AI agents are now creating a new chapter. The next frontier is no longer analyzing faster, but letting agents act: working together across planning, procurement, manufacturing, logistics, and operations.

For more than 50 years, 麻豆原创 has helped organizations run their supply chains by connecting business context, intelligence, and execution across end-to-end processes.听

We are proud to share that 麻豆原创 has been positioned in the Market Shapers quadrant of the inaugural Gartner庐 Emerging Market Quadrant for Integrated Supply Chain AI Agents 鈥� Established Vendors. This is Gartner’s first formal assessment of this emerging category. In our view, it reflects the progress we have made in bringing AI agents into the heart of supply chain operations, and the direction we continue to take.听

Gartner describes the Market Shapers quadrant this way:听

From assistants to orchestrated agents听

Supply chain decisions never happen in isolation. A supplier delay can ripple through production schedules, logistics, customer commitments, and financial results. Yet much of the AI in supply chains today still works within a single function or a single task. It can point out a problem, but people still must weigh the trade-offs and act across several systems.听

With Autonomous Supply Chain Management, people define goals and priorities, AI assistants orchestrate activity across domains, and agents execute the work within governed, end-to-end processes. Through , role-specific assistants coordinate specialized AI agents across design, planning, sourcing, manufacturing, delivery, and operations. Together, they sense disruptions, analyze the impact, simulate alternatives, and execute coordinated responses, with people making the decisions that matter most.听听

Joule is where it all comes together. People express what they want to achieve, and Joule Assistants take it from there.听 Each assistant is tailored to a specific role, while agents handle specialized tasks within business-defined guardrails.听

For example, when a planner describes a goal, the Supply Chain Planning Assistant coordinates demand, inventory, and capacity agents to build and simulate scenarios, so the planner can focus on evaluating outcomes and making the decision. People direct. Assistants coordinate. Agents execute.听

And because 麻豆原创 supply chain agents work within the broader 麻豆原创 Autonomous Suite, a supply chain decision can trigger the right financial, workforce, or customer action as part of the same coordinated response.

Agents that act where the work happens听

We believe AI agents create real value only when they move from recommendation to execution safely. Greater autonomy must be accompanied by greater accountability. As agents take on more responsibility, organizations need visibility into what those agents are doing and why. That requires three things working together.听

First, agents need to work inside the system of record. 麻豆原创 agents operate directly in 麻豆原创 Cloud ERP and the 麻豆原创 supply chain management suite executing production orders, convert requisitions, or direct warehouse tasks, with no middleware between decision and action. They can also extend coordination to trading partners through 麻豆原创 Business Network.听

Second, agents need business context. 麻豆原创 Business Data Cloud, 麻豆原创 Knowledge Graph, and our supply chain semantic model give agents a shared understanding of business entities, relationships, and rules. That helps them understand what a signal means across domains and how a disruption will affect the business.听

Third, agents need governance. Through 麻豆原创 Business AI Platform, Joule Studio lets customers and partners build agents, and 麻豆原创 AI Agent Hub governs both 麻豆原创 and non-麻豆原创 agents with identity enforcement, audit trails, and explainability. Joule Work gives people one place to monitor and act on agent recommendations.

Delivering real results for customers听

Customers will not value us by how many agents we build, but by the outcomes those agents deliver. Recognition is only meaningful when it reflects real impact for our customers, that are already putting supply chain agents to work with capabilities available today.听

Takeda Pharmaceuticals will use Industry AI for regulated manufacturing, where agents monitor production KPIs, flag deviations, and initiate corrective workflows in GxP environments. The expected results include up to 10% productivity gain and 25% less stock-out revenue loss.听

Lemvigh-Mueller deployed three coordinated agents to handle supplier order confirmations, reaching more than 90% touchless processing of 100,000 confirmations a year.听

RWE is applying Autonomous Asset Management to its offshore wind fleet, where AI agents analyze past incidents, identify root causes, and generate pre-filled work orders for technicians.听

Looking ahead听

Supply chains don’t become autonomous overnight. The journey happens step by step: starting with agents that recommend, then agents that act within clear guardrails, and expanding autonomy as data, processes, and governance mature.听听

As we work with our customers and partners, our agent coverage will continue to expand every quarter across supply chain domains.听听

We are grateful to our customers, partners, analysts, and 麻豆原创 teams whose collaboration shapes our products and strategy. We see this recognition as an important milestone, and we remain committed to helping organizations turn their supply chains into a source of resilience and lasting business value.听

Devesh Mishra, General Manager and Chief Product Officer for 麻豆原创 Supply Chain Management

*Gartner, Emerging Market Quadrant for Integrated Supply Chain AI Agents 鈥� Established Vendors, Tess Frenzel, Carlton Sapp, October 28, 2026.
GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.听
Gartner does not endorse any company, vendor, product or service depicted in its publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner publications consist of the opinions of Gartner’s 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 publication, including any warranties of merchantability or fitness for a particular purpose.听

Those days have long since been lost to history.  

From a single building in Bengaluru, 麻豆原创鈥檚 presence in India has expanded across the country, transforming a small operation into one of 麻豆原创鈥檚 most important global innovation hubs. Today, more than 18,000 employees are on the payroll, making it 麻豆原创鈥檚 second largest location. 

麻豆原创鈥檚 presence in the country has two closely connected dimensions: 麻豆原创 India, led by Manish Prasad, and 麻豆原创 Labs India, led by Sindhu Gangadharan.  

麻豆原创 India is a regional business organization serving India, Sri Lanka, and Bangladesh. With locations in Bengaluru, Delhi, Hyderabad, Gurugram, Mumbai, and Pune, 麻豆原创 Labs India drives product development and innovation for 麻豆原创 solutions used worldwide. 

麻豆原创鈥檚 commitment and trust in 麻豆原创 Labs India to deliver innovation is evident with the opening of 麻豆原创 Labs India Innovation Park, located in Bengaluru, . Inaugurated in August 2025, this 鈧�194 million investment is 麻豆原创鈥檚 largest research and development hub outside of Germany and can accommodate an additional 15,000 employees.听

From local solutions to global impact 

Solving local challenges and then scaling the solutions globally has become a hallmark of 麻豆原创 Labs India. Teams working to help customers navigate India鈥檚 complex tax reporting requirements laid the foundation of 麻豆原创 Document and Reporting Compliance. Today, this solution serves customers in more than 50 countries. 

That pattern of turning local expertise into global product innovation is now visible in 麻豆原创鈥檚 AI strategy. 

With Sudhakar Singh, who leads the AI Security office, and the global head of engineering for Joule and head of 麻豆原创 Business AI, Rahul Lodhe, both based at 麻豆原创 Labs India, the location plays a visible role in shaping the agenda for the Autonomous Enterprise.听

Teams in India engineered , an AI solution grounded in 麻豆原创鈥檚 business data and context and integrated across 麻豆原创 and non-麻豆原创 systems. Accessed via a conversational interface, Joule has evolved from a copilot and听now orchestrates background AI capabilities and 听

One example is the Data Product Creation Agent, designed to help鈥� users overcome the lack of quality data products鈥攐ne of the hidden bottlenecks in AI.听听

In a matter of months, with help from domain experts beyond 麻豆原创 Labs India, the proof-of-concept (PoC) became the Data Product Creation Agent.听听

The speed of development was astonishing, says Leena Kharti, development manager for the team. Kharti, who has worked in development for 16 years, attributes this to the mindset shift in her team. 鈥淲e got into the habit of thinking differently: today there鈥檚 a blocker, so let’s work on something else that moves us forward. In AI, speed is a feature, and that mindset made all the difference,鈥� she says.听听

The same spirit of innovation extends beyond 麻豆原创 Labs India’s own teams to the wider network of founders, engineers, and emerging technology firms around them鈥攁nd the ideas it generates.听

India’s startup ecosystem鈥攖he third largest in the world鈥攈as become a powerful source of innovation, complementing the country’s deep engineering talent and entrepreneurial culture. This offers 麻豆原创 Labs India a valuable pipeline of new ideas, technologies, and partnerships that can help shape the next generation of enterprise software.听

To tap into that energy, 麻豆原创 Labs India launched the 麻豆原创 Startup Studio program in Bengaluru in June 2026. The six-month accelerator program offers selected startups the opportunity to co-create enterprise-ready solutions with 麻豆原创 teams and scale through 麻豆原创’s global ecosystem.听

This year’s cohort included ANSCER Robotics, Pulse Energy, and QNu Labs, whose work spans enterprise AI, robotics, industrial intelligence, and quantum-safe technologies.听听

While the focus areas of this year鈥檚 cohort differ, the program鈥檚 goal is the same: to turn emerging technologies into practical business solutions. This provides 麻豆原创 an early window into innovations that could help define the future of the Autonomous Enterprise.听

鈥�India has all the ingredients to win in AI鈥� 

India has an outstanding talent pipeline with more than 2.5 million STEM graduates in 2026 and one of the world鈥檚 most dynamic startup ecosystems. Combined with 麻豆原创 Labs India鈥檚 growing role in innovations such as Joule and agentic AI, these strengths are shaping the solutions 麻豆原创 delivers to customers worldwide. 

As 麻豆原创 advances its vision of the Autonomous Enterprise, the importance of 麻豆原创 Labs India lies not simply in its size but in its influence on what 麻豆原创 ships to its global customer base.听听

鈥淚ndia has all the ingredients required to win in AI,鈥� says CEO of 麻豆原创 SE Christian Klein. 鈥淔irst, the quality of technical talent and universities is extremely strong. Second, India is investing heavily in building the infrastructure that AI ecosystems need. And third, it鈥檚 a country that is pro-business with a strong focus on becoming an even better place to invest.鈥澨�

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.

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.

International Space Station鈥檚 use of tabular AI and expanded partnerships demonstrate the art of the possible with 麻豆原创 AI

BERLIN 鈥� (NYSE: 麻豆原创) today announced a series of AI innovations at its annual 麻豆原创 TechEd conference that expands its portfolio for developers.

The enhancements to the Joule Studio and Joule Work solutions and 麻豆原创 Business AI Platform, including the latest tabular foundation model from 麻豆原创, offer new tools to build and extend AI agents, a richer data and business context for those agents to reason with, and strengthened governance controls to run them responsibly across the enterprise.

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 use no-code and pro-code tools to build and extend agents, choosing from more than 70 AI models. Customers in the Joule Studio early adopter program also can design and run n8n workflows in Joule Studio, with broader availability to follow. The 麻豆原创 Business Data Cloud and 麻豆原创 Knowledge Graph solutions 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. Importantly, 麻豆原创 customers鈥� data remains their data. 麻豆原创 does not share customer data with anyone to train or improve large language models.

鈥溌槎乖� TechEd has always been where builders shape what comes next,鈥� said Philipp Herzig, CTO of 麻豆原创 SE. 鈥淭his year, we are showing how we are moving from AI learning your business to running it. Joule Work is where you put your intent and get work done, in one experience across the entire 麻豆原创 landscape and beyond. That is the shift we are delivering at scale. Everything underneath runs on 麻豆原创 Business AI Platform, delivering faster time to value through out-of-the-box business understanding, higher accuracy, enterprise-readiness that is secure, governed and compliant from the first deployment, and a unique context layer built on over 50 years of process and industry expertise, encoded into every agent action. This is AI built where real work happens.鈥�

Joule Work and Joule Studio Make the Autonomous Enterprise Real

New capabilities for Joule Work will help make AI agents more effective at handling complex business processes via open protocols including Agent2Agent (A2A) and Model Context Protocol (MCP). These innovations advance Joule Work to better understand context, make decisions and execute multistep business workflows across enterprise systems and business domains:

• New agentic thinking capabilities that plan and orchestrate the steps assistants and agents need to reliably complete complex, multistep business workflows across 麻豆原创 landscapes.
• New capabilities in 麻豆原创 Knowledge Graph will incorporate data from additional 麻豆原创 and non-麻豆原创 sources that are unique to each customer. Users can query and explore their customized data in natural language and get accurate answers grounded in their own information.
• Advancements in interoperability allow third-party agents to securely invoke Joule Agents through a governed agent gateway. This enforced access control enables agents from different providers to collaborate across business workflows without sacrificing governance or control.
• New 麻豆原创 Enterprise AI Gateway solution will extend governance to AI interactions, offering a centralized way to manage AI traffic across the enterprise that will consolidate authentication, policy enforcement, routing, monitoring, usage tracking and auditing.
• New Joule Assistant and 麻豆原创 AI Agent Hub expand governance capabilities, and a new AI Governance Assistant helps customers meet compliance requirements for the EU AI Act. These are designed to address critical barriers to scaling enterprise AI: keeping AI systems classified, compliant and operating within defined organizational guardrails and context.

麻豆原创 also announced that Joule Studio has new observability and administrative controls to give developers and administrators visibility into agent behavior, model usage and spend. Joule Studio is 麻豆原创鈥檚 AI-first development environment that is accessible through Joule Work for building and extending enterprise agents. Joule Studio will run a time-bound promotion for select customers, where deploying and running custom agents on the 麻豆原创-managed Joule Studio runtime will remain free through December 2026. The design-time tooling for building and testing agents and n8n workflows with AI-assisted development will remain free under fair-use limits.

麻豆原创 Business Data Cloud as the Grounding Engine of the Autonomous Enterprise

麻豆原创 Business Data Cloud Ontology Manager in 麻豆原创 Business Data Cloud (麻豆原创 BDC) will provide an AI-native authoring experience for building ontologies across 麻豆原创 and third-party data. By helping AI understand how data from different teams, systems and applications relate to one another, AI agents can more accurately reason and make decisions across the enterprise. The solutions complement 麻豆原创 Knowledge Graph, which provides rich business context for 麻豆原创 data.

Additionally, 麻豆原创 is expanding its lakehouse capabilities with Dremio, adding native support for Apache Iceberg and Apache Polaris plus a high-performance, federated query engine that will enable unified data access without moving or copying the data. 麻豆原创 expands its partnership with Databricks, bringing the full scope and latest innovations of data engineering and enterprise AI capabilities into 麻豆原创 Business Data Cloud as 麻豆原创 Databricks. Core innovations such as Lakeflow, Agent Bricks, Unity Gateway, and Lakebase will help customers to simplify data management and provide a strong data foundation for their AI journey. With a deeper integration to 麻豆原创 Business Data Cloud, a new 麻豆原创 Application Data Foundation, as well as an integration between Joule and Genie, 麻豆原创 and Databricks are helping customers establish an enterprise-wide lakehouse as the foundation for the Autonomous Enterprise and Joule Work.

The 麻豆原创 Reltio solution, recently announced at 麻豆原创 Connect, will bring multidomain master data management to 麻豆原创 BDC. It unifies and enriches customer, supplier and product records across 麻豆原创 and non-麻豆原创 systems into trusted, reusable data products that can power applications and AI agents.

Tabular Foundation Model Trusted for Business, Validated in Space

麻豆原创 announced its latest tabular AI model, 麻豆原创-RPT-2.0, which will expand beyond classification and regression to support anomaly and outlier detection, and richer AI-powered analysis through embeddings. These native, multi-table capabilities will further reduce manual engineering while delivering faster, deeper insights from structured data. In addition, a new 麻豆原创-RPT-TS model will help customers predict values across time-indexed business metrics for key forecasting use cases using the 麻豆原创 Integrated Business Planning solution without requiring custom machine learning.

In partnership with HPE, 麻豆原创 also announced the deployment of 麻豆原创-RPT on the HPE Spaceborne Computer-2 aboard the International Space Station (ISS), one of the most significant edge-compute validations of any enterprise AI model to date. 麻豆原创-RPT is planned to run directly onboard the ISS to support anomaly triage, predict issues before failures escalate, reconstruct missing sensor values from historical data, and generate explainable mission-health reports for astronauts and ground teams 鈥� all without requiring data to be sent back to Earth for analysis.

鈥淭he same trusted 麻豆原创-RPT model that powers global supply chains also can work in space,鈥� said Herzig. 鈥溌槎乖粹�檚 deep-rooted, proven expertise has led to the International Space Station鈥檚 use of our tabular foundation model to predict potential mission-critical failures in orbit. With 麻豆原创-RPT and TabPFN from Prior Labs, we bring the unique capabilities of tabular AI into the enterprise and across every frontier.鈥�

Out-of-the-Box Assistants and Agents Define Our Advantage

For five decades, 麻豆原创 has built the systems of record that run your business. We will deliver, out of the box, a suite of assistants and agents across five horizontal domains all running on 麻豆原创 Business AI Platform 鈥� with industry specialization 鈥� that provides a baseline for systems of intelligence and systems of action.

Ecosystem Partners Help Expand Platform Capabilities

The global 麻豆原创 partner ecosystem brings new capabilities to the ongoing operation and extension of solutions on 麻豆原创 Business AI Platform, helping customers advance toward the Autonomous Enterprise. At 麻豆原创 TechEd, the following partner announcements were highlighted:    

A collaboration with Intel closes the loop between enterprise workflows and autonomous robots, bridging informational technology and operational technology through physical AI.

In addition, new 麻豆原创 solution extensions with , and extend autonomous capabilities for customers, helping them run their core processes end to end, without handoffs between systems.

Beyond technology, 麻豆原创 is empowering its strong developer community with new enablement and engagement with tens of thousands joining for leading up to 麻豆原创 TechEd, new reference architectures, tutorials and hands-on opportunities attendees can try onsite at the event and at home.

To learn more about today鈥檚 announcements visit our .

Visit the . Get 麻豆原创 news via  and .

About 麻豆原创

As a global leader in enterprise applications and business AI, 麻豆原创 (NYSE: 麻豆原创) stands at the nexus of business and technology. For over 50 years, organizations have trusted 麻豆原创 to bring out their best by uniting business-critical operations spanning finance, procurement, HR, supply chain, and customer experience. For more information, visit .

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This press release was created with the assistance of artificial intelligence and has been reviewed by the editorial team.

This document contains forward-looking statements, which are predictions, projections, or other statements about future events. These statements are based on current expectations, forecasts, and assumptions that are subject to risks and uncertainties that could cause actual results and outcomes to materially differ. Additional information regarding these risks and uncertainties may be found in our filings with the Securities and Exchange Commission, including but not limited to the risk factors section of 麻豆原创鈥檚 2024 Annual Report on Form 20-F.
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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.

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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.

Today鈥檚 finance leaders are expected not only to report on business performance, but also to help shape it. They need a foundation that connects financial and operational data, supports end-to-end processes, and helps teams move from insight to action with speed and confidence.

Recognized for cloud ERP for finance

For us, this recognition is about more than technology. In our opinion, it reflects the importance of a strong digital foundation that brings together financial and management accounting, operational context, trusted data, and embedded intelligence to help finance teams work more effectively.

At the heart of is a unified data model and common journal that can connect financial information with the business processes behind it. We believe this approach can help organizations reduce reconciliation effort, gain a more multidimensional view of performance, and give finance teams the context they need to make informed decisions.

As the role of finance continues to evolve, we remain focused on helping customers simplify core processes while creating the foundation for new capabilities in automation and AI. This combination of connected data, processes, and intelligence is central to our vision for the future of finance.

Connecting financial and operational decisions

Finance transformation increasingly depends on connecting processes across the enterprise. By bringing together finance with purchasing, sales, inventory, and other operations, organizations can better understand what鈥檚 driving financial outcomes and address issues in context.

麻豆原创 helps bring financial and operational processes together across the enterprise. We believe this approach can help finance teams improve transparency, strengthen governance, and resolve issues earlier, including during the financial close.

AI is becoming an important part of that connected experience. Joule and 麻豆原创鈥檚 growing portfolio of persona-based assistants are designed to work within business processes and established controls. These capabilities can help finance professionals identify anomalies, automate selected activities, address exceptions, and focus their expertise on higher-value work.

Recent innovations announced at 麻豆原创 Connect in 2026 build on this direction. New Joule Assistants for finance, 麻豆原创 Enterprise Financial Consolidation, and are designed to connect data and workflows more closely while supporting the governance and auditability finance teams require.

Supporting customer transformation

demonstrates how a connected finance foundation can support transformation at scale. Through its 鈥淕roup Finance Backbone鈥� initiative, the company worked with 麻豆原创 to harmonize finance operations across manufacturing, sales, and financial services. BMW Group established one integrated finance template supporting organization-wide reporting, standardized approximately 800 ground-level processes, and improved data comparability and granularity across 15 major source systems.

We believe customer outcomes like these demonstrate the importance of combining standardized processes, trusted data, and central governance to help global organizations improve transparency and prepare for future innovation.

Building the future of finance

Finance leaders are balancing a growing range of priorities, from improving operational performance and managing risk to preparing their organizations for AI-powered innovation. 麻豆原创 remains focused on helping them address those priorities with a unified foundation that connects data, processes, and people across the enterprise.

We are proud to be recognized as a Leader in this Magic Quadrant and remain committed to helping customers build finance organizations that are intelligent, connected, and ready for what comes next.

Learn more

To learn more about 麻豆原创鈥檚 position in the 2026 Gartner Magic Quadrant for Cloud ERP for Finance, .

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

*Gartner, Magic Quadrant for Cloud ERP for Finance, 2026 by , , October 13, 2026

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

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.

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.

TechWolf gives enterprises a continuously updated view of the work their people do and the skills they have, using its proprietary data model called context graph for work. The acquisition will bring this context graph, along with TechWolf’s AI models and applied AI research team, into 麻豆原创, establishing work intelligence as a strategic 麻豆原创 asset. The deal is expected to close in Q4 2026, subject to usual closing conditions, including regulatory approval. Terms of the deal were not disclosed. 

TechWolf works from existing HR and business systems that customers connect. It builds its model of the organization at three levels: the work itself, down to the tasks inside a job; the skills people have and apply; and the external labor market. It maps that against the customer鈥檚 business strategy, so the leaders responsible for hiring, reskilling and redeployment are working from better information.

TechWolf is expected to become an intelligent core of the 麻豆原创 SuccessFactors portfolio, supporting the company鈥檚 talent and work intelligence strategy. TechWolf will power workforce and talent strategies across 麻豆原创 SuccessFactors, bringing skills mapping, workforce planning and organizational redesign informed by context from across the Autonomous Enterprise. Once the transaction closes, TechWolf and 麻豆原创 will unify skills and work data to inform HR and business leaders to guide decisions about development, deployment and workforce transformation.

鈥淭echWolf鈥檚 proprietary context graph for skills and work provides an excellent grounding layer for agent queries regarding work and skills planning and talent management,鈥� said Manoj Swaminathan, president and chief product officer for 麻豆原创 Autonomous Suite and member of the Extended Board of 麻豆原创 SE. 鈥淭his grounding layer perfectly matches our strategy. It makes token usage more efficient, lowers the cost of deploying workforce agents and will make Joule more intelligent in AI-driven HR scenarios such as skills-based hiring, workforce planning and role redesign.鈥�

Joint 麻豆原创 and TechWolf customers using the technology are seeing measurable results from the partnership the two companies have had. Building on that foundation, 麻豆原创 and TechWolf will jointly co-innovate a concrete set of new AI-powered workforce and skills optimization products to be designed and developed post-close. 麻豆原创 plans to integrate TechWolf鈥檚 solutions to a greater degree, offering customers even better insights and work intelligence to help organizations plan reskilling, internal mobility and hiring.

“Organizations everywhere are trying to figure out how AI is reshaping work, and what their workforce needs to look like because of it,” said Andreas De Neve, CEO and co-founder of TechWolf. “We have spent eight years building the evidence layer to answer those questions. It’s always been our mission to connect skills, tasks and work to help employers solve AI transformation, and joining 麻豆原创 will allow us to extend that mission across a broader set of business context and larger scale. In this next chapter, we plan to build a world-class AI company.鈥�

Subject to closing and required consultation, 麻豆原创鈥檚 current plans are for TechWolf to remain as an independent entity under CEO Andreas De Neve in its Ghent headquarters, with offices in London, New York and San Francisco, and for its platform to remain available to both 麻豆原创 and non-麻豆原创 customers. J.P. Morgan served as exclusive financial advisor to TechWolf.

Visit the . Get 麻豆原创 news via  and .

About TechWolf

TechWolf is a leading applied AI company that helps large enterprises understand how AI is transforming work and what that means for their workforce. Its platform brings together skills, work, and market intelligence, into a single ; Its open-source models have been downloaded more than two million times.听 TechWolf co-exists with systems enterprises already use, including 麻豆原创 SuccessFactors and non-麻豆原创 applications, and connects with AI tools and agents. Customers include HSBC, Atlas Copco Group, GSK, Ericsson, AMD, MetLife, PayPal and Booking.com. The company offers EU and US data-residency, standard encryption, access controls and certifications including ISO/IEC 42001 and ISO/IEC 27001 that support EU AI Act governance and security. Learn more at .

About 麻豆原创

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

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This document contains forward-looking statements, which are predictions, projections, or other statements about future events. These statements are based on current expectations, forecasts, and assumptions that are subject to risks and uncertainties that could cause actual results and outcomes to materially differ. Additional information regarding these risks and uncertainties may be found in our filings with the Securities and Exchange Commission, including but not limited to the risk factors section of 麻豆原创鈥檚 2025 Annual Report on Form 20-F.
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EssilorLuxottica shares how 麻豆原创 supports its global business processes and enables continuous innovation.

From iconic brands to advanced eye care solutions, discover how cloud technology helps the company operate at scale and enhance customer and professional experiences worldwide.

Read to learn more.

With a global retail network that includes Sunglass Hut, LensCrafters, Vision Express, and Apollo, the company operates at the intersection of medical technology, consumer fashion, and retail.

From eyewear to smart wearables

In recent years, EssilorLuxottica has extended its brand into wearable technology, blending eyewear with digital capabilities. Smart glasses equipped with cameras, microphones, speakers, and integrated display technologies represent a new frontier where utility, lifestyle, and connectivity converge.

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This evolution is not just about hardware innovation. It reflects a broader ambition: transforming eyewear into an intelligent interface between users and digital services. The company鈥檚 collaboration with 麻豆原创 brings that vision into sharper focus.

Powered by AI, connected to the backbone

EssilorLuxottica runs its retail network of 15,000 stores on 麻豆原创 S/4HANA Retail and is co-innovating with 麻豆原创 to pilot AI-driven use cases based on Joule. The idea is to help any retailer bring 麻豆原创鈥檚 AI capabilities directly into smart glasses, creating a hands-free interface between employees and enterprise systems. Using voice interaction, store associates can access inventory and availability data in real time, receive contextual customer insights, and identify opportunities for more personalized service, such as cross-sell and upsell recommendations. In this way, a spoken request can become an immediate action in the backend system.

Empowering humans

EssilorLuxottica is initially exploring two practical applications for the technology: supporting technicians in the field and empowering associates on the retail floor. For field operations, wearable AI can provide real-time guidance and documentation support, helping technicians navigate complex tasks. In stores, smart glasses could give associates immediate access to customer profiles, order history, inventory data, and ordering capabilities鈥攁ll through natural voice interaction.

The potential was demonstrated live at this year鈥檚 麻豆原创 Sapphire Madrid, where an AI-enabled pair of glasses helped a store associate identify a returning customer, retrieve their purchase history, reorder products, and suggest relevant add-ons based on customer and purchase context, all within a seamless conversation.

Opportunities ahead: from pilot to platform

The use cases, currently in a test phase, were developed in just two weeks. They signal broader transformation potential, with the teams targeting commercialization of both pilots by the end of the year. EssilorLuxottica operates a complex global supply network that encompasses hundreds of sites and distribution centers, which presents significant opportunity for scaling AI-enabled workflows across the enterprise.

The implications extend beyond retail: the EssilorLuxottica鈥撀槎乖� collaboration illustrates how established industries can use AI and integrated business processes to create new ways of working and serving customers.

By transforming eyewear into an intelligent interface connected to enterprise systems, the company is redefining both the product and the experience around it.

Top photo courtesy of EssilorLuxottica.

Unlike traditional esports tournaments that play out over a few hours, Race to World First pushes teams through days and sometimes weeks of continuous competition. Players face extreme cognitive load, limited recovery time, and the constant pressure to execute flawlessly against the most complex encounters ever designed in gaming. At that level, performance is no longer just about mechanics. It’s about endurance. And endurance creates a data challenge.

Performance analytics beyond the game

Over the last several years, Team Liquid has become one of the most innovative organizations in esports.

Together, we have built data platforms, advanced analytics capabilities, and AI-powered solutions such as Joule Agents that help players, analysts, and coaches access insights faster than ever before. As we discussed our next innovation journey with the team, one question emerged quickly: What if coaches could see not only how players are performing in the game, but also how they are performing as human beings?

Historically, game statistics, biometric measurements, and wellness data were often analyzed independently, limiting the ability to understand how they influence one another. Therefore, the next evolution of esports analytics is not collecting more data. It is connecting data.

For Race to World First, we helped Team Liquid bring gameplay analytics, player wellness information, and biometric signals from wearable devices together in 鈥攁nd relationships that were previously difficult to identify became visible.

This sounds simple, but it fundamentally changes how coaching decisions are made.

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One unified view of the entire team

For the first time, Team Liquid鈥檚 coaches, analysts, and performance experts can see how physical readiness, stress indicators, recovery metrics, and in-game performance interact鈥攊n one view instead of separate dashboards and spreadsheets.

During Race to World First, waiting is often not an option. Fatigue develops in real time, and physical and mental stress accumulate throughout the competition. With live data streams, Team Liquid’s coaching staff can now monitor conditions as they unfold and identify early warning signals before they impact performance.

AI that amplifies human expertise

Data becomes even more valuable when AI helps make sense of it. That is where comes into play. By connecting previously isolated datasets, Joule analyzes trends across gameplay, wellness, and biometric information simultaneously. Coaches receive proactive recommendations from the underlying data.

Imagine receiving insights such as: “Players鈥� reaction times are getting longer, and stress indicators are elevated. Consider a recovery period before the next progression attempt.” Or: “This tactic has historically underperformed with the current roster configuration. Alternative strategies have delivered stronger results under similar conditions.”

AI does not replace human expertise; it amplifies it by giving Team Liquid鈥檚 coaches access to information that would otherwise remain hidden across thousands of data points.

Proving ground for human performance

This collaboration represents something bigger than esports. Competitive gaming has become one of the world’s most sophisticated environments for exploring human performance鈥攊ts scale, intensity, and speed create a unique proving ground.

Team Liquid鈥檚 challenges are not so different from those organizations face in business: bringing fragmented data together, turning information into action, and helping people make better decisions under pressure. It is the question we started with, asked at a larger scale: How are people performing, not only within systems and processes, but as human beings?

Race to World First raises the stakes. By combining connected data in 麻豆原创 Business Data Cloud with AI-powered insights from Joule, we are exploring what becomes possible when technology helps organizations understand performance holistically鈥攎ost importantly, how to help people perform at their best when it matters most.

In esports, success will still be measured in milliseconds. Sustaining that precision for weeks is a different challenge, one determined by how well a team is supported by connected data, intelligent analytics, and a deeper understanding of the people behind the numbers.

Because in the end, championships are won by teams. Data simply helps those teams unlock their full potential.

Benjamin Blau is chief process and information officer at 麻豆原创.

To better drive success, our unified service release model will continue to deliver expanded capabilities seamlessly. This update demonstrates the real-world impact of our strategy, especially as businesses increasingly embed AI into their core operations.

Realizing value: documented progress and strong customer adoption

Our core mission remains to provide guidance that is simple, predictable, and directly connected to measurable business outcomes. We are delighted to see this approach gain substantial momentum. Hundreds of organizations have adopted our Advanced Success Plan and Max Success Plan, validating that deeper engagement leads to stronger, long-term results. Our customers鈥� success is our priority. We bring our entire portfolio together to help create a seamless experience, custom-fit to customer needs.

Empowering businesses with next-generation capabilities

To help organizations navigate in today鈥檚 dynamic market, we鈥檝e focused our services on three strategic growth drivers:

• Powering the Autonomous Enterprise: We help streamline processes and boost efficiency by pairing intelligent tools with expert guidance. This includes AI-powered capabilities like Joule for 麻豆原创 for Me for proactive insights and agentic case resolution to help deliver faster, more intelligent support.
• Enabling targeted business transitions: We deliver clear, structured pathways that help businesses confidently unlock 麻豆原创鈥檚 newest AI capabilities and innovations across specific lines of business. These tailored migration paths can simplify transition and integration processes.
• Driving front-office impact with productivity AI: Our focus can deliver measurable productivity gains for front-office teams through deeply embedded AI capabilities, enhanced release guidance, and strategic engagement planning aligned with business outcomes.

Expanding and integrating support for every stage of the 麻豆原创 journey

Our is streamlined into three success plans (Foundational, Advanced, and Max) with three supplemental offerings (development services, application management, and professional services), all designed to complement each other to support continuous adoption, innovation, and transformation at every stage of the journey. The focus is on clearly defining what each offering provides, when to use it, and the specific outcomes it can deliver.

Success plans: evolving as the primary engagement model

Our success plans continue to strengthen their position as they offer expanded solution area coverage and AI-powered capabilities. The Foundational Success Plan has full 麻豆原创 solution area coverage, providing clearer pathways to Advanced Success Plan and Max Success Plan engagements. New expert-led AI services are now available, with enhanced 麻豆原创 Build coverage to support AI maturity journeys. Furthermore, upgraded release guidance tooling helps improve planning consistency, and automated provisioning for Max Success Plan customers can streamline the engagement process.

The tangible impact of these integrated AI capabilities is already evident, with Joule for 麻豆原创 for Me seeing strong adoption with nearly 200,000 users year-to-date, while agentic case resolution uses AI agents to help automatically handle and resolve support cases, working to reduce resolution times and minimize the need for manual escalation.

Development services: unlocking the full potential of 麻豆原创 BTP

In the current release, our development services have been expanded to help unlock the full potential of 麻豆原创 Business Technology Platform (麻豆原创 BTP).

New extensibility packages offer structured guidance and best practice frameworks for custom applications and integrations to enable faster delivery and consistent quality. Furthermore, all managed development engagements formally embed clean core principles, which help make custom solutions upgrade-safe and future-proof. In addition, AI-assisted tooling is now standard in eligible engagements, working to further reduce timelines and improve code quality.

Application management: sustaining continuity and performance

Application management, which handles the day-to-day operations and ongoing optimization of a customer鈥檚 live 麻豆原创 environment, continues to evolve as a critical pillar to help ensure continuity and performance across 麻豆原创 environments.

麻豆原创 enables customers to operate the Autonomous Enterprise with confidence by continuously governing, securing, and optimizing autonomous applications and platform services. AI-assisted monitoring and intelligent incident remediation help proactively identify risks, reduce resolution times, and improve operational resilience. Through an integrated adopt-to-operate approach, 麻豆原创 can connect adoption, success planning, and application management into a seamless lifecycle experience, helping customers accelerate value realization, maximize business outcomes, and sustain transformation success.

Professional services: structured implementation and transformation

Our professional services can deliver targeted, one-time project expertise, including end-to-end implementations, complex migrations, upgrades, and system optimizations.

With the new release, 麻豆原创 is simplifying its professional services portfolio to support transformation journeys by aligning with success plans and integrating proven expertise into scalable engagement models. Refreshed outcome-based services help expand coverage across 麻豆原创 solutions and are designed to align with the Advanced Success Plan and Max Success Plan. Furthermore, formalized handoff protocols between success plans and professional services help foster seamless continuity of project context.

A partnership for continuous success

This unified portfolio release underscores 麻豆原创鈥檚 collective strength and shared commitment to help transform customers鈥� 麻豆原创 experience with a streamlined support portfolio built for impact:

• Modular choice: Easily scale services to match exact goals.
• Ongoing optimization: Continuously unlock software potential with proactive guidance.
• One connected system: Enjoy a frictionless, cohesive experience across the full suite.

Transforming your business shouldn’t be complicated. At 麻豆原创, we partner with you to turn complex transformation into measurable, lasting business results. Explore our newly evolved 麻豆原创 Services and Support portfolio to see how it can help maximize value and drive your strategic goals forward.

Dr. Uwe Grigoleit is senior vice president of Customer Evolution & Portfolio at 麻豆原创.

We believe this recognition reflects our ongoing commitment to helping organizations navigate an increasingly complex world of work through innovation, global scale, and AI that helps connect workforce decisions to business outcomes. 

A new era of HCM 

Organizations today face unprecedented workforce challenges.

Skills requirements are evolving rapidly. Workforces are becoming increasingly distributed. Business priorities shift faster than traditional planning cycles can accommodate. At the same time, leaders are being asked to make workforce decisions with greater speed, precision, and confidence. As these pressures increase, the role of HR and HCM technology is fundamentally changing.

Organizations no longer need systems that simply record workforce data or automate HR processes. They need connected, intelligent systems that can help anticipate workforce needs, surface recommendations, remove friction from everyday work, and help people make better decisions. This is why we announced our vision for Autonomous HCM at 麻豆原创 Sapphire in May.

As part of 麻豆原创’s broader vision for the Autonomous Enterprise, Autonomous HCM brings together trusted workforce and business data, embedded intelligence, AI, and HR processes to help organizations respond more effectively to changing workforce needs. The goal is not simply to automate more tasks. It’s to help organizations understand what’s happening, determine what to do next, and execute with greater speed and confidence. Achieving this requires trusted workforce and business data working together to provide the context needed for better decisions and better outcomes. 

Bringing Autonomous HCM to life

Over the past year, 麻豆原创 has continued to invest in capabilities designed to help organizations move more seamlessly from workforce insight to workforce action. From new Joule and AI agents to and , these capabilities help connect workforce intelligence, decision-making, and execution across HR processes. Our acquisition of SmartRecruiters extends this approach to talent acquisition, helping connect hiring decisions to workforce planning, skills intelligence, and the broader employee lifecycle. Next month at at 麻豆原创 Connect, we’ll share new innovations and customer stories that further demonstrate how 麻豆原创 SuccessFactors can help organizations automate work, adapt more quickly to change, and drive better workforce outcomes, ultimately moving towards Autonomous HCM.

Creating measurable impact

Organizations around the world are already working towards this reality.

has embedded AI capabilities within 麻豆原创 SuccessFactors solutions to support employee development, goal setting, recruiting, and career conversations. By giving employees and managers access to AI-assisted tools and insights, Timken is simplifying HR processes, improving employee development conversations, and enabling more informed workforce decisions.

is demonstrating how AI can help organizations move from workforce insight to workforce action. With 麻豆原创 SuccessFactors solutions, the company has streamlined recruiting processes across more than a dozen industries, reducing recruitment duration by 75% and improving hiring efficiency fourfold. AI-generated job descriptions, competency-based interview questions, and workforce insights are helping create a more efficient, consistent, and skills-based approach to talent management.

These examples demonstrate an important shift. AI is no longer limited to providing information. It’s helping employees, managers, and HR teams make better decisions and take action more quickly and effectively.

Looking ahead

We are grateful to our customers whose continued trust and innovation make this recognition possible.

As we look ahead, our focus remains on helping organizations connect workforce insight with action, enabling leaders to make better decisions, respond more quickly to change, and create better outcomes for employees and the business. about our position in the 2026 Gartner庐 Magic Quadrant™ for HCM Suites for 1,000+ Employee Enterprises.

Gartner, Magic Quadrant for HCM Suites for 1,000+ Employee Enterprises, By , , , , , , 31 August 2026 
Gartner does not endorse any company, vendor, product or service depicted in its publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner 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 publication, including any warranties of merchantability or fitness for a particular purpose. 
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Last week, 麻豆原创 customers gathered at the Swiss Smart Factory in Biel, Switzerland, to experience firsthand how robots and 麻豆原创 software can work together to solve real business challenges.

Embodied AI refers to AI agents that interact with the world through a physical body鈥攅nabling machines to autonomously perceive, understand, reason, and act in real environments. By connecting these agents to and 麻豆原创 Business AI Platform, 麻豆原创 brings business context into that physical execution: robots that don’t just carry out tasks, but understand the business decisions those tasks serve.

Warehouse automation, asset inspection, and material handling are just some of the business scenarios where embodied AI is beginning to create value. Bringing those scenarios to life requires more than a robot. It requires business context from 麻豆原创 applications, integration expertise to connect systems and robots, and the right robots to execute the task.

鈥淕enerating market interest for embodied AI and transforming it from an exciting technology topic into a practical 麻豆原创-connected business value demanded a new format,鈥� explained 麻豆原创 Switzerland CTO Alexander Finger, who was a key driver behind the event.

Unlike traditional innovation jams, an embodied AI jam requires robots and a space where people can safely work with them side by side.

The Swiss Smart Factory provided exactly that environment for 麻豆原创 Switzerland to host the event. Bringing together customers, robot manufacturers, system integrators, and 麻豆原创’s embodied AI experts created a unique opportunity to move from discussion to hands-on experimentation and real-world use cases.

Viable use cases in days, not weeks

Embodied AI may well be all about hardware and software, but Finger says accelerating progress is ultimately about bringing people together. At the jam, customers and partners were paired with system integrators and robot manufacturers aligned to their business challenges.

While some teams explored how inspection drones could connect to solutions such as 麻豆原创 Asset Performance Management, others investigated how humanoids could support processes with 麻豆原创 Digital Manufacturing.

The result was a level of progress that typically takes weeks to achieve.

鈥淔inding where embodied AI creates real business value鈥攁nd shaping a solution to deliver it鈥攖ypically takes weeks of distributed back-and-forth,鈥� said Lukasz Ostrowski, head of the embodied AI initiative at 麻豆原创. 鈥淭hree days of dedicated, focused time with customers changed that. We could test ideas, challenge assumptions, and iterate in real time until we arrived at something concrete that neither side could have defined alone. What we learn with each customer like this doesn’t stay with that customer鈥攊t shapes how we build for the rest of the industry.鈥�

The physical dimension makes embodied AI tangible

For Finger, embodied AI only becomes meaningful when customers can experience it firsthand.

Seeing a robot perform tasks informed by business processes and objectives makes the potential business value far easier to understand than a slide deck or demo alone.

This is why the Swiss Smart Factory plays such an important role; it provides a safe environment where customers, robot manufacturers, system integrators, and 麻豆原创’s embodied AI experts can work and explore embodied AI in action together.

As of January 2027, 麻豆原创 Switzerland will become a member of Swiss Smart Factory, enabling it to host future embodied AI jams as well as shorter discovery formats like those already used for other AI customer-facing events.

While 麻豆原创 Customer Experience Labs show customers how 麻豆原创 applications, data, and AI can solve business challenges, the Swiss Smart Factory adds a physical dimension. It gives customers a hands-on environment to explore how robots can be connected, act in a business context, and create tangible business value.

Bringing embodied AI to more customers globally

鈥溌槎乖� is richer when we talk to customers,鈥� Finger concluded, reflecting on the success of the jam. Beyond the speed with which teams developed use cases and architectural proposals, one outcome stood out: customers left the event wanting to continue the conversation and further explore their embodied AI ambitions with 麻豆原创.

As 麻豆原创 Switzerland expands its offerings of embodied AI events, more customers will be able to experience embodied AI firsthand and explore how robots, 麻豆原创 applications, and business processes can work together to create value.

The top team鈥檚 rowers focused entirely on maximizing individual power. If the boat slowed, they rowed harder in isolation, disrupting the oars鈥� synchronized rhythm and creating water drag. Meanwhile, the junior rowers knew they were individually weaker, so they rowed in harmony.

Enterprises have faced countless variations of this problem: implementing systems that maximize productivity at the individual or team level but actively hinder the wider enterprise. Many organizations are experiencing something similar with AI today.

AI and sub-optimization

Sub-optimization is a systemic failure that occurs when the performance of a specific part of a system is maximized, inadvertently hampering the performance of the entire system. There are three intertwined themes: intensity, context, and prediction, which, taken together, explain how AI can sub-optimize an organization by making individuals and local systems stronger while straining the broader organization.

Research reinforces this disconnect between individual or even company-wide AI adoption and the value it delivers. shows near-universal enterprise AI adoption: 89% of respondents report regular AI use in at least one business function, but only 37% report an earnings before interest and taxes (EBIT) impact from AI at the enterprise level. Even worse: only six percent of companies can be categorized as high performers that already capture significant organization-wide value from AI.

What makes it so hard to move from AI adoption to measurable value capture? I believe there are three themes that influence a company鈥檚 ability to benefit its entire organization.

Intensity

AI tools often don鈥檛 reduce work; they intensify it. A found that employees who heavily use AI worked faster, took on a broader range of tasks, and worked longer hours, often without being asked. So, what appears to be higher productivity in the short run is actually silent workload creep and mounting pressure as employees manage new AI workflows and do more with less. that the most mentally taxing form of AI engagement was oversight; AI tools that require direct monitoring increased feelings of being overwhelmed by the volume of information at work.

It is easy to see why AI can feel intense: tasks that once required days can now be prompted into existence almost immediately. People start more things; they do more analysis and write more memos. However, like an eight-lane highway that suddenly narrows to a single-lane toll booth, individuals must still consume all this output. This bottleneck only compounds at the organizational level, as all employees produce more than ever, leaving both individuals and the organization as a whole struggling to keep up. Creation has scaled. Absorption has not.

The solution isn鈥檛 necessarily to use less AI, but to change where and how AI shows up. AI should understand user intent and surface the insights needed to answer the question, rather than generating static assets or requiring you to switch between different apps and systems.

If your question creates more things, it鈥檚 not helping absorption. 麻豆原创鈥檚 answer is , a central workspace across 麻豆原创 and non-麻豆原创 systems that uses AI agents to handle tasks.

Ask, 鈥淲hich stores run out of 65鈥慽nch TVs in the next 72 hours, and where is stock I can move?鈥� It will pull data across systems and orchestrate agents to act on the user鈥檚 behalf. In this case, 麻豆原创鈥檚 answer is autonomous action combined with a highly individual user experience for that specific situation, not more assets to be absorbed. If employees can avoid juggling systems and consuming assets, they can spend more time exercising judgment on actions that matter. This is how AI can alleviate intensity.

Context

Most AI systems understand the world, but not the enterprise in which they operate. There is a difference between a system of record鈥攖ransactions, master data, process logic鈥攁nd tacit knowledge鈥攅mails, chats, unwritten rules. And enterprises run on both. If AI only sees the system of record, its answers might be technically correct but contextually wrong because they don鈥檛 reflect the organization’s lived practice.

Even the most ostensibly basic questions require company context. Asking 鈥淲hich suppliers can I source coconuts from?鈥� requires knowledge of an organization鈥檚 process landscape across procurement, supply chain, compliance, finance, and other domains. This type of enterprise knowledge is usually scattered across process models, policies, chats, spreadsheets, and applications, so it鈥檚 tough to maintain. And even if they find it, agents cannot turn it into action without procedural knowledge of the involved people鈥攖he unwritten rules, decisions, and steps鈥攖hat make a process executable.

preview continuously captures institutional knowledge and makes it usable for both people and agents. It turns written inputs, chat inputs, process knowledge, policy guidance, and application logic into reusable building blocks that AI agents can consume. Blocks are captured once, governed centrally, and reused across the company. So when someone asks Joule Work about coconuts, the answer is driven by the company鈥檚 memory and reflects actual rules the process owners agreed upon鈥攆or example: “Only source from Brazil; others require formal exception approval.”

麻豆原创 Company Memory is not a one鈥憈ime implementation; it鈥檚 continuous. In this way, company knowledge behaves like infrastructure, ensuring agents act contextually, not just correctly, as policies and teams change.

Prediction

Business decisions are fundamentally prediction problems that rely on structured data. Most organizations use LLMs, which are great at unstructured data like text but for architectural reasons not so great at working with and generating the structured numerical data that underpins good predictions. Delay prediction, forecasting, anomaly detection, stock optimization, and credit risk are everyday operating questions that depend on structured, tabular data and forward-looking judgment.

Asking LLMs for reliable forecasts on enterprise tables is simply the wrong tool for the job. At the same time, traditional custom machine learning approaches are too slow for many real-time questions: after extracting data, sending it to specialists, and waiting weeks, the question often has changed by the time the answer comes back. This combination means predictive capabilities are either restricted to specialists or rendered inaccurate by generic LLMs; in either case, the organization鈥檚 decision-making is weakened.

Reliable forecasting and risk assessment should be a system property, not an individual hack. 麻豆原创-RPT-1.5 and TabPFN 3 are models that excel with tabular data and will integrate with Joule Work and 麻豆原创 Business Data Cloud for forward-looking questions directly on live tables.

麻豆原创-RPT-1.5 for 麻豆原创 data and TabPFN 3 for any tabular data are specialized prediction engines for structured data. They enable decision-makers working in the core systems to ask, 鈥淪hould I reroute volume? What鈥檚 the probability of on鈥憈ime delivery? What鈥檚 the cost delta across scenarios?鈥� and get answers grounded in real enterprise data.

Availability across the organization eliminates specialist bottlenecks and better equips the enterprise to handle uncertainty through prediction. This enables informed top-level decisions that really move the needle for a company.

AI for the benefit of the whole organization

AI has already proven it can make people more capable, but that does not automatically help the wider organization. AI shouldn鈥檛 be about optimizing isolated tasks; it should be about reshaping how work, knowledge, and decisions flow through the company.

Joule Work, 麻豆原创 Company Memory, and 麻豆原创-RPT-1.5/TabPFN 3 are great examples of how 麻豆原创 designs system-level capabilities. They offer a unified engagement layer, a living institutional memory, and a prediction engine for structured business data that elevate AI from individual-level hacks into a collective benefit for the enterprise.

This is AI that bridges the individual-to-institutional value gap, moving from simply getting AI into the company to generating value throughout the company.

Florian Kunzke is global director of AI Strategy at 麻豆原创.

The hand-crafted stringing on the face of the racket, the customized design, the packaging and distribution, and the systems that enable each touchpoint are all part of a global supply chain that ensures the racket gets from the warehouse into the gear bag of the world No. 5 exactly when he needs it.

That journey鈥攆rom raw materials and global suppliers through manufacturing, warehousing, customs, and last-mile delivery鈥攊s a story. And 麻豆原创 helps power it.

The carefully curated racket, along with Wilson鈥檚 shoes, shirt, cap, and shorts de Minaur chooses to wear as he competes at the highest level, are just a few of the thousands of products Wilson manufactures and delivers to athletes and customers around the world.

Behind each product is an interconnected global operation spanning manufacturing, warehousing, retail, e-commerce, and B2B channels and a digital foundation that helps Wilson keep it all moving seamlessly.

Powering a global sporting goods business

Wilson is a global leader in sports equipment and apparel, with a legacy of more than a century of innovation across tennis, basketball, baseball, golf, and other sports. From developing high-performance equipment for the world鈥檚 best athletes to creating products for players at every level, Wilson combines deep sporting expertise, innovation, and craftsmanship to help athletes perform at their best.

For almost two decades, Wilson has trusted 麻豆原创 to run its global operations, connecting hundreds of employees across finance, sales, logistics, warehousing, and other critical retail functions.

Wilson’s 麻豆原创 landscape spans core enterprise resource planning (ERP), supply chain management, data and analytics, procurement, travel, global trade, integration and enterprise architecture. Its current environment includes 麻豆原创 ERP Central Component (麻豆原创 ECC), 麻豆原创 Analytics Cloud, 麻豆原创 Datasphere, , and 麻豆原创 Business Technology Platform, as well as 麻豆原创 Ariba, 麻豆原创 Concur, and 麻豆原创 LeanIX solutions, among others.

And Wilson’s 麻豆原创 digital transformation is continuing. The company is preparing for a major 麻豆原创 S/4HANA transformation beginning in 2027, which will unlock new capabilities across areas such as extended warehouse management (EWM), transportation, quality, and omnichannel operations. As part of its 麻豆原创 S/4HANA journey, Wilson uses Joule to assist with code conversion marking the beginning of introducing into its business operations

One connected foundation for a connected customer experience

Beyond professional athletes such as de Minaur that choose to utilize Wilson to perform on and off the court, Wilson’s global customers interact with the company in many ways鈥攖hrough retail stores, e-commerce, and B2B channels. Behind those experiences is a complex network of products, inventory, orders, warehouses, and business processes that need to work together.

Built for the pace of sport

The world of professional sports moves quickly. A tennis match can turn in a matter of seconds. Consumer goods organizations like Wilson run an equally dynamic and demanding global operation: designing, manufacturing, moving, and selling products to customers and athletes around the world.

A major event, such as a Grand Slam or a Wilson ambassador winning a tournament, can bring heightened demand for the products athletes use and fans want to buy. This requires Wilson to coordinate inventory, production, warehouses, retailers, and e-commerce across markets and respond quickly as demand changes. 麻豆原创 solutions such as 麻豆原创 Extended Warehouse Management, 麻豆原创 Global Trade Services, 麻豆原创 Concur and 麻豆原创 Ariba provide Wilson with the digital foundation to run smarter, operate faster, and respond to the ever-changing demands running a global retail business

This September in New York fans will see Alex de Minaur compete with his trusted Wilson racket and adorn his new customized Wilson kit. What they won鈥檛 see is the integrated ecosystem and the multifaceted journey behind every single racket, every pair of shoes, every piece of apparel鈥攁nd the 麻豆原创 technology powering it.

Tennis is a game of preparation. When I walk onto the court, every detail matters鈥攆rom how the racket feels in my hand to the kit I’m wearing. Knowing that Wilson and the technology behind their business makes sure everything is ready exactly when I need it, that’s the kind of confidence that lets me focus on competing at my best.

Alex de Minaur

Whether it鈥檚 a game of tennis or the demands of a global business, success depends on having everything working in harmony. With 麻豆原创, Wilson can perform at its best.

And on the court, Alex de Minaur knows he can trust that everything is in place when the moment matters most.

NTT DATA is a $30+ billion global leader in AI, digital business and technology services, serving 75% of the Fortune Global 100.

The 麻豆原创 solutions will help NTT DATA replace multiple legacy HR systems with one unified platform for people data and processes, strengthening decision-making, employee experience and workforce planning. 麻豆原创 SuccessFactors solutions will serve as the system of record for people and talent data, working alongside NTT DATA’s existing employee service platform and specialist workforce planning tools. 麻豆原创 Business Data Cloud will connect this data with insights across other business functions.

鈥淭he initiative reflects NTT DATA’s view of talent as a strategic differentiator and AI as a capability that should be embedded across enterprise organizations,鈥� said Stijn Nauwelaerts, Chief People Officer, NTT DATA, Inc. 鈥淯ltimately, this is about creating an environment where our people feel empowered to do their best work, wherever they are in the world.鈥�

The deployment builds on a strategic partnership between 麻豆原创 and NTT DATA spanning more than 36 years, during which NTT DATA has collaborated as an 麻豆原创 platinum partner, global service partner and global reseller for 麻豆原创. In 2025, NTT DATA adopted 麻豆原创 Cloud ERP Private solutions to modernize its core systems.

麻豆原创 SuccessFactors solutions will now be deployed internally at NTT DATA over a 12-month period, with the company applying its own 麻豆原创 expertise to design and roll out the platform. This will create a single, authoritative source of HR data and processes, laying the foundation for faster, more consistent HR services across the organization. Leading its own implementation will also strengthen NTT DATA’s ability to guide clients through AI-driven HR transformation, with firsthand experience of the solutions it delivers.

鈥淣TT DATA is demonstrating how AI and cloud technology can redefine the employee experience,鈥� said Thomas Saueressig, Chief Customer Officer and Member of the Executive Board of 麻豆原创 SE. 鈥淲ith a unified, intelligent HR platform, the company will unlock new levels of productivity and scale a people strategy that supports a connected workforce worldwide.鈥�

Visit the . Get 麻豆原创 news via  and .

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

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

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

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

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

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

Now let鈥檚 dive into the releases from Q2 2026.

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

Joule

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

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

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

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Joule Work mobile app
General availability

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

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

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

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

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

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

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

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Autonomous SCM

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

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

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

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

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

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

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Autonomous Finance

Project Billing Price Verification Agent
Beta release

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

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

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

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

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

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Autonomous Spend

Expense Automation Agent
General availability

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

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

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

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

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

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

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

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

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

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Autonomous CX

Order Reliability Agent
Beta release

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

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

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

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

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

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Joule with 麻豆原创 Order Management Services
Beta release

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

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

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

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麻豆原创 Business AI Platform

Build

Joule Studio
麻豆原创 Early Adopter Care

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

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

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

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

and .

Contextualize and Reason

Generative AI hub, enhancements

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

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

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

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

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

.

麻豆原创 Joule for Consultants, enhancements

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

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

and .

麻豆原创 Document AI enhancements

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

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

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

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麻豆原创 Domain Models

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

麻豆原创 Domain Models will help:

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

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

.

Govern

麻豆原创 AI Agent Hub enhancements

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

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

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Process Consulting Agent
General availability

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

.

Enterprise Content Research Agent
General availability

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

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WalkMe solutions, AI knowledge indexing
General availability

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

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WalkMe solutions, AI knowledge referencing
General availability

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

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WalkMe solutions, pinned AI
General availability

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

.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

is the digital tool for customers and partners to easily interact with 麻豆原创 and get immediate guidance to the best solution. With this portal, users can access important alerts, metrics, and insights about their 麻豆原创 product portfolio from a single access point. The integration of Joule in 麻豆原创 for Me is not just a feature drop or a UX redesign. This is an inflection point in how customers access products, support, and self-service.听

We are moving from a click-and-search portal to a conversational, agent-driven enterprise ecosystem. For users, this is an experiential shift with significant business outcomes. By taking over the tedious work of clicking, searching, parsing, and diagnosing, Joule helps free up IT administrators, consultants, and business leaders to focus on what truly matters: driving innovation and scaling their business.

鈥淭he successful launch of Joule in 麻豆原创 for Me is the result of strong collaboration, innovation, and a shared commitment to improving customer experience. It demonstrates how 麻豆原创 continues to turn its AI strategy into real, tangible value, delivering solutions that are not only powerful but also practical and user-centric.鈥�

Gerlinde Wallner, Organizational Change Manager and Coach, Strategy & Operations, 麻豆原创

What can users expect from this new unified, AI鈥憄owered entry to 麻豆原创 for Me?

• Effortless navigation across the portal
• Fast access to relevant information
• Advanced self-service with guided support
• Accelerated task execution without needing to know where to click

In their fast-paced business environments, users don鈥檛 have to search through complex menus or multiple touchpoints. Joule in 麻豆原创 for Me can simplify their path to support. They can simply ask, explore, and act to experience personalized, conversational access to support, self-service, and key tasks in 麻豆原创 for Me.

鈥淛oule is transforming 麻豆原创 support by making it more intuitive and intelligent. We can guide users conversationally to the right outcome鈥攏o searching, no guesswork鈥攁ccelerate self-service and task execution, and deliver context-aware, personalized support directly within 麻豆原创 for Me.鈥�

Corinne Reisert, VP, Customer Support Experience 麻豆原创 for Me, Global Customer Support, 麻豆原创

In addition to introducing Joule in 麻豆原创 for Me, 麻豆原创 takes advantage of AI-powered agentic case resolution, which brings AI agents into support workflows to help analyze new cases, detect duplicates, suggest routings, and draft responses. For select priority cases, AI agents can recommend replies, which helps reduce manual effort, improve triage accuracy, and shorten resolution timelines. This is available now to 麻豆原创 customers.For more information, see .

Joule in 麻豆原创 for Me is being rolled out in phases as of May 2026, at no extra cost to customers.

鈥淲ith 麻豆原创 runs 麻豆原创, we show our customers how we scale agents across the enterprise to deliver real outcomes. Joule in 麻豆原创 for Me exemplifies how conversational and agentic AI can fundamentally transform the way users operate and offer customers a simple and intuitive path to access 麻豆原创鈥檚 services and support.鈥�

Benjamin Blau, Chief Process & Information Officer, 麻豆原创

While Joule in 麻豆原创 for Me already helps deliver a simpler and more intuitive way to access information, support, and guided actions, this is just the starting point. As 麻豆原创 continues to advance its AI strategy, customers can look forward to new scenarios, expanded agent capabilities, and deeper integration across services and support processes. This launch establishes the foundation for a more conversational and autonomous customer experience, one that will continue to evolve as 麻豆原创 brings the next generation of AI-powered innovations to life.

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

As one of the听industry鈥檚 most trusted independent听peer review听platforms,听TrustRadius听is known for its rigorous verification process and commitment to unbiased, customer鈥憀ed insights. These听awards are based听on real听user experiences,听making听them听especially meaningful.

This recognition reinforces a clear message: organizations are turning to听麻豆原创 SuccessFactors solutions not just to manage HR but to modernize it. As companies move towards more autonomous, AI-driven ways of working, they need HCM solutions that bring together data, insights, and action.听That鈥檚听exactly what the 麻豆原创 SuccessFactors portfolio can deliver.

Momentum across the portfolio

This year鈥檚听results highlight strong and growing momentum.

麻豆原创 SuccessFactors听increased from听12听Top Rated awards听in 2025听to 19听in 2026, reflecting听deeper customer satisfaction across听the HCM landscape.

Recognized categories include:

• HR Management
• Workforce Analytics
• Talent Management
• Compensation Management
• Workforce Management
• Applicant Tracking
• Talent Intelligence
• Corporate Learning Management
• Payroll
• International Payroll
• Pay Equity
• Recruiting Automation
• Employee Performance Management
• HR Compliance
• HR Service Delivery
• Employee Onboarding
• Succession Planning
• Diversity, Equity, and Inclusion (DEI)

This breadth reflects the strength of 麻豆原创 SuccessFactors solutions as a unified suite鈥攃onnecting people, processes, and data across the workforce. 麻豆原创听SuccessFactors听solutions can provide听the foundation to turn those connections into real-time insight and action.

What听our听customers are听saying

Across听thousands听of听verified听reviews,听customers听consistently听point to one thing: impact.听From operational efficiency to better decision-making and improved employee experiences, 麻豆原创 SuccessFactors solutions are helping organizations move faster and work smarter.

• 鈥淲ith 麻豆原创 SuccessFactors HCM AI, we get helpful, actionable insights to make the best decisions. For instance, the insights we gain help us streamline HR operations, especially when it comes to managing our payroll.鈥� 鈥斕�

• 鈥溌槎乖� SuccessFactors is our core platform and supports our finance and HR processes. We use every module for recruiting, compensation, and learning. It supports our HR transformation and lays the foundation for our data.鈥� 鈥斕�

• 鈥溌槎乖� SuccessFactors HCM stands out among other human capital management solutions due to its comprehensive suite of听cloud鈥慴ased听tools, strong global compliance capabilities, and seamless integration with other 麻豆原创 systems.鈥� 鈥斕�

• 鈥淔or enhancing employee experience, AI offers personalized recommendations for their learning and development, which increases their productivity and engagement.鈥� 鈥斕�

• 鈥溌槎乖� SuccessFactors HCM is considered a 鈥榖est of breed鈥� for a reason. The fact that it does allow for听in鈥慸epth听customization, and its ability to be tailored not only to individual business needs, but also it allows for best practice听follow鈥憉p听while ensuring organizations remain compliant with several legal requirements.鈥� 鈥斕�

• 鈥淲e听leverage听听(ECP)听for our payroll engine and for our payroll calculations.听Having ECP makes everyone’s life easier鈥� the payroll control center will simulate the payroll before you run the actual payroll. That gives you a lot of analytics and KPIs to analyze results or potential issues well in advance.鈥� 鈥斕�

• “In our organization, we mainly use Joule in 麻豆原创 SuccessFactors to automate and complete tasks through natural conversation, which听eliminates听manual steps. [It] plays听a big role in eliminating the constant back and forth and guesswork involved in finding accurate information, as well as completing routine tasks, for example, workforce insights, budget and planning, document retrieval, etc. Additionally, it makes navigating [麻豆原创听SuccessFactors] seamless.鈥� 鈥斕�

Looking听ahead

We鈥檙e听incredibly grateful to the customers that shared their experiences on听TrustRadius听with insights that continue to guide our innovation.

Building on听the听introduction of听Autonomous HCM at 麻豆原创 Sapphire,听our focus is clear: helping HR听move beyond managing听processes to听orchestrating work. By听bringing听together AI, data, and workflows, 麻豆原创 SuccessFactors solutions enable organizations to听operate听with greater speed, clarity, and confidence, so they can not only adapt to change but actively shape what comes next.

Learn more about the impact customers are seeing with听.

Lara Albert is chief marketing officer for 麻豆原创 SuccessFactors.

In an industry rapidly shifting toward AI-powered operations, Martur Fompak International saw an opportunity to reimagine its material flow through the strategic implementation of technology. Building on the efficient, people-driven processes it already had in place, the company partnered with 麻豆原创 and Humanoid鈥攁 UK-based robotics and AI company鈥攖o explore how integrating embodied AI鈥損owered robotics could redefine material flow across its automotive manufacturing environment. Using Joule and embodied AI capabilities from 麻豆原创, Martur Fompak International now connects production signals and business context directly to autonomous execution, creating a context-aware automation system that prioritizes, picks and delivers materials while adapting in real time to changing business conditions.

Built on 麻豆原创 S/4HANA and enabled by the 麻豆原创 Extended Warehouse Management application, the solution enriches humanoid robots with real-time knowledge of tasks, attributes and exception handling. Guided by material data, storage locations, sequencing and production priorities provided via embodied AI, humanoid robots execute material flows across a live automotive manufacturing environment鈥攊dentifying, transporting and delivering materials to the line while continuously confirming back into 麻豆原创 solutions. Together with autonomous mobile robots (AMRs), the company has created a fully automated, scalable material flow that boosts throughput, improves accuracy and reduces reliance on manual coordination. By assigning repetitive, non-value-adding and physically demanding tasks to robots, Martur Fompak International is enabling its people to focus on safer, more meaningful and higher-value work that drives productivity and innovation.

鈥淥ur humanoid robot collaborates with digital production systems to ensure seamless coordination across order management, logistics and production, enabling scalable AI adoption and improving efficiency, consistency and operational resilience,鈥� said 脰zlem Alt谋n谋艧谋k, Group Intelligent Technologies Director at Martur Fompak International. 鈥淭he deployment of our humanoid solution, powered by an embodied AI layer and enabled through the Joule Studio solution, proves that combining cognitive autonomy with physical automation can transform execution, accelerate decisions and scale intelligent enterprise capabilities across the organization.鈥�

鈥淢artur Fompak International exemplifies what it means to turn AI ambition into real business value on the shop floor,鈥� said Emmanuel Raptopoulos, Chief Revenue Officer, EMEA, MEE and APAC, 麻豆原创 SE. 鈥淏y embedding 麻豆原创 Business AI directly into their physical operations, they are not only boosting throughput and operational resilience鈥攖hey are setting a new standard for what an intelligent, AI-first factory looks like. This is exactly the kind of end-to-end transformation that defines the future of manufacturing. We are proud to congratulate Martur Fompak International on being named the sole winner in the AI Excellence category at the 2026 麻豆原创 Innovation Awards鈥攁 testament to their boldness in turning intelligent enterprise vision into real-world impact.鈥�

Early results show increased throughput, fewer errors and a scalable, AI-driven intralogistics model. A future target of up to five times greater work efficiency has been set for mass production, with work orders expected to be completed faster, more consistently and with greater precision across production flows. With 400 daily production line feeds and 100% 麻豆原创 software鈥揹riven decision making already in place, Martur Fompak International is advancing beyond traditional automation, pioneering a scalable, intelligent factory that represents a new standard for the automotive industry.

Looking ahead, Martur Fompak International plans to further expand its autonomous operations across additional production lines, leveraging 麻豆原创 Business Technology Platform to scale AI-driven workflows and integrations鈥攕upporting both operational efficiency goals and broader sustainability commitments.

Visit the听. Get 麻豆原创 news via听 and .

Media Contact:
Ekin Tayali, +34 673019169, ekin.tayali@sap.com, CET
麻豆原创 麻豆原创 Room; press@sap.com

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

They are expected to manage risk, ensure compliance, and deliver strategic value, all while navigating talent shortages and increasing operational complexity. And most are doing it without the end-to-end visibility they need.

Workflows are disconnected, decision-making is reactive, and policies are inconsistently enforced. I have heard this from customers across every industry and, frankly, it is a problem that traditional approaches to procurement technology haven鈥檛 fully solved.

That鈥檚 what makes this moment different. At 麻豆原创 Sapphire, we introduced the Autonomous Enterprise, a fundamental shift in how businesses operate, with AI assistants and agents powering end-to-end execution at scale, with governance built in. Critically, this isn鈥檛 just about adding AI features to existing tools. It is about moving from AI in applications to AI on applications鈥攊ntelligence that works across your entire landscape, not just inside individual products.

Autonomous Spend Management: From concept to reality

Autonomous Spend Management is a core pillar of the Autonomous Enterprise vision, designed to address the fragmentation that holds procurement and finance teams back. By applying agentic AI across procurement, travel, expenses, and external workforce processes, we鈥檙e creating continuity where disconnection exists today鈥攊ntelligent systems that orchestrate activities, connect context, and surface the right insights at the right moment.

What this means for the people doing the work is equally significant. When AI handles routine execution, decision-makers get time and clarity back. They can intervene earlier, with better information, and focus on more strategic work that actually moves the needle.

To bring this to life, we are introducing a new set of Joule Assistants, AI-powered teammates designed to support procurement and spend management across the full life cycle:

• Category Management Assistant: Analyzes spend patterns, delivers market intelligence, and helps build sharper category strategies
• Sourcing Assistant: Manages the entire sourcing life cycle, from drafting RFPs and bids to recommending negotiation strategies
• Supplier Management Assistant: Provides comprehensive oversight of the supply base, from intelligent classification to continuous multi-dimensional risk monitoring
• Contract Assistant: Streamlines contract authoring, flags renewal opportunities, and connects supplier selection through to contract execution
• Requisition Assistant: Guides users to the right buying channel, auto-fills fields, and uses advanced trade-off analyses to help maximize volume discounts
• Buying Assistant:Helps professional buyers identify spend leakage, surface optimal suppliers, and automate order consolidation
• Receiving Assistant: Auto-creates goods receipts and service entry sheets and guides users through quality tracking so nothing falls through the cracks
• Invoicing Assistant: Handles invoice capture, duplicate detection, and payment proposals so finance teams can close faster with fewer errors
• Services Procurement Assistant: Manages the full SOW life cycle from creation through compliance tracking
• Travel Assistant: Simplifies trip planning with pre-spend estimates, streamlined approvals, and built-in compliance guidance
• Expense Management Assistant: Automates expense reporting, capturing details, flagging errors, and keeping everything compliant

The Autonomous Spend Management capabilities run across our cloud ERP application portfolio, including 麻豆原创 Cloud ERP Private, for end-to-end coverage across business processes and systems.

Why connected processes are critical

Connection is just as powerful as intelligence, and that conviction runs through everything we  announced this week. AI can only do so much if the underlying processes are still fragmented.

In next-gen 麻豆原创 Ariba Buying, new Joule Agents support purchasing and policy management through a more intuitive, persona-driven experience, guiding users toward compliant, contract-linked options while improving catalog management and document traceability. Deeper integration with 麻豆原创 S/4HANA Private Cloud Edition and 麻豆原创 ERP Central Component means these capabilities work with existing ERP investments, not around them.

麻豆原创 Ariba Contracts now brings contract creation, approvals, and compliance tracking into a single unified workspace. AI-assisted drafting lets teams create contracts using natural language, while centralized visibility into terms, pricing, and key dates keeps data consistent and connected to downstream procurement processes.

We also introduced a new Joule Agent in 麻豆原创 Ariba Intake Management to automate how procurement requests are captured and routed across 麻豆原创 and non-麻豆原创 systems. And expanded supplier evaluation capabilities in 麻豆原创 Ariba Supplier Lifecycle and Performance let teams segment performance data by geography, business unit, or category 鈥� with insights feeding directly into to inform sourcing and procurement decisions.

Expanding visibility into services spend and supporting adoption

Nowhere is the need for connected processes more apparent than in asset-intensive industries. In oil and gas, mining, and utilities, external workers can make up 40% of the workforce, yet most organizations are still managing them through manual processes and disconnected systems. The risks are real: expired certifications, overpayments, and poor visibility into work billed versus work actually done.

New 麻豆原创 Fieldglass capabilities address these challenges by bringing together the full contractor life cycle, from the moment a worker arrives on site through to final payment. Organizations can now automate time tracking, verify worker credentials and safety requirements before granting site access, maintain tighter controls over equipment, and dramatically reduce the manual effort involved in invoicing.

We鈥檙e also using AI to accelerate SOW creation by automatically recommending worker roles based on the SOW description and historical buyer data, which reduces manual setup and improves consistency from the start. And to support adoption, WalkMe Premium is now integrated with 麻豆原创 Fieldglass and 麻豆原创 Ariba, providing in-app guidance for tasks such as creating statements of work, approving timesheets, and hiring candidates.

The future of spend management

Autonomous Spend Management marks a fundamental shift from managing processes to delivering business outcomes. From chasing cost savings to actively shaping resilience, margin, and growth. From reacting to events to anticipating them.

The real strategic implication is this: Spend does not happen in isolation. Every contract and invoice has a downstream effect on financial performance. When those decisions are made in context鈥攚ith AI connecting procurement, supply chain, and finance鈥攖he enterprise doesn鈥檛 just run more efficiently, it runs as one system.

That鈥檚 what we are building, and what we announced this week marks a significant step forward.

For more details on this week鈥檚 announcements, see the . For more details on the latest updates in travel and expense, please refer to the

Etosha Thurman is co-business lead and chief marketing officer for 麻豆原创 Finance & Spend Management.

In his kickoff keynote at 麻豆原创 Sapphire Orlando, Florida, Klein and other 麻豆原创 Board members detailed how 麻豆原创 plans to bring agentic AI to the world’s most critical business workflows so that humans and AI can meet the accelerating demands of global business profitably, strategically, and safely.

鈥淭oday I鈥檓 super proud to launch our new 麻豆原创 Business AI Platform, which forms the basis for our vision of the future of business: the Autonomous Enterprise, where agents run the business and you can focus on what truly matters,鈥� Klein said.

Enterprise AI is at an inflection point, Klein told his 30,000-strong in-person and virtual keynote audience, and 麻豆原创 is in a unique position to deliver what customers need to turn their businesses into autonomous enterprises.

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The business AI imperative

Across industries, organizations are investing heavily in artificial intelligence, yet many still struggle to translate that investment into meaningful business value. At 麻豆原创 Sapphire, the message was clear: This isn鈥檛 a technology problem; it鈥檚 a context and execution problem.

While 80% accuracy may be sufficient for consumer AI applications, Klein said, 鈥淓ighty percent is just not good enough when you run the world鈥檚 most business-critical businesses. They [LLMs] should not guess; they should deliver accurate, compliant, and secure outcomes.鈥� 

Klein acknowledged that while adoption of AI has become near-universal, tangible business value remains elusive. Citing a recent Stanford AI survey, he noted that almost every company is now using AI, but seeing only limited return.

The reason, he argued, lies in a structural gap. Above the waterline of enterprise AI, LLMs continue to improve at tasks trained on publicly available data, while below it lies what enterprises truly need: AI that understands mission-critical business data, end-to-end processes, and operates within security, compliance, and governance frameworks.

ERP as the foundation for business AI

麻豆原创鈥檚 answer to this challenge begins with what Klein described as 鈥渢he brain of every company: its ERP system.鈥� For over 50 years, 麻豆原创 has had solutions with incredibly deep process and data domain know-how alongside the governance requirements, compliance controls, and company-specific configurations that define how businesses actually run.

Now, as part of the company鈥檚 new vision, 麻豆原创 plans to infuse this institutional knowledge into AI agents, enabling them to navigate thousands of business processes, select from more than 7 million data fields, and verify identity and access authorizations before returning any output.

鈥淲e鈥檙e bringing together LLMs with 50 years of business know-how stored in our ERP. But to do this, we had to do nothing less than completely reinvent our company,鈥� he told the audience. 鈥淭oday we are very excited to show you the new 麻豆原创 and our vision for the Autonomous Enterprise.鈥�

麻豆原创 Business AI Platform

To bring this vision to life, 麻豆原创 executives on stage announced a series of important innovations, beginning with the launch of the new 麻豆原创 Business AI Platform, a unified architecture bringing together 麻豆原创 Business Technology Platform, 麻豆原创 Business Data Cloud, and AI Foundation under a single roof.

鈥淭he heart of this new platform is the rich context layer,鈥� said Klein. 鈥淗ere, we infuse the deep ERP business domain know-how into the AI agents. Through our knowledge graphs, our AI agents have now a compass, a map, to find the right process and data in your ERP universe. And to provide the agents even more context, we are also introducing our new 麻豆原创 Domain Models. They have been trained on 麻豆原创’s code to even better understand the business logic of your company.鈥�

But, he said, 麻豆原创 is going further: 鈥淏ecause you run your business not only with 麻豆原创 solutions, our AI agents have to also understand non-麻豆原创 data. That’s why we included our 麻豆原创 Business Data Cloud in the context layer to build a single semantical data layer across 麻豆原创 and non-麻豆原创. No more silos, no spaghetti data sprawl鈥攂ecause no AI agent can compensate for a broken data model.鈥�

Echoing Klein, 麻豆原创 CTO Philipp Herzig, who presented the platform in detail, said it has been designed to close the agent adoption gap in the enterprise by delivering outcome, speed, enterprise-readiness, and context. 鈥淚t’s the place where you build, contextualize, reason, and govern AI,鈥� he said.

Herzig explained that the platform is structured around three layers: the context layer which Klein referenced, the build layer, and the governance layer. 鈥淎gents are only as powerful as the context they operate on,鈥� he said. 鈥淟acking context is the number one reason why enterprise AI projects fail to deliver value.鈥�

Within the build layer of the new platform, the new Joule Studio is designed to understand a company鈥檚 business challenges and enables the building of new AI agents quickly and easily.

The third tier is the governance layer, anchored by the new 麻豆原创 AI Agent Hub built on 麻豆原创 LeanIX. This provides a single command center to discover, manage, and govern all AI agents鈥斅槎乖� and non-麻豆原创. It will be generally available in Q3 and included in 麻豆原创 Business AI Platform at no additional charge.

Underscoring the changing AI marketplace, Herzig was joined on stage by KPMG Global Head of Advisory Rob Fisher, who told the audience: 鈥淲hat I鈥檓 hearing from clients is a clear shift; they鈥檙e moving from AI pilots to embedding integrated AI and agents into how work gets done. Where we see leaders really separating from the pack is in the execution and the organizational adaptability.鈥�

麻豆原创 Autonomous Suite

Building on the platform, 麻豆原创 Executive Board Member Muhammad Alam, 麻豆原创 Product & Engineering, announced the transformation of 麻豆原创鈥檚 SaaS application portfolio into the 麻豆原创 Autonomous Suite, described as the most significant evolution of 麻豆原创鈥檚 applications business in the company鈥檚 history.

The suite spans five domains: Autonomous Finance, Autonomous Spend, Autonomous Supply Chain Management, Autonomous HCM, and Autonomous CX, with more than 200 agents and over 50 assistants available in the coming months. Each assistant is mapped to core business roles and carries defined KPIs tracked through 麻豆原创 AI Agent Hub.

鈥溌槎乖� Autonomous Suite brings together the depth of our process expertise, semantically rich data, and built-in governance and compliance,鈥� said Alam. 鈥淭hese agents are designed with outcomes as a core objective. Each assistant has a defined set of ROI KPIs that you can expect it to deliver.鈥� 

鈥淯nderpinning the autonomous suite are out-of-the-box agents鈥攈undreds of agents cutting across all core business processes,鈥� he shared. 鈥淭hese agents come together into what we call assistants, or Joule Assistants. We’ve mapped these assistants to roles across the core processes of an organization, because we know that the first step 
in realizing value from AI is to empower your people to do more, do it better, or do things that just weren’t possible to be done before.鈥�

Turning to Joule itself, Muhammad said 麻豆原创 is fundamentally reimagining how users will interact with 麻豆原创 applications in the future.

鈥淲e call this Joule spaces and along with the familiar Joule conversations experience and Joule Studio 2.0, it is now part of what we call Joule Work,鈥� he explained.

鈥淛oule Work represents a massive step forward in super-charging the capabilities of Joule as we know it today,鈥� Alam said. 鈥淲ith Joule Work, we’re bringing a claw-based agentic harness to Joule along with computer and file access, better support for open standards such as MCP and A2A, access to a more complete knowledge base, and, of course, amazing visualizations on the fly.鈥�

Industry AI: H&M and Sector-Specific Transformation

During the keynote, 麻豆原创 Chief Operating Officer Sebastian Steinhaeuser introduced the Industry AI initiative, delivering AI-powered solutions built on decades of sector-specific expertise across 26 industries. In life sciences, he highlighted how 麻豆原创 customer Takeda is achieving up to 10% productivity gains, up to 25% reduction in revenue loss from stock-outs, and up to five percent reduction in safety stock through Autonomous Regulated Manufacturing.

He was also joined on stage by H&M Group CDIO Ellen Svanstr枚m, who discussed how the fashion retailer is embedding AI across its value chain. Built on RISE with 麻豆原创, 麻豆原创 Business Data Cloud, 麻豆原创 Commerce Cloud, and 麻豆原创 SuccessFactors solutions, H&M has developed a Store Intelligence Agent that processes real-time signals to generate actionable recommendations for store managers. Svanstrom also demonstrated the AI-powered InStore Concierge, a customer-facing agent that bridges digital and physical retail through personalized outfit recommendations and real-time availability.

RISE with 麻豆原创 and 麻豆原创 GROW: Path to the Autonomous Enterprise

Returning to the keynote stage, Klein emphasized that technology adoption alone does not create business value. Simply plugging AI agents into your system landscape will drive zero value, he said. 鈥淢oving to the Autonomous Enterprise requires serious change management. Adoption of AI goes hand-in-hand with business process change and end user enablement.鈥�

To support customers on this journey, 麻豆原创 announced a comprehensive reset of its RISE with 麻豆原创 and 麻豆原创 GROW offerings. RISE with 麻豆原创 customers will receive contractual commitment to activate three Joule Assistants within the first year, with the Max Success Plan extending adoption across the full enterprise.  

麻豆原创 GROW customers will receive more than 20 AI assistants from day one, with an AI-enabled toolchain designed to support go-live in weeks. New partnerships with Palantir and Accenture will support the most complex migration scenarios.

Closing: The Autonomous Enterprise

Klein closed the keynote by asking Joule to summarize the key takeaways and noting that 麻豆原创 is evolving from being a software company to becoming a business AI company.

鈥淲e showed how to turn the promise of business AI into reality with 麻豆原创 Business AI Platform, which provides the data processes and governance AI need to deliver accurate and secure outcomes at scale; we introduced the 麻豆原创 Autonomous Suite, where applications reason, decide, and act for you; and we showed how to manage change management with RISE with 麻豆原创. Together with customers and partners, we showed how 麻豆原创 is helping companies realize the vision of the Autonomous Enterprise.鈥�

鈥淲e鈥檝e been reinventing how businesses run for over 50 years, and now by infusing 麻豆原创鈥檚 ERP brain into the new 麻豆原创 Business AI Platform, we鈥檙e solving one of the biggest challenges businesses are facing today: how to turn AI into business value,鈥� he said. 鈥淚t鈥檚 the end of long negotiations, supply chain disruptions, financial blind spots, and the beginning of better: Welcome to the Autonomous Enterprise.鈥�

麻豆原创 Business Data Cloud (麻豆原创 BDC) is the data foundation of that platform, the business data fabric that anchors universal business context, serving as the trusted knowledge core听for every enterprise application and agent.听

The future of agentic organizations will be driven by AI with the deepest organizational knowledge. That future doesn’t start with AI models; it starts with whether your data foundation can give agents the business context they need to act autonomously. 

Today, we are introducing innovations that move organizations closer to becoming an autonomous enterprise.

Turn all your data into business outcomes 

A business data fabric architecture ensures every agent, application, and decision draws from the same trusted business context. And today, we are introducing new business data fabric capabilities that bring multi-model, unified master data, and embedded governance to your agentic foundation. 

• 麻豆原创 HANA Cloud natively available in 麻豆原创 Business Data Cloud:听麻豆原创 HANA Cloud听is听now a听core听component of 麻豆原创 Business Data Cloud.听As the AI database听for 麻豆原创 BDC,听麻豆原创听HANA Cloud provides a听unified听in-memory engine听for agents to reason across transactional, analytical, and multi-model workloads听such as spatial, graph, and vector.听In practice, this means agents can navigate relationships across customers and suppliers, analyze geographic dependencies, or perform semantic search in real time.听And because every workload runs on a single in-memory engine with native workload management, inference time drops dramatically, lowering TCO and improving the predictability of AI听costs.听With 麻豆原创 HANA Cloud, 麻豆原创 Databricks, and 麻豆原创 Snowflake, 麻豆原创 Business Data Cloud delivers听intelligent compute for听every data and AI workload.
• Reltio in 麻豆原创 Business Data Cloud: With the completed acquisition of Reltio, 麻豆原创 is bringing multi-domain master data management capabilities directly into 麻豆原创 Business Data Cloud, helping customers unify, cleanse, and harmonize data across 麻豆原创 and third-party听sources. Reltio鈥檚 AI-based entity resolution identifies and merges related records听into a single, consistent view of business entities.听Low-latency delivery and Model Context Protocol support enable real-time, multi-agent workflows across听your data landscape: a procurement agent, for example, can assess supplier risk and trigger action almost instantly using trusted, real-time data. Together, this becomes a golden record system of context that Joule Agents use to deliver faster time-to-value for business AI.
• 麻豆原创 Master Data Governance natively available听in听麻豆原创 Business Data Cloud:听Unified master data is only as valuable as the governance applied to it. To ensure data is AI-ready, governance must听shift听from regulator to value accelerator. 麻豆原创 Master Data Governance is now a core component of 麻豆原创 Business Data Cloud, governing master data and policies across听your听business data fabric.听This results in听embedded听AI governance that accelerates agent deployment, ensuring every agent operates on data听products听that听are听verified听and aligned to your business policies.听
• 麻豆原创 AI Core integration with 麻豆原创 Business Data Cloud: 麻豆原创 is introducing deeper integration between 麻豆原创 Business Data Cloud and 麻豆原创 AI Core, enabling AI models to be grounded directly in trusted business data, semantics, and governance. Batch inference can now be embedded into business-ready data products, continuously enriching the data that powers Joule with predictions, classification, and听AI听outputs.听听

“This is where 麻豆原创 Business Data Cloud fits into the vision: not as a centralized system, but as an enabler of cultural change through its unique capabilities. These capabilities allow teams to preserve mission-critical business context across financial and non-financial data.”

Jannie Affeld, VP Finance Systems and ERP, Google 

Transform outcomes with Joule Agents 

麻豆原创 is bringing agentic AI directly into the business data fabric through Joule Agents, introducing new capabilities that streamline data management, analytics, and planning through a conversational experience: 

• Data product search and creation: Joule Agents simplify how users discover and create data products. With natural language prompts, users can identify relevant 麻豆原创 and third-party data sources, perform joins and transformations automatically, and apply business context and governance policies.  
• Automated planning and analytical modeling: Joule Agents enable data modelers and planning teams to generate analytical and planning models using AI. By defining dimensions, granularity, and data sources, users can automatically create models aligned with best practices. Teams can also initiate planning cycles, manage versions, and apply calculations without deep technical expertise. 
• Easily听surface business听insights:听Business users听can听ask complex analytical questions in natural language and receive context-aware insights across lines of business. Powered by governed data products听in 麻豆原创 Business Data Cloud and 麻豆原创 Knowledge Graph,听Joule听understands relationships, processes, and business logic听to deliver听more accurate and complete answers without requiring manual exploration.
• 麻豆原创 Analytics Cloud story generation: Joule accelerates 麻豆原创 Analytics Cloud story creation by transforming data models, queries, and business context into dashboards and visualizations automatically. Users can continue the conversation, drilling into KPIs, identifying drivers, and exploring trends in a single workflow. 

Extend context across your open data ecosystem

Last year, we introduced 麻豆原创 BDC Connect, a capability to share data and metadata with zero copies, preserving meaning across every cloud and platform. We are excited to announce 麻豆原创 BDC Connect for Amazon Athena, continuing our promise of openness and choice.

This enables 麻豆原创 data products to be discovered and consumed directly within AWS without replication or loss of context. As a result, teams can build analytics, applications, and AI agents faster while ensuring they operate on trusted, governed business data.

Together with existing partners across Snowflake, Databricks, Google BigQuery, and Microsoft Fabric, 麻豆原创 Business Data Cloud delivers a connected, open data ecosystem so organizations can extend business context across their entire landscape with zero copies. 

General availability is planned for H2 2026.

“Compute can happen anywhere, data can stay at the source when needed, but business context is managed once, centrally, in 麻豆原创 Business Data Cloud.”

Malin Persson, CIO at Ericsson

Get started today 

Build your trusted foundation for agentic AI with 麻豆原创 Business Data Cloud.  

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

Employees at , a global fashion retailer with tens of thousands of employees, had to navigate multiple systems to piece together data across sales and procurement. Answering a single question could take up to 10 minutes.

Today, they just ask Joule. What used to take 10 minutes now takes about three seconds, driving a 70% increase in operational efficiency and a 50% reduction in manual errors.

Using capabilities in , LC Waikiki partnered with 麻豆原创 and to build a custom AI-driven experience that dynamically interprets user requests, applies role-based context, performs the necessary queries, and connects data across systems to present a complete view in one place. It then links people directly to the relevant transaction.

At 麻豆原创, stories like these inspire our vision for the enterprise in which AI transforms how people and processes work鈥攐ne where people set the direction and AI executes. We call it the the Autonomous Enterprise.

In the Autonomous Enterprise, decisions are grounded in real-time intelligence, workflows are automated end-to-end, and AI proactively improves every function while empowering people to do their best work.

The Autonomous Enterprise also provides fully governed AI you can trust, so you can achieve more. Making this a reality for companies is critical because AI is now essential to how all work gets done. It is increasingly involved in decisions that carry financial, operational, and regulatory consequences.

Joule: One place to direct the entire business

In the Autonomous Enterprise, Joule Work, announced at 麻豆原创 Sapphire, is the next step in the evolution of how people engage with and execute end-to-end business processes. Joule Work is a dynamic workspace that adapts to intent, keeps people focused on outcomes, and delegates execution to AI.

Through Joule Work, you can say goodbye to manually coordinating work across multiple applications and interfaces. Instead, tell Joule what you want to accomplish. Joule Assistants with role and process context will coordinate teams of Joule Agents to surface the right insights and automate routine work across departments and systems. Rather than static, disjointed systems, you get workspaces that pull together information and menus from various systems that fit your specific needs, in real time.

Joule Work is available now to customers in the 麻豆原创 Early Adopter Care program. 麻豆原创 Early Adopter Care program for the Joule Work desktop app is planned for Q2 2026; general availability for both is planned for H2 2026. The Joule Work mobile app is generally available now.

We also announced that Joule鈥檚 bi-directional Agent-to-Agent (A2A) capabilities will be generally available in Q4, enabling third-party agents to securely call on Joule Agents and act within enterprise processes, extending interoperability in both directions across 麻豆原创 and non-麻豆原创 environments. Agents built in Joule Studio will natively support A2A protocols, enabling interoperability and scalability for multi-agent execution.

麻豆原创 Autonomous Suite: The operational core of the modern enterprise

While Joule Work empowers every individual to do their best work and expand their impact, the 麻豆原创 Autonomous Suite transforms how entire business functions, or 鈥渁utonomous domains,鈥� work.

麻豆原创 Autonomous Suite spans five domains: finance, spend, supply chain, human capital management, and customer experience. These domains will operate as a single system, so workflows and agents run across functions without fragmenting into separate tools, separate data, or separate decisions. This approach allows AI recommendations to reflect your full operating reality.

With 麻豆原创鈥檚 integrated suite of business applications and industry-leading business data, AI in the Autonomous Enterprise is grounded in the specifics of how key business functions actually work. This foundational context for transformative AI outcomes is where 麻豆原创鈥檚 unique experience comes in. For decades, we have been trusted to run our customers鈥� most important functions. 麻豆原创 Autonomous Suite infuses our deep knowledge of business processes into your AI, along with the data context and operational guardrails it needs to be truly effective and reliable at enterprise scale.

Each organization is also unique. Over time, your business has defined how your work gets done. These are the rules, workflows, and how systems respond when something unexpected happens, like a failed transaction, so processes don鈥檛 break. In the Autonomous Enterprise, AI delivers its greatest value by respecting these boundaries, turning your unique ways of working into a true advantage.

At 麻豆原创 Sapphire, we announced new Joule Assistants and Joule Agents, spanning the domains of the Autonomous Enterprise, to help organizations move from managing work to directing outcomes. These new assistants and agents will roll out through the end of this year.

麻豆原创 Business AI Platform: The foundation of the Autonomous Enterprise

The 麻豆原创 Business AI Platform turns the vision of human-led, AI-driven business operations into something enterprises can build and run. It enables them to move from AI experimentation to execution by grounding agents and applications in real business context that governs it all at enterprise scale.

At the center is , a fully managed environment that empowers enterprises to build and manage the full lifecycle of AI agents, applications, extensions, and workflows. Intent-based development capabilities allow people to describe what they need in natural language. A Joule Agent then generates structured requirements, specifications, code, and test artifacts grounded in 麻豆原创 process and data context.

Developers can work within the tools they already use, including VS Code and MCP-enabled toolchains, and choose their preferred agent frameworks, such as , , and .

Through deep integration with the , 鈥攁nd the new 麻豆原创 Domain Models trained on 麻豆原创 code, customer data, metadata, and business processes鈥擩oule Agents reason over real, semantically rich enterprise data rather than generic knowledge. 麻豆原创 Domain Models are available through the 麻豆原创 Early Adopter Care program, with general availability planned for Q3 2026.

Speed and governance, no longer a tradeoff, are built into the 麻豆原创 Business AI Platform. At 麻豆原创, we believe that corporate governance鈥攊ncluding approval flows, compliance processes, identity management, and the ability to audit decision-making鈥攎ust carry into how AI is deployed, updated, and scaled. Joule Studio runtime provides a secure, production-ready, fully managed environment for deploying agents, helping organizations meet compliance standards while reducing infrastructure complexity.

An enhanced 麻豆原创 AI Agent Hub also provides a vendor-agnostic command center to discover, inventory, and govern 麻豆原创 and non-麻豆原创 AI agents and MCP servers across the enterprise. Integration with and further embeds governance and architecture transparency into the development process.

The 麻豆原创 AI Agent Hub leverages enterprise-wide process intelligence to continuously track where AI agents are creating value and can proactively surface where they can deliver even more, because we believe AI needs to remain accountable for outcomes in addition to uptime. 麻豆原创 AI Agent Hub is generally available, with additional capabilities rolling out through 2026. See release timelines in the .

Empowering everyone to solve business challenges with AI

We are making the Autonomous Enterprise a reality because at 麻豆原创, we believe that companies of all sizes need far more than marginally better AI models or the latest bolt-on solutions. They deserve AI-driven outcomes that increase innovation, revenue, and margins.

The Autonomous Enterprise is what brings our vision to life: AI grounded in your data, connected across your most important processes, and governed to fit how your business runs.

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