teams can streamline goods receipts, service entry sheets, returns, and quality tracking with the Receiving Assistant, reducing manual steps while reinforcing purchase order and contract compliance. For supplier returns, the Return Goods to Supplier Agent helps identify slow-moving or obsolete stock and uses material details and return quantities to trace the original supplier and purchase order. It then prepares a draft return purchase order and notifies the responsible purchaser, who reviews the proposal and agrees on the return terms with the supplier. This combines automated preparation with human oversight to reduce administrative effort, help release working capital tied up in excess inventory, and lower the financial risk of unresolved dead stock.

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The general availability for Receiving Assistant is now underway in a phased approach.

Requisition Assistant

• Purchase Requisition Creation Agent
• Purchase Requisition Optimization Agent

Employees can find the items and services they need and prepare policy-compliant purchase requests with the Requisition Assistant. The Purchase Requisition Optimization Agent searches and compares options across multiple catalogs in real time, ranking alternatives against priorities such as price, quality, supplier preference, and delivery time. To simplify request preparation, the Purchase Requisition Creation Agent turns free-text requests or documents into ready-to-submit purchase carts, guides users to the appropriate buying channels, and autofills forms while checking policies and validating key details. These capabilities help employees balance purchasing needs with company preferences, reduce errors before submission, and shorten requisition-to-order cycles, with an estimated 15% increase in requisition staff productivity.

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The general availability for Requisition Assistant is now underway in a phased approach.

Operational Procurement Assistant

• Invoice Reminder Agent
• Material Spend Advisory Agent

Procurement teams can reduce manual follow-up and strengthen contract compliance across the procure-to-pay cycle with the Operational Procurement Assistant. It monitors and drives the processing of purchase requisitions, purchase orders, supplier contracts, scheduling agreements, service entry sheets, and supplier invoices. Teams can identify off-contract spending, quantify potential savings, and spot quick wins with the Material Spend Advisory Agent, while accountants receive invoice insights through the Invoice Reminder Agent. By reducing manual steps, keeping contracts and sourcing data up to date, and simplifying supplier quote management, the assistant helps accelerate procurement execution, reduce errors and delays, and improve contract utilization.

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The general availability for Operational Procurement Assistant is now underway in a phased approach.

麻豆原创 Ariba Contracts, AI-assisted contract creation from documents

Suppliers can create contracts in 麻豆原创 Ariba Contracts from existing contract PDFs. The feature extracts relevant contract details, recommends required fields, and provides a preview where users can review, edit, or override values before creating the contract. This reduces repetitive data entry, supports more consistent contract data, and helps accelerate contract onboarding and digitization.

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This capability is generally available.

麻豆原创 Ariba Contracts, AI-assisted contract creation from prompts

Contract managers can start new contracts in 麻豆原创 Ariba Contracts using natural-language prompts directly within the contract creation experience. The feature translates a small number of inputs into contract header fields in seconds, helping users focus on their intent rather than navigating through separate steps. Users review and confirm the proposed details before creating the contract, reducing effort while maintaining human control as contract volumes grow.

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This capability is generally available.

麻豆原创 Ariba Contracts, AI-assisted contract field recommendations

Contract managers can complete contracts faster in 麻豆原创 Ariba Contracts with context-aware field recommendations. The feature suggests relevant values from reliable data sources for fields such as supplier, company code, and payment terms, helping users navigate organizational rules and dependencies with less manual effort. Explainable recommendations support accuracy and compliance while allowing contract managers to retain control and flexibility over final values.

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This capability is generally available.

Concur Travel, AI-assisted trip changes

Business travelers and travel coordinators can manage itinerary changes through a natural-language conversation with Joule in Concur Travel. They can find a trip by describing it, review the estimated cost of a modification before confirming it, and update flights, hotels, or rental cars without switching to separate booking tools. This provides real-time support that reduces manual work and helps users make travel changes faster.

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This capability is generally available.

Concur Expense, AI-assisted approval manager

Approval managers can use AI-powered insights in Concur Expense to focus their review on the transactions that require attention. The feature identifies unusual patterns, inconsistencies, and missing context, while routine, lower-risk expenses recede from view to reduce review noise. Explainable AI reasoning clarifies why an item was flagged, and direct access to receipts, expense history, and supporting details helps managers quickly validate decisions. Integrated into existing approval workflows, the feature helps speed up reviews, improve consistency, and maintain accountability without requiring approvers to be expense-policy experts.

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This capability is available in 麻豆原创 Early Adopter Care.

Concur Expense, AI-assisted corporate card management

Card administrators and business travelers can simplify virtual card management with AI-driven automation in Concur Expense. Administrators can translate policies written in natural language into spending limits, merchant category controls, and approval workflows, while travelers can request cards and funds conversationally through familiar messaging channels. This reduces administrative effort, accelerates access to funds, and supports compliance through upfront spending guardrails. The feature is currently available only to eligible customers enrolled in the corresponding 麻豆原创 Early Adopter Care program.

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This capability is available in 麻豆原创 Early Adopter Care.

Concur Travel, AI-assisted travel filters

Business travelers can find suitable flights faster with AI-assisted travel filters in Concur Travel. By describing preferences such as a preferred airline or departure time window in plain language, they can narrow search results without scanning numerous options or adjusting filters individually. This helps travelers identify relevant itineraries more efficiently, simplify decision-making, and book with greater confidence.

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This capability is generally available.

Concur Travel, AI-assisted travel search

Corporate travelers can find relevant rail stations more easily with AI-assisted travel search in Concur Travel, even when they do not know the exact station name or local spelling. They can search in supported languages using station names, cities, countries, or points of interest, with support for typos, omitted accents, and different word orders. This helps travelers identify suitable stations with less effort and plan rail journeys with greater confidence.

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This capability is generally available.

Autonomous CX

Case Management Assistant

• Case Action Recommendation Agent
• Case Preparation Agent
• Case Processing Agent
• Case Management Orchestration Agent

Service representatives can move from case intake to resolution with clearer context and guided actions using the Case Management Assistant. Four agents released in Q3 support this journey: the Case Preparation Agent analyzes incoming requests and extracts key customer context, giving representatives an informed starting point, while the Case Action Recommendation Agent recommends next best actions and helps prioritize tasks based on the live case. The Case Processing Agent assists with processing steps across relevant scenarios and domains, while the Case Management Orchestration Agent coordinates the specialized agents throughout the workflow. By reducing back-and-forth and supporting case summaries and customer communications, the assistant helps teams deliver more efficient, consistent service, with estimated benefits including a 35% reduction in diagnosis effort, a 50% reduction in misrouting, and a 20% reduction in resolution time.

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The general availability for Case Management Assistant is now underway in a phased approach.

Deal Qualification Assistant

• Lead Conversion Agent
• Lead Enrichment Agent
• Lead Qualification Agent
• Lead Routing Agent
• Deal Qualification Orchestration Agent

Sales teams can prioritize promising prospects and accelerate opportunity creation with the Deal Qualification Assistant. It deduplicates lead data, assesses fit and intent, and automates routing and engagement. Four agents released in Q3 connect these activities: the Lead Enrichment Agent enhances incoming lead records with relevant external data, ensuring sales teams have the context needed to engage effectively, while the Lead Qualification Agent categorizes prospects as qualified, nurtured, or disqualified based on ideal customer criteria. After an account executive confirms qualification, the Lead Conversion Agent creates the required account, contact, and opportunity records, preserving lead details and engagement history. The Deal Qualification Orchestration Agent coordinates the specialized agents across the process. This reduces research and data entry while improving pipeline quality, with estimated benefits including a 35% reduction in manual deal-qualification effort and a 10% increase in the lead-to-meeting ratio.

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The general availability for Deal Qualification Assistant is now underway in a phased approach.

麻豆原创 Enterprise Service Management, AI-assisted document extraction

Service and support staff can extract relevant information from image and PDF attachments in 麻豆原创 Enterprise Service Management. The feature converts unstructured document content into structured data鈥攕uch as invoice or contract-account details鈥攁nd supports retrieval of related entities from 麻豆原创 back-end systems, including 麻豆原创 S/4HANA Cloud. This accelerates document handling, improves data accuracy, and connects incoming documents more smoothly to existing workflows and business processes.

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This capability is generally available.

麻豆原创 Omnichannel Promotion Pricing, AI-assisted promotional text translation

Promotion planners can use 麻豆原创 Omnichannel Promotion Pricing to translate promotional text into more than 150 languages. AI-powered translation reduces the manual work required to maintain multilingual content and helps keep messaging consistent across global promotion campaigns.

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This capability is generally available.

麻豆原创 Engagement Cloud, AI-assisted content composer

Marketers can accelerate campaign creation with the AI-powered content composer in 麻豆原创 Engagement Cloud, generating personalized copy and brand-consistent images directly within the campaign editor. Drawing on product catalog data, audience attributes, and reference campaigns, they can create headlines, body copy, calls to action, and hero images while retaining control to preview, edit, or regenerate each element. This integrated workflow helps teams launch campaigns faster, scale personalization, and reduce tool switching.

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This capability is generally available.

麻豆原创 Business AI Platform

Mistral on 麻豆原创 Business AI Platform

Organizations can use Mistral’s enterprise AI platform, including Mistral Vibe Work and AI Studio, hosted on 麻豆原创 Business AI Platform, to enhance productivity. Employees can interact with multilingual large language models to chat, think, search, write, upload files, and receive answers to questions, helping them automate meaningful daily work from a single environment. The offering also provides a no-code environment for teams to design, customize, deploy, and operate AI agents and workflows end-to-end. Mistral models are deployed locally on 麻豆原创 Cloud Infrastructure to support data processing within a trusted region, helping organizations address regulatory requirements, regional compliance mandates, and geopolitical considerations. A fully sovereign version on 麻豆原创 Sovereign Cloud, supporting legal, operational, technical, and data sovereignty, is planned for early 2027.

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This capability is generally available.

麻豆原创 Analytics Cloud, AI-assisted prompt insights

Story designers can embed large language model prompts as widgets in 麻豆原创 Analytics Cloud stories, enabling story consumers to generate AI responses directly within their analytics experience. The feature can provide structured analysis of tables or answer broader business questions, helping users create data-driven narratives and decision support without moving internal data to external AI tools. This reduces manual analysis effort and can cut the time required to analyze 麻豆原创 Analytics Cloud data by 60%.

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This capability is generally available.

麻豆原创 Analytics Cloud, AI-assisted story code generation

Business analysts can build customized dashboards more efficiently with AI-assisted story code generation in 麻豆原创 Analytics Cloud. The feature automatically generates JavaScript and explains its functionality and coding choices, helping analysts understand, modify, and extend scripts to create low-code analytics applications. This reduces manual coding effort, supports faster delivery of analytics solutions, and gives analysts greater flexibility to tailor dashboards to business needs.

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This capability is generally available.

麻豆原创 Datasphere, AI-assisted SQL generation

Data modelers can create and refine SQL views more efficiently with AI-assisted SQL generation in 麻豆原创 Datasphere. Using natural language prompts in the SQL View Builder, they can generate SQL code for new or existing views. This helps reduce manual coding effort and increase modeling productivity.

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This capability is generally available

Generative AI hub enhancements

麻豆原创-RPT-1.6
麻豆原创-RPT-1.6, now available on generative AI hub, is our latest model in the 麻豆原创-RPT family. It delivers the strongest gains in prediction quality to date, particularly for high-cardinality classification, and is validated across 197 real-world enterprise tasks.

• Explainability: Available in all 麻豆原创-RPT-1.6 variants, this includes column-level feature importances and the ability to surface specific historical rows most influential to each prediction, giving business users and decision-makers a clearer basis for trusting and acting on AI-driven outputs.
• Deep Context Mode: Only available in 麻豆原创-RPT-1.6-large, this allows the model to draw on significantly larger reference datasets to improve prediction quality. It is intended for use cases with large historical data, such as invoice records spanning multiple fiscal years or product catalogs in the tens of thousands. This mode trades higher latency and cost for higher accuracy, and can be enabled via a single parameter in the prediction configuration.

Tabular Orchestration
Tabular Orchestration, now available on generative AI hub, is a new managed workflow for 麻豆原创-RPT models. It handles context management and data access, thereby removing the need to build and maintain custom data pipelines. Teams register their data source and scenario configuration once at design time. Every inference is a single API call, with the workflow automatically handling data ingestion and context selection before routing the prediction request to the specified 麻豆原创-RPT model. This setup is designed to easily carry forward as the workflow expands to support additional tabular foundation models beyond 麻豆原创-RPT in future releases.

Prior Labs’ TabPFN-3.5-Plus
Business analysts and AI teams can turn structured business data into actionable predictions faster with TabPFN-3.5 Plus, now available in 麻豆原创 AI Core. Developed by Prior Labs, an 麻豆原创 company, the tabular foundation model helps organizations predict outcomes such as payment delays, supplier risks, customer churn, and upsell opportunities鈥攚ithout task-specific model training or tuning. Unlike large language models, which excel at language and knowledge tasks, TabPFN-3.5 Plus is purpose-built for the rows and columns that underpin critical business decisions.

The model works with data as it exists in business systems, handling missing values, mixed data types, and inconsistent fields without preprocessing. Through in-context learning, it uses labeled examples to make predictions from raw tabular data, including columns containing thousands of distinct values, such as product codes or customer identifiers. This reduces the manual preparation and trial-and-error configuration associated with traditional machine learning, helping teams move from business data to predictions in minutes.

For customers, this means a faster path to predictive insights with less model-development effort. Finance teams can anticipate payment delays, procurement teams can assess supplier risks, and customer-facing teams can identify churn risks and upsell opportunities. By making prediction more accessible, TabPFN-3.5 Plus helps organizations use their structured data to inform decisions and act earlier on emerging risks and opportunities.

Learn more here and . .

Enterprise Architecture Assistant

• Enterprise Architecture Management Agent

Enterprise architects can find answers and maintain more complete, up-to-date architecture inventories without leaving 麻豆原创 LeanIX using the Enterprise Architecture Assistant. Released in Q3, its Enterprise Architecture Management Agent enables architects to query and analyze inventory data across multiple dimensions, drawing on internal documentation from repositories such as 麻豆原创 LeanIX, Confluence, and SharePoint. Architects can identify missing fields and conduct gap analysis within their workflow, reducing the need to manually search documents or switch between applications. By bringing contextual research and analysis into 麻豆原创 LeanIX, the assistant helps shorten information searches, simplify navigation and data management tasks, and strengthen inventory quality and governance.

Get started .

The general availability for Enterprise Architecture Assistant is now underway in a phased approach.

Data Management Assistant

• Transition Data Quality Scope Agent
• Transition Data Quality Evaluation Agent
• Transition Data Quality Prioritization Agent
• Transition Data Quality Validation Agent
• Transition Data Quality Automated Resolution Agent
• Data Management Orchestration Agent

Data management teams can identify, prioritize, and correct data quality issues across the transformation life cycle with the Data Management Assistant. Its Q3 lineup connects six agents within a governed workflow: the Transition Data Quality Scope Agent selects relevant KPIs, while the Transition Data Quality Evaluation Agent analyzes execution results to identify trends and severity. Teams can focus on the most important corrections with the Transition Data Quality Prioritization Agent, then identify source systems and verify proposed fixes through the Transition Data Quality Validation Agent. The Transition Data Quality Automated Resolution Agent executes corrections only after human approval, with the Data Management Orchestration Agent coordinating the specialized agents throughout the process. By combining ongoing monitoring, actionable recommendations, and sandbox validation before production deployment, the assistant helps teams reduce correction risks, detect emerging issues, and maintain data governance at scale.

Get started .

The general availability for Data Management Assistant is now underway in a phased approach.

Process Transformation Assistant

• Process Consulting Agent

Business users and process analysts can uncover opportunities for improvement and turn insights into prioritized actions with the Process Transformation Assistant in 麻豆原创 Signavio. It combines conversational process mining with data preparation, tailored recommendations, and structured value cases to reduce configuration effort and support faster decisions. In Q3, the Process Consulting Agent lets users explore processes through natural-language questions, using multi-agent reasoning to retrieve relevant data, perform analyses, and translate findings into contextual recommendations and next-best actions. This helps users without specialist analytical skills contribute to process improvement while freeing analysts from time-consuming searches. Estimated benefits of the agent include a 90% reduction in time spent on complex data searches per artifact and a five percent reduction in average time spent analyzing, designing, modeling, and monitoring a process.

Get started .

The general availability for Process Transformation Assistant is now underway in a phased approach.

ALM Dashboard Generation Agent

IT and operations teams can use the ALM Dashboard Generation Agent to create graphical dashboard cards from system and service data in 麻豆原创 Cloud ALM through natural-language prompts. The agent helps users build personalized, role-specific monitoring dashboards without extensive data expertise, making it easier to tailor views to operational needs and deliver relevant insights to each user. This can reduce dashboard creation and maintenance effort by 75% and increase business productivity by 25% through faster access to customized information.

Get started .

This capability is generally available.

ALM Alert Resolution Agent

Alert processors can use the ALM Alert Resolution Agent in 麻豆原创 Cloud ALM to analyze alert details, including error messages, thresholds, related issues, and system context, and gain a comprehensive view of the current situation. The agent automates initial triage, searches help documentation, knowledge base articles, and 麻豆原创 Notes, and provides recommended troubleshooting and resolution steps directly on the Alert Details page. This helps reduce mean time to resolution, lower manual triage and troubleshooting effort, improve system uptime, and standardize incident resolution with consistent, AI-guided workflows.

Get started .

This capability is generally available.

麻豆原创 Regulatory Insights

Compliance officers can use 麻豆原创 Regulatory Insights to manage complex and evolving compliance requirements. The solution extracts and structures control requirements from lengthy regulatory documents, compares regulatory versions to identify new, changed, or withdrawn requirements, and analyzes the extent to which existing internal controls provide coverage. AI-driven recommendations help identify potential controls for closing gaps, while validation and contextual analysis support accurate mapping of related regulatory and control language. APIs enable integration with existing compliance and risk management systems, helping organizations reduce manual effort, improve transparency, maintain consistent data, and strengthen compliance processes at scale. By helping teams identify regulatory changes earlier and understand complex documents faster, the solution can help reduce penalties or rework costs from missed changes by 25% and reduce the average time spent comprehending regulatory documents by 70%.

Get started .

This capability is available as beta release.

麻豆原创 Cloud ALM, AI-assisted operations score optimization

IT and operations leads responsible for 麻豆原创 S/4HANA landscapes in RISE with 麻豆原创 can use the AI assistant in 麻豆原创 Cloud ALM Operations View to understand Operations Index scores and their day-to-day impact. The assistant translates technical metrics, KPIs, and configuration data into clear, context-aware explanations, identifies the primary factors affecting current scores, and highlights the systems, configuration gaps, or runtime issues that need attention first. This helps operations owners prioritize actions, allocate effort effectively, and make faster decisions aligned with clean core principles without manually interpreting every dashboard chart or KPI.

Get started .

This capability is generally available.

Industry AI

Spare Parts Request Processing Agent

Spare Parts teams can use the Spare Parts Request Processing Agent to process requests from multiple input channels and initiate the creation of sales quotations in the target system. The agent identifies the appropriate parts and involves experts when request details are missing or ambiguous, helping ensure accurate responses without requiring specialists to manually manage every request. This reduces the time and effort required to respond to customers, shortens the time to create quotes, increases spare-parts sales, and improves customer satisfaction.

Get started .

This capability is available in 麻豆原创 Early Adopter Care.

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

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

We believe the 2026 evaluation recognized 麻豆原创 for strengths in areas that are increasingly important to enterprise commerce: using AI to make commerce more intelligent, supporting complexity at global scale, and connecting commerce with operational data.

For 麻豆原创, these areas come to life in how we help customers simplify complex commerce and prepare for what comes next.

Making commerce more intelligent with AI

AI is changing how customers discover products, interact with businesses, and complete purchases. It is also creating opportunities to simplify work for commerce teams.

helps bring AI into customer experiences and commerce workflows while maintaining the business controls required for enterprise transactions.

Ericsson provides an example of what this can look like in practice. Generating a customer quote could once take more than two weeks. Today, after digitizing and streamlining its commerce processes with 麻豆原创 Commerce Cloud, many of those requests can be completed in minutes or even seconds. With that digital foundation in place, Ericsson is now exploring how AI agents could support quote creation while employees remain responsible for oversight and decision-making.

The goal is straightforward: use AI to reduce complexity, make commerce easier for customers, and give employees more time to focus on higher-value work.

Supporting complexity at enterprise scale

For global enterprises, commerce rarely means managing a single storefront or business model. Businesses may need to operate across multiple brands, markets, sites, languages, currencies, channels, and customer types while maintaining consistency and control.

麻豆原创 Commerce Cloud helps enterprises manage that complexity and provides the flexibility to evolve as their businesses grow.

Daimler Truck North America demonstrates the value of building that foundation for the long term. Roughly 30,000 users interact with its commerce platform every day, and DTNA reports approximately 40% compound annual growth over the lifetime of the platform. Those results have been built through years of investment in platform modernization, cloud migration, dealer collaboration, and alignment between business and IT.

As businesses expand and customer expectations evolve, having a commerce platform that can grow and adapt with the organization becomes increasingly important.

Connecting commerce with your operations

A great commerce experience depends on what happens behind the storefront.

Customers expect accurate products, prices, inventory, entitlements, and order information when they need it. Businesses need commerce to work with the operational processes and enterprise data behind every interaction.

麻豆原创 Commerce Cloud connects commerce with those operations, helping businesses deliver more consistent customer experiences while simplifying execution across the enterprise.

, noted in this year鈥檚 report as a recent platform release, extends this approach to mid-sized and growing businesses running 麻豆原创 Cloud ERP. Purpose-built for B2B companies looking to modernize customer engagement without adding complexity, it turns the pricing, availability, order, and invoice data already in their ERP into a direct self-service experience for customers, with faster deployment, lower total cost of ownership, and no middleware required.

That connection is also central to Autonomous Unified Commerce from 麻豆原创: connecting customer, commerce, operational, and financial processes on a common data foundation, while embedding AI into business workflows to help organizations execute more efficiently and drive profitable growth.

Looking ahead

AI is creating new ways for customers to discover products, get answers, and complete purchases through AI-powered interfaces and assistants beyond the traditional storefront. Commerce is evolving to support these new channels via the Universal Commerce Protocol (UCP) and Model Context Protocol (MCP).

The real work is getting AI into the daily operations that actually drive revenue, reduce cost, and help businesses serve their customers better. Our focus is to help customers navigate these changes by making complex commerce simpler, supporting growth at enterprise scale, and creating more connected and intelligent ways of working.

We are proud to mark 麻豆原创’s 12th recognition as a Leader and grateful to the customers, partners, and 麻豆原创 teams who continue to move commerce forward.

To learn more and explore the digital commerce platforms market, access a complimentary copy of the .

Kollen Glynn is global head of 麻豆原创 Commerce Cloud for 麻豆原创 Customer Experience at 麻豆原创.

Gartner, Magic Quadrant for Digital Commerce Platforms, Aditya Vasudevan, Penny Gillespie, Sandy Shen, Jason Daigler, Mike Lowndes, 19 October 2026.
Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner鈥檚 research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
Gartner and Magic Quadrant are trademarks of Gartner, Inc. and/or its affiliates.
麻豆原创 was recognized as 麻豆原创 Hybris in 2014, 2016 and 2017.

This is the sixth time that 麻豆原创 LeanIX has positioned as a Leader in the annual report.听

We believe this reflects our unique strength in helping enterprises breathe life into their enterprise architecture data, discover and govern their AI portfolios, and make rapid transformation a business capability听

The analyst firm positioned 麻豆原创 LeanIX furthest in Completeness of Vision and highest in Ability to Execute, once again. The news is particularly relevant for business leaders as enterprise architecture tools are becoming increasingly important for AI adoption and governance.

Why enterprise architecture matter for AI

Enterprise architecture tools document up-to-date information about organizations鈥� application portfolios and tech stacks. Visibility on this information is essential for leveraging agentic AI and governing it to ensure return on investment.

Enterprise architects are not in the driving seat for AI governance, but with the right tools, they can provide the data chief AI officers and AI governance boards need to make rapid, informed decisions. The 麻豆原创 LeanIX portfolio is able to discover, store, and provide that essential enterprise architecture intelligence.听

鈥淥ur goal is to empower our customers to confirm return on technology investment in the AI era with dynamic, enterprise architecture intelligence that helps drive business transformation,鈥� 麻豆原创 LeanIX Chief Product Officer Dominik Rose said. 鈥淭ransformation never ends, but the AI revolution is the most fundamental step forward that technology has taken this century. Our business transformation solutions empower organizations to take that step in their stride with comprehensive, current EA data.鈥�

What is the Gartner Magic Quadrant for EA Tools?

A Gartner Magic Quadrant is a culmination of research in a specific market, offering a wide-angle view of the relative positions of the market鈥檚 competitors.鈥疊y applying a graphical treatment and a uniform set of evaluation criteria, a Magic Quadrant helps quickly ascertain how well technology providers are executing their stated visions and how well they are performing against Gartner鈥檚 market view.

A Magic Quadrant provides a view of the four types of technology providers in any given field: 

• Challengers execute well today or may dominate a large segment, but do not demonstrate an understanding of market direction.
• Niche players focus successfully on a small segment or are unfocused and do not out-innovate or outperform others.
• Visionaries understand where the market is going or have a vision for changing market rules, but do not yet execute well.
• Leaders execute well against their current vision and are well-positioned for tomorrow.

麻豆原创 LeanIX has been positioned as a Leader in the Gartner Magic Quadrant for Enterprise Architecture Tools for six consecutive years, and this year was positioned furthest in Completeness of Vision and highest in Ability to Execute.

For enterprise architects and senior leaders, Gartner evaluates EA Tools against real-world enterprise needs, including: 

• Capturing, structuring, analyzing, and presenting models
• Supporting change, transformation, and optimization
• Assessing and managing an evolving IT portfolio
• Managing EA knowledge and resources to address stakeholder needs
• Identifying and tracking sustainability initiatives

To see the full analysis and learn why 麻豆原创 was once again listed as a Leader for EA tools, .

Neil Sheppard is a senior content marketing specialist for 麻豆原创 LeanIX.

Gartner does not endorse any vendor, product or service depicted in its research publications and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner鈥檚 business and technology insights organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
Gartner and Magic Quadrant are trademarks of Gartner, Inc and/or its affiliates.

It requires AI that can drive outcomes by executing and coordinating work across the customer journey and the business processes behind it, grounded in trusted data and governed at every step.

With the 麻豆原创 Customer Experience Q3 2026 release, 麻豆原创 is bringing the vision to life as part of the broader . New AI assistants and innovations across sales, service, marketing, and unified commerce help organizations coordinate customer-facing activities with broader business processes across the enterprise.

These capabilities are now available to select customers, providing an opportunity to explore Autonomous CX in action.

Explore the highlights of the Q3 2026 release below. For full sub-solution details, see our recaps for , , , , , and . Additional announcements from 麻豆原创 CX Connect are available here.

Autonomous sales and revenue: from leads to qualified opportunities

Pipeline generation remains one of the most important activities in any organization. Yet, lead qualification often gets bogged down in manual reviews, disconnected systems, and handoffs that slow sales teams down just when prospect interest is at its highest level. To help streamline this process, 麻豆原创 is introducing the , which automates lead qualification, enrichment, routing, and follow-up activities. The assistant leverages specialized agents to move leads efficiently through the qualification process.

Behind the scenes, the coordinates the journey from lead capture to qualified opportunity. The gathers prospect intelligence and company context and the evaluates fit and engagement signals to prioritize opportunities. The then manages the transition from qualified lead to sales opportunity.

Together, these capabilities create a more connected lead-to-opportunity process. Sales teams can then reduce administrative effort and improve lead quality while focusing more time on customer engagement and revenue generation.

Autonomous service: from case resolution to lasting customer relationships

The need for coordinated execution continues after the sale. When customers need help, they want it fast and without being transferred or having to explain the problem twice. The goal is to resolve cases quickly and get customers back to business faster.

The new helps accelerate the entire case life cycle from intake to resolution.

• automates case intake by categorizing requests, gathering context, generating summaries, and routing cases
• continues case execution after preparation, automates processing steps, and prepares customer communications
• evaluates case status continuously and identifies the most effective next step

The brings all these capabilities together into a unified workflow. With routine steps automated, service teams deliver fast and accurate service that keeps customers coming back. The same capabilities are also available for , extending agentic case handling to shared service functions such as HR and finance.

Autonomous marketing and engagement: from campaigns to customer engagement

Marketing teams are under immense pressure to deliver increasingly personalized experiences across more channels, audiences, and content and often with less budget. At the same time, they are expected to demonstrate measurable impact on business outcomes including revenue and growth鈥攏ot just marketing performance metrics such as opens, clicks, and engagement.

To address this complexity, 麻豆原创 is introducing the and . These assistants are designed to support campaign and content planning, execution, and optimization.

At the center is the , which helps translate customer insights and business priorities into coordinated engagement actions across the campaign lifecycle.

• helps marketers create and refine target audiences
• translates campaign goals into executable automation programs
• provides conversational access to performance data and surfaces optimization opportunities

For the Content Assistant, the helps generate tailored copy and creative variants, and coordinate messaging across channels while aligning execution to audience and campaign objectives.

With planning, execution, and performance working together, marketers have more room to focus on strategy, creativity, and the customer experience.

Unified commerce innovations

Autonomous CX depends on more than customer-facing intelligence. Understanding what a customer wants is only the first step. Delivering on that intent requires the operational systems behind the experience, including pricing, inventory, fulfillment, and financial processes, to coordinate and execute in real time. When customer demand is directly connected to business operations, organizations can consistently turn customer intent into promised outcomes.

New innovations across retail operations and ERP-connected commerce help organizations better align customer demand with business execution and deliver seamless experiences across channels.

The latest release of continues to strengthen retailers鈥� capabilities by expanding integration with . Retailers can apply centrally managed promotions consistently across digital and physical channels. New demand and inventory segmentation capabilities better align inventory and demand across retail, e-commerce, and wholesale channels, while operational enhancements improve day-to-day store execution.

With , B2B companies running 麻豆原创 Cloud ERP Public Edition can turn ERP data into a more seamless customer self-service experience. Native 麻豆原创-managed integration provides access to pricing, availability, orders, and invoices across all channels, while a preconfigured, clean core approach accelerates time to value and lowers total cost of ownership.

The next stage of customer experience

This is only the beginning. 麻豆原创 continues to invest in new AI assistants and specialized agents to help organizations work more efficiently, respond more intelligently, and deliver better customer outcomes.

As Autonomous CX evolves, customers can expect even more capabilities that extend automation, orchestration, and decision-making across the end-to-end customer journey.

Learn more about 麻豆原创 CX in Q3 2026鈥�&苍产蝉辫;

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.

You benchmark features, compare pricing, run a proof of concept, and choose the platform your sales team finds most intuitive. The ERP conversation happens in a different room, with different stakeholders, on a different cycle.

This separation feels logical on an org chart. In practice, it is one of the most costly fictions in enterprise technology planning.

The enterprise reality

doesn鈥檛 operate in a vacuum. It operates inside a business that already has a system of record, one that holds pricing rules, credit limits, inventory positions, order histories, contract terms, and fulfillment status. That system is the ERP. When CRM and ERP are evaluated and deployed independently, the downstream consequence is not just a technical integration challenge. It is a structural gap in the information available to every customer-facing employee, every day, in every interaction that matters.

When CRM and ERP are built on separate data models, integration becomes a permanent overhead. Sales reps without ERP data access spend an estimated 20 to 30% of their selling time on non-selling activities such as sourcing pricing approvals, checking inventory availability, or validating order status–work that should happen automatically in the background of a connected system. The sales motion slows. Forecast accuracy degrades. And the promised ROI of the CRM investment erodes quietly, deal by deal.

According to Gartner庐 Peer Insights™ responses to the question, “What were the key factors that drove your decision?”, based on the response of actual users, 49% of 麻豆原创 Sales Cloud users and 43% of 麻豆原创 Service Cloud users cited pre-existing relationships as a deciding factor in their purchase decision. This is more than double the market average of about 20%.

At first glance, this looks like vendor loyalty. We believe it is something more deliberate than that.

What native integration unlocks

Pre-existing relationships in enterprise software carry specific, quantifiable value. When an organization already runs 麻豆原创 S/4HANA, choosing 麻豆原创 Sales Cloud and 麻豆原创 Service Cloud means working within a known data architecture rather than building a bridge between two foreign systems. The integration patterns are pre-established. The master data such as customers, products, pricing conditions etc is already structured in a way 麻豆原创 products understand natively. The implementation risk profile is fundamentally different from deploying a point-solution CRM and then spending months, and significant budget, building and maintaining connectors to the ERP back-end. This is not loyalty to a logo. It is a rational assessment of where integration risk lives.

The outcomes that follow from this architectural alignment are measurable. Customers running 麻豆原创 S/4HANA and together have achieved a 38% reduction in sales cycle duration, a 12% increase in opportunity win rate, and . These numbers are not the result of superior user experience alone. They are the result of sales processes that can draw on real-time ERP data like current inventory, live pricing, or credit exposure, without requiring the sales rep to leave the CRM, make a phone call, or wait for a nightly data sync. The integration is the mechanism of the outcome, not a supporting feature of it.

Many enterprises rely on disparate point solutions for marketing, sales, commerce, and service. Stitching these together with custom middleware and integrations is not just expensive, it’s fragile. Organizations have entire teams dedicated to building and maintaining integrations, manually transferring data between systems, and firefighting connectivity issues. The result is poor customer experiences, delayed insights, and limited scalability.

According to an , organizations that natively connect 麻豆原创 CX solutions with 麻豆原创 ERP unlock five compelling economic benefits:

1. Native iFlows and prebuilt connectors reduce integration and middleware costs by 25 to 80%, delivering six- to seven-figure savings over five years.
2. Seamless front-to-back connectivity drives revenue growth through higher customer registrations, improved retention, better sales conversion rates, and larger deal sizes; projecting a 30 to 441% revenue uplift over five years.
3. AI-driven automation and elimination of manual handoffs boost productivity by 25 to 90% across every stage of the sales cycle, from marketing to service.
4. Organizations go live and begin realizing customer experience improvements in as few as six months, significantly faster than point-solution alternatives.
5. Built-in compliance support reduces regulatory risk without custom development.

Together, these benefits deliver a validated five-year ROI of 214% to 726%.

Validation from our customers

This director of Customer Service and Support in the consumer goods industry highlights Integration and reliability strengths of 麻豆原创 Sales Cloud:

This marketing manager in the retail industry highlights the seamless integration enabling customer service teams to enhance customer experience:

Suite advantage is a strategic choice, not a convenience

麻豆原创 continues to invest in tighter, event-driven integration between these platforms, moving away from batch replication toward real-time, API-first connectivity. The adoption of 麻豆原创 Integration Suite pre-built integration packages, combined with RISE with 麻豆原创 and 麻豆原创 GROW bundling, is lowering the barrier for customers to activate these scenarios out of the box. For organizations on the path to an intelligent enterprise, the integration between 麻豆原创 S/4 HANA and 麻豆原创 Sales Cloud and 麻豆原创 Service Cloud integration is no longer a project, it is a product.

CRM value is not created in isolation, it is created at the intersection of customer data and operational data, of sales motion and business process, of pipeline intelligence and ERP reality. For organizations already running 麻豆原创, that intersection is not something to build. It is something to activate.

Gartner Peer Insights content consists of the opinions of individual end users based on their own experiences with the vendors listed on the platform, should not be construed as statements of fact, nor do they represent the views of Gartner or its affiliates. Gartner does not endorse any vendor, product or service depicted in this content nor makes any warranties, expressed or implied, with respect to this content, about its accuracy or completeness, including any warranties of merchantability or fitness for a particular purpose.
GARTNER and PEER INSIGHTS are trademarks and service marks of Gartner, Inc. and/or its affiliates and are used herein with permission. All rights reserved.

Its real potential emerges when AI connects with the data and processes behind public services, turning a citizen request into coordinated action across the administration.

For public institutions, there is another essential requirement: innovation must go hand in hand with transparency, accountability, and digital sovereignty. 麻豆原创 combines decades of process expertise with enterprise applications, data, AI, and sovereign cloud technologies.

S.Public, the new 麻豆原创 Experience Center location for the public sector, brings these capabilities to life. It officially opened in Berlin on October 12, 2026.

From a citizen request to coordinated action

Damage in a public space can be reported digitally in a matter of moments. But that is where the actual administrative process begins: determining responsibilities, planning the work, checking resources and budgets, procuring materials if needed, and closing the case. 麻豆原创 solutions can connect citizen engagement, maintenance, procurement, and financial management across this end-to-end process.

At S.Public, a playground scenario makes this tangible. A citizen notices damage, such as a broken swing, and reports it by smartphone. The steps that follow show how a single report becomes coordinated action. The real innovation is not the reporting form itself, but the intelligently connected processes behind it.

When AI understands the administrative context

The same principle applies to public funding. Applications involve documents, eligibility requirements, and financial information. AI can check submitted materials for completeness and consistency, flag missing information early, and provide employees with an explained recommendation for further review. The final decision remains with the responsible public authority.

A funding application for a solar installation illustrates this approach in Berlin. The 麻豆原创 capability behind it is broadly applicable: AI is embedded where employees evaluate information and manage processes. Rather than automating isolated tasks, it supports the broader administrative context.

When digital sovereignty meets AI

How can sensitive data be used with AI while remaining under an organization’s own control? 麻豆原创 Sovereign Cloud addresses four dimensions of digital sovereignty: data, operational, technical, and legal sovereignty.

At S.Public, this concept becomes something visitors can actually walk through. The new Sovereign Cloud Showroom makes 麻豆原创’s security and sovereignty concept physically tangible for the first time. Visitors pass through three security zones: administration, control, and restricted zones. They experience firsthand how the operating environment for 麻豆原创 Sovereign Cloud and the protection of classified information work together.

A public sector scenario at a seaport shows how this can be combined with intelligent analysis. 麻豆原创 solutions and the AI assistant Joule bring together selected data from internal and external systems in a sovereign environment. Data on vessel arrivals, terminals, containers, and available inspection capacity is turned into a prioritized overview. Analysts can query it in natural language, while the responsible public officials decide which inspections to carry out.

More than a place for demonstrations

The three examples point to a common development: public services become more intelligent when applications, data, and AI work together across organizational and functional boundaries while accountability remains clear.

S.Public creates a place where customers, partners, and experts can explore and discuss these possibilities. The 麻豆原创 capabilities behind them extend far beyond Berlin.

The future of public services will not be determined by how much technology public administrations adopt, but by what they enable for people and society.

Andre Bechtold is president of 麻豆原创 Industries & Experiences and chief revenue officer of Industry AI at 麻豆原创.

As organizations become more dependent on connected infrastructure, cloud services, AI, and third parties, weaknesses in one part of the ecosystem can cascade across the business.

A Board is not expected to make technical decisions. Its role is to exercise informed oversight: set expectations, test whether accountability is clear, require evidence of resilience, and ensure that management integrates security and compliance into strategy, investment, operations, and crisis preparedness.

The following questions are designed for a Board of Directors and its relevant committees. They broaden the original cybersecurity focus to include compliance, privacy, data governance, third-party assurance, regulatory change, and demonstrable control effectiveness. 

1. Are security and compliance roles, decision rights, and escalation paths clearly defined?

Treating security or compliance as an isolated specialist function weakens governance. Accountability should extend across executive leadership, business operations, technology, legal, risk, privacy, internal audit, and the security organization. The Board should understand who owns material risks,鈥痺hat is the risk appetite鈥痺ho can accept them, and how significant incidents, control failures, vulnerabilities, or regulatory concerns reach the appropriate committee or full Board.

Evidence to request:

• Does the security and compliance leadership team have direct access to executive management and the Board?
• Are material risks escalated before they materialize into鈥痑ctive incidents?
• Do responsibilities cover prevention, detection, response, recovery, regulatory obligations, and assurance?

2. Are security and compliance built into strategy, investment, and transformation decisions?

Security and compliance should shape strategic choices from the beginning, not be added after commitments have been made. Major investments, cloud programs, acquisitions, new products, AI initiatives, and supplier selections should be assessed alongside听security and compliance听considerations.听Early involvement helps identify structural risk while decisions are still practical to change.听

Evidence to request:

• Which strategic decisions require a documented security and compliance review?
• What risks or control requirements influenced the final decision?
• Are funding and accountability aligned with the risk created by the initiative?

3. Do we have a transparent, decision-ready view of security and compliance risk?

Boards need more than technical activity reports. Management should translate important threats, vulnerabilities, control gaps, legal obligations, and business dependencies into clear enterprise risk scenarios. Reporting should show potential operational, financial, legal, and reputational consequences, as well as the residual risk that remains after controls are applied. 

Evidence to request:

• Which scenarios could materially affect our strategic objectives?
• How are likelihood, business impact, and control effectiveness assessed?
• What residual risks have been accepted, by whom, and for how long? 

4. Is our compliance strategy prepared for regulatory change?

Compliance听should be managed as a continuing capability rather than a periodic response to individual requirements. The Board should assess whether regulatory developments are听monitored, interpreted consistently, assigned to accountable owners, and translated into controls, technology roadmaps, evidence, and reporting. This includes requirements affecting security, privacy, data sovereignty, operational resilience, AI, and third-party services.听

Evidence to request: 

• Which emerging requirements could materially affect the business?
• How quickly can new obligations be translated into operational controls? 
• Can management demonstrate compliance consistently across regions, business units, and technology environments?

5. Are legacy systems and technical debt creating unacceptable security or compliance exposure?

Systems that are no longer fully supported can accumulate security weaknesses, limit monitoring, complicate recovery, and make compliance evidence harder to produce. The Board should view aging critical systems as an enterprise risk and investment issue, not just an IT lifecycle matter. Where modernization is delayed, management should document compensating controls, ownership, and a time-bound plan. 

Evidence to request: 

• Which critical systems are out of support or approaching end of support?
• What business processes and regulatory obligations depend on them?
• What is the modernization plan and how is interim risk independently validated?

6. Are AI security, compliance, and governance embedded in the operating model?

AI introduces security, legal, privacy, data, ethical, and operational questions that require coordinated governance. Whether AI is developed internally or obtained from a third party, management should know what data it can access, how outputs are used, which decisions听remain听subject to human review, and how systems are tested and听monitored听as they change.

Evidence to request:

• Who is accountable for approving and monitoring AI use cases?
• What controls protect sensitive data and prevent unauthorized use?
• How are legal, ethical, security, compliance, and human-rights considerations evaluated and evidenced?

7. Are identity and access management strong enough for our risk profile?听

Compromised credentials and excessive access can allow attackers or insiders to reach critical systems and data. Identity and access management should be treated as听a business听control, not simply an administrative technology process. Strong governance connects employee, contractor, third-party, and system identities to reliable business records and removes access when it is no longer required.听

Evidence to request: 

• Are strong authentication and least-privilege access applied to critical systems?
• How quickly is access changed when roles, employment, or supplier relationships change?
• Are privileged and non-human identities monitored, reviewed, and time-limited where appropriate? 

8. Does our culture measurably reduce human and conduct risk?

Policies and technical controls depend on people making sound decisions. A mature program goes beyond annual training by addressing role-specific exposure, leadership behavior, phishing and social engineering, secure ways of working, speak-up channels, and consequences for repeated noncompliance. The Board should seek measurable evidence that behavior and risk are improving. 

Evidence to request: 

• Which roles face the highest security and compliance exposure?
• What do simulations, incidents, training results, and behavioral indicators show over time?
• How visibly do leaders reinforce expected behavior and accountability?

9. Are third-party and supply-chain risks continuously assessed and governed? 

Security and compliance obligations do not stop at the enterprise boundary. Critical suppliers, cloud providers, software dependencies, advisers, and other partners can create operational, regulatory, data, concentration, and geopolitical exposure. Third-party risk should be integrated into enterprise risk management and monitored throughout the relationship, not treated as a one-time procurement exercise. 

Evidence to request: 

• Which third parties are critical to operations, data, or regulatory obligations?
• What independent evidence supports their security, compliance, and resilience claims?
• How are concentration, subcontractor, geographic, and exit risks managed? 

10. Are we prepared to detect, contain, disclose, and recover from a major incident? 

Prevention alone is not enough. The Board should understand whether the organization can identify significant events early, contain their impact, make timely legal and regulatory decisions, communicate credibly, restore priority services, and learn from the event. Readiness should cover ransomware, account takeover, data compromise, fraud, third-party disruption, and other plausible business scenarios. 

Evidence to request: 

• When were crisis, incident response, business continuity, and recovery plans last exercised together?
• Are decision rights clear for regulatory disclosure, law enforcement, ransom demands, customer communication, and service restoration?
• What gaps were found in recent exercises or incidents, and are remediation actions closed?

From reactive oversight to demonstrable resilience 

These 10 questions help a Board move the discussion from technical activity to governance, accountability, evidence, and business outcomes. Effective oversight requires an ongoing dialogue with management, constructive challenge of assumptions, and decisions that reflect the organization鈥檚 risk profile, regulatory obligations, and digital maturity.

Modernization can be an important governance lever when it improves standardization, transparency, control automation, resilience, and the ability to produce reliable compliance evidence. Responsibility, however, remains shared across leaders, business owners, technology teams, security, compliance, legal, risk, audit, employees, and critical partners. 

Board takeaway

Security and compliance are not separate from business strategy. They are essential to continuity, lawful operation, stakeholder trust, and long-term value protection. 

Marielle Ehrmann is chief security officer and a member of the Supervisory Board of 麻豆原创 SE.

Note: This piece has been adapted from the German article 鈥淐ybersicherheit 鈥� Zehn Leitfragen f眉r den Aufsichtsrat鈥� by original authors Dr. Friederike Rotsch and Sebastian Lange alongside Marielle Ehrmann.

Following eight months of close collaboration with 麻豆原创, DACHSER has reached a major milestone on this journey: the successful migration of human capital management (HCM) to 麻豆原创 S/4HANA via .

Nearly a century has passed since Thomas Dachser founded the company in 1930 with a single truck, transporting cheese from the Allg盲u region to the Rhineland and returning with steel. Yet, logistics and warehousing remain the core business of the transport logistics company, which is headquartered in the town of Kempten in south-west Germany. “Our core business is what we call our road business, meaning everything transported by road, across both dry goods and food segments,” says Herbert Egner, head of the IT Products Finance/HR division at DACHSER, who has nearly 40 years of experience in software development at the company.

Currently, DACHSER operates globally with approximately 427 company-owned branches across 43 countries. Out of its roughly 37,500 employees worldwide, 19,000 are based in Germany. Total revenue in 2025 reached 鈧�8.3 billion. “On one hand, we are a deeply tradition-conscious family business, now managed by the third and fourth generations. On the other hand, we place a massive emphasis on innovation鈥攁nd not just at the executive level,” Egner notes. “Every single employee should have the opportunity to contribute here.”

In terms of digitization, the company is currently replacing a multitude of small, manual processes with IT-based solutions. Following DACHSER鈥檚 licensing of the 麻豆原创 Business Technology Platform (麻豆原创 BTP) in 2024, employees can now leverage its low-code tools to build apps for their own workflows. For instance, instead of using physical clipboards and paper checklists to document damage to transport vehicles, DACHSER inspectors now use low-code applications to capture that information digitally.

麻豆原创 BTP and the 麻豆原创 Integration Suite also played a vital role in helping DACHSER move HCM to the cloud鈥攚hich, alongside the license conversion to 麻豆原创 S/4HANA, marks one of DACHSER鈥檚 most significant IT upgrades in recent years. “While we are a long-standing 麻豆原创 customer, we are certainly not your typical 麻豆原创 user,” Egner explains. “For transport and warehouse management, we still rely on our proprietary systems, which we began developing back in the 1980s. At the time, 麻豆原创 did not yet offer the specific system depth we required for our logistics processes.”

Finance software from 麻豆原创 has been in use at DACHSER since 2009, with journal entries transferred from the company’s internal systems to 麻豆原创 via an interface. In October 2024, the finance solution was migrated from an on-premise environment under 麻豆原创 ERP Central Component (麻豆原创 ECC) and 麻豆原创 HANA to the Microsoft Azure cloud. As part of RISE with 麻豆原创, DACHSER then successfully migrated its HCM payroll and time management modules for its 19,000 employees in Germany to the cloud in June 2026. This intensive eight-month collaboration between 麻豆原创 and DACHSER successfully established a cornerstone of the company鈥檚 global HCM transformation.

“For HCM, we chose RISE with 麻豆原创. However, when we decided to move to the cloud three years ago, it wasn’t yet mature enough to meet our specific needs,” Egner says. “Once our three-year contract with Azure expires, RISE with 麻豆原创 could become a viable model for our finance environment as well.”

The go-live was flawless: the very first payroll run for all employees in Germany in July 2026 went off without a hitch, and all payments were distributed on time. The system then entered a four-week hypercare phase, during which the project team closely monitored ongoing operations before regular support took over in August. With this step, DACHSER has established the necessary future-proof 麻豆原创 architecture for its HR operations. This opens up new possibilities for the company, allowing it to focus heavily on implementing new features within the 麻豆原创 SuccessFactors HCM Suite.

麻豆原创 S/4HANA transformation

“When we say we want to be the world’s most digitally integrated logistics service provider by 2030, this applies not only to digitizing our processes but also to integrating our acquisitions, which bring their own 麻豆原创 systems with them,” Egner says. “Under our ‘麻豆原创 First’ strategy, we roll out finance and HCM in these countries before deploying our proprietary transport and warehouse management systems.” This approach was recently executed in Italy and is currently planned for several acquired companies in the Iberia region.

DACHSER鈥檚 goal is to integrate the 麻豆原创 licenses of acquired companies into a global enterprise agreement with 麻豆原创, regardless of the country in which the systems are running. In the future, this will be handled via a semi-automated workflow. “When it comes to licensing in particular, it is crucial for us to have a unified agreement moving forward on how to manage these assets, ensuring that neither side faces these administrative overheads anymore,” Egner explains.

In connection with the license conversion from 麻豆原创 ECC to 麻豆原创 S/4HANA, both companies sat down once again to realign on their future path to mutual success. “Here at DACHSER, we are noticing a new openness from 麻豆原创, as well as a deeper understanding of us as a customer,” says Egner. “This has been received very positively on our end. People are working on both sides, and we want to shape this partnership in a way that allows both parties to benefit based on mutual respect.”

麻豆原创’s latest global research with Oxford Economics reveals that organizations are already using AI across approximately 25% of business tasks, yet despite this momentum, only nine percent of organizations have fundamentally adapted their business around AI.

That gap matters. It suggests that while AI adoption is accelerating, most companies are not embedding it into how the business actually operates.

This is precisely the challenge the is designed to solve: moving AI beyond individual productivity gains to orchestrating end-to-end business processes and delivering value across the enterprise.

鈥淐onversations with clients have fundamentally shifted. Customers are no longer asking about how to deploy AI, they鈥檙e demanding ways to harness it rapidly and to scale it to unlock maximum business value while minimizing risk,鈥� explained DK Sinha, CEO of Accenture Edge. 鈥淧rograms like are built exactly for that: combining standardized delivery, unrivaled industry expertise, and AI-enabled accelerators to help organizations capture the full potential of their transformation. The result is greater speed, predictability, and confidence as they build their digital core and reinvent with AI.鈥�

麻豆原创 often describes the Autonomous Enterprise as AI built on data, processes, and governance.  That sequence is important. Research consistently shows that poor data quality remains one of the biggest obstacles to enterprise AI, and mission-critical business processes cannot be “almost right.” AI needs the same controls, auditability, and compliance that organizations already expect from their ERP systems.

Why do so many AI initiatives stall?

The Oxford Economics findings point to an important conclusion: the biggest obstacle is no longer AI technology, it is business readiness.

Many organizations have added AI to individual applications. Far fewer have transformed their businesses to connect AI to the information, processes, and governance needed to create enterprise-wide value and move from piecemeal to enterprise-wide AI:

That distinction explains why so many pilots struggle to scale.

鈥淎I can only support intelligent business decisions when it is grounded in how the business operates, including who the customer is, which contract applies, what inventory is available, which approval policies are in place, and which regulatory requirements must be met,鈥� said Javier Navarro, 麻豆原创 global head at SEIDOR. 鈥淎s a trusted 麻豆原创 partner, our role is to help clients connect AI to the data, processes, and governance that enable real business value.鈥�

Most AI knows natural language. Enterprise AI must know the language of your business. This is the difference between using AI and running an AI-powered business, one where humans and AI work together across the enterprise, with AI grounded in trusted business data, governed processes, and enterprise-grade controls to deliver accurate, secure, and compliant outcomes for better and faster decision-making.

Medium-sized fast-growth companies have the greatest opportunity

Fast-growth companies are in a unique position: they have the agility to move faster than large enterprises, yet they still have the scale to generate meaningful value from AI. These businesses have unique competitive advantages, including fewer systems to integrate, shorter decision-making chains, less technical debt, and typically more willingness to adopt modern, cloud-native platforms. Autonomous enterprise capabilities represent a unique opportunity for these businesses to punch above their size.

This means they can deploy autonomous capabilities in weeks, not years, and see ROI far more quickly, turning agility into a genuine growth advantage against slower competitors.

 麻豆原创’s Autonomous Enterprise strategy is designed precisely for this reality. It provides AI agents with a structured understanding of business entities, relationships, and processes鈥攇iving them the context needed to support real business execution.

Realizing that value, however, also requires rethinking how business transformation itself is delivered to fast-growth companies that are focused on fast outcomes.

The most challenging part is not implementing AI technology, but redesigning the operating model to eliminate dependency on consultants and put more power in the hands of the customer. Traditionally, transformation was designed around consultant-driven delivery, resulting in high costs, long timelines, and knowledge dependence. We challenged this model based on three principles: customer enablement-first, customer ownership, and AI-driven continuous learning.

鈥淎I allows us to build knowledge directly into the organization through each stage of the transformation journey,鈥� said Jiro Furuya, CEO, Freedom Consulting. 鈥淎t the customer level, AI provides real-time guidance and support for day-to-day operations; at the consultant level, tools such as 麻豆原创 Joule for Consultants enhance problem-solving and accelerate deliver; and underneath, a centralized data foundation continuously learns from every project, ensuring that knowledge compounds over time. This model and 麻豆原创 GROW Fast methodology allow us to deliver go-lives in six-week waves, significantly accelerating time-to-value while ensuring that capability stays within the customer organization.鈥�

The power of ecosystem

Clearly, no company becomes an Autonomous Enterprise alone. Technology alone doesn’t create an Autonomous Enterprise鈥攂usiness transformation does. And while 麻豆原创 provides the trusted platform, our rich ecosystem of partners translates the capabilities into measurable business outcomes.

Partners bring the industry expertise, business process knowledge, and change management capabilities to help customers identify the highest-value use cases, integrate 麻豆原创 with the broader IT landscape, develop industry-specific AI agents, and accelerate adoption鈥攖urning the promise of AI into enterprise-wide value much faster than companies could achieve on their own.

As a result, 麻豆原创 has launched a 鈧�100 million partner fund to accelerate customer adoption of AI assistants and agents, recognizing that innovation at scale requires a vibrant ecosystem. For fast-growing companies, this dramatically lowers the barrier to adoption.

The first chapter of AI was about helping people work faster. The next chapter is about helping businesses think, decide, and operate more intelligently. That is the promise of the Autonomous Enterprise.

Becoming an Autonomous Enterprise is not just about AI itself, but about bringing together 麻豆原创, partners, and transformation expertise to accelerate outcomes. For fast-growth companies, this is an opportunity to compete differently, scale more intelligently, and define the next generation of market leaders.

Santina Franchi is president of the 麻豆原创 Corporate Segment.

But while there is enormous excitement about what AI can do, we are still measuring progress the wrong way. The number of models, pilots, or agents a company deploys tells us very little. What matters is whether AI transforms the core processes where business value is actually created.

Can it steer demand fulfillment to high-margin business when supply is disrupted? Can it help release a pharmaceutical batch without compromising quality and compliance? Can it resolve a critical maintenance issue before it turns into an outage?

This is where enterprise AI becomes real and where generic intelligence reaches its limits.

From pharmaceutical supply chains and manufacturing quality management to asset maintenance, trading, and retail, the hardest industry problems are deeply contextual. They are shaped by decades of interconnected processes, strict regulations, and operational constraints. Solving them demands more than a powerful model. It requires trusted business data, integrated processes, rock-solid governance, and a deep industry domain expertise–the very foundation upon which we are building . At the same time it demands a team which is obsessed with customer problem solving and world-class forward deployed engineers.

This convergence of industry depth and forward deployed engineering (FDE) is where the next wave of enterprise value will be unlocked.

Start with the value, not the technology

AI innovation too often turns into a search for problems to solve. We take the opposite approach: start with the customer’s desired business value and work backward.

Where is margin leaking? Why is there lost sales? What operational decisions take too long? What production deferment can be prevented?

The goal is no longer just incremental productivity for individual users. It is moving toward autonomous operations. Across industries, organizations need to compress the cycle between detecting an issue, simulating options and impact, deciding the right response, executing corrective actions, and learning from outcomes–in a compounding loop. What once took days can increasingly happen in minutes.

Through our forward-deployed engineering model, we co-develop tailored capabilities alongside customers. We solve high-stakes challenges that have no off-the-shelf answers, then turn those proven solutions into scalable software capabilities for entire industries. This is not innovation from a distance. It is built with the people who live these challenges every day.

Autonomous operations make this possible. Within clearly defined business guardrails, AI agents identify disruptions, model scenarios, evaluate trade-offs, orchestrate execution, and improve via feedback loops–with human experts firmly in the loop and on-the-loop to validate critical decisions and steer strategic direction.

Co-innovation as a catalyst for impact

The most valuable AI solutions are never built in isolation. They are forged inside live operating environments.

Takeda Pharmaceuticals, a global leader in research-based biopharmaceuticals, is applying this thinking to its future operating model.

According to Rebecca Kaufmann, senior vice president and head of Enterprise Platforms a , “AI represents the key enabler for Takeda’s transformation for the future. We are combining the 麻豆原创 industry and technology knowledge with our organizational real-life experience. The supply chain planning assistant will provide us with AI-detected root causes and AI-suggested remediation and also automated performance tracking resulting in a self-healing capability.”

That ambition–shifting from passive detection to systems that continuously learn, recommend, and self-heal—is taking hold across sectors.

Doehler, a global producer of natural ingredients for the food, beverage, life sciences, and nutrition industries, is already demonstrating what this looks like on the factory and logistics floor.

“Our people’s greatest value lies in deciding what to do next, and now they can focus entirely on that,” said Stephan Schissler, co-chief information officer at Doehler GmbH. “The Logistics Issue Resolution Agent detects the root cause, recommends the resolution, and turns a multi-hour firefight into a one-click decision. Our logistics team has recovered roughly a third of the time once lost to troubleshooting, and our processes are becoming self-resolving rather than exception-driven.”

These examples reinforce a decisive rule: AI delivers maximum return when it eliminates operational drag, freeing professionals to focus on judgment, strategy, and business outcomes.

AI that thinks like the industry

As AI moves from passive recommendation to intelligent execution, industry context becomes paramount.

The question is no longer whether an AI agent can take an action. The question is whether it understands which action is compliant, safe, and commercially viable within a specific environment and organizational guardrails.

• In life sciences, autonomous manufacturing coordinates processes from design-to-release while strictly adhering to compliance-critical quality and compliance criteria.
• In energy and natural resources, agents go beyond detecting maintenance alerts to dynamically schedule crews, stage spare parts, and rebalance operations.
• In engineer-to-order manufacturing, systems orchestrate complex quotations, adapt production schedules, and accelerate new product introductions.

Deep industry context is what makes these outcomes trustworthy.

Building intelligence that compounds

麻豆原创鈥檚 Industry AI portfolio is focused across seven core domains: Autonomous Asset Management, Autonomous Commodity Management, Autonomous Adaptive Production, Autonomous Regulated Manufacturing, Autonomous Revenue Growth Management, Autonomous Unified Commerce, and Autonomous Project Delivery.

While the use cases differ, the principle is the same: sustainable value comes from end-to-end value streams, not optimizing functional silos.

 鈥淏usiness performance depends on how quickly a company can respond when conditions change,鈥� said Andre Bechtold, president of 麻豆原创 Industries & Experiences and chief revenue officer of Industry AI at 麻豆原创. “By applying 麻豆原创鈥檚 industry knowledge across connected processes, Industry AI helps customers act earlier, limit disruption, and keep operations on track.”

The measure that matters

, one message from our customer discussions was unmistakable: the ambition across sectors is to move past fragmented experimentation and embed agentic intelligence directly into mission-critical workflows.

Our initial wave of early adopter customers has proven the compounding power of this model: Customer friction points sharpen our agentic solutions, stronger agentic solutions strengthen standard, and stronger standards accelerate the time to value the next customer can achieve.

The future of enterprise AI will not be decided by who launches the most agents. It will be decided by who can bring intelligence into the decisions and workflows where business value is created every day.

When AI understands the industry, operates within trusted boundaries, and learns alongside human experts, autonomy stops being an abstract vision. It becomes a better way to run the business.

For a complete overview of the Industry AI innovations and product announcements unveiled at 麻豆原创 Connect in 2026, see the and related 麻豆原创 News coverage.

Dominik Metzger is president and global head of Industry AI at 麻豆原创.

Finance leaders today are expected to do far more than report results. They鈥檙e expected to shape business outcomes, guide decisions, and respond quickly to change. Yet many finance processes still operate in isolation rather than in concert. Invoice approval and payment execution happen in separate places.

Planning, consolidation, and reporting often rely on separate systems and workflows. And governance often misses critical information by operating in a silo. The result is delays between identifying an issue, making a decision, and taking action.

This year at 麻豆原创 Connect, 麻豆原创 announced new capabilities designed to help close those gaps. These include , a new payment service embedded in 麻豆原创 Cloud ERP; 麻豆原创 Enterprise Financial Consolidation, a new addition to 麻豆原创鈥檚 enterprise performance management portfolio; and an expanded set of Joule Assistants that bring AI-powered support to finance, governance, and compliance processes.

While each innovation addresses a different challenge, they share a common objective. Finance teams should spend less time moving information between systems and more time acting on insights.

Embedding payments into finance operations

One of the most persistent sources of friction in finance is the payment process itself.

An invoice may be approved in the ERP system, but completing the payment often still involves banking portals, payment files, middleware, and reconciliation activities. Each additional handoff introduces complexity and can slow execution.

麻豆原创 Pay is designed to simplify that process by embedding payment execution directly into . When an invoice is approved, payment and reconciliation can occur within the same workflow and the same business context.

The launch also marks 麻豆原创鈥檚 entry into payment services through , a wholly owned 麻豆原创 subsidiary established earlier this year. As a regulated payment-service provider, Tereina enables 麻豆原创 to deliver services natively within finance workflows rather than relying solely on third-party providers. This helps keep payment execution, reconciliation, controls, and auditability connected to the underlying business transaction.

The service supports EFT, wire transfers, checks, and stablecoin-based settlement for cross-border transactions. More broadly, 麻豆原创 offers customers a wide range of payment options, spanning bank API integrations, connectivity to more than 10,000 financial institutions through SWIFT, digital-currency payments, and now natively embedded payment services.

For finance organizations, the opportunity is not simply faster payment execution. It is reducing the effort required to connect systems, manage exceptions, and reconcile information after the fact while keeping payment processing, reconciliation, controls, and auditability within a single workflow.

麻豆原创 Pay is now generally available in the United States and the United Kingdom.

Connecting performance management across the enterprise

The same challenge appears across enterprise performance management.

Planning, consolidation, close processes, and external reporting all rely on the same financial reality. Yet they are often managed through separate tools, data models, and processes. Maintaining a consistent view of performance can become increasingly difficult as organizations grow and business conditions change.

At 麻豆原创 Connect, we announced to help address that challenge.

Built on , the solution brings together statutory and management consolidation with close and disclosure capabilities on a common governed data foundation. By connecting these activities more closely, finance teams can spend less time reconciling information and more time understanding performance and responding to change.

The solution also includes new Joule Assistants for consolidation and disclosure processes. These assistants help identify anomalies, resolve exceptions, and support disclosure preparation while maintaining auditability and human oversight.

For organizations operating mixed environments–some 麻豆原创, some non-麻豆原创 environments–麻豆原创 Enterprise Financial Consolidation provides flexibility by supporting data from multiple sources. It also offers a path forward for customers looking to modernize existing consolidation environments and adopt a more AI-enabled approach to performance management.

麻豆原创 Early Adopter Care is planned for the first quarter of 2027.

Expanding AI across finance and governance

AI鈥檚 potential in finance extends beyond answering questions or generating summaries. The larger opportunity is helping teams complete work that has traditionally required significant manual effort, specialist knowledge, and cross-functional coordination.

At 麻豆原创 Connect, 麻豆原创 expanded its portfolio of Joule Assistants to address these opportunities.

The new assistants support areas including accounting, revenue recognition, pricing, disclosure management, project operations, governance, and trade compliance. Embedded directly into existing workflows, they are designed to help teams complete tasks more efficiently while preserving review processes and accountability.

Some focus on helping finance professionals understand complex calculations and reporting requirements. Others support pricing decisions, disclosure preparation, project execution, and continuous monitoring of risks and controls.

Just as important is governance.

As organizations deploy more AI agents across business functions, they need assurance that those agents operate within the same boundaries that govern human users. New governance, security, and compliance assistants are designed to extend existing controls, access management, and monitoring capabilities to AI-driven activities, helping organizations apply consistent oversight across both human and digital actors.

General availability for the Governance Assistant and International Trade Assistant is planned for Q4 2026.

From financial insight to immediate action

Finance has always played a central role in helping organizations understand performance. Increasingly, it is also expected to help organizations respond in real time.

That makes speed, visibility, and execution just as important as reporting accuracy.

The innovations 麻豆原创 announced at 麻豆原创 Connect reflect that shift. Whether simplifying payments, connecting performance management processes, or embedding AI directly into everyday finance work, each reduces the operational distance between identifying an issue and acting on it.

For finance teams, the ultimate goal is not simply faster processes or faster reports. It is making better decisions with greater confidence and executing those decisions with less friction.

Learn more about how 麻豆原创 is helping organizations modernize finance operations across ERP, planning, treasury, payments, and accounting .

Lawrence Martin is general manager and chief product officer for 麻豆原创 Finance & Spend.
David Imbert is chief marketing officer for 麻豆原创 Financial Management.

Procurement leaders operate in a very different environment than they did just a few years ago. Beyond cost and compliance, they must now strengthen resilience, manage supplier risk, support workforce strategies, and help businesses respond faster to changing market conditions.

Yet, many of the systems supporting these responsibilities remain disconnected. Supplier intelligence lives in one place, workforce information in another, and spend data often requires significant preparation before it can drive decisions.

At 麻豆原创 Connect, we announced a series of innovations across the 麻豆原创 Ariba, 麻豆原创 Fieldglass, and 麻豆原创 Concur portfolios designed to help organizations address those challenges: new AI assistants for spend analysis and contingent workforce management, embedded supplier risk intelligence through a new partnership with Moody鈥檚, a new solution extension with ORO Labs that orchestrates procurement across complex enterprise landscapes, and new 麻豆原创 Concur capabilities that simplify high-volume administrative work.

Together, these innovations point to a broader shift toward , a domain within the . AI connects information, insights, and execution, while people remain in control of the decisions that matter most.

Better spend decisions start with better visibility

Many procurement organizations still struggle to turn spend data into actionable insight quickly enough to support decision-making.

According to the 2026 Economist Enterprise report, , sponsored by 麻豆原创, digital transformation is now the top strategic priority for procurement over the next 18 months, and improving spend analytics is one of its biggest drivers, cited by 53% of executives. These findings reflect growing pressure to improve decision-making and business impact.

Earlier this month, 麻豆原创 announced the availability of 麻豆原创 Ariba Spend Analysis and Insights, giving organizations a new way to bring together spend data across 麻豆原创 Ariba, 麻豆原创 Fieldglass, 麻豆原创 Concur, 麻豆原创 Cloud ERP, and other systems into a single foundation for analysis and action.

Building on that foundation, we announced the new Spend Analytics Assistant at 麻豆原创 Connect. Planned for general availability in Q1 2027, it helps procurement teams identify opportunities for cost savings, compliance, supplier consolidation, and risk mitigation. Rather than searching through reports, teams see highlighted areas that merit attention, along with supporting context to help them prioritize and act, so they spend more time on decisions and less on data preparation.

Bringing supplier risk into everyday decisions

Supplier risk is now a strategic concern across every industry, yet risk data often remains disconnected from the sourcing and purchasing decisions where it matters most.

At 麻豆原创 Connect, we also announced a new partnership with Moody鈥檚 that embeds supplier risk monitoring directly into . By bringing financial health, operational risk, and regulatory status into the same environment where supplier performance and relationship data are managed, procurement teams can evaluate risk as part of everyday decision-making rather than periodic reviews.

The partnership also strengthens the foundation for AI-powered procurement. When risk information is embedded directly within procurement workflows, it becomes part of the business context used to help identify emerging issues and support earlier intervention. This gives teams a more complete view of supplier relationships and greater ability to respond before potential disruptions affect operations.

General availability is planned for Q4 2026.

Connecting procurement across complex enterprise landscapes

The disconnection that slows procurement decisions often extends beyond data; it also shows up across the systems themselves. For many organizations, procurement unfolds across a mix of 麻豆原创 and third-party applications, each supporting different parts of the process. Coordinating work across that kind of landscape, without disrupting the systems teams already rely on, can be one of modern procurement鈥檚 more persistent challenges.

To solve this, 麻豆原创 announced a new solution extension developed with ORO Labs: 麻豆原创 Ariba Procurement Orchestration by ORO Labs. It provides a purpose-built orchestration layer that connects procurement experiences, workflows, and systems across complex environments spanning both 麻豆原创 and third-party applications.

For employees, this means a more unified experience and a simpler way to initiate and navigate procurement work. Low-code/no-code workflows let teams adapt processes as business requirements and policies change, without disrupting underlying systems. Organizations can also apply agentic AI within these workflows while preserving the controls, approvals, and human oversight that enterprise operations depend on.

The solution extension works alongside Joule Work, with Joule Work serving as the primary engagement layer. General availability is planned for November 2026.

Making external workforce management more proactive

For many organizations, contingent workers have become an essential part of how they scale skills, execute projects, and adapt to changing business needs. Managing those workers, however, often requires coordination across procurement, HR, hiring managers, suppliers, and compliance teams.

We also announced the new Contingent Workforce Assistant, designed to bring greater visibility to that process. Planned for general availability in Q4 2026, the assistant helps organizations manage the contingent worker life cycle by surfacing issues such as onboarding delays, pending approvals, compliance requirements, and other exceptions before they affect assignments. By bringing relevant information into a single experience, teams can address issues earlier while maintaining governance and auditability.

This proactive approach addresses one of the persistent challenges in workforce management: identifying issues early enough to act before they become operational problems.

Simplifying travel and expense management through targeted automation

Not every productivity challenge in spend management requires a major transformation initiative.

In many organizations, the greatest source of friction in travel and expense management comes from routine administrative work, such as retrieving receipts, locating invoice images, preparing audit documentation, and reviewing user permissions. Individually small, these tasks collectively consume significant time across finance, audit, compliance, and expense management teams.

To address this, 麻豆原创 Concur announced new additions to . Building on capabilities already used to support tax, audit, compliance, and reporting, 麻豆原创 plans to introduce new micro-apps that help organizations export employee expense information, simplify access-governance reviews, and retrieve vendor invoice documentation in bulk.

The approach is intentionally practical: rather than redesigning entire processes, the micro-apps target specific operational pain points. Customer adoption of the existing micro-apps points to strong demand for this focused automation.

Additional micro-apps are planned for general availability in Q4 2026.

Turning spend intelligence into action

Most organizations have plenty of spend data. The persistent challenge is turning that information into timely decisions and meaningful business outcomes.

The innovations announced this year at 麻豆原创 Connect address that challenge from multiple angles, improving visibility into spend, bringing risk intelligence closer to procurement decisions, connecting procurement processes across complex enterprise landscapes, improving external workforce management, and reducing administrative effort across travel and expense.

Better decisions require better visibility, stronger context, and fewer barriers between identifying an issue and responding to it. This is the goal of Autonomous Spend Management: applying agentic AI across every spend process so disconnected workflows operate as one intelligent, governed system.

This matters more as procurement takes on a larger role in driving resilience, growth, and business performance. For organizations that can identify opportunities faster, respond to risk earlier, and spend less time on manual coordination, spend becomes a source of advantage rather than a cost to control.

Baber Farooq is chief marketing officer for Spend Management at 麻豆原创.

With the 2H 2026 release and throughout the remainder of the year, we’re taking the next step toward Autonomous HCM, bringing together agentic AI, trusted workforce data, and HR applications to help deliver the outcomes that matter most to organizations. At its core are three pillars: automate the HR function to release capacity from administrative work, redesign how tasks, workflows, roles, and teams are structured across people and AI, and reinvent how organizations recruit, develop, move, and reward their people.

This vision comes to life through working together across 麻豆原创 applications to help carry out end-to-end HR processes and help people make better decisions, faster, all within a single workspace: . This is what it looks like when AI works across an organization鈥檚 data, applications, and processes: not more AI for the sake of AI, but AI that delivers impact you can measure.

Building on the 麻豆原创 investments organizations have already made, the innovations we鈥檙e delivering throughout the second half of 2026 help automate, redesign, and reinvent work to realize the full promise of Autonomous HCM.

These innovations span the full employee lifecycle:

Plan & hire

Finding, hiring, and onboarding the right talent gets easier and faster, with every step aligned to business needs.

• As AI drives up application volumes, recruiters risk spending more time clearing a queue than assessing if a hire works for the business. can address this with repeatable, configurable screening that helps narrow large applicant pools to a focused shortlist. builds on that, with results now fully reportable in Report Builder, giving recruiters visibility into the reasoning behind AI-assisted decisions. Together, they help free up the time and attention to do what the business actually needs鈥攁pply their judgement to identify and engage the candidates most likely to succeed.

• helps put agentic capability directly inside the recruiter workflow so hiring never loses momentum, with monitoring pipelines and booking interviews through agentic scheduling, Winston Interview delivering a conversational AI-led screening experience, and the Interview Summary Agent automatically capturing and posting a structured summary to the candidate profile.

• Helping new hires get up to speed and start contributing faster, the equips HR teams to deliver personalized, timely onboarding at scale, with intelligent prioritization, simplified onboarding task creation, and automated custom form conversion.

Manage & pay

These innovations help organizations work more efficiently and stay compliant while giving employees fast, seamless support to reduce the routine inquiry burden on HR teams.

• The helps build trust in payroll so employees can have confidence in every paycheck. Administrators can quickly identify anomalies, resolve issues, and stay ahead of compliance, while employees can get direct access to clear, confident answers about their pay without raising a support ticket.

• The can reduce manual effort, prevent bottlenecks, and keep HR processes moving, proactively resolving workforce and payroll data issues, automating employee and job changes, and streamlining position management. It can also detect approval bottlenecks, nudge approvers, and recommend next steps.

• The can give HR administrators a smarter way to manage day-to-day system operations鈥攆rom audit readiness and compliance tracking to time and benefits configurations鈥攔educing routine maintenance and helping HR teams drive continuous improvement and innovation.

Develop & grow

These innovations connect skill-building, learning, and career development into a more intentional experience designed for continuous growth.

• The helps move organizations to proactive workforce readiness, connecting compliance monitoring, upskilling, and development in one experience. It can guide the right learning actions at the right time for employees, managers, and admins, from flagging compliance deadlines to identifying skill gaps and recommending targeted learning.

• People perform at their best when they know their employer is invested in their development. The helps employees close skill gaps, build personalized development plans, and set meaningful goals. For leaders, conversational talent discovery through Joule helps surface internal candidates, successors, and experts with a clear explanation of what shaped each result, helping organizations match the right talent to opportunities and grow people from within.

The next step toward Autonomous HCM

These are not standalone capabilities. Together, they are Autonomous HCM in action: AI working with trusted data and HR processes to remove friction, surface insight, and give people the space to focus on outcomes that matter. The organizations building that way today are the ones that will lead tomorrow.

Discover what鈥檚 new in the 麻豆原创 SuccessFactors 2H 2026 release with the or .

Rebecca Carr is president of HCM Products for 麻豆原创 SuccessFactors and CEO for SmartRecruiters.

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At , 麻豆原创 showcased the Autonomous Enterprise in action, including Joule Work, 麻豆原创 Autonomous Suite, and 麻豆原创 Business AI Platform.

麻豆原创 launched the Autonomous Enterprise at 麻豆原创 Sapphire in 2026. At this year鈥檚 麻豆原创 Connect, customers are weighing in on how they鈥檙e using it and how much farther they can go with it. Customers like Novartis, Nestle, and more are actually showing proof of the results. Trust and business context are key factors when it comes to reliable AI systems, and by its innovations built inside 麻豆原创 Autonomous Suite, 麻豆原创 is proving this is the beginning of better.

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鈥淚t’s time to show you the Autonomous Enterprise in action underpinned by great customer stories,鈥� 麻豆原创 CEO Christian Klein told his keynote audience at the start of the event.  

Klein reminded the audience that 麻豆原创 introduced the Autonomous Enterprise at its 麻豆原创 Sapphire conference in May 2026 as 鈥渢he beginning of better.鈥� Since then, he said, 麻豆原创 has worked with hundreds of customers to implement Joule Work as the AI engagement layer for the Autonomous Enterprise. 

A new UI for AI 

鈥淭his month, we are rolling out Joule Work for all customers,鈥� Klein said, describing it as a new UI for AI. He noted that for decades, users had to learn how 麻豆原创 worked, but with Joule Work, 麻豆原创 learns how people work. 鈥淔rom now on, the more than 400 million 麻豆原创 users can talk in their own language to 麻豆原创, and Joule Work gets the job done.鈥� 

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Klein explained that Joule Work is based on three pillars: conversations, where users ask questions and delegate tasks to agents; spaces, which display content, insights, and shared work; and Joule Studio, where customers extend Joule or build agents tailored to their own processes.   

He called 麻豆原创鈥檚 deep business and data context its 鈥渟ecret sauce.鈥� That context includes 麻豆原创 Knowledge Graph, which maps more than 7 million data fields, 50 million tables, more than 50,000 public and private APIs, 400 data products, and extensive process knowledge. 鈥淭hat’s why Joule doesn’t guess,鈥� he said. 

麻豆原创鈥檚 110,000 employees are already using Joule to perform hundreds of thousands of tasks every day, he shared: 鈥淲e are already seeing productivity gains in central functions like HR, finance, and procurement of over 20% and even bigger gains in more complex analytical and planning tasks.鈥� 

麻豆原创 Autonomous Suite 

Klein then turned his attention to the 麻豆原创 Autonomous Suite, the second layer in the Autonomous Enterprise. The suite initially covers five domains: finance, procurement, supply chain, human resources, and customer experience. 麻豆原创 Autonomous Suite is designed for work where a good answer is insufficient and 鈥渢he right answer鈥� is required every time, he said.  

鈥淟LMs are probabilistic and only give you the most likely answer,鈥� Klein said. He contrasted that probabilistic approach with 麻豆原创’s embedded agents. Because the agents operate within deterministic application workflows, they use the same data, follow the same rules, and leave the same audit trail as the applications themselves. Customers can choose the level of autonomy appropriate to each process, retaining human approval where risk is high and allowing greater automation for routine work. 

Since 麻豆原创 Sapphire, Klein said 麻豆原创 has created more than 20 Joule Assistants and over 200 Joule Agents, and is aiming for more than 400 agents by year-end. 鈥淏ut the real story isn鈥檛 the number of agents. It鈥檚 the business outcomes they deliver,鈥� he said. 

麻豆原创 Business AI Platform 

鈥淭hese agents can only understand your business so well because of the 麻豆原创 Business AI Platform,鈥� Klein said. 鈥淭he platform gives customers the best frontier model for every agentic use case, the deepest out-of-the-box business context and 麻豆原创-managed governance.鈥�  

Building on Klein鈥檚 remarks, Manoj Swaminathan, president and chief product officer of 麻豆原创 Autonomous Suite, described the 麻豆原创 Business AI Platform as the infrastructure that enables the Autonomous Enterprise. The platform is organized around three capabilities: building agents, contextualizing them with business data, and governing how they operate. 

Swaminathan explained how the context layer makes agents more accurate. Joule Studio spans no-code and professional development and offers access to more than 70 models, a strategy he said reduces lock-in and controls token costs.  

鈥淛oule Studio is natively integrated with our context layer,鈥� he said. 鈥淪o, the agents you build are more accurate, faster to deploy, and more efficient to run. Our knowledge graph is the map with the processes, data flows, and business tools. The agents find the right processes among thousands and the right information among over 7 million feeds. 麻豆原创 Business Data Cloud brings that understanding to all your data, one consistent view, the processes, policies, and logic to make it meaningful.鈥� 

Swaminathan said no AI can compensate for a broken data model. To help agents understand customers鈥� business rules, 麻豆原创 Domain Models are trained on 麻豆原创鈥檚 code. For predictions, 麻豆原创 provides two engines grounded in how a customer’s business operates: 麻豆原创-RPT, 麻豆原创’s model family trained on its business data, and the Prior Labs tabular AI model.

Jerseys from Jersey 

During his section of the keynote, Jan Gilg, global president of Customer Success & Americas, used an imaginary startup 鈥� 鈥淛erseys from Jersey鈥� 鈥� to invite a handful of customers to the stage to answer his questions and validate aspects of 麻豆原创鈥檚 Autonomous Enterprise strategy.  

The customers included Novartis, which is piloting Joule sourcing agents for bid analysis and negotiation support; Morgan Foods, which described a supply-chain transformation built on 麻豆原创 S/4HANA and 麻豆原创 Integrated Business Planning; Nestl茅, which emphasized the importance of a unified digital core; and 麻豆原创 customer and partner PwC, which spoke about the value of the Industry AI portfolio from 麻豆原创. 

Summing up their comments, Gilg noted that the elements of the 麻豆原创 Autonomous Suite 鈥渁re more than a collection of functional components. It鈥檚 one system working together like connective tissue that provides connectivity, context, and control.鈥�

Gilg also made several product announcements, including the launch of 麻豆原创 Pay, an embedded payments service that can automatically execute and reconcile supplier payments once an invoice is approved. He also announced 麻豆原创鈥檚 planned acquisition of TechWolf, a Belgian workforce-intelligence startup designed to help close skills gaps. 

Security and governance 

In the closing section of the keynote, 麻豆原创 Chief Security Officer Marielle Ehrmann focused on trust and governance. She noted that ransomware handovers that once took eight hours now take 22 seconds with AI, and while 83% of enterprises use AI daily, only 13% have real visibility into it.  

鈥淭he biggest threat to companies is not malicious AI; it鈥檚 unmanaged AI,鈥� Ehrmann said. To counter that threat, she said every agent should have a verified owner, a risk tier, and scoped access rights, supported by zero-trust checks on identity, purpose, and data sensitivity as well as human oversight. 

She emphasized that 麻豆原创 Business AI Platform incorporates regulatory and localization requirements, applies user authorizations, keeps customer data from being used to train foundation models, and supports three levels of oversight: human-in-the-loop approval, human-on-the-loop monitoring, and human-in-command override authority. 鈥淭he more autonomously an agent operates, the tighter the oversight applied,鈥� Ehrmann said, calling the principle 鈥渇reedom within boundaries.鈥� 

A clear shift among customers 

Wrapping up the keynote, Klein emphasized that trust is key to everything 麻豆原创 does and has been for over 50 years. 鈥淲e are delivering the most trusted and governed AI in the industry,鈥� he said, but added, 鈥淎I also requires significant change management to ensure it delivers the desired outcomes for your business.鈥� 

That is where 麻豆原创鈥檚 business transformation solutions come in, he said. 麻豆原创 Signavio measures process value before and after a change to help justify token spending; 麻豆原创 LeanIX discovers and tracks AI agents; and 麻豆原创 WalkMe supports user adoption and trust. Klein also highlighted an agent-led migration toolchain. Together, he said, these capabilities mean that the days of multi-million-dollar migration spend are gone.

鈥淭his is no longer the start of something better. This is better,鈥� Klein told the audience. He described a clear shift among 麻豆原创 customers: 鈥淔rom manual steps to connected workflows, from reactive decision-making to predictive insights, from humans in spreadsheets to humans in and on the loop–all powered by our AI platform to keep AI accurate, governed, and extensible.鈥�

And, most importantly, Klein said, 鈥淚ts ready for you today鈥� This is the Autonomous Enterprise.鈥� 

According to research from the , 72% of executives believe organizations claiming to transform work with AI are primarily automating existing processes rather than fundamentally redesigning how work gets done. Yet, 88% say redesigning work and roles creates significant enterprise value. And HR is ranked as the No. 1 owner of that work. As a result, HR and business leaders are looking at how technology can support their need to continuously align people, skills, and work with evolving business priorities.

This is the journey to . Autonomous HCM is the future of human capital management, where AI automates end-to-end processes, drives context-aware decisions, and optimizes workforce operations at scale.

As we think about Autonomous HCM, we have defined three pillars:

1. Automate the HR function to release capacity from administrative work: HR teams currently spend a significant share of their time on operational activities such as payroll, service delivery, recruiting administration, and workforce management. AI can reduce much of that burden, creating capacity for more strategic work. But automation is not the destination. It’s the foundation. The true value comes from what organizations do with the time and resources they regain.
2. Redesign how tasks, workflows, roles, and teams are structured throughout the enterprise, across both people and AI: For decades, companies optimized around jobs, functions, and fixed processes. AI challenges many of those assumptions. The opportunity is no longer simply to make existing work more efficient. It is to rethink work itself. This is not just a technology initiative. It is one of the most important business transformation challenges organizations face today.
3. Reinvent how organizations recruit, develop, move, and reward their people: Most talent processes were built for a world characterized by stable jobs and predictable career paths, but that world is rapidly changing. Organizations that succeed will create talent ecosystems that can adapt as quickly as the business itself.

These three pillars are all interconnected: automation releases capacity, work redesign determines how value is created, and talent-system reinvention makes the new model sustainable.

What makes this possible is 麻豆原创鈥檚 unique ability to bring together trusted workforce data, connected business processes, ontology, and AI within a single business context. Together, this context enables organizations to move beyond isolated HR activities and make strategic workforce decisions that drive the most urgent business outcomes. This context is what matters.

During 麻豆原创 Connect, 麻豆原创 announced that it has entered into an agreement for 麻豆原创 to acquire TechWolf. TechWolf provides a leading AI work intelligence platform, which helps organizations understand the work their people do and the skills they have, giving them the right context and the right grounding. Once the transaction closes, TechWolf is expected to power workforce and talent strategies across the 麻豆原创 SuccessFactors portfolio, bringing skills mapping, workforce planning, and organizational redesign informed by context from across the Autonomous Enterprise to guide decisions about development, deployment, and workforce transformation.

New Innovations to 麻豆原创 SuccessFactors HCM Suite

While Autonomous HCM is our vision for the future, we have several new innovations spanning the entire 麻豆原创 SuccessFactors HCM suite, available this month, including:

A growing portfolio of AI assistants

New AI assistants orchestrate work end-to-end and support decisions in real time. Many of the agents that we announced at 麻豆原创 Sapphire are available this month, including assistants for payroll, HR service, learning, recruiting, onboarding, and career and development. We have more agents coming soon, including the new Workforce Planning Assistant to support customers using . It is powered by connected workforce, contingent labor, and financial data from 麻豆原创 SuccessFactors, , and , to help business and HR leaders forecast workforce needs and create plans for the most effective mix of hiring, reskilling, redeployment, and external talent to address changing workforce demands.

Significant updates to our global benefits solutions

Benefits are among the most important decisions employees make, and they can also be some of the most complex for HR teams to support. To help make benefits administration more efficient for HR and benefits decisions more personalized for employees, 麻豆原创 announced two new innovations built natively into 麻豆原创 SuccessFactors Employee Central. The new agentic admin console for U.S. benefits provides HR administrators with a centralized view of benefits enrollment activity and exceptions, helping them quickly identify and resolve issues with built-in monitoring and natural language support. By reducing time investigating enrollment issues and navigating reports, organizations can improve oversight and manage complex enrollment periods more efficiently. Additionally, the new benefits decision support tool uses employee data already in the system to recommend coverage aligned to individual needs and budgets, resulting in a more personalized and intelligent enrollment experience for employees. 

New integration of WalkMe Learning Arc with

Creating and deploying learning content can often take weeks, making it difficult for organizations to respond quickly to changing business needs. With this new integration, learning teams can describe learning requirements in natural language, and AI generates courses and quizzes while maintaining author control. Learning experiences can then be created and deployed directly within 麻豆原创 SuccessFactors Learning in hours rather than weeks.

Shaping Autonomous HCM with our customers

As we look forward, we know that what has worked in the past will not take us to the future. The goal is not simply to showcase technology, but to prove how connected AI can help organizations translate strategic decisions into measurable workforce and business results faster and more effectively. Rather than organizing our innovation around individual products or modules, we are focusing our innovation around the most important outcomes our customers are trying to achieve鈥攁nd in doing so, leaning into 麻豆原创鈥檚 strength in connecting data, workflows, and processes across the entire 麻豆原创 portfolio. We will co-innovate with select customers to define, validate, and measure outcome-driven approaches that connect workforce decisions directly to business value, proving the value of our innovations in real time.

The journey starts now

We are at a defining moment for HR leaders. The future of work will not be shaped solely by technology. It will be shaped by the leaders who can translate transformation into meaningful outcomes for their organizations and their people.

Autonomous HCM is the future of HCM. But it does not start tomorrow. The journey starts today.

To learn more about the latest 麻豆原创 SuccessFactors innovations and how 麻豆原创 is helping organizations advance their journey toward Autonomous HCM, .

Siva Sundaresan is general manager and chief product officer for 麻豆原创 SuccessFactors.

These announcements build on the Autonomous Customer Experience strategy we introduced at 麻豆原创 Sapphire. That premise is straightforward: AI must understand customer intent, make context-aware decisions, and execute within business guardrails. Following through on a customer commitment requires AI grounded in real-time, enterprise-wide business data, with a deep understanding of the processes needed to orchestrate work from intent to outcome.

A quote needs accurate pricing and terms. An order depends on inventory, credit, payment, and fulfillment. A service interaction requires information about the customer, the product, and the commitments the company has already made.

Customers generally do not see this operational complexity. They experience whether the company followed through.

Joule Assistants move from providing answers to driving business outcomes

Many AI tools can reason about what should happen next and recommend actions. But executing complex enterprise processes requires more than that.

AI needs to understand the live state of the business, including customer commitments, products, pricing, agreements, orders, payments, policies, and operational constraints. It must then navigate the approvals, exceptions, and cross-application steps required to act. Without that grounding, proposed actions may appear reasonable but can be inappropriate, infeasible, or break down in execution.

This is the direction behind the latest Joule Assistant innovations. New and enhanced combine role-specific intelligence with business and process context across marketing, commerce, sales, service, finance, operations, and compensation, helping users move from understanding what should happen to getting the work done.

The innovations apply this approach to moments where fragmented context, manual steps, and handoffs can slow the customer experience.

• Campaign Assistant and Content Assistant help teams move from campaign ideas to execution faster by reducing the effort required to plan, create, refine, and activate marketing campaigns.
• Shopping Assistant makes product discovery easier and more relevant to what the customer is trying to accomplish.
• Quoting Assistant brings customer, pricing, and commercial context together so sellers can prepare quotes faster and spend less time assembling information across systems.
• Service Planning and Operations Assistant translates service demand into coordinated plans, reducing the manual work required to align people, resources, and customer commitments.
• Incentive Compensation Assistant and Recurring Receivables Assistant bring the same intelligence to critical processes behind the customer experience, helping resolve questions, reduce administrative work, and keep compensation and payment workflows moving.

Together, the new assistants announced at 麻豆原创 Connect extend the approach introduced at 麻豆原创 Sapphire. Sales Assistant brings engagement insights, pipeline signals, and deal context together so sellers can prioritize the right opportunities, prepare for every interaction, and spend more time building relationships and moving deals forward. Deal Closing Assistant unifies customer, product, pricing, and fulfillment intelligence to help teams build optimal quotes, surface relevant products with pricing and availability insights, and shorten the path from negotiation to confirmed orders.

The goal is not to add another conversational interface. It is to move work forward by bringing together the business context, process knowledge, and controls required to act.

Human judgment remains important where decisions require approval, oversight, or exception handling. Within defined guardrails, AI can assemble context, determine and execute appropriate actions, and escalate when human intervention is needed.

麻豆原创 Order Management Services coordinates execution across ERP landscapes

Order management becomes more difficult when an order crosses ERP systems, legal entities, plants, distribution centers, and business units.

Each transition can introduce different information about pricing, inventory, credit, fulfillment status, or ownership of the next action. Consolidating every ERP system before improving order execution is not always a practical starting point.

麻豆原创 is expanding to provide highly scalable, resilient, real-time omnichannel order orchestration across 麻豆原创 S/4HANA and other ERP systems.

Built on a cloud-native, event-driven architecture, 麻豆原创 Order Management Services coordinates order execution across channels, systems, business units, and third-party partners without requiring organizations to consolidate their ERP landscapes first.

The expanded capabilities provide a common layer for validating orders, checking inventory across locations, coordinating transactions between business units, and monitoring progress through fulfillment.

This gives organizations greater flexibility to add, remove, and experiment across channels, while enabling the order management layer to detect disruptions, trigger exception workflows, and keep execution aligned with the original customer commitment.

In complex enterprise landscapes, this combination of scale, flexibility, and reliable integration helps businesses deliver consistent omnichannel experiences while improving operational efficiency.

Complete business context across the customer journey

Customer intent enters the business long before an order reaches a fulfillment system. It begins when a customer discovers a product, engages with a campaign, requests a quote, or starts a purchase journey.

To deliver on that intent, customer-facing interactions must remain connected to the pricing, inventory, ordering, fulfillment, and financial systems that support them.

麻豆原创 Commerce Cloud, cloud ERP edition, is now generally available. First introduced at 麻豆原创 Sapphire, the edition allows midsize and growing companies to extend 麻豆原创 Cloud ERP with commerce capabilities that use ERP data for pricing, availability, orders, invoices, and other transactional processes.

For more than a decade, 麻豆原创 Commerce Cloud has supported some of the world’s largest and most complex digital commerce environments. With 麻豆原创 Commerce Cloud, cloud ERP edition, we are extending that approach to companies that want a more standardized, 麻豆原创-managed path to ERP-connected commerce.

By using 麻豆原创 Cloud ERP as the system of record, organizations can connect digital commerce with pricing, ordering, fulfillment, and financial processes without building and maintaining those connections independently. The result is a faster path to value, lower operational complexity, and an ERP-aligned commerce model designed to scale as the business grows.

The same principle applies as customer interactions move across teams. A new native connection between 麻豆原创 Engagement Cloud, enterprise edition, and 麻豆原创 Sales Cloud synchronizes contact and company information while bringing relevant engagement signals directly into sales workflows. Instead of reconstructing customer context, sellers can continue conversations with a clearer understanding of prior engagement.

Customer experience depends on execution

AI can understand customer intent, make context-aware decisions, and act within defined guardrails. The harder question is whether the business can carry that action across the systems, teams, and processes responsible for delivery.

The innovations announced at 麻豆原创 Connect address different parts of that challenge. Joule Assistants bring business and process context into the flow of work and help execute the next step. 麻豆原创 Order Management Services orchestrates orders across complex ERP landscapes. 麻豆原创 Commerce Cloud, cloud ERP edition, connects digital commerce directly with ERP pricing, availability, ordering, fulfillment, and financial processes.

Together, they advance a common principle: Autonomous Customer Experience depends on connecting customer-facing intelligence with the operational context and processes required to act.

Customers ultimately do not experience recommendations, models, or workflows. They experience whether the business followed through and delivered.

As AI begins to act on behalf of the business, the quality of that follow-through will depend on the business context, process knowledge, and operational reality behind every action.

Additional information on the 麻豆原创 Connect announcements, including availability details and product-specific innovations, is available in the . You can also watch the to see these innovations in action.

Balaji Balasubramanian is president and chief product officer of 麻豆原创 CX.

Many of the futures we have explored are no longer hypothetical. They are showing up in how work is organized and experienced today. The fundamental question now is whether organizations are shaping those changes deliberately or simply reacting as they unfold.

combines monitoring of more than 500 business, academic, government, and economic resources with original global mixed methods research involving 2,718 employees, 500 C-suite leaders, 60 HR executives, and founders of AI-native companies. It points to a fundamental concern: organizations may be optimizing the future of work for efficiency when they should be designing it for value.

The productivity trap

In October 2025, we explored 10 predictions for work, workforces, and work practices, each with two possible futures. A year later, the evidence is strongest for what we call the “AI Upgrade” future, in which AI鈥檚 value is measured through efficiency, productivity gains, and time savings. Signals of broader work transformation are emerging, but they remain less established.

The emphasis on productivity is understandable because time savings, output, and cost reductions are relatively easy to measure. The harder question is what organizations do with the capacity AI can create. They can ask employees to produce more of the same work, or they can redesign work, roles, teams, and talent systems around what people and AI can achieve together.

Work transformation needs a north star

Related research from our found that 88% of employees said their jobs have changed because of AI, yet half of those changes occurred without meaningful guidance from managers or their organizations. Employees are using AI to simultaneously offload tasks and expand the scope of their work, often without a shared playbook or a clear view of the intended outcome. 

Before organizations can transform work intentionally, they need a clear north star for what work should become. Yet between 55% and 70% of employees said they had received no clear organizational guidance on the work shifts they are expected to navigate. At the same time, 72% of C-suite leaders said that most organizations claiming to transform work with AI are really automating existing jobs faster. Without a shared vision for work transformation, organizations risk defaulting to isolated experimentation and efficiency-first use cases, creating uneven outcomes and limiting the combined potential of people and AI.

Six shifts to guide better work design

We endorse six shifts in work and organizational design that are both enabled by AI and associated with work-enriching characteristics well established in organizational psychology as leading to increased employee motivation, effort, performance, and retention:

• From specialist to generalist: Apply core expertise across a broader range of problems and tasks.
• From static jobs to dynamic roles: Organize work around flexible responsibilities and outcomes that evolve with business needs.
• From pyramid to diamond organizational design: Reduce coordination layers and concentrate more people closer to where core work happens, enabling them to own end-to-end delivery.
• From output to impact: Evaluate work by the value it creates, not simply by activity or deliverables.
• From centralized authority to decentralized decision-making: Move decisions closer to the people and teams with the context to act.
• From people management to self-leadership: Give employees greater ownership of their direction, performance, and growth.

Through an experimental study, we validated how all six shifts can enrich work. For example, employees perceived 77% greater autonomy under decentralized decision-making, while dynamic roles were perceived as 49% more challenging than static jobs. The full report examines these effects across all six shifts, compares where organizations are today with where employees believe they need to be, and offers a prioritized path for leaders to get started.

Work transformation requires HR transformation

If work shifts this substantially, the function responsible for the people agenda cannot assume its own role and structure will remain the same. HR was built for fixed jobs, predictable career paths, established hierarchies, and talent systems designed for consistency. AI challenges each assumption.

With this context, we see HR鈥檚 new mandate as focused on three interconnected pillars:

• Automate: Use automation and intelligent systems to release capacity from administrative work and create space for more strategic contribution. C-suite leaders estimate that 49% of HR work will be fully automated within five years, compared with 35% of enterprise work overall.
• Redesign: Lead enterprise work redesign by determining how tasks, workflows, roles, and teams are structured across people and AI. Executives ranked work redesign as the transformation pillar that will create the greatest enterprise value over the next five years and identified HR as its leading owner.
• Reinvent: Overhaul recruitment, development, mobility, performance, and rewards to support how work now gets done and who succeeds in the new way of working. Seventy-five percent of executives agree that organizations can succeed in the AI era only if they reinvent how they measure, reward, and grow people.

In the full report, we also explore four plausible futures for how these pillars might come together to shape HR鈥檚 strategic contribution, depending on where leaders believe the people agenda can create the greatest value.

The future of work is a choice

The future of work is already being built. The opportunity now is to build it intentionally, creating better work for people and greater value for organizations. 

Explore how our 10 predictions from 2025 are tracking, the evidence behind six work shifts for better employee and business outcomes, and four possible futures for how HR can create strategic value in an AI-enabled enterprise. .

Dr. Autumn D. Krauss is chief scientist of the Future of Work Research Lab for 麻豆原创 SuccessFactors.

The dates for 麻豆原创 Business Suite 7 maintenance have not changed: mainstream maintenance remains available through December 31, 2027, followed by optional extended maintenance through December 31, 2030.

At the close of the optional extended maintenance period, several support arrangements will expire simultaneously, including 麻豆原创鈥檚 current reselling and support agreements for third-party databases and Java. What we want customers to understand is what the expiring third-party agreements mean for their systems鈥攑articularly for systems running on third-party databases or Java. Customers using these technologies should prepare now and plan accordingly. 

According to Michael Bloch, member of the Board at the German-speaking 麻豆原创 User Group (DSAG), 鈥淓arly information is essential for companies operating 麻豆原创 landscapes. We appreciate the open communication about the support implications after 2030. This transparency gives customers an important basis for planning. The significance of these developments will differ across organizations depending on their individual circumstances. What matters now is drawing the right conclusions and establishing a clear strategy for the way forward. However, any decision should be based on a comprehensive assessment of all available alternatives and their respective long-term consequences.鈥�

What ends on December 31, 2030

At the close of the optional, extended maintenance at the end of 2030, several support arrangements expire simultaneously. Here are more details of what this means for our customers.

麻豆原创’s current reselling and support agreements with major third-party database providers for these legacy environments

The existing reselling and support arrangements enable 麻豆原创 and the technology providers to work closely together to build and improve tooling optimized for 麻豆原创 workload on third-party databases and to support, when an issue evolves at the integration between 麻豆原创 software and a third-party database or Java. 麻豆原创鈥檚 current reselling and support arrangements with major third-party database providers are scheduled to end for these legacy environments. With expiration of these arrangements, the foundation for providing these joint services to 麻豆原创 customers ceases to exist.

Once these agreements expire, 麻豆原创 will no longer provide database-specific support for third-party databases in these legacy environments, including for customers that originally licensed their third-party database through 麻豆原创 and enrolled in 麻豆原创 Maintenance and Support.

For all affected third-party databases, 麻豆原创 support will no longer cover the database-related corrections available today. What does that mean in practice? Today, when a customer raises a support ticket on a third-party database issue, 麻豆原创 handles it end-to-end, routing it through integrated internal processes directly to the database vendor’s team. After 2030, that integrated support path will no longer exist.

麻豆原创鈥檚 support arrangements for Java

麻豆原创鈥檚 current support arrangements for Java (Java 8/JEE5) used with 麻豆原创 NetWeaver Application Server Java 7.5 (麻豆原创 NetWeaver AS JAVA 7.5) are scheduled to conclude at the end of 2030. This is the central constraint: once these arrangements expire, 麻豆原创 maintenance, including customer-specific maintenance, cannot replace the support and rights they provide. 麻豆原创 NetWeaver AS JAVA 7.5 with all the add-ons will fall into customer specific maintenance starting 2031 without a follow-on release.

What happens after December 31, 2030

Implications for customer-specific maintenance

After the extended maintenance period expired at the end of December 2030, on-premise 麻豆原创 Business Suite 7 systems will run under customer-specific maintenance. However, what 麻豆原创 can deliver under customer-specific maintenance is even further restricted specifically for the third-party database or Java side.

For such systems, 麻豆原创 will not be able to provide comprehensive support for issues involving third-party databases or Java. This includes kernel patches and kernel security patches for third-party database environments. Also, 麻豆原创 may no longer be able to provide support and even downloads of database tooling associated with third-party databases. This includes databases, administration tooling, backup tooling, and upgrade and migration tooling for third party databases.

Again, this is a reminder of an important consequence of the expiring agreements with third parties. An issue may occur that cannot be resolved within the support framework available after 2030. That is, as laid out, especially the case for third party DB or Java issues. Customers should therefore not regard customer-specific maintenance for affected environments as a sustainable long-term operating model of business-critical systems.

Implications for conversion

Timing matters for another reason. Tooling for a听system conversion from 麻豆原创 Business Suite 7 on a third-party database to 麻豆原创 S/4HANA or 麻豆原创 Cloud ERP will only be available before the end of reseller/support agreements at the听end of 2030. After that point, only greenfield transition scenarios (new implementations) will be supported.

For 麻豆原创 solutions that rely on AS Java, 麻豆原创 provides solution-specific transition recommendations. Detailed product impacts and technical guidance are documented in the relevant 麻豆原创 Notes.

Recommended paths for our customers

麻豆原创鈥檚 strategic recommendation

Customers should use the time available to move toward 麻豆原创 Cloud ERP and the 麻豆原创 Autonomous Suite. This is the path to continued innovation and a fully supported, future-ready environment. The right sequence will depend on each organization鈥檚 starting point, business priorities, and landscape complexity.

Customers should plan to complete their transformation to 麻豆原创 Cloud ERP, together with the appropriate go-to solutions in the 麻豆原创 Autonomous Suite, by the end of 2030. Starting early gives organizations greater control over scope, timing, and risk.

Transition option for 麻豆原创 ERP

Qualifying customers whose transformation is underway, but that need additional time can consider Transition Option for 麻豆原创 ERP. This time-bound subscription offering is intended for the largest and most complex 麻豆原创 Business Suite 7 customers that may not complete their move by the end of extended maintenance. It is designed to support business continuity from 2031 through 2033 while enabling a structured path to 麻豆原创 Cloud ERP. Systems must be moved to 麻豆原创 ERP, private edition on 麻豆原创 HANA before December 31, 2030, and further eligibility requirements apply. The transition option does not support Java-dependent capabilities.

In select cases, switching to 麻豆原创 HANA database

Customers that intend to continue operating 麻豆原创 Business Suite 7 on premise can remove the third-party database dependency by moving the relevant system to 麻豆原创 HANA before the end of 2030. This does not extend mainstream or optional extended maintenance. The system would still enter customer-specific maintenance, with the limitations that this entails, and 麻豆原创 does not recommend this as the strategic long-term path.

Moving forward together

Our customers do not need to make these decisions alone. We are working with customers, partners, and our user groups to make the implications transparent and support realistic planning.

The maintenance dates announced in 2020 remain in place. On top, after 2030, dependencies on affected third-party databases and 麻豆原创 NetWeaver Java cannot be comprehensively supported even under customer-specific maintenance. Our customers have defined paths forward, but some choices must be implemented before the end of 2030.

Now is the time to assess affected systems, clarify dependencies, and discuss the best path together with 麻豆原创 or an 麻豆原创 partner. Acting early gives organizations the freedom to sequence the work around their business priorities, reduce uncertainty, and protect continuity.

We will continue to communicate transparently and support our customers as they move towards 麻豆原创 Cloud ERP and the 麻豆原创 Autonomous Suite.

Learn more

Maintenance Strategy

2020 announcement

• 2020 maintenance announcement for 麻豆原创 Business Suite 7
• Interview on the 2020 maintenance announcement

麻豆原创 ERP/transition

• Updates for 麻豆原创 ERP, private edition, transition option

麻豆原创 Notes

• 1648480:
• 3464370:
• 3591251:
• 3768400:
• 3832186:

Philipp Herzig is CTO and a member of the Extended Board of 麻豆原创 SE.

Joule Work, Joule Assistants and 麻豆原创 Business AI Platform help organizations turn business AI into trusted outcomes across every core function.

LAS VEGAS 鈥� (NYSE: 麻豆原创) today showcased the Autonomous Enterprise in action at 麻豆原创 Connect, demonstrating how Joule Work and Joule Assistants generate value for customers across every line of business.

“This is a breakthrough moment for business AI,” said Christian Klein, CEO of 麻豆原创 SE. “The Autonomous Enterprise has arrived. With Joule and 麻豆原创 Autonomous Suite, built on 麻豆原创 Business AI Platform, we’re helping organizations like Novartis, Morgan Foods, Nestl茅 and PwC connect intelligence across every function to deliver trusted business outcomes.”

At 麻豆原创 Connect, these customers shared how the Autonomous Enterprise is already driving measurable value across their businesses:

• 鈥淲e’ve co-developed and recently launched a pilot of 麻豆原创’s Joule Sourcing Assistant,鈥� said Christoph Buerki, Head of Procurement, Novartis. 鈥淥ur vision is for AI agents to handle the manual heavy lifting, enabling sourcing professionals to focus on strategy, supplier engagement and value creation.鈥�
• 鈥淎t Morgan Foods, we鈥檙e already putting the pieces in place for agentic adoption, especially through our technical foundation. The biggest shift will be moving from reactive to predictive, where agents can connect demand signals, inventory levels and supplier status faster than ever before,鈥� said Steve Hankins, Chief Financial Officer, Morgan Foods, 鈥淥ur superpower will come from people on top of agents. Our approximately 1,000 employees are what make our supply chains work鈥攖he technology helps make them even better.鈥�
• 鈥淥ur 麻豆原创 S/4HANA transformation across 140 countries is creating a connected digital core with consistent processes, trusted data and greater visibility across markets and functions. With that foundation, Joule and the Accounts Receivable Assistant can automatically prioritize collections work based on risk and business context, recommend the next best action and help our finance teams respond faster to change,鈥� said Luca Dell’Orletta, Global Head IT Innovation & Enterprise Architecture, Nestl茅.
• 鈥淚n professional services, billing can be one of the more error-prone processes, with accuracy playing an important role in the client experience. 麻豆原创鈥檚 Billing Assistant brings greater intelligence to this critical process, helping identify potential issues earlier so our people can deliver better client outcomes and build stronger relationships,鈥� said Jill Notz, Chief Accounting Officer, Controller, Business Services Leader, PwC. Building on the success of the Billing Assistant, PwC and 麻豆原创 are into new areas of joint AI innovation.

麻豆原创’s Autonomous Enterprise Scales Up

Joule Work is beginning its customer rollout as a new AI user interface that lets employees interact with 麻豆原创 solutions in their own language and complete work across 麻豆原创 and third-party data. Grounded in business context from 麻豆原创 Knowledge Graph, which maps more than 7 million data fields, Joule provides information users can rely on. At 麻豆原创, Joule Work is already live with 110,000 employees, with the company seeing 20% productivity gains across finance, HR and procurement. A select group of customers also is using Joule Work in their business environments.

• 鈥淧artnering within the Early Adopter Program for Joule Work Desktop has delivered more momentum in just a few months than we saw in all the years before,鈥� said Peter Wied, Director of Complexity Management at Reinhausen. 鈥淲e see huge potential in making access to our 麻豆原创 S/4HANA data significantly easier and faster through natural language. This has the potential to permanently transform how our teams interact with business data.鈥�
• 鈥淭he dedicated team has been phenomenal. Their proactive communication, focus on accelerating architecture activation, rapid remediation of defects and overall support have enabled us to become Joule ready much sooner than would have been possible had we needed to navigate these challenges on our own,鈥� said Keith Smith, 麻豆原创 Platform Architect, CONA Services. 鈥淭he CONA team is excited about the progress made to date and looks forward to exploring and testing Joule’s capabilities in greater depth over the coming months as we continue our adoption journey.鈥�

When a query is not enough, Joule Assistants execute operational work directly within business workflows, using established data, authorizations and audit trails. Customers control the level of autonomy. 麻豆原创’s approach is also open: through the Agent2Agent protocol, Joule can connect with third-party AI and agents, bringing governed business context into broader AI workflows.

At 麻豆原创 Connect, 麻豆原创 showcased how these capabilities generate outcomes across 麻豆原创 Autonomous Suite:

• In finance, controllers can automate revenue recognition, disclosures and trade compliance, with early deployments of the International Trade Assistant reducing trade classification effort by up to 50%.  
• In supply chain, planners can turn multi-hour disruptions into one-click resolutions across planning, manufacturing and logistics.
• In HR, leaders can determine the best way to close workforce gaps, while employees can make smarter benefits choices tailored to their needs.
• In procurement, decision-makers can identify savings opportunities against peer benchmarks and manage the full contingent workforce life cycle.
• In customer experience, organizations can turn customer engagement into sales opportunities with AI-powered campaigns, quoting and customer workflows.
• Across industries, 麻豆原创 is collaborating with customers to apply AI to specialized industry processes, helping organizations in manufacturing, retail, commodity trading and asset-intensive industries improve decision-making, automate routine actions and build more resilient operations.

The Platform Powering Business AI You Can Trust

麻豆原创 Business AI Platform gives organizations one place to build, contextualize and govern AI across 麻豆原创 and third-party environments. With Joule Studio, business users and developers can build and extend skills and agents from no-code to pro-code workflows, choosing from more than 70 AI models. 麻豆原创 Business Data Cloud and 麻豆原创 Knowledge Graph ground those agents in trusted business data and context, helping organizations select the right model for each use case and better manage AI value and consumption.

麻豆原创-managed governance helps organizations scale AI with transparency, accountability and control. 麻豆原创 AI Agent Hub in 麻豆原创 LeanIX solutions provides a central inventory of enterprise agents, while 麻豆原创鈥檚 independently ISO/IEC 42001-certified governance architecture supports regulatory requirements, established authorizations and auditable AI operations.

To help organizations adopt AI at scale, 麻豆原创’s Business Transformation Management portfolio, including 麻豆原创 Signavio, 麻豆原创 LeanIX and 麻豆原创 WalkMe solutions, provides the toolchain to analyze processes, govern applications and agents, and drive adoption across the enterprise.

麻豆原创 Brings Embedded Payments to Cloud ERP

麻豆原创 also announced that it is entering the payments market with 麻豆原创 Pay, powered by Tereina, an 麻豆原创 company. Embedded directly in 麻豆原创 Cloud ERP, 麻豆原创 Pay executes and reconciles payments within the same workflow when an invoice is due, reducing manual file handling and external processing dependencies, while giving customers greater control.

Partners Expand the Reach of Autonomous Business

麻豆原创 is extending the Autonomous Enterprise beyond 麻豆原创’s own application portfolio through new partnerships that bring specialized intelligence directly into 麻豆原创 workflows.

A new partnership between 麻豆原创 and Moody鈥檚 Corporation is designed to embed Moody鈥檚 supplier risk monitoring data into 麻豆原创 Ariba Supplier Lifecycle and Performance. This gives procurement teams continuously updated insights into supplier financial health, operational risk and regulatory status at the point of decision. Organizations will be able to move from periodic risk reviews toward continuous monitoring, enabling Joule Agents to flag supplier risk signals and surface alternatives before disruption affects the business.

An expanded partnership with ORO Labs brings procurement orchestration to 麻豆原创 customers as a solution extension. 麻豆原创 Ariba Procurement Orchestration by ORO Labs is a purpose-built orchestration layer that connects procurement workflows end to end across heterogeneous enterprise environments, giving business users a seamless experience for intake, actions and visibility.

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About 麻豆原创

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We believe this recognition reflects 麻豆原创’s commitment to helping enterprises deliver intelligent, personalized customer experiences, enhanced by AI, built on trusted data and connected end-to-end business processes, and grounded in real-life business context. Joule Agents, Joule Assistants, , and together provide the agentic marketing backbone to advance this vision of the Autonomous Enterprise.

AI is evolving from assisting marketers to executing and acting on their behalf, creating a fundamental opportunity for organizations to completely rethink how they engage customers. Agentic marketing enables AI agents and assistants to understand context, take action, and help brands deliver more relevant personalized experiences.

Trust is the foundation to this shift. As AI takes on greater responsibility, organizations need solutions built with security, compliance, governance, and trusted business data so customers can confidently rely on AI-driven interactions and decisions.

Why agentic marketing matters

Consumers are becoming more comfortable using AI, reshaping their expectations of the brands they love and the organizations they interact with. 麻豆原创’s indicates that 33% of consumers now trust AI assistants to complete purchases on their behalf, while 31% say interactions with AI assistants make them more loyal to a brand. The trend is even more pronounced across younger consumers. Across generations, we see AI playing an influential role in purchasing decisions and long-term customer loyalty. For example, a consumer might ask an AI assistant to reorder a household product, book a trip, or recommend a new product based on past preferences. For those interactions to succeed, customers must trust that the AI is acting on accurate information, protecting their data, and making decisions within appropriate guardrails.

Organizations from manufacturers to fashion brands are seasoned in using generative AI to create content, build audiences, and improve marketing efficiency. Attention is turning to agentic marketing: moving from AI that recommends to AI that acts.

Marketing context meets business reality

Most marketing platforms understand customer interactions well: clicks, opens, purchases, and browsing behavior. Those signals matter, but they only tell part of the story. The most valuable signals often live elsewhere across the enterprise in inventory availability, order status, fulfillment performance, product profitability, return behavior, and subscription and replenishment cycles.

Marketing often sees customer behavior, while ERP systems see business reality. The greatest opportunity for AI emerges when those worlds come together. Because 麻豆原创 sits at the heart of how many of the world’s leading enterprises operate, marketers can access the operational intelligence that lives within the systems running the business, enabling AI to make decisions based not only on what customers are doing, but on what the business can actually deliver. In an age of agentic marketing, that distinction matters.

Why 麻豆原创 was recognized as a Leader

麻豆原创 was recognized for the following strengths by the IDC MarketScape:

• With the agentic layer of Joule, marketers get real-time, context-aware personalization without having to reconcile 麻豆原创 and non-麻豆原创 data themselves, since Joule Agents and Joule Assistants draw on a harmonized 麻豆原创 Business Data Cloud foundation. Teams receive role-based guidance for navigating complex workflows across Autonomous Domains, while everyday tasks such as retrieving information, connecting enterprise data, and completing multi-step processes happen independently, freeing staff from manual execution. In 麻豆原创 Engagement Cloud, this means personalization and segmentation scale on their own, and the 4Q25 CX release lets customer service issues resolve autonomously, backed by 麻豆原创’s enterprise data layer and 麻豆原创 Knowledge Graph, instead of requiring escalation.
• Because Joule Studio is low code, organizations with reasonable levels of in-house development capabilities can build custom agents themselves, without waiting on IT or writing code, while still connecting to Joule, the 麻豆原创 Customer Experience portfolio, and third-party systems grounded in 麻豆原创 Knowledge Graph and 麻豆原创 Business Data Cloud. For sales and marketing teams, this means faster deal cycles and higher conversion: a sales-assistant agent, for example, automatically surfaces buying patterns and product recommendations from purchase history, sparing reps the manual analysis.
• Marketers running campaigns across email, digital ads, wallet, conversational, and web can manage them all from a single platform instead of stitching together point solutions. Because the engagement events service connects external data sources into real-time segmentation, campaigns stay relevant as customer behavior shifts. AI-assisted tools such as natural-language product search, AI-generated consumption summaries, and an AI-assisted report builder cut down the manual work of building segments and reporting, enabling marketers to focus on strategy while AI handles recommendations, translation, prediction, and reporting at scale.

AI that drives business results

Organizations around the world are using 麻豆原创’s AI-powered customer engagement capabilities to create more connected experiences and drive business results. For example:

• uses an AI-powered data foundation to boost fan subscriptions by 50% and improve pre-game fan engagement by 80%.
• Molton Brown combines AI-enhanced commerce and personalization to create seamless customer experiences that strengthen loyalty while maintaining 100% uptime during peak periods.
• connects AI-driven shopping experiences, such as virtual beauty tools, and personalized product recommendations to deliver engaging and elevated customer experiences.

Advancing toward the Autonomous Enterprise

Being named a Leader in the IDC MarketScape: Worldwide Agentic Marketing for B2C Enterprises 2026 Vendor Assessment is an important milestone for 麻豆原创 and the evolution of agentic marketing as a category.  As organizations move from experimenting with AI to deploying agents that can reason, recommend, and act, we believe this recognition validates 麻豆原创’s approach of combining AI, trusted business data, and connected business processes to help customers navigate this next phase of transformation.

The vision of the Autonomous Enterprise is one in which AI-powered agents help organizations connect customer, operational, and business data to make and execute decisions with greater speed, relevance, and scale. In this future, AI doesn’t replace human expertise; it amplifies it. Intelligent assistants and agents can help teams act faster and more effectively, with governance and guardrails that keep people responsible for the strategy, creativity, and judgment that build lasting customer relationships.

Ultimately, the success of agentic AI will depend not only on what it can do, but on how much customers and businesses trust it to act on their behalf.

Learn more

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Sara Richter is chief marketing officer for 麻豆原创 Engagement Cloud at 麻豆原创.