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

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

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

Gartner describes the Market Shapers quadrant this way:听

From assistants to orchestrated agents听

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

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

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

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

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

Agents that act where the work happens听

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

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

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

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

Delivering real results for customers听

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

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

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

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

Looking ahead听

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

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

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

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

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

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

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

From vision to execution

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

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

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Designed for better business outcomes

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

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

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

Joule Work

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

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

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

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

麻豆原创 Autonomous Suite

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

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

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

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

麻豆原创 Business AI Platform

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

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

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

Physical AI by 麻豆原创

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

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

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

Joule Studio: Extend, build, and integrate your agents

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

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

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

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

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

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

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

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

麻豆原创 Business Data Cloud

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

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

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

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

Governance

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

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

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

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

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

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

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

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

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

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

has been developing innovative products for the energy market for over 100 years. Sustained high demand and long-term growth prospects create a dynamic environment for innovation.

The company also innovates with AI. They use it within their products to create customer value and to automate manual processes for employees. And they are adding more automation to help them meet growing demand amid skilled labor shortages. Reinhausen鈥檚 expertise in AI innovation is among the reasons the company joined 麻豆原创鈥檚 early adopter program for Joule Work.

鈥淧artnering within the Early Adopter Program for Joule Work Desktop has delivered more momentum in just a few months than we saw in all the years before,鈥� said Peter Wied, Director of Complexity Management at Reinhausen. 鈥淲e see huge potential in making access to our 麻豆原创 S/4HANA data significantly easier and faster through natural language. This has the potential to permanently transform how our teams interact with business data.鈥�

AI that answers complex business questions while running agent-driven workflows at scale

Soon, all 麻豆原创 customers will enjoy the benefits of Joule Work when it rolls out later this month. We built it because the promise of AI goes far beyond text generation and fact-finding. It can help people make better decisions, informed by their data, and get answers to complex questions. 麻豆原创 Business AI Platform ensures those answers are all grounded in the context of how a specific business runs.

These are advantages companies can only get from 麻豆原创 because 麻豆原创 Business AI Platform encodes decades of process and industry knowledge right into our applications, knowledge graph, and Joule Work. The platform also combines best-in-class third-party models with 麻豆原创鈥檚 own foundation models for ABAP code generation and predictions on structured data, alongside 麻豆原创鈥檚 proprietary domain models, which are pre-trained on 麻豆原创’s code, data models, and business processes.

This means every AI interaction can draw on the model best suited for the task鈥攚hether that’s generating clean code, predicting a business outcome, or answering a process question鈥攚ith the accuracy and cost-efficiency that business-critical work demands. And through 麻豆原创 Business Data Cloud, Joule Work accesses all the data it needs from a single unified source that breaks down silos and unifies data across 麻豆原创 and non-麻豆原创 sources.

Now, is generally available in 麻豆原创 Business Data Cloud. It helps organizations eliminate fragmented records with a single trusted view of customers, suppliers, products, and employees across 麻豆原创 and non-麻豆原创 systems.

But we know sharper decisions and reliable business answers are only part of the equation. Organizations need AI to take action and coordinate work across all their business processes. Earlier this year, Deloitte more than 500 leaders and found 74% said they would redesign or rebuild almost half of their business processes around AI agents.

Through the 麻豆原创 Autonomous Suite, 麻豆原创 provides a library of reliable, ready-to-use Joule Agents across every business function, so companies can hit the ground running. And Joule Work brings the most powerful business AI orchestration engine in the industry to run those agentic workflows at scale.

Build and connect anything

Joule Work and the 麻豆原创 Business AI Platform provide an out-of-the-box competitive advantage for the AI era, but every company has its own unique advantages that should be strategically extended. This is why Joule Work鈥檚 Develop capability provides access to Joule Studio, soon to be generally available.

In Joule Studio, customers can customize and build Joule Agents with tools that embed process context and semantically rich data. They can also connect third-party systems with open standards because Joule Studio leverages over 11,800 pre-built business accelerators. Joule Studio is more than five times faster than do-it-yourself options, according to benchmarking done by .

With Joule Studio, anyone can describe what they want to build in natural language, while developers can use pro-code tools all in the same environment. New agents are deployed directly into Joule Work and inherit 麻豆原创 security and governance.

Warehouse automation company KNAPP used Joule Studio to build an that helps workers identify and solve problems without waiting for an expert. Issue resolution times have dropped by about 90%, while documentation and training times fell by about 80%.

In our industry, AI is not the future anymore; it is already happening. And that is exactly what our AI delivery pilot demonstrates every day. Our partnership with 麻豆原创 makes complexity simple. When a problem appears on the warehouse floor, 麻豆原创 Business AI Platform helps us solve it in seconds.

Florian Aufegger, Senior 麻豆原创 EWM Solution Consultant, KNAPP AG

As organizations build and deploy more agents, governance also has to evolve from compliance checkpoints to an integral part of the entire agent life cycle. Trust remains the biggest barrier to AI adoption for 71% of technology buyers, according to . This is why , now generally available, helps organizations govern every agent, LLM, and MCP server across 麻豆原创 and non-麻豆原创 environments. New capabilities, including 麻豆原创 Signavio Agent Mining, measure the outcomes every agent delivers and identify opportunities to further optimize those outcomes.

Start scaling AI, and your business outcomes, today

BCG’s latest found that the companies farthest ahead scaling AI deliver 2.4 times the revenue growth and 2.8 times the EBITDA growth of those falling behind.

Our customers, Reinhausen and KNAPP, show what that looks like up close: an hour of manual work reduced to seconds, and problems on the warehouse floor solved without waiting for an expert. Our goal is to make results like these repeatable for every 麻豆原创 customer.

If you want to see more, the new are now live, and starts October 27, in person and online. You can also of Joule Work and 麻豆原创 Business AI Platform.

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

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

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

Get all the news: .听

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

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

A new UI for AI 

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

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

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

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

麻豆原创 Autonomous Suite 

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

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

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

麻豆原创 Business AI Platform 

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

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

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

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

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

Jerseys from Jersey 

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

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

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

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

Security and governance 

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

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

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

A clear shift among customers 

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

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

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

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

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

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

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

What ends on December 31, 2030

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

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

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

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

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

麻豆原创鈥檚 support arrangements for Java

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

What happens after December 31, 2030

Implications for customer-specific maintenance

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

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

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

Implications for conversion

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

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

Recommended paths for our customers

麻豆原创鈥檚 strategic recommendation

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

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

Transition option for 麻豆原创 ERP

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

In select cases, switching to 麻豆原创 HANA database

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

Moving forward together

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

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

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

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

Learn more

Maintenance Strategy

2020 announcement

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

麻豆原创 ERP/transition

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

麻豆原创 Notes

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

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

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

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

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

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

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

麻豆原创’s Autonomous Enterprise Scales Up

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

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

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

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

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

The Platform Powering Business AI You Can Trust

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

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

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

麻豆原创 Brings Embedded Payments to Cloud ERP

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

Partners Expand the Reach of Autonomous Business

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

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

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

Visit the . Get 麻豆原创 news via  and .

About 麻豆原创

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

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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.

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.听
• 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.

Demand shifts quickly, supply networks are more exposed to disruption, cost and margin pressure remain constant, and the decisions that determine whether a company can respond with confidence rarely sit inside one function.

The enterprise is left with a critical question: How do you move fast enough to capture opportunity without putting fulfillment, margin, or customer trust at risk?

Many of the world鈥檚 largest organizations navigate this challenge on a regular basis. It is exactly the kind of moment that exposes the limits of how enterprises currently operate. Connecting the dots across functions, systems, and decisions still takes too much time, too much manual effort, and too much stitching across fragmented landscapes. By the time teams have gathered the data, aligned the functions, modeled the trade-offs, and agreed on a response, the environment has already shifted.

This is why we introduced the Autonomous Enterprise at 麻豆原创 Sapphire. The goal is to sense change earlier, understand its impact across the enterprise, coordinate the right response, and keep people in control of important decisions. This is a fundamental shift in how businesses can operate: intelligence that is continuous, decisions grounded in real-time context, and an enterprise that moves as a connected system rather than a collection of disconnected parts.

Autonomy at scale

An Autonomous Enterprise is an organization that can continuously sense what is happening across its operations, reason over those signals using business context and established rules, and act across end-to-end processes without depending on manual coordination at every step. AI assistants and agents advance work across the enterprise in alignment with the goals, policies, and constraints defined by humans.

Every AI-driven action is auditable and traceable. Human judgment is deliberately embedded in decisions that require accountability and exceptions that fall outside defined parameters.

Three principles underscore the Autonomous Enterprise:

1. Process knowledge: Deep, industry-specific understanding of how a business truly runs
2. Business data: Enriched, connected, contextual data that gives AI something real to work with
3. Governance: The backbone that keeps everything upright, traceable, and within policy

Beneath it all is the 麻豆原创 platform, ensuring every layer works in concert, every agent operates within guardrails, and every outcome can be traced back to a decision made by a human.

Intelligence that works across the business

The average business landscape probably doesn鈥檛 look like one system, one vendor, or one clean stack. Your processes still have to run end to end across all of it: record to report, plan to make, source to pay, hire to retire, order to cash. If AI is going to work in the enterprise, it has to work across this landscape, not inside one application or vendor boundary.

IDC shows that more than 50% of business decisions still take between one and seven days. That is the gap we are closing鈥攆rom days to moments.*

At the core of the Autonomous Enterprise is the 麻豆原创 Autonomous Suite. Joule becomes the way you interact, as a single entry point into your business. In the middle, the 麻豆原创 Autonomous Suite connects your core domains: finance, supply chain, spend, HCM, and customer experience. And underneath, everything is grounded in your business context, your data, your processes, your rules, your governance.

With 麻豆原创鈥檚 unified foundation of applications, data, and business context, AI is embedded directly into how work gets done, enabling autonomous, end-to-end execution rather than isolated use cases.

The operating model behind this is built on a clear division of responsibility: people set priorities, policies, and guardrails. Assistants understand role and process context and coordinate activity across domains. Agents carry out the defined work, detecting signals, triggering actions, and resolving routine tasks continuously in the background.

And while automation is a part of this, the bigger shift is intelligence and optimization. The system is no longer following predefined workflows. It is using business context to understand what is happening, and what should happen next. This is the shift from systems of record to systems that help run the business.

Autonomous Finance shows what changes

Finance offers a clear example of how this model changes the work itself. Many finance organizations still contend with manual steps, fragmented data, and slow cycles. In a volatile environment, that lag translates directly into slower responses to risk, missed opportunities, and diminished confidence in the decisions that shape performance.

With Autonomous Finance, more of that work can be handled by the system, allowing finance teams to spend less time chasing numbers and more time shaping decisions. The function begins to move from reconciling the past to shaping the future.

Autonomous Finance is not one capability, one agent, or one use case. It is built across the entire finance process, from planning to revenue management, treasury, closing, compliance, and tax. Within each area, assistants are supported by specialized agents working continuously in the background. Some focus on forecasting, some on billing, some on cash, and some on closing. The important point is that these capabilities are connected, so decisions in one area can flow into the others. Connected assistants, specialized agents, continuous optimization. That is the model.

The impact across these areas compounds. Finance teams reclaim meaningful capacity as manual reporting, reconciliation, and transaction processing give way to continuous intelligence. Cash cycles compress. Close timelines shorten. Forecasting becomes more accurate and more responsive to changing conditions.

Because these capabilities are connected, improvements in one area reinforce the others: faster billing flows into better cash visibility, which flows into stronger planning confidence, which flows into more decisive action at the executive level. Compliance strengthens as well, not through added controls, but through better intelligence embedded in the process itself, supporting requirements across ISO, SOC, and SOX with greater accuracy and less manual effort.

The result is not incremental improvement in isolated tasks. It is a fundamentally different operating posture for the finance function, one where the system handles orchestration and people direct outcomes.

Industry AI adds depth

Autonomous domains give breadth across business functions, while Industry AI provides the depth of knowledge. The same supply chain problem looks very different in life sciences, in industrial manufacturing, in agribusiness, in retail, or in energy. The rules, regulations, data models, and value chains are different.

麻豆原创 is not starting from generic AI and trying to teach it how an enterprise works. We start with decades of industry and process knowledge, already embedded in the systems that run the world鈥檚 most complex businesses. Our AI is grounded in sector-specific processes, end-to-end value chains, operational realities, and compliance requirements. And our ecosystem extends this with specialized expertise, so organizations can adapt the intelligence to their markets and their industries.

This is not AI for the sake of AI. This is AI applied to the real operating model of each industry.

The path forward

That is the real shift: not AI operating in isolated tasks, but AI helping the enterprise continuously sense, reason, act, and learn. People remain in control throughout, while the system handles the orchestration required to bring together the right data, context, and decision at the right moment.

The Autonomous Enterprise marks a shift from managing processes to directing outcomes. It moves organizations from reacting to events to anticipating them, and from stitching together decisions after the fact toward helping the business move as one connected system.

This does not require waiting for a perfect, fully transformed landscape. Organizations can begin by applying AI on top of existing landscapes and evolving their business as they go. That work is already underway with many of our customers. What they have in common is that they are starting now, moving faster, making better decisions, and building the foundation for a more autonomous enterprise, step by step.

This is a journey. And it begins with the recognition that the enterprise of the future will not be defined by how efficiently it executes predefined processes, but by how intelligently it can sense change, weigh trade-offs, and move with confidence when it matters most.

For more on 麻豆原创鈥檚 broader Autonomous Enterprise announcement, read The Future of the Enterprise Is Autonomous. For more details on 2026 麻豆原创 Sapphire announcements, see the .

Manoj Swaminathan is general manager and chief product officer of 麻豆原创 Autonomous Suite, Finance & Spend, and member of the Extended Board of 麻豆原创 SE.
Eric van Rossum is chief marketing officer of 麻豆原创 Global Product Marketing and chief product officer of 麻豆原创 Industries and Globalization.

*IDC Resource Map for 麻豆原创, 麻豆原创 Custom Survey 2026: Enterprise Process Automation Survey鈥� April 2026, sponsored by 麻豆原创, doc #US54531626 _RMD , May 2026

The difference is not the model. It is context: the ability for AI to understand how a business actually runs. 

Much of today鈥檚 AI discussion centers on agents, along with models and benchmarks. Which model performs best? Which system completes the most tasks? Which interface feels most natural? These factors matter, but they do not solve the central enterprise challenge.

Companies run workflows that cut across teams, policies, approvals, authorizations, and data. They plan, source, produce, hire, pay, and serve through systems that carry real business consequences. AI only creates durable value at scale when it operates inside this reality.

Models generate answers. An agent can complete a task. But running a business requires something more. It requires an understanding of how work gets done, who is authorized to act, which rules apply, and how decisions connect across functions. Without that context, AI simply can鈥檛 deliver on its promise.

That is one reason I believe AI raises the premium on software with deep business context. It allows companies to fundamentally reinvent how work gets done. When AI agents understand end鈥憈o鈥慹nd processes, they can operate across functions, execute workflows autonomously, and coordinate actions in real time. Instead of automating individual steps, AI can run processes end to end, freeing employees from repetitive coordination and enabling them to focus on higher鈥憊alue judgment, oversight, and strategy.

This is what we describe as the Autonomous Enterprise, a fundamental shift from systems of execution to systems that can reason, decide, and act. A vision where 麻豆原创 is poised to lead. 

For more than five decades, we have powered the core processes that run the world鈥檚 leading organizations. Our systems don鈥檛 just store data; they encode how businesses actually operate: their processes, rules, and decisions. Our ERP is the institutional memory and the brain of many companies across industries and around the globe. Our new 麻豆原创 Business AI Platform brings together enterprise data, processes, and governance into a unified context for AI.

Building on this foundation, Joule is the interaction layer that connects people with AI and redefines how they interact with software. Joule Assistants collaborate with users, while Joule Agents execute business workflows end to end. This is how intelligence becomes embedded directly into operations, not added on top. We call this the .

“Show me how my financial forecast for the year could change based on the latest pipeline and supply chain data.” On the surface, this looks like a simple prompt directed to a large language model.听But disconnected from enterprise systems, the answer is听mere听speculation.

Grounded in the full context of the business,听the system first identifies the correct business process from听hundreds听of听mission鈥慶ritical processes and understands the specific configuration that governs how this process runs in your organization. It then selects exactly the right data from听millions听of听data fields stored across the ERP landscape. Finally, every step is checked against identity, authorization, and access controls, ensuring the result is accurate, compliant, and trustworthy. This is how enterprises move beyond generic, probabilistic answers toward decisions they can rely on.

Reaching this state requires more than adding a chatbot or layering AI on top of existing systems. Many enterprises still operate with fragmented landscapes, data spread across systems, and processes shaped by years of incremental change. In this environment, AI cannot simply be “bolted on” or layered onto fragmented, outdated systems. It does not accelerate progress. It amplifies inefficiency and risk. Companies must rethink how their processes, data, and infrastructure work together and how humans and AI share responsibility. This is not only a technical shift. It is a change鈥憁anagement challenge. 

New technology only creates value when it is accompanied by real change. AI does not replace transformation. It raises the return on transformation done well. And it comes to life only when every element of the system鈥攖he agent, the process, and the human鈥攚orks together by design. People need to understand how to work with AI agents, and processes must be intentionally shaped to embed intelligence where decisions and execution happen.

This is why change management is foundational. It means reskilling employees, re鈥慹ngineering processes to connect them directly with data and AI, and modernizing the underlying landscape. 

That is why we are introducing new听AI-led RISE with 麻豆原创 and 麻豆原创 GROW听offerings听and fundamentally resetting our services model: to help companies modernize, navigate change, and turn AI from potential into sustained business value at their own pace.听

This marks the beginning of a new era of enterprise software:听where intelligence is not separate from听operations but embedded within them.听The companies that lead will not be those with the most advanced models in isolation, but those that connect AI to the way their business actually runs鈥攚ith context, governance, and trust.听

This is the dawn of the Autonomous Enterprise, and 麻豆原创 is uniquely positioned to help the world鈥檚 leading organizations realize its full potential. 

Christian Klein is CEO of 麻豆原创 SE.

Every week brings another announcement about smarter copilots, more capable agents, or new orchestration layers designed to automate work across the enterprise. The progress is undeniable. But much of the market is not optimizing for how businesses operate.

That distinction is more important than many realize. Because enterprises do not run on prompts. They run on execution.

A global manufacturer deciding how to reroute inventory during a supply chain disruption needs more than simply an answer. It must evaluate supplier alternatives, inventory availability, customer commitments, and financial tradeoffs simultaneously. A CFO forecasting liquidity exposure during market volatility needs context that a simple chatbot interaction can鈥檛 provide. These are interconnected operational decisions shaped by dependencies, preferences, approvals, financial consequences, and tradeoffs that ripple across the business in real time.

In countless conversations I鈥檝e had with executives over the past year, the discussion inevitably shifts from AI capability to operational reality. The models are improving quickly. The harder question is whether AI understands the business environments it is operating within.

Today, too much of the AI conversation still assumes that better models alone will produce better business outcomes. They will not. Enterprises are discovering that intelligence disconnected from operational context 鈥� the processes, the data, the rules and policies that govern and protect your organization 鈥� can generate activity without creating much progress. In some cases, it can create more fragmentation and risk.

A generated recommendation may sound convincing while missing critical dependencies elsewhere in the system. An AI agent may automate one workflow efficiently while disrupting planning assumptions in another. Enterprises do not suffer from a shortage of AI outputs. They suffer from a shortage of AI systems capable of understanding operational consequences.

That is the real challenge now emerging in enterprise AI and solving it requires something deeper than orchestration. It requires context.

For decades, enterprise software has quietly served as the operational backbone of the global economy. Finance systems, supply chains, procurement networks, workforce planning platforms, manufacturing operations, and customer fulfillment processes all run through interconnected systems that capture not just information, but the logic of how businesses function. They contain years of accumulated process knowledge and data, governance structures, authorizations, policies, and economic relationships that shape every decision a company makes. They are the institutional memory of the enterprise.

In the AI era, that business context becomes enormously valuable. Without it, AI鈥檚 outputs remain educated guesses rather than grounded judgments.

When AI is grounded directly inside operational processes, it can begin to reason across the full reality of the enterprise. That changes the role software plays inside organizations. Enterprise systems are beginning to participate directly in execution itself.

AI can identify risks earlier, coordinate responses across functions, recommend actions in real time, and automate routine execution within defined boundaries. Not as isolated agents operating independently, but as intelligence connected to the economic and operational fabric of the enterprise itself. 

Importantly, autonomy in enterprise does not mean removing humans from decision-making. It means reducing the friction, fragmentation, and administrative drag that prevents organizations from operating with speed and coherence at scale. 听People still define priorities, make judgment calls, and hold accountability. But AI can help coordinate and execute the operational work surrounding those decisions.

Consider a supplier disruption affecting a critical manufacturing component. Most AI systems today can summarize the issue or predict likely delays based on learned patterns. But operationally grounded AI can move beyond insight into coordinated execution. It can identify affected production schedules, evaluate inventory positions globally, assess alternative sourcing options, estimate financial exposure, flag customer delivery risks, and recommend actions across procurement, logistics, finance, and customer operations simultaneously.

That is not simply workflow automation. It鈥檚 an entirely new way for humans and systems to interact.

This is also why I believe the AI era will increase the strategic importance of enterprise systems, not diminish it.

As AI moves closer to execution, the systems that matter most will be the ones capable of grounding intelligence in operational and transactional reality. The value shifts toward systems that understand permissions, policies, dependencies, processes, financial consequences, and organizational accountability at enterprise scale.

This shift also changes how leaders should think about transformation.

The first phase of enterprise AI adoption focused heavily on experimentation. Companies tested copilots, deployed pilots, and automated isolated tasks. Few delivered productivity gains and fewer fundamentally changed how organizations operate.

The companies that lead in the next phase will approach AI differently. They will connect intelligence directly to the operational systems where decisions carry real economic consequences. They will recognize that trustworthy AI depends not only on governance, but on context, data quality, process integrity, and transactional understanding.

Most importantly, they will understand that successful AI adoption in enterprises is not only a technical shift. It is a change management challenge. Real value comes to life only if AI agents, processes, and humans work in concert.

The future belongs to enterprises that strike this balance: humans defining priorities and holding accountability, while intelligent systems coordinate and execute with precision 鈥� enabling businesses to navigate an increasingly complex world with greater resilience, productivity, and intelligence.

Christian Klein is CEO of 麻豆原创 SE.

.

That idea framed a , where Thomas Saueressig, chief customer officer and member of the Executive Board of 麻豆原创 SE, and Jan Gilg, global president of Customer Success & Americas and member of the Extended Board of 麻豆原创 SE, set out the company鈥檚 case for the Autonomous Enterprise.

Their message was clear: As AI moves from promise to practice, customers are no longer asking whether it matters; they are asking how to make it deliver measurable results across the business.

鈥淓very day, billions of people wake up trusting that the world simply runs,鈥� Saueressig said.

But making that happen is anything but simple. Saueressig pointed to the hidden complexity behind everyday routines 鈥� from power grids balancing supply and demand in real time to global supply chains moving goods across countries and continents. Enterprise operations, he argued, are the invisible backbone of modern life, even if most people never see them.

Gilg picked up that thread by focusing on the pressure customers now face as they try to translate AI ambition into business value. Excitement is high, he said, but so is urgency.

Customers want to scale AI across the enterprise and connect it to core processes where it can have tangible impact. But according to Gilg, the real obstacle is not the AI itself. It is the enterprise landscape around it.

鈥淭he elephant in the room: AI in the enterprise is complex,鈥� he said, pointing to the disconnected applications and fragmented data many organizations still contend with.

That challenge led directly to 麻豆原创鈥檚 vision for the 鈥� one in which AI is embedded into business processes, connected through trusted data, and governed in a way that makes it reliable at scale.

The Autonomous Enterprise vision

鈥淚t鈥檚 this need for trusted, seamless integration that led us to our vision for the Autonomous Enterprise,鈥� Gilg said.

He presented it not as a future concept, but as a practical operating model in which AI drives end-to-end execution within a trusted governance framework, with people remaining in control.

Saueressig cast 麻豆原创鈥檚 role as helping customers get there: 鈥淥ur goal is to help you become an Autonomous Enterprise step-by-step. … We are making AI value real today.鈥�

He linked that approach to RISE with 麻豆原创, 麻豆原创鈥檚 AI offerings, and the 麻豆原创 Services and Support Portfolio with its Ssuccess plans, which are designed to help customers put innovation to productive use. The emphasis, he said, is on creating value throughout the transformation journey

鈥淲hen you are fully committed to RISE with 麻豆原创, we are committed to support you at every step,鈥� Saueressig said. That commitment spans even the most complex and hybrid landscapes, he said, stressing that no customer will be left behind.

Lockheed Martin: Readiness over transformation in a high-stakes environment

That customer-first approach set up the next part of the keynote, where customers took the stage to share firsthand how they are transforming their businesses in the real world 鈥�  no theory, no abstraction, just practical experience.

Opening the customer round, Lockheed Martin positioned transformation not as an end goal, but to ensure constant readiness in one of the world鈥檚 most demanding environments.

鈥淭ransformation is not the goal. Readiness is for us,鈥� said Maria Demaree, SVP and CIO of Lockheed Martin Corporation, stressing that the stakes are 鈥渉uman鈥� when systems support national defense and allied missions. Readiness, she explained, means the ability to move 鈥渨ith speed, clarity, and confidence across the enterprise.鈥�

Through its largest transformation investment in the company鈥檚 history, Lockheed Martin is redesigning processes end-to-end, connecting fragmented systems, and embedding AI into a model-based enterprise built on 麻豆原创.

Operating in a highly regulated environment with strict security and data requirements, the company is focused on reducing cycle times and improving responsiveness. Demaree emphasized that 鈥渢ransformation doesn鈥檛 start with technology. You must rethink your processes.鈥� 麻豆原创鈥檚 role, she said, has evolved from vendor to trusted partner understanding Lockheed Martin鈥檚 business and the environment it works in.

Aeropuertos Argentina: From reactive winter operations to proactive AI-driven control

Aeropuertos Argentina made history by becoming the first Latin American customer to take the 麻豆原创 Sapphire keynote stage. The company used the spotlight to share a hands-on example rooted in operational urgency and showed how a clean core and focused innovation can quickly deliver results.

Managing 90% of Argentina鈥檚 commercial flights, they need to keep airport operations running during severe winter weather. This has historically relied on manual, fragmented processes 鈥� driving up costs, safety risks, and environmental impacts. To address this, the company developed an AI agent called Smart Network for Operative Winter (SNOW) to orchestrate weather data, runway sensors, maintenance processes, and operational procedures.

鈥淲e passed from a reactive to a proactive model,鈥� said Gustavo Sabato, Chief Information Officer of Aeropuertos Argentina, highlighting expected benefits, including a 16% cost reduction and lower CO鈧� emissions. Time to value was fast: from idea to operation in 12 weeks, with rollout starting at two airports and expanding to six more this upcoming winter.

A key enabler was upgrading from 麻豆原创 R/3 to 麻豆原创 S/4HANA in 2023 and building the solution on 麻豆原创 Business Technology Platform.  While integrating multiple non-standardized data sources was challenging, the result is now that the company operates with 鈥渙nly one version of the truth,鈥� said Sabato, and requires minimal manual intervention. The company plans to scale the approach beyond Argentina and into processes at other airports they manage elsewhere, reinforcing that strong technical fundamentals are essential to turn AI into real operational outcomes.

Exxon Mobil: Clean core and solid data foundation

ExxonMobil is rethinking how its operations will remain agile and nimble amid the rapid changes driven by the global shift toward new energy sources.

Bill Keillor, Vice President of ExxonMobil Global Services Company, said the energy giant launched a business-led transformation to simplify processes and unlock data that had become fragmented after decades of customization. 鈥淥ur goal is not short-term optimization but long-term agility: standardizing on industry best practices, establishing a clean core, and becoming upgrade stable,鈥� he said.

He emphasized that both the transformation and the company鈥檚 AI ambitions depend on a strong foundation. 鈥淚f you can鈥檛 get this foundation right, you will continue to pay the price for it,鈥� he said.

Keillor closed with three pieces of advice for any transformation: be crystal clear on strategy and align leadership behind it; put strong governance in place to enable fast, consistent decisions; and choose partners who challenge you and are in for the long run.

Levi Strauss: AI at scale

As Levi Strauss accelerated its shift toward a direct-to-consumer business, it recognized that greater speed and scale would require a lean technology landscape. Jason Gowans, Chief Digital and Technology Officer, said the company started by consolidating nine ERP systems into a single global foundation with RISE with 麻豆原创, standardizing processes and establishing a clean core.

That unified backbone now supports Levi鈥檚 ambitious AI strategy, with already more than 1,000 AI agents in production across the business. The impact is already visible; one example is wholesale order processing. While 80% of orders already flow through automatically, the remaining 20% 鈥� often submitted by smaller customers through handwritten notes, emails, or unstructured documents 鈥� previously took two to five days to process manually.

鈥淣ow, with the agents that we鈥檝e built on top of 麻豆原创, that process takes 20 to 30 minutes,鈥� Gowans said. For Levi Strauss, the lesson is clear: standardization does not limit agility; it makes it possible.

Migration powered by AI

These customer examples illustrated that transformation usually follows a shared path: modernizing the core, moving to the cloud, and unlocking innovation along the way. 

麻豆原创 then showed how AI-powered agents can help customers accelerate that journey through a more integrated, AI-driven approach to transformation at scale. Migration and modernization assistants, , are designed to analyze systems, data, custom code, configuration, testing, and rollout as part of one connected process. By replacing fragmented manual work with coordinated automation, activities that once took weeks 鈥� from landscape analysis to custom-code assessment 鈥� can now be completed in a single weekend.

The world doesn鈥檛 break because of change

Gilg then widened the lens, arguing that every major technology wave brings uncertainty. But every one of these waves has in fact made the world better off by creating more jobs, new business models, and new revenue streams that people couldn鈥檛 imagine before. In the same way, he argued, enterprise software will become even more essential because of AI.

That is because the core needs of business remain the same: systems that work, people who care, and teams that collaborate. In Gilg鈥檚 framing, AI will not replace enterprise software. It will live inside it, embedded in the processes that keep companies running.

Saueressig brought the keynote back to its opening image: a world people trust to function. In a time of rapid change and unprecedented disruption, he asserted, resilience matters more than ever.

鈥淭he world doesn鈥檛 break because of change,鈥� he said. 鈥淚t breaks when change moves faster than resilience. And that鈥檚 where 麻豆原创 comes in.鈥� Underscoring the importance of people in times of change, he emphasized that beyond technology and AI, transformation remains deeply human, shaped by the people who build and use it. 鈥淭he future isn鈥檛 written by AI.  It is written by us,鈥� he said.

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.

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

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

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

Autonomous Spend Management: From concept to reality

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

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

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

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

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

Why connected processes are critical

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

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

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

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

Expanding visibility into services spend and supporting adoption

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

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

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

The future of spend management

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

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

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

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

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

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,听, 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.

Market volatility, liquidity pressures, and currency fluctuations are exposing the limits of traditional planning models built around fixed timelines and after-the-fact analysis. To keep pace, finance teams need the ability to continuously sense change, understand its impact, and steer performance with confidence.

The challenge is that many organizations are still planning with processes designed for a different era. Siloed data, manual workflows, and episodic planning cycles make real-time decision-making difficult, limiting visibility across the entire business. reinforces the urgency: 72% of organizations still find financial planning, budgeting, and forecasting too time-consuming.* In a volatile environment, that lag translates directly into slower responses to risk, missed opportunities, and diminished confidence in the decisions that shape performance.

This is why finance needs a new operating model, one that moves beyond periodic exercises and toward continuous steering. At 麻豆原创 Sapphire, we are introducing 麻豆原创 Enterprise Planning, a new flagship offering designed to close the gap between insight and action, enabling planning to continuously drive business performance.

The shift from periodic planning to continuous steering

Traditional financial planning has always provided structure, but too often that structure comes at the expense of agility. Planning occurs in fixed windows. Teams work from historical snapshots, static assumptions, and fragmented inputs. By the time a variance is understood or a scenario is modeled, the business may already be operating in a fundamentally different environment.

麻豆原创 Enterprise Planning is designed to move organizations beyond these constraints through a continuous approach to planning and execution built on speed, confidence, and control. Finance teams gain the ability to detect signals as they emerge, evaluate constraints in real time, and connect plans directly to execution.

This Sense-Reason-Act model represents a fundamental shift in how planning operates. Rather than waiting for a planning cycle to surface issues, agents continuously monitor for material changes and respond through guided, explainable decisions embedded in everyday processes. At the same time, 麻豆原创 Analytics Cloud continues to support the iterative Plan-Do-Check-Act cycles that finance teams rely on for strategic and tactical planning across mid- to long-term horizons, including model creation, forecasting, variance analysis, and scenario simulation. Together, these two approaches create a planning ecosystem that is both responsive in the moment and disciplined over time.

The solution embeds Joule Agents directly into the planning process, helping connect strategy to operations in real time. Agents can interpret internal and external data signals, model their impact on KPIs, simulate scenarios, recommend actions, and orchestrate planning workflows with built-in governance and explainability. Planning shifts from a single point in time to continuous workflows. When decisions are made, Joule Agents can update plans to support downstream execution. General availability is planned for Q3 2026.

Built on 麻豆原创 Analytics Cloud and 麻豆原创 Business Data Cloud, these capabilities form a more connected, intelligent planning ecosystem that enables organizations to act decisively and with full transparency.

Why governed data and connected planning matter

Continuous planning is only as reliable as the data it is built on. Without a unified data foundation, even the most advanced analytics cannot produce trustworthy outcomes. As automation increases, this challenge becomes more acute: decisions execute faster, but errors can scale just as quickly.

That is why our approach is not AI in isolation. 麻豆原创 Enterprise Planning is built using 麻豆原创 Business Data Cloud data products and the 麻豆原创 Analytics Cloud solution. 麻豆原创 Analytics Cloud remains the foundation for strategic and tactical planning cycles, while 麻豆原创 Business Data Cloud provides the governed data foundation underpinning the entire ecosystem. This helps ensure compliance, auditability, and enterprise-wide trust, which becomes even more critical as AI-driven automation expands.

Continuous planning in practice

What makes this vision tangible is how it shows up in real financial workflows. By continuously monitoring market signals and financial positions, these solutions help organizations reduce the lag between insight and action, improving both speed and decision quality. This is the Sense-Reason-Act model at work: sensing shifts in currency markets, reasoning through the impact on cash positions, and acting through guided decisions that keep the business aligned with its financial objectives.

More broadly, the Autonomous Finance domain brings together Joule Assistants and Joule Agents to provide CFOs and finance organizations with more insight, control, and support across their operations. Beyond planning, specialized Joule Assistants coordinate multiple agents to support key finance processes including financial closing, billing, governance, and tax and compliance. The result is a finance function where intelligence is embedded across the full operational scope, not confined to a single workflow.

Because these agents are delivered within 麻豆原创鈥檚 planning and finance solutions, they carry a native understanding of enterprise data, planning semantics, and mission-critical business processes. The goal is not to replace finance expertise, but to augment it. This gives teams the foresight needed to navigate complexity with greater confidence.

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

The future of finance is continuous

The future of finance will be defined by the ability to connect data, processes, and decisions across the enterprise in a continuous loop. Organizations that can sense change as it happens, reason through its impact using trusted and governed data, and act by connecting plans back to execution will be best positioned to navigate volatility with the agility and discipline that modern finance demands.

With 麻豆原创 Enterprise Planning, organizations can move beyond static planning cycles and toward a more intelligent, continuous approach to steering performance.

For more details, refer to the and the .

Lawrence Martin is chief product officer and head of Public Cloud Engineering at 麻豆原创.
David Imbert is head of Finance Product Marketing at 麻豆原创.

*IDC Spotlight, sponsored by 麻豆原创, The Rise of Dynamic Planning in the Agentic AI Era, #US54493826, April 2026

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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The conversation has moved beyond what AI can do into how it should be applied, placing HR at the center of decisions that will shape people, culture, and business outcomes for years to come.

While we have often talked about the 鈥渇uture of work,鈥� the simple fact is that future is already here. The question is whether organizations are ready to operate differently.

AI requires a fundamental rethinking of how work gets done, grounded in the data, systems, and processes that run today鈥檚 organizations. And getting it right starts with one clear principle: humans must remain firmly at the center鈥攏ot as operators of process, but as leaders of judgement, strategy, and change.

What Autonomous HCM means for HR leaders

At 麻豆原创, this is the foundation of our vision for the Autonomous Enterprise, announced at in Orlando, where AI assistants can run core HR processes end-to-end, so people are empowered to focus on their most meaningful work while staying firmly in control of outcomes.

brings together agentic AI, HR applications, and real business context鈥攇rounded in deep process expertise and enterprise-grade governance鈥攖o help organizations anticipate workforce needs and respond with greater precision as business priorities change.

With the new HCM innovations announced at 麻豆原创 Sapphire, we are building on the existing breadth and depth of 麻豆原创 SuccessFactors with new AI-native functionality that amplifies how HR can help shape the business and elevate what employees are capable of.

Automate work with Joule Assistants

The first shift is automation; not as task replacement, but as a new way of working. A new generation of , delivered through Joule as 麻豆原创鈥檚 AI engagement layer, bring this to life by orchestrating agents to execute work end-to-end and support decisions in real-time.

These assistants are not just automating tasks; they are guided by employees to reduce manual effort and support a growing range of HR scenarios:

• Payroll becomes proactive, not reactive: The coordinates multiple to prepare payroll runs, identify issues early, and guide administrators to faster resolution, shifting payroll from reactive process to proactive execution. Working alongside the Core HR Assistant and , it helps organizations manage employee data, track time and attendance, and pay employees with greater accuracy and less manual work.
• Talent acquisition flows more seamlessly end-to-end: The helps keep hiring moving from intelligent matching to interview coordination, providing real-time guidance to recruiters and hiring managers. Once a candidate accepts, the takes over to support a smooth transition for new employees. These new Joule Assistants connect talent acquisition processes between and the broader 麻豆原创 SuccessFactors HCM suite.
• HR services become faster and more intuitive: The helps administrators resolve common HR questions instantly, directing employees to the right next step and reducing service center volume while improving the overall employee experience.

Reimagine the workforce with AI-driven planning

As AI becomes part of how work gets done, organizations must rethink workforce planning as a continuous leadership discipline, not a periodic exercise. Today, 62% of C鈥憇uite executives say they are dissatisfied with how well people data connects to business performance, according to , making it harder to turn strategy into action. The new workforce planning capability within 麻豆原创 Enterprise Planning supports a shift toward strategic work redesign, inclusive of both agents and people, by helping leaders link workforce decisions directly to HR, business, and financial needs.

This workforce planning capability connects data from , , and 麻豆原创 SuccessFactors, creating a unified foundation for workforce decision鈥憁aking across employees and contingent labor. Together, this moves workforce planning beyond static models. Leaders gain clear scenario insight and the ability to combine human judgment with AI to align workforce and investment decisions.

At a more granular level, constant change means business and HR leaders are often dealing with organizational changes. The new AI鈥慹nabled organizational modeling for replaces slow, disconnected modeling approaches with an integrated experience that supports scenario planning and impact analysis, enabling leaders to evaluate organizational choices with greater accuracy and alignment. With this approach, leaders can quickly explore alternative organizational structures and understand implications before changes are implemented. Whether adjusting roles, teams, or reporting lines, organizational modeling becomes a practical leadership tool, supporting thoughtful change while maintaining data integrity and minimizing disruption. The result is a clearer, more proactive approach that helps organizations make smarter workforce decisions in a constantly evolving business landscape.

Elevate people through continuous upskilling

When it comes to skills, the rise of generative AI has once again accelerated the pace of change. New jobs are emerging, new skills are required, and processes that have worked for decades are being completely reimagined.  The new Workforce Upskilling Assistant delivers personalized, AI-driven learning directly where work happens, in collaboration tools, mobile, desktop and 麻豆原创 SuccessFactors鈥攈elping organizations keep skills aligned with where the business is headed. By orchestrating multiple Joule Agents, it supports content creation and generation, adaptive micro-learning, and reinforcement, enabling leaders and managers to identify critical skill gaps and accelerate upskilling, particularly in fast-moving areas such as AI.

By delivering learning in the tools and channels employees already use, the Workforce Upskilling Assistant turns workforce and business data into timely, bite鈥憇ized learning moments. Rather than relying on scheduled courses or standalone systems, HR learning teams can quickly convert existing content to deliver learning to the right person at the right time.

A new standard for human-centered Autonomous HCM

麻豆原创鈥檚 Autonomous Enterprise vision sets a new standard for how HR leads in an AI-driven world, one where AI assistants and agents take on the work of coordination, so people can focus on leading and shaping outcomes. As AI becomes embedded into how work runs, HR is uniquely positioned to guide what matters most, moving from coordinating processes to guiding decisions, building resilient teams, strengthening trust, and ensuring the workforce is ready for what鈥檚 ahead.

That is the promise of an Autonomous HCM platform: human expertise elevated by AI, delivering meaningful impact for both people and the business.

Learn more about how 麻豆原创 is delivering Autonomous HCM by catching the replay of the HCM Innovation .

Dan Beck is general manager and chief product officer for 麻豆原创 SuccessFactors.

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�

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麻豆原创 is solving one of the biggest challenges businesses face today: turning AI into real outcomes.

At 麻豆原创 Sapphire in 2026, 麻豆原创 unveils its vision of the Autonomous Enterprise, where humans and AI work together to execute mission-critical processes more effectively, adapt faster, and drive better business results.

This vision is powered by 麻豆原创 Business AI Platform, which unifies business data, processes, and governance into a trusted foundation for AI, enabling accurate, secure, and scalable outcomes.

Building on this foundation, the 麻豆原创 Autonomous Suite brings AI into applications with agents that can execute end-to-end processes, helping organizations automate operations, increase resilience, and unlock new value across the enterprise.

The company introduces a unified 麻豆原创 Business AI Platform, deepening partnerships with Anthropic, Amazon Web Services, Google Cloud, Microsoft, NVIDIA and Palantir

ORLANDO听鈥� At 麻豆原创 Sapphire in 2026, (NYSE: 麻豆原创) introduced the to help enhance the world’s most critical business workflows, so that humans and AI work together to meet the accelerating demands of global business profitably, strategically and safely.

鈥淔or the mission-critical processes of our customers, ‘almost right’ just isn鈥檛 good enough,鈥� said Christian Klein, CEO of 麻豆原创 SE. 鈥淏y uniting 麻豆原创 Business AI Platform with 麻豆原创 Autonomous Suite, we anchor AI agents in the business processes, data and governance so they can deliver accurate, compliant and secure outcomes, unlocking new sources of revenue and meaningful cost savings.鈥�

The Autonomous Enterprise includes a unified AI platform for building, contextualizing and governing agents, an autonomous suite that executes core business operations and a new user experience that redefines how people work with enterprise software.

Introducing 麻豆原创 Business AI Platform

麻豆原创 Business AI Platform is a new foundation for building and deploying enterprise AI grounded in real business context. 麻豆原创 Business AI Platform now unifies 麻豆原创 Business Technology Platform, 麻豆原创 Business Data Cloud and 麻豆原创 Business AI into a single, governed environment.

At its core is the 麻豆原创 Knowledge Graph solution, which gives AI agents a structured map of business entities, processes and relationships across a customer’s 麻豆原创 landscape. Joule Studio is 麻豆原创’s AI-first solution for building enterprise agents, applications and agentic workflows. Developers can build using the no-code, pro-code and AI frameworks of their choice on 麻豆原创-managed infrastructure that is secure, scalable and optimized for enterprise AI.

Deploying 麻豆原创 Autonomous Suite Across Every Business Function and Industry

Building on this foundation, 麻豆原创 also introduced 麻豆原创 Autonomous Suite, which enables 麻豆原创’s existing business applications with AI agents capable of running processes from start-to-finish.

The suite will deploy more than 50 domain-specific Joule Assistants across finance, supply chain, procurement, human capital management and customer experience. These assistants will automate end-to-end processes by orchestrating a subset of over 200 specialized agents to execute precise tasks. For example, the new Autonomous Close Assistant can compress the financial close process from weeks to days by automating journal entries, reconciliation and error resolution across the entire process.

麻豆原创 also launched Industry AI, expanding its deep industry portfolio through seven autonomous solutions that will enable start-to-finish industry processes and embed sector-specific process logic, data models and regulatory requirements. At 麻豆原创 Sapphire, 麻豆原创 showcased its work with European energy giant RWE to leverage Industry AI, helping reduce unplanned downtime across its offshore wind turbines. With 麻豆原创’s Autonomous Asset Management scenario, AI agents are designed to analyze data from thousands of past incidents, identify the likely root cause and generate pre-filled work orders with the right tools and proven fixes from other sites.

Designing the Autonomous User Experience

The company also revealed Joule Work, redefining how users engage with 麻豆原创 software. Instead of navigating individual applications and entering data across several screens, users will now interact primarily with Joule. By describing a desired business outcome, Joule will orchestrate the right combination of workflows, data and agents to get it done.

Joule Work goes beyond conversation, proactively surfacing relevant insights and automating routine tasks behind the scenes so work moves forward even when humans aren’t actively steering it. It will be available on desktop, mobile and voice across 麻豆原创 and non-麻豆原创 systems.

Accelerating the Customer Journey Toward Autonomy with 鈧�100 Million Infusion

麻豆原创 evolved its customer and partner programs to help accelerate the organization’s journey to the Autonomous Enterprise. To catalyze adoption, the company has launched a 鈧�100 million fund for 麻豆原创 partners to help customers deploy 麻豆原创-built AI assistants and agents. The fund is also available to partners that extend or build new partner agents on the new 麻豆原创 Business AI Platform using Joule Studio.

麻豆原创 has enhanced its RISE with 麻豆原创 and 麻豆原创 GROW offerings to accelerate AI adoption. Both include access to the Joule Assistants portfolio; RISE with 麻豆原创 customers will have three assistants activated within their first year, while 麻豆原创 GROW customers receive full portfolio access at onboarding. 麻豆原创 S/4HANA on-premises and 麻豆原创 ERP Central Component (麻豆原创 ECC) customers are not excluded: those that commit to transitioning the majority of their current landscape to 麻豆原创 Cloud ERP gain access to select AI scenarios, bridging the gap between their current landscape and their cloud destination

麻豆原创 also introduced new agent-led transformation tooling that can reduce ERP migration efforts by more than 35 percent, driving faster and more predictable projects by automating system analysis, code remediation, configuration and testing at scale.

Lastly, 麻豆原创 announced a full slate of strategic partnerships across each category:

• Platform and suite partnerships include Anthropic, with Claude among the foundation models 麻豆原创鈥檚 AI platform will leverage to power Joule agents across HR, procurement and supply chain; Amazon Web Services, bringing zero-copy data integration between 麻豆原创 Business Data Cloud and Amazon Athena; Google Cloud and Microsoft, enabling bidirectional agent-to-agent interoperability between Joule and external agent frameworks; Mistral AI and Cohere, delivering sovereign model options on 麻豆原创’s cloud infrastructure; , providing visual AI workflow orchestration inside Joule Studio; NVIDIA, whose OpenShell provides the trusted secure runtime for Joule Studio; and , bringing AI agents into 麻豆原创 Service Cloud to handle customer interactions with full access to business data and service processes.
• Implementation partnerships include Palantir and Accenture, partnering on complex data migration scenarios, and for AI-powered cloud ERP migrations.

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About 麻豆原创

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

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