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.

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

Supporting Sovereign Cloud Requirements 

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

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

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

Exploring the Value of AI  

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

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

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

Visit the . Get 麻豆原创 news via  and .

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

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

Space is unforgiving. Vacuum, radiation, orbital debris, and solar storms can produce mission-critical crises at any moment. Adding to the complexity, onboard sensors and mission system telemetry produce too much time-sensitive data to be routed back to Earth for analysis. Latency has to be reduced to ensure the safety of the spacecraft. 

Enter 麻豆原创’s tabular AI foundation model, , which makes predictions directly from structured data鈥攅ven with some data missing鈥攁nd without requiring the usual model training or fine-tuning. Running directly onboard the ISS on HPE Spaceborne Computer-2, it supports anomaly triage in real-time telemetry data, predicts issues before failures escalate, reconstructs missing sensor values based on historical data, and generates explainable mission-health reports for astronauts and ground teams.

“This is proof that tabular AI on 麻豆原创 works at the edge of possibility,” said Mayank Shrivastava, vice president of Generative AI and Foundation Models at 麻豆原创 SE. “Space is the ultimate stress test for AI. If a foundation model can predict mission-critical failures in orbit, under radiation, energy, and connectivity constraints, imagine what it can do for a global enterprise. This is the same model that will predict supplier risk, cash flow anomalies, and equipment failures across your enterprise. What works in orbit works in your supply chain.” 

Imagine this: in orbit, data arrives in fragments, connectivity comes and goes, and you cannot send a technician when something starts to fail. You have to anticipate problems with a variety of signals. Enterprises face the same challenge on the ground: a supplier whose deliveries are quietly slipping, a payment pattern that鈥檚 drifting, a compressor that鈥檚 running hot. Predictive models that are able to spot early warning signs in a satellite are also built to find those same signals in your business data, so you can act before small problems become critical ones.

How 麻豆原创 strengthens business functions with models for structured data 

麻豆原创-RPT is part of 麻豆原创’s portfolio of tabular foundation models, or TFMs, that includes the model by Prior Labs, and , which orchestrates, manages, and governs TFMs, predictions, and workflows. Large language models (LLMs) excel at language and reasoning, but TFMs are built for the structured tabular data that underpin real-world operations, such as the sensor-driven data used in this project.  

麻豆原创-RPT is about 50,000 times more energy-efficient than state-of-the-art LLMs. This is especially important in space, where every watt is constrained. 麻豆原创-RPT is also highly adaptable. It requires no pre-training as conditions evolve. 

Outcomes that will be delivered onboard include real-time predictions for computer and mission health status, an overall mission risk score, an assessment of whether manual intervention is needed, human-readable explanations of AI-driven findings, and ad hoc reports for crew and ground teams. 

鈥淗PE Spaceborne Computer-2 continually demonstrates the advantages of having a high-performance compute solution aboard the ISS, supporting advanced technologies and on-going research in space鈥� said Scott Wiest, Vice President and Global Sales Chief Technologist, HPE Fellow. 鈥淒eploying 麻豆原创鈥檚 tabular AI on HPE Spaceborne Computer-2 shows what鈥檚 possible with real-time detection and prediction using AI in the ultimate edge environment.鈥� 

From orbit to enterprise 

The deployment of 麻豆原创-RPT on HPE鈥檚 Spaceborne Computer-2 on the ISS, by early 2027, will show how it strengthens business functions on earth. The constraints of space, including extreme energy limits, intermittent connectivity, radiation interference, and zero tolerance for failure are relevant to challenging use cases on earth. These include disaster relief operations, agriculture in remote areas, distributed manufacturing, and spread-out supply chains.  

Every insight aboard the ISS accelerates 麻豆原创’s road map for enterprise edge AI and key use cases that leverage structured data for the most important analytical and operational processes, even in the most challenging settings.  

Looking ahead, the mission lays the groundwork to support space-based data centers, deep space exploration, and other collaborations on space missions. Our long-term vision for space-based compute infrastructure is anchored in proven, efficient, and accurate AI. 

Explore how to deliver tabular AI on 麻豆原创, learn more about the specialized capabilities behind it, and try the 麻豆原创-RPT AI model with your own data on the . 

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

AI has gone from insight to action and now, with physical AI, it comes into the physical world.

Enterprises have made substantial investments in both AI and physical AI. AI helps business systems deliver remarkable insights and recommendations on financials, inventory, and supply chains. Physical AI creates value every day with physical systems鈥攕uch as industrial robots, humanoids, sensors, or robot dogs鈥攄eployed across production lines, warehouses, field service teams, and logistics networks.

As automation, robotics, and sensing technologies have matured across industries, the volume and richness of the physical signals coming from physical execution has grown substantially; physical operations today generate continuous intelligence about factors such as asset health, task completion, quality, throughput, and exceptions.

Today, however, there is a gap between what the business knows and the intelligence generated by physical operations on the ground. This gap is one of the most common sources of operational friction across industries. It contributes to delayed responses, ill-informed decisions, and physical operations that never enter the business record. As a result, revenue and margins decrease while risk exposure increases.

From enterprise intelligence to physical action

This year at 麻豆原创 TechEd, 麻豆原创 revealed how our physical AI removes this operational friction: the system that runs your business is the same system that controls your physical operations.

The enterprise knowledge in your business system triggers the physical actions; physical signals arrive with the business context already attached;听physical operations in the field flow back into 麻豆原创 as structured data intelligence. Every physical action leaves a traceable, auditable record tied to the relevant business process.

The full context of your business is behind every physical action; verified outcomes are brought back into 麻豆原创 in a secure and governed way for follow-up actions.

When the system that runs your business is also the system that governs how, when, and why physical tasks are executed, the result is a fundamentally different kind of operational capability. For enterprises already running 麻豆原创, this means physical execution inherits the intelligence, context, and governance already in place.

This is how physical AI by 麻豆原创 transcends automation and extends the Autonomous Enterprise into the real world.

“Foxconn’s vision for intelligent manufacturing requires physical systems that understand business context, not just motion commands,” said Dr. Wang, head of Physical AI at Foxconn. “With physical AI by 麻豆原创, warehouse tasks from 麻豆原创 Extended Warehouse Management directly orchestrate our humanoid robots and autonomous transport systems. Asset inspections triggered by maintenance plans flow to our robotic platforms and return verified outcomes back to 麻豆原创. This closed loop between enterprise systems and physical execution is what sets this collaboration apart: the robot knows why it acts, and the business system knows what happened.”

How physical AI by 麻豆原创 capabilities are designed to transcend automation

1. Business-driven physical action

Physical AI is built to ground every physical execution in the business context: order priorities, safety rules, inventory status, and compliance constraints. Every action has a clear reason tied to business outcomes, and the business system remains in control of what gets executed, when, and why. The result is physical execution that is intelligent from the start, not just automated.

2. Physical evidence into digital workflows

Every physical task creates a complete audit trail and physical observations flow back into 麻豆原创 automatically, transforming field outcomes into business insights rather than isolated records in a separate system. Physical world events become inputs to the decisions and processes that follow.

3. Enterprise-governed execution

Built on the 麻豆原创 Business AI Platform, physical AI capabilities are designed to provide traceability, auditability, and human-in-the-loop control as first principles. Physical world data flows into 麻豆原创 Business Data Cloud as new structured data products, enriching planning, predictions, and operational strategy over time.

4. Vendor and hardware agnostic

Working across device platforms, physical systems, and AI models of the customer’s choosing, customers retain freedom over their physical layer. 麻豆原创 provides the business context, orchestration, and governance. Partners provide the physical execution.

Physical AI at work with customers and partners

础谤莽别濒颈办-尝骋: physical AI executes and records inbound storage tasks

础谤莽别濒颈办-尝骋 is a joint venture between Turkish durable consumer, Ar莽elik and South Korea鈥檚 LG Electronics, manufacturing air conditioners. Physical AI by 麻豆原创 enables a humanoid robot to execute and record inbound storage tasks. This has addressed the costly and persistent issue of incorrect storage placements in inbound logistics with potential savings of millions of euros.

• Associated 麻豆原创 solutions: 麻豆原创 Extended Warehouse Management
• Physical AI ecosystem partners:
• Video:

Intel: physical AI in autonomous warehouse operations

Intel is one of the world’s largest semiconductor manufacturers, operating a vast global warehousing and logistics network and serves as both a听 proof-of-concept environment and a technology collaborator. By integrating 麻豆原创鈥檚 enterprise workflows and warehouse processes with Intel-powered edge AI and robotics technologies, warehouse transport tasks generated in 麻豆原创 are automatically translated into robot missions and executed autonomously in the warehouse.

麻豆原创 provides the business context, workflow governance, and process orchestration, ensuring that every physical action aligns enterprise priorities and operational goals. Intel provides the physical AI hardware and software foundation at the edge, delivering the compute AI and real-time robotics capabilities that enable autonomous systems to perceive, decide, and act in dynamic environments. The combined capabilities of 麻豆原创 and Intel create a closed-loop system; business decisions become physical actions and operational outcomes are continuously fed back into business systems.

In a proof of concept demonstrated at Intel鈥檚 Chengdu site in China, material movement is automated end-to-end, reducing both transport distances and repetitive manual handling. Physical AI capabilities from 麻豆原创 and Intel demonstrate how enterprise decisions can directly drive physical execution in logistics workflows.

• Associated 麻豆原创 systems: 麻豆原创 Extended Warehouse Management
• Physical AI ecosystem partners:

Holcim: industrial asset inspections

Holcim is a Swiss multinational building materials and sustainable construction company, operating in heavy industry. The company has significant infrastructure assets with inspection, maintenance, and safety use cases. Since attending the Embodied AI Jam held in August 2026, Holcim has implemented physical AI capabilities for asset inspections with industrial drones and quadruped dogs.

On-site quadruped dogs inspect conveyor belts in dark, dusty areas and the status of kilns in extreme temperatures. Drones inspect assets in heights difficult for humans to reach. And end-to-end automation starts and finishes in a 麻豆原创 system. In 麻豆原创鈥檚 asset management solution, the maintenance plan triggers an action for the industrial drone/quadruped dog; once the action is executed, a maintenance notification is created back into 麻豆原创.

• Associated 麻豆原创 systems: 麻豆原创 Plant Maintenance
• Physical AI ecosystem partners: , ,
• Video:

Start your physical AI journey today

From warehouse operations to asset maintenance, from safety inspections to keeping your operations compliant, physical AI capabilities from 麻豆原创 have the potential to transform your business. It connects the intelligence of your enterprise with real-world execution; the 麻豆原创 system that you trust to run your business is the same system that informs the actions of your physical systems, be it a robot, an industrial drone, or a robodog.

As a part of Industry AI, you can begin your physical AI journey today with warehouse operations and asset inspection use cases or a physical AI use case, tailor-made to your requirements. Contact 麻豆原创 via email for more information.

Lukasz Ostrowski is global head of Physical AI and Robotics at 麻豆原创.

The story began in 1996, when around 700 developers and technical experts gathered in Orlando, Florida, to explore a new generation of enterprise software.

鈥淚t wasn鈥檛 called ‘TechEd’ back then,鈥� explained Karl Kessler, vice president of Product Management for ABAP AI at 麻豆原创, who attended the Orlando event as a product manager for 麻豆原创 Technology. 鈥淚t was originally called the 麻豆原创 Technical Developers Conference.鈥�

The event came at a time of technological change. 麻豆原创 was opening up new ways to make 麻豆原创 R/3 business processes accessible over the internet. BAPI programming interface provided access to 麻豆原创 business logic through defined interfaces, while ALE made it easier to exchange messages between distributed systems. 麻豆原创 was also working on a 鈥渂usiness framework鈥� designed to integrate distributed business processes.

This raised a question that seems obvious today: how could enterprise software evolve into a platform for an increasingly connected IT landscape?

From technical training to hands-on experience

The event gave developers an opportunity to explore these new possibilities. But its format was still a far cry from the multifaceted platform for knowledge sharing, experimentation, and community that 麻豆原创 TechEd would become over the following three decades.

鈥淚n the beginning, it was all very academic,鈥� Kessler recalled. 鈥淭here were no keynotes or product demos on big stages. We went straight into an introduction to the architecture of 麻豆原创 R/3, and the program consisted mainly of lectures: less conference spectacle, more technical classroom.鈥�

The second event, held in Los Angeles later in 1996, offered a glimpse of how quickly the 麻豆原创 ecosystem was evolving. 麻豆原创 and Microsoft showcased new possibilities involving Internet Explorer, SQL Server, and Microsoft Office 97. Hands-on sessions were introduced for the first time, giving participants the opportunity to work with 麻豆原创 software themselves.

鈥淎nd 鈥榟ands-on鈥� really meant getting your hands dirty back then,鈥� Kessler emphasized. 鈥淧articipants had to install the software on their own laptops. Ready-to-use images that you can deploy in just a few clicks today? Not a chance.鈥�

For the first time, participants had access to a software environment where they could experiment for themselves鈥攕omething that has since become a staple of 麻豆原创 TechEd.

Where technology meets community

In the years that followed, this shift continued. With the introduction of 麻豆原创 NetWeaver, technologies like Java, portals, the web, and XML became increasingly prominent alongside the traditional ABAP world. 麻豆原创 TechEd was evolving from an event where technology was explained into a place where developers could try it out and discuss it for themselves.

The community, too, began to play a bigger role. The 麻豆原创 Developer Network created new opportunities for exchange: knowledge was no longer shared only from the stage, but also between sessions, online, and across company boundaries.

Kessler witnessed this evolution firsthand. From 1996 until the onset of the pandemic, he attended every 麻豆原创 TechEd event in the U.S. and Europe. He initially took part as a product manager and speaker, later helping shape the program as a track lead and, for several years, an agenda lead.

Throughout the 2000s, 麻豆原创 TechEd increasingly became a community experience. Kessler has particularly vivid memories of the events in Las Vegas: 鈥淒uring the day, there were lectures and four-hour hands-on sessions. But the day didn鈥檛 end when the official program did. People went out for dinner together, saw shows, or met up at the hotel in the evening. Many of us were there for practically a whole week. And if we had time afterward, we might take a trip to the Grand Canyon or Death Valley.鈥�

By then, 麻豆原创 TechEd had become much more than a conference program. Alongside the technical content, it was the encounters, conversations, and shared experiences that helped shape the community.

New formats emerged from around 2000 onward, further strengthening that community spirit. Demo Jam gave developers a stage to showcase what they had built. The Community Clubhouse created space for conversations beyond traditional presentations. Roadmap sessions offered a look at the 麻豆原创 technologies to come, while hands-on sessions turned attendees from listeners into active participants.

鈥淥ver time, exchanging ideas became at least as important as the technology itself,鈥� Kessler recalled. 鈥淭he more complex the topics became, the more important it was to share experiences, try things out together, and learn from one another.鈥�

Technology was no longer simply presented. It was explored, discussed, and developed further–together.

When the future becomes the present

Throughout the 2000s, the technological foundations of enterprise software continued to evolve. Initially, the focus had been on connecting 麻豆原创 systems with other applications through open interfaces and web technologies. Increasingly, however, a different question came to the fore: what if vast amounts of data could be made available almost in real time, without having to move and process them so extensively first?

Around 2010, 麻豆原创 TechEd brought this idea to life. Vishal Sikka introduced 麻豆原创 HANA and in-memory computing, an approach that processes data directly in main memory. It challenged established assumptions about database performance. Initially focused on analyzing large volumes of data, 麻豆原创 HANA later became a foundation for applications as well.

For 麻豆原创 TechEd, this marked another step forward. The event was no longer just about learning new technologies; it was also about exploring their potential and understanding what they could mean in practice. An idea that had once sounded like science fiction became the starting point for new applications and business processes.

鈥淚t was the transition from 鈥榃hat if?鈥� to 鈥楾his is how we do it,鈥欌�� Kessler said.

Meanwhile, the community continued to evolve. The 麻豆原创 Mentor program gave particularly engaged community members a new role as sounding boards, ambassadors, and representatives of the wider community. Technology and people were evolving together.

鈥淣ot every prediction from back then came true exactly as expected,鈥� Kessler reflected. 鈥淪ome ideas disappeared, while others developed in completely different ways. But time and again, concepts that had sounded futuristic at 麻豆原创 TechEd went on to become part of everyday working life.鈥�

With 麻豆原创 Fiori, 麻豆原创 S/4HANA, and the cloud, new topics arrived at 麻豆原创 TechEd that would go on to shape the 麻豆原创 landscape. Developers could experience and discuss how enterprise software was changing鈥攁nd what those changes meant for their own work.

The focus was no longer just on what a system could do technically. Increasingly, the questions were about how enterprise software should be used: on which devices, through which interfaces, with what data, and with how much flexibility.

The future was becoming more tangible. It was no longer confined to architecture diagrams and technical concepts; it was visible on screens, in mobile applications, and in cloud environments.

At 麻豆原创 TechEd, these changes were not just presented but explored and developed further together. What once opened up new possibilities became an integral part of the 麻豆原创 landscape.

The next future Is already taking shape

Over the years, 麻豆原创 TechEd continually found new ways to bring technological developments to life and bring the 麻豆原创 community together. New formats, greater interaction, and hands-on experiences had all helped shape the event. The community had long since become much more than an audience: it was an essential part of what made 麻豆原创 TechEd what it was.

Then came 2020. 鈥淭he global pandemic suddenly took away so much of what we had come to take for granted,鈥� Kessler said. 鈥溌槎乖� TechEd still took place, but it went digital. There were no exhibition halls where people could meet, no spontaneous reunions or conversations in the corridors, no discussions after a session that unexpectedly sparked a new idea.鈥�

Hands-on experiences and exchanges with others had to take place under entirely different conditions. Something that had grown over many years could not simply be replicated online, one-to-one. 鈥淭hat鈥檚 when we realized just how much 麻豆原创 TechEd depends on personal interaction,鈥� Kessler recalled.

One of Kessler鈥檚 most memorable experiences shows how important that interaction was, even behind the scenes: the 鈥渟peaker ready room.鈥� Every year, around 130 speakers gathered in a large ballroom during 麻豆原创 TechEd week to prepare for their sessions and help one another out. If they ran into problems with connectivity, licenses, updates, or integrating different components, they worked together to find solutions鈥攐ften on the spot and under time pressure.

Over the years, this gave rise to a speaker community made up of familiar faces and newcomers who worked alongside 麻豆原创 IT and the event organizers to make 麻豆原创 TechEd happen. For Kessler, the ready room embodies the event鈥檚 distinctive sense of teamwork. Everyone wanted the sessions to succeed and participants to have the best possible experience. The shared mindset was simple: we鈥檒l make it work鈥攖ogether.

In 2026, 麻豆原创 TechEd finds itself at another technological turning point. Artificial intelligence is changing how software is developed, used and increasingly executed. Developers are working with AI-powered tools. Applications can take on tasks. Agents can execute processes and interact with one another.

鈥淲e have no more idea today of what will be commonplace 10 years from now than developers in 1996 could have known which of their futuristic ideas would eventually become standard practice,鈥� Kessler said. 鈥淏ut that鈥檚 exactly the point. The future looked different at every 麻豆原创 TechEd, yet the questions behind it have remained much the same: how can technology better support people? How can systems work together? How can we make complex processes simpler? What becomes possible when technical boundaries are pushed?鈥�

So, what connects the old hands attending their 30th 麻豆原创 TechEd with the young talents joining for the first time in 2026?

Kessler鈥檚 answer is simple: 鈥淲e all want to know what comes next鈥攁nd discover together what鈥檚 possible.鈥�

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� 

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.

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

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

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

麻豆原创 and NVIDIA are also working to:

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

Joule Studio runtime: Governance above the execution layer

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

麻豆原创 engineering contributions to NVIDIA OpenShell

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

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

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

In early July 2026, partner Bluestonex invited customers to an immersive hands-on workshop day at the Bluestonex AppHaus Oswestry, Shropshire, UK. The focus was placed on two industries: retail and supply chain. During the event, attendees watched inspiring demos of new 麻豆原创 AI capabilities powered by the and took part in interactive sessions to experience industry innovation and learn along the pragmatic, human-centered innovation approach.

Different showcases for retail and supply chain

Two different showcases shed light on different applications and scenarios from the retail and supply chain industries.

The first showed how data can be surfaced and acted on meaningfully via the use of AI and smart application functions including 麻豆原创 Business Data Cloud (麻豆原创 BDC) and Bluestonex鈥檚 flagship master data management and governance solution, Maextro. The second showed different supply chain and warehouse use cases from the retail and manufacturing industry, where the participants could experience the demo with physical mobile devices and interactive stock movements as if they were in the warehouse setting itself.

Two work streams to explore AI-infused innovation

After learning about the art of the possible brought about by the latest 麻豆原创 Business AI solutions of the Autonomous Enterprise, the workshop attendees split into two work streams to explore different aspects of AI-infused innovation:

1. The first group went through a process of rapid ideation and prototype development with a fast-paced innovation approach. Participants got hands-on, creating both low-fidelity and high-fidelity designs. To do so, they were shown how different AI tools, such as , are changing the way applications are built.
2. To explore the skills needed for successful AI adoption and to address the culture shift, the second session used LEGO庐 Serious Play庐 and explored the human side of AI transformation. The method is used for skill-building exercises to then tackle a central challenge: building a model that represents how AI could fundamentally change an existing business. The participants learned a set of guiding principles for AI that they can apply in their day-to-day business.

麻豆原创 AppHaus Network partners acting as front-runners

The workshop is a good example of how 麻豆原创 partners such as Bluestonex act as front-runners when it comes to exploring the and adopting AI in a meaningful way. During the workshop, experts helped customer teams identify appropriate use cases, design a concept for an AI agent, and create a real business solution that can be built with the latest 麻豆原创 technology, encouraging and enabling them to conquer this new environment with all its opportunities. The guiding workshop principle was to create value in their respective company context.

The feedback given by some of the participants seems to acknowledge that:

• “Wow. Mind blown! Great team showing the art of the possible. Excellent.”
• 鈥淩eally great day overall. I always love the real-world scenarios. The hands-on sessions were a highlight, and it’s a great visual display of what’s possible.”
• “Great way to generate ideas, meeting other people, engaging format. The real-world scenarios stood out.”
• “The space itself鈥攖he Bluestonex church鈥攚as a highlight. Love the 麻豆原创 Experience Center content on the big, beautiful screen.”

Looking back from two perspectives

Richard Henry, head of Growth, 麻豆原创 BTP, Data, & Innovation at Bluestonex, looks back: 鈥淭he addition of the latest demo content powered by the 麻豆原创 Experience Center to Bluestonex鈥檚 industry workshop model marks another step in our mission to help customers realize the Autonomous Enterprise. By combining the latest innovation stories, industry experiences, and 麻豆原创 Business AI with proven 麻豆原创 AppHaus methodologies, we move beyond inspiration to action. Through hands-on workshops, design thinking, and immersive experiences, organizations can explore, validate, and prioritize real business opportunities. In the end, our focus remains the same: we want to turn innovation with the latest 麻豆原创 solutions into practical outcomes and adoption, all for the benefit of our customers.鈥�

Tobias Gollwitzer, senior director and regional lead EMEA, 麻豆原创 AppHaus Walldorf, concludes: 鈥淭his workshop is just another proof of the exceptional agility, the dynamic approach, and attitude of our 麻豆原创 AppHaus Network partner in the UK. The team invites and motivates 麻豆原创 customers to be among the first to conquer new 麻豆原创 industry-specific technologies, grasp and move towards 麻豆原创鈥檚 Autonomous Enterprise solutions to their best advantage.鈥�

Imke Vierjahn is communications lead for 麻豆原创 AppHaus Network.

The grand opening event was scheduled along the local 麻豆原创 Connect Day events for AI & Data Leaders, so guests from the regional business and technology community were welcomed at the new creative space. Near 麻豆原创鈥檚 Newtown Square campus, the factory-style sovanta AppHaus now invites 麻豆原创 customers to hands-on co-innovation work. Experts from sovanta help teams explore and design use cases based on the latest AI-infused technologies provided by the Autonomous Enterprise. As the company puts it, 鈥淥thers talk about agents. We build them.鈥�

Speakers with three different perspectives

The grand opening event started with a welcoming note given by Claus Heinrich, founder and CEO of sovanta; Christian Heinrich, president of sovanta America; and David Robinson, president, 麻豆原创 North America and Americas Customer Success Functions. They shared their 麻豆原创 and partner perspectives and fully agreed that the most important insights came from their joint customers. So, they offered the stage to two long-standing sovanta customers, Elanco Animal Health and Endress+Hauser. Both shared first-hand experiences of their innovation work and journey鈥攁nd what AI-powered business transformation looks like in their concrete business contexts.

“What we value most about sovanta is how they collaborate with us: honest, well-organized, and always a step ahead in a rapidly evolving landscape,鈥� Sunil Guna, senior director, ERP lead at Elanco Animal Health, said. 鈥淥ver the past year, they have become a true trusted advisor, and the sovanta AppHaus Newtown Square gives us the place to keep turning ideas into real impact with 麻豆原创.鈥�

“The Autonomous Enterprise is not built in a boardroom鈥攊t is co-created, use case by use case, in spaces exactly like this one. Having sovanta bring that capability directly to North American customers, right here in Newtown Square, is a genuine milestone for 麻豆原创 and for the customers we build the future with together,” 麻豆原创鈥檚 Robinson said.

Looking back 鈥� and ahead

Claus Henrich, who is a former board member of 麻豆原创 AG, looked back on the years of building and growing sovanta as a company: 鈥淪ince founding sovanta in Germany in 2009, our approach has been to work closely with our customers on real business challenges and turn them into meaningful innovation. That approach has paid off, and bringing it to the U.S. with our sovanta AppHaus Newtown Square is an important next step. I鈥檓 proud of what we鈥檝e built and grateful for the trust of 麻豆原创 and our customers.鈥�

Global innovation support, going broader and deeper

The new location is designed in a creative factory style, reminiscent of the hands-on innovation work that the sovanta team performs with all its customers. Like all other 麻豆原创 AppHaus Network members, its co-innovation work follows the .

Thomas Biedermann, global head of 麻豆原创 AppHaus Network, commended the partner team for opening this new location not far from Philadelphia: 鈥滿y heartfelt congratulations to the sovanta team for being the second partner of our global network community to open a second AppHaus location in the U.S. It shows that the innovation approach we share is not only broadly adopted across the globe but also intensified by our partners, going broader and deeper, so to say. We wish you and your customers lots of success!鈥�

Working closer with the American 麻豆原创 ecosystem

Christian Heinrich explained the advantage as follows: 鈥淓stablishing the sovanta AppHaus Newtown Square strengthens our presence at the heart of the U.S.-American 麻豆原创 ecosystem and creates new opportunities to collaborate even more closely with 麻豆原创 and our customers in the U.S. As part of the 麻豆原创 AppHaus Network, we can combine proven human-centered innovation approaches with sovanta鈥檚 AI and 麻豆原创 expertise to move from ideas to tangible business outcomes faster. Our 麻豆原创 AppHaus location is our sovanta Innovation Factory brought to life, where we turn innovation from one-off projects into a scalable, repeatable approach.鈥�

Rolling up our sleeves

鈥淭his isn鈥檛 just a new location,鈥� Sven Arndt, chief technology officer, sovanta America, said. 鈥淚t鈥檚 a place built around our customers and their challenges. We want to sit down together, understand where AI can create real value, and turn the most promising use cases into solutions that work in practice. I鈥檓 excited to welcome our first customers to Newtown Square, roll up our sleeves, and build what鈥檚 next together.鈥�

For more information, the new can be found at 3407 West Chester Pike, Newtown Square, PA 19073, United States of America.

Imke Vierjahn is communications lead for 麻豆原创 AppHaus Network.

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

Starting point at Daikin

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

Disconnected systems led to complexity

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

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

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

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

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

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

Journey to an Autonomous Enterprise

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

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

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

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

For commerce teams, delivering that experience is anything but simple. Behind every purchase sit catalogs, promotions, customer accounts, inventory, payments, orders, and fulfillment. A seemingly straightforward storefront change can quickly become part of a much larger release.

麻豆原创 Commerce Cloud and Vercel are working together to give teams a more flexible way forward. continues to manage the commerce data and processes behind the transaction. Vercel runs the customer-facing experience and gives teams the infrastructure and workflow to build, review, release, and operate it.

The tools to transform customer experience are here, but tools alone don鈥檛 win. You must consider operating models too. Our partnership with 麻豆原创 Commerce Cloud pairs Vercel鈥檚 web stack鈥攊ncluding world-class performance, faster iteration, and scale that holds up under peak demand鈥攚ith 麻豆原创鈥檚 trusted data and processes, and governance built in from the start.

Jeanne DeWitt Grosser, Chief Operating Officer, Vercel

The operating model is straightforward. Teams can change the storefront without having to change everything behind it at the same time.

A faster starting point for cutting-edge storefronts

Consider a commerce team preparing to enter a new market. It needs a localized storefront, a different customer journey, and a campaign built for that audience. In a tightly connected architecture, those changes can become dependent on a broader release involving pricing, inventory, orders, payments, and fulfillment.

Separating the storefront gives the team more freedom to work. It can design and release the experience for that market while 麻豆原创 Commerce Cloud continues to provide consistent product data, prices, availability, customer information, and order processes.

Vercel is developing Next.js storefront templates for 麻豆原创 Commerce Cloud to help teams get started. The templates connect to core capabilities such as product discovery, content, cart, checkout, and customer journeys.

The templates are backed by Vercel global delivery, managed scaling, deployment workflow, and observability, which improve engineering velocity and faster performance yielding more conversions.听

As a result, frontend teams gain room to move, while commerce teams retain control of the rules that protect revenue and customer commitments.

What this changes for commerce teams

Campaigns and customer expectations move quickly. Vercel creates a preview deployment for each change, giving developers, designers, marketers, and business teams a working version to review before it reaches production. Teams can test the experience against 麻豆原创 Commerce Cloud services, gather feedback, and release approved storefront changes with fewer dependencies on a larger backend release.

The same approach helps organizations manage different brands, regions, languages, and buying models. A consumer placing a quick order has different expectations from a business buyer working with negotiated prices, an account-specific catalog, or complex purchasing rules. Teams can build a distinct experience for each audience with 麻豆原创 Commerce Cloud powering the operations behind it.

In addition, Vercel鈥檚 global network, edge routing, and caching bring storefront content closer to customers. Its managed infrastructure is built to scale with demand, including the traffic associated with major campaigns and peak shopping periods. Built-in observability gives teams visibility into traffic, errors, latency, and calls to external services, helping them identify problems that could affect the shopping experience.

Making AI impactful in commerce

AI-assisted development can dramatically accelerate the path from idea to experience. But speed without trusted context can simply produce more low-value experiences, faster and at greater cost.

Connected to 麻豆原创 Commerce Cloud, AI experiences can draw on trusted commerce data and processes. This gives teams a stronger foundation for building impactful customer journeys that are accurate, brand-aligned, and connected to how the business actually operates.

Leveraging Vercel鈥檚 AI SDK, development teams get a common toolkit for building great commerce applications using the AI model provider of your choice.

For teams starting with an idea for a new interface, Vercel鈥檚 v0 offering can help marketers and developers design, iterate, and turn that idea into an experience they can review and refine.

The right storefront strategy depends on the business

There is no single storefront approach that fits every commerce operation.

麻豆原创 Commerce Cloud, composable storefront, is available for organizations that want a closely integrated, 麻豆原创-managed experience. Vercel provides the new 麻豆原创 templates on the Vercel Frontend Cloud for teams building highly differentiated experiences on their own cadence using storefront technology used by millions of developers.听 Both options are backed by 麻豆原创 Commerce Cloud with market-leading commerce capabilities to drive profitability for growing companies and the world鈥檚 largest enterprises.

Your customers expect storefronts to be fast and easy to use. They also expect accurate prices, reliable availability, and an order that arrives as promised. 麻豆原创 Commerce Cloud and Vercel bring those two sides of commerce together: an experience that can keep changing, backed by the data and processes that keep the business running.

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

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

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

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

Powering a global sporting goods business

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

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

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

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

One connected foundation for a connected customer experience

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

Built for the pace of sport

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

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

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

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

Alex de Minaur

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

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

has long used frustration-free, quiz-style games to create these connection moments for people living with Alzheimer鈥檚 and dementia. Now, with help from 麻豆原创 and , the organization is exploring how AI can personalize its games at scale.

鈥淲hat we saw, as AI was coming in, was that we could take two or three more steps to really personalize the experience and trigger positive memories for each individual,鈥� Bruce Elliott, CEO and cofounder of Memory Lane Games, says. 鈥淏ut, as a six-person startup on the Isle of Man, a little island in the middle of the Irish Sea, it was a daunting task. We had a brilliant vision, but to bring it together we needed support.鈥�

Collaboration for good

Through the MovingWorlds鈥� platform, Memory Lane Games collaborated with 麻豆原创 and EY to move beyond generic reminiscence content, creating an AI prototype that is designed to help transform personal details, family photos, and life experiences into memory games.

This project was part of , a MovingWorlds cooperation between 麻豆原创, EY, and Microsoft that provides social enterprises鈥攖hat often have limited resources, complex technical needs, and a lack of access to partners鈥攚ith strategic coaching, pro-bono consulting, expert-led workshops, and access to a global partner ecosystem at no cost.

For employees at companies like 麻豆原创 and EY, these engagements are equally transformative. Real-world problem-solving with social enterprises is among the most effective forms of experiential learning, helping to develop skills that benefit both the individual and the organization.

Through this program, Memory Lane Games gained access to the technical support needed to explore how generative AI could deepen personalization in its games. To start, the project team鈥擶ade Tsai, global client technology architecture leader at EY, and 麻豆原创 developers Robin Baeurle and Michael Zadikowitsch鈥攍ooked at what the organization had done in the past and how it could be transformed by generative AI. The intersection of personalization and images was where the team landed.

Turning memories into personalized games

The foundation of the project team鈥檚 prototype is a persona profile that the AI can reference. A caregiver or loved one creates a profile for the person living with dementia with basic information like age bracket, gender, first language, places they鈥檝e lived, what they did for work, and cultural background as well as other details like past vacations, favorite foods, hobbies, pets, and more. The goal is to capture the culture of the individual, not personal data. Using that persona profile, the AI suggests topics and then generates games by pulling in open-source images and writing multiple-choice questions.

For example, a demo of the Memory Lane Games prototype showed the creation of a persona profile of a 75-to-80-year-old woman who lived in Savannah, Georgia, was an elementary school teacher for 35 years, vacationed on Hilton Head Island and in the Blue Ridge Mountains, had a tabby cat named Magnolia, and enjoys Motown and soul music, gardening, birdwatching, and cooking Southern classics like pecan pie and shrimp and grits. The AI prototype generated several games for her: Savannah鈥檚 Historic Squares, Blue Ridge Mountain Getaways, Savannah鈥檚 Southern Kitchen Favorites, and Magnolia the Cat and Backyard Birds.

鈥淎t the start, we didn鈥檛 know how to solve this problem of extracting metadata from images, or even how to collect images, where to find them, and how to create these games,鈥� Zadikowitsch says. 鈥淎nd then we started experimenting and trying things out鈥攇enerating questions and then finding images, which didn鈥檛 work well, then finding images and then generating questions, which worked better.鈥�

The prototype is currently in the testing stages. Once it is deployed, there is the possibility to expand game personalization with photos submitted by a caregiver or loved one.

Tsai explains that current AI models can identify objects in images, but directing them to extract metadata and EXIF data, which contains the exact date, timestamp, and GPS coordinates indicating when and where the photo was taken, can take it a step further.

The layers of information stored within a photo file can be a treasure trove for Memory Lane Games鈥� AI prototype. 鈥淲e can use that information as a hint to extrapolate what else we can pull from the surroundings that could help reshape that memory experience,鈥� Tsai says. 鈥淚t鈥檚 going beyond where that photo is taken.鈥�

AI with tangible human impact, not just productivity gains

While the AI prototype helps Memory Lane Games create personalized games more efficiently, its greater promise is human: helping people living with dementia and loved ones connect through joyful memories.

鈥淥ften in the context of AI, the typical audience is people working at other companies, not, for example, people with dementia,鈥� Baeurle says. 鈥淭here鈥檚 a lot of potential that goes to waste in not realizing that these audiences can also benefit from digital solutions and, specifically, AI-powered solutions.鈥�

For the project team, that human outcome was the guiding principle. 鈥淲hatever we build, it should spark joy,鈥� Baeurle adds.

鈥淐aregivers are really busy. Family members are really busy. Care staff in care homes are very busy. If we can take one photo from a family and a couple lines of text and create six to 10 questions that can really pull out all of those memories, trigger a positive memory, and start those wonderful stories鈥攖hat鈥檚 what [this collaboration] has been able to help us deliver,鈥� Elliott says.

AI makes it possible to create memory games not only about a city, but about a neighborhood, a local landmark, or another detail closely tied to someone鈥檚 life. The more personal the prompt, the more likely it is to encourage conversation and connection.

鈥淲e know that social isolation is one of the top modifiable risks for dementia in older age, so the more we can get people talking and those neurons firing, just by letting them talk about something they want to talk about, is powerful,鈥� Elliott says.

The future of AI-enabled memory care

What this collaboration between Memory Lane Games, 麻豆原创, EY, and MovingWorlds showed is that AI has a place in memory care. 鈥淭he team demonstrated that AI can help create engaging [memory] games. I think this was the goal,鈥� Zadikowitsch says.

Tsai attended the United Nation鈥檚 International Telecommunication Union (ITU) this summer, after winning the 鈥淎I for Good for Entrepreneurship鈥� category in EY鈥檚 inaugural AI for Impact Challenge with this Memory Lane Games project.

The project team is confident that this is just the beginning for AI-enhanced memory care and has already come up with more use cases: an AI companion optimized for dementia that runs on speech-based interfaces and emotional and voice intelligence, adding more languages since dementia patients often revert to their first language, and using generative AI to create images of places for which no photos exist.

鈥淲e鈥檝e always taken a very simple yet scalable approach. And this [project] has taken both of those: kept it simple for the user but made it infinitely scalable. And I think that鈥檚 where the real magic of AI is,鈥� Elliott says. 鈥淎I is the key to us scaling, and we needed this collective help, this collaboration, in order to test those interesting hypotheses. And we鈥檝e seen positive early results.鈥�

For Memory Lane Games, that magic is not technology for technology鈥檚 sake. It is the possibility of helping more people reconnect with the stories, places, and people that make life feel familiar and spark joy.

MovingWorlds has supported more than 3,000 successful projects across 110 countries, unlocking more than $50 million in pro-bono consulting expertise for social enterprises. If you are a social enterprise in need of skilled support in AI or another line of business, for support on the MovingWorlds’ platform. 

With updates to and , partners now get a self-service purchasing experience that cuts out that complexity, with pricing transparency comparable to what partners get on AWS and Azure marketplaces.

Smarter path from opportunity to purchase

The updated experience connects the full purchasing journey in one place, 麻豆原创 for Me: from finding the right solution, to submitting a quote, to completing the transaction.

Start with the right catalog

Partners start with a personalized catalog for their specific opportunity. Partner authorization, customer eligibility, product prerequisites, and related products are shown upfront 鈥� searchable, sortable, and packaged in an intuitive way, so there is no need to cross-reference multiple systems or wait for someone else to confirm what can be sold.

Know your pricing before you ask

Price transparency is built into the experience. With shopping carts powered by 麻豆原创鈥檚 own commerce solutions, partners can see what features are included in the product and can explore options and simulate quotes with full pricing visibility from the start, without waiting on internal teams to pull numbers together. More than 70% of B2B buyers prefer to research and validate independently before talking to a sales rep, and this experience is built around that.

Guided selling: No guesswork and easier to learn

麻豆原创’s portfolio is broad, and pricing can get complex, especially for partners newer to selling 麻豆原创, that previously had to track price lists, prerequisites, and product relationships on their own before building a quote. The experience now surfaces eligibility requirements, dependencies, and configuration options as partners work, so nobody starts from scratch.

Test scenarios before you submit

Before requesting approval, partners can run simulations to validate pricing and test different quote configurations before quote approval. Fewer surprises going in means fewer revisions coming back.

Faster approvals, less waiting

Internal approvals and administrative complexity have , and for many deals, that friction sits in the quote and approval stage. Partners now submit pre-validated, perfectly priced quotes only when they have tried and tested different pricing possibilities in the cart, and even complex scenarios move through a streamlined approval process with fewer delays.

End-to-end, fully digital

Once a quote is approved, the purchase completes digitally from start to finish: no chasing signatures, no manual uploads, no last-mile handoffs. Self-service e-commerce has become the , and the move to 麻豆原创 for Me and 麻豆原创 Store reflects that shift.

See it in action

The experience looks different depending on your partner model. For CC Flex partners, the journey includes commission visibility at every step, from the configurator through to the approved quote. For VAR partners, the flow moves from cart configuration to fully automated quote generation and straight through to checkout, with a confirmation email from 麻豆原创 once the order is placed.

What’s next

Upcoming enhancements will introduce embedded AI assistance through Joule, guiding partners through the purchasing journey with in-context support at every step. We will also include full partner cloud pricelist coverage and the ability to manage post sales via this kind of guided digital buying journey soon.

Whether you are managing a straightforward opportunity or a complex deal, the updated experience in 麻豆原创 Store and 麻豆原创 for Me gives partners clearer pricing, fewer roadblocks, and more control over how they close.

Start your next quote: Visit .

Shreya Datta is senior director of 麻豆原创 Marketplace Direct Business at 麻豆原创.

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麻豆原创’s Laura Marwood speaks with Casey West, a developer advocate at Google Cloud, about the realities of building mission鈥慶ritical AI applications, how organizations should think about AI adoption, and how the 麻豆原创鈥揋oogle Cloud collaboration has evolved over the years.

It’s about responsible, thoughtful AI adoption 鈥� where guardrails, deep problem understanding, and strategic collaboration matter far more than speed or hype.

West made a point that stayed with me. Using AI simply to generate more output is only the beginning. The more powerful use case is to treat AI as a collaborator in the thinking process: something that can challenge assumptions, ask questions we might not have asked, and help us see a problem from a different angle. In that role, AI is not replacing judgment, it is creating useful friction around it.

That shift matters because the work of building technology is changing. Developers are no longer just writing deterministic logic and controlling every outcome in advance. Increasingly, they are working with systems that are creative, probabilistic, and less predictable by design. That opens up huge possibilities, but it also raises the bar. Quality, guardrails, metrics, and trust become even more important when software starts to reason in ways that are not always fully scripted.

The advice West offered to developers was simple and probably more durable than any specific tool or framework: fall in love with the problem, not the solution. The technologies will change. The models will change. The implementations will change. But the ability to understand a problem deeply, stay curious, and apply AI with judgment will remain valuable.

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At a time when so much of the conversation around AI feels breathless, that feels like a good way to approach this moment: not with blind enthusiasm, and not with fear, but with curiosity and discipline. What are we trying to understand? What are we trying to improve? Where would better questions, better feedback, or better judgment make the biggest difference?

That is where AI starts to feel less like a wave we have to chase, and more like a capability we can shape with purpose. AI gives us new ways to build, learn, and decide. The question is not just how much more we can produce with it, but how much better our thinking can become when we use it well.

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

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

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

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

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

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

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

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

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

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

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

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

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

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This document contains forward-looking statements, which are predictions, projections, or other statements about future events. These statements are based on current expectations, forecasts, and assumptions that are subject to risks and uncertainties that could cause actual results and outcomes to materially differ. Additional information regarding these risks and uncertainties may be found in our filings with the Securities and Exchange Commission, including but not limited to the risk factors section of 麻豆原创鈥檚 2025 Annual Report on Form 20-F.
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The agreement, concluded at the end of 2025, marks a significant step in migrating Nokia鈥檚 麻豆原创 landscape to the RISE with 麻豆原创 journey. By adopting RISE with 麻豆原创 Methodology, Nokia will follow a structured, end-to-end approach to migrating its ERP landscape covering processes, data, applications and operating models while gaining continuous access to innovation and embedded AI capabilities delivered through 麻豆原创鈥檚 cloud ERP portfolio.

Nokia has selected Microsoft Azure as the cloud platform underpinning the transformation, providing the global scale, security and performance required to support the company鈥檚 most business-critical enterprise workloads. 鈥淣okia鈥檚 decision reflects a clear commitment to business-led transformation,鈥� said , Global President Customer Success Europe, APAC, Middle East and Africa and Member of the Extended Board, 麻豆原创 SE. 鈥淩ISE with 麻豆原创 Methodology provides Nokia with a structured road map, integrated toolchain and continuous access to innovation. It enables the company to modernize its ERP landscape while keeping a clean core and building a strong foundation for enterprise AI.鈥�

A Structured Approach to ERP Transformation

RISE with 麻豆原创 is designed as a comprehensive business transformation framework rather than a point solution. It combines a standardized transformation methodology, integrated tools and expert guidance to help organizations move from legacy ERP environments to RISE with 麻豆原创.

麻豆原创 will operate and manage the 麻豆原创 S/4HANA software environment in the cloud, allowing Nokia to shift focus from infrastructure management to business outcomes. The approach supports process standardization, operational simplification and ongoing innovation, rather than a one-time system migration.

Nokia has been on a business and technical transformation journey with its next-generation 麻豆原创 S/4HANA software environment, covering finance and key logistics capabilities, supported by 麻豆原创 solutions and applications. These include 麻豆原创 S/4HANA for central finance, 麻豆原创 Master Data Governance, 麻豆原创 Extended Warehouse Management, 麻豆原创 Global Trade Services and 麻豆原创 S/4HANA Cloud for advanced ATP. AI-enabled functionality embedded in 麻豆原创鈥檚 cloud applications will be progressively adopted as part of the journey.

鈥淭his agreement builds on our existing work with 麻豆原创 and Microsoft and supports Nokia鈥檚 ambition to secure how we run our core business operations,鈥� said Marek O膷kay, VP, Global Head of IT Procurement & Vendor Management, Nokia. 鈥淏y applying RISE with 麻豆原创 Methodology on Microsoft Azure, we are strengthening a structured and future ready path for business growth 鈥� one that simplifies our ERP landscape, enables continuous innovation and strengthens our commitment for AI driven processes.鈥�

Microsoft Azure as the Cloud Foundation

Microsoft Azure will serve as the cloud foundation for Nokia鈥檚 RISE with 麻豆原创 journey, aligning with Nokia鈥檚 broader cloud and data strategy. Nokia already operates parts of its 麻豆原创 landscape on Azure, and consolidating workloads on a single hyperscale platform is expected to deliver benefits in performance, security latency and operational resilience.

As part of the agreement, Microsoft will collaborate closely with 麻豆原创 and Nokia throughout the transformation, supporting migration activities and ongoing optimization.

鈥淭his collaboration demonstrates how cloud platforms, enterprise applications and AI can come together to support complex, global business transformations,鈥� said Joacim Damgard, CVP, Europe North Microsoft. 鈥淏y running 麻豆原创 S/4HANA on Azure within the RISE with 麻豆原创 journey, Nokia is creating a scalable and secure foundation for continuous innovation.鈥�

Building on a Longstanding 麻豆原创 Relationship

Nokia has been an 麻豆原创 customer for decades. In recent years, the company has been consolidating multiple ERP systems into a unified 麻豆原创 S/4HANA software landscape as part of its next-generation ERP program.

The move to RISE with 麻豆原创 helps secure that journey, providing a structured methodology to help accelerate transformation, reduce complexity and unlock cloud native capabilities.

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

Instead, they hit friction:

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

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

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

The agentic era is accelerating this shift dramatically.

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

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

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

The customer experience reality: ambition outpacing execution

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

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

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

A new model for customer experience built on trusted enterprise data

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

At the heart of this partnership:

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

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

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

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

Why this partnership matters

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

For commerce leaders:

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

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

For marketing leaders:

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

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

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

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

Unlocking new value for enterprises

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

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

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

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

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

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

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

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

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

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

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

The technology to do this better has been around for a while, but the habits haven’t caught up. That version exists鈥攎any organizations just aren’t using it yet.

What still happens most of the time: the team finds the tool, sends an email to IT, copies procurement, waits for a vendor quote, chases someone for budget approval, gets the contract sent to legal, and follows up three weeks later to ask where things stand. Somewhere in that chain, someone is managing a spreadsheet no one else can find.

This is not a niche problem. Companies experience significantly higher error rates, longer processing times, and limited visibility into spending patterns, and those inefficiencies compound the longer they go unaddressed. Meanwhile, now say they prefer a rep-free buying experience. They want to research, evaluate, and buy on their own terms, not wait for someone to send them a PDF.

The gap between what buyers expect and what many procurement processes actually deliver is wide, and it’s getting harder to ignore as AI adoption accelerates. Business teams do not want to wait weeks to access a new capability. But the approval chain wasn’t built for that speed.

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麻豆原创 Store was built for exactly this. More than 3,600 麻豆原创 and partner solutions鈥攁pplications, extensions, AI-powered tools, integrations鈥攁re all accessible through a single marketplace where 麻豆原创 customers can explore, trial, quote, and purchase within their existing 麻豆原创 environment.

Listings clearly outline integration requirements, pricing, and deployment options upfront. Many solutions offer free trials or demos with no sales call required. And when a customer is ready to buy, the platform handles automated entitlement checks, pre-verified product dependencies, and flexible payment options, so there are no handoffs to a procurement inbox that may or may not be monitored.

One of the less-discussed advantages is speed of access. Customers can go from purchase to provisioned access in less than an hour: no license delays stalling a project, no waiting on a quote that takes three business days to arrive. And because 麻豆原创 Store integrates directly into the customer’s existing 麻豆原创 environment, new solutions do not require a separate onboarding process to connect with the tools already in place.

Organizations using advanced procurement platforms report compared to traditional manual processes. The operational case is clear. The harder part is cultural: getting teams to actually stop managing approvals through email when that is what they’ve always done.

The companies moving fastest on software adoption right now are not necessarily using better tools. They’re spending less time managing the process around the tools.

To discover 麻豆原创 and partner solutions, request quotes, start trials, and manage purchases through a more centralized experience, .

Don Larcinese is global vice president of 麻豆原创 Marketplace Sales & Enablement.

Across industries, customers are moving from pilots to production. They are investing in systems that can run pricing, orders, fulfillment, and service end-to-end, with AI actively driving decisions and outcomes. They are not looking for more tools. They are looking for results.

This is where 麻豆原创 has a clear point of view. Customer experience (CX) only works when it is connected directly to execution.

麻豆原创 Customer Experience is integrated with 麻豆原创 Cloud ERP across pricing, order management, fulfillment, billing, and service. AI operates inside these processes using real business data. That means every customer interaction can reflect what the business can deliver.

This is the shift now underway, and it is creating immediate opportunities for partners.

麻豆原创 partners are the ones who bring this to life. They take product capabilities and turn them into working solutions that improve conversion, increase fulfillment accuracy, and reduce service cost.

The next wave of growth will be led by partners who move quickly and build on this foundation.

Customer experience is now measured by what gets done

Customer experience is no longer judged by engagement metrics alone. It is judged by outcomes. Customers expect:

• Accurate pricing at the moment of purchase
• Real product availability, not estimates
• Orders that are fulfilled as promised
• Service that resolves issues without repetition or delay

When these things work, the experience works. When they fail, the problem is immediately visible.

AI is increasing the speed of every interaction. It is also exposing execution gaps faster than ever before. If pricing, inventory, or order data are inconsistent, customers see it instantly.

This is why customer experience and execution can no longer be separated.

AI is now driving actions, not just insights

AI is already acting inside key business processes. As seen in the , in 麻豆原创 CX today:

• Marketing, content, and campaign assistants can orchestrate segmentation, content creation, and optimization based on live performance signals.
• Commerce, merchandising, shopping, and order management assistants can connect discovery, conversion, and fulfillment to real-time inventory and pricing.
• Sales assistants help guide deal qualification and deal execution by linking pipeline signals to pricing, availability, and fulfillment data.
• Case and service management assistants help automate routine interactions while maintaining full context across orders, entitlements, and history.

These are not future scenarios. These capabilities are available and in use. But they only work when they are connected to trusted business data.

Without that, AI creates errors at scale. With it, AI drives measurable improvement.

Autonomous CX connects experience to execution

Autonomous CX connects core products and processes across the business, operating on a shared business context. It brings together 麻豆原创 Commerce Cloud, 麻豆原创 Sales Cloud, 麻豆原创 CPQ, 麻豆原创 Service Cloud, 麻豆原创 Field Service, 麻豆原创 Engagement Cloud for marketing, and 麻豆原创 Cloud ERP across finance, supply chain, and order management.

This is not a set of disconnected applications. It is a unified system where customer interactions and operational processes run on the same data foundation. Pricing, inventory, orders, and service are consistent across every touchpoint.

As a result, AI can move from recommendation to execution, working to ensure that every interaction is grounded in what the business can deliver. It can also remove the integration gaps that slow down CX execution.

麻豆原创 CX partners are moving faster from projects to outcomes

This shift is changing what customers expect from partners. Customers are not asking for system implementations. They are asking for outcomes such as:

• Faster time to deploy
• Higher conversion rates
• Improved order accuracy
• Lower cost to serve

麻豆原创 provides a strong starting point with embedded assistants, standard integrations, and prebuilt industry scenarios. Partners are building on this to deliver complete solutions. This is where differentiation happens.

Where partners are creating value today

The opportunity is not theoretical. It is already visible in active partner work.

Across 麻豆原创 CX:

• The cloud ERP edition of 麻豆原创 Commerce Cloud can connect storefront, pricing, ordering, and fulfillment in one model.
• 麻豆原创 Revenue Growth Management and 麻豆原创 Retail Execution support trade planning and in-store performance.
• Intelligent applications help package AI use cases across marketing, sales, and service.
• 麻豆原创 Service Cloud with partner integrations such as Parloa enables automated, context-aware service interactions.

麻豆原创 CX partners are turning these capabilities into repeatable offerings. Examples include:

• Industry packages for retail and CPG combining commerce, pricing, and fulfillment
• Preconfigured deployments of 麻豆原创 Sales Cloud and 麻豆原创 Service Cloud that reduce time to go-live
• Integration connectors linking 麻豆原创 CX with existing commerce, loyalty, and service platforms
• Extensions to CPQ and sales workflows that improve deal margin and approval speed
• Service automation scenarios that reduce manual case handling using real order and entitlement data
• AI-driven discovery connected directly to 麻豆原创 Commerce and 麻豆原创 Commerce, order management

These are practical, deployable solutions that can deliver measurable results.

The ecosystem is expanding what鈥檚 possible

麻豆原创 is strengthening this model through partnerships. Recently announced partnerships with companies such as Amazon Web Services, Google Cloud, Parloa, and Vercel enable new interaction models like conversational commerce, AI-driven search, and composable digital experiences.

What matters is that these experiences connect back to 麻豆原创 for execution. Orders, pricing, fulfillment, and service remain consistent across every channel. This gives partners the freedom to innovate on the experience layer while relying on 麻豆原创 for reliable execution.

A new economic model for partners

The economics for partners are changing. With Autonomous CX, partners can build:

• Industry solutions that can be reused and scaled
• Implementation packages that shorten delivery timelines
• Extensions and integrations that apply across customers
• Ongoing services for AI optimization and governance
• New offerings built around AI assistants, AI agents, and orchestration
• Higher-value transformation programs that combine AI, data, and process design

This creates a more predictable and repeatable revenue model. It also strengthens long-term customer relationships.

Now is the time to act with 麻豆原创 CX

Customers are making decisions now. They are selecting platforms and partners that can deliver AI-driven execution across customer experience. They are looking for partners who can:

• Connect CX to ERP processes
• Deliver solutions that work out-of-the-box and scale
• Improve measurable business outcomes

麻豆原创 provides the foundation. The platform is in place. The capabilities are real. The next step is execution.

Partners who move now can define the use cases, build the offerings, and lead in their industries. The momentum is already building. This is the moment to accelerate it.

What partners should do next

To move from opportunity to execution, partners can act now.

• See what is available today. Explore the latest AI-driven CX capabilities and partner opportunities in the .
• Understand the foundation for Autonomous CX. Learn how 麻豆原创 connects experience to execution with the .
• Deepen expertise and accelerate readiness. Gain a stronger understanding of Autonomous CX and partner use cases:
• Engage now and shape the next wave. Join the upcoming executive briefing to understand how to position and deliver these solutions:
  • June 18:
• Strengthen credibility and scale impact. Advance your go-to-market and positioning through certification:

麻豆原创 is not asking partners to start from scratch. The platform, capabilities, and ecosystem are already in place. The opportunity now is to build, differentiate, and lead.

Karl Fahrbach is chief partner officer at 麻豆原创.
Balaji Balasubramanian is president and chief product officer for 麻豆原创 Customer Experience.

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.

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�

During our event keynote, I took time to preview this exciting new venture, which was formally announced at 麻豆原创 Sapphire, alongside our CEO and chairman of the Executive Board, Christian Klein.

We explored how the Autonomous Enterprise is 麻豆原创鈥檚 new north star and vision. It鈥檚 the future of business where AI transforms how people work and processes run.

Building on the Suite-as-a-Service foundation established last year at 麻豆原创 Sapphire, 麻豆原创 is reinventing itself for AI-native operations that move customers from point-solution AI to enterprise-wide autonomous operations.

With this launch, we鈥檙e also re-framing some of the misconceptions and hesitations that we know our customers have about AI.

AI isn鈥檛 technology for technology鈥檚 sake; it鈥檚 about driving outcomes, like real-time intelligence, automated end-to-end workflows, and continuous improvement鈥攁ll important aspects that AI can deliver.

麻豆原创 partners are 麻豆原创鈥檚 force multiplier to bring the Autonomous Enterprise to life, expanding reach, credibility, and adoption. To help 麻豆原创 partners evolve their practices to align with this vision, we also announced significant new investments in the partner ecosystem during the event.

麻豆原创 Business AI partner-led adoption program

The Partner Summit at 麻豆原创 Sapphire in Orlando included the launch of a new offer that funds听partners who听activate, extend, build, and deploy 麻豆原创 Business AI for customers. The 麻豆原创 Business AI partner-led adoption program is a significant opportunity for partners to guide their clients to unlock the potential of 麻豆原创 Business AI Platform.

At 麻豆原创 Sapphire, 麻豆原创 Executive Board Member and Chief Operating Officer Sebastian Steinhaeuser commented on our need to deepen our partner investments: 鈥溌槎乖� pledges 鈧�100M to our partner ecosystem today to fast forward AI adoption and accelerate our customers path to the Autonomous Enterprise. Partners are able to tap into this fund when they support our customers in the adoption and consumption of 麻豆原创-delivered agents as well as by working with them to extend agents and build custom agents on our 麻豆原创 Business AI Platform.鈥�

The program has four packages:

• Adoption: AI Assistant activated and deployed
• Launch: Joule Studio partner-built custom agent or听workflow/pro-code application
• Performance: Joule Studio partner-built custom agent and听workflow/pro-code application
• Enterprise: Minimum of three Joule Studio partner-built custom agents and听workflow/pro-code application

麻豆原创 will actively nominate customers with potential for this program. Partners can also reach out to 麻豆原创 with their suggestions. If the customer qualifies, 麻豆原创 will approve the proposed services, and the 麻豆原创 partner will execute a statement of work with the customer and share the required documentation with 麻豆原创 for funding.

麻豆原创 Business AI and data validated partner program

This new program distinguishes partners that demonstrate a holistic approach to 麻豆原创 Business AI and data along with deep expertise and close alignment with our 麻豆原创 Business AI and data strategy. Built on the foundation of the existing Competency Framework for the 麻豆原创 PartnerEdge program and enhanced by 麻豆原创 Business AI and data requirements, this designation signals to customers that these partners are capable and genuinely invested in delivering the full promise of the Autonomous Enterprise, including guidance with autonomous domain blueprint adoption and building agentic scenarios on 麻豆原创 Business AI Platform.

Partner agent race to 麻豆原创 TechEd

Building on the strong momentum of the partner agent race to 麻豆原创 Sapphire, in which partners submitted more than 680 agents, 麻豆原创 is inviting partners to join the next chapter of the program. Partner agent race to 麻豆原创 TechEd spotlights enterprise-grade, secure, scalable, production鈥慸eployed AI agents in live environments, built on 麻豆原创 Business AI Platform.

For the partner agent race to 麻豆原创 TechEd, agents must be:

• Developed with Joule Studio and deployed to 麻豆原创 Business AI Platform runtime
• Deployed on 麻豆原创 Business AI Platform using 麻豆原创 Cloud SDK for AI and AI Foundation

Extensions of 麻豆原创-delivered agents must also be created with Joule Studio, and agents that only integrate via direct APIs or integration services for 麻豆原创 BTP are out of scope for this project. 麻豆原创 encourages partners to begin preparing eligible agents as soon as the timeline, evaluation criteria, and other details are released in the coming weeks.

End-to-end partner enablement strategy

麻豆原创 is introducing a structured enablement path that takes partners from awareness to action. 麻豆原创 has combined large-scale enablement and market recognition programs to scale the partner ecosystem for the Autonomous Enterprise and 麻豆原创 Business AI Platform.

The enablement plan includes a deep-dive curriculum for sales, presales, consultants, and developers. Partners can take advantage of hands-on workshops, regional innovation days in priority markets, and Hack2Build sprints focused on agent-based use cases. Dedicated Autonomous Enterprise and 麻豆原创 Business AI Platform pages on听麻豆原创 Partner Portal provide all the information partners need for success, regardless of where they are in their AI journey.

麻豆原创 will deepen this enablement strategy with the upcoming听鈥淎I era powered by the Autonomous Enterprise鈥� learning, covering 麻豆原创 Business AI Platform, Joule Studio, and more for learners who want hands-on platform experience. 麻豆原创听will also host live webinars connecting partners directly to the Autonomous Enterprise narrative, platform strategy, and key commercial updates. Partners can find additional curated learning and enablement content on the .

A soundtrack for the future, a symphony of success

Closing out our keynote for Partner Summit at 麻豆原创 Sapphire in Orlando, we underscored how critical our partners are to this moment.

Our focus is to enable partners to adopt and extend the Autonomous Enterprise and 麻豆原创 Business AI Platform, accelerating ecosystem鈥憀ed growth in support of 麻豆原创鈥檚 AI鈥慺irst strategy. Our goal is ambitious: We want to see every 麻豆原创 customer enjoy an AI experience with 麻豆原创 in the next year. Let鈥檚 get them on the cloud and toward our vision.

Together, we鈥檒l keep pushing the tempo, layering innovation, and building the soundtrack for the Autonomous Enterprise, ultimately creating a symphony of success for our customers.

Karl Fahrbach is chief partner officer of 麻豆原创.

From application development to automation, integration, and now agentic AI, we have pushed forward so organizations can move faster, solve bigger challenges, and create with confidence.

At 麻豆原创 Sapphire, we鈥檙e taking a giant step forward in making that mission a reality.

I鈥檓 thrilled to announce Joule Studio, a bold new, fully managed offering that empowers enterprises to build and manage the full life cycle of AI agents, applications, and workflows. Joule Studio brings 麻豆原创 Business AI Platform to life, empowering organizations to build agents that are natively grounded in live business data, end-to-end processes, and rich business semantics that already exist across your 麻豆原创 landscape.

Let鈥檚 look at what users can accomplish with Joule Studio.

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Build faster with intent-based development

To connect business needs and technical execution, we鈥檝e placed intent-based development capabilities at the heart of the Joule Studio experience. Users can simply describe their goals in natural language, enabling anyone in the business to quickly create an automated solution or digital assistant.

When triggered, Joule Studio:

• Sets the business context for user鈥檚 request with 麻豆原创 Signavio Process Consultant Agent, 麻豆原创 Knowledge Graph, and 麻豆原创 Domain Models.
• Understands the customer landscape with 麻豆原创 LeanIX, including third-party solutions.
• Generates a complete, structured flow of artifacts, including a product requirements document that captures the business outcome, technical specifications with implementation-ready details, code scaffolding, test artifacts, and a live working preview.
• Creates a highly traceable flow from idea to implementation, ensuring a direct, seamless handoff from business users to developers. It fundamentally shifts enterprise agentic development from a slow, manual translation of requirements into a rapid, structured, and 麻豆原创-aligned workflow.

“Joule Studio generated an end-to-end solution in 10 to 15 minutes, replacing three to four days of manual development and coordination.”

Vanitha Ponnusamy, Sony

Develop agentic solutions your preferred way

Joule Studio pairs the simplicity of intent-based capabilities with unprecedented openness, providing developers with the freedom to create agentic solutions their way, using their preferred frameworks and tools without being locked into a single approach.

For example, developers can deepen and adapt Joule Studio-generated solutions using the tools and agentic IDEs they already know and love, such as Visual Studio Code, Cursor, and others. Additionally, Joule Studio offers new pro-code capabilities that support frameworks such as LangChain, Pydantic AI, and LlamaIndex, as well as an embedded n8n environment for visual multi-agent orchestration.

Harness best-in-class partnerships: n8n and Vercel

To build truly transformative AI solutions, developers need the freedom to use the tools they already love. That is why we are thrilled to announce new embedded partnerships with Vercel and n8n, giving Joule Studio users the ultimate flexibility to orchestrate complex workflows and build stunning user experiences鈥攁ll without sacrificing 麻豆原创鈥檚 enterprise-grade security and governance.

Vercel for blazing-fast, custom digital experiences

While 麻豆原创-oriented frameworks like UI5 and 麻豆原创 Fiori remain the gold standard for enterprise consistency, our new partnership with Vercel gives developers unparalleled choice for custom frontend design. By leveraging Vercel within the 麻豆原创 ecosystem, developers can rapidly build highly flexible, custom web interfaces for their AI agents using popular frameworks like Next.js. This enables teams to deliver lightning-fast, consumer-grade digital experiences that prioritize speed and custom design, while securely preserving 麻豆原创 enterprise controls.

n8n for visual workflow orchestration at enterprise scale

Creating intelligent agents is just the beginning; integrating them into end-to-end business processes is where the real value is unlocked. We are bringing an embedded, fully managed n8n environment directly into Joule Studio. By using n8n within Joule Studio, teams can visually orchestrate multi-agent systems and bring AI right into the process flows they are designed to support, ensuring agents act with perfect timing and context. Developers get the beloved n8n experience they already know, complemented by seamless access to 麻豆原创 systems, Joule Studio capabilities, and 麻豆原创-managed services for identity and operations. It is the ultimate combination for delivering powerful, enterprise-ready automations faster than ever.

Deploy enterprise-ready agents securely

Building powerful agents is only half the equation; realizing their full value comes from running them securely and reliably at enterprise scale. To help our customers do this, 麻豆原创 is introducing a managed Joule Studio runtime service that enables organizations to deploy agents, applications, and workflows in a secure, production-ready environment with zero infrastructure management required.

Joule Studio runtime does the heavy lifting for our customers by managing all the complex operational capabilities needed for enterprise scale; runtime configuration, cluster management, storage, and model access are delivered seamlessly out-of-the-box. Underpinning this runtime is also the NVIDIA OpenShell, which places each agent inside an isolated, sandboxed environment with configurable policies and guardrails 鈥� ensuring agents can operate autonomously while staying within defined boundaries and preventing unchecked access to sensitive enterprise systems.

This governed foundation provides IT teams with built-in observability and lifecycle management. With controlled deployments, standardized schema validation, and deep integration with 麻豆原创 Business Transformation Management solutions like 麻豆原创 Signavio and 麻豆原创 LeanIX as well as 麻豆原创 Cloud Application Lifecycle Management allow teams to monitor agent usage, costs, and business impact over time. It creates an always-on cycle of continuous improvement, where AI monitors performance, surfaces insights, and proposes the next round of fixes.

Agents deployed on Joule Studio runtime will be equipped with persistent, long-term memory powered by 麻豆原创 HANA Cloud, enabling them to retrieve user preferences and context across multiple sessions.

Bring agents into the flow of everyday work

Ultimately, the value of agentic AI is realized when people can effortlessly interact with it. With the new Joule Work engagement layer, we are bringing the apps, agents, and workflows your teams build directly into the flow of everyday work, providing a personalized, intent-based workspace that reduces context switching and accelerates task completion.

“Across 48 diverse scenarios, Joule Studio consistently delivered high-quality code, with only a handful of instances requiring minor refinements to reach full functionality.”

Suraj Gahalyan, Accenture

Joule Studio: 麻豆原创 Business AI Platform in action

Joule Studio is more than just a powerful development environment; it is the ultimate expression of the unified coming together. While the broader market struggles with disconnected point solutions that lack business context and keep AI stuck in endless pilot modes, 麻豆原创 Business AI Platform bridges every system, process, and decision to deliver true enterprise-wide value.

Joule Studio acts as the engine that brings the three foundational pillars of the 麻豆原创 Business AI Platform to life in one seamless workflow:

• Build: We are taking organizations from idea to enterprise impact by providing a unified workspace that enables the seamless creation of agents, applications, and workflows. Whether leveraging intent-based development or our embedded partnerships with n8n and Vercel, teams can turn ideas into solutions without operational overhead.
• Contextualize and reason: An agent is only as smart as the data it understands. Through deep integration with the 麻豆原创 Knowledge Graph, 麻豆原创 Business Data Cloud, and 麻豆原创 Domain Models, every solution built in Joule Studio is natively anchored in universal business context. This means agents reason over real, semantically rich business data, understanding relationships and process logic, for reliable performance from day one.
• Govern: Speed and control are no longer a tradeoff. By tapping into 麻豆原创 AI Agent Hub, fully managed Joule runtime, and solutions like 麻豆原创 Signavio and 麻豆原创 LeanIX, Joule Studio embeds enterprise-grade governance, observability, and lifecycle management directly into the development process.

By unifying these capabilities, Joule Studio allows your best people to do their best work. It eliminates integration complexity and fragmented security, empowering your organization to transition from isolated AI experiments into a secure, autonomous enterprise.

Get started today

Joule Studio is ushering in a new era of enterprise-grade agentic development. While the rest of the market struggles to bridge the gap between basic LLMs and real-world business execution, Joule Studio delivers a definitive advantage: agents, applications, and workflows that are natively grounded in 麻豆原创 live data, processes, and business semantics.

I am pleased to share that now through the end of 2026, 麻豆原创 customers and partners can receive free design-time access, including AI-assisted development capabilities under fair-use limits. This is your opportunity to redefine how your business operates and turn your existing 麻豆原创 landscape into an unparalleled AI engine. Equip your teams to build with speed and confidence today.

• Learn more at

We cannot wait to see the incredible agentic solutions your teams will bring to life!

Michael Ameling is president of 麻豆原创 Business Technology Platform and a member of the Extended Board of 麻豆原创 SE.

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

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

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

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

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

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

Claude brings additional agentic capabilities and connectivity to Joule

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

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

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

Bringing AI into the systems enterprises already trust

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

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

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

For 麻豆原创 customers, this shift promises step-change productivity. But it also raises a hard requirement: Enterprise AI agents must be safe, governable, and auditable by design.

This is the context for 麻豆原创鈥檚 deep technical collaboration on 麻豆原创 Business AI Platform with , an open source secure runtime for autonomous AI agents. This collaboration is not about 麻豆原创 鈥渁dopting鈥� a runtime. It is about 麻豆原创 actively shaping, hardening, and productizing the execution layer for enterprise agentic AI鈥攖ogether with NVIDIA.

Why this matters to 麻豆原创 customers

For 麻豆原创 customers, the value of this collaboration is concrete and practical. It enables:

• AI agents that operate inside 麻豆原创 processes without bypassing governance
• Security models aligned with enterprise IAM and compliance frameworks
• Clear audit trails for agent actions across systems
• Confidence to move from pilots to production

Most importantly, it avoids a false choice between innovation and control. Customers do not have to bolt security on later, or redesign their risk models to accommodate AI agents. Instead, security and governance are built into the execution model from the start.

The real enterprise challenge: Trusting agents that act

When AI systems move from generating responses to executing actions, the risk profile fundamentally changes. Agentic systems can touch systems of record, cross application and data boundaries, and operate without human review at every step.

In all enterprise environments, especially regulated ones, this makes execution safety and governance the defining challenge. Traditional chatbot-era controls are insufficient once agents can access shells, files, networks, credentials, and APIs.

麻豆原创 customers know this reality well. Business AI is only valuable if it can be:

• Inspected and audited
• Constrained by policy
• Trusted by security and compliance teams

Solving this problem requires more than infrastructure primitives or application-level rules alone.

NVIDIA OpenShell: The foundation

NVIDIA OpenShell addresses a critical layer of the problem: secure, sandboxed execution of autonomous agents.

As an open source runtime, OpenShell introduces strong capabilities, including:

• Isolated execution environments
• Policy enforcement for filesystem and network access
• Runtime-level containment that limits blast radius even when agent logic fails

These capabilities form a foundational layer for autonomous agents to execute safely. In practice, enterprises need that execution layer aligned with business context and governance.

Enterprises expect clarity on questions such as:

• Which business role authorizes an action?
• Which process context applies?
• How actions map to enterprise policies and audit trails?

This is where 麻豆原创鈥檚 contribution becomes decisive.

What 麻豆原创 brings: Enterprise semantics, governance, and scale

麻豆原创 is co-developing and contributing to OpenShell based on enterprise reality.

1. Enterprise-driven runtime requirements

麻豆原创 operates at a level of scale and responsibility that few software providers do: mission-critical processes, regulated industries, and millions of transactions per hour.

By bringing real 麻豆原创 agentic workloads into the collaboration, 麻豆原创 provides the operational proving ground that OpenShell needs to mature from a powerful runtime into an enterprise-hardened one.

This includes shaping requirements around:

• Isolation boundaries that match enterprise risk models
• Policy enforcement aligned with real business constraints
• Auditability that stands up to customer and regulatory scrutiny

2. Co-development of OpenShell capabilities

麻豆原创 is committing engineering capacity to the OpenShell open-source code base, with a focus on areas that matter specifically to enterprises: runtime hardening, policy modeling, enterprise identity integration, and auditing and governance hooks.

麻豆原创 is helping define how secure agent execution must work for enterprises; not just theoretically, but in production.

3. Joule Studio runtime: From runtime safety to enterprise control

Where OpenShell secures execution, Joule Studio runtime provides the enterprise harness that makes agents usable and governable in business systems:

• Business-aware policy semantics like roles, skills, life cycle
• Enterprise identity and access control
• Observability and auditability across agent behavior
• Deployment and operational governance across landscapes

This ensures that agent autonomy is always framed by business intent and accountability, not just technical permissions.

OpenShell answers: 鈥淐an this action safely execute?鈥�; Joule Studio runtime answers: 鈥淪hould this action happen at all?鈥�

Raising the bar for enterprise agentic AI

This collaboration represents more than an integration. It reflects a shared intent to define what 鈥渆nterprise-grade鈥� actually means for autonomous AI systems.

By combining NVIDIA鈥檚 runtime and security innovation and 麻豆原创鈥檚 enterprise productization, governance expertise, and operational scale, 麻豆原创 and NVIDIA are working toward an integrated solution for trusted agent execution鈥攐ne that enterprises can inspect, govern, and rely on.

For 麻豆原创 customers, this means AI agents that are not just powerful, but designed to earn trust in the environments where trust matters most.

Andre Lamego is senior vice president and chief product officer of 麻豆原创 BTP Fabric