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

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

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

Gartner describes the Market Shapers quadrant this way:聽

From assistants to orchestrated agents聽

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

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

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

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

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

Agents that act where the work happens聽

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

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

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

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

Delivering real results for customers聽

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

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

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

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

Looking ahead聽

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

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

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

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

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

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

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

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

Why agentic marketing matters

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

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

Marketing context meets business reality

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

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

Why 麻豆原创 was recognized as a Leader

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

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

AI that drives business results

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

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

Advancing toward the Autonomous Enterprise

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

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

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

Learn more

Download the .

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

Today, alongside the announcement of and the broad availability of NVIDIA OpenShell, we鈥檙e deepening our collaboration with NVIDIA to help make enterprise AI agents safer, more governable, and easier to audit by design.

As announced this year at 麻豆原创 Sapphire, 麻豆原创 and NVIDIA have a deep technical collaboration, with plans to bring 麻豆原创 Business AI Platform together with OpenShell, an open-source secure runtime for autonomous AI agents. This is more than a planned integration, it reflects a shared effort to help define what 鈥渆nterprise-grade鈥� means for autonomous AI systems.

As part of our ongoing co-development activities, 麻豆原创 engineers are contributing directly to the OpenShell codebase and embedding OpenShell within 麻豆原创 Business AI Platform. , an AI-first development environment, provides a complete execution and governance stack for enterprise AI agents, while OpenShell supports deploying agents with a focus on safety and security.

麻豆原创 and NVIDIA are also working to:

• Help harden the OpenShell runtime for enterprise-scale deployment, including in regulated industries with strict security and compliance requirements
• Connect OpenShell鈥檚 runtime isolation with the business governance layer of Joule Studio runtime, linking technical execution boundaries to enterprise authorization models, IAM frameworks, and audit trails
• Work toward FedRAMP, FIPS, and regulated-industry enablement on a committed road map
• Advance open research and AI safety through the , of which 麻豆原创 and NVIDIA are both founding members alongside other industry leaders

Joule Studio runtime: Governance above the execution layer

runtime helps determine whether an action should execute at all鈥攁pplying business authorization, role-based policy, and process context before a request in action reaches the runtime. OpenShell provides an open-source runtime boundary designed to help build and deploy autonomous, self-evolving agents more safely. It governs how the agent executes, what it can see and do, and where inference goes. Together, the two aim to give enterprise customers execution containment and business accountability in a single integrated stack.

麻豆原创 engineering contributions to NVIDIA OpenShell

麻豆原创 engineers are contributing to four areas of the OpenShell project. On platform architecture, 麻豆原创 has helped drive decomposition of the runtime into independently deployable components, separating the supervisor and agent execution layers so they can scale independently. For Kubernetes-native operations, contributions include operator support, image pull secret management for private container registries, custom volume claim templates, and foreground deletion of sandbox resources. 麻豆原创 has also worked to minimize the gateway image footprint and extend compute driver support for heterogeneous enterprise infrastructure. Contributions to supervisor health monitoring and structured log-level configuration bring the runtime to enterprise observability standards.

Joule Studio runtime is available free for 麻豆原创 customers and partners through October 2026鈥�

Andre Lamego is senior vice president and chief product officer for 麻豆原创 Business AI Platform Fabric at 麻豆原创.

We believe this recognition reflects 麻豆原创鈥檚 long-term investment in helping manufacturers connect sourcing, engineering, planning, and supplier collaboration on a single foundation.

Direct materials procurement doesn鈥檛 get enough credit for how hard it actually is. Unlike indirect spend, direct materials sit at the intersection of engineering, planning, finance, and supplier relationships鈥攐ften all at once. A design change triggers a sourcing event. A commodity price spike ripples into contract terms. A supplier quality issue lands on the production floor before procurement even knows it happened.

The organizations that manage this well don鈥檛 just have good sourcing tools. They have connected systems, trusted data, and the ability to act quickly when conditions change. Solving for that complexity is what 麻豆原创鈥檚 direct spend portfolio is built to do.

Connecting design, sourcing, and execution

麻豆原创鈥檚 direct spend offering is built with an industry-specific lens. A cookie-cutter approach to procurement business processes does not work for direct materials.

An engineering-oriented industry like automotive or aerospace, where a design change can trigger a sourcing event, has fundamentally different needs than a brand- or asset-oriented industry such as consumer goods or chemicals, where commodity exposure and supplier scale dominate. 麻豆原创 builds for both, with purpose-built offerings tuned to how each actually works.

What our offerings share is a single, unbroken data model running from new product introduction through sourcing, contract award, and purchase order execution, all within 麻豆原创 Business Suite: no bolt-on connectors, no manual handoffs between systems.

This matters more than it might seem. Data fragmentation across the design-to-source-to-settle workflow remains one of the biggest barriers to AI delivering real value in direct spend. When procurement decisions are connected to engineering, planning, supplier, and financial data on a shared foundation, AI has meaningful context to work with.

A network and ecosystem that extends the core

The sourcing suites are the center of gravity, but what surrounds them is equally important for manufacturers managing complex, multi-tier supply chains.

麻豆原创 Business Network connects millions of trading partners across roughly 190 countries, with more than US$7.7 trillion in annual commerce flowing through it. The network supports forecast collaboration, purchase order execution, inventory management, logistics visibility, quality processes, and supplier communication. For global manufacturers managing complex supply chains, that scale can be operationally significant.

Customers are already seeing the benefits of a more integrated approach to direct spend. For example, selected 麻豆原创鈥檚 direct materials sourcing solution because of its close integration with core ERP processes, including bill of materials management, production planning, and cost calculation. By connecting sourcing more closely to product development and manufacturing workflows, the company is helping procurement, engineering, and suppliers work from the same foundation throughout the product life cycle.

Additional 麻豆原创 capabilities extend the process further across product development, commodity management, supply planning, supplier collaboration, financing, and analytics. Together, these capabilities help organizations manage direct spend as part of an integrated process that spans from early design collaboration through supplier payment, supported by a unified data foundation.

Taken together, these capabilities span a much broader scope than traditional sourcing software, from early design collaboration through supplier payment, all governed on 麻豆原创 Business Data Cloud as a unified analytics foundation.

Where AI stands today, and where it鈥檚 going

Today, 麻豆原创 delivers AI-powered supplier and item recommendations, invoice data extraction, category strategy insights, supplier risk scoring, and Joule capabilities embedded throughout the suite. These are production capabilities already helping customers streamline procurement processes.

Looking ahead, 麻豆原创 plans to expand its AI capabilities across the direct spend life cycle, with new agentic experiences focused on areas such as market intelligence, supplier discovery, RFx orchestration, negotiation support, contract authoring, and supply chain impact analysis. These innovations will be delivered through Joule Work and Joule Studio 2.0, a no-code/low-code environment built on 麻豆原创 Business AI that enables organizations to create and manage agents grounded in live business data and end-to-end process context.

The foundation underneath this road map is what makes it credible. Agents are only as good as the data and process context they can access. When that foundation is fragmented across disconnected systems, AI adds noise as often as it adds value. When it鈥檚 unified, agents can execute with greater relevance and accuracy.

Who this is built for

The 麻豆原创 Ariba offering for direct spend works with any ERP backend, with the added benefit of integrating out of the box with 麻豆原创 Cloud ERP. For aerospace and defense customers with data sovereignty requirements, 麻豆原创 offers deployment options for sovereign cloud that few vendors in this space support. For brand- and asset-oriented enterprises, a consolidated source-to-pay platform spanning both direct and indirect spend can simplify governance while reducing complexity.

For manufacturers operating complex global supply chains, the value of a natively integrated approach is straightforward: fewer manual handoffs, better visibility across functions, faster response to disruptions, and AI that can draw on connected business data rather than isolated point solutions.

What this recognition means to us

We鈥檙e grateful to the customers and partners whose real-world feedback shapes how we build these products. We believe an IDC MarketScape recognition reflects their experiences as much as it reflects our road map, and we don鈥檛 take that lightly.

This recognition energizes us. For more than 50 years, 麻豆原创 has helped organizations run their most critical business processes, and direct spend is one of the areas where that experience comes together most clearly. We鈥檒l continue investing in deeper integration, broader network capabilities, trusted AI, and the industry expertise our customers rely on. We鈥檙e grateful to the customers and partners that continue to help shape these solutions and help us deliver meaningful innovation across the direct spend life cycle.

Fang Chang is executive vice president and chief product officer for 麻豆原创 Procurement and External Workforce Solutions at 麻豆原创.
Baber Farooq is chief marketing officer for 麻豆原创 Spend Management.

*IDC MarketScape: Worldwide AI-Enabled Direct Spend 2026 Vendor Assessment, IDC #US54900426, September 2026.

For many enterprise leaders, however, the gap between strategic ambition and operational reality has remained frustratingly wide. That gap is exactly what the Autonomous Enterprise podcast set out to close.

Launched on June 1, 2026, the weekly series has now published 16 episodes spanning three format categories: 麻豆原创 Deep Dives, with executive leaders on strategy and product; Customer Deep Dives, with companies narrating their own transformation journeys; and Industry Deep Dives, with selected external voices shaping entire industries.

Available on Spotify, Apple Music, and all major podcast platforms, as well as on 麻豆原创 channels and LinkedIn, the show has rapidly established itself as a direct and candid voice on enterprise AI adoption.

What the first 16 episodes reveal

Across more than a dozen conversations鈥攚ith guests that include 麻豆原创 executives, global customers, and external thought leaders鈥攆our themes emerge with striking consistency.

Autonomous enterprise is available today, not tomorrow

In episode 13, Uma Rani, president and chief product officer for 麻豆原创 Cloud ERP Private, plainly stated that the Autonomous Enterprise is not a future road map item; it is available now for customers on the 2025 and 2023 releases. The show’s earliest episodes reinforced this framing: Peter Maier, leading Strategic Customer Engagements in the Office of the CEO, opened episode one by grounding the 麻豆原创 Sapphire vision in practical execution, while Manoj Swaminathan used episode three to unpack the architecture of 麻豆原创 Autonomous Suite, the layer where AI assistants and agents begin to execute end-to-end processes.

The decisive advantage is business context, not model capability

Michael Ameling, formerly president of the 麻豆原创 Business AI Platform at 麻豆原创, explained in episode four how 麻豆原创 Knowledge Graph and domain models, combined with frontier models including Anthropic’s Claude, give enterprise AI the semantic grounding that generic LLMs lack. The platform brings together 50 years of 麻豆原创 process knowledge to enable agents that act relevantly, reliably, and responsibly鈥攖hree conditions every guest returned to when discussing trustworthy AI at scale.

Transformation is a people and change management challenge as much as a technology one

In episode two, Ferose V R articulated a shift from humans adapting to systems toward systems adapting to humans, through agents that understand context and intent. Sanjay Kulkarni, senior vice president and global head of Global Delivery Hub at 麻豆原创, brought a practitioner’s precision to episode 12: his 12,500-strong services organization measures AI value not by deployment volume, but by measurable business outcomes and the readiness of the people responsible for them. The same message came from Geoff Scott, CEO and chief community champion of Americas’ 麻豆原创 Users’ Group (ASUG), in episode eight: trust, skill development, and genuine human-AI collaboration are the variables that separate transformations that scale from those that stall.

Geographic diversity shapes adoption, but the direction is universal

In episode nine, 麻豆原创 Labs India Managing Director Sindhu Gangadharan mapped how Indian enterprises are approaching autonomous capabilities: fast adoption, high ambition, and a particular emphasis on workforce readiness. Manos Raptopoulos, global president of Customer Success, Europe, APAC, Middle East, and Africa, covered in episode 11 what AI ambition looks like from Frankfurt to Fukuoka, illustrating how regional context varies while the structural imperatives remain the same. And 麻豆原创 EMEA President Augusta Spinelli used episode five to define the two concrete paths to the Autonomous Enterprise鈥擱ISE with 麻豆原创 and 麻豆原创 GROW鈥攁s well as what agent-led transformation looks like from an execution standpoint.

In addition, customer episodes add the texture that strategy alone cannot provide. Achim Loebke from Uniper and Daniele Tedesco from Essity both shared in-depth, unfiltered accounts of what AI transformation looks like from the inside, including the organizational friction, the decisions that require human judgment, and the early indicators of measurable impact. These conversations, deliberately not positioned as product showcases, have consistently drawn the highest listener engagement.

Why this series matters for the 麻豆原创 ecosystem

The Autonomous Enterprise podcast fills a gap by offering candid, executive-level conversations where the honest complexity of AI adoption is treated as the point, not a problem to be managed. Its target audience鈥擟IOs, CDOs, COOs, CFOs, and senior transformation leads鈥攁re precisely the people making the investment and governance decisions that will determine how quickly 麻豆原创 customers move from AI ambition to AI execution.

With more than 40 episodes planned for its first year, the series is building a cumulative body of insight that serves both as a reference for practitioners navigating their own transformation journeys and as evidence of the breadth and depth of 麻豆原创’s engagement with its customer community.

New episodes drop weekly; listen on Spotify, Apple Music, and all major podcast platforms.

Benedikt Gieger is Product Strategy and AI lead in Digital Manufacturing at 麻豆原创 SE.
Julia Kloppenburg is a technology consultant in Customer Engagement & Adoption at 麻豆原创 SE.

It鈥檚 a noticeable shift that could fundamentally change how enterprises operate. But according to Manish Sood, CEO of Reltio, an 麻豆原创 company, and co-author of Agentic Intelligence, it also exposes a challenge companies have wrestled with for decades: fragmented, inconsistent, and poorly connected data.

鈥淭here is tremendous enthusiasm around agentic AI right now, and for good reason,鈥� said Sood. 鈥淎lmost every company is experimenting with it in some form or the other. But there is a very significant gap between ambition and underlying readiness.鈥�

Research conducted by Harvard Business Review Analytic Services underscores that readiness gap: 94% of organizations surveyed are exploring or implementing agentic AI, while only 15% believe their data foundation is truly ready.

鈥淎n AI demo can tolerate a lot of imperfections and gaps in the data,鈥� said Sood. 鈥淎n AI agent operating inside a real business context cannot. The moment we give software the ability to make decisions or to take action, the quality, timeliness, and context of underlying data becomes much more consequential.鈥�

AI highlights legacy data challenges

Sood believes fragmentation, trust, and governance are three recurring barriers when it comes to AI adoption. Because data remains scattered across hundreds or even thousands of enterprise systems, governance models built for traditional enterprise systems must evolve for an environment in which AI agents can initiate actions.

While these are familiar challenges, the speed and autonomy AI introduces is much different.

Historically, humans have compensated for fragmented systems via tapping into institutional knowledge. Employees know which spreadsheet to check, which colleague to call, or which exception to make. AI agents do not inherently possess that organizational context.

鈥淚f a human has incomplete information, we can often recognize it, ask another question, or find someone who knows the history,鈥� said Sood. 鈥淚n comparison, an autonomous agent may simply act.鈥�

The question for enterprises therefore shifts from whether they can build an AI agent to whether they can trust the environment in which that agent operates.

From digital filing cabinets to connected intelligence

Many organizations still operate around what Sood refers to as the 鈥渇iling cabinet鈥� model. When businesses digitized, individual functions created their own systems for marketing, finance, sales, supply chain, service, and human resources. Moving those systems to the cloud did not necessarily eliminate the underlying silos.

鈥淭he intelligence age requires us to move from storing information to connecting that information,鈥� he said.

This connected foundation supports what Sood and his co-author describe as 鈥渃o-agency,鈥� a new relationship between people and intelligent machines.

Historically, enterprise technology followed a relatively simple model: humans made decisions and machines executed them. AI changes that equation. Machines can contribute speed, scale, pattern recognition, and continuous operation, while people provide judgment, experience, accountability, and an understanding of nuance.

鈥淚f the human sees one version of the customer and the agent sees another, you don鈥檛 have co-agency; you have conflict,鈥� said Sood.

Turning insight into action

These implications extend beyond technology architecture as agentic intelligence requires companies to rethink strategy, investment, workflows, governance, and the relationship between people and machines. It鈥檚 a notable shift that moves from insight to action.

鈥淚nsight by itself does not create economic value. Action does,鈥� Sood shared.

For example, predicting that a piece of industrial equipment is likely to fail can provide valuable information. But an intelligent system capable of coordinating maintenance, locating the necessary parts, and scheduling service before the failure occurs can potentially transform that insight directly into a business outcome. In order for this to happen, however, trusted information needs to be available when and where decisions happen.

According to Sood, four responsibilities are necessary for organizations preparing for this environment:

1. Connect critical enterprise data.
2. Contextualize it with relationships, history, policies, and interactions.
3. Operationalize that trusted context at the point of decision.
4. Govern how AI agents act.

鈥淒efine where an agent can act, where a human needs to intervene, how decisions are traced, and how systems learn from mistakes,鈥� Sood explained. 鈥淭he goal isn鈥檛 simply to make more data available to AI. The goal is to make the enterprise understandable through AI.鈥�

Building for the intelligence age

As AI models continue to improve, Sood believes competitive advantage will increasingly come from proprietary data, business context, workflows, processes, and feedback mechanisms surrounding them.

Trust will also become more consequential as organizations delegate greater authority to AI. As a result, companies will need confidence not only in what an agent knows, but where its information came from, how current it is, and what boundaries govern its actions.

鈥淭he companies that win won鈥檛 simply be the companies with the most sophisticated AI,鈥� said Sood. 鈥淭hey will be the companies that create environments in which AI can understand the business, operate with trusted context, and act responsibly.

Today, at the in Atlanta, 麻豆原创 unveils the latest innovations for 麻豆原创 Signavio, 麻豆原创 LeanIX, WalkMe, and 麻豆原创 Cloud ALM to show how their integrated capabilities become the system on which enterprises can rely for end-to-end agent operations.

麻豆原创 Signavio expands AI capabilities and advances AI agent excellence

麻豆原创 Signavio is bringing AI deeper into every stage of process transformation while giving enterprises the foundation to run AI agents with confidence. Enterprises no longer question whether to use AI. They ask where it matters most, and how to trust the agents that act on their behalf. With its latest release, 麻豆原创 Signavio answers on two fronts: building AI agents into every part of the suite to make transformation faster and more accessible, and helping organizations achieve by giving their agents the knowledge and governance they need to deliver real value.

The latest release of the introduces process mining capabilities, analyzing process performance data across 麻豆原创 and non-麻豆原创 environments, surfacing bottlenecks, and recommending where agents can be applied to create the most value.

Knowing where to act is only the starting point. Realizing that potential requires processes that are designed for AI from the ground up. The re-designed brings in AI-native process design, integrated governance, real-time collaboration, and suite-wide connectivity into a single canvas, making process modeling faster, more accessible, and built for the way transformation teams actually work.

Good process design sets the intent. closes the loop with AI-native and increasingly agent-driven analysis, providing an ever-more autonomous monitoring layer that watches how work runs and helps teams act on recommendations with confidence.

To enable agents to act on those recommendations within organizational context and guardrails, preview will offer both people and AI agents a single, consistent source of an organization’s rules, standards, and process know-how.

麻豆原创 Signavio provides the process intelligence layer where AI supports both the people analyzing processes and the agents acting on them. That leads to the question of the enterprise architecture underpinning AI agents.

Bring architecture intelligence to life with 麻豆原创 LeanIX

Enterprise architecture has long been one of the most strategic functions in any organization, yet its value rarely travelled beyond the architecture team. 麻豆原创 LeanIX is changing that by delivering architecture intelligence directly to the CIO making portfolio decisions, to the transformation office managing change at scale, and to the AI governance leads navigating an expanding agent landscape.

That requires architectural knowledge that is active, not just documented. The enterprise knowledge repository stores approved architecture principles, security standards, and technical decisions as governed knowledge that agents reason over, turning a documented standard into active context that shapes agent design and runtime behavior. The has been extended to combine live workspace data, industry benchmarks, and 麻豆原创 best practices to support complex architecture decisions, with human judgment retained throughout.

The provides the foundation for enterprise-grade AI governance, inventorying agents, large language models, and MCP servers across the enterprise, mapping them to capabilities and owners so governance becomes a continuous operational discipline. The new AI Governance Assistant embeds EU AI Act and NIST compliance intelligence directly into this inventory, automating risk classification and reassessing automatically when systems change.

As AI reshapes the enterprise faster than traditional governance can handle, 麻豆原创 LeanIX shows that architecture intelligence is no longer purely reserved for the experts. It is the architectural backbone the whole business needs to move forward into the AI-native era with confidence.

But architectural confidence does not automatically become employee behavior. That gap is where the WalkMe portfolio operates.

Adoption and execution across every application

Transformation only delivers when people actually change how they work.

Three years into the enterprise AI era, the numbers make that gap painfully clear: Gartner research shows 87% of IT leaders say AI adoption has not happened automatically, and only eight percent of employees use enterprise AI in any meaningful way. That鈥檚 happening largely because AI still sits outside the flow of work. With the latest innovations announced today, WalkMe is closing that gap.

Contextual AI assistance proactively surfaces AI tools, including Joule Agents, assistants, and skills, directly where people are already working, across 麻豆原创 and non-麻豆原创 applications. The right capability reaches the right person at the right moment, with the governance controls to manage what agents run, who can access them, and when they trigger.

But surfacing AI is only part of the answer. Agents still need to act, and much of the enterprise is not reachable through an API. WalkMe’s UI-native agent solves the connectivity problem by operating the interface itself, extending Joule’s reach into legacy systems, heavily customized ERP environments, and third-party applications that standard agent architectures simply cannot touch.

Two new capabilities sustain this at scale. AI Authoring lets teams describe the experience they want in plain language and generates a live, deployable plan. With AI Insights, leaders can query how workflows and content are performing and receive analysis, charts, and dashboards built from real data.

More than simply providing access to AI, its real business impact is felt when people start actually using AI, consistently, in the moments that count.

Sustaining that performance, from the first transformation project through live operations, requires one platform to govern the entire life cycle.

One life cycle, one governance model

The measure of agentic AI is what happens after deployment. 麻豆原创 Cloud ALM governs the full journey: from managing cloud transformation end to end with AI assistants and Joule spanning system analysis, configuration, testing, and rollout. Once agents are live, its AI agent monitoring capability tracks every agent action so operations teams can confirm AI is working as designed, catch deviations before they compound, and demonstrate the business outcomes that justify the investment.

Business Transformation Management solutions from 麻豆原创 drive the by operating as a system: 麻豆原创 Signavio defines how work should run and measures whether agents are improving it; 麻豆原创 LeanIX governs where agents operate and whether they can be trusted; WalkMe ensures people and agents can execute together across every application; 麻豆原创 Cloud ALM governs the transformation and deployment life cycle, validating that agents are tested, trusted, and ready before they go live, and monitoring their performance in production to protect the outcomes the business invested in.

Business Transformation Management solutions from 麻豆原创 are the system that keeps all of it in motion.

Andre Wenz is general manager and chief product officer for Business Transformation Management at 麻豆原创.

The real measure of an isn’t how intelligent its AI is. When you add the customer to the picture, the conversation shifts to making their lives easier, helping them achieve their business goals, and earning their trust and confidence.

This customer-centric perspective and the possibilities of AI reshape how organizations think about work.

One of our life sciences customers is already putting this vision into practice. Every day, the company receives tens of thousands of customer emails across sales, service, and support. Each message must be reviewed, categorized, prioritized, and routed to the right team, often requiring employees to navigate a patchwork of legacy systems, disconnected applications, and manual workflows. As customer expectations continue to rise, simply adding more people to keep pace is no longer a sustainable strategy. By enabling intelligent agents to coordinate work across complex business processes, the company is transforming how customer requests are handled while improving both operational efficiency and customer experience.

This is more than another wave of process automation. It represents the beginning of the Autonomous Enterprise, where enterprise operations become more efficient without sacrificing quality, governance, or control.

Powered by agentic AI, intelligent agents are becoming active participants in executing business processes, collaborating with one another, and driving measurable business outcomes. Their defining characteristic is not just intelligence. They act autonomously within defined guardrails while humans remain accountable for the decisions they make, the actions they authorize, and the business outcomes they deliver.

With that, entire operating models begin to evolve as intelligent agents handle routine coordination, accelerate decision-making, and enable the business to respond with greater speed and resilience. In a volatile environment where customer expectations and market dynamics continue to accelerate, the Autonomous Enterprise is emerging not simply as a technology strategy, but as a new operating model for delivering better outcomes for customers and business alike.

The evolution of service delivery in the AI era

AI is fundamentally reshaping how enterprise software services are delivered and consumed. Service delivery is evolving to proactive, AI-powered operations where intelligent agents automate routine tasks, predict and prevent issues, accelerate incident resolution, and continuously optimize system performance. This enables service delivery organizations to scale efficiently, improve customer experience, and shift human expertise toward higher-value innovation and strategic customer engagement.

The shift to agentic AI makes AI fluency a foundational skill for becoming more productive and efficient in day-to-day activities. 聽Effective collaboration with intelligent agents can streamline routine tasks, generate higher-quality insights, and free up time for higher-value activities while facilitating innovation-focused customer advisory and strategic decision-making.

Rather than replacing expertise, agentic AI augments teams by enabling faster decisions, automating routine work, and delivering better customer outcomes. Key areas include:

• Customer engagement readiness: AI consolidates customer context, surfaces key insights and risks, and recommends next best actions to help teams prepare faster and engage more effectively.
• Intelligent service delivery processes: AI automates repetitive tasks, orchestrates workflows, predicts issues, and accelerates resolution, enabling more proactive and efficient service delivery.
• Connected Intelligence: AI captures expertise, recommends relevant knowledge, identifies patterns across engagements, and continuously optimizes delivery processes and best practices.

Together, these capabilities enable us to work alongside our customers as their partners, anticipating needs, tackling their challenges, and delivering intelligent, outcome-focused services.

How customers adopt AI as they become autonomous enterprises

For customers, service delivery becomes more proactive, personalized, and outcome-driven. Technical challenges and incidents are no longer assessed solely through a technical lens. They are evaluated based on their impact on business outcomes, customer experience, operational resiliency, and organizational performance.

Instead of reacting to incidents after they occur, AI agents continuously monitor environments, predict potential issues, autonomously resolve common problems, and provide faster, more intelligent support, resulting in higher service reliability, quicker resolution times, and a more seamless customer experience. This shift enables organizations to prioritize response and remediation according to business criticality rather than technical severity alone.

One of our manufacturing customers leverages Joule Agent to continuously monitor ERP system health, identify root causes of issues, recommend corrective actions, and autonomously execute approved resolutions. The solution delivers approximately 3,000 hours of annual effort savings, minimizes unplanned business downtime, and enables standardized, 24/7 AI-powered reporting that aligns with the firm鈥檚 enterprise reporting requirements.

Customers are ready to scale AI across their organizations, but they expect transparency, governance, and clarity on where AI acts autonomously and where human judgment remains essential. While enthusiasm for AI鈥檚 potential remains strong, customers are seeking clear insights into where AI鈥檚 potential lies within their organization. Three themes consistently emerge:

• Measurable ROI: Customers expect AI to deliver a measurable impact beyond innovation. Through our success plans, 麻豆原创 partners with customers to define success metrics, align AI initiatives to business priorities, and drive outcomes across execution speed, productivity, experience, and operational resilience.
• End-to-end use cases: Point solutions optimize individual tasks, but customers seek enterprise-wide value. Cross-functional workflows such as claims processing and procure-to-pay, where multiple AI agents collaborate, deliver far greater impact than isolated copilots. One of our American customers leverages AI for proactive monitoring, AI-powered root cause analysis, automated exception resolution, and autonomous execution of approved actions, accelerating issue resolution while minimizing manual effort. With up to 80% gains in operational efficiency and improved on-time vendor payment by 25%, the company streamlined its business processes and enhanced supplier collaboration.
• Governance is a business imperative: AI governance is no longer an IT agenda. It became a strategic imperative as business leaders now shape AI decisions, recognizing that trust is the foundation of enterprise adoption. Leading organizations are investing in AI innovation and AI governance in parallel, because sustainable scale requires both.

To address evolving customer needs, the Global Delivery Hub offers personalized success plans that tailor service experiences from self-guided resources to strategic partnerships, helping customers achieve continuous value and success. 聽Delivering this level of personalized, AI-enabled customer success requires a fundamentally different approach to service delivery.

At the core of this transformation are four guiding pillars that enable the Global Delivery Hub to combine human expertise with intelligent automation and deliver consistent, outcome-driven experiences.

Four C鈥檚 power the Global Delivery Hub鈥檚 AI-driven delivery model

The transition to agentic AI is not defined by technology alone. It is defined by how organizations prepare their people, evolve their ways of working, and establish the right foundations for responsible autonomy. Service delivery organizations that successfully embrace this shift will combine human expertise with intelligent automation to create more agile, resilient, and outcome-driven services.

Our AI-driven delivery model is built on four critical pillars: customers, capabilities, collaboration, and controls, required to unlock the full potential of AI while maintaining trust, accountability, and business alignment.

• Customer: Through the combination of intelligent agents, automation, and human expertise, service delivery teams can better understand customer needs, anticipate challenges, and provide more proactive, personalized advisory. This creates a stronger partnership model where AI continuously optimizes delivery services and accelerates customer success.
• Collaboration: Driving value with AI requires stronger, more connected, agile, and cross-functional collaboration across the organization. Product, engineering, delivery, and customer teams must work as one team, continuously aligning technology, operations, and customer outcomes to accelerate innovation and business value.
• Capabilities: Realizing the full potential of agentic AI is not simply a technology transformation; it is a people transformation. The future of delivery will be shaped by professionals who combine deep operational expertise with the ability to work effectively alongside intelligent agents. Technical excellence must complement AI fluency, automation skills, and the ability to leverage data-driven insights to make faster, better decisions. The organizations that succeed will be those that empower their people to adapt, learn, and collaborate with AI, creating a workforce that is not only AI-enabled, but AI-empowered.
• Controls: As AI becomes more autonomous, governance must evolve from a set of controls into a strategic capability that enables trust, scale, and responsible decision-making. Organizations need clear principles that define where AI agents can act independently, where human oversight is required, and how decisions are monitored and improved. Strong governance provides the foundation to scale agentic AI with transparency, accountability, and alignment to business objectives.

Without these foundational pillars, organizations risk adding complexity rather than creating value. Those that take a customer-centric approach while investing in the right capabilities, cross-functional collaboration, and appropriate controls will be better positioned to scale agentic AI, strengthen operational resilience, accelerate innovation, and deliver superior customer outcomes.

As agentic AI continues to reshape enterprise operations, these capabilities are becoming the foundation for the next generation of software service delivery.

Leading the next era of software service delivery

Organizations that act early will be better equipped to unlock the value of autonomous capabilities while ensuring governance, trust, and alignment with strategic business priorities. As software service delivery shifts to proactive, AI-powered operations, customers increasingly view providers as strategic advisors and AI co-pilots that help navigate their transformation journey.

By combining intelligent agents, automated workflows, and human expertise, organizations can proactively address challenges and achieve stronger business outcomes. To succeed in this new model of engagement, customers should focus on key areas that will accelerate their path toward an Autonomous Enterprise:

• Adopt a mindset of continuous adaptation: The AI landscape will continue to evolve rapidly. Customers that treat AI adoption as an ongoing transformation will be better equipped to innovate, respond to change, and build a resilient foundation for the autonomous enterprise.
• Build AI-ready teams and operating models: As delivery becomes increasingly collaborative between humans and intelligent agents, teams need a clear understanding of what is automated, what requires human judgment, and how to engage when exceptions occur. Preparing employees with AI fluency, updated processes, and new ways of collaboration will be critical to maximizing the value of agentic AI.
• Early alignment on shared outcomes, governance, and ways of working: Successful AI-enabled delivery requires clear alignment between customers, 麻豆原创, and partners on business objectives, success metrics, decision rights, and accountability. Establishing joint KPIs, governance models, and escalation paths early helps ensure AI-driven operations remain transparent, trusted, and focused on measurable business value as autonomous capabilities evolve.

The shift to agentic AI is not simply about automating existing services. It is about creating a new partnership model focused on continuous improvement, shared accountability, and business outcomes. Customers that prepare today will be better positioned to take advantage of the agility, resilience, and innovation enabled by the Autonomous Enterprise.

Sanjay Kulkarni is global head of Delivery Hub and Customer Value Group at 麻豆原创.
Julia Kloppenburg is a technology consultant in Customer Engagement & Adoption at 麻豆原创 SE.
Benedikt Gieger is Product Strategy and AI lead in Digital Manufacturing at 麻豆原创 SE.

For most enterprises, the integration environment they built over the last decade was never designed for what’s being asked of it now. Point-to-point connections, legacy middleware, fragmented tooling across dozens of systems: these were manageable constraints when the work was batch processing and scheduled dataflows.

Agentic AI changes the equation entirely. Autonomous agents need real-time access to data across the full application estate, consistent governance, and an integration layer that can scale without becoming a bottleneck. The organizations that have already modernized their integration foundation are finding that they have a meaningful head start.

麻豆原创 commissioned IDC to conduct an in-depth analysis of organizations using 麻豆原创 Integration Suite, including its advanced event mesh capability. 麻豆原创 Integration Suite is 麻豆原创’s flagship integration platform as a service, delivered on 麻豆原创 Business AI Platform, the unified foundation for 麻豆原创’s AI, data, and integration capabilities.

For this Business Value White Paper, IDC conducted in-depth interviews with eight organizations across manufacturing, consumer products, energy, fintech, healthcare, retail, and transportation 鈥� enterprises with an average of 48,529 employees and $14.53 billion in annual revenue, operating across the U.S., Germany, Denmark, India, and the UK. The results were quantified from actual outcomes, not a modeled composite.

What the IDC Business Value White Paper found: 麻豆原创 Integration Suite customers are achieving a 368% three-year return on investment with an eight-month payback, generating an average of $47,900 in annual benefits per integrated application, or $9.36 million per organization.

Numbers that matter

The IDC Business Value White Paper documents measurable impact across the full breadth of what integration touches in a modern enterprise:

• Integration speed and scale
  • 78% more application integrations
  • 41% less time to complete per application integration
  • 99% more application messages processed
• Operational reliability
  • 58% fewer unplanned outages
  • 35% faster to resolve integration errors
  • 19% fewer integration errors
• Business process automation
  • 49% more business processes automated
  • 21% efficiency gains for business process teams
  • 32% less time to onboard a business partner
• Developer and team productivity
  • 29% improvement in development team productivity
  • 31% faster development life cycle for new applications
  • 32% more efficient application integration teams

These aren’t projections. They are the average outcomes across eight real enterprises, documented through in-depth interviews by IDC analysts Shari Lava, group vice president for AI, Data, and Automation, and Matthew Marden, research vice president for Business Value Strategy.

More than an 麻豆原创 platform

One finding in this study deserves particular attention for customers and partners evaluating 麻豆原创 Integration Suite in mixed-vendor environments: on average, 64% of integrated applications in the study are non-麻豆原创, and 82% of integrations touch at least one non-麻豆原创 application.

麻豆原创 Integration Suite is not an 麻豆原创-only platform. It is the integration backbone for the full, heterogeneous application environment that modern enterprises actually operate, one where 麻豆原创 and non-麻豆原创 systems must work together reliably at scale.

One study participant described what that means in practice: “麻豆原创 Integration Suite is our main connection to the outside world. Every time anyone needs to connect to our 麻豆原创 systems, it goes through [麻豆原创] Integration Suite. It’s our main front door for API access, for data access, for collaborating with us, and for transferring data in and outbound into our 麻豆原创 systems.”

Built for the AI era

The timing of this study matters. Organizations are moving fast on agentic AI, deploying autonomous agents that need to orchestrate, monitor, and act across their entire application estate in real time. Integration is no longer a back-office concern; it is the operational layer on which AI-driven automation either succeeds or stalls.

As part of 麻豆原创 Business AI Platform, 麻豆原创 Integration Suite brings together API management, event-driven architecture through advanced event mesh, and AI-native capabilities 鈥� giving organizations the governance, observability, and real-time connectivity that agentic AI workloads require. That includes native support for MCP, LLM connectivity, and agent-to-agent orchestration, making it a ready foundation for autonomous enterprise processes.

Study participants described this directly. One said: “The real value of [麻豆原创] Integration Suite is the combination of its different capabilities: cloud platform integration, API management, and advanced event mesh. It’s the full set of these capabilities that makes it so important for us.”

Another captured the business case for reliability in AI-driven environments: “What we’re starting to see with 麻豆原创 Integration Suite is a level of reliability and standardization in the integrations we’re building, which enables positive levels of further scalability. We no longer have that kind of point-to-point integration, which had been a risk and a source of technical debt for us.”

Business case is clear

A 368% three-year ROI and eight-month payback is a compelling headline. But what sits behind those numbers is equally significant: enterprises can connect more of their application estate, automate more of their business processes, resolve failures faster, and onboard partners more quickly 鈥� all on a platform that is ready for the AI workloads they are investing in right now.

“We can innovate more with 麻豆原创 Integration Suite because it’s easier and the developers are happier working with the tool. It gives good results, so we continue to build on the foundations we have already created.”

That is the right foundation for what comes next.

.

Sid Misra is chief marketing officer of Technology Foundation for 麻豆原创 Business AI Platform at 麻豆原创.

Source: IDC Business Value White Paper, sponsored by 麻豆原创, The Business Value of 麻豆原创 Integration Suite (Doc #US54820926-BVWP, August 2026) , IDC Business Value Snapshot, sponsored by 麻豆原创, The Business Value of 麻豆原创 Integration Suite (Doc #US54820926-BVS, August 2026)

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.

It鈥檚 about making consequential decisions inside complex processes that require a deep industry specific context of data, regulations, human workflows, and processes.

For example, tasks like deploying and coordinating thousands of field technicians to restore energy grids after a storm and reduce unplanned downtimes or like keeping critically important production lines running and service levels high despite global supply chain disruptions involve many difficult decisions.

“These problems are incredibly hard to solve,” says Dominik Metzger, president of Industry AI at 麻豆原创, because solving them demands immense organizational change, especially in highly regulated industries.

Frontier LLMs are not enough

There is a growing realization among business leaders that solving these challenges requires more than just a powerful AI language model. In these situations, AI must do more than generate suggestions. It needs to act on insights and context to support the execution of business processes. This requires agentic AI that can work with trusted business data, apply industry-specific knowledge, and take actions in ways that are reliable, explainable, and useful to the people closest to the work.

AI is most valuable when it understands the context in which decisions are made. Manufacturers need to balance demand, production capacity, supplier risk, and quality requirements. Energy companies need to manage assets, safety, sustainability, and regulatory obligations. Life sciences companies need to innovate while meeting strict compliance expectations.

To help solve the most complex problems facing large enterprise customers, 麻豆原创 is bringing together 50 years of deep industry聽expertise, leading AI engineering know-how, and customer-facing forward-deployed delivery capabilities in one organization聽to combine forward-deployed engineering with strong productization capabilities.

This enables 麻豆原创 to move beyond custom AI solutions and build, productize, and scale end-to-end AI transformations for industry-specific business problems.

Think of it this way: the Autonomous Enterprise is 麻豆原创鈥檚 strategic direction, 麻豆原创 Business AI Platform is its foundation, and Industry AI acts as a highly focused customer transformation offering, solving industry-specific challenges for individual customers to generate substantial business value.

What makes Industry AI different

The Industry AI portfolio is built on three differentiators: first, more than 50 years of 麻豆原创鈥檚 industry and process expertise across 26 industries; second, the richness of 麻豆原创 customers’ data footprint and ontologies in an existing system of record; and third, a dedicated forward-deployed engineering (FDE) workforce to solve problems that do not have off-the-shelf answers, customer by customer.

Forward-deployed engineering embeds AI specialists, such as data scientists and AI builders, directly with customers to solve high-value, industry-specific problems, rather than relying solely on packaged software. Metzger explains that forward-deployed engineering involves 鈥済etting obsessed with the problems of our customers鈥� and immersing 麻豆原创鈥檚 agentic AI developers in the challenges these customers face. 鈥淲orking directly with customers, we will build, deploy, and scale Industry AI applications to deliver tangible business value for our customers,鈥� he says.

Lessons learned by building these tailored solutions with selected customers will be productized as a standardized platform offering for many more customers to deploy and use. This model allows fast scaling and delivery, while also building up 麻豆原创鈥檚 platform and solution portfolio of high-value agentic solutions that are close to customer needs and current industry priorities.

Getting up close with customers

麻豆原创鈥檚 decision to establish Industry AI as a focused business offering reflects the conviction that true value from agentic AI is created in close collaboration with the industry experts who face specific business challenges every day. This proximity is the fastest way to identify the most critical problems, develop and test practical AI solutions, and refine them based on real-world experience. Most importantly, it allows to deliver tangible business value and prove the impact of AI in practice.

This approach helps reduce the gap between a promising idea and a solution. On the one hand, teams can move more quickly from identifying a need to deploying a solution that delivers measurable value. It also ensures that what is built reflects real-world needs rather than assumptions made far from the customer environment.

For 麻豆原创鈥檚 enterprise customers, the promise of the Industry AI offering is not simply smarter software. It is a more practical path to the Autonomous Enterprise. Instead of asking teams to adapt to generic tools, Industry AI can help bring deep intelligence into the processes people already use and the decisions they already make, while staying connected to the business data, controls, and applications that keep organizations running.

Benefits

Examples of these benefits are easy to describe. An energy provider avoids costly downtime by identifying a likely spare part demand early and recommending relevant suppliers. A retailer adjusts inventory positions and trade promotions based on simulated scenarios and real demand signals before stockouts occur. A pharmaceutical manufacturer ensures the safe and reliable release of life-saving drugs through a highly precise, high-quality, and fully automated batch release process.

That matters because it can help organizations move faster, improve quality, and free employees to focus on higher-value work. It can also help companies turn industry knowledge into a lasting advantage, especially as AI becomes a larger part of how businesses operate.

For 麻豆原创, the formation of the Industry AI unit represents a strategic leap, combining industry-specific expertise, end-to-end offerings, and a value-based offering in a way that is clearly designed to differentiate 麻豆原创 from competitors.

麻豆原创鈥檚 differentiation from other AI platform providers and more traditional forward-deployed engineering companies is not simply about providing custom AI services: the focus is on turning industry-specific expertise into scalable, repeatable offerings that can be deployed across customers, creating a more sustainable and differentiated model for delivering AI value.

Specifically, the Industry AI offering is positioned as an all-in-one commercial package, including platform consumption and cloud services, solutions, forward-deployed engineering, and expert support, with pricing based on real customer business value and a single contract.

It is based on 麻豆原创鈥檚 unique deep industry-specific knowledge, business AI platform, and knowledge graph, enabling tailored processes for customers. In addition, 麻豆原创鈥檚 approach is grounded in customers鈥� business logic, decades of experience and enterprise grade governance, all implemented in standard products, differentiating it from some recently announced market offerings.

What comes next

As the World Economic Forum has highlighted, successful AI scaling depends not only on the technology itself, but also on practical changes to how people work, how decisions are made, and how organizations govern new capabilities.

麻豆原创 solves industry problems that generic AI鈥攅ven if super powerful鈥攃annot. Ultimately it is about enterprise and business process transformation, not AI deployment only. This then can redefine how AI transforms industries, by moving beyond isolated use cases toward autonomous, end-to-end agentic execution.

Agentic AI builds on the economic potential of generative AI, which McKinsey has estimated could add US$2.6 to $4.4 trillion annually to the world economy. This represents the next stage of enterprise AI adoption: a shift from content generation to autonomous execution, and from isolated pilots to operational deployments.

Agent sprawl

That shift creates a new governance challenge. Agent sprawl occurs when AI agents are created, deployed, or connected across systems faster than the enterprise can inventory them, assign ownership, control permissions, monitor behavior, and optimize or retire them when they are no longer fit for purpose.

Underscoring this shift, found that 98% of companies have already deployed AI agents or plan to do so. But as adoption accelerates, governance is struggling to keep pace. According to the same report, less than half of the organizations surveyed have visibility into an inventory of AI agents.

The mechanics of agent sprawl are familiar to any technology leader who has navigated a wave of SaaS adoption. Individual teams, motivated by genuine productivity goals, deploy agents independently. Each one is designed for a specific task鈥攁 marketing automation agent, a supply chain monitoring agent, an HR onboarding bot鈥攁nd each works in isolation. Without a centralized platform or governance framework, the organization accumulates a fragmented landscape of agents that do not interoperate, cannot be audited consistently, and accumulate technical debt faster than they generate value.

, the average global Fortune 500 enterprise will have more than 150,000 AI agents in use, yet only 13% of organizations believe they have the right governance in place to manage those agents. Max Goss, senior director analyst at Gartner, told his audience at a London conference in April: 鈥淎s CIOs and IT leaders see an explosion of AI agents across their organizations, many are contending with an ungoverned sprawl of agents that expose their organizations to a range of risks, including misinformation, oversharing, and data loss.鈥�

He added: 鈥淢any organizations resort to blocking or restricting the use of AI agents, but this is not a long-term solution. If employees are unable to work in the sanctioned tools, they will likely go around the organization鈥檚 controls and start using shadow AI, which presents far greater risks. Organizations need to find a balance where they can govern agents and manage sprawl, but also safely empower employees to innovate with these tools.鈥�

Agents typically need broad, cross-environment permissions to function, but those permissions are rarely governed with the same rigor applied to human users. The risk posed by unmanaged or rogue AI agents in the enterprise is therefore real and growing.

AI agent security concerns

Publicly reported enterprise-security examples also point to agents leaking sensitive information or acting outside their intended scope, including cases where malicious instructions caused agents to bypass guardrails, delete production records, or trigger irreversible financial transactions.

The security concern is what registers most sharply with enterprise technology leaders. With chatbots and early generative AI, a security failure typically meant bad output: an inaccurate or inappropriate response that could usually be corrected after the fact. In the agentic era however, the consequences of an agent failure or security breach can be far more damaging because agents can take action, call tools, access systems, and initiate business processes.

That is why agent governance is no longer only an IT operations issue. It increasingly touches board-level concerns: risk ownership, regulatory exposure, data protection, auditability, operational resilience, and accountability for autonomous decisions.

The emerging AI governance platform

Leading organizations are beginning to treat agent governance not as a compliance overhead but as a strategic capability that determines whether AI investments compound as advantages or liabilities. As a result,  effective AI agent governance has quickly become a boardroom topic and is contributing to the emergence of a new platform category: the AI governance platform.

The category is still forming, but its purpose is becoming clear. Enterprises need a way to discover agents, understand what they do, control what they can access, verify whether they are compliant, and monitor how they behave in production.

麻豆原创 is one of the agentic AI pioneers in this emerging category. Through its 2023 acquisition of LeanIX, 麻豆原创 gained a foundation in enterprise architecture management. This has quickly become a recognized differentiator for 麻豆原创 AI Agent Hub鈥攑ositioning AI artifacts like agents, models, and MCP servers within the full architecture and business context of the organization.

麻豆原创 AI Agent Hub builds on this foundation as a command center for managing and governing AI agents and related AI assets across an enterprise, even when they come from different vendors and run on different systems.

As 麻豆原创 CTO Philipp Herzig explained on stage at this year’s 麻豆原创 Sapphire event, 麻豆原创 AI Agent Hub is intended to provide a governance layer of record for the enterprise agent ecosystem. 鈥淎gents are everywhere,鈥� he said. 鈥淪ome are great, some are not, and almost no one has a consistent picture鈥攏o central governance, no clear view of what each agent does, whether it adds value or whether it adheres to your policies.鈥�

He added: 鈥溌槎乖� AI Agent Hub changes that. One entry point and command center to discover, manage, and govern all AI agents, LLMs, and MCP servers in your landscape鈥攙endor-agnostic. [麻豆原创] AI Agent Hub allows you to discover all your agents in context: your landscape, your business processes. Once you have identified the right agents, you can control their risk and define architectural decisions or compliance rules.鈥�

A closing window

Given the pace of AI agent deployment, the window for implementing effective enterprise governance before a serious incident occurs is narrowing. For CIOs, CEOs, and company boards, the question is no longer whether to govern AI agents. It is whether governance gets designed into the architecture from the start or retrofitted after the first serious failure.

Organizations that treat agent governance as a strategic priority in 2026 will be better positioned to scale AI as a durable competitive advantage. Those that defer could spend 2027 cleaning up: in enterprise technology, the cost of speed without structure eventually gets paid. But with AI agents, the bill arrives faster鈥攁nd at greater scale鈥攖han anything that has come before.

To meet these expectations, organizations must move beyond siloed commerce channels toward a connected autonomous CX model that integrates commerce, order sourcing, fulfilment, and customer support across the full customer journey.

The Advanced Success Plan for 麻豆原创 Customer Experience supports this shift by accelerating omnichannel capability build-out through outcome-based governance. It helps organizations move from fragmented execution to coordinated operations鈥攅nabling consistent cross-channel experiences, aligning commerce with sales and service execution, and improving conversion and repeat purchase through end-to-end alignment.

Reality of B2B buying

B2B buying is rarely linear anymore. Buyers often switch between digital channels, sales teams, and service touchpoints before deciding. Research from McKinsey鈥檚 B2B Pulse highlights that buyers typically engage across multiple interaction channels before making a decision.**

This reflects how digital commerce has become a core part of the buying process, with 58% of global B2B retailers selling on at least three e-commerce platforms.鈥�

At the same time, expectations have shifted. Consistency across channels is assumed, digital self-service is often the starting point, and speed and transparency are baseline requirements.

That creates a dual challenge for organizations: improving customer experience while also managing increasing operational complexity behind the scenes.

Convergence of commerce, sales, and service

B2B environments combine complex operations with tightly connected customer journeys. Organizations must manage customer-specific pricing and contracts, large and dynamic product catalogs, multi-step approvals, distributed fulfilment models, and integrations across commerce, ERP, sales, and service systems. Customers, however, do not see this complexity. They experience a single journey, from discovery to purchase, fulfilment, and support, and expect it to feel seamless.

A typical journey may include discovering products online, aligning pricing with sales, placing orders through self-service channels, and resolving issues via service interactions. Expectations for real-time visibility, fast issue resolution, and consistent engagement continue to rise, as highlighted in research such as DHL鈥檚 B2B E-Commerce Trends. The same DHL report also shows that 78% of B2B retailers expect website sales to grow over the next three to five years, which reinforces how central digital channels have become.鈥�

Yet many organizations still operate in silos. The result is often inconsistent data, limited visibility across interactions, slower issue resolution, and disconnected customer experiences. This is why omnichannel transformation is not about adding channels, but about connecting commerce, sales, and service into a unified operating flow.

Process excellence as a foundation

As complexity increases, end-to-end process alignment becomes critical. Customers experience outcomes, not systems, and those outcomes depend on how well processes are connected across commerce, sales, service, and fulfilment.

Breakdowns typically occur when sales agreements are not reflected in commerce systems, fulfilment is not aligned with order promises, service teams lack customer context, or data differs across channels. These are not isolated system issues, but symptoms of disconnected processes.

In practice, that can mean a customer sees one price in the portal, a different one in the quote, and another one in the order confirmation. It can also mean service teams have to ask customers to repeat information that already exists elsewhere in the organization. Even when the underlying technology is in place, the experience still feels broken if the process is not connected.

Organizations that invest in process excellence are better positioned to deliver consistent experiences, reduce friction, improve operational efficiency, and scale complex B2B models. Process alignment also makes it easier to respond to change, because teams can adapt faster when the underlying journey is not held together by disconnected handoffs.

Why omnichannel matters

Omnichannel in B2B is not just about offering more ways to buy. It is about making those ways work together. When the customer starts on one channel and finishes on another, the handoff needs to feel natural. If not, the customer experience becomes fragmented very quickly.

That matters because B2B buyers are increasingly comparing their business purchasing experience to the consumer experiences they already know. They expect simple navigation, transparent pricing, reliable order updates, and a service team that understands the full context of the account. In other words, they want the convenience of digital commerce without losing the support and complexity that B2B purchasing often requires.

This is also where many companies struggle. They may have a strong storefront, but weak back-end coordination. Or they may have good sales support, but poor visibility once the order has been placed. Omnichannel transformation closes that gap by connecting the customer-facing experience with the operational processes behind it.

Accelerating outcomes with the Advanced Success Plan for 麻豆原创 Customer Experience

The Advanced Success Plan for 麻豆原创 Customer Experience helps organizations accelerate omnichannel capability build-out through outcome-based governance and alignment between strategy and execution.

Rather than focusing only on implementation, the emphasis is on measurable outcomes across the full customer journey. This includes enabling consistent omnichannel experiences, aligning 麻豆原创 Commerce Cloud, 麻豆原创 Sales and Service Cloud, and fulfilment processes, managing catalog and contract complexity at scale, improving order sourcing and fulfilment coordination, and strengthening end-to-end process alignment.

The goal is to ensure all capabilities operate as one connected system rather than separate functions.

That becomes especially important in B2B environments where a single transaction can involve multiple stakeholders, custom pricing rules, approval steps, and several systems working together at once. Without clear governance and alignment, even well-designed digital tools can create confusion instead of clarity.

With the right operating model, however, organizations can turn complexity into a strength. They can reduce friction for customers, improve efficiency internally, and create a more reliable buying experience across every channel.

The Advanced Success Plan for 麻豆原创 Customer Experience includes access to Business Process Best Practices which helps customers understand the end-to-end process flow and best practices for executing business processes across 麻豆原创 Sales and Service. This service showcases reference processes and bridges the gaps that can occur during rapid implementations of solutions in complex landscapes or when implementation of multiple solutions creates fragmented processes without taking into consideration unique end-to-end view.

Conclusion

Omnichannel commerce and B2B digital transformation are reshaping how organizations engage customers and deliver value. Success depends on connecting commerce, sales, service, and fulfilment into a unified operating model supported by strong end-to-end processes.

Organizations that focus on process excellence and outcome-based governance are better positioned to scale effectively and meet rising customer expectations. The Advanced Success Plan for 麻豆原创 Customer Experience enables this by connecting strategy to execution and supporting consistent outcomes across the customer journey.

For B2B companies, the real shift is not just digital adoption. It is building a model where channels, processes, and teams work together in a way that feels simple to the customer, even when the operation behind it is complex.

Nikola Stojanovski is a product manager for the Advanced Success Plan for 麻豆原创 Customer Experience.
Tara Tracey is global product owner for the Advanced Success Plan for 麻豆原创 Customer Experience.

*Digital Commerce 360, 2025
**McKinsey, 2021
鈥燚HL, 2025

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

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

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

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

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

Now let鈥檚 dive into the releases from Q2 2026.

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

Joule

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

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

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

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Joule Work mobile app
General availability

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

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

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

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

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

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

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

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Autonomous SCM

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

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

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

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

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

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

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Autonomous Finance

Project Billing Price Verification Agent
Beta release

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

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

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

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

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

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Autonomous Spend

Expense Automation Agent
General availability

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

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

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

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

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

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

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

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

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

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Autonomous CX

Order Reliability Agent
Beta release

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

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

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

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

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

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Joule with 麻豆原创 Order Management Services
Beta release

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

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

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

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麻豆原创 Business AI Platform

Build

Joule Studio
麻豆原创 Early Adopter Care

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

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

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

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

and .

Contextualize and Reason

Generative AI hub, enhancements

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

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

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

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

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

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麻豆原创 Joule for Consultants, enhancements

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

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

and .

麻豆原创 Document AI enhancements

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

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

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

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麻豆原创 Domain Models

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

麻豆原创 Domain Models will help:

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

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

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Govern

麻豆原创 AI Agent Hub enhancements

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

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

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Process Consulting Agent
General availability

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

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Enterprise Content Research Agent
General availability

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

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WalkMe solutions, AI knowledge indexing
General availability

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

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WalkMe solutions, AI knowledge referencing
General availability

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

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WalkMe solutions, pinned AI
General availability

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The real barrier is not budget or technology

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

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

Define what success actually looks like

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

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

Design the strategy before building the integrations

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

Validate every critical decision with expert guidance

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

Measure whether the strategy is delivering

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

Where cycles and silos break

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

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

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

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

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

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

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

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

AI inching closer to enterprise maturity

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

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

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

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

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

Global businesses meeting key AI challenges

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

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

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

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

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

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

Future of value from AI is the Autonomous Enterprise

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

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

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

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

Visit the . Get 麻豆原创 news via  and .

Media Contacts:
Alex Vaught, 麻豆原创, +1 (206) 678-5712, alex.vaught@sap.com, PST
Ilaina Jonas, 麻豆原创, +1 (646) 923-2834, ilaina.jonas@sap.com, EST
Daniel Reinhardt, 麻豆原创, +49 151 168 10 157, daniel.reinhardt@sap.com, CEST
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.鈥� Additional information regarding these risks and uncertainties may be found in our filings with the Securities and Exchange Commission, including but not limited to the risk factors section of 麻豆原创鈥檚 2025 Annual Report on Form 20-F. 
漏 2026 麻豆原创 SE. All rights reserved.  
麻豆原创 and other 麻豆原创 products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of 麻豆原创 SE in Germany and other countries. Please see  for additional trademark information and notices.  

When suppliers send order confirmations as PDF files, even minor discrepancies in price, quantity, or delivery dates can trigger significant manual effort within procurement. For Lemvigh鈥慚眉ller, one of Denmark鈥檚 largest wholesalers within steel, plumbing, heating and electrical products, this has long been a familiar challenge, consuming substantial time and resources.

The company has now tackled the very point where earlier automation initiatives often stalled. With a new solution based on several specialized AI agents, developed on 麻豆原创 technology and implemented in close collaboration with NTT DATA Business Solutions, supplier PDF order confirmations can now be read, interpreted, compared, and processed automatically鈥攄irectly against 麻豆原创 systems.

鈥淲e have previously tried both RPA and traditional automation approaches without really achieving the desired effect. The key difference this time is that we broke the task down into multiple independent AI agents, each responsible for a specific part of the process. Together, they now handle what previously required manual review,鈥� says Frederik Aakerlund, IT director at Lemvigh鈥慚眉ller.

10 weeks from idea to AI agents in production

The project originated with an e-mail from Jess Frederiksen, an AI鈥憇avvy project manager in Lemvigh鈥慚眉ller鈥檚 Market and Procurement organization. After successfully matching an order confirmation with a purchase order using ChatGPT as an experiment, he approached the IT director to explore whether this could be turned into a fully integrated system solution.

From the initial tests to production deployment, the entire project took just 10 weeks. According to Lemvigh鈥慚眉ller, this short implementation timeline was critical in allowing the solution to demonstrate tangible business value quickly and build internal support.

鈥淭his was not a long-running project. In 10 weeks, we moved from idea to AI agents in production, already delivering measurable value to our procurement officers,鈥� Aakerlund says.

Over time, Lemvigh鈥慚眉ller expects the solution to free up resources equivalent to three to four full-time employees. These resources will instead be redeployed to higher-value activities, including handling the most complex and exception鈥慸riven orders.

鈥淭he objective is not to reduce headcount, but to use our expertise more effectively. The AI agents take care of routine tasks, enabling procurement officers to focus on cases where their experience genuinely matters,鈥� Aakerlund adds.

More than 100,000 order confirmations automated

Each year, Lemvigh鈥慚眉ller sends approximately 175,000 purchase orders to more than 2,000 suppliers. While part of this volume is handled in a structured manner via EDI, around 60% of supplier order confirmations are still received as unstructured documents.

With the coordinated AI agents in place, the company can now automatically identify delays, quantity changes, and price discrepancies鈥攁nd respond significantly faster.

鈥淧reviously, when order confirmations were handled manually, it could take hours or even days before changes were reflected across the organization. Today, the AI agents update the data almost immediately, allowing customers to receive a much more accurate picture of deliveries far sooner,鈥� says Klaus Heinemann, head of 麻豆原创 ERP at Lemvigh鈥慚眉ller, who led the development together with the project team. 鈥淚n addition, we now identify price discrepancies before the final invoice is issued, saving time both for us and for our suppliers.鈥�

Multiple AI agents orchestrated in a single workflow

The solution is built around three cooperating AI agents, each with a clearly defined role in the process. One agent handles incoming e-mails and attachments, a second extracts and structures data from PDF documents, and a third compares the extracted information against purchase orders in 麻豆原创 to determine whether there is a match or a deviation.

As a result, complex and unstructured supplier data can be processed in a unified, automated workflow without requiring procurement officers to open and manually review lengthy PDF files.

鈥淲hat makes this solution robust is the interaction between the agents. Each agent is highly specialized, but they are orchestrated in a way that ensures the process flows seamlessly from start to finish,鈥� Heinemann explains.

During the project, the importance of master data quality also became increasingly clear.

鈥淚n areas such as Incoterms and other master data, we identified improvements that need to be addressed. This has been an important learning not just for this initiative, but for our broader work with AI,鈥� he says.

While it is still too early to measure the full impact on customer experience, error rates, or claims, expectations are that faster and more precise handling of supplier confirmations will, over time, lead to fewer surprises and significantly improved delivery transparency. Internally, the solution has been met with strong interest and curiosity among employees.

鈥淧rocurement officers clearly recognize the value of being relieved from the most tedious routine work. This has sparked a constructive dialogue about how technology can best support their day鈥憈o鈥慸ay responsibilities,鈥� Heinemann says.

Business AI with a clear business outcome

According to Lemvigh鈥慚眉ller, the investment is expected to deliver a return within a relatively short timeframe.

鈥淲e are talking about quarters rather than years when it comes to ROI. That is why it was essential for us to get the solution into production quickly and focus on processes with a clear and measurable impact,鈥� Aakerlund says.

For 麻豆原创, the project serves as a concrete example of how artificial intelligence can be embedded directly into core business processes rather than remaining a disconnected experiment.

鈥淢any companies talk about AI agents primarily in terms of automation. Lemvigh鈥慚眉ller demonstrates that the real challenge鈥攁nd the real opportunity鈥攍ies in coordination,鈥� says David Pontoppidan, head of AI at 麻豆原创 for the Nordics and Baltics. 鈥淚t is the orchestration of three specialized agents directly within the core process that makes this solution robust. This is also where many multi鈥慳gent initiatives fail, not due to limitations of individual agents but because of insufficient coordination. Lemvigh鈥慚眉ller has succeeded by anchoring the solution in its 麻豆原创 landscape, where data, business rules, and governance frameworks are already firmly established.鈥�

He continues: 鈥淚nnovation is not about company size. Lemvigh鈥慚眉ller shows that a Danish organization with short decision paths and a pragmatic approach to technology can move faster than many large global enterprises that are still in the planning stage. Ten weeks from idea to production is far from the norm, but perhaps it should be.鈥�

Designed for operations and scalability

The solution was implemented in close collaboration with NTT DATA Business Solutions, which was responsible for making the solution production鈥憆eady and fully integrated into Lemvigh鈥慚眉ller鈥檚 麻豆原创 landscape.

鈥淏y distributing responsibilities across multiple AI agents, Lemvigh鈥慚眉ller has been able to automate a complex process without losing transparency or control. This has enabled a fast and secure transition from pilot to production and ensures a more robust solution that can easily be expanded as new requirements emerge,鈥� says Kristian Dahl, 麻豆原创 UX manager at NTT DATA Business Solutions.

According to Dahl, the modular, agent鈥慴ased architecture was a key enabler in moving efficiently from proof of concept to live operation.

First step in a broader AI agent strategy

Initially, the AI agents have been deployed for selected supplier inboxes and business areas. However, Lemvigh鈥慚眉ller already sees significant potential in applying the same agent鈥慴ased approach across additional administrative processes.

鈥淭his is the first AI agent solution we have put into production. The experience has given us the confidence to consider similar approaches across other areas, including invoice processing and order management,鈥� Aakerlund concludes.

Ellen Vig Nelausen is a Nordic Integrated Communications Expert at 麻豆原创.

Over the past several years, procurement has steadily expanded its influence inside the enterprise. As supply chains faced unprecedented disruption, procurement leaders became trusted advisors to the C-suite on resilience, risk management, and sustainability. Their visibility increased and so did the expectations placed upon them.

Now, the playbook is being rewritten once again.

Research from the 2026 Economist Enterprise Report titled , sponsored by 麻豆原创, finds that financial performance has reemerged as the primary benchmark of procurement鈥檚 success. Drawing on a global survey of 2,648 C-suite executives, the report found that 54% cite cost control as procurement鈥檚 greatest contribution to the business, up from 43% just one year earlier. The findings point to a function that is increasingly stretched, yet positioned to deliver measurable business outcomes鈥攊f it can navigate a difficult balancing act.

Cost control returns to center stage

The shift is not surprising given the environment. Persistent inflation, tariff uncertainty, and continued investment in supply chain resilience have pushed cost management back to the top of the executive agenda. At the same time, companies are investing in dual sourcing, nearshoring, and inventory buffering to reduce exposure鈥攕trategies that strengthen resilience but often raise costs. Procurement is expected to offset those increases elsewhere.

What makes this moment particularly challenging is that procurement鈥檚 broader responsibilities have not diminished. Teams are still expected to manage geopolitical risk, advance sustainability, and support digital transformation. The mandate has expanded significantly, often without a corresponding increase in capacity, tools, or operating model support. Delivering savings, limiting cost increases, and managing input costs while fulfilling a growing strategic role is the defining tension facing procurement leaders today.

AI is becoming procurement’s digital imperative

Technology, and AI in particular, is increasingly seen as the key to resolving tension. In the Economist Enterprise study, 60% of executives identified digital transformation as procurement’s top strategic priority over the next 12 to 18 months, up sharply from 38% in 2025. More than half (56%) identified AI as the primary driver of that transformation.

The emergence of agentic AI is accelerating expectations further. More than half of executives are planning to implement or evaluate agentic AI capabilities within the next 12 to 18 months. Unlike earlier generations of AI that focused primarily on generating insights, agentic AI introduces the ability to execute workflows鈥攆rom guided buying experiences to automated purchase order creation鈥攅nabling procurement to move beyond recommendations and drive actions.

Even so, executive expectations remain grounded. Only 9% of survey respondents want AI to lead most procurement decisions within three years. Procurement鈥檚 highest-value work continues to rely on human judgment, strong supplier relationships, and the ability to navigate complex trade-offs. AI plays a critical role in strengthening these capabilities, but it does not replace them.

Realizing that potential, however, requires the right foundation. Connected data, clear governance, and close collaboration across procurement, finance, IT, and operations are prerequisites for generating AI outputs that are reliable and accountable.

Category management takes on greater importance

As procurement balances cost pressures with broader business priorities, category management is emerging as a critical discipline. The report found that category and demand management are expected to receive the second highest level of digital investment among procurement disciplines over the next three years, trailing only spend and performance analytics.

This reflects the growing complexity of procurement decisions. Category leaders are no longer simply awarding projects to the lowest-cost supplier. They are expected to weigh cost, risk, sustainability, and supplier performance simultaneously鈥攁 level of complexity that demands better analytics and faster insight-to-action capabilities.

That level of nuance can strengthen procurement鈥檚 impact, but it can also slow execution. More sophisticated category strategies require better data, sharper analytics, and faster insight-to-action capabilities. The report found that category strategy has become the third most common source of process delays, behind only contracting and sourcing.

Success increasingly depends not on collecting more data, but on turning data into confident decisions quickly. Organizations that close that gap will be better positioned to execute strategy, not simply develop it.

Procurement’s strategic value is being tested

Perhaps the most striking finding in the report is a growing confidence gap. While nearly three-quarters of executives still believe procurement collaborates effectively across the organization, that figure dropped from 90% in 2025 to 74% in 2026. Confidence in procurement鈥檚 role in shaping digital transformation strategy also declined meaningfully over the same period.

These numbers do not signal a retreat from procurement鈥檚 strategic importance. Rather, they reflect a broader shift in how enterprise decisions are being evaluated. As AI democratizes access to data across the organization, more stakeholders have the information to question decisions and demand clearer evidence of value.

Procurement leaders are now expected to control costs, manage risk, strengthen resilience, and help guide AI adoption, often simultaneously and without additional resources. The question is no longer whether procurement belongs at the leadership table. It is whether procurement can consistently deliver the value expected of it across an expanding and increasingly complex set of priorities.

Procurement鈥檚 next chapter

The Economist Enterprise findings paint a picture of a function at a pivotal moment. Cost savings has returned as the primary mandate, yet procurement is still expected to manage risk, protect supply continuity, and lead digital transformation.

Meeting those expectations will require more than layering AI onto existing processes. It demands connected data foundations that make AI outputs trustworthy, visibility across suppliers and spending, and technology that enables teams to move from reactive decision-making to proactive intervention.

The leaders who will define procurement鈥檚 next chapter are those who can turn AI, data, and connected processes into faster decisions, stronger resilience, and measurable business impact.

Gordon Donovan is vice president of Research for Procurement and External Workforce at 麻豆原创.

They see a personalized ad, explore a product through an AI assistant, respond to an offer in their inbox, and open your app expecting everything to simply work. They expect relevance, continuity, and immediacy across every interaction in their entire journey.

Instead, they hit friction:

• They鈥檙e asked to repeat information they already shared.
• Promotions don鈥檛 reflect inventory availability.
• Fulfillment updates arrive late or not at all.
• And when support is needed, no one has the complete picture.

These are not isolated breakdowns. They are systemic customer experience failures.

In the era of agentic AI, those moments compound quickly. Customers now assume brands understand who they are, what they need, and what is happening in real time, and they expect businesses to act on that intelligence instantly across the entire customer journey.

The agentic era is accelerating this shift dramatically.

This goes beyond surfacing insights or recommendations. Agentic AI systems are increasingly capable of planning, reasoning, decision-making, and coordinated action across networks of agents. AI is fundamentally reshaping how customer experiences are created, delivered, and optimized in real time.

But it also exposes a fundamental truth: when AI moves faster than your data, systems, and processes, it reveals everything that鈥檚 broken.

That tension鈥攂etween rising customer expectations and disconnected CX reality鈥攊s exactly what 麻豆原创 and Google Cloud are solving together with .

The customer experience reality: ambition outpacing execution

Most organizations want to deliver seamless, connected experiences, but they struggle to operationalize those moments.

According to 麻豆原创 research, 78% of businesses say AI will be essential for retaining customers in 2026. Yet fewer than two in five share their customer data across CX (37%) or CRM (39%) platforms.

This is an enterprise operations problem, and its impact is felt across the entire CX business. And when these touchpoints are disconnected, the customer feels it first. And by the time the company feels that friction, it may be too late to win that customer back.

A new model for customer experience built on trusted enterprise data

麻豆原创 Customer Experience plays a key role in the expansion of 麻豆原创 and Google Cloud’s partnership to enable a fundamentally new approach to CX鈥攐ne that connects data, AI, engagement, and commerce in real time.

鈥淎gentic customer experience starts deeper in the stack than the customer ever sees鈥攊n the quality of the data, the way processes run, and the strength of the platform underneath. With 麻豆原创 and Google Cloud, we鈥檙e building that foundation together, so AI moves beyond insight to action. This delivers continuous, intelligent experiences with the control, context, and execution required at enterprise scale.鈥�

Muhammad Alam, Member of the Executive Board of 麻豆原创 SE, 麻豆原创 Product & Engineering

麻豆原创 Commerce Cloud endorses Universal Commerce Protocol: powering the next era of agentic commerce

As digital commerce enters the age of AI-driven experiences, a new standard is emerging to reshape how consumers discover and purchase products.

The Universal Commerce Protocol is an open standard designed to enable AI agents to manage the entire shopping journey, from product discovery to checkout and even post-purchase support.

Unlike traditional commerce integrations that rely on fragmented APIs and bespoke connections, UCP creates a shared language for retailers, payment providers, and AI systems. This allows intelligent agents to interact directly with commerce platforms, dramatically simplifying how products are surfaced, evaluated, and purchased.

In essence, UCP transforms commerce from a series of disconnected steps into a unified, agent-driven experience.

, long known for powering enterprise-grade digital commerce for global brands, is now aligning with this new paradigm. As part of the growing UCP ecosystem, 麻豆原创 plans to work with Google to enable its customers to participate in AI-native commerce experiences that extend beyond traditional storefronts and help its merchants鈥� products be discovered and purchased across the Gemini app and Google Search, including AI Mode.

鈥淥ur goal with UCP is to build an open, trusted standard for the future of AI-driven commerce,鈥� said Ashish Gupta, VP/GM, merchant shopping at Google. 鈥淗aving a leader like 麻豆原创 endorse the protocol is critical as we work toward bringing seamless, secure agentic commerce to everyone.鈥�

For merchants, this means dramatically lower integration costs, faster onboarding into new AI-driven channels, and the ability to reach new customers beyond their storefront experiences.

In addition, 麻豆原创 Commerce Cloud will leverage Google Gemini capabilities to power a Shopping Assistant that brands can deploy directly to their own customers. This enables organizations to offer a real-time, AI-driven shopping experience across their digital touchpoints. The Shopping Assistant engages shoppers through chat, voice, and text to help them discover products, answer questions, and turn intent into transactions.

Unlike traditional storefront interactions, the Shopping Assistant can create a persistent, conversational experience that follows the customer across the shopping journey, continuously refining recommendations and guiding decisions in real time.

It can also curate creative ideas鈥攕uch as themed outfits or complete event concepts鈥攂y intelligently combining products based on customer requests. By unifying behavioral signals, real-time inventory, and promotional intelligence, it can increase conversion rates, improve average order value, and ensure every recommendation is both relevant and fulfillable.

麻豆原创 Engagement Cloud and Google Cloud: how agents work together for marketing

This new expanded partnership comes alongside another historic milestone for 麻豆原创 and Google Cloud, announced earlier this year. A fundamentally different approach to marketing execution is now offering marketers a new model for engagement that is built on trusted enterprise data.

By combining customer data and real-time signals like inventory, orders, and fulfillment status with operational truth, marketing teams can now build, launch, and optimize personalized customer engagements, grounded in business context and executed at scale through an autonomous multi-agent framework.

At the heart of this partnership:

• 麻豆原创 Business Data Cloud聽(麻豆原创 BDC) connects semantically rich data across the enterprise with AI to help enable real-time insights and drive personalized interactions grounded in business context. This includes 麻豆原创 Business Data Cloud Connect for Google BigQuery with bidirectional, zero-copy data access and built-in enterprise-grade governance and security.

• Google BigQuery聽unlocks real-time signals across the Google ecosystem, such as geolocation, weather, ad engagement, and rich analytics, for AI-driven segmentation, personalization, activation and analytics.

• 麻豆原创 Customer Experience聽solutions can provide the real-time behavioral context: customer profiles, transactions, orders, service interactions, and consented engagement data.

• 麻豆原创 Engagement Cloud can activate enterprise data, AI insights, and predictions through intuitive tools and AI agents to help securely orchestrate real-time, personalized interactions across the entire customer life cycle.

Why this partnership matters

The collaboration between 麻豆原创 and Google reflects a broader shift in how commerce and marketing teams operate.

For commerce leaders:

• From search to agents: Consumers are no longer just searching. AI agents are acting on their behalf, making decisions, and completing purchases.
• From channels to ecosystems: Commerce is moving beyond owned channels into distributed, AI-powered environments like search, assistants, and chat interfaces.
• From integration to interoperability: Open standards like UCP eliminate the need for one-off integrations, enabling scalable participation in the AI economy.

With UCP, AI agents can seamlessly access product catalogs, manage carts, process payments, and handle post-purchase workflows, all without forcing retailers to rebuild their infrastructure.

For marketing leaders:

• From prompt to performance: Agentic intelligence becomes operational where business goals, enterprise signals, and marketing processes direct AI agents, translating into real customer interactions and automated lifecycle journeys.
• From manual to generative: Advanced generative capabilities powered by Google Gemini models, such as Nano Banana 2, introduce new agentic skills that help marketing teams dynamically generate messaging, imagery, and campaign variations.
• From dark data to unified data context: With every interaction grounded in business context and continuous engagement signals, messages become truly dynamic. Text messages can turn into immersive conversations with Google Rich Communication Services (RCS) and advertising creative and offers can continuously evolve.

Agents collaborate across 麻豆原创 and Google Cloud to personalize, activate, and continuously optimize campaigns in real time across engagement channels and media networks.

鈥淲ith this partnership, 麻豆原创 and Google Cloud bring together connected AI and a unified data foundation to create real-time understanding of the customer and business context. This enables organizations and CX teams to move from fragmented interactions to continuous, intelligent execution鈥攅mbedding AI into end-to-end processes and unlocking meaningful gains in productivity, speed, and business impact.鈥�

Jan Gilg, Global President Customer Success & Americas, Member of the Extended Board

Unlocking new value for enterprises

For 麻豆原创 Commerce Cloud customers, this partnership can unlock several strategic advantages:

• Increased discoverability in AI-driven shopping experiences
• Faster time-to-market through standardized integrations
• Ownership of customer relationships, even in third-party AI environments
• Future-proof architecture aligned with emerging commerce standards

As AI continues to compress the distance between intent and transaction, accessibility to agents becomes just as important as visibility in search results. In this new model, success in commerce is no longer defined by storefront experience alone鈥攊t鈥檚 defined by how effectively your products, data, and systems can be accessed, interpreted, and transacted on by AI agents.

For customers, this partnership unveils a new network of interoperable AI agents, grounded in enterprise data and shared context聽across 麻豆原创 and Google. Organizations can achieve measurable outcomes, including:

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

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

鈥淲hat matters is delivering real value to our customers. As customer experience becomes more agentic, organizations need to move faster, stay connected, and operate consistently across every interaction. By bringing commerce, marketing, and service together, we help our customers reduce complexity, respond more quickly, and deliver more relevant experiences that strengthen engagement and drive sustainable growth.鈥�

Manos Raptopoulos, Global President Customer Success Europe, APAC, Middle East & Africa and Member of the Extended Board 麻豆原创 SE

The future of AI-driven commerce and marketing: what this means for your CX strategy

The partnership between 麻豆原创 and Google for marketing and commerce聽marks a foundational shift toward what many are calling an agentic revolution鈥攁 world where AI doesn鈥檛 just assist CX teams and shoppers but actively participates in the buying process and shapes their customer experience.

For enterprise CX leaders, the message is clear: success in this new era will depend on how well your commerce and marketing platform can communicate with AI agents. With 麻豆原创 and Google Cloud, 麻豆原创 Customer Experience is positioning itself鈥攁nd its customers鈥攖o thrive in that future.

At the same time, the research also makes clear that ambition and vision are not enough.

The orchestration gap

Nearly half of the executives surveyed by IDC recognize the substantial benefits of end-to-end orchestration鈥攂ut many have yet to take decisive action. The true challenge lies not in understanding its value, but in bridging the gap between strategic intent and effective execution.

IDC surveyed C-level leaders across industries and regions. The message was consistent. Leaders understand the destination, but they want clearer guidance on the blueprint to execution. As the IDC white paper said, 鈥淎 majority of executives see value in an orchestrated supply chain and believe they must move in that direction, yet the precise steps are unclear, and they are not sure where best to start鈥攖hey need help.鈥�

That tension is familiar. Many organizations have made progress in operational silos, but far fewer have connected design, planning, procurement, manufacturing, logistics, and service into a truly coordinated operating model.

What orchestration really means

An orchestrated supply chain connects people, processes, and technology to provide agility and deliver continuous improvement, despite persistent disruption. Agentic AI is fundamentally reshaping orchestration, enabling systems to analyze, decide, and coordinate across functions in real time. From sourcing and procurement to planning, manufacturing, logistics, and delivery, the focus shifts from optimizing individual functions to achieving enterprise-wide alignment.

Traditional linear supply chain models were built for a more predictable world. Today鈥檚 reality is different. Geopolitical uncertainty, AI-driven disruption, climate pressures, regulatory complexity, and rising customer expectations are constant. Decisions made in one area now ripple quickly across the rest of the supply chain.

Orchestration addresses this reality by establishing a shared foundation of contextually relevant information, designing processes to operate together, and enabling systems that support end-to-end decision-making and execution. Technology plays a critical role, but orchestration ultimately depends on organizational alignment: clear roles, shared metrics, and coordinated processes. Instead of optimizing planning or execution in isolation, orchestration evaluates trade-offs based on their impact on the entire supply chain and the broader business.

Different leaders, shared outcomes

The IDC research also highlights how perspectives on orchestration vary by role. COOs focus on enterprise performance, CSCOs balance transformation with operational demands, and CPOs emphasize cost and risk exposure in direct materials, including mitigation strategies. Orchestration must deliver value across these perspectives while maintaining a unified, end-to-end view.

This diversity of perspective reinforces why orchestration matters. Success comes from respecting functional priorities without allowing silos to drive disconnected decisions, enabling coordinated decision-making that optimizes the whole, not just the parts.

Efficiency and agility, not trade-offs

Many executives perceive a trade-off between efficiency and preparedness, but orchestration changes the equation. With integrated data, shared context, and agile tools, companies can respond faster, reduce disruption response times, and make informed trade-offs鈥攄emonstrating that agility and efficiency can reinforce each other rather than compete.

As one procurement leader told in the IDC white paper, 鈥淲e have to be able to be both resilient and efficient, or at least be able to make informed trade-offs quickly. Right now, we have neither the necessary supply chain integration nor agile enough tools to be able to do that.鈥� Without integrated supply chains and shared context, that balance is difficult to achieve. When companies can identify issues earlier and respond faster, recovery times shrink, translating into lower costs, less expediting, and more reliable customer service.

The role of agentic AI

Agentic AI is emerging as a practical, transformational path to supply chain orchestration. AI-driven agents can monitor signals, evaluate scenarios, and recommend or initiate actions within defined guardrails. Across 麻豆原创 customer environments, AI delivers value in supplier onboarding, predictive maintenance, and rapid rebalancing of inventory or capacity. Leaders generally prefer AI as an advisor rather than a fully autonomous decision-maker, reflecting the continued importance of human judgment, accountability, and experience. Orchestration works best when AI strengthens decision-making while keeping people firmly in the loop.

Data, platforms, and the role of 麻豆原创 Supply Chain Management

Effective orchestration requires more than connectivity, it depends on harmonized data, coordinated processes, and contextual intelligence embedded where work happens. As supply chains extend across multi-tier supplier networks, logistics partners, and service providers, the challenge is no longer access to data, but making data usable, contextual, and actionable at scale.

The IDC research underscores that true end-to-end orchestration must span both internal operations and external ecosystems. Much of the most critical information鈥攔isk signals, capacity constraints, execution status鈥攍ives outside the enterprise. Without a common data foundation, organizations struggle to move from insight to action.

This is where plays a distinct role. 麻豆原创 brings together an end-to-end portfolio of supply chain applications, deeply integrated with ERP and line-of-business systems, and connected externally through . Planning, sourcing and procurement, manufacturing, logistics, and service operate as a coordinated system rather than isolated domains.

At the data layer, provides a normalized foundation that can harmonize operational, transactional, and network data. This shared context provides visibility, analytics, and AI. On top of it, embedded and extensible AI鈥攊ncluding agentic AI and Joule鈥攕upports orchestrated decision-making, helping to accelerate time to decision and time to recovery while keeping people engaged and in control.

Turning priority into practice

The move toward orchestrated supply chains is well underway, but progress remains uneven. Only a minority of organizations consider themselves close to full, end-to-end orchestration. Technology alone is not the constraint. Data readiness, clarity of outcomes, organizational alignment, and change management matter just as much.

Leading organizations start with clear objectives, invest in people alongside platforms, and build pragmatic road maps that prioritize time to value. Orchestration is not a single project. It is a progression that evolves as capabilities mature.

At 麻豆原创, our focus is on making orchestration practical and accessible. Through 麻豆原创 Supply Chain Management solutions and 麻豆原创 Business Network, we help organizations align teams, connect processes, integrate partners, and embed AI into core supply chain activities鈥攅nabling better decisions, faster execution, and sustained enterprise impact in a constantly changing world.

Hagen Heubach is chief marketing officer for Supply Chain Management at 麻豆原创.

IDC White Paper, sponsored by 麻豆原创, Orchestrating the End-to-End Supply Chain: Strategic Priority, Practical Reality, #US54385326-WP, April 2026

What it cannot say is why this supplier keeps slipping, what resolved a similar dispute last time, or that the same supplier has a delayed shipment in logistics and a renegotiated contract in procurement at the same moment.

The reasoning behind enterprise decisions has stayed locked in human judgment, scattered across systems.

For 50 years, enterprise software has been an excellent system of record. Closing the reasoning gap on top of it is what enterprise AI was always meant to do.

From AI-first to AI-native

The first wave, the AI-first approach, added intelligence inside existing applications. A feature can summarize an invoice or suggest a journal entry, but it lives within one application and cannot see across the landscape. Three barriers keep it confined: It lacks business and process context, it sits on disconnected systems without a shared data model, and it lacks the governance to be accountable at scale.

Meanwhile, the pace of change is unforgiving. Agentic systems, new interaction models, and new ways of grounding AI in business data are arriving faster than most architectures can absorb. As 麻豆原创 CEO Christian Klein noted this year at 麻豆原创 Sapphire, 80% accuracy may suffice for consumer AI; it is nowhere near enough for the world鈥檚 most business-critical processes. Bolting more intelligence onto isolated applications will not close that gap. It only multiplies the silos.

So what does it actually take to move beyond isolated AI features and build an enterprise that reasons, learns, and acts as one, without sacrificing the trust, governance, and reliability the business depends on? It is the question CIOs, CTOs, and enterprise architects are working through right now.

The foundation behind the Autonomous Enterprise

It takes a new foundation, and that is exactly what 麻豆原创鈥檚 provides.

This is not a white paper that sits on a shelf; it is the technology foundation 麻豆原创 is actively building to bring the Autonomous Enterprise to life: a business where agents, orchestration, and data work in one continuous loop to turn intent into trusted outcomes.

The shift it enables is from AI-first to AI-native, where software operates across the landscape as a system of context: an intelligence layer connecting data, process knowledge, decision history, and semantics. Agents reason over the whole picture, not fragments. Every interaction feeds intelligence. Every correction becomes a learning signal. Value shifts from software as a service to outcome as a service.

AI-native paves the way for the Autonomous Enterprise: one system of context that understands disputes in service, delays in logistics, and contract changes in procurement all at once, and can act on them with full governance and accountability.

Philipp Herzig, CTO and Member of the Extended Board, 麻豆原创 SE

Crucially, AI-native does not replace what already works. It pairs two complementary paths. The deterministic path keeps the predictable, rule-based execution that compliance depends on. The probabilistic, AI-native path adds reasoning that learns from data and experience. One is reliable but rigid. The other is powerful, but without context and control, often confidently wrong. Context engineering, guardrails, and observability bind the two, turning raw capability into reasoning the enterprise can trust.

The architecture delivers this through four reimagined layers that together form a cognitive core:

• The user experience layer shifts interaction from navigating apps to stating intent, with Joule as the central engagement point.
• The process layer turns applications into capability providers that expose stable APIs, events, and data for agents to orchestrate.
• The foundation layer is where data and AI come together as the intelligent core: orchestration, reasoning, and model services on one side; 麻豆原创 Business Data Cloud and the 麻豆原创 Knowledge Graph on the other, with 麻豆原创-trained models, including 麻豆原创-RPT-1 for structured business data, sitting alongside leading third-party models in one governed generative AI hub.
• The platform layer provides the runtime, governance, and harness that turn stateless models into reliable enterprise agents.

It defines the cornerstone architectural building blocks for agentic systems across experience, process, data, and platform, turning 麻豆原创鈥檚 unique business context into a living system of intelligence

What does this look like in practice? A finance analyst asks Joule to resolve high-value disputes likely to delay payment. Joule does not act alone. It coordinates AI assistants, which in turn direct specialist AI agents through agentic orchestration: the assistant decomposes the goal, delegates to a finance agent and a service agent, and reconciles their results. People set direction; assistants coordinate; agents execute. Those agents draw on the right information through context engineering, find the correct data through semantic grounding in 麻豆原创 Knowledge Graph, and act within governed boundaries, routing only exceptions to a human. Each resolution becomes a decision trace that makes the next one smarter.

This is not theoretical. During the 2026 keynote at 麻豆原创 Sapphire, 麻豆原创 COO Sebastian Steinhaeuser pointed to life sciences customer Takeda, which is achieving up to 10% productivity gains, up to 25% reduction in revenue loss from stock-outs, and up to five percent reduction in safety stock through autonomous regulated manufacturing. That is what AI-native looks like at work.

Frontier models are available to everyone. Business context is not. Each resolved dispute, each corrected decision, each completed process adds to it, compounding with every interaction.

Trust is engineered in, not bolted on. A set of cross-cutting, 麻豆原创-managed qualities holds the layers together: integration, identity, security, observability, and extensibility, with resilience, compliance, and sustainability handled by the platform.

Autonomy only creates value when it is governed, so agents become first-class principals with their own agent identity, scoped to a bounded subset of permissions and audited like any enterprise actor. Harness engineering wraps each model with the sandboxing, memory, and guardrails that make it dependable.

As the paper puts it, the model reasons but the harness governs, and it is the harness, not the model, that determines the ceiling. Open standards such as the Model Context Protocol and Agent2Agent protocol let agents interoperate across the enterprise, while sovereign cloud options keep data residency and compliance built in.

This direction is being shaped with the customer community, not handed down to it: the architecture carries forewords from the leaders of the German-Speaking 麻豆原创 User Group (DSAG) and Americas鈥� 麻豆原创 Users’ Group (ASUG) alongside 麻豆原创鈥檚 own.

The North Star is a living document. Published openly on , it will keep evolving as the technology and the agentic ecosystem advance, and as customer feedback shapes the design. If you build with 麻豆原创 or build on 麻豆原创, this is your invitation: Read the architecture, push back where it should be sharper, and contribute. The same invitation extends to the wider 麻豆原创 Architecture Center site, where 麻豆原创鈥檚 reference architectures are being built openly with the community. 

Read the AI-Native North Star Architecture and 听辞谤 .

Beyond the architecture itself is a single commitment: building systems that learn rather than dictate. For 麻豆原创 customers, 50 years of process knowledge, governed data, and trusted decision frameworks compound into a new kind of enterprise intelligence that is reliable, transparent, and deeply human.

The Autonomous Enterprise will not arrive as a single product launch. It will be built layer by layer, decision by decision, on the foundation described here, one grounded interaction at a time.

is head of the Office of the CTO at 麻豆原创.
is vice president of the Office of the CTO at 麻豆原创.

As discussed in a new whitepaper, , this perspective is grounded in interviews with supply chain leaders across six industries: automotive electronics and software, agricultural equipment, chemicals, global technology, automotive supply, and home appliances.

Their experiences reveal where companies are investing, where adoption challenges remain, and where the next wave of value is likely to emerge.

Supply chains are entering an era of permanent disruption

Four structural forces are reshaping global supply chains simultaneously: geopolitical instability, economic pressure, demographic shifts, and accelerated digital transformation.

Since 2017, relative to trade among closer partners, signaling growing fragmentation in global commerce. , while labor shortages and digital skill gaps continue to constrain operations.

Europe alone could face by 2028, and 63% of companies cite .

Together, these pressures are pushing supply chains beyond the limits of the traditional 鈥減lan-source-make-deliver鈥� model.

Companies are shifting from optimization to AI-enabled orchestration

Supply chains are increasingly viewed as strategic levers for resilience, service differentiation, and competitive advantage.

Across all six companies interviewed, each is investing in at least three forward-looking AI use cases in planning alone.

• A leading agricultural equipment company has deployed more than 1,000 AI agents to support orchestration, scenario planning, and value chain visibility. A global chemicals company is embedding AI across planning and scenario management while emphasizing explainability and trust.
• A home appliance company is applying AI selectively to improve forecasting, transport optimization, warehouse safety, and logistics costs.

The common theme: organizations are redesigning how the enterprise senses, decides, and acts.

Resilience is now defined by decision velocity

In today鈥檚 fragmented environment, resilience is no longer about static buffers. It is about how quickly companies can convert disruption signals into coordinated action across sourcing, production, planning, and logistics.

• An automotive electronics and software company centralized electronics ordering across roughly 30 plants and redesigned crisis-management processes, reducing disruption response times by approximately 95%.
• A global technology company adopted a regional 鈥渢wo-leg鈥� supply chain model, using inventory strategically to respond faster to disruptions.

The emerging differentiator is not forecast accuracy alone, but the speed from disruption detection to execution. Visibility remains important, but visibility without coordinated action is no longer enough.

Trust and governance are the biggest barriers to scaling AI

Despite rapid interest, . The challenge is not model accuracy alone; it is trust, explainability, fragmented systems, and manual overrides.

• One global chemicals company found that scaling AI depended less on technical performance and more on whether users could understand and trust the outputs. This led to stronger human-in-the-loop governance and progressive autonomy thresholds.
• A major automotive electronics company requires transparent, traceable AI reasoning before planners rely on AI-generated recommendations.

The path to autonomy will be incremental: companies will first augment human decision-making, then automate routine and semi-structured decisions as governance, trust, and data maturity improve.

The next frontier is the Autonomous Enterprise

The Autonomous Enterprise is an operating model where AI workflows, contextual business data, and embedded governance work together to anticipate disruption, coordinate action, and continuously improve performance.

The shift is moving from isolated copilots to coordinated agent-to-agent workflows spanning the supply chain.

In autonomous production environments, supplier reliability agents can monitor vendor risk while workforce orchestration agents align labor capacity with demand. Procurement agents execute sourcing decisions, and production planning agents dynamically rebalance schedules in response to changing conditions.

A similar pattern is emerging in asset management, where alert-processing, maintenance, warehouse replenishment, and goods-movement agents collaborate to resolve operational issues with minimal human intervention.

The business impact is significant. Agentic AI has by 20 to 30%, , and helped .

Collectively, these improvements mark the transition from reactive supply chains to systems that can increasingly anticipate, decide, and execute autonomously.

Building the autonomous supply chain

Capturing this opportunity requires three capabilities that remain fragmented in many organizations today:

• Organizational intelligence: The ability to detect patterns, anticipate risks, and reason across constraints
• Contextual data: Trusted operational data, business rules, workflows, and policies that ground AI decisions in enterprise reality
• Embedded execution: Integrating intelligence directly into workflows so actions can move from recommendation to execution without manual intervention

This creates a virtuous cycle: better data improves decisions, better decisions improve processes, and improved processes generate richer operational data over time.

Importantly, companies do not need to rebuild the enterprise from scratch. Deterministic systems of record remain essential for control, compliance, and auditability. The real transformation lies in rewiring how decisions are made and governed.

Organizations moving fastest are focusing first on high-value, high-frequency decisions such as forecasting, inventory optimization, disruption sensing, transport planning, procurement workflows, maintenance, and customer-service resolution.

The bottom line

The future of supply chain management will not be defined by more digital tools alone. It will be defined by the ability to operate the supply chain as a connected, adaptive, and increasingly autonomous system.

For leaders who move first, supply chain will evolve from a cost-management function into a competitive differentiator, enabling faster time to market, stronger service levels, and greater resilience. The organizations that lead will not be those running the most AI pilots. They will be the ones using AI to redesign how the enterprise senses, decides, and acts across the end-to-end supply chain.

For more information about Autonomous Supply Chain Management, download the white paper, .

Hagen Heubach is chief marketing officer for Supply Chain Management at 麻豆原创.

Demand for professionals who can build, govern, and orchestrate these agents is rising faster than supply, making graduates with hands-on agent-building experience among the most sought-after profiles in today’s job market.

This year at 麻豆原创 Sapphire, 麻豆原创 laid out its vision for the Autonomous Enterprise, where AI agents manage and execute business processes end to end. For universities, this raises an immediate question: How do graduates get ready for a world where AI agents are part of daily operations?

麻豆原创 is now providing new no-cost offerings and resources for universities that give lecturers and students hands-on access to AI agent building, process management, and enterprise architecture tools. The goal is to help higher education keep pace with the rapid adoption of agentic AI in industry and prepare graduates for a changing job market.

Preparing the next generation of AI agent builders

麻豆原创 has put together a new set of offerings and resources that help universities embed agentic AI-related concepts and technology into their teaching hands-on. Three offerings, each covering a different angle of agentic AI, are now accessible at no cost for academic lecturers and their students:

• : Before building an agent, the process it will operate in must be understood. 麻豆原创 Signavio Process Transformation Suite gives lecturers and their students access to process mining, modeling, and process transformation capabilities. They can model and analyze existing processes, spot inefficiencies, and design improved workflows that include AI agents. Additionally, students and lecturers can now experience process modeling with 麻豆原创 Signavio Process Modeler as part of 麻豆原创 Learning Hub, student edition.
• : For students to understand where agents sit within an organization’s IT landscape, this is the tool. Newly available at no cost for academic lecturers via 麻豆原创 Learning Hub, student edition, 麻豆原创 LeanIX lets students model enterprise architectures and reason about what changes when introducing AI agents into an existing system landscape.
• : Lecturers and their students can access an agent-building environment from 麻豆原创 and leverage various enablement resources. These allow students to explore configuring and building an AI agent, either in a guided demo experience or in a live system hands-on.

What makes this especially valuable is how the pieces connect. Students can explore different components of agentic AI hands-on using 麻豆原创 solutions. They learn that building an agent is only part of the job. Understanding process context, architectural and governance implications is equally important.

Collaboration with educational institutions globally

麻豆原创 will also collaborate intensively on embedding agentic AI into teaching with lecturers from more than 10 universities globally, including:

• Budapest University of Technology and Economics, Hungary
• E枚tv枚s Lor谩nd University, Hungary
• Hasso Plattner Institute, Germany
• HEC Montr茅al, Canada
• Karlsruhe Institute of Technology, Germany
• National University of Singapore Business Analytics Centre, Singapore
• TEC de Monterrey, Mexico
• Technical University of Munich, Germany
• Tongji University, China
• Technical University of Dresden, Germany
• University of California, Irvine, U.S.

The institutions will get exclusive early access to 麻豆原创’s latest agent building platform capabilities, benefit from agent building deep dives for students with 麻豆原创 experts, and from the opportunity to articulate academic needs with regards to teaching agentic AI related concepts hands-on to 麻豆原创.

鈥淲e want students to work with the same tools and scenarios that companies are using right now,鈥� Dr. Katharina Schaefer, head of Academic Partnerships at 麻豆原创, said. 鈥淏y giving lecturers free access to our agent-building resources, we make it easy for them to bring that reality into their courses. Students who build AI agents on real enterprise processes during their studies will have a head start when they enter the job market.鈥�

For faculty, the practical element is what counts. Students do not just read about AI agents in a textbook. They build them on real systems with real constraints.

“What excited me is that students get to work with enterprise-grade tools, thanks to this new platform,” said Prof. Jes煤s Aguilar-Gonzalez, TEC de Monterrey. “Students from our School of Engineering & Sciences build agents connected to real business processes and have to think about architecture and governance. That is much closer to what they will face in their first job than any textbook exercise.”

What sets this apart is its enterprise context: Agentic AI is taught in connection with business processes and the system landscape that supports them, so students learn how AI fits into real operations rather than experimenting in isolation.

Building the workforce of the future

As part of the , 麻豆原创 has been partnering with more than 2,800 educational institutions for decades to enable students to learn, research, and innovate with business applications and technology. With these offerings, 麻豆原创 supports students in developing sought-after 麻豆原创 skills, preparing them for job opportunities worldwide.

Ready to bring agentic AI into your classroom? Visit the or reach out via universityalliances@sap.com to get started.

The version for , part of the 麻豆原创 Services and Support portfolio, is the helping organizations adopt, activate, and scale the 麻豆原创 Customer Experience and AI innovations announced at 麻豆原创 Sapphire.

The proactive, expert-led engagement model is built to de-risk transformation, accelerate time to value, and sustain measurable outcomes across customer experience initiatives. It combines guided adoption, prescriptive functional and technical assistance, AI-powered best practices, and continuous value realization aligned to the realities of modern customer experience (CX): AI at the core, unified data, omnichannel at scale, retention over acquisition, service-led growth, and persistent skills gaps in a rapidly evolving digital landscape.

At its heart, the Advanced Success Plan for 麻豆原创 Customer Experience brings together the right expertise at the right time, program governance, solution experts, value advisors, and adoption specialists. This helps teams execute faster and smarter with 麻豆原创 Customer Experience.

What sets the Advanced Success Plan apart

• Outcome-based: Business outcomes and key value indicators are co-defined with teams, with milestones and workstreams aligned to deliver measurable Autonomous CX results.
• Proactive by design: AI Assistants, adoption checks, and innovation accelerators are embedded throughout, reducing risk and compressing time to value as agentic capabilities evolve.
• Continuous enablement: Role-based best practices and coaching are tied directly to the Autonomous CX road map, closing skills gaps at pace as new AI and platform capabilities become available.
• Cross-solution orchestration: Unified processes and shared business context across marketing, commerce, sales, and service break silos and enable enterprise-scale execution.

This is the first of a planned series to deep dive on the topics below. Here, we start with introducing how the Advanced Success Plan for 麻豆原创 Customer Experience helps operationalize seven macro trends shaping modern customer experience.

1. AI鈥憄owered customer experiences

AI now underpins everything from next best engagement to intelligent service resolution. The Advanced Success Plan embeds AI adoption patterns directly into the delivery approach, identifying high value use cases, calibrating data prerequisites, and guiding model governance.

The results are prioritization of high鈥慽mpact starting points, a plan to scale with guardrails, accelerating time from pilot to production and grounding every decision in 麻豆原创鈥檚 CX AI capabilities and product road map.

2. Hyper鈥�personalization at scale

Personalization demands more than algorithms; it requires clean, consent鈥慳ware data, robust decisioning, and experimentation discipline. The Advanced Success Plan delivers:

• Data readiness assessments and integration patterns to enrich customer profiles and segments
• Governance and testing playbooks to validate personalization hypotheses at scale
• Prescriptive journeys to operationalize next best action across every customer channel

The result: hyper personalization moves from proof of concept to standard operating model.

3. Unified customer data and breaking down silos

Siloed data undermines CX. We help establish a unified data foundation and harmonized identities, aligning business, data, and integration teams. With technical guidance and adoption accelerators, users can move faster toward a single view of the customer to fuel analytics, personalization, and service excellence.

The results are unified profile use cases, data quality baselines, and source鈥憃f鈥憈ruth decisions to reduce duplication and latency.

4. Omnichannel commerce and B2B digital transformation

Modern buyers expect seamless journeys across web, mobile, marketplace, and partner portals, especially in B2B. The plan accelerates omnichannel capability build鈥憃ut by uniting commerce, order sourcing, pricing, and fulfilment patterns, supported by outcome鈥慴ased governance.

The result: Channel consistency, catalogue and contract complexity, and the alignment of service and sales motions are all addressed, driving measurable improvement in conversion rates and repeat purchase.

5. Customer retention over acquisition

Acquisition costs are rising and retention is the new growth engine. The Advanced Success Plan helps operationalize retention strategies, churn prediction, intelligent engagement, loyalty, and proactive service across the CX stack.

The result: We align metrics such as retention rate, customer lifetime value, and service鈥憈o鈥憆evenue contribution, and ensure the data foundation supports them.

6. Service as a revenue driver

Service is no longer a cost center; it鈥檚 a growth channel. We guide users to productize services, monetize value鈥慳dded offerings, and embed outcome鈥慴ased contracts. The plan includes:

• Playbooks for cross鈥憇ell/upsell from service interactions
• Knowledge and field service patterns to improve first鈥憈ime fix and attach rates, KPI frameworks for service鈥憀ed growth

The result: With prescriptive governance and AI鈥慸riven intelligence, service organizations move from reactive cost management to consistent, measurable contribution to top鈥憀ine revenue and customer retention.

7. Navigating digital transformation complexity and skills gaps

Large transformation programs falter on orchestration and capability enablement. The Advanced Success Plan addresses both by:

• Establishing a cadence of value sprints and decision forums
• Providing role鈥慴ased enablement covering functional and technical assistance, data, product ownership, end-user adoption, and change management
• AI-guided best practices embedded throughout delivery to eliminate rework and accelerate quality outcomes across Industry AI scenarios

Organizations execute with confidence, even amid shifting requirements, resource constraints, and rapidly evolving agentic AI capabilities.

Measurable outcomes

• Accelerated time to first value through prioritized, AI-ready use cases aligned to capabilities
• Higher adoption and sustained performance via continuous enablement
• Reduced program risk through proactive governance, telemetry, and structured decision forums
• Measurable gains in conversion rates, customer retention, and service-led revenue contribution across the full CX stack

Getting started

• Define Autonomous CX priorities: Identify two to three priority outcomes for the next two quarters facilitated by the 麻豆原创 Value Management service.
• Assess readiness: Evaluate data, integration, governance, and enablement gaps to define a 12 to 18 month engagement plan.
• Engage the Advanced Success Plan: Align workstreams, milestones, and metrics with our expert team.聽
• Industrialize and scale: Convert proven delivery patterns into reusable accelerators, deployable across regions and lines of business.

This series will examine each of the seven trends in depth, demonstrating how the Advanced Success Plan for 麻豆原创 Customer Experience translates CX strategy into repeatable execution and measurable business outcomes.

Tara Tracey is a global product owner at 麻豆原创.

The agents help organizations deliver measurable results: a greater than 50% reduction in packaging compliance review hours, scenario simulation time cut from a day to 20 minutes, up to 80% reduction in manual GHS classification effort, and over 20% fewer packaging compliance errors.

The agents handle multi-step workflows that previously required hand-offs between teams and systems, including sustainability reporting preparation, packaging and product compliance assessments, carbon footprint simulation, and workplace safety documentation. They address mounting pressure across the enterprise: giving finance teams visibility into how carbon exposure affects forecasts; helping procurement teams manage regulatory risk without slowing down innovation; enabling supply chain teams to spot emission hotspots while maintaining service levels; and supporting operations in connecting safety observations to proactive, audit-ready actions.

New AI sustainability agents

The Sustainability Regulatory Readiness Agent helps organizations prepare for upcoming sustainability regulations such as the by translating materiality assessments into a defensible reporting scope and mapping the right data and metrics to each disclosure requirement. This enables sustainability teams to capture, validate, track, and ultimately disclose ESG information with far less manual effort.

For finance teams that need to manage carbon costs and disclosure risk while balancing the financial implications of sustainability performance, the agent automates financial-grade data mapping between material topics, regulatory requirements, and 麻豆原创 finance data, improving audit readiness and turning an existing materiality assessment into a clear, defensible reporting scope. Unlike a standalone sustainability point solution that only surfaces issues or a generic AI model that drafts narrative text, this agent works inside and the broader 麻豆原创 landscape to keep reporting scopes aligned to policy and keep underlying data structured and traceable.

The Footprint Optimization Agent brings together carbon, energy, and waste data from across Scope 1, 2, and 3 sources and pinpoints where emissions and other environmental impacts are highest across products, plants, and supply chains. It then runs side鈥慴y鈥憇ide simulations of different reduction levers and turns the results into reports, supplier requests, and targeted initiatives that support decarbonization projects and ESG goal tracking. For operations, the agent makes it easy to test 鈥渨hat鈥慽f鈥� operational changes and see their projected impact on carbon and other environmental footprints. It reduces scenario simulation time from approximately one day to about 20 minutes, making operational decisions based on real impact projections available at workers鈥� fingertips. This directly addresses the financial implications of carbon exposure: with ESG data often derived from industry averages that can vary by 30 to 40% or more from actual values, the ability to simulate and act on granular, accurate data carries significant margin protection value.

The Packaging Compliance Agent reads and interprets evolving packaging regulations starting with the , maps supplier and product documentation to a structured data model, infers and flags missing information, and checks product designs for conformity at scale. It turns scattered, often unstructured packaging data into an auditable compliance record for each SKU, shipment, and product run, reducing manual review effort and error rates in the process.

Procurement and sourcing teams facing growing pressure to ensure supplier eligibility, material compliance, and traceability while managing cost and availability now have an agent that helps protect revenue by catching packaging issues before they block orders or trigger fines. This equates to a greater than 50% reduction in manual compliance review hours and over 20% reduction of packaging compliance assessment errors. As sustainability moves to the transaction level鈥攃ompliance per SKU, per shipment, per product run鈥攖his kind of automated, embedded compliance capability becomes an operational necessity.

The GHS Classification and Labeling Agent collects the required input data, applies the relevant Globally Harmonized System (GHS) rules, and proposes classifications and label elements that can be used directly in downstream product compliance processes.

By automating these steps, it delivers up to an 80% reduction in manual efforts and a 60% reduction in GHS labeling and classification errors. For product and compliance teams that must keep launches on schedule and avoid shipment holds or market access denials, the agent embeds GHS product compliance into everyday workflows, turning a historically expert鈥慸riven, error鈥憄rone process into a consistent, auditable control point across the portfolio.

The Workplace Safety Agent supports workplace safety by analyzing reported observations and proposing follow-up tasks, risk assessments, and controls. It generates updated, approved safety instructions based on those observations to help organizations strengthen safety governance. With operations under increased pressure to ensure safe work environments without compromising service and speed of production, the agent delivers proactive, standardized safety management at scale, reducing the risk of incidents and unplanned downtime. At the same time, HR and EHS leaders can point to a clear trail of actions and updated instructions to demonstrate continuous improvement in safety culture to employees, regulators, and boards.

Only AI can deliver sustainability at scale

To ensure compliance and enhance strategic decision-making, sustainability data needs to become granular. It should move beyond a record of what happened and become a driver of future outcomes. To reach this level of insight, sustainability data needs to be analyzed at transaction level. Getting transaction-level data at scale is not something that can be done manually.

Granular sustainability data allows businesses to ensure compliance, control carbon and cost exposure, safeguard product marketability, and strengthen supply chain transparency and resilience. Perhaps most important is the ability to embed sustainability into business performance and across all business functions. This final point is the key to unlocking sustainable business autonomy.

In the sustainability context, becoming an Autonomous Enterprise means that sustainability policies are executed automatically inside enterprise workflows. This includes connecting financial and sustainability data for trusted steering, automating disclosure and performance insights, and blocking non-compliant shipments. Ultimately, sustainability becomes a governing factor in enterprise decisions, as opposed to a reporting or compliance activity.

Enterprise autonomy entails gradual AI maturation:

• Intelligence: Faster visibility into reporting and materials compliance risks across the enterprise
• Optimization: Data-driven decisions that balance cost, risk, and sustainability impact
• Autonomy: Actions executed directly within operational workflows, eliminating manual coordination

The choices enterprises make now鈥攈ow data is structured, how decisions are supported, and how sustainability is integrated鈥攚ill determine whether they can safely scale automation later or whether complexity and risk increase as systems evolve.

With the Autonomous Enterprise, leaders can deliver sustainable outcomes at scale.

Why 麻豆原创?

AI needs three things to successfully run autonomously: business and process context, data connection and integration, and a reliable governance structure.

Generic models can read data, but without business context they cannot reason how a business actually runs. They see tables, not operations, and provide recommendations that may be commercially or operationally unviable. Without data that is integrated and connected across all business departments, AI has to perform in siloes, unaware of how sustainability decisions might impact financial targets, or how procurement decisions affect supply chain risk. 麻豆原创’s rich ERP data foundation ensures that enterprise AI has the full business picture, not just fragments of it.

Finally, AI that lacks governance and cannot be audited or controlled can be more harmful than helpful to a business. 麻豆原创’s more than five decades of business process expertise anchored in governance, risk, and compliance, mean that AI for enterprise deployment can be managed safely and reliably. Sustainability agents operate within defined parameters, ensuring that automation scales without sacrificing control or compliance.

This is the foundation that makes everything possible. Without it, an enterprise has AI experiments. With it, it has an operating model.

Sophia Mendelsohn is chief sustainability and commercial officer at 麻豆原创.
Gunther Rothermel is chief product officer of 麻豆原创 Sustainability.

While expectations for reliable, on-time delivery remain high, organizations are navigating faster鈥慶hanging demand, more complex global networks, and increasing pressure on cost and working capital. And they鈥檙e looking for ways to turn insight into action more quickly and consistently across the supply chain.

麻豆原创 has been helping organizations build more connected and intelligent supply chains for over 50 years. At 麻豆原创 Connect in October, we introduced 麻豆原创 Supply Chain Orchestration, establishing a foundation for detecting issues, coordinating responses, and connecting execution across complex supply networks.

The innovations announced this week at 麻豆原创 Sapphire extend that vision further. By introducing a new set of AI-driven assistants and agents, we鈥檙e moving orchestration toward an autonomous operating model, where planning, manufacturing, logistics, and asset operations increasingly anticipate, coordinate, and resolve without manual intervention at every step.

AI grounded in real operations

AI delivers lasting value in supply chain management only when it is embedded where work actually happens. Autonomous agents do not operate independently of enterprise applications; they rely on deeply integrated processes and trusted data. Precision, compliance, and resilience depend on this foundation. Without it, AI does not scale or earn trust.

At 麻豆原创, the Autonomous Enterprise represents a vision for how organizations will run their businesses in the future: with insight, decision-making, and execution increasingly connected, while people remain firmly in control. Autonomous Supply Chain Management is a practical step toward that vision.

Autonomous Supply Chain Management reflects an evolution in how planning, execution, and operations work together. People define goals and priorities, assistants orchestrate activity across domains, and agents execute the work鈥攁ll within governed, end鈥憈o鈥慹nd processes.

At 麻豆原创 Sapphire, we鈥檙e introducing , enabled by new Joule Assistants and Industry AI scenarios that apply this model to daily operations across planning, manufacturing, logistics, engineering, and asset management. General availability will be phased throughout 2026, starting now.

Joule Assistants across the supply chain

Rather than disconnected AI tools, the following assistants will be embedded directly into core 麻豆原创 supply chain applications, where deep process knowledge, semantically rich business data, and enterprise鈥慻rade governance already exist.

Each will support a distinct area of responsibility while sharing context, data, and outcomes across the supply chain:

• Asset and Service Assistant: Changes how work gets detected and dispatched, turning signals and anomalies into action rather than queue items
• Business Network Assistant: Extends this coordination outward across suppliers, logistics providers, and service partners so execution doesn鈥檛 stall at the edges of the enterprise
• Logistics Assistant: Keeps warehouse and transportation execution moving as conditions change, coordinating agents rather than waiting for human handoffs at every step
• Manufacturing Assistant: Connects shop floor signals with broader operational context so teams can act on disruptions faster
• Planning Assistant: Helps planners stay ahead of exceptions and constraints without having to manually piece together signals from across the network
• Product Design Assistant: Helps engineering and manufacturing teams stay aligned as products evolve, surfacing the downstream implications of changes before they create rework or delays

From assistants to autonomous agents

In addition to these assistants, 麻豆原创 is delivering more than 60 purpose鈥慴uilt agents across supply chain processes. These agents are designed to sense events, analyze impact, and take guided action within defined business guardrails, helping coordinate execution while keeping people firmly in control.

In manufacturing, agents such as the Production Excellence Agent and Production Master Data Readiness Agent continuously monitor production, quality, and machine signals to detect issues early and keep routings and work instructions aligned with enterprise plans. In asset and service operations, the Asset Performance Alert Processing Agent and Technician Briefing Agent are designed to assess asset conditions, prioritize work, and increase first time fix rates, helping reduce downtime and improve responsiveness.

Beyond supply chain-specific scenarios, these assistants and agents will also extend into 麻豆原创’s cloud ERP environment, including , supporting 麻豆原创鈥檚 broader Autonomous Enterprise strategy. General availability will be phased through 2026, starting now.

Building on this foundation, 麻豆原创 Industry AI adds industry-specific intelligence that complements the core assistants. Rather than standalone features, Industry AI brings together purpose-built agents, process expertise, and business data to drive measurable outcomes. This value-led approach helps organizations apply AI in ways that reflect regulated requirements, complex production models, and asset-intensive operations 鈥� accelerating information across entire industry value chains.

People remain responsible for strategy, oversight, and the decisions that require judgement. What changes is how consistently high-volume, time-sensitive coordination happens across the supply chain.

Where this shows up in practice

The Autonomous Enterprise is our vision, and the innovations we鈥檝e announced at 麻豆原创 Sapphire are concrete steps that customers can build on within current 麻豆原创 environments. They are focused on addressing value leakage caused by fragmented handoffs, delayed decisions, and manual work.

In planning, new capabilities will connect commercial decisions directly with supply planning, linking promotion and pricing plans to inventory and replenishment to reduce stockouts, minimize write-offs, and improve planning consistently. New capabilities include vendor-managed inventory, transportation load building, deployment optimization, and co- and by-product planning.

In manufacturing and engineering, updates to will strengthen compliance and traceability in regulated environments. AI capabilities in the engineering-to-manufacturing handover will help teams understand the downstream impact of design changes before they reach the shop floor, surfacing implications for bills of materials, routings, lead times, and costs directly in context.

In , new Joule Agents will support execution-level decisions across warehouse and transportation operations, validating inbound receipts, aligning labor with real workload, and helping organizations respond faster to shifting constraints. Predictive labor planning in will allow operations teams to anticipate workforce needs rather than react to gaps.

In asset and service management, a new 麻豆原创 Field Service and Asset Management solution will bring planning, scheduling, dispatching, and field execution together in a single experience, connected to so work execution, parts usage, and costs stay aligned across service, operations, and finance.

These capabilities will become available in phases through 2026, aligning with customers鈥� existing 麻豆原创 landscapes. Together, they represent incremental but meaningful progress toward more connected, automated, and resilient supply chain operations.

The path forward

Supply chains don鈥檛 become autonomous overnight. This evolution happens workflow by workflow, expanding automation where it delivers real value, while keeping people firmly in control. As AI becomes embedded in execution, supply chain teams spend less time monitoring and firefighting, and more time shaping decisions, managing trade-offs, and building resilience.

This shift is bigger than any single organization. In a new white paper,聽, we explore how leading organizations are moving beyond isolated AI pilots toward AI embedded across end-to-end supply chain processes, and what it takes to get there. This article draws on multiple sources, including analytical support from McKinsey & Company.

That鈥檚 the direction we are moving, from reacting toward supply chains that anticipate, absorb, and adapt. What we鈥檙e introducing at 麻豆原创 Sapphire reflects that commitment.For more details on all announcements made this week, please refer to the .

Dominik Metzger is president and chief product officer of 麻豆原创 Supply Chain Management.

AI is what brings that ambition within reach. It helps companies act faster, personalize at scale, and engage in new ways. But it is also raising expectations. Every interaction now reflects how well the business runs.

When a customer places an order or asks for help, the experience depends on what happens behind the scenes. If pricing is inaccurate, inventory is uncertain, or fulfillment falls short, the experience breaks.

That is why customer experience is now defined by execution. Customers do not experience systems or intent. They experience outcomes.

Agentic AI can increase speed, intelligence, and personalization. But speed alone does not improve customer experience. It amplifies what is already there. When execution is aligned with process, data and governance, AI drives better outcomes. When it is not, AI exposes the disconnect.

Aligning experience and execution

Autonomous CX brings agentic AI directly into the processes that run the business instead of layering it on top of disconnected systems. It connects AI assistants across marketing, commerce, sales, and service onto a shared business context across 麻豆原创 CX, 麻豆原创 Cloud ERP, supply chain, and connected systems. Orders, inventory, pricing, and financials are defined once and used consistently, so decisions are based on live operational reality.

At the center of this shift are AI assistants and autonomous agents. Assistants coordinate multiple agents across end-to-end customer workflows, from discovery to fulfillment, engagement to service, and issue to resolution.

At 麻豆原创 Sapphire, we highlighted assistants that make this real across the portfolio:

• In marketing, Content Assistant and Campaign Assistant orchestrate intent understanding, content creation, segmentation, optimization, and campaign execution within governance controls.
• In commerce, Merchandising Assistant, Shopping Assistant, and Order Management Assistant connect discovery, conversion, and fulfillment to operational reality.
• In sales, Sales Assistant, Deal Qualification Assistant, and Deal Closing Assistant move sellers from signal to execution.
• In service, Case Management Assistant and Service Management Assistant improve resolution and service quality, with additional assistants purpose-built for self-service, HR service, and accounts receivable workflows.

AI-driven discovery and engagement grounded in business reality

麻豆原创鈥檚 collaboration with Google follows the same principle: connect AI-driven discovery and engagement to business execution.

Together, 麻豆原创 and Google are focused on three priorities: first, applying the latest AI models, including Gemini, to deliver high-quality customer experiences; second, supporting industry standards and open protocols to enable interoperability across ecosystems; third, enabling seamless, personalized journeys across channels and Google surfaces such as Shopping and Gemini.

By combining 麻豆原创鈥檚 governed business data with Google鈥檚 AI capabilities, assistants and agents can connect customer intent from storefronts, search, and AI-driven channels to 麻豆原创 commerce and order processes. This ensures that what customers see reflects what the business can fulfill.

This is also why 麻豆原创 is adopting and expanding how 麻豆原创 product data can power AI-driven experiences wherever customer intent originates. This keeps experiences aligned with pricing, inventory, and fulfillment in real time.

麻豆原创 Commerce Cloud innovations

麻豆原创 continues to be recognized in analyst evaluations, including the Gartner庐 Magic Quadrant™ for Digital Commerce, where 麻豆原创 has been positioned as a Leader for 11 consecutive times.

, trusted by the largest enterprises, now extends to mid-market and growing companies on 麻豆原创 Cloud ERP. The new 麻豆原创 Commerce Cloud, cloud ERP edition delivers a standardized, end-to-end approach, reducing complexity, leveraging AI natively, and accelerating time to value. It connects discovery through fulfillment via tight integration with 麻豆原创 Cloud ERP.

For digitally mature organizations, 麻豆原创 is expanding composable commerce with new and modular cart and checkout services. These services integrate with core processes such as pricing, promotions, loyalty, tax, payments, inventory, sourcing, and order management across 麻豆原创 and non-麻豆原创 touchpoints. This helps organizations modernize their architecture while maintaining end-to-end execution.

麻豆原创 is also expanding its ecosystem with Vercel to accelerate storefront development and deployment with optimized performance, scalability, and composable front-end experiences.

In payments, 麻豆原创 Unified Payment, powered by Adyen, embeds global processing directly into the commerce flow to simplify integration and improve conversion. 麻豆原创 also continues to enhance its open payment framework with pre-integrated providers, such as Checkout.com and PayPal, giving customers flexible provider choices that are easy to configure and use.

Together, these capabilities reduce total cost of ownership, speed deployment, and make it easier to deliver better experiences at scale.

Sales execution turns insight into action

Customer experience extends into sales execution, where teams need clear next steps and confidence those actions can be fulfilled.

We introduced new innovations, including field sales capabilities for retail execution processes in consumer goods companies and other field-selling environments. These capabilities provide rich mobile experiences that work offline, making it easier to plan store visits, capture in-store activity, and manage execution in real time.

Sales leaders gain connected insights tied directly to pricing, inventory, and order processes, leading to more consistent execution and better outcomes.

Scaling trusted autonomous service

Autonomous CX is strengthened through partnerships that extend execution while preserving trust and governance.

Our combines its agentic AI-driven voice and digital self鈥憇ervice with service, order, and entitlement data from 麻豆原创 Service Cloud. AI-driven automation can handle routine interactions with full context, escalating seamlessly and with continuity to service teams when human expertise is needed. This approach helps organizations scale service without breaking trust and ensures customer interactions remain connected to real business processes.

麻豆原创 is also expanding its partnership with Amazon to scale AI-driven service across voice and digital channels, enabling faster, more consistent resolution while keeping service execution grounded in real-time business data.

Industry AI in action

We are also showcasing Industry AI scenarios that demonstrate how assistants and autonomous capabilities operate in real business environments.

Autonomous Revenue Growth Management supports trade planning teams and key account managers in consumer products companies that sell through retailers, with applicability to agribusiness and wholesale distribution. Industry鈥憇pecific Joule Assistants provide AI鈥慸riven insights across trade planning and execution, helping teams identify growth opportunities, optimize commercial terms and respond more quickly to performance signals. The result is more predictable growth with fewer downstream exceptions.

Unified commerce supports merchandising and operations teams across retail, wholesale, and direct-to-consumer models. Unified commerce connects demand, inventory, and customer data across channels, with Joule Assistants guiding decisions on assortment, pricing, and placement. The result is more consistent execution and faster decisions.

The next phase of customer engagement

Across these innovations and Industry AI scenarios, the pattern is clear. AI delivers value only when it acts on shared, trusted context. When experience and execution stay aligned, speed becomes a source of trust instead of risk.

This is how 麻豆原创 is approaching the future of customer experience: as a coordinated system where every decision is visible, and every promise can be kept.

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

The CX innovations and Industry AI scenarios highlighted here are planned for general availability in Q3 2026.
The capabilities announced as part of 麻豆原创鈥檚 Autonomous Enterprise run across 麻豆原创 Cloud ERP, including 麻豆原创 Cloud ERP Private.
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In his kickoff keynote at 麻豆原创 Sapphire Orlando, Florida, Klein and other 麻豆原创 Board members detailed how 麻豆原创 plans to bring agentic AI to the world’s most critical business workflows so that humans and AI can meet the accelerating demands of global business profitably, strategically, and safely.

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

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

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The business AI imperative

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

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

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

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

ERP as the foundation for business AI

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

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

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

麻豆原创 Business AI Platform

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

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

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

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

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

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

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

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

麻豆原创 Autonomous Suite

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

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

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

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

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

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

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

Industry AI: H&M and Sector-Specific Transformation

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

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

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

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

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

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

Closing: The Autonomous Enterprise

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

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

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

The autonomous enterprise reflects a fundamental shift in how organizations operate using real鈥憈ime intelligence to guide decisions, orchestrate processes end to end, and continuously adapt as conditions change. AI becomes embedded into the fabric of the enterprise, helping every function operate with greater speed, resilience, and confidence.

The foundation of the autonomous enterprise is the 麻豆原创 Business AI Platform, which infuses AI with the process knowledge, data, and governance organizations depend on. 

Business Transformation Management solutions from 麻豆原创 help power the 麻豆原创 Business AI Platform by bringing together insights and enterprise knowledge that have long been fragmented and isolated in silos.

Business Transformation Management solutions from 麻豆原创 help deliver the promise of the autonomous suite. Here鈥檚 how.

麻豆原创 Agent Hub: Command center for agentic governance

Now available, the helps organizations discover, inventory, govern, and evaluate AI agents across the enterprise landscape. In fact, it鈥檚 already being used by 150 companies with over 100, 000 agents under management. 麻豆原创 AI Agent Hub acts a system of records for all AI agents, large language models (LLMs), and Model Context Protocols (MCP) servers.

In the context of 麻豆原创 Business AI platform, 麻豆原创 AI Agent Hub underpins the governance pillar, ensuring organizations can deploy and manage AI agents safely and at scale.

In addition to the enterprise architecture context that 麻豆原创 LeanIX provides, along with an giving agents access to architecture data, 麻豆原创 AI Agent Hub enables enterprise architects to apply proven governance practices, such as mapping to business capabilities, to the entire agentic landscape. The addition of agent mining capabilities supported by 麻豆原创 Signavio provides visibility into the behavior of AI agents, their conformance with policies, and their business impact.

From the standpoint of the Autonomous Enterprise, the insight the hub provides is not only necessary, it鈥檚 critical.

New AI capabilities

The new Enterprise Architecture Assistant from 麻豆原创 LeanIX is supported by several new agents, including two highlighted here. The Enterprise Content Research Agent draws on internal business content to enrich architecture data, while the Enterprise Architecture Web Research Agent scans the web for relevant vendor and application information.

These enhancements are part of a broader set of AI capabilities in 麻豆原创 LeanIX. The solution now makes it easier to create surveys, automate tasks, perform calculations, and plan target architectures. In addition, significantly improved semantic search enables Claude, AI co鈥憄ilots, and other agents to seamlessly access and work with enterprise architecture data.

In 麻豆原创 Signavio Process Transformation Suite, we redesigned 麻豆原创 Signavio Process Modeler with an AI-first architecture, modernized user experience and deeper integration with 麻豆原创 Autonomous Suite. 麻豆原创 Signavio also introduced the Process Transformation Assistant to enable business users to conduct sophisticated process analysis through natural language prompts. The assistant can identify high-impact opportunities for agent deployment, accelerating the time from question to decision and providing context-aware process insights to anyone.

Looking ahead to a new paradigm

Despite the rapid pace of change brought about by agentic AI, we are still in the early days of this technological revolution. To succeed and continue to ride the wave of innovation, companies need to aggregate and organize their procedural knowledge about how they operate.  This knowledge is often fragmented across many structured and unstructured sources鈥攕uch as process models, application logic, documents, and chats鈥攖o create a coherent view of how the business s runs.

This foundation enables agents to understand and act within the business context. In turn, agents will continuously contribute back, enriching and evolving this knowledge repository over time.

At 麻豆原创 Signavio we call this storehouse 鈥渃ompany memory.鈥� Company memory, comprised in part of process atoms, captures all the knowledge of operational practices, business rules, preferences, and more so that it can be accessed by agents as needed to check conformance and change behavior.

To enable the Autonomous Enterprise, you need to capture the tribal wisdom and unstructured knowledge your company depends on to operate today. That is what process atoms and a centralized company memory, accessed and updated by agents, do for you. In the future, it鈥檚 hard to imagine how any enterprise will succeed without the context, learning, and guidance that company memory delivers.

Business transformation never stops

As our research has shown, . That鈥檚 why you need a capability in place that allows for planning, managing, and realizing value from every transformation you undertake.

This year at 麻豆原创 Sapphire , we talked about all the ways our solutions support this capability as well as all the ways our solutions continue to evolve in the era of the autonomous enterprise, allowing you to adapt, innovate, and thrive into the future.

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Andre Wenz is chief product officer of 麻豆原创 Signavio.
Dominik Rose is chief product officer of 麻豆原创 LeanIX.