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�

• 鈥淲别听濒别惫别谤补驳别听听(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.
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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.

麻豆原创 and Anthropic today announced plans to expand their collaboration to deliver advanced AI solutions to enterprise customers, making Claude, Anthropic鈥檚 AI model, a primary reasoning and agentic capability embedded across 麻豆原创’s AI-enabled solution portfolio, powered by Joule and Joule agents.  

Unveiled today at 麻豆原创 Sapphire, Anthropic and 麻豆原创 will collaborate to embed Claude鈥檚 agentic capabilities into the newly announced 麻豆原创 Business AI Platform to advance 麻豆原创鈥檚 vision of the Autonomous Enterprise in the agentic AI era.

The collaboration builds on 麻豆原创鈥檚 more than 50-years of business application know-how across processes, data, and governance. This complements 麻豆原创鈥檚 open ecosystem approach to supporting any model and provides greater customer choice and flexibility to meet evolving AI requirements. 

Connecting directly to 麻豆原创 Business AI Platform, Claude will empower agents to carry 鈥媜ut tasks鈥攆rom closing the books at quarter-end and answering complex employee leave questions to rerouting supplier orders mid-shipment鈥攃oordinating across 麻豆原创 S/4HANA, 麻豆原创 SuccessFactors and 麻豆原创 Ariba solutions, and other systems via MCP.

鈥淥ur open platform means we鈥檙e tightly integrated with world-leading companies across our portfolio. Together with Anthropic, we鈥檙e building something uniquely valuable for our customers,” said Christian Klein, CEO of 麻豆原创 SE. “The Autonomous Enterprise requires AI that understands business context and acts within the controls organizations depend on, and our partnership with Claude plays a key role in this.”

“We built Claude to support the work that helps businesses run: closing the books, rerouting delayed orders, or approving expenses, to name a few. With Claude on 麻豆原创 Business AI Platform, that work happens inside the systems enterprises have already invested in, with the trust and governance 麻豆原创 customers rely on,” Daniela Amodei, co-founder and president of Anthropic, said.

Claude brings additional agentic capabilities and connectivity to Joule

Joule from 麻豆原创 is an AI-enabled business assistant that helps teams make faster, smarter decisions by embedding contextual, more secure AI directly into 麻豆原创 and non-麻豆原创 business workflows. Now, 麻豆原创 is expanding Claude鈥檚 capabilities to Joule with plans to integrate Anthropic鈥檚 advanced agentic AI capabilities across the newly announced 麻豆原创 Business AI Platform.

With a deeper use of Claude and access to Anthropic鈥檚 frontier models, 麻豆原创 customers can expect additional capabilities, such as:

• Better reasoning on complex business tasks: Claude will empower agents to take real action for hundreds of thousands of 麻豆原创 customers, across finance, 鈥嬧�婬R, procurement, and supply chain. Agents leveraging Claude connect to 麻豆原创 Business AI Platform to understand business context grounded in 麻豆原创 data, make 鈥嬧�媘ore accurate decisions, and operate safely within defined processes. For example, a Treasury Manager can ask Joule to prepare a CFO briefing for a bank meeting, and within minutes receive a completed presentation populated with live data and analysis as well as flagged financial risks. Work that previously took hours of manual effort now takes minutes. 
• Agentic AI that understands business context: Claude works with business context from across 麻豆原创鈥檚 enterprise systems and other tools connected through MCP. It takes action step by step: looking up data, making updates, triggering approvals, moving a task forward. Anthropic and 麻豆原创 will work strategically to build custom agents and agentic workflows in 麻豆原创鈥攐ptimizing for key industries such as public sector, healthcare, education, life sciences and utilities. This combines 麻豆原创’s expertise in enterprise applications and AI with Claude’s reasoning and agentic capabilities.

Bringing AI into the systems enterprises already trust

As AI moves from advising to acting, trust is critical, especially in the enterprise and in regulated industries. Anthropic is bringing safe, reliable AI into processes that enterprises already trust. When AI adjusts an order, triggers a workflow, or makes a recommendation inside an 麻豆原创 customer’s environment, it does so within the same controls that govern human decisions: the approvals, policies, and compliance frameworks already wired into 麻豆原创 solutions.

Together, Anthropic and 麻豆原创 plan on bringing this model to life by combining Claude with 麻豆原创鈥檚 depth and scale, helping organizations move from experimentation into the core of how their organizations operate.

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

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

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

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

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

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

Won’t AI then replace software altogether?

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

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

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

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

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

The new stack: What this actually looks like

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

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

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

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

For fast-growing companies: 麻豆原创 GROW Fast

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

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

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

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

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

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

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

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

Held from April 20鈥�24, Hannover Messe is the world鈥檚 leading industrial trade fair.

On day one, Christian Klein, CEO of 麻豆原创 SE, stopped by the 麻豆原创 booth before joining German Chancellor Friedrich Merz and other industrial leaders on the center stage to discuss the importance of moving from AI ambition to real-world execution.

And visitors to the 麻豆原创 booth experienced that shift firsthand, following the production of the ginger shot.

Packaged in a neat blue box, the ginger shot was refreshing but that wasn鈥檛 the only takeaway. The real takeaway was how 麻豆原创鈥檚 new set of AI-powered manufacturing and supply chain innovations can deliver connected .

Supply chain orchestration

From AI and data and then using 麻豆原创鈥檚 agentic AI, visitors saw what supply chain orchestration looks like in practice. 麻豆原创 uses , trusted data, and applications to help manufacturers听sense, analyze, and act in real time.

At the booth, visitors saw human operators interact with an ANYbotics robot through Joule using natural language to run live, remote field service inspections; Uhlmann鈥檚 high-tech glass-fronted packing machine, PacXplorer, in action opposite the CNC machine from DMG MORI that was creating spare parts for the PacXplorer; and, at end of the production cycle, AIMBO鈥檚 robot handling the picking and packing of the ginger shot. Both AIMBO and ANYbotics are part of 麻豆原创鈥檚 growing network of physical AI partnerships.

In addition to many tours held in German and English, day one also saw tours in Japanese, Chinese, and Portuguese鈥擝razil was the partner country at Hannover Messe 2026.

Equipped with headphones to block out the noise of the crowds at the booth, visitors heard how 麻豆原创鈥檚 AI can deliver trusted orchestration and smarter execution for and .

Live AI use cases demonstrate functions and benefits

Operations and insights use case

Here, visitors experienced 麻豆原创鈥檚 vision of supply chain orchestration. In this vision, supply chain orchestration acts as the nerve center of the enterprise. It uses external alerts such as natural disasters, port congestions, or supplier routes to optimize enterprise logistics and planning using agents.

Benefits can include faster response times with AI-assisted monitoring and automated alerts; improved decision-making with data-driven, operational decisions powered by integrated business AI capabilities; and seamless integration with end-to-end connectivity from supply chain planning through to manufacturing execution and quality control.

Top AI functions

• can assist with order release and real-time monitoring.
• A physical AI robot inspects hazards, analyzes inspection data, and identifies root causes.
• Supply optimization analysis helps summarize insights, analyze, and explain the time-series optimization planning run.

Smart production use case

DMG MORI demonstrated production at its CNC machine鈥攁s part of an end-to-end process鈥攆rom engineering to planning to production.

As the white robotic arm of the CNC machine silently moved the pusher spare part听after the milling process, visitors learned about the benefits of integration, from design to tool management, CNC programs to as part of a seamless, integrated process. The production operator dashboard offers the operator on the machine AI capabilities and insights to operational and maintenance information.

The process then continues through to logistics execution with 麻豆原创 Logistics Management, which helps combine warehousing and transportation capabilities for smaller warehouses.听 This听features an AI-powered logistics assistant that can cut through the noise, automatically gathering, summarizing, and prioritizing critical shipment information. It can also provide real-time shipping prices, bringing to life trusted orchestration and smarter execution.

Top AI functions

• uses natural language to help streamline warehouse and transportation operations.
• can provide manufacturing information and support decision-making throughout the workflow.

Intelligent packaging use case

Uhlmann’s PacXplorer and 麻豆原创 highlighted a fully integrated, high-speed packaging line from 麻豆原创 S/4HANA, to 麻豆原创 Digital Manufacturing, down to Uhlmann鈥檚 automation layer to produce the packaged ginger shot. The ginger shots were moved away from the line by a mobile autonomous robot from Symovo. This use case showed visitors how 麻豆原创 supports regulated industries such as pharma and life sciences. 听

Highlighted benefits include increased operational speed with higher throughput thanks to decreased order processing time, built-in regulatory compliance, reduced manual intervention, inventory transparency, and data integrity across the entire production chain.

Top AI functions

• Condition monitoring-led services can enhance asset uptime and service efficiency by combining AI-driven insights and seamless collaboration across the service ecosystem.
• AI-empowered flow analysis enables quick process modeling and engineering optimization.
• Intelligent exception handling is embedded in agent-driven processes.
• Joule’s integrated AI agents can support decision-making throughout the workflow.
• Joule can help power order and line insights.

Humanoid use case

At the final stop before getting their ginger shots, visitors watched an intelligent humanoid robot perform physical tasks at the end of the packaging line, bridging the gap between digital planning and physical execution, highlighting 麻豆原创鈥檚 Project Embodied AI.

Benefits of humanoids include increased operational speed with higher throughput due to a decreased order processing time; increased business uptime and cost efficiency especially in areas dangerous or difficult for humans; inventory transparency with real-time data integrity across the warehouse; and physical-digital alignment eliminating misalignment between planning and execution.

Top AI functions

• Joule and Joule Studio can enable robots to understand the physical world, make autonomous decisions, and learn from their environment for smarter operations.

More than a quick refuel

At the end of their visit, visitors got so much more than a quick refuel to slake their thirst. Following the creation of the ginger shot from recipe development and planning to production with mixing, filling, and packing, visitors came away with a clear understanding of how 麻豆原创 is connecting insight to execution with trusted orchestration and smarter execution. And, it is this trusted orchestration and smarter execution that is building the resilience every manufacturer needs in today鈥檚 world.

Customers can deploy Joule Agents in 麻豆原创 CX Solutions to build, launch, and optimize marketing campaigns

Gemini Enterprise acts as a central hub for agents to take action across 麻豆原创 and Google Cloud platforms

LAS VEGAS 鈥� (NYSE: 麻豆原创) and Google Cloud today announced a new partnership that will help marketers put AI agents to work at scale.

Through new integrations between the 麻豆原创 Engagement Cloud, 麻豆原创 Customer Experience (麻豆原创 CX) and Joule solutions and Gemini Enterprise, joint customers can now deploy agents that securely access unified data stored across both ecosystems to execute complex marketing strategies based on high-level goals defined by the user.

Together, 麻豆原创 and Google Cloud provide a unified foundation for data and AI agents to operate across both ecosystems. Gemini Enterprise will act as a central hub for data integrations and multi-agent coordination, allowing agents to take action across a customers鈥� 麻豆原创 and Google Cloud solutions. These integrations will be supported by the 麻豆原创 Business Data Cloud Connect solution for Google and BigQuery, which enable bidirectional, zero-copy data access between the two platforms, with enterprise-grade security and governance. Capabilities across both Gemini Enterprise and agent gateway APIs from 麻豆原创 will allow customers鈥� agents to more securely exchange context, trigger actions and optimize outcomes across platforms, enabling true multi-agent orchestration.

The integration allows marketers to prompt an agent within 麻豆原创 Engagement Cloud with a clear objective like, 鈥淚ncrease repeat purchases from the last 30 days,鈥� or 鈥淢aximize customer lifetime value while reducing campaign operational costs.鈥� An agent, like a Joule Agent, will handle the end-to-end process鈥攆rom content personalization to visualization to conversational engagement.

鈥淭his is more than a data integration; it鈥檚 a leap forward for AI agents that can collaborate naturally and execute seamlessly,” said Balaji Balasubramanian, President and Chief Product Officer, 麻豆原创 Customer Experience and Consumer Industries. 鈥淏y combining 麻豆原创 Business Data Cloud Connect for Google with interoperable AI agents across 麻豆原创 and Google Cloud, we鈥檙e giving organizations a path from AI experimentation to AI-enabled customer experience at scale. Marketers can spend less time on manual tasks and more time shaping the customer journey.

鈥淭o realize the full potential of agentic AI, businesses need their systems to speak the same language,鈥� said Kevin Ichhpurani, President, Global Partner Ecosystem at Google Cloud. 鈥淏y uniting 麻豆原创鈥檚 enterprise data and customer engagement platform with Google Cloud鈥檚 AI, we鈥檙e enabling marketers to move beyond simple automation to multi-agent orchestration, driving dynamic campaigns that reason and adapt to market shifts in real time.鈥�

According to from 麻豆原创 Engagement Cloud, more than half of marketers say fragmented, outdated data prevents them from acting in the moment. 麻豆原创 and Google Cloud are helping remove that roadblock by unifying data and letting AI agents turn insights into action. Using Joule with 麻豆原创 Engagement Cloud, campaigns can move from planning to activation automatically without manual stitching across tools.

Customers will benefit from autonomous campaign generation, optimization and continuous improved performance. Businesses will achieve faster speed-to-market, lower operational overhead and always-on optimization that drives higher ROI, while giving teams more time to focus on strategy and end-to-end campaign execution.

While marketing is the first example, and will be available to customers in H2 2026, this multi-agent orchestration model is designed to support high-value use cases across the 麻豆原创 CX portfolio, laying the foundation for AI-driven customer experience, powered by trusted, unified real-time data and interoperable agents.

For more information about 麻豆原创 Customer Experience solutions, visit .

For more information about Gemini Enterprise, visit .

Visit the . Get 麻豆原创 news via  and .

About Google Cloud

Google Cloud offers a powerful, optimized AI stack 鈥� including AI infrastructure, leading models like Gemini, data management capabilities, multicloud security solutions, developer tools and platform, as well as agents and applications 鈥� that enables organizations to transform their business for the Agentic Era. Customers in more than 200 countries and territories turn to Google Cloud as their trusted technology partner.

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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Instead, they immediately encounter friction. They鈥檙e asked to repeat information they鈥檝e already shared across multiple channels and departments. Then they see an offer for the item they just purchased, rather than something similar or new. And when they encounter an issue down the line, customer support doesn鈥檛 recognize their history.

Micro moments like these do not feel minor to customers anymore. They feel inexcusable. Customer expectations have changed faster than most brands can keep up. Customers now assume brands know who they are, what they need, and what鈥檚 happening right now. And they expect brands to act on that knowledge instantly.

At the same time, businesses are embracing a new era of AI. Dubbed “agentic AI,” it represents a paradigm shift where AI doesn鈥檛 just analyze or recommend products, but increasingly plans, decides, and acts through a network of agents. This creates a massive opportunity for customer experience (CX) leaders today, in particular marketers, who, according to McKinsey, are leading in AI adoption amongst business functions. But it also raises the stakes.

Because when AI moves faster than your data, systems, and processes, it exposes everything that鈥檚 broken. That tension鈥攂etween rising expectations and disconnected reality鈥攊s exactly what 麻豆原创 and Google Cloud are addressing together.

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The marketer鈥檚 reality: ambition outpacing execution

According to recent , more than half of marketers say fragmented or outdated data prevents them from acting in the moment. Insights arrive too late. Activation requires manual stitching across tools. And even the best strategies stall before they ever reach customers.

It is clear that most organizations genuinely want to deliver great customer experiences. But fragmentation is what stands in the way of delivering connected, meaningful engagements.

On one side: Customers expect effortless, relevant, and real-time experiences. On the other hand, organizations still operate with fragmented data, siloed teams, and delayed insights.

Our latest reveals that customers are increasingly frustrated: 45% say brands can鈥檛 keep up with changing expectations, and 44% say interactions feel less personal than before.鈥�

AI accelerating the engagement divide 

The disconnect between what customers feel and what businesses believe is the “.” Customer signals live across disconnected systems. Data arrives late or without context. Execution happens separately from insight. And while customers feel this friction immediately, many companies do not realize how disconnected their experiences truly are in their customers’ eyes. Now, AI is accelerating this divide.

Agents can generate content, launch campaigns, and optimize engagement at unprecedented speed. But when those agents act on incomplete, outdated, or fragmented data, they only exacerbate inconsistency and poor customer experiences.

When talking to our customers, it鈥檚 clear that there is no shortage of ambition when it comes to AI. In our research, 78% of brands say AI will be integral to their customer retention efforts this year. But only 46% of brands can connect their data in a way that is accessible to power AI sustainably.

The real challenge for CX leaders today is ensuring that AI has the right foundation: trusted data, unified context, and direct connection to execution.

New model for engagement built on trusted enterprise data

麻豆原创 and Google Cloud are expanding their partnership to enable a fundamentally different approach to marketing execution, one grounded in trusted enterprise data and real-time signals, accelerated with multi-agent coordination, and delivered at scale through 麻豆原创 and Google鈥檚 customer engagement solutions.

麻豆原创 provides both operational truth for elements such as inventory, orders, and fulfillment status, and deep customer knowledge across customer experience interactions. Google Cloud brings additional real-time signals and analytics, along with advanced AI. Combined, they create a shared, real-time understanding of the customer, grounded in business and situational context.

At the heart of this partnership:

• 麻豆原创 Business Data Cloud (麻豆原创 BDC) connects semantically rich data across the enterprise with AI to enable real-time insights and drive personalized interactions grounded in business context. This includes 麻豆原创 Business Data Cloud Connect for Google BigQuery.
• Google BigQuery unlocks real-time signals across the Google ecosystem, such as geolocation, weather, and rich analytics, through bidirectional, zero-copy data access with 麻豆原创 BDC, while ensuring enterprise-grade governance and security.
• 麻豆原创 Customer Experience applications provide the real-time behavioral context 鈥� customer profiles, transactions, orders, service interactions, and consented engagement data.
• 麻豆原创 Engagement Cloud activates enterprise data and AI insights and predictions to securely orchestrate real-time, personalized interactions across the entire customer life cycle.

With these innovations, marketers can finally move from insight to execution automatically.

To realize the full potential of agentic AI, businesses need their systems to speak the same language. By uniting 麻豆原创’s enterprise data and customer engagement platform with Google Cloud’s AI, we鈥檙e enabling marketers to move beyond simple automation to multi-agent orchestration, driving dynamic campaigns that reason and adapt to market shifts in real time.

Kevin Ichhpurani, President, Global Partner Ecosystem at Google Cloud

From prompt to performance: how agents work together for marketing

Another critical element of this new execution model is agent interoperability. Gemini Enterprise acts as a central hub for multi-agent coordination, enabling  customers鈥� agents to securely exchange context and take action across platforms. Meanwhile, Joule acts as the engagement layer within 麻豆原创 applications, executing tasks, orchestrating campaign and content workflows, and optimizing marketing outcomes. Working together, 麻豆原创 and Google are enabling true multi-agent orchestration connected to trusted enterprise data.

Within this broader CX transformation, 麻豆原创 Engagement Cloud is where agentic intelligence becomes operational for marketing teams. It is the environment where enterprise signals, generative media, and AI agents translate into real customer interactions and automated lifecycle journeys.

Advanced generative capabilities powered by Google Gemini models, for example, Nano Banana 2, introduce new agentic skills that help CX teams dynamically generate messaging, imagery, and campaign variations. Through assistants and agents in Joule, these capabilities become embedded directly into marketing workflows, allowing brands to adjust tone, localize content, and respond instantly to changing conditions.

It is not just content generation and personalization that are being rewired. With unified data context and interoperable agents, mobile messaging can turn into immersive conversational experiences with Google Rich Communication Services (RCS) and advertising audiences, and creative, which can continuously evolve based on real-time performance and business signals, transforming campaigns into intelligent, self-optimizing systems.

And through this multi-agent network, marketers will not need to build every step of a campaign manually. Instead, they define the goal, gain more time to focus on strategy and creativity, and let agents handle the rest.

For example, a marketer can prompt:

• 鈥淚ncrease repeat purchases from customers in the last 30 days.鈥�
• 鈥淢aximize customer lifetime value while reducing campaign operational costs.鈥�

And from there:

• Joule Agents coordinate content production, grounded in customer and enterprise data, understand business context, customer history, and constraints
• Google鈥檚 Gemini Models and agents generate creative variations, messaging, and channel-specific content
• Agents collaborate across 麻豆原创 and Google Cloud to personalize, activate, and continuously optimize campaigns in real time across engagement channels and media networks

This is more than a data integration. It鈥檚 a leap forward for AI agents that can collaborate naturally and execute seamlessly. By combining 麻豆原创 Business Data Cloud Connect for Google with interoperable AI agents across 麻豆原创 and Google, we鈥檙e giving organizations a path from AI experimentation to AI-empowered customer experience at scale. Marketers can spend less time on manual tasks and more time shaping the customer journey.

Balaji Balasubramanian, President and Chief Product Officer, 麻豆原创 Customer Experience and Consumer Industries

Clear business outcomes for marketing teams

By enabling a network of interoperable AI agents and grounding them in enterprise data and shared context across 麻豆原创 and Google, organizations can achieve measurable outcomes, including:

• Faster speed-to-market through autonomous campaign and content generation
• Lower operational overhead by eliminating manual execution steps
• Always鈥憃n optimization that continuously improves performance
• Higher ROI through relevant, timely, and consistent engagement at scale

Marketers can spend less time managing workflows and more time shaping strategy, creative direction, and customer value.

Beyond campaigns: continuous engagement at enterprise scale

While marketing is a natural starting point, this is just the beginning. Customer engagement does not live in one system or team. Engagement spans commerce, service, sales, supply chain, and operations. A brand promise made in a message must be fulfilled by inventory. A personalized offer depends on pricing, availability, and delivery. And a single customer service interaction can shape the future of customer loyalty and lifetime value.

This multi-agent model is designed to support high-value use cases across the 麻豆原创 Customer Experience portfolio, laying the foundation for an AI-driven customer experience powered by trusted, unified, real-timedata.

In an AI-driven world, customer experience goes beyond any single interaction鈥攊t’s defined by every touchpoint a customer has with your company.

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

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

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

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

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

Some highlights from Q1 2026:

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

Joule

Joule, enhancements

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

To set up, see: , , and .

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

For more information, see .

麻豆原创 Joule for Consultants, enhancements

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

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

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

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

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

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

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

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

Get started .

SECTION

麻豆原创 Business AI for supply chain

Project Setup Agent
Beta release

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

Get started .

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

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

Get started .

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

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

Get started .

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

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

Get started .

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

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

Get started .

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

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

Get started .

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

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

and get started .

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

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

Get started .

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

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

Get started .

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

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

Get started .

麻豆原创 Business AI for finance

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

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

Get started .

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

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

Get started .

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

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

Get started .

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

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

Get started .

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

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

Get started .

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

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

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

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

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

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

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

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

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麻豆原创 Business AI for spend management

Expense Report Validation Agent
General availability

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

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

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

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Expense Automation Agent
麻豆原创 Early Adopter Care

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

See the demo .

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

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

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Policy Navigator
麻豆原创 Early Adopter Care

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

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麻豆原创 Business AI for procurement

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

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

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麻豆原创 Business AI for customer experience

Catalog Optimization Agent
General availability

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

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

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

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麻豆原创 Business AI for IT and developers

Joule Studio code editor and Joule Studio CLI

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

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

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Joule with 麻豆原创 Datasphere
General availability

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

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麻豆原创 Document AI, enhancements

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

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

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

Generative AI Hub in AI Foundation, enhancements

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

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

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

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

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

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

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

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

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

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

See .

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

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

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麻豆原创 Business AI for industries

Tender Analysis Agent
General availability

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

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

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

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

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

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Joule with 麻豆原创 Intelligent Clinical Supply Management
General availability

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

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

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

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麻豆原创 Business AI for business transformation management

Joule with 麻豆原创 Signavio solutions
General availability

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

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

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

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

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

Get started .

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

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

With the听听1H鈥�2026 release,听we鈥檙e听deepening those connections across the HR lifecycle. This release focuses on听four听core priorities:听connected,听suite-wide听AI; unified experiences that adapt to how organizations work;听processes designed for clarity, accuracy, and compliance; and stronger foundations for skills and long-term growth.听Together, these innovations help organizations听anticipate听needs earlier, reduce friction in daily work, and move forward with greater confidence.听

Connected AI that works across HCM

AI in HR delivers the greatest impact when it works continuously across the entire workforce lifecycle鈥攏ot as isolated features, but as connected capabilities that share context and insight.

The 1H鈥�2026 release expands听suite-wide听agentic AI听across 麻豆原创 SuccessFactors solutions, helping听employees听get clearer answers, act sooner, and keep听work moving听across roles and responsibilities.听A connected network of听听now supports areas such as recruiting, workforce administration, payroll, learning, performance, and talent development鈥攚orking together behind the scenes to help听anticipate听next steps and surface relevant guidance.

This release also introduces a growing听workforce knowledge network, bringing trusted external expertise and research directly into the flow of work through Joule.听Teams can now access expert-backed global employment guidance and听research-driven听insights without leaving their workflows鈥攕upporting听faster, more听confident decisions.

To听further听support learning in the flow of work,听intelligent Q&A in听听now helps employees find information more easily. AI听can deliver instant,听context-aware听responses drawn directly from an organization鈥檚 learning content,听along with relevant links and resources,听so employees can get answers quickly without searching through courses or documentation.听

Unified experiences that adapt to how work gets done听

As HR听tasks听become more embedded in听day-to-day work, experiences need to feel intuitive, connected, and responsive听wherever work happens. In the 1H 2026 release,听麻豆原创 SuccessFactors solutions continue to unify experiences across the suite, giving employees, managers, and HR teams what they need听in听the moment.听

• Connected recruiting and onboarding:听Native integration between听 solutions, , and听听can bring AI-enabled听recruiting, core HR, and onboarding together into a single, continuous experience, helping hiring teams move faster while听maintaining听consistency from candidate through new hire.听

• Tailored experiences,听built faster:听The new听extensibility wizard听can provide guided, step-by-step support for creating custom extensions on听听(麻豆原创 BTP) directly within听麻豆原创听SuccessFactors solutions, making it easier to adapt experiences to unique business needs while preserving governance.听
• Simpler, clearer employee moments:听A redesigned, configurable 401(k) experience听in听听for U.S.听employees helps simplify enrollment and management by clearly explaining employer contributions and guiding deferrals and beneficiary setup, helping employees make informed decisions with confidence.听

Processes designed for clarity, accuracy, and compliance听

In the 1H鈥�2026 release, 麻豆原创 SuccessFactors introduces new capabilities that help organizations bring greater clarity and rigor to pay practices.

With听pay听transparency insights听in the , organizations can analyze compensation patterns and potential pay gaps, supporting transparent, data-driven pay practices in-line with evolving regulatory expectations,听including in the EU.听

Skills governance听for sustainable growth听

Preparing for听what鈥檚听next requires trusted, consistent skills data that organizations can rely on across HR, talent, and workforce planning.

In the 1H鈥�2026 release,听we are听strengthening听the听听with enhanced听skills governance, providing a centralized interface to help manage skills, apply governance standards, and ensure alignment across 麻豆原创 SuccessFactors solutions and partner applications. This helps organizations improve听skills听data quality, maintain consistency at scale, and make more confident,听skills-based听decisions.听

A connected foundation for the future 

This听release听reinforces听麻豆原创鈥檚 continued focus on an intelligent, connected HCM听foundation鈥攐ne designed to evolve with your organization and support confident decisions at every stage of work. By bringing together data, AI, and experiences across the HR lifecycle, these听enhancements help organizations reduce friction today while听laying听the groundwork for听tomorrow.

To explore what鈥檚 included in this release, check out the or watch the overview .

Bianka Woelke is group vice president and head of Application Product Management for 麻豆原创 SuccessFactors.

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Team Liquid, the world鈥檚 largest esports organization, is turning to Joule to transform how it manages the vast amounts of data generated in competitive gaming.

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

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

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

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

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

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

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

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

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

Matt Dillman is a senior videographer at 麻豆原创.

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

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

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

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

Building the foundation for enterprise-grade AI

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

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

Modernizing the business logic that runs the enterprise

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

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

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

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

Connecting AI to business operations

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

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

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

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

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

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

Experience agentic AI at NVIDIA GTC

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

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

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

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

.

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

麻豆原创 is expanding the Joule solution across 麻豆原创 Concur solutions and introducing new automation capabilities:

• A new integration between Joule and Microsoft 365 Copilot, now available, embeds travel and expense tasks into everyday productivity tools. Employees can create and submit expense reports, upload receipts, book travel and receive policy guidance in 麻豆原创 Concur solutions without leaving Microsoft applications.
• Two new Joule Agents further streamline expense compliance and reporting.
  • Expense Automation Agent automatically creates and populates expense reports for employees so all they have to do is review, refine and submit.
  • Expense Pre-Submit Audit Agent validates receipts and flags discrepancies before submission to reduce report rejection and reimbursement delays.
  • Both agents are currently available through the 麻豆原创 Early Adopter Care program with general availability expected later this year.
• New AI-based rule creation tools simplify the complex task of managing policy rules in the Complete by 麻豆原创 Concur and Amex GBT, Concur Travel and Concur Expense solutions.
• The 麻豆原创 Sales Cloud solution now integrates with Booking Agent to streamline workflows and enhance productivity for sales teams.

麻豆原创 Concur and American Express Global Business Travel (Amex GBT) , an AI-enabled codeveloped solution for booking, servicing, payments and expensing. New capabilities include AI-enabled travel support with handoff to a live travel counselor and a specialized home page for travel managers. Concur Expense also integrates with Amex GBT Egencia for customers worldwide.

Joint customers of 麻豆原创 Concur solutions and can now create and manage American Express Virtual Cards in Concur Expense, supporting employee spending with controls and added security. The virtual cards can also be used in Concur Travel. This capability is available now to select U.S.-based American Express庐 Corporate and Business customers using Concur Expense with availability for all such customers planned for Q3 2026.

麻豆原创 Concur teams up with Visa to integrate Concur Expense and Visa through the Visa Commercial Integrated Partner program. Initially, real-time notifications (RTN) from Visa card swipes will automatically create expenses in Concur Expense. This capability is planned to be available through 麻豆原创 Early Adopter Care in Q3 2026. 麻豆原创 Concur solutions will now support RTN from all major credit card networks.

Additionally, 麻豆原创 Concur solutions are advancing corporate travel with enhanced booking, expanded global access and intelligent traveler support. The new experience in Concur Travel supports guest bookings, expanded Cleartrip content in India and additional airline options. TripIt Pro adds Image to Plan with Apple Intelligence and expanded Risk Alerts to help travelers organize itineraries and monitor disruptions.

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

I鈥檓 pleased to announce that next鈥慻en 麻豆原创 Ariba is now available, marking the next phase in 麻豆原创鈥檚 journey to reimagine source鈥憈o鈥憄ay for the age of AI. This milestone represents the transition from announcement to execution, bringing a fundamentally rebuilt platform into customers鈥� hands.

This milestone comes alongside strong industry recognition. 麻豆原创 has been named a Leader in the 2026 Gartner庐 Magic Quadrant™ for Source鈥憈o鈥慞ay Suites, an acknowledgment that aligns with the delivery of next-gen 麻豆原创 Ariba and our continued focus on platform modernization and AI鈥慸riven innovation at enterprise scale.

Why rebuilding the foundation matters

As procurement leaders know, AI鈥檚 potential is widely recognized, but its impact has often been uneven. Confidence is high, yet results depend on more than algorithms alone. AI only delivers value when it is supported by the right data, processes, and platform architecture. That belief shaped our decision to rebuild 麻豆原创 Ariba from the ground up.

Independent analyst firm as 鈥渁 complete reengineering of the largest and most entrenched source鈥憈o鈥憄ay platform in the world,鈥� emphasizing that this move goes far beyond adding AI features to legacy systems. Instead, it establishes the architectural foundation required for AI to operate reliably and at scale.

This aligns with what we consistently hear from customers that sustainable impact requires modernization at the core.

An AI鈥憂ative source-to-pay platform built on 麻豆原创 Business Technology Platform

Next鈥慻en 麻豆原创 Ariba is built on (麻豆原创 BTP), providing a unified, real鈥憈ime data foundation across the source鈥憈o鈥憄ay lifecycle. This shift enables tighter integration with , improved extensibility, and faster innovation delivery.

By moving to 麻豆原创 BTP, 麻豆原创 Ariba can support open APIs, cross鈥憇uite data consistency, and the responsiveness required for intelligent, AI鈥慸riven procurement operations鈥攃apabilities that are increasingly expected of leading source鈥憈o鈥憄ay platforms but are difficult to achieve on legacy architectures.

Current next鈥慻en capabilities will continue to be delivered incrementally throughout 2026 and into 2027, giving customers flexibility to adopt innovation at a pace that aligns with their business priorities.

Embedded intelligence with Joule: moving from insight to action

A defining element of next鈥慻en 麻豆原创 Ariba is the deep integration of directly into procurement workflows. Rather than treating AI as an optional add鈥憃n, next鈥慻en 麻豆原创 Ariba can embed intelligence where work happens鈥攕upporting faster, more informed decisions while reducing friction across everyday processes.

Early capabilities include:

• A Bid Analysis Agent, which can automatically evaluate complex bid scenarios, including total cost considerations
• AI-assisted contract support to help automate routine inquiries, generate summaries, and provide instant access to contract details

These capabilities reflect a broader shift from systems that require constant manual input to platforms that actively support outcomes.

A more unified, intuitive procurement experience

Next鈥慻en 麻豆原创 Ariba also addresses long鈥憇tanding fragmentation across the source鈥憈o鈥憄ay lifecycle.

Key improvements include:

• 麻豆原创 Ariba Intake Management, now globally available, providing a single entry point for procurement requests
• A simplified 麻豆原创 Fiori鈥慴ased user experience, delivered through a central launchpad
• A modernized contract lifecycle, supported through integration with Icertis Contract Intelligence

Together, these improvements are designed to make procurement easier to engage with while working to ensure processes remain connected, compliant, and intelligent behind the scenes.

What this means for customers

For existing 麻豆原创 Ariba customers, next鈥慻en 麻豆原创 Ariba provides choice and continuity. Customers can:

• Transition to next鈥慻en 麻豆原创 Ariba on a voluntary basis.
• Access next鈥慻en capabilities without commercial implications.
• Run current and next鈥慻en environments in parallel during an active transition, reducing risk and disruption.

麻豆原创 is providing tools, services, and advance timelines to support a managed transition, allowing organizations to move forward with confidence rather than urgency.

The foundation for what comes next

Next鈥慻en 麻豆原创 Ariba is not an endpoint, it is the foundation for the future of procurement.

With an AI鈥憂ative architecture, embedded agentic intelligence, and a unified user experience, this new generation of 麻豆原创 Ariba helps organizations move beyond transactional efficiency toward smarter decisions, greater resilience, and measurable business outcomes.

As Ardent Partners observed, this rebuild has the potential to act as a catalyst鈥攏ot just for 麻豆原创 Ariba customers, but for the broader procurement technology landscape. By combining scale, data, and AI in a fundamentally new way, next鈥慻en 麻豆原创 Ariba is helping define what modern source鈥憈o鈥憄ay platforms can deliver.

With the solution now available, we look forward to partnering with customers as they move from vision to value. Together, we can shape the future of intelligent procurement.

Baber Farooq is senior vice president of Product Marketing for 麻豆原创 Ariba and 麻豆原创 Fieldglass.

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

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

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

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

From static dashboards to dynamic workspaces

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

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

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

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

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

Challenges and 麻豆原创鈥檚 answers

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

Accuracy

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

Trust

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

Complexity

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

Why this matters now

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

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

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

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

According to industry , auto executives expect AI to boost product value by 22% and digital service value by 37% within three years. As vehicle portfolios expand鈥攅lectric, hybrid, software-defined, and increasingly customized鈥攖he operational complexity for automakers and suppliers has risen sharply. Nowhere is this more evident than in service parts management and new product introduction (NPI).

Service parts planners sit at the intersection of engineering, supply chain, manufacturing, and customer service. Their task is deceptively simple: ensure the right parts are available at the right time and place across a vehicle鈥檚 lifecycle. In reality, they grapple with fragmented data, limited inventory visibility, unpredictable demand signals, and compressed timelines鈥攅specially as new models and components are introduced at unprecedented speed. High data quality, tight orchestration across systems, and rapid decision-making are no longer nice to have, they are business-critical.

This is where becomes transformative. Instead of treating NPI as a linear, manual, and reactive process, AI agents can fundamentally reimagine how service parts planning is executed. By embedding AI directly into the planning workflow, service parts planners are supported鈥攏ot replaced鈥攂y intelligent systems that operate with full contextual awareness. These AI agents can monitor real-time data across inventories, supplier readiness, historical demand patterns, external risk factors, and engineering changes, as well as orchestrate the NPI process end to end.

In practice, this means planners move from firefighting to foresight. AI agents can automate sequential NPI steps, flag potential bottlenecks before they materialize, and dynamically adjust plans as conditions change. A single, unified dashboard provides transparency across the entire process, while built-in what-if simulations allow planners to test scenarios鈥攕upplier delays, demand spikes, geopolitical disruptions鈥攂efore decisions are locked in. Crucially, humans remain firmly in control. AI augments judgment, improves speed, and enhances confidence, rather than operating in a silo.

Platforms like 麻豆原创 Business Technology Platform (麻豆原创 BTP), combined with Joule and the agent builder capability in Joule Studio, can enable this multi-agent approach at enterprise scale. By integrating AI seamlessly with core business processes, automakers can ensure that intelligence flows across functions, rather than being trapped in silos. The result is not just automation, but orchestration鈥攚here systems, data, and people work in concert.

The impact is tangible. Automakers can significantly reduce planning cycle times and improve time-to-market for new products. Planning risk is lowered through continuous what-if analysis that incorporates both internal and external variables. Service readiness improves, ensuring that customers experience continuity and reliability even as product complexity increases. At an ecosystem level, this translates into greater resilience, lower costs, and higher customer satisfaction.

More broadly, this use case points to a shift in how we should think about customer-specific AI in automotive. The future will not be defined solely by smarter vehicles, but by smarter enterprises鈥攚here AI agents support decision-making across the value chain, from product inception to end-of-life service. In an industry under pressure to innovate faster, operate leaner, and remain sustainable, AI-driven operations are fast becoming a competitive necessity. The automotive ecosystem is evolving. Those who embrace AI not just as a technology but as a new operating model will be best positioned to lead it.

Sindhu Gangadharan is head of Customer Innovation Services and managing director for 麻豆原创 Labs India.

Joule is an AI solution that turns siloed data and tasks into intelligent, connected workflows that help improve decisions, speed up end-to-end processes, and create a unified AI experience across 麻豆原创 and non-麻豆原创 systems. With AI agents for all core functions, powered by 麻豆原创 business process expertise, an AI strategy scales faster and wider.

In this context, the unique value of combining Joule with 麻豆原创 Signavio is the powerful combination of deep process context from 麻豆原创 Signavio and orchestration across 麻豆原创 applications, including but not limited to 麻豆原创 S/4HANA, 麻豆原创 Business Technology Platform (麻豆原创 BTP), and 麻豆原创 SuccessFactors solutions.

After months of successful collaboration with customers in the 麻豆原创 Early Adopter Care program, this launch marks a major step toward delivering a conversational experience, making it easier than ever to explore, understand, manage, and transform processes with 麻豆原创 Signavio solutions.

But how does this work in practice? Let鈥檚 look at an example.

Joule in action

Imagine accessing , an organization鈥檚 single source of truth for process alignment, in order to understand more about a particular process, order-to-cash. What once might have taken hours of investigation can now happen in minutes through a simple conversation.

Asking Joule, 鈥淲ho is the process owner of the order-to-cash process?鈥� prompts Joule to suggest the process that most closely matches the query, then retrieve the owner information from the process diagram attributes.鈥�

Following up with 鈥淧rovide me with a description of the process flow鈥� means Joule skills convert the process visual into a clear process description. You can dive deeper into the process as well, perhaps by comparing the difference in process execution in different regions. Just ask, and Joule skills provide a textual summary that highlights the key differences between the two process models.

Joule offers a connected user experience across 麻豆原创 and non-麻豆原创 systems, allowing employees to ask questions and interact with Joule from anywhere. In other words, while working in 麻豆原创 Signavio, users can interact with data and capabilities from other systems, or while in other systems, users can access 麻豆原创 Signavio capabilities.

Simple and seamless

This connectivity and seamless access is designed to simplify day-to-day tasks in multiple ways, and Joule with 麻豆原创 Signavio offers skills across three use cases: informational, navigational, and transactional. Plus, additional analytical capabilities are planned for future releases. As a summary, these use cases鈥攐r interaction patterns鈥攃omprise the following:

• Informational:鈥疛oule acts as an intelligent assistant, helping users understand how to perform specific tasks or generating a textual comparison between processes. For example, ask Joule: 鈥淲hat鈥檚 the difference between draft and published process models?鈥濃�痮r 鈥淗ow can I set up a dashboard?鈥�
• Navigational: Joule simplifies content navigation across 麻豆原创 Signavio solutions and guides users through the 麻豆原创 Signavio Process Collaboration Hub to access various assets such as process and journey models, and value accelerators. For example, prompt Joule: 鈥淥pen the Order-to-Cash process model published for EMEA鈥� or鈥�淔ind accelerators for the Financial Process鈥�
• Transactional: Joule enables users to perform actions conversationally, such as creating and deleting assets within the 麻豆原创 Signavio Process Transformation Suite, including processes, journey models, and dictionary items. For example, tell Joule: 鈥淐reate a new dictionary term and link it to the Returns process.鈥濃�痮r 鈥淒elete the journey model called Sales Process.鈥�

Unlocking value with agentic AI

Right now, Joule works across 麻豆原创 applications as a process companion, meaning that 麻豆原创 Signavio process context and intelligence is accessible to the user across all Joule-compatible applications, including, among others, 麻豆原创 S/4HANA, 麻豆原创 SuccessFactors solutions, and 麻豆原创 BTP. But this is just the beginning.

麻豆原创 is developing Joule Agents embedded into every business function and accessed with role-based assistants, which use 麻豆原创鈥檚 process expertise to automate complex workflows and deliver AI value at scale. Joule Agents for 麻豆原创 Signavio solutions can help accelerate content discovery and process analysis, create value cases, and enhance user onboarding.

The age of AI brings an expectation of immediate insights and context-based support when and where you need it, and Joule with 麻豆原创 Signavio solutions means process navigation and execution is no exception.

For clearer decision-making, seamless integration, and enhanced automation, visit the to learn how to activate Joule and get your own conversation started.

Lucas de Boer is Global Marketing program lead for 麻豆原创 Signavio.

Building resilience and control across every category of spend

Organizations are under increasing pressure to manage costs, improve agility, and drive measurable outcomes. 麻豆原创鈥檚 connected and intelligence-driven source-to-pay suite is designed to help customers meet these challenges head on.

麻豆原创 Ariba solutions can deliver broad and deep functionality across the full source-to-pay lifecycle, spanning sourcing, contracting, procurement, invoicing, and supplier management. With the industry鈥檚 largest supplier network, 麻豆原创 enables buyers and suppliers to collaborate with confidence, consistency, and scale.

麻豆原创鈥檚 investment priorities: platform modernization and agentic AI innovation

Rather than reflecting external judgments, 麻豆原创鈥檚 strategic focus is centered on advancing its platform foundation, AI capabilities, and user experience to help customers operate with greater intelligence and agility. Our long鈥憈erm investments concentrate on three core areas:

Modernizing the platform for the future

A platform update arriving in 2026 will complete the modernization of 麻豆原创鈥檚 technical architecture. This modernized foundation can deliver greater extensibility, improved performance, and faster delivery of innovation, particularly in agentic and generative AI.

Built as an AI-native architecture, the next-generation platform can embed intelligence directly into workflows to help anticipate needs, guide decision-making, and automate actions across the entire source-to-pay process. This positions 麻豆原创 to deliver the first truly AI-native source-to-pay suite built for the future of procurement.

Expanding intelligence with Joule

plays a central role in bringing intelligence and insight to every stage of the source-to-pay process. Joule鈥檚 advanced AI agents can help automate tasks, support decision-making, enhance compliance, and unlock new productivity across sourcing, procurement, and supplier collaboration.

Reimagining the user experience

麻豆原创 is delivering an updated, consistent UI/UX across 麻豆原创 applications. For procurement teams, this means smoother navigation, modernized interfaces, and enriched contextual intelligence, including enhanced supplier 360 profiles and strengthened collaboration capabilities.

Strengthening global scale and operational flexibility

麻豆原创 continues to demonstrate industry-leading global scale, supporting high-volume transactions and diverse compliance requirements across regions and industries. With multiple cloud deployment options across major hyperscalers and a robust portfolio of security and regulatory certifications, including FedRAMP, customers can operate confidently wherever they do business.

Connected solutions across 麻豆原创 Business Network and the intelligent suite

麻豆原创 Business Network remains the largest supplier network in the source-to-pay market, spanning more than 190 countries. 麻豆原创鈥檚 broader spend ecosystem鈥攊ncluding innovations such as , , and 鈥攅nables organizations to unify data, intelligence, and processes across sourcing, procurement, invoicing, and spend management.

Importantly, 麻豆原创鈥檚 capabilities extend well beyond traditional source-to-pay. Through connected solutions covering travel and expense, contingent workforce management, external labor, and additional spend categories, 麻豆原创 provides a truly comprehensive spend management platform that can deliver visibility and control across the full spectrum of enterprise spend.

As organizations increasingly operate in heterogeneous application landscapes, 麻豆原创 helps deliver openness, security, and extensibility so customers can maintain cohesive and connected processes throughout 麻豆原创 and non-麻豆原创 environments.

Customer impact: outcomes that scale

Customers across industries and regions continue to demonstrate what鈥檚 possible with 麻豆原创鈥檚 source-to-pay solutions. Organizations report meaningful improvements in areas such as compliance, cost optimization, supplier collaboration, operational efficiency, and workforce productivity鈥攆rom managing millions of invoices to running global sourcing initiatives to scaling AI-powered automation across distributed operations.

麻豆原创 remains deeply committed to helping procurement and finance organizations navigate complexity with confidence. Our investments in platform modernization, agentic AI, user experience, and cross-suite integration are all grounded in a single mission: to help customers achieve sustainable, long-lasting impact.

We’re grateful for this recognition and energized by the opportunity to deliver even greater value in the years ahead.

. Read the full Gartner Magic Quadrant for Source-to-Pay Suites report .

Fang Chang is EVP and chief product officer for 麻豆原创 Procurement and External Workforce Solutions.
Baber Farooq is senior vice president and head of Market Strategy for 麻豆原创 Procurement and External Workforce solutions.

*Gartner Magic Quadrant for Source-to-Pay Suites, January 21, 2026 – ID G00833291, by Micky Keck, Kaitlynn Sommers, Lynne Phelan, Magnus Bergfors, Alex Brady

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

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

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

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

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

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

Here are some of the highlights from Q4 2025:

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

Joule

Joule with Microsoft 365 Copilot
General availability

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

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

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Joule Analytics Center
General availability

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

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Joule Preview Landscape
General availability

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

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麻豆原创 Joule for Consultants, product enhancements
General availability

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

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

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

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

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Joule deep research capability
Beta release

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

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

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麻豆原创 Business AI for human resources

Performance Preparation Agent
General availability

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

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

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

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

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

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

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

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

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

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

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

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

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麻豆原创 Business AI for supply chain

Production Planning and Operations Agent
Beta release

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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麻豆原创 Business AI for finance

Accounting Accruals Agent
Beta release

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

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

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International Trade Classification Agent
Beta release

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

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

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

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

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

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Change Record Management Agent
Beta release

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Joule with 麻豆原创 Risk and Assurance Management
General availability

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

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

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麻豆原创 Business AI for spend management

Booking Agent
General availability

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

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

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Receipt Analysis Agent
麻豆原创 Early Adopter Care release

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

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

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麻豆原创 Business AI for procurement

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

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

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

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

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

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

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麻豆原创 Business AI for customer experience

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

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

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

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

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

麻豆原创 Business AI for IT and developers

Joule studio, agent builder
General availability

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

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

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Generative AI Hub in AI Foundation, enhancements
General availability

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

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

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

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

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

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

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麻豆原创-ABAP-1 foundation model

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

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

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Prompt Optimizer

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

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Orchestration registry

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

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New models available

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

For more information on new and deprecated models,

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麻豆原创 Document AI, enhancements
General availability

Vision-enabled information extraction

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

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Processing of e-mail attachments

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

Get started with and .

Document workflows

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

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

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Built-in transport management

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

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

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

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

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

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

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

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麻豆原创 Business AI for industries

Utilities Customer Self-Service Agent
General availability

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

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

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Tender Analysis Agent
General availability

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

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

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

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

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

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

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

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

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

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

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

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麻豆原创 Business AI for business transformation management

Dashboard Analyzer Agent for 麻豆原创 Signavio
Beta release

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

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

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Screen Guide Agent for 麻豆原创 Signavio
Beta release

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

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

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Workspace Administration Agent for 麻豆原创 Signavio
Beta release

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

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

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Value Case Creation Agent for 麻豆原创 Signavio
Beta release

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

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

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Process Content Recommender Agent for 麻豆原创 Signavio
Beta release

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

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

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

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

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

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

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

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

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麻豆原创 LeanIX, AI Agent Hub
General availability

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

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

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

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

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

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Philipp Herzig is CTO of 麻豆原创 SE.

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

Heartland Dental is the largest dental support organization in the United States, with over 3,000 supported doctors in more than 1,900 supported practices across 39 states and Washington, D.C. But beyond numbers, what sets Heartland apart is its tight knit community and people culture. As Jerome shared, “The first thing I associate with Heartland is community; we鈥檙e doctor-led. In our support role, we don鈥檛 tell dentists how to practice. Our role is to make their lives easier鈥攅nabling dentists to concentrate on patient care.”

Making lives easier is a vision Heartland shares not only for its supported dentists but for its own operations. The company’s digital journey began “backwards” starting in 2018 with 麻豆原创 Business Technology Platform (麻豆原创 BTP), instead of with ERP, to resolve disparate data from all over. Once 麻豆原创 BTP was established and adopted by the organization, it then began to incorporate .

What makes this journey truly stand out is how Heartland is using technology to serve people, supported through seamless integration with tools like 麻豆原创 Concur solutions, embedded听AI鈥攕uch as smart invoice management鈥攁nd embedded analytics. These features are freeing up time and resources so teams can focus on what matters most: supporting doctors and improving patient care.

The team has rolled out听麻豆原创 Build Work Zone across its supported practices and is investing in听AI tools like 听to help employees access information faster, automate repetitive tasks, and focus on what really matters鈥攑atient care and experiences. Jerome explained, “Just like we take the administrative burden off our supported doctors, 麻豆原创 takes the tech burden off us, so we can focus on supporting doctors and their teams.鈥�

Heartland’s next big milestone is going live with 麻豆原创 S/4HANA Cloud Public Edition听and taking AI a step further to automation.

Heartland Dental鈥檚 story shows that with the strategic adoption of technological innovations, it鈥檚 possible to build a future-ready healthcare support organization grounded in people and purpose.

Chris Putvinski is a communications specialist at 麻豆原创.

Since the launch, has empowered organizations to streamline operations for increased efficiency, reduced manual workloads, and untangled multi-channel complexity. With this approach, businesses can deliver on customer promises with seamless customer experience. This momentum has also been recognized in the market, as 麻豆原创 Order Management Services was named a Leader in by IHL Group for its robust capabilities and enterprise readiness.

, a leading German home improvement retailer, is already seeing the benefits. With 麻豆原创 Order Management Services, Hornbach connects digital and physical stores with full visibility into day-to-day transactions, providing omnichannel retail experience at scale to its customers.

However, the retail landscape is evolving continuously. While profitable growth is critical to businesses, earning and sustaining customer loyalty now is becoming more important. Ahead of the curve, 麻豆原创 has heavily invested in expanding capabilities in the 麻豆原创 Order Management Services bundle to help retailers deliver on customer promises with intelligence, scalability, and adaptability, leading to boosts in customer loyalty.

At NRF 2026, 麻豆原创 is unveiling new and enhanced capabilities that power retailers to not only operate more efficiently but also achieve loyalty-led growth through every order.

AI in 麻豆原创 Order Management Services

Joule in 麻豆原创 Order Management Services: 麻豆原创鈥檚 AI copilot, Joule, is now available in 麻豆原创 Order Management Services. Access order-related data, analysis, and insights through conversations in natural language and visual display.

Order Reliability Agent: Accelerate operational efficiency with the Order Reliability Agent in 麻豆原创 Order Management Services. Proactively mitigate and resolve any potential issues and gaps, such as stock discrepancies or process bottlenecks, to help ensure every order is fulfilled seamlessly and to boost customer loyalty.

AI-assisted copy generation and translations: Create promotional copy in seconds and translate it into any language with AI assistance, helping to reduce manual workload and accelerate time-to-market.

UI enhancements

Workflow-optimized UI: The enhanced and unified UI in 麻豆原创 Order Management Services can deliver a consistent user experience across order, inventory, and fulfillment operations. Teams can now work faster, reduce training time, and maintain full visibility across every step of the order lifecycle.

Watch the 麻豆原创 Order Management Services  to get a closer look at the AI capabilities in action. Visit the 麻豆原创 booth at NRF 2026, January 11 鈥� 13, to learn more about 麻豆原创 Order Management Services and catch an in-person demo.

Emilie Fournelle is head of Product Management for 麻豆原创 Order Management Services at 麻豆原创.

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

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

1. New categories of AI foundation models unlock enterprise value

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

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

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

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

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

2. Software evolves toward AI-native architecture

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

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

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

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

3. Agentic governance becomes mission-critical

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

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

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

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

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

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

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

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

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

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

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

5. Deglobalization drives sovereign AI offerings

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

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

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

Executing on the 2026 AI themes

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

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

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

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

As companies race to implement this new technology, they鈥檙e discovering it鈥檚 not all as smooth as envisioned. Following a recent , high costs and fuzzy business value are creating speed bumps. As it turns out, the technology itself isn鈥檛 the problem, it鈥檚 how and why we use it. Like any shiny new gadget, AI agents only matter when they solve real problems that make people鈥檚 lives easier. So, what kinds of issues are they good at tackling? And how do we make sure we鈥檙e designing systems that serve actual humans and are not just chasing the latest tech trend?

That鈥檚 where things get interesting: deciding when you truly need an agent, how much freedom it should have, and what challenges and tasks it鈥檚 meant to address all while ensuring it genuinely helps people, instead of just ticking the 鈥渨e use AI鈥� box. The real magic happens when humans and agents team up, working side by side for the best results. How do we make the most of this human-agent partnership?

If you鈥檙e looking for a practical way to get started, the 麻豆原创 AppHaus Joule Agent Discovery and Design workshops offer a hands-on approach to help tackle these exact questions. With a blend of human-centered design methods, these workshop formats put people first, working to ensure agentic systems aren鈥檛 just flashy but genuinely useful.

Want to try it out for yourself? Here鈥檚 how you can run your own workshops and define impactful agentic systems.

A toolkit to build human-centered agentic solutions

The Joule Agent workshops are offered as two different formats, each designed to guide you through a different stage of building effective agentic systems: the Joule Agent Discovery workshop and the Joule Agent Design workshop. Together, these workshops provide a hands-on, human-centered path for creating AI agents that can truly deliver value.

First stop: Joule Agent Discovery workshop

The Joule Agent Discovery workshop is a structured approach to uncover the most valuable opportunities for agentic technology. It focuses on real-world challenges and identifying where automation can make the biggest impact. In two to three hours, participants dive into questions such as: What specific inefficiency or challenge needs solving? What could be automated? Who would benefit most from automation? What needs to be achieved with the automation? How complex and variable is the problem at hand?

The workshop also introduces participants to agentic technology and examines how much the selected challenges would benefit from it. By the end of this workshop, participants identify one or more high-value use cases that are well-suited to agentic technology. This helps ensure that efforts are focused on meaningful improvements rather than adopting technology for its own sake.

Second stop: Joule Agent Design workshop

Next is the Joule Agent Design workshop, which brings together those closest to the process鈥攅nd users and business experts鈥攖o define the details of the agent: its responsibilities, required skills, and how it will collaborate with people. The workshop follows a practical structure:

1. Define the focus area: Clarify what target users need to achieve within the selected process and identify which aspects would benefit most from automation.
2. Identify tasks to delegate: Use the metaphor of 鈥渉iring a super-specialist鈥� to decide which responsibilities should remain with people and which can be assigned to agents. Exercises help determine how many agents are needed, the risks of automating certain tasks, and where consistency versus autonomy is required.
3. Describe the super-specialist job: Draft a job description for each agent, outlining necessary skills and responsibilities.
4. Instruct the super-specialist: Define the instructions or workflow, including information requirements, decision points, and where human involvement is needed.

By the end of the workshop, each agent is described in detail, including its tasks, required knowledge and tools, and an initial set of instructions. This forms the foundation for configuring the agent鈥檚 system prompt.

The workshop material also offers guidance on structuring the system prompt based on the gathered information, ensuring a smooth transition from workshop insights to practical implementation. The entire process is designed to be completed in a single day and can be conducted virtually in Mural.

Learning how to run these workshops

To help ensure that anyone can confidently run these workshops鈥攏o advanced degrees or secret codes required鈥攁 set of self-paced courses are available and can be completed at the individual pace of the learner:

• : This course offers a comprehensive introduction to the Joule Agent Discovery workshop. It provides a clear, step-by-step guide, explains the exercises in detail, and gives practical advice on how to facilitate effective sessions. You鈥檒l gain the skills to identify agentic opportunities and successfully lead your team through the process.
• : This short webinar also centers on the Joule Agent Discovery workshop, but it specifically highlights a practical method for assessing the agentic potential of automation ideas. Consider it your quick reference for making informed decisions about automation.
• : The latest addition to our curriculum, this course walks you through the Joule Agent Design workshop step by step. It covers each exercise, shares real-world examples, and offers facilitation tips. You鈥檒l also learn how to adapt the workshop format for various time constraints and organizational needs.

All the resources required to facilitate these workshops are freely available on the website. Learners can simply visit the site, explore the materials, and start their agentic journey with confidence to turn ideas into new useful, human-centered AI solutions.

Karen Detken is an expert user experience designer at 麻豆原创 AppHaus.

is a next-generation HR help desk and ticketing system, purpose-built for HR. It helps organizations break through those barriers by connecting AI, trusted data, and intuitive experiences to help transform HR service delivery from reactive support to proactive, human-centered impact.

From HR help desk to human impact

麻豆原创 SuccessFactors Enterprise Service Management gives HR teams a unified HR service delivery platform to help manage service requests, automate workflows, and deliver consistent, guided interactions that can build trust and compliance. By moving beyond a reactive “ticketing” mindset, HR can anticipate employee needs, operating with speed, transparency, and insight, which enables the entire organization to work more efficiently.

Unlike IT-centric tools that treat employees as tickets, 麻豆原创 SuccessFactors Enterprise Service Management brings together context, compliance, and connection鈥攅nabling HR to create experiences that feel personal, intelligent, and effortless.

AI for HR: delivering context-driven automation

Automation is only as effective as the context behind it. Unlike standalone AI add-ons, Joule, 麻豆原创鈥檚 AI copilot, can make every interaction faster, smarter, and more human. Employees can ask questions in everyday language and receive contextual answers grounded in verified HR data and policies.

HR teams benefit from AI-generated case summaries, pre-built templates, personalized e-mail drafts, intelligent case recommendations, and intelligent ticket deflection to help reduce manual effort and speed resolution. With agentic AI, the system continuously learns from every interaction, automatically surfacing insights, suggesting next best actions, and driving service accuracy. HR teams don鈥檛 just automate tasks; they deliver smarter, compliant, and more personalized support all within a single, secure HR system.

Connected data that powers intelligent decisions

Unlike many platforms that rely on integrations or duplicated data to connect with HR systems, the 麻豆原创 SuccessFactors portfolio is connected by design. Deeply integrated with , 麻豆原创 SuccessFactors Enterprise Service Management can connect every transaction, service case, and policy into a single source of truth for HR. Combined with and , this foundation can turn data into actionable intelligence: surfacing trends, predicting service demand, and improving quality with each interaction. Pre-configured HR scenarios accelerate implementation and reduce time-to-value by providing best-practice case and form definitions that can be tailored to unique organizational processes. Standard integration flows enable HR teams to update employee data directly from within the case, making it easier to get up and running quickly without starting from scratch.

Experience that feels effortless

A great HR service experience isn鈥檛 just fast鈥攊t feels easy. serves as the digital front door for 麻豆原创 SuccessFactors Enterprise Service Management, 麻豆原创 SuccessFactors Employee Central, and Joule.

Employees can find answers, submit requests, and collaborate in the flow of work:

• Submit requests and find answers through guided experiences.
• Initiate AI-powered knowledge searches to reduce overall case volume.
• Extend workflows quickly with seamless integration with .

Customer impact in action

Across industries, 麻豆原创 customers are replacing fragmented, IT-led tools with a purpose-built HR service platform, creating measurable gains in employee satisfaction and HR efficiency.

• 鈥淏y moving to next-generation service requests with [麻豆原创 SuccessFactors] Enterprise Service Management, we鈥檝e streamlined complex processes like 401(k) transfers and dramatically improved speed, compliance, and employee confidence.鈥�  鈥� 麻豆原创 customer, Technology
• 鈥淓mployees start in [麻豆原创 SuccessFactors] Work Zone with Joule, search knowledge first, and only create a case when needed. That reduces case volume and service rep workload.鈥� 鈥� 麻豆原创 customer, Consumer Goods
• 鈥淲e鈥檙e designing for consistency鈥攗sing targeted forms aligned to service categories. Data duplication is minimized while the People Profile serves as the single source of truth.鈥� 鈥� 麻豆原创 customer, Manufacturing
• 鈥淥ur front door is multi-channel鈥攅-mail, phone, and Teams chat鈥攁ll anchored in the HR knowledge base with smart routing to the right teams.鈥� 鈥� 麻豆原创 customer, Aviation
• 鈥淥ne feature that truly stood out for us was the timeline. Finding information in our old system used to be a challenge, but with [麻豆原创 SuccessFactors] Enterprise Service Management, the timeline view has been a game changer. It gives our teams instant visibility into every interaction and drives new levels of efficiency and clarity. 鈥� 麻豆原创 customer, Banking
• 鈥淭he guided steps for HR service representatives have transformed how our team works. Instead of navigating complex processes, they now follow clear, intuitive paths that ensure every case is handled consistently and efficiently. It鈥檚 like having an intelligent assistant built right into the workflow.鈥� 鈥� 麻豆原创 customer, Telecommunications
• 鈥淭he [麻豆原创 SuccessFactors] Employee Central data mashup within case management has been an absolute game changer. Having real-time employee data visible directly in each case means no more switching between systems. Our HR team can make faster, more informed decisions with complete context at their fingertips.鈥� 鈥� 麻豆原创 customer, Manufacturing

From service efficiency to experience intelligence

By uniting AI, data, and experience within the 麻豆原创 ecosystem, 麻豆原创 SuccessFactors solutions can turn HR service delivery into a .

Organizations can achieve:

• Faster resolution for service issues
• Personalized experiences at scale for every employee
• Actionable insights that improve both HR efficiency and employee engagement

In a world where agility and trust define competitive advantage, HR is no longer a back-office function鈥攊t鈥檚 a driver of workforce productivity, organizational resilience, and enterprise-wide success.

Ready to see it in action?听,听gives you 30 days of hands-on access to explore the solution at no cost. Start with the use cases most relevant to your team and experience firsthand how 麻豆原创 SuccessFactors Enterprise Service Management grows alongside your needs.

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

Beyond the technologies being deployed, the ability to use them meaningfully in daily work is becoming central. This is also seen as the major challenge of AI adoption. Developing AI skills has thus become a strategic priority.

For 麻豆原创 customers and partners, the question is how to upskill their teams effectively. Because without the right skills and abilities, the best software is useless.

Here you’ll find seven tips for developing AI skills specifically for the 麻豆原创 context.

1. Learn the fundamentals of AI

There are many learning resources for the basics in the form of e-learning courses or webinars. Some are even free, such as many courses from 麻豆原创. We’ve compiled key : on AI in general, Joule, and 麻豆原创 Business AI, as well as the important topic of ethics and responsible AI.

2. Self-assessment: Where do I stand?

Everyone should understand the fundamentals of AI; after that, you can deepen your knowledge in targeted areas. The can help with this self-reflection. Here you can rate yourself in the areas of awareness, m, knowledge, and application skills. The topic areas include Joule, including copilot and agents; embedded AI; machine learning services on 麻豆原创 Business Technology Platform; 麻豆原创 Build and Joule Studio; responsible AI; and implementation of 麻豆原创 Business AI. First results show that many respondents show a high motivation, while technical application skills still need more improvement. 

3. Deepening by topic and role: Which AI skills are still needed?

Once you’ve assessed yourself as a team or individual, the various 麻豆原创 learning offerings鈥攊nformation also 鈥攃an help you create your own learning plan. Looking at the relevant topic areas, your own role, and preferred learning formats makes orientation and selection easier.

4. Set learning goals and document successes

Many people find it helpful to set concrete learning goals and schedule specific learning times; for example, in their own calendar. Documenting and reflecting on learning progress and “aha” moments also helps you and others, whether through blogs on 麻豆原创 Community, a personal learning journal in digital notes, or simply verbally within your team. AI certification like for might also be a more formal way to check and document your skills.

5. Learn from and with others

In peer learning, you learn through barcamps, workshops, discussion rounds, communities, study groups, networking meetups, or promptathons, a type of hackathon where small groups solve challenges from their daily work using AI tools. Along with 麻豆原创, companies like Deutsche Telekom and Continental are already using this format.

Learning through exchange in communities鈥攕uch as the with its many blogs and discussions鈥攊s another format for learning with peers. also offers numerous led by 麻豆原创 experts where you can ask questions.

6. Learn through experience and doing

With such generic technologies as AI, it’s important for everyone to explore for themselves where AI can help them and to try things hands-on. Whether in learning projects where you experiment with and reflect on new AI tools, or in team workshops. For workshops, the with templates can help鈥攊ncluding for , , 麻豆原创 Business AI design, and 麻豆原创 Business AI exploration. Discovery and exploration should happen at the strategic level, but it’s also very helpful at the team level. The practice systems in can also assist with hands-on practice as you see Joule and embedded AI features in many training systems.

7. Regularly review and update your AI skills: How can I keep up to date?

The field of AI is evolving rapidly, so regular learning, updating, and trying out new tools is essential. Podcasts, the resources mentioned in this article, , and the diverse array of can help with this. Or why not build your own news-update agent for your own context?

Summary and outlook

AI learning in the 麻豆原创 ecosystem is a business-critical, continuous task. You only understand AI by applying it and actively engaging with it. Additionally, it’s important to adapt to these new technologies鈥攐r even to completely rethink tasks and processes. Complete a self-assessment, create a learning plan by yourself and with your peers, and book the learning offerings relevant to you today.

Thomas Jenewein is a business development manager at 麻豆原创.

This month at , we announced a wave of 麻豆原创 Business AI innovations all built on the same technology foundation that powers our that we are now delivering to our customers and partners, allowing them to add even more value in the future.

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

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

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

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

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

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

Bringing together the power of 麻豆原创 BDC and Snowflake

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

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

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

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

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

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

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

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

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

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

Digital sovereignty made in Germany, for Europe

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

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

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

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

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

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

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

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

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

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

No 麻豆原创 TechEd without ABAP news

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

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

What鈥檚 next: embodied AI and quantum

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

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

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

Read more about the partnerships and implementations here.

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

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

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

Philipp Herzig is CTO of 麻豆原创.