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

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

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

North: Cohere鈥檚 enterprise-grade AI agent platform

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

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

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

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

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

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

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

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

Try it today: .

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

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

This is the context in which 麻豆原创 redesigned the 麻豆原创 Services and Support portfolio eight months ago. It鈥檚 built around a simple premise: transformation isn’t a milestone but a continuous journey, and every organization deserves a partner to guide it at every stage.

The response from customers was clear. Success plans from 麻豆原创 became the default way organizations engage with 麻豆原创’s cloud portfolio, from early cloud deployments to complex, multi-solution transformations powered by 麻豆原创 Business AI.

As of today, November 2, 2026, Max Success Plan鈥斅槎乖粹�檚 most strategic level of engagement鈥攊s available to every customer, whether they choose to work with 麻豆原创 directly or through a trusted partner. For those working through a partner, it is now accessible via Cloud Choice (CCFlex) and Resell (VAR), with managed service providers coming soon.

With this, success plans are open to every 麻豆原创 customer, regardless of how they engage with 麻豆原创. has been included in every 麻豆原创 cloud subscription from day one. has long been available across 麻豆原创鈥檚 full customer base, both directly and through partners. now helps close the loop鈥攖he same complete, tiered path to value that 麻豆原创 has offered directly since February 2026聽is now available to every customer, regardless of how they choose to engage with 麻豆原创. 聽

The evidence: why guidance determines who wins with AI

The data on this is stark. RAND Corporation found that 80% of enterprise AI initiatives fail to deliver their intended business value鈥攖wice the failure rate of standard IT projects. S&P Global Market Intelligence found that 42% of companies abandoned most of their AI initiatives in 2025, up from 17% the year before. And MIT鈥檚 widely cited GenAI Divide report found that 95% of organizations investing in generative AI see zero measurable return on their P&L, while that same research found that pairing AI adoption with an experienced external partner roughly doubles deployment success, 66% versus 33% for internal-only efforts.

The differentiator isn鈥檛 the technology stack. It鈥檚 guided execution.

This is exactly the gap success plans were built to close. They are 麻豆原创鈥檚 cloud engagement model: the structured, continuous guidance customers have been asking for, helping organizations adopt the latest innovations and stay ready for whatever comes next. Foundational Success Plan, Advanced Success Plan, and Max Success Plan can scale that guidance to match a customer鈥檚 complexity and AI ambition. And Max Success Plan, as 麻豆原创鈥檚 most strategic level of engagement, can be that experienced external partner the research points to, built directly into the 麻豆原创 relationship, not bolted on beside it.

The proof: what success plans actually deliver

IDC’s research found that organizations using achieved $4.9 million in average annual benefits, 28% less time spent on incident management, and a 39% reduction in impactful security breaches*. customers saw a 381% three-year ROI, $3.4 million in annual average benefits, and 19% quicker time to market for new features and functions**. And customers achieved a 521% three-year ROI, $4.4 million in end-user productivity gains per organization per year, and a 24% improvement in business processes***.

Guidance that pays for itself鈥攁t every level. Full findings for each success plan are available on .

One clear goal: consistent guidance, regardless of how you reach 麻豆原创

When 麻豆原创 introduced the success plans, the goal was clear: give every 麻豆原创 cloud customer a clear, predictable path to value, one plan included with every subscription and two additional that scale with ambition. What became apparent almost immediately was how many of those customers already reach 麻豆原创 through a partner鈥攁nd how natural the next question was: why should the path to value look any different because of that?

Extending Max Success Plan through partner channels closes a gap that had nothing to do with the value of the offering and everything to do with how customers happened to reach it. That relationship should no longer determine the level of guidance available.

鈥淎s AI raises the stakes for successful transformation, customers need consistent, strategic guidance regardless of how they engage with 麻豆原创. By making Max Success Plan available through our partner channels, we are expanding access to the expertise and support customers need to turn innovation into lasting business value.鈥�

Thomas Saueressig, Chief Customer Officer and Member of the Executive Board of 麻豆原创 SE

Max Success Plan: strategic guidance, built into every engagement

Max Success Plan is 麻豆原创鈥檚 most comprehensive success plan, designed for organizations navigating complex transformations, scaling 麻豆原创 Business AI, or operating in fast-moving business environments. It combines strategic expertise, proactive guidance, and hands-on support to help customers continuously evolve and maximize the value of their 麻豆原创 investments.

Key capabilities include:

• A designated success plan manager who helps orchestrate the end-to-end customer experience with deep knowledge of the customer鈥檚 business and 麻豆原创 landscape
• Cross-solution process improvement and transformation advisory that can connect strategy to execution across the full 麻豆原创 landscape
• Tailored AI use case adoption, including prototype development to validate ideas and de-risk investment before scaling
• Everything in the Advanced and Foundational Success Plans, like guided transformation, proactive risk detection, and mission-critical support, so nothing is lost, only added to

Max Success Plan can be directly integrated into how customers run and evolve their 麻豆原创 solutions. It can be continuous, proactive, and tightly connected to the product road map customers are already investing in. That holds true whether a customer reaches 麻豆原创 through a partner or engages directly.

鈥淔or many of our customers, the partner relationship is the primary one. Extending Max Success Plan into Cloud Choice and Resell means our partners can now sell that same strategic service offering themselves.鈥�

Karl Fahrbach, Chief Partner Officer at 麻豆原创

Available now: growing to meet you where you are

Max Success Plan is now available to organizations working with Cloud Choice (CCFlex) or Resell (VAR) partners. Managed service provider (MSP) is currently piloting with a few customers, allowing MSP partners to align their delivery models and prepare for broader availability.

This phased approach reflects a key insight since February: the organizations that get the most from success plans are those that adopt at a pace matching their own readiness.

As 麻豆原创 builds toward the , where AI increasingly supports execution while people set the direction and hold accountability, the guidance layer around that technology matters as much as the technology itself.  Expanding Max Success Plan across partner models gives more customers access to the strategic guidance they need to adopt innovation, navigate continuous transformation, and realize greater value from their 麻豆原创 investments.

The guidance is in place. The next step is yours.

The difference between an AI initiative that transforms a business and one that quietly stalls is rarely the technology. It is the guidance around it鈥攖he expertise that connects a product roadmap to a business outcome and keeps that connection strong as both evolve.

That guidance is now available to every 麻豆原创 customer, however they choose to reach us. Success plans remain the starting point for any 麻豆原创 cloud engagement, for customers mapping out their next transformation and for the partners they rely on to get there.

Ready to take the next step? to find the level of engagement that best fits your organization. Partners can explore the success plans within the partner portal.

Ville Leivo is global head of Cloud Success and Premium Portfolio at 麻豆原创.

Ryseff, J. and Narayanan, A. Why AI Projects Fail and How They Can Succeed. RAND Corporation, 2025.
S&P Global Market Intelligence. Generative AI Shows Rapid Growth but Yields Mixed Results. Voice of the Enterprise: AI & Machine Learning, October 2025.
Challapally, A., Pease, C., Raskar, R., and Chari, P. The GenAI Divide: State of AI in Business 2025. MIT NANDA (Project NANDA, MIT Media Lab), July 2025.

*IDC White Paper, sponsored by 麻豆原创, The Business Value of Using 麻豆原创鈥檚 Foundational Success Plan, doc ##US54140525-BVWP, April 2026
**IDC White Paper, sponsored by 麻豆原创, The Business Value of Using 麻豆原创鈥檚 Advanced Success Plan, doc #US54296426-BVWP, April 2026
***IDC White Paper, sponsored by 麻豆原创, The Business Value of Using 麻豆原创鈥檚 Max Success Plan, doc #US54437226-BVWP, May 2026

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

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

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

AI governance announcements from 麻豆原创 TechEd

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

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

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

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

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

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

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

麻豆原创 and AI governance in depth

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

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

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

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

Governance should not be an afterthought

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

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

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

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

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

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

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

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

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

Extend the business data fabric with an open lakehouse foundation

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

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

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

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

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

Accelerate your journey to the cloud

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

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

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

Build a trusted system of context across domains

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

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

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

Turn enterprise knowledge into reusable business context for AI

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

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

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

Get started

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

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

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

From vision to execution

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

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

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Designed for better business outcomes

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

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

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

Joule Work

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

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

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

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

麻豆原创 Autonomous Suite

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

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

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

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

麻豆原创 Business AI Platform

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

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

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

Physical AI by 麻豆原创

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

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

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

Joule Studio: Extend, build, and integrate your agents

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

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

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

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

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

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

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

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

麻豆原创 Business Data Cloud

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

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

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

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

Governance

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Migration and modernization assistants go live in customer transformation projects

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

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

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

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

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

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

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

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

Expanding across the full transformation life cycle

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

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

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

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

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

Learn more at .

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

The planned collaboration aims to bring together 麻豆原创鈥檚 enterprise application portfolio, 麻豆原创 Sovereign Cloud and 麻豆原创 Business AI capabilities with NTT鈥檚 global infrastructure, full-stack integrated operations, deep industry AI expertise and global customer reach to help customers benefit from cloud and AI innovation while retaining control over sensitive data, operations, technology and security. The planned collaboration reflects the shared ambition of 麻豆原创 and NTT to advance secure, resilient and sovereign digital transformation for regulated industries.

Supporting Sovereign Cloud Requirements 

As an initial area of cooperation, 麻豆原创 and NTT plan to work together to provide a broad set ofsovereign capabilities that combine the 麻豆原创 Sovereign Cloud On-Site offering with NTT-managed infrastructure and operations to customers in key markets. 

麻豆原创 Sovereign Cloud On-Site is 麻豆原创鈥檚 deployment offering for customers with stringent sovereignty, regulatory and compliance requirements. It enables 麻豆原创-managed cloud solutions to be operated in a customer-owned or customer-selected data center. This provides organizations with a high degree of sovereignty across local physical control, data residency and operational assurance, while helping customers address applicable local legal and regulatory requirements.

By combining the capabilities of 麻豆原创 Sovereign Cloud with NTT鈥檚 global reach and expertise in advising, designing, delivering and operating secure infrastructure, the companies intend to support customers in geographies where strong local control is required. These capabilities are intended to help governments, defense organizations, defense industry companies, critical infrastructure operators and other regulated enterprises modernize mission-critical processes while maintaining control over sensitive data and operations and addressing compliance requirements. 

Exploring the Value of AI  

Building on this foundation, 麻豆原创 and NTT intend to explore broader opportunities to help governments, critical infrastructure operators and highly regulated industries modernize mission-critical processes and adopt AI in a secure, responsible and sovereign manner.

鈥淲ith this cooperation, 麻豆原创 and NTT aim to give customers a practical path to cloud and AI innovation while maintaining the control, governance and trust they require,鈥� said Dominik Asam, member of the Executive Board and Chief Financial Officer, 麻豆原创 SE. 鈥淔or public sector organizations and regulated industries, sovereignty is not just a compliance requirement, it is the foundation for resilience, competitiveness and the responsible adoption of AI. Together, we want to help customers innovate while meeting their sovereignty requirements.鈥� 

鈥淥ur customers are looking for practical ways to use AI to solve real business challenges and create new value,鈥� said Yutaka Sasaki, Representative Member of the Board, Senior Executive Vice President and Chief Financial Officer, NTT. 鈥淏y combining 麻豆原创鈥檚 business applications and AI capabilities with NTT鈥檚 deep understanding of industry needs and mission-critical operations, we aim to help organizations transform how they work, make better decisions and accelerate business outcomes through AI.鈥�

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Media Contact:
Clara Villanueva, +1 (610) 235-7247 聽clara.villanueva@sap.com, ET
麻豆原创 麻豆原创 Room; press@sap.com

This press release was created with the assistance of artificial intelligence and has been reviewed by the editorial team.
This document contains forward-looking statements, which are predictions, projections, or other statements about future events. These statements are based on current expectations, forecasts, and assumptions that are subject to risks and uncertainties that could cause actual results and outcomes to materially differ.聽 Additional information regarding these risks and uncertainties may be found in our filings with the Securities and Exchange Commission, including but not limited to the risk factors section of 麻豆原创鈥檚 2025 Annual Report on Form 20-F.
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Today鈥檚 finance leaders are expected not only to report on business performance, but also to help shape it. They need a foundation that connects financial and operational data, supports end-to-end processes, and helps teams move from insight to action with speed and confidence.

Recognized for cloud ERP for finance

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

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

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

Connecting financial and operational decisions

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

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

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

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

Supporting customer transformation

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

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

Building the future of finance

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

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

Learn more

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

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

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

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

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

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

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

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

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

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

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

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

麻豆原创 S/4HANA transformation

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

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

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

The release combines AI agents that can work directly with core business processes and adds new regulatory compliance capabilities and industry-specific innovations across asset-intensive industries, retail, and energy sectors. Together, these enhancements help strengthen the foundation for organizations that need to scale AI responsibly.

Building the foundation for enterprise AI

As organizations explore how AI can drive business outcomes, one challenge keeps surfacing: AI alone is not enough. To operate at enterprise scale, AI needs access to trusted data, process context, business rules, and governance.

This is why the conversation about AI increasingly becomes a conversation about ERP. AI agents require more than information. They need to understand what actions are permitted, which controls must be respected, and when decisions require human review. 麻豆原创 Cloud ERP Private can provide this foundation through integrated business processes, harmonized data, and enterprise-grade governance. FPS02 helps extend these capabilities, bringing intelligence directly into the processes where business happens.

AI embedded within business processes

FPS02 introduces AI agents and AI-assisted capabilities across key business domains, helping organizations automate routine work while maintaining oversight and accountability.

In finance, agents and AI-assisted capabilities help support processes spanning planning and forecasting, period-close activities, working capital management, tax processes, and compliance operations. Rather than requiring employees to manually identify issues and coordinate follow-up activities, 麻豆原创 Cloud ERP Private and analytical business insights can surface risks, recommend actions, and help manage exceptions.

In sales order-to-cash processes, agents enable sales teams to quickly identify fulfillment issues, receive resolutions, and create or update sales orders directly in their workflow, which helps minimize delays and improve customer satisfaction.

Joule and 麻豆原创 Document AI help streamline quality management by uploading, extracting, and matching certificate data, flagging exceptions, and creating pre-filled defects from natural-language inputs and source documents to help accelerate review, accuracy, and inspection release.

By receiving key observations, recommendations, and a clear action plan聽directly within business processes, employees can focus on judgment and expertise rather than information gathering and exception management.

Compliance built into the core

FPS02 also delivers important regulatory capabilities directly within 麻豆原创 Cloud ERP Private.

The release includes support for IFRS 18 indirect-method cash flow statement reporting, helping organizations prepare for evolving financial reporting requirements. Additional updates include support for Germany’s AUSWIB1M foreign-trade reporting requirements and Canada First Nations Tax determination with GST499-1 return generation. By incorporating these capabilities into standard business processes, organizations can address compliance obligations while maintaining consistent governance and operational efficiency.

Industry innovation across critical operations

FPS02 deepens industry-specific capabilities across asset-intensive operations, upstream oil and gas, defense and security, and retail.

For asset-intensive industries, enhancements further integrate permit-to-work processes into maintenance operations, helping ensure operational controls are embedded directly within workflows. Upstream oil and gas organizations can gain new capabilities that extend Joule into production and revenue accounting, while 麻豆原创 Document AI helps interpret gas plant statements and propose accounting entries for review. Defense and security organizations can benefit from innovations supporting equipment allocation and ERP operations in limited-connectivity environments.

Retail organizations gain four new data products that help expose retail master data through 麻豆原创 Business Data Cloud, working to create a stronger foundation for future AI-driven processes. FPS02 also introduces operational enhancements including support for partial order fulfillment scenarios, GS1 2D barcode enablement, and expanded product traceability through serial-number integration between point-of-sale and inventory processes.

Advancing the Autonomous Enterprise

The Autonomous Enterprise is not built through a single AI breakthrough or software release. It emerges through the combination of trusted business data, integrated processes, governance, and intelligent capabilities working together across the enterprise.

麻豆原创 Cloud ERP Private 2025 FPS02 helps advance that foundation in concrete terms: by embedding intelligence within business processes, strengthening compliance capabilities, and expanding industry-specific innovation.

To explore the full FPS02 release and its implications for your business, visit .

Maura Hameroff is chief marketing officer for 麻豆原创 Cloud ERP Private and RISE with 麻豆原创.

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

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

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

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

Embedding payments into finance operations

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

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

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

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

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

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

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

Connecting performance management across the enterprise

The same challenge appears across enterprise performance management.

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

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

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

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

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

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

Expanding AI across finance and governance

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

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

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

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

Just as important is governance.

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

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

From financial insight to immediate action

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

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

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

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

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

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

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

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

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

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

Better spend decisions start with better visibility

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

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

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

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

Bringing supplier risk into everyday decisions

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

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

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

General availability is planned for Q4 2026.

Connecting procurement across complex enterprise landscapes

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

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

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

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

Making external workforce management more proactive

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

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

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

Simplifying travel and expense management through targeted automation

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

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

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

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

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

Turning spend intelligence into action

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

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

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

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

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

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

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

A new UI for AI 

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

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

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

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

麻豆原创 Autonomous Suite 

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

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

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

麻豆原创 Business AI Platform 

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

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

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

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

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

Jerseys from Jersey 

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

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

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

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

Security and governance 

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

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

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

A clear shift among customers 

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

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

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

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

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

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

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

Joule Assistants move from providing answers to driving business outcomes

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

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

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

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

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

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

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

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

麻豆原创 Order Management Services coordinates execution across ERP landscapes

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

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

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

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

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

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

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

Complete business context across the customer journey

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

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

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

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

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

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

Customer experience depends on execution

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

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

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

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

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

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

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

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

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

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

Why agentic marketing matters

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

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

Marketing context meets business reality

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

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

Why 麻豆原创 was recognized as a Leader

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

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

AI that drives business results

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

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

Advancing toward the Autonomous Enterprise

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

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

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

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

As AI reshapes the way organizations attract, develop, and support talent, leaders need more than systems of record. They need intelligent solutions that help them understand what’s happening across their workforce, anticipate what’s next, and take action with confidence.

We believe this recognition reflects 麻豆原创’s continued investment in AI-powered innovation across 麻豆原创 SuccessFactors solutions and our commitment to helping organizations build a more connected, intelligent, and proactive approach to workforce management.

鈥淎I is rapidly expanding what HCM platforms can do, but buyers need providers that combine innovation with real operational depth and consistent execution,鈥� said David Wilson, CEO and founder of Fosway Group. “麻豆原创 SuccessFactors has accelerated its rollout of AI features across HCM and expanded its integration with AI agents, offering more options to HR leaders. It is a Strategic Leader in the 2026 Fosway 9-Grid™ for Cloud HR, and 麻豆原创鈥檚 Excelling Trajectory in 2026 reflects its increased market performance and customer advocacy as well as continued innovation in both AI and core capabilities.”

Building the foundation for Autonomous HCM

This recognition comes at a pivotal moment for HR.

As AI continues to reshape work, organizations are moving beyond questions of efficiency and automation and focusing on a broader challenge: how to build more agile, adaptable, and resilient workforces.

The next era of HCM will be defined not by automating processes, but also by helping organizations make better decisions. That’s why we introduced our vision for Autonomous HCM earlier this year at 麻豆原创 Sapphire.

As part of 麻豆原创’s broader vision for the Autonomous Enterprise, Autonomous HCM brings together trusted workforce and business data, embedded AI, and HR processes to help organizations connect workforce decisions more closely to business outcomes.

As skills requirements evolve and business priorities shift, leaders need more than workforce data. They need the ability to understand implications, evaluate options, and act quickly.

At its core, Autonomous HCM is about closing the gap between knowing and doing. By bringing together workforce data, business context, and AI, organizations can move from understanding workforce challenges to taking action on them.

Traditionally, organizations have relied on reports, dashboards, and manual processes to identify workforce issues and determine next steps. While these tools provide valuable insights, they often leave leaders to interpret information and coordinate actions across disconnected systems and processes.

Autonomous HCM envisions a different approach. By combining AI, workforce intelligence, and business context, organizations can surface recommendations, automate routine work, and help employees, managers, and HR teams take action directly within the flow of work.

Whether identifying emerging skills gaps, recommending learning opportunities, supporting hiring decisions, streamlining employee support, or helping managers respond to workforce trends, AI can help organizations move more quickly from insight to action and achieve better outcomes.

Turning AI into action

Over the past year, 麻豆原创 has continued to invest in innovations that help organizations connect workforce insights with better decisions and outcomes, including:

• , helping employees, managers, and HR teams get work done more efficiently
• , bringing workforce and business data together to support better decision-making
• , helping streamline HR service delivery and employee support
• The acquisition of SmartRecruiters, extending our connected HCM approach to talent acquisition by linking hiring decisions more closely to workforce planning, skills intelligence, and the broader employee lifecycle

Next month at , we’ll share new innovations and customer stories that demonstrate how organizations are using our solutions to adapt more quickly to change, create more agile workforces, and move toward the vision of Autonomous HCM.

Delivering meaningful outcomes

The journey toward Autonomous HCM is already underway for many organizations.

is using 麻豆原创 SuccessFactors solutions and Joule to transform payroll and employee support experiences. By implementing AI capabilities, such as Explain Pay, the company has reduced payroll processing time by 90%, from five days to four hours, and expects to reduce payroll support costs by 80% while delivering faster, more personalized assistance to employees.

is embracing AI as part of its broader workforce transformation strategy. Through 麻豆原创 SuccessFactors solutions and Joule, the company is using AI to support talent acquisition, skills development, and employee growth while creating a more personalized and accessible employee experience. The company is also advancing its journey toward becoming a skills-based organization, using AI to help connect people with learning and development opportunities.

These examples highlight how organizations are using AI not just to automate work, but to improve decision-making, enhance employee experiences, and create measurable business value. They also demonstrate how the journey toward Autonomous HCM is already taking shape across organizations today.

Looking ahead

AI is creating new opportunities for organizations to rethink how work gets done and how people can thrive.

Our focus remains on helping organizations connect workforce insight with action through trusted data, embedded AI, and end-to-end HR processes. By bringing together workforce and business data in a unified foundation, 麻豆原创 SuccessFactors solutions help organizations build toward Autonomous HCM and create better outcomes for employees and the business.

We are grateful to our customers whose trust, partnership, and innovation make recognitions like this possible.

聽and how 麻豆原创 SuccessFactors solutions are helping organizations build the foundation for Autonomous HCM and the future of work.聽

About the Fosway 9-Grid™
Fosway Group is Europe鈥檚 #1 HR industry analyst. The Fosway 9-Grid™ is a five-dimensional market analysis model that helps organisations compare solutions based on Performance, Potential, Market Presence, Total Cost of Ownership and Trajectory.
The Fosway 9-Grid™ for Cloud HR provides a unique assessment of the principal Cloud HR options available to organizations in EMEA. The analysis is based on extensive independent research and insights from Fosway鈥檚 Corporate Research Network of over 250 organizations, including BP, HSBC, PwC, Sanofi, Shell, and Vodafone.
Visit the Fosway website at .

His dream led to the founding of Colombina and聽to聽a colorful corporate logo inspired by a character from an Italian opera who swung on the moon.聽In聽the 1960s,聽his son, Don Jaime H.聽Caicedo,聽took over the company and drove a major transformation,聽diversifying its offerings and聽expanding聽sales abroad.

Expansion 

That early spirit of reinvention has continued to shape Colombina鈥檚 growth, turning a family-founded confectionery business into a diversified food company with international reach.聽Today, Colombina is a聽multinational聽leader in food production with a portfolio of 19 categories, including confectionery,聽cookies,聽chocolate,聽sauces,聽preserves,聽ice cream, and coffee, along with a global workforce of more than聽8,000.聽聽

From seven processing plants聽and聽39 distribution centers in Colombia, Venezuela, Central America,聽and the U.S., Colombina serves聽750,000 customers聽in聽more than聽90 countries worldwide and generates annual sales of almost聽US$1 billion.聽

But with a global footprint spanning manufacturing, distribution, and retail, the company had to continually review and adapt specialized technology solutions for each market segment.  

Colombina鈥檚 leadership recognized that this fragmented approach made it difficult to ensure the聽integrity, availability, and confidentiality聽of business information at scale,聽and that the company聽needed聽to聽standardize聽processes across dozens of markets while continuing to grow sales and manage costs.聽

IT priorities 

Colombina聽identified聽key areas in need of standardization, particularly finance and supply chain management, and recognized that a鈥痷nified technology foundation鈥痺as critical to sustaining growth and operational efficiency.鈥� Security鈥痑lso聽emerged聽as a top priority, shaping many of the technology decisions that would follow.聽

To address these challenges,聽it embarked on a major transformation led聽by CIO Jes煤s Brand, a long-time ASUG Colombia chapter president and respected figure across the region. Colombina鈥檚 objective was to effectively use data to achieve its business goals faster and with less risk.聽

By moving to the cloud, Colombina was seeking to streamline product planning, manufacturing operations and models, and quality management processes. It also focused on simplifying supply chain tasks, including inventory, warehousing, delivery and transportation, order promising, and logistics material identification.  

RISE with 麻豆原创 

A long-term 麻豆原创 customer, Colombina signed up for RISE with 麻豆原创,聽migrating from 麻豆原创 ERP Central Component to 麻豆原创 S/4HANA Cloud Private聽Edition in a full, simultaneous migration聽executed across聽16 countries.聽The solution聽covers key business functions聽including finance, asset management, manufacturing, supply chain, and sales, but the company went a step further.聽

To聽harmonize its operational data, Colombina integrated its cloud ERP with a broad ecosystem of 麻豆原创 solutions, including聽麻豆原创 Fiori, 麻豆原创 SuccessFactors, 麻豆原创 Integrated Business Planning,聽麻豆原创 Analytics Cloud, and聽麻豆原创 Ariba,聽among others.聽聽

At the same time, John Carlos Jaramillo, an IT project manager at Colombina for over 20 years, noted that governance and security were strengthened through 麻豆原创 Cloud Application Services, which helps run, manage, and continuously optimize the company鈥檚 expanded 麻豆原创 landscape.聽

Jaramillo also highlighted the value derived from engaging with the 麻豆原创 Enterprise Architect, particularly through strategic advisory and guidance on adoption opportunities. He emphasized the benefit of gaining a clearer understanding of the contractual entitlements available to the organization, enabling more effective聽utilization聽of existing 麻豆原创聽investments聽and maximizing the value of their licensed capabilities.聽

He also聽highlighted the聽guidance provided around key strategic initiatives, including clean core, helping drive a sustainable and extensible ERP landscape; 麻豆原创 Cloud ALM, enabling stronger operational visibility and application life cycle management practices; and Joule Base, supporting the exploration of AI-driven capabilities and accelerating the organization’s journey toward business innovation and increased user productivity.聽

Security benefits 

Among the overall security benefits, the new system provides聽protection聽at different layers, including the operating system, applications,聽and聽users, while delivering聽low downtime and quick, thorough resolutions. Before implementation,聽system downtime was a significant issue for Colombina;听now,聽with double the number of application servers,聽downtime has been聽reduced聽and most customer issues can be resolved within 45 minutes.聽

Colombina鈥檚 IT team also reports high confidence when looking ahead to updates, with reduced concerns thanks to predictable update windows that typically take place at the weekend. In addition, the new system provides early watch alerts, enabling the IT team to get in front of potential risks on a day-to-day basis. 麻豆原创 for Me helps monitor and maintain the flow of these activities and gives a clear view of current infrastructure, operating system, activities, and risk profiles. 

Tangible results 

The impact has been tangible: Colombina now offers next-day delivery and has improved average customer response times by more than a third. Workload distribution has improved dramatically, while the business has seen fewer customer returns, stronger demand planning, and more efficient HR management. 

But the company is not stopping there. Having introduced聽Joule聽for 麻豆原创 SuccessFactors, Colombina is聽preparing聽to聽activate聽Joule Agents across its business and is implementing 麻豆原创聽Databricks for data models,聽working with 麻豆原创聽Business Data Cloud聽to build the right data foundation聽for future聽AI capabilities.聽

To answer this question, moved to one cloud ERP that enabled a unified, intelligent enterprise while reducing complexity and empowering people.

Starting point at Daikin

At the forefront of the story was the goal of being the global leader in the HVAC industry. As a result, Daikin pioneered groundbreaking technologies that revolutionized commercial AC. With a of over 100 years of engineering, the company has grown to operate globally in . However, its journey to global recognition was peppered with growing pains.

Disconnected systems led to complexity

Years of rapid growth alongside acquisitions left the company with a legacy ERP that was fragmented, inconsistent, and lacked the standardization required to operate efficiently. Multiple disconnected systems and workflows meant that employees were dealing with a myriad of processes, all differing depending on which business unit they were working with. This increase in manual work and confusion around processes added avoidable complexity to day-to-day tasks, as employees spent more time than necessary solving for problems.

Together, EY and 麻豆原创 teams worked toward an end goal by posing the question: how do we simplify operations, empower employees, and drive customer experience? Daikin鈥檚 industry is unique, with highly distributed networks and complex supply chains. Companies operating in this environment must continuously adapt to industry standards and regulations, all while delivering the reliable product experience they鈥檙e known for. Daikin turned to EY and 麻豆原创 because the company needed a partnership that understood the nuances of the industry within which it operates.

Daikin鈥檚 focus from the beginning was on the people who would be impacted most by this transformation and ensuring that their everyday experience evolved into one that is simpler, consistent, and productive. With AI at the core, Daikin can look to the future and see one unified system where AI moves beyond isolated cases and becomes embedded end-to-end.

To achieve this vision, Daikin established 麻豆原创 Cloud ERP as its digital core and single source of truth. Going live in just 10 months, modernized finance, logistics, procurement, and sales workflows for more than 300 frontline users, demonstrating early results of 10% improved counter efficiency and a 20% faster financial close.

EY鈥檚 industry expertise paired with 麻豆原创 technology helped ensure that the people who use the technology every day no longer log-in to multiple systems and customers get the right product at the right time and price.

Employees gained single-screen experiences, faster access to information, reduced manual effort, simplified training and onboarding, and more confidence in decision-making. Dealers gained better visibility, faster onboarding experiences, and more consistent service interactions.

Journey to an Autonomous Enterprise

Now, Daikin doesn鈥檛 have to worry about which inefficiencies it will have to confront for an approaching launch.

The early results demonstrate what鈥檚 possible when AI-driven insights are paired with human-centered delivery: improved efficiency, faster financial close, better data governance, enhanced forecasting and operational decision making, and accelerated delivery using AI-assisted implementation capabilities.

This not only empowers end users but creates a scalable foundation that can grow alongside Daikin. With over 400 branches worldwide, EY and 麻豆原创 created a blueprint for Daikin鈥檚 global transformation with future automation and intelligent decision-making in mind.

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Tara Milosavljevic is a Partner Marketing manager at 麻豆原创.

For , Race to World First is a test of endurance as much as execution. The competition can require teams to compete continuously for weeks against some of the world鈥檚 most complex raid encounters, where fatigue, stress, recovery and coordination can influence performance while coaches still need to make decisions in real time.

That challenge has traditionally been difficult to solve because critical information often sits in separate systems. Gameplay metrics, combat logs, wearable-device signals, player wellness information and operational data can each provide insight. However, when analyzed in isolation, they leave coaches with an incomplete view of how player condition and in-game execution affect one another.

The solution helps Team Liquid bring those signals into a unified, AI-enriched analytics environment. Coaches, analysts and performance experts can view physical readiness, stress indicators, recovery metrics, scheduling context and in-game performance together, helping them identify patterns that are hard to detect across disconnected tools and spreadsheets.

The solution extends the connected data foundation with AI-powered recommendations. By analyzing trends across gameplay, wellness and biometric information, Joule can help coaches surface early warning signs and compare tactics against historical performance patterns. With Joule, coaches can also evaluate whether recovery breaks, role adjustments or strategy changes may help the team sustain precision during progression attempts.

鈥淎s we prepare for Race to World First, having player performance, health and game data in one place changes how we approach coaching and strategy,鈥� said Dr. Jesse Hart, Senior Director of Sports Science and Analytics, Team Liquid. 鈥溌槎乖� Business Data Cloud allows us to make decisions backed by data rather than intuition during one of the most demanding competitive periods of the year.鈥�

麻豆原创 and Team Liquid have collaborated since 2018 on esports performance, analytics and AI innovation, including solutions designed to support competitive preparation and real-time decision-making. 麻豆原创 Business Data Cloud marks the next evolution of that work by showing how connected data and AI can help teams turn fragmented information into action under pressure. The same challenge exists beyond esports: organizations across industries need trusted data, intelligent insights and human expertise working together so people can perform at their best when it matters most.

Visit the . Get 麻豆原创 news via  and .

Media Contact:
Martin Gwisdalla, +49 6227 7-67275, martin.gwisdalla@sap.com, CET
麻豆原创 麻豆原创 Room; press@sap.com

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

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

Performance analytics beyond the game

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

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

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

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

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

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One unified view of the entire team

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

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

AI that amplifies human expertise

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

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

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

Proving ground for human performance

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

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

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

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

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

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

Moving to 麻豆原创 Cloud ERP Private as part of the RISE with 麻豆原创 journey, the company will consolidate its enterprise resource planning (ERP), business process intelligence, and service capabilities in a unified cloud-based subscription model. This creates the foundation to support further growth, optimize processes and integrate new technologies.

鈥淢oving to 麻豆原创 Cloud ERP Private is a strategic decision that goes well beyond IT infrastructure. With RISE with 麻豆原创, we are creating the foundation to run HARTING as a more agile, data-driven business鈥攕tandardizing our global operations, accelerating the integration of new technologies and unlocking the potential of AI across our processes. This contract marks a pivotal step in how we intend to grow and compete over the next decade,鈥� said Philip Harting, CEO, HARTING Technology Group.

麻豆原创 Cloud ERP Private will enable HARTING to transition to a future-proof, scalable and innovation-driven ERP environment. Cloud transformation will allow HARTING to consistently automate and standardize operations and infrastructure, direct IT resources toward value-adding topics and innovation, and continuously benefit from updates and new functionalities. At the same time, RISE with 麻豆原创 opens additional potential in the area of artificial intelligence and data-driven business models, with integrated AI functionalities and new automation options enabling faster, more substantiated data-based decisions. As a long-standing consulting partner, NTT DATA Business Solutions AG will accompany HARTING on its transformation path.

鈥淚ndustrial companies are under real pressure to move faster, operate leaner and integrate AI into their core processes as a present-day advantage. HARTING’s move to 麻豆原创 Cloud ERP Private with RISE with 麻豆原创 reflects exactly the kind of decisive, long-term thinking that separates technology leaders from those who wait, and positions them to scale efficiently and respond to market change with confidence,鈥� said Dirk Haeussermann, Managing Director, 麻豆原创 Germany.

Visit the . Get 麻豆原创 news via  and .

Media Contact:
Mariana Guerrero, +1 786 255 6334, mariana.guerrero@sap.com, ET
麻豆原创 麻豆原创 Room; press@sap.com

Top image courtesy HARTING Technology Group

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

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

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

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

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

Powering a global sporting goods business

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

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

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

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

One connected foundation for a connected customer experience

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

Built for the pace of sport

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

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

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

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

Alex de Minaur

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

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

Now, agentic AI could fundamentally change that equation, creating a different model for 麻豆原创 transformation: one built around faster delivery, lower costs, and greater customer self-sufficiency.

That was the central argument put forward during a recent examining what its participants described as 鈥渄eshoring,鈥� which uses AI agents to rethink work that organizations previously distributed between expensive onshore resources and lower-cost offshore teams.

For Stuart Browne, founder and CEO of , an independent 麻豆原创 consultancy that spent the past seven years helping companies chart their journeys from 麻豆原创 ECC to 麻豆原创 S/4HANA, the opportunity starts with questioning assumptions the industry has accumulated over decades.

鈥淭he only way we deliver this in the future is by changing the way we鈥檝e delivered it in the past,鈥� he said. 鈥淪o we鈥檝e got to find new ways of accelerating the migration, and I think AI is probably the best bet for that.鈥�

Why not faster, better, and cheaper?

The traditional technology transformation triangle says organizations might have projects that are faster, better, or cheaper, but generally only two of the three.

Browne challenged that premise by drawing an analogy to NASA鈥檚 鈥渇aster, better, cheaper鈥� approach to missions. He argued that 麻豆原创 transformations should similarly reconsider the assumption that improving one dimension requires sacrificing another.

鈥淲hy can鈥檛 you choose all three of these things?鈥� he asked.

That question becomes increasingly relevant as companies confront the remaining volume of 麻豆原创 S/4HANA migrations while simultaneously dealing with economic pressure, scarce 麻豆原创 skills, and the complexity of global transformation programs.

Ranjeet Panicker, senior vice president and head of Business Transformation at 麻豆原创, framed the challenge around the complete life cycle of an 麻豆原创 project, from initial discovery and analysis through design, build, and ultimately run.

At every stage, customers are asking the same questions: How can the project be completed faster? How can it cost less? How can my organization extract more value from the investment?

From offshoring to 鈥渄别蝉丑辞谤颈苍驳鈥�

For decades, mechanisms for reducing delivery costs have been moving work offshore. The economics were relatively straightforward as certain activities were moved to locations where labor costs are lower.

Browne argued that those savings can obscure another problem. While offshore resources may cost less, distributing work between offshore and onshore teams can increase the overall volume of work through handoffs, communication issues, rework, and coordination.

Agentic AI introduces another possibility. Instead of asking where human labor should be located, organizations ask whether some of that labor needs to be performed manually at all.

鈥淲hy can we not reduce the volume of work and reduce the cost of work?鈥� Browne asked.

That question led to the concept of deshoring鈥攔eplacing portions of location-based delivery with AI agents capable of performing or accelerating specific 麻豆原创 transformation activities.

After analyzing roughly 180 typical activities involved in an 麻豆原创 ECC to 麻豆原创 S/4HANA migration, Browne鈥檚 research concluded that AI could potentially produce about a 60% cost reduction by compressing effort and allowing tasks previously requiring highly experienced people to be performed by less experienced workers augmented by AI.

Some of the most attractive candidates are highly skilled activities that consume significant amounts of time, including writing functional and technical specifications, performing fit-gap analysis, and analyzing custom code.

鈥淚f your run rate is a million a month,鈥� Browne noted, 鈥渆liminating months from a program can dramatically change its economics.鈥�

The 80% solution with humans handling the last mile

AI may be capable of producing what Browne characterizes as an 80% solution within hours. It means experienced people review, challenge, and refine that work rather than spend time manually producing everything from scratch.

鈥淲hat AI produces shouldn鈥檛 be fully trusted without review,鈥� he said. None of this means eliminating experienced 麻豆原创 professionals. Instead, the emerging model redistributes where their expertise is applied.

The objective is not autonomous transformation. It accelerates the majority of the work while concentrating human expertise on the 鈥渇inal mile鈥� that includes judgment, validation, design decisions, and other activities where experience adds the most value.

Custom code could be an early breakthrough

One area where Browne believes AI is already producing significant change is custom code analysis. Early 麻豆原创 S/4HANA migrations often focused on getting existing customizations into the new environment and then determining how to remediate them. Agentic AI creates another option that determines whether the customization needs to exist at all.

According to Browne, AI can now analyze an entire custom code base, reverse engineer functional specifications, and determine whether the same business requirement can instead be met using standard 麻豆原创 functionality.

Rather than migrating large amounts of legacy customization and addressing it later, customers could potentially understand their custom-code landscape and identify opportunities for fit-to-standard before even selecting a systems integrator.

鈥淵ou can actually plan the fit-to-standard of your custom code before your SI’s have even been appointed,鈥� Browne said.

The implication is significant because customers have already paid for standard 麻豆原创 capabilities that may eliminate the need for some custom functionality while creating an environment that is simpler to maintain and upgrade.

麻豆原创 is building agents across the transformation life cycle

Panicker sees similar opportunities emerging across the broader 麻豆原创 implementation life cycle.

The terminology of onshore and offshore itself may eventually become less relevant, he argued, because organizations will increasingly think about transformation work in terms of skills rather than locations. AI-led skills can be delivered through assistants and agents and applied across different stages of a cloud transformation.

麻豆原创 is targeting areas including system analysis, data management, custom code, configuration, testing, rollout, and project management.

Panicker described an assistant as a collection of agents supporting a particular topic area.

A system-analysis capability can examine the overall transition. Data-management capabilities can address data quality. Custom-code agents can support analysis, recommendations, and, in some scenarios, automated remediation. Configuration assistants can evaluate current and target states, while testing agents can help automate test scripts.

According to Panicker, these capabilities are connected with 麻豆原创 Cloud ALM running on 麻豆原创 Business Technology Platform, along with a data and knowledge foundation designed to provide the customer-specific context agents need. 麻豆原创鈥檚 overall objective, he said, is to reduce transformation effort by approximately 35%.

Testing could become the next frontier

Testing can consume substantial amounts of time, particularly in industries with security, compliance, or validation requirements. AI raises a more complicated question: How much of that testing can organizations eventually delegate to agents?

鈥淚f I can get the code to get remediated by an agent,鈥� Panicker said, 鈥渃an I allow an agent to do the testing and accept the testing?鈥�

The answer will determine where humans remain directly involved in transformation workflows. Customers must decide not only if an agent can perform a task, but whether they have enough confidence in the agent鈥檚 knowledge, context, and guardrails to delegate responsibility for the outcome.

Context separates useful agents from hype

麻豆原创 programs generate enormous amounts of organization-specific information around architecture decisions, risk registers, test scripts, requirements, and other artifacts that evolve throughout a transformation.

Browne believes connecting AI to this continuously changing body of knowledge will be essential. 鈥淭he ability to converse not with a static LLM, but to converse with that world as well, I think is what will make good agents even better,鈥� he said.

Organizations may therefore need to rethink not only how they perform 麻豆原创 work, but how they capture information. Programs traditionally run across Excel, Word, PowerPoint, SharePoint, and other repositories may increasingly need AI-native knowledge environments capable of providing agents with usable business and system context.

鈥淭hat鈥檚 where I think this will be won or lost,鈥� Browne said.

AI could also change who holds the knowledge

Perhaps one of the biggest changes involves something less technical: who possesses expertise. 麻豆原创 implementations have traditionally depended heavily on experienced consultants and systems integrators. Customers frequently lack comparable knowledge, creating an imbalance that can make it difficult to challenge recommendations or independently evaluate major design decisions.

Browne believes someone with only six months of 麻豆原创 experience could potentially move up the knowledge curve in weeks in ways that previously might have taken years.

Panicker agreed that access to knowledge is becoming far less constrained.

His own experience at 麻豆原创 was initially concentrated in technical roles. Learning the business context surrounding that expertise often required finding someone willing to explain it. AI potentially allows professionals to move outside those traditional 鈥渟wim lanes.鈥�

Challenging the accepted 麻豆原创 timeline

麻豆原创 customers and consultants have grown accustomed to transformations, migrations, and upgrades taking a certain number of months or years. Those timelines have become assumptions embedded into planning.

Both Browne and Panicker believe those assumptions now deserve to be challenged.

鈥淚t鈥檚 about compressing the time that these tasks make and connecting the decision-makers to be able to make better decisions more quickly,鈥� Browne said.

Panicker similarly argued that organizations should stop accepting conventional project durations without asking what AI capabilities have been introduced to shorten them. 鈥淲hy can鈥檛 we do this sooner?鈥� he asked.

For Browne, the shift is already underway: 鈥淚鈥檓 not suggesting for one moment that we can press a button and deliver a whole program. Complexities around testing, change management, and design decisions remain.鈥� But he believes customers should stop treating agentic transformation as something waiting over the horizon. 鈥淭hat world is here now,鈥� he said.

Panicker鈥檚 message was equally straightforward: don鈥檛 wait on the sidelines: 鈥淟ean in, be curious about the technology. Ultimately, the sooner you can get to the outcome that you鈥檙e driving from a business standpoint, the more value you can bring.

Returning humans to the Moon鈥攁nd building the foundation for an enduring presence in deep space鈥攔equires thousands of people, multiple government agencies, international partners, commercial providers, and highly complex systems to work together with extraordinary precision.

During the 麻豆原创 NOW event in Washington, D.C., Dr. Lori Glaze, associate administrator for NASA鈥檚 Human Spaceflight Mission Directorate, offered attendees an inside look at the Artemis program and the operational discipline behind it. I later joined Dr. Glaze for a conversation about managing complexity, sustaining momentum, and using emerging technology to support mission outcomes.

The discussion offered lessons that extend far beyond space exploration. For public sector organizations and enterprises undergoing their own transformations, NASA鈥檚 experience demonstrates how ambitious goals become achievable: one tested capability, one informed decision, and one coordinated team at a time.

Building momentum one mission at a time

NASA鈥檚 Artemis program is designed as a sequence in which every mission tests capabilities and generates knowledge for the next.

Artemis I, completed in 2022, successfully tested the Space Launch System rocket and the Orion spacecraft without a crew. Artemis II built on that foundation with the first crewed flight of the program, launching four astronauts in April 2026 for a nearly 10-day journey around the Moon before their safe return approximately nine days later.

During the mission, the crew tested Orion鈥檚 life-support and maneuvering systems, traveled farther from Earth than any humans before them, conducted scientific observations, and safely re-entered Earth鈥檚 atmosphere at nearly 24,000 miles per hour.

But the mission was not only about setting records. Every observation, test, and operational decision produced information that NASA can apply to what comes next.

鈥淓ach test flight in this program is going to inform the next steps of our mission,鈥� Glaze said.

NASA is now preparing for Artemis III, targeted for 2027. The mission will test critical rendezvous and docking capabilities between Orion and commercial human landing systems developed by Blue Origin and SpaceX. Those tests are intended to reduce risk before Artemis IV, currently targeted as the program鈥檚 first crewed lunar landing mission in 2028.

This incremental approach offers an important transformation principle: Meaningful progress does not require solving the entire future at once; it requires designing each milestone to validate assumptions, reduce risk, and create a stronger foundation for the next decision.

Standardization creates the capacity to accelerate

Speed is often associated with moving quickly. At NASA, it also means reducing unnecessary reinvention.

Glaze explained that one of the agency鈥檚 priorities is standardizing the architecture supporting future Artemis missions. Although exploration frequently involves building something that has never existed before, NASA also needs repeatable systems and processes that can support a more regular cadence of missions.

鈥淲e want to do this over and over again, so we need to standardize our architecture,鈥� she said. Standardization does not eliminate innovation. It creates the stable foundation upon which innovation can move faster.

NASA relies on an extraordinary range of technologies to support that complexity, including 麻豆原创 solutions. But technology alone does not make a mission like Artemis possible. Its value comes from how effectively it connects people, processes, information, and decisions around a shared objective.

This is equally relevant to organizations modernizing their finance, workforce, procurement, supply chain, and operational systems. When information and processes remain fragmented across different platforms, teams spend significant time reconciling data, navigating interfaces, and recreating decisions.

A connected digital backbone can reduce that friction. It provides a shared operational foundation so that organizations can scale proven processes, introduce new capabilities, and respond to change without rebuilding the enterprise each time.

Complexity demands faster, better-informed decisions

The scale of the Artemis program is difficult to overstate.

NASA must coordinate launch vehicles, spacecraft, landers, spacesuits, scientific instruments, communications, logistics, personnel, budgets, commercial contractors, and international partners. Each element has its own timeline, dependencies, and risks鈥攁nd all of them must ultimately come together at precisely the right moment.

Glaze said NASA is working to streamline decision-making by placing the right expertise closer to the work. Subject matter experts have been embedded with contractors and industry suppliers so that issues can be identified, evaluated, and resolved more quickly. The objective is to shorten the distance between insight and action.

That challenge is familiar across government. Leaders often have access to enormous amounts of information but lack a unified view of what is changing, where pressure is building, or which intervention will have the greatest impact.

Glaze identified this as one of the most promising applications for artificial intelligence: helping teams absorb large volumes of data, understand status across complex programs, and identify the areas that require attention.

For organizations, the opportunity is to move from systems that primarily document what has happened to systems that can help interpret conditions, anticipate risks, and support the next best action.

Autonomy works best when it expands human capability

NASA is already applying autonomous technology beyond administrative processes.

Robotic vehicles exploring the Moon and Mars can evaluate terrain, select safer routes, schedule scientific activities, manage communications, and avoid hazards with limited intervention from Earth. AI can also help researchers analyze the enormous scientific datasets generated by NASA missions and focus their attention on the most valuable discoveries.

These capabilities illustrate an important distinction: autonomy is not necessarily about removing people from the mission. It is about allowing technology to manage complexity at a scale and speed that enables people to make better decisions.

The same principle applies to the Autonomous Enterprise. Embedded AI can help coordinate routine processes, detect emerging issues, and recommend actions while keeping people responsible for judgment, accountability, and mission outcomes.

Partnerships turn ambition into capability

No single organization could accomplish the Artemis mission alone.

NASA鈥檚 architecture brings together government teams, commercial space companies, traditional aerospace manufacturers, international space agencies, scientific institutions, and military partners. Each contributes a specific capability to the larger mission.

For Artemis III, NASA is coordinating with Blue Origin and SpaceX on commercial landing systems. The Orion spacecraft includes a service module provided by the European Space Agency. Future lunar exploration plans also involve mobility systems, habitats, scientific instruments, and infrastructure developed through additional public-private and international partnerships.

This ecosystem is not adjacent to the mission; it鈥檚 how the mission gets done.

For public sector transformation, partnerships can provide specialized expertise and innovation that would be difficult for one organization to develop independently. But successful ecosystems require more than contracting. They require shared objectives, clearly defined responsibilities, trusted information, and mechanisms for making coordinated decisions.

Trust is the ultimate operating system

When I asked Glaze for her most important leadership advice, her answer was direct: surround yourself with smart people and trust them. 鈥淣o one person can do these things,鈥� she said. 鈥淭hey require thousands of people to achieve these amazing things.鈥�

Technology, architecture, and process all matter. But none of them can substitute for teams that understand the mission and are empowered to act.

NASA鈥檚 progress under Artemis demonstrates what becomes possible when a bold vision is supported by disciplined execution. The agency is testing before scaling, standardizing where it can, bringing expertise closer to decisions, using technology to expand human capability, and building an ecosystem around a clearly defined mission.

Whether the objective is returning to the Moon, modernizing a government agency, or transforming a global enterprise, the lesson is the same: the most ambitious outcomes are achieved when people, data, processes, and partners move forward together.

Jamison Braun is senior vice president and managing director for U.S. Public Services at 麻豆原创 America.

The company鈥檚 history goes back more than 100 years, and what began as a small textile shop in Aarhus, Denmark, is now an international retailer with 鈧�12 billion in revenue.

麻豆原创 has supported Salling Group for over 20 years and is central to its operations, said Alan Jensen, CIO and executive vice president at Salling Group. Recently, the company has modernized its ERP system to 麻豆原创 S/4HANA Cloud via RISE with 麻豆原创.

With this cloud-based infrastructure in place, the retailer is ready to begin its AI transformation.

Improving everyday life

Salling Group鈥檚 reason for introducing AI is threefold: improve customer experience, simplify for employees, and boost operational efficiency. 鈥淲e want to make everyday life better for our customers by having the right product for the right price every time they need it,鈥� Jensen said. 鈥淲e also want to make every day better for our employees, so the tools and systems they work with are intuitive and easy to use.鈥� This aligns with the company鈥檚 purpose to improve everyday life for customers, colleagues, and the communities it is a part of.

The company views AI as a key enabler, focusing on how to turn AI into real business value for customers, employees, and the company overall. One such area where AI will have real impact on the retailer is logistics, Jensen said. Currently, Salling Group uses in its 29 distribution centers. The application helps manage high volumes of goods and run sustainable, risk-resilient operations via digitalized warehouse processes in the cloud. For Salling Group, this means on-time delivery to stores and efficient supply chain operations.

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What鈥檚 next

Salling Group is using 麻豆原创 solutions and AI to modernize its retail operations end-to-end, with a clear emphasis on customer experience, employee productivity, and supply chain excellence鈥攚hile ensuring new technologies deliver tangible business outcomes.

When it comes to 麻豆原创, Jensen is looking forward to seeing how the Autonomous Enterprise will further Salling Group鈥檚 success with AI.

鈥淭he Autonomous Enterprise looks very exciting,鈥� Jensen said. 鈥淭here is definitely a lot in how we can improve the way we run our business every day, so we need to be curious and see how we can use it.鈥�

鈥淲e started by fixing the basics,鈥� said Siani, Transformation and Technology director at Ipiranga, one of Brazil鈥檚 largest fuel distributors.

Ipiranga operates almost 6,000 service stations nationwide. Headquartered in Rio de Janeiro, the company has more than 6,200 B2B customers and 1,500 convenience stores. Ipiranga generates $120.7 billion in revenue, yet despite its size and market leadership, the company faced a critical problem: repeated project failures.

Working on the root cause

鈥淏efore we could start investing in new technology, we needed to take a good look at why some of our initiatives were not generating the expected value for the business,鈥� Siani said, speaking at the TAC Insights conference for in Toulouse. 鈥淲e needed a rigorous RCA.鈥�

RCA, or root cause analysis, is a structured approach to problem-solving that focuses on identifying the underlying causes of issues rather than just addressing surface-level symptoms.

鈥淚nstead of making new investments, we decided to dig deeper and identify the causes,鈥� she explained. 鈥淲hen we examined the company鈥檚 history, RCA confirmed that the high level of customization was limiting our ability to deliver at the required speed. Our inability to adopt the best technologies and functional best practices was directly impacting the company鈥檚 ability to evolve and drive business growth.鈥�

Speaking the language

From the outset, the Technology team recognized that the case for the ERP transformation rested on demonstrating how inconsistent processes and the lack of standardized best practices hindered the organization’s ability to respond quickly and remain competitive in the market.

鈥淲hen we went to the Board to secure the budget for the digital transformation, we didn鈥檛 start by talking about technology,鈥� Sian sharedi. 鈥淲e started by discussing what the business needed to achieve its strategic objectives faster. Then, we listed the obstacles preventing that progress along with the technology capabilities required to remove those barriers.鈥�

By translating technical challenges and opportunities into business language, Siani helped Ipiranga鈥檚 leadership understand that innovation without a strong foundation would not take the business to the level of efficiency required.

鈥淏y identifying these root causes, we were able to avoid a common trap,鈥� she explained. 鈥淚nstead of investing in new technology without fixing the foundation, we shifted our strategy. We refocused on聽 tools to process integration, governance, and operational discipline.鈥�

Getting approval for a big investment

Ipiranga partnered with 麻豆原创 to assess critical processes across operations, finance, and commercial operations.

“麻豆原创 brought in business experts to pinpoint how a heavily customized ERP system was slowing decision-making and limiting visibility and innovation,” Siani said. Rather than focusing on 麻豆原创 functionalities, they asked fundamental business questions: What does your financial process look like? How does your order-to-cash process work?”

Armed with these insights and a clear understanding of what was slowing decision-making, Siani returned to the board with a clear message: “Fix the foundation, or transformation will fail.”

As a result, she got approval to implement a new ERP system, driven by business value and ROI. What ensued was a massive integration effort, with a targeted go-live scheduled for December 31, 2026.

With support from Accenture and 麻豆原创, Ipiranga adopted a clean core strategy, ensuring minimal customization and long-term scalability. Overall, 104 legacy systems were analyzed, 64 systems will be integrated and 40 decommissioned, and over 750 interfaces are being built. Of course, all developments had to pass strict governance gates to guarantee the clean core.

People driving change

Technology may enable transformation, but people make it successful. Over 350 professionals from a variety of business and technical teams are involved in the ongoing project. Crucially, leadership played a direct role. With executive sponsorship and transparent communication, resistance to change has been minimal.

鈥淲e were careful not to impose change, but to explain the impacts clearly and discuss them with a multidisciplinary team,鈥� said the IT expert, who is also a rowing champion. 鈥淲e made sure to prepare teams early and embed change management at every phase.鈥�

The team prioritized initiatives with the highest return on investment (ROI) and paused non-essential projects. They also selected world-class partners鈥攊ncluding Accenture as the implementation partner and Amazon Web Services (AWS) as the hyperscaler鈥攖o help ensure a high-quality, successful transformation.

The company鈥檚 transformation is now in the middle of a critical milestone (SIT1). The expected ROI is over $40 million, but more importantly, the company has built something far more valuable than a new system.

鈥淲e will have a solid operational foundation, integrated, scalable ,and efficient processes, and a culture that understands how transformation goes beyond technology. Technology is simply the path,鈥� Siani said.

Ipiranga鈥檚 journey offers a powerful reminder that digital transformation is not about tools; it鈥檚 about assertive fundamentals. RCA can reveal issues technology alone cannot fix. It demonstrates that preparation is as important as execution, and that clean core strategies reduce long-term complexity.

“In effect, the path to successful transformation doesn鈥檛 start with innovation. It starts with clarity, discipline, clear goals that together determine the correct technology,鈥� Siani concluded.

Managing logistics operations has become increasingly challenging in recent years. The coronavirus pandemic and its repercussions caused unprecedented disruption across global supply chains, and today, hostilities in the Strait of Hormuz are severely hindering shipping traffic.

In situations like these, the only option many companies have to protect their manufacturing and delivery timelines from delays and uncertainties is to carry larger inventories. This inevitably leads to higher costs.

Now, a combination of container tracking technology and real-time data is revolutionizing supply chain visibility, enabling businesses to keep their inventories at efficient levels. Hapag-Lloyd, the world’s fifth-largest shipping company, provides live position data for its containers. 麻豆原创 uses this information in its ERP systems and 麻豆原创 Business Network Global Track and Trace cloud solution, connecting it to the shipment and material data that is stored there. Which means that, as well as being an 麻豆原创 customer, Hapag-Lloyd is an 麻豆原创 supplier and partner, too.

Karsten Schmidt, director and product owner of the Live Position and Track & Trace tools at Hapag-Lloyd, and Sven York Pohl, chief expert for Digital Adoption at 麻豆原创 SE, discuss how the collaboration between the companies began, how it is benefiting users already, and what opportunities lie ahead.

Q: Tracking technology has advanced rapidly in recent years. What sparked the wave of innovation we鈥檝e seen here?

Pohl: During the coronavirus pandemic, we experienced serious delays in the movement of goods and materials. And we realized that logistics processes need to be much more transparent.

Schmidt: Until recently, we could only track certain milestone events in sea shipping, such as a container arriving at a port or being loaded onto a ship. We knew when a container had been loaded onto a ship or when it left the Port of Hamburg, but if it failed to arrive in Munich as planned, we had no way of knowing what had happened to it.

Q: So how do you track containers today?

Schmidt: After the pandemic, we decided to equip all our dry containers with IoT tracking devices that continuously transmit location data. By mid-2024, we had fitted these devices to 90% of our container fleet. Since then, we have been able to track more than 2 million containers worldwide and provide position data every 15 minutes for shipments on land鈥攁nd every six hours for shipments by sea. This means that our customers can check the precise location of 鈥渢heir鈥� containers online in real time.

Q: How did the collaboration between Hapag-Lloyd and 麻豆原创 begin?

Pohl: I was involved in an 麻豆原创 transformation project at Hapag-Lloyd, which meant I had frequent meetings with Karsten. We realized early on that position data had immense potential, both for Hapag-Lloyd as a shipping company and for its customers. If you know exactly when an intermediate product will arrive, or how many days late it will be, you can plan your downstream production steps with pinpoint precision. And this obviously works best if position data is visible not only on a separate website, but also in the applications you use to manage your production and other business processes. So Karsten and I began discussing how we could feed position data into 麻豆原创 ERP systems.

Q: Which 麻豆原创 applications use this data?

Pohl: The 麻豆原创 Business Network Global Track and Trace cloud solution offers companies transparent shipment information in real time along the entire supply chain. And it enables this data to be embedded natively in back-end systems. Which means, for example, that you can integrate automated alerts directly into 麻豆原创 S/4HANA that show where a container is currently located. The data can also be leveraged in 麻豆原创鈥檚 cloud-based supply chain collaboration platform, 麻豆原创 Business Network for Logistics, to seamlessly connect shippers with logistics services providers and carriers. And, aside from ensuring visibility, real-time data such as that provided by Hapag-Lloyd maximizes supply chain security and drives sustainability by enabling businesses to track materials from their source to the finished product.

Q: Who benefits most from Hapag-Lloyd’s tracking data?

Schmidt: Logistics managers, definitely. Because they need precise information about where their containers are located and when they will arrive. The tracking data also provides insights into how sustainable a transportation chain is. Warehouse planners benefit too, because they can keep inventory levels as low as possible, without them dropping too low. And as well as enabling carriers to plan their routes with maximum efficiency, tracking data can also help streamline and optimize payment processes.

Q: Would you say that the current conflict in the Middle East has highlighted the value鈥攁nd necessity鈥攐f using tracking technology to plan shipping routes?

Schmidt: Absolutely. A huge number of containers have been transported from the Persian Gulf across Saudi Arabia to the Red Sea port of Jeddah to be shipped on from there. Our container tracking solution keeps me constantly updated on the land route I have chosen and on where my container is currently located.

Q: What happens if the container is held up somewhere? How does the system help?

Schmidt: That鈥檚 what we鈥檙e working on right now. Usually, when customers want to know where their container is, they call our customer service number. But with 15,000 containers held up in the Persian Gulf, we were unable to handle the sheer volume of inquiries we received by phone. So we gave all our customers access to our live positioning service, even those who had not booked it. The solution we implemented to deal with this exceptional situation showed us the potential that was there going forward to simplify our customers鈥� processes鈥攁nd our own.

Pohl: Looking ahead, 麻豆原创 users will be able to ask their software what options they have when shipments get held up, and what alternative modes of transportation are available to them.

Schmidt: If, for example, a ship is delayed and misses its scheduled rail connection, the system will provide a predictive service, telling the user whether onward transportation by truck is possible and how much longer the journey will then take.

Pohl: We will also use Joule, the flagship AI brand that is built into 麻豆原创鈥檚 cloud portfolio, for this service, so that users will be able to ask AI to suggest specific actions.

Schmidt: Obviously, when goods are transported over long distances, and especially by sea, you can never rule out delays entirely. But what we can do is remove that feeling of helplessness and of having no room to maneuver when events occur that are out of my control.

Top image via Hapag-Lloyd

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

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

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

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

1. From AI use cases to intelligent business processes

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

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

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

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

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

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

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

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

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

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

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

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

Where we go from here

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

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

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

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

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

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

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

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

25,000 tests with 600 participants

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

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

Simplification and standardization ensure consistent data

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

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

Cloud technology is not an end in itself

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

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

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

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

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

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

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

AI-driven demand forecasts optimize warehouse stocks

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

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

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

Top image courtesy of Swarovski

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

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

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

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

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

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

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

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

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

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

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

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

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

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