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

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

Extend the business data fabric with an open lakehouse foundation

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

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

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

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

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

Accelerate your journey to the cloud

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

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

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

Build a trusted system of context across domains

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

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

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

Turn enterprise knowledge into reusable business context for AI

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

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

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

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

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At 麻豆原创 TechEd in 2026, 麻豆原创 announced a series of innovations in Joule Work, Joule Studio, and 麻豆原创 Business AI Platform.

From building and extending AI agents to connecting business data and governing AI experiences, see how 麻豆原创 helps customers move towards the Autonomous Enterprise. Hear 麻豆原创 leaders and industry voices share what鈥檚 new, their perspectives, and more.

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�

But while there is enormous excitement about what AI can do, we are still measuring progress the wrong way. The number of models, pilots, or agents a company deploys tells us very little. What matters is whether AI transforms the core processes where business value is actually created.

Can it steer demand fulfillment to high-margin business when supply is disrupted? Can it help release a pharmaceutical batch without compromising quality and compliance? Can it resolve a critical maintenance issue before it turns into an outage?

This is where enterprise AI becomes real and where generic intelligence reaches its limits.

From pharmaceutical supply chains and manufacturing quality management to asset maintenance, trading, and retail, the hardest industry problems are deeply contextual. They are shaped by decades of interconnected processes, strict regulations, and operational constraints. Solving them demands more than a powerful model. It requires trusted business data, integrated processes, rock-solid governance, and a deep industry domain expertise–the very foundation upon which we are building . At the same time it demands a team which is obsessed with customer problem solving and world-class forward deployed engineers.

This convergence of industry depth and forward deployed engineering (FDE) is where the next wave of enterprise value will be unlocked.

Start with the value, not the technology

AI innovation too often turns into a search for problems to solve. We take the opposite approach: start with the customer’s desired business value and work backward.

Where is margin leaking? Why is there lost sales? What operational decisions take too long? What production deferment can be prevented?

The goal is no longer just incremental productivity for individual users. It is moving toward autonomous operations. Across industries, organizations need to compress the cycle between detecting an issue, simulating options and impact, deciding the right response, executing corrective actions, and learning from outcomes–in a compounding loop. What once took days can increasingly happen in minutes.

Through our forward-deployed engineering model, we co-develop tailored capabilities alongside customers. We solve high-stakes challenges that have no off-the-shelf answers, then turn those proven solutions into scalable software capabilities for entire industries. This is not innovation from a distance. It is built with the people who live these challenges every day.

Autonomous operations make this possible. Within clearly defined business guardrails, AI agents identify disruptions, model scenarios, evaluate trade-offs, orchestrate execution, and improve via feedback loops–with human experts firmly in the loop and on-the-loop to validate critical decisions and steer strategic direction.

Co-innovation as a catalyst for impact

The most valuable AI solutions are never built in isolation. They are forged inside live operating environments.

Takeda Pharmaceuticals, a global leader in research-based biopharmaceuticals, is applying this thinking to its future operating model.

According to Rebecca Kaufmann, senior vice president and head of Enterprise Platforms a , “AI represents the key enabler for Takeda’s transformation for the future. We are combining the 麻豆原创 industry and technology knowledge with our organizational real-life experience. The supply chain planning assistant will provide us with AI-detected root causes and AI-suggested remediation and also automated performance tracking resulting in a self-healing capability.”

That ambition–shifting from passive detection to systems that continuously learn, recommend, and self-heal—is taking hold across sectors.

Doehler, a global producer of natural ingredients for the food, beverage, life sciences, and nutrition industries, is already demonstrating what this looks like on the factory and logistics floor.

“Our people’s greatest value lies in deciding what to do next, and now they can focus entirely on that,” said Stephan Schissler, co-chief information officer at Doehler GmbH. “The Logistics Issue Resolution Agent detects the root cause, recommends the resolution, and turns a multi-hour firefight into a one-click decision. Our logistics team has recovered roughly a third of the time once lost to troubleshooting, and our processes are becoming self-resolving rather than exception-driven.”

These examples reinforce a decisive rule: AI delivers maximum return when it eliminates operational drag, freeing professionals to focus on judgment, strategy, and business outcomes.

AI that thinks like the industry

As AI moves from passive recommendation to intelligent execution, industry context becomes paramount.

The question is no longer whether an AI agent can take an action. The question is whether it understands which action is compliant, safe, and commercially viable within a specific environment and organizational guardrails.

• In life sciences, autonomous manufacturing coordinates processes from design-to-release while strictly adhering to compliance-critical quality and compliance criteria.
• In energy and natural resources, agents go beyond detecting maintenance alerts to dynamically schedule crews, stage spare parts, and rebalance operations.
• In engineer-to-order manufacturing, systems orchestrate complex quotations, adapt production schedules, and accelerate new product introductions.

Deep industry context is what makes these outcomes trustworthy.

Building intelligence that compounds

麻豆原创鈥檚 Industry AI portfolio is focused across seven core domains: Autonomous Asset Management, Autonomous Commodity Management, Autonomous Adaptive Production, Autonomous Regulated Manufacturing, Autonomous Revenue Growth Management, Autonomous Unified Commerce, and Autonomous Project Delivery.

While the use cases differ, the principle is the same: sustainable value comes from end-to-end value streams, not optimizing functional silos.

 鈥淏usiness performance depends on how quickly a company can respond when conditions change,鈥� said Andre Bechtold, president of 麻豆原创 Industries & Experiences and chief revenue officer of Industry AI at 麻豆原创. “By applying 麻豆原创鈥檚 industry knowledge across connected processes, Industry AI helps customers act earlier, limit disruption, and keep operations on track.”

The measure that matters

, one message from our customer discussions was unmistakable: the ambition across sectors is to move past fragmented experimentation and embed agentic intelligence directly into mission-critical workflows.

Our initial wave of early adopter customers has proven the compounding power of this model: Customer friction points sharpen our agentic solutions, stronger agentic solutions strengthen standard, and stronger standards accelerate the time to value the next customer can achieve.

The future of enterprise AI will not be decided by who launches the most agents. It will be decided by who can bring intelligence into the decisions and workflows where business value is created every day.

When AI understands the industry, operates within trusted boundaries, and learns alongside human experts, autonomy stops being an abstract vision. It becomes a better way to run the business.

For a complete overview of the Industry AI innovations and product announcements unveiled at 麻豆原创 Connect in 2026, see the and related 麻豆原创 News coverage.

Dominik Metzger is president and global head of Industry AI at 麻豆原创.

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

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

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

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

Better spend decisions start with better visibility

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

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

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

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

Bringing supplier risk into everyday decisions

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

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

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

General availability is planned for Q4 2026.

Connecting procurement across complex enterprise landscapes

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

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

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

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

Making external workforce management more proactive

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

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

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

Simplifying travel and expense management through targeted automation

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

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

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

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

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

Turning spend intelligence into action

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Build and connect anything

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

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

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

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

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

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

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

Start scaling AI, and your business outcomes, today

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

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

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

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

I鈥檝e spent most of my career building supply chain technology to drive business outcomes at scale. Earlier in my career, nobody was talking about AI. But I was helping build what we鈥檇 now call an autonomous supply chain.

At scale, manual decision-making simply cannot keep pace with growing consumer expectations. That鈥檚 why the next phase of supply chain AI needs to move beyond analysis to help organizations turn insights into action across the processes and applications that move goods. 

Decision velocity: where supply chain speed is won or lost 

Even the most intelligent supply chains can struggle to turn a meaningful signal into a well-governed response. I think of that as decision velocity: how quickly an organization can recognize change and coordinate the right response based on relevant business context.听

In supply chain, decisions are inherently interconnected. Planning affects manufacturing, which depends on product and asset readiness, suppliers, logistics partners, and more. I hear this from our customers today: each decision could be rational; the sum of them was not.听

AI can automate individual tasks, but isolated intelligence only creates isolated value. 

Agents need to understand how a decision in one domain affects the next. That鈥檚 where supply chain applications become critical, providing the business context, connected processes, and controls that agents need to act. 

New Joule Agents extend AI across the supply chain 

This year at 麻豆原创 Connect, we’re expanding the capabilities of six Joule Assistants introduced earlier at 麻豆原创 Sapphire and adding specialized Joule Agents that can take on critical supply chain tasks. These capabilities will help organizations respond faster and automate more routine work while keeping people at the center of informed decision-making: 

• The Product Design Assistant will be able to interpret product designs, recipes, and specifications and orchestrate compliant product changes, helping accelerate time to market.听
• The Supply Chain Planning Assistant will help planners sense, simulate, and resolve exceptions in a single workspace, helping teams understand downstream impacts and address disruptions before they ripple across the enterprise.听
• The Manufacturing Assistant will help teams respond to plant-floor disruptions as they happen, while new capabilities in the Logistics Assistant span warehousing, outbound optimization, fulfillment, and transportation dispatching, helping turn logistics insight into action.听听
• New agentic capabilities for the Asset Operations Assistant will help translate equipment and maintenance signals into action by supporting the initiation, screening, and simulation of maintenance requests. And the Business Network Assistant will improve collaboration with suppliers and logistics partners in areas such as sourcing, contracting, and exception handling. 

What connects these scenarios is the ability for agents to operate within the business context already governing the supply chain, so an insight in one area can inform the next and, when authorized, trigger coordinated action.听General availability for these new capabilities is planned for Q4 2026.听

Customer results show autonomy taking hold 

Customers are already putting this shift into practice.  

Takeda Pharmaceuticals will use the Supply Chain Planning Assistant to help pinpoint likely causes behind planning issues and propose corrective steps, supporting adaptive planning processes over time.  

Rebecca Kaufmann, senior vice president and head of Enterprise Platforms at Takeda Pharmaceuticals, said, 鈥淲e are combining the 麻豆原创 industry and technology knowledge with our organizational real-life experience. The Supply Chain Planning Assistant will provide us with AI-detected root causes and AI-suggested remediation and also automated performance tracking resulting in a self-healing capability.鈥澨�

Doehler, a global producer of natural ingredients for the food, beverage, life sciences, and nutrition industries, is applying AI to logistics issue resolution.  

Stephan Schissler, CIO at Doehler GmbH, says, “Our people’s greatest value lies in deciding what to do next鈥攁nd now they can focus entirely on that. The Logistics Issue Resolution Agent detects the root cause, recommends the resolution, and turns a multi-hour firefight into a one-click decision. Our logistics team has recovered roughly a third of the time once lost to troubleshooting, and our processes are becoming self-resolving rather than exception-driven.”听

This is how autonomy delivers practical value: moving from manually managing exceptions toward more intelligent supply chain processes. 

Building autonomy on a foundation of trust 

As AI takes on a greater role in supply chain decisions, trust must be built into how it operates. Leaders need to understand what AI decided and why, define what it is authorized to complete, intervene when necessary, and know who is accountable. 

That is the direction behind everything we are introducing at 麻豆原创 Connect in 2026. By grounding Joule Assistants and their agents in the processes and data of the 麻豆原创 Supply Chain Management portfolio, we can help organizations move from insight to coordinated action while keeping people at the center of the decisions that matter most.听

For supply chain leaders, that is where autonomy becomes tangible: less time spent on routine work and resolving exceptions, faster responses when conditions change, and more time for the strategic decisions that strengthen resilience, efficiency and customer value. 

Ultimately, it comes down to decision velocity you can trust. 

Devesh Mishra is general manager and chief product officer for 麻豆原创 Supply Chain Management.

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

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

Get all the news: .听

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

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

A new UI for AI 

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

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

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

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

麻豆原创 Autonomous Suite 

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

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

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

麻豆原创 Business AI Platform 

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

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

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

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

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

Jerseys from Jersey 

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

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

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

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

Security and governance 

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

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

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

A clear shift among customers 

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

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

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

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

According to research from the , 72% of executives believe organizations claiming to transform work with AI are primarily automating existing processes rather than fundamentally redesigning how work gets done. Yet, 88% say redesigning work and roles creates significant enterprise value. And HR is ranked as the No. 1 owner of that work. As a result, HR and business leaders are looking at how technology can support their need to continuously align people, skills, and work with evolving business priorities.

This is the journey to . Autonomous HCM is the future of human capital management, where AI automates end-to-end processes, drives context-aware decisions, and optimizes workforce operations at scale.

As we think about Autonomous HCM, we have defined three pillars:

1. Automate the HR function to release capacity from administrative work: HR teams currently spend a significant share of their time on operational activities such as payroll, service delivery, recruiting administration, and workforce management. AI can reduce much of that burden, creating capacity for more strategic work. But automation is not the destination. It’s the foundation. The true value comes from what organizations do with the time and resources they regain.
2. Redesign how tasks, workflows, roles, and teams are structured throughout the enterprise, across both people and AI: For decades, companies optimized around jobs, functions, and fixed processes. AI challenges many of those assumptions. The opportunity is no longer simply to make existing work more efficient. It is to rethink work itself. This is not just a technology initiative. It is one of the most important business transformation challenges organizations face today.
3. Reinvent how organizations recruit, develop, move, and reward their people: Most talent processes were built for a world characterized by stable jobs and predictable career paths, but that world is rapidly changing. Organizations that succeed will create talent ecosystems that can adapt as quickly as the business itself.

These three pillars are all interconnected: automation releases capacity, work redesign determines how value is created, and talent-system reinvention makes the new model sustainable.

What makes this possible is 麻豆原创鈥檚 unique ability to bring together trusted workforce data, connected business processes, ontology, and AI within a single business context. Together, this context enables organizations to move beyond isolated HR activities and make strategic workforce decisions that drive the most urgent business outcomes. This context is what matters.

During 麻豆原创 Connect, 麻豆原创 announced that it has entered into an agreement for 麻豆原创 to acquire TechWolf. TechWolf provides a leading AI work intelligence platform, which helps organizations understand the work their people do and the skills they have, giving them the right context and the right grounding. Once the transaction closes, TechWolf is expected to power workforce and talent strategies across the 麻豆原创 SuccessFactors portfolio, bringing skills mapping, workforce planning, and organizational redesign informed by context from across the Autonomous Enterprise to guide decisions about development, deployment, and workforce transformation.

New Innovations to 麻豆原创 SuccessFactors HCM Suite

While Autonomous HCM is our vision for the future, we have several new innovations spanning the entire 麻豆原创 SuccessFactors HCM suite, available this month, including:

A growing portfolio of AI assistants

New AI assistants orchestrate work end-to-end and support decisions in real time. Many of the agents that we announced at 麻豆原创 Sapphire are available this month, including assistants for payroll, HR service, learning, recruiting, onboarding, and career and development. We have more agents coming soon, including the new Workforce Planning Assistant to support customers using . It is powered by connected workforce, contingent labor, and financial data from 麻豆原创 SuccessFactors, , and , to help business and HR leaders forecast workforce needs and create plans for the most effective mix of hiring, reskilling, redeployment, and external talent to address changing workforce demands.

Significant updates to our global benefits solutions

Benefits are among the most important decisions employees make, and they can also be some of the most complex for HR teams to support. To help make benefits administration more efficient for HR and benefits decisions more personalized for employees, 麻豆原创 announced two new innovations built natively into 麻豆原创 SuccessFactors Employee Central. The new agentic admin console for U.S. benefits provides HR administrators with a centralized view of benefits enrollment activity and exceptions, helping them quickly identify and resolve issues with built-in monitoring and natural language support. By reducing time investigating enrollment issues and navigating reports, organizations can improve oversight and manage complex enrollment periods more efficiently. Additionally, the new benefits decision support tool uses employee data already in the system to recommend coverage aligned to individual needs and budgets, resulting in a more personalized and intelligent enrollment experience for employees. 

New integration of WalkMe Learning Arc with

Creating and deploying learning content can often take weeks, making it difficult for organizations to respond quickly to changing business needs. With this new integration, learning teams can describe learning requirements in natural language, and AI generates courses and quizzes while maintaining author control. Learning experiences can then be created and deployed directly within 麻豆原创 SuccessFactors Learning in hours rather than weeks.

Shaping Autonomous HCM with our customers

As we look forward, we know that what has worked in the past will not take us to the future. The goal is not simply to showcase technology, but to prove how connected AI can help organizations translate strategic decisions into measurable workforce and business results faster and more effectively. Rather than organizing our innovation around individual products or modules, we are focusing our innovation around the most important outcomes our customers are trying to achieve鈥攁nd in doing so, leaning into 麻豆原创鈥檚 strength in connecting data, workflows, and processes across the entire 麻豆原创 portfolio. We will co-innovate with select customers to define, validate, and measure outcome-driven approaches that connect workforce decisions directly to business value, proving the value of our innovations in real time.

The journey starts now

We are at a defining moment for HR leaders. The future of work will not be shaped solely by technology. It will be shaped by the leaders who can translate transformation into meaningful outcomes for their organizations and their people.

Autonomous HCM is the future of HCM. But it does not start tomorrow. The journey starts today.

To learn more about the latest 麻豆原创 SuccessFactors innovations and how 麻豆原创 is helping organizations advance their journey toward Autonomous HCM, .

Siva Sundaresan is general manager and chief product officer for 麻豆原创 SuccessFactors.

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

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

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

Joule Assistants move from providing answers to driving business outcomes

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

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

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

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

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

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

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

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

麻豆原创 Order Management Services coordinates execution across ERP landscapes

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

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

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

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

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

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

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

Complete business context across the customer journey

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

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

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

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

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

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

Customer experience depends on execution

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

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

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

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

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

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

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

Many of the futures we have explored are no longer hypothetical. They are showing up in how work is organized and experienced today. The fundamental question now is whether organizations are shaping those changes deliberately or simply reacting as they unfold.

combines monitoring of more than 500 business, academic, government, and economic resources with original global mixed methods research involving 2,718 employees, 500 C-suite leaders, 60 HR executives, and founders of AI-native companies. It points to a fundamental concern: organizations may be optimizing the future of work for efficiency when they should be designing it for value.

The productivity trap

In October 2025, we explored 10 predictions for work, workforces, and work practices, each with two possible futures. A year later, the evidence is strongest for what we call the “AI Upgrade” future, in which AI鈥檚 value is measured through efficiency, productivity gains, and time savings. Signals of broader work transformation are emerging, but they remain less established.

The emphasis on productivity is understandable because time savings, output, and cost reductions are relatively easy to measure. The harder question is what organizations do with the capacity AI can create. They can ask employees to produce more of the same work, or they can redesign work, roles, teams, and talent systems around what people and AI can achieve together.

Work transformation needs a north star

Related research from our found that 88% of employees said their jobs have changed because of AI, yet half of those changes occurred without meaningful guidance from managers or their organizations. Employees are using AI to simultaneously offload tasks and expand the scope of their work, often without a shared playbook or a clear view of the intended outcome. 

Before organizations can transform work intentionally, they need a clear north star for what work should become. Yet between 55% and 70% of employees said they had received no clear organizational guidance on the work shifts they are expected to navigate. At the same time, 72% of C-suite leaders said that most organizations claiming to transform work with AI are really automating existing jobs faster. Without a shared vision for work transformation, organizations risk defaulting to isolated experimentation and efficiency-first use cases, creating uneven outcomes and limiting the combined potential of people and AI.

Six shifts to guide better work design

We endorse six shifts in work and organizational design that are both enabled by AI and associated with work-enriching characteristics well established in organizational psychology as leading to increased employee motivation, effort, performance, and retention:

• From specialist to generalist: Apply core expertise across a broader range of problems and tasks.
• From static jobs to dynamic roles: Organize work around flexible responsibilities and outcomes that evolve with business needs.
• From pyramid to diamond organizational design: Reduce coordination layers and concentrate more people closer to where core work happens, enabling them to own end-to-end delivery.
• From output to impact: Evaluate work by the value it creates, not simply by activity or deliverables.
• From centralized authority to decentralized decision-making: Move decisions closer to the people and teams with the context to act.
• From people management to self-leadership: Give employees greater ownership of their direction, performance, and growth.

Through an experimental study, we validated how all six shifts can enrich work. For example, employees perceived 77% greater autonomy under decentralized decision-making, while dynamic roles were perceived as 49% more challenging than static jobs. The full report examines these effects across all six shifts, compares where organizations are today with where employees believe they need to be, and offers a prioritized path for leaders to get started.

Work transformation requires HR transformation

If work shifts this substantially, the function responsible for the people agenda cannot assume its own role and structure will remain the same. HR was built for fixed jobs, predictable career paths, established hierarchies, and talent systems designed for consistency. AI challenges each assumption.

With this context, we see HR鈥檚 new mandate as focused on three interconnected pillars:

• Automate: Use automation and intelligent systems to release capacity from administrative work and create space for more strategic contribution. C-suite leaders estimate that 49% of HR work will be fully automated within five years, compared with 35% of enterprise work overall.
• Redesign: Lead enterprise work redesign by determining how tasks, workflows, roles, and teams are structured across people and AI. Executives ranked work redesign as the transformation pillar that will create the greatest enterprise value over the next five years and identified HR as its leading owner.
• Reinvent: Overhaul recruitment, development, mobility, performance, and rewards to support how work now gets done and who succeeds in the new way of working. Seventy-five percent of executives agree that organizations can succeed in the AI era only if they reinvent how they measure, reward, and grow people.

In the full report, we also explore four plausible futures for how these pillars might come together to shape HR鈥檚 strategic contribution, depending on where leaders believe the people agenda can create the greatest value.

The future of work is a choice

The future of work is already being built. The opportunity now is to build it intentionally, creating better work for people and greater value for organizations. 

Explore how our 10 predictions from 2025 are tracking, the evidence behind six work shifts for better employee and business outcomes, and four possible futures for how HR can create strategic value in an AI-enabled enterprise. .

Dr. Autumn D. Krauss is chief scientist of the Future of Work Research Lab for 麻豆原创 SuccessFactors.

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

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

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

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

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

麻豆原创’s Autonomous Enterprise Scales Up

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

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

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

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

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

The Platform Powering Business AI You Can Trust

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

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

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

麻豆原创 Brings Embedded Payments to Cloud ERP

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

Partners Expand the Reach of Autonomous Business

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

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

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

Visit the . Get 麻豆原创 news via  and .

About 麻豆原创

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

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

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

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

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

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

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

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

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

Bring architecture intelligence to life with 麻豆原创 LeanIX

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

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

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

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

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

Adoption and execution across every application

Transformation only delivers when people actually change how they work.

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

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

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

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

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

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

One life cycle, one governance model

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

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

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

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

Last week, 麻豆原创 customers gathered at the Swiss Smart Factory in Biel, Switzerland, to experience firsthand how robots and 麻豆原创 software can work together to solve real business challenges.

Embodied AI refers to AI agents that interact with the world through a physical body鈥攅nabling machines to autonomously perceive, understand, reason, and act in real environments. By connecting these agents to and 麻豆原创 Business AI Platform, 麻豆原创 brings business context into that physical execution: robots that don’t just carry out tasks, but understand the business decisions those tasks serve.

Warehouse automation, asset inspection, and material handling are just some of the business scenarios where embodied AI is beginning to create value. Bringing those scenarios to life requires more than a robot. It requires business context from 麻豆原创 applications, integration expertise to connect systems and robots, and the right robots to execute the task.

鈥淕enerating market interest for embodied AI and transforming it from an exciting technology topic into a practical 麻豆原创-connected business value demanded a new format,鈥� explained 麻豆原创 Switzerland CTO Alexander Finger, who was a key driver behind the event.

Unlike traditional innovation jams, an embodied AI jam requires robots and a space where people can safely work with them side by side.

The Swiss Smart Factory provided exactly that environment for 麻豆原创 Switzerland to host the event. Bringing together customers, robot manufacturers, system integrators, and 麻豆原创’s embodied AI experts created a unique opportunity to move from discussion to hands-on experimentation and real-world use cases.

Viable use cases in days, not weeks

Embodied AI may well be all about hardware and software, but Finger says accelerating progress is ultimately about bringing people together. At the jam, customers and partners were paired with system integrators and robot manufacturers aligned to their business challenges.

While some teams explored how inspection drones could connect to solutions such as 麻豆原创 Asset Performance Management, others investigated how humanoids could support processes with 麻豆原创 Digital Manufacturing.

The result was a level of progress that typically takes weeks to achieve.

鈥淔inding where embodied AI creates real business value鈥攁nd shaping a solution to deliver it鈥攖ypically takes weeks of distributed back-and-forth,鈥� said Lukasz Ostrowski, head of the embodied AI initiative at 麻豆原创. 鈥淭hree days of dedicated, focused time with customers changed that. We could test ideas, challenge assumptions, and iterate in real time until we arrived at something concrete that neither side could have defined alone. What we learn with each customer like this doesn’t stay with that customer鈥攊t shapes how we build for the rest of the industry.鈥�

The physical dimension makes embodied AI tangible

For Finger, embodied AI only becomes meaningful when customers can experience it firsthand.

Seeing a robot perform tasks informed by business processes and objectives makes the potential business value far easier to understand than a slide deck or demo alone.

This is why the Swiss Smart Factory plays such an important role; it provides a safe environment where customers, robot manufacturers, system integrators, and 麻豆原创’s embodied AI experts can work and explore embodied AI in action together.

As of January 2027, 麻豆原创 Switzerland will become a member of Swiss Smart Factory, enabling it to host future embodied AI jams as well as shorter discovery formats like those already used for other AI customer-facing events.

While 麻豆原创 Customer Experience Labs show customers how 麻豆原创 applications, data, and AI can solve business challenges, the Swiss Smart Factory adds a physical dimension. It gives customers a hands-on environment to explore how robots can be connected, act in a business context, and create tangible business value.

Bringing embodied AI to more customers globally

鈥溌槎乖� is richer when we talk to customers,鈥� Finger concluded, reflecting on the success of the jam. Beyond the speed with which teams developed use cases and architectural proposals, one outcome stood out: customers left the event wanting to continue the conversation and further explore their embodied AI ambitions with 麻豆原创.

As 麻豆原创 Switzerland expands its offerings of embodied AI events, more customers will be able to experience embodied AI firsthand and explore how robots, 麻豆原创 applications, and business processes can work together to create value.

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

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

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

Building momentum one mission at a time

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

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

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

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

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

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

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

Standardization creates the capacity to accelerate

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

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

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

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

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

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

Complexity demands faster, better-informed decisions

The scale of the Artemis program is difficult to overstate.

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

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

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

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

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

Autonomy works best when it expands human capability

NASA is already applying autonomous technology beyond administrative processes.

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

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

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

Partnerships turn ambition into capability

No single organization could accomplish the Artemis mission alone.

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

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

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

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

Trust is the ultimate operating system

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

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

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

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

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

AI as a key element

In the AI category, the Austrian energy company OMV prevailed with its transformation project. Building on an existing 麻豆原创 S/4HANA landscape, the transformation focused on intelligent automation, advanced analytics, and AI-driven decision-making in core business processes. A key factor for success was a step-by-step approach: instead of a hasty implementation, OMV first laid a solid foundation and introduced AI in a targeted manner into the user experience and business processes. The focus was on regulation, security, and continuous evaluation. The clear principle was to standardize, integrate, and establish a stable data foundation before it scaled more broadly. Early in the process, the company relied on Joule in 麻豆原创 SuccessFactors solutions and 麻豆原创 S/4HANA. In the next step, the transformation will continue so OMV can make even greater use of AI.

Intelligent water supply

With a data-driven platform for monitoring its water network, the Belgian water utility Farys was recognized by the jury in the Innovation category. With the help of 麻豆原创 technology and AI, the utility can now deploy resources more efficiently and optimize its processes in a targeted way. Water leaks can be detected early before greater damage occurs, water quality issues can be predicted, and energy consumption can be reduced. The result: less downtime, lower repair costs, and a noticeably better quality of service. Having a unified solution also fosters cross-departmental collaboration by breaking down existing data silos. In the future, risk assessments for pipelines and facilities will be integrated, and data-driven decisions will help determine where repairs will have the greatest impact. With this strategy, Farys is preparing its infrastructure for the demands of climate change.

Achieving goals through teamwork

E.ON UK won the Best Team category with its 麻豆原创 S/4HANA transformation project. With its group-wide project running from 2021 to 2027, the company aims to standardize processes and build a future-proof, scalable IT landscape. The central challenge was to consolidate various legacy systems in a highly regulated market. What sets this project apart is the close collaboration between the company, IT, and external partners. The company used workshops, road shows, and a targeted key user network to focus on team culture and cohesion from the very beginning. The result is not only a successful technical transformation, but above all a lived team culture鈥攁 key factor to the project鈥檚 success.

A new data foundation

In the Transformation category, Electrica Furnizare came out on top. The starting point was a fragmented system in which data was scattered and an overarching overview was missing. The company鈥檚 existing infrastructure was neither scalable nor able to respond flexibly to growing regulatory and customer requirements. With the introduction of 麻豆原创 S/4HANA combined with 麻豆原创 Business Technology Platform (麻豆原创 BTP), 麻豆原创 Customer Experience solutions, and 麻豆原创 Business AI, Electrica Furnizare achieved one of the largest transformations in the utility sector. The result: a single, reliable data source for all departments, end-to-end optimized processes, and a solid foundation that paves the way for future innovations and the full deployment of AI capabilities.

Better customer service through comprehensive modernization

Loudoun Water鈥檚 far-reaching modernization project won the Customer Experience category, with the company using 麻豆原创 S/4HANA, 麻豆原创 Service Cloud 2.0, and 麻豆原创 SuccessFactors solutions to optimize its processes. With the introduction of 麻豆原创 Service Cloud 2.0, Loudoun Water is the first utility company worldwide to take this step. The effort paid off: customer service is faster and more modern, work orders are better organized, billing and payments are managed more efficiently, and time tracking for employees has been simplified. The migration was carried out in a single, coordinated step, creating a future-proof, scalable foundation on which Loudoun Water can further expand its position as a technology leader in the industry.

Innovation as a driver of the energy transition

The winners of the first Energy and Utilities Innovation Award demonstrate the wide range of possibilities through which companies in the energy and utilities sector are actively shaping digital transformation. Whether AI-driven decision-making processes, intelligent infrastructure monitoring, or a consistent realignment of the IT landscape, all the projects have one thing in common: they rely on solid foundations, step-by-step implementation, and close collaboration among all stakeholders. Melanie Fiolka, go-to-market & community engagement lead for Utilities at 麻豆原创, summarizes it as follows: 鈥淭he energy and utilities industry is undergoing profound change. The winners of the first Energy and Utilities Innovation Award demonstrate how vision is turned into tangible value through clear strategies, solid data foundations, and the adoption of AI.鈥�

The award makes it clear that innovation in the industry is no longer the exception, but has become a strategic necessity.

Under the official banner of Climate Cooperation in a Fractured World, delegates spread across the city and the tone was noticeably different from previous years. Fewer pledges, more blueprints. Less “what should we aim for,” more “who is going to finance and build it.”

Sustainability is a driver of growth

If there was a single reframing that ran through the week, it was this: sustainability is not a cost of growth, it is a driver of it.

That shift was visible in how decarbonization was discussed. Conversations that once centered on targets now centered on operations: Scope 3 emissions, value-chain engagement, procurement and logistics decisions, energy demand reduction. Practitioners repeatedly pointed to an “execution gap”鈥攖he distance between climate strategies on paper and projects that are actually permitted, financed, and built鈥攁nd to the unglamorous work of unblocking infrastructure and untangling supply-chain bottlenecks as the real frontier.

Electrification gave the growth argument its clearest expression. The launch of the Electrify Now initiative, which aims to lift electricity’s share of final energy demand from roughly 20% today to 35% by 2035, was framed as an industrial strategy.听Nearly doubling electricity’s share of energy demand in under a decade is an acceleration, and the week’s energy-transition summits were clear about what it demands: scaling renewables at pace, doubling down on energy efficiency, and, above all, building out the grid infrastructure to carry it. Speeding up permitting and resolving supply-chain constraints were named repeatedly as the bottlenecks that will decide whether the target is met.听

The heatwave outside made that case tangible. As cooling demand surges and extreme weather stresses networks, a clean, resilient electricity system is fast becoming a precondition for business continuity and not just decarbonization.听UK-focused sessions highlighted the substantial clean energy investment flowing into the country since 2024 as evidence that the in its own right.听

The same logic ran through the finance agenda. Sessions on moving from risk to resilience and from risk to opportunity focused on mobilizing capital for adaptation and climate-resilient infrastructure, and on the practical challenge of connecting available capital with investable projects through better data, governance, and pipeline development.

Nature is now on the agenda

Perhaps the most striking development of the week was where nature sat on the agenda, and where it is headed. Speakers were blunt about the underlying problem: our economic system is very good at valuing what we take from nature and very poor at valuing nature itself. Worse, we actively pay to destroy it. Figures cited during the week put global investment flows that harm nature at around US$7.5 trillion a year, against roughly $250 billion flowing into activities that help it. As one speaker put it, the task is not to lament that imbalance, but to get the economics right and to start treating nature as something that can be measured, managed, and steered with the same discipline as carbon or financial risk.

That 鈥済etting the economics right鈥� is fast becoming a data challenge for business. Work such as the on the economics of landscape restoration suggests that investing in nature can generate returns comparable to investing in factories, railways, or other conventional infrastructure. As nature-related risks and opportunities are codified into emerging frameworks and regulation, companies will have to treat nature as a set of measurable data points: impacts and dependencies per site, per supplier, and per product line, rather than a one鈥憃ff narrative in a sustainability report.

Governments have levers too, from requiring companies to stress test for nature-related risk, to shaping incentives so that capital flows toward restoration rather than degradation. For corporate leaders, that translates directly into new categories of information that need to be captured and governed: nature鈥憆elated financial exposure, land use and biodiversity metrics, and nature鈥憄ositive investment pipelines. What was once an externality is quickly becoming a set of operational KPIs.

, professor at the London School of Economics, noted that this was the first year nature was represented at the event, but also how far it still has to travel: “Today here in the outdoor tent, next year in the big room.” The implication for businesses is that the organizations that move nature into their core data models and decision frameworks now are better positioned when this topic inevitably moves from the tent to the board agenda.

The AI warning: get sustainability data in now

Underpinning nearly every theme was data. Location-specific climate analytics were described as “the new lens” for understanding financial risk, and AI featured in almost every discussion of how organizations can gain visibility and control over complex energy, water, and supply chain systems.

But the sharpest point made during the week was a warning. As Stephen Jamieson, chief marketing officer of , put it: “If we don’t get sustainability data into AI right now, AI will optimize around it. AI works within the systems, the data, and the constraints you give it. If your sustainability priorities live only in documents and presentations rather than in your data and processes, AI will optimize confidently in entirely the wrong direction.”

The logic is uncomfortable, but hard to argue with. Sustainability now plays out at the transaction level鈥攕uch as carbon cost per shipment, Scope 3 exposure per supplier, packaging compliance per SKU鈥攁nd the volume, granularity, and pace of those requirements exceed what manual processes and fragmented tools can manage. An organization whose cannot see its financial constraints, or whose supply chain system cannot see supplier regulations, hands its AI a broken map.

麻豆原创鈥檚 answer to this is the Autonomous Enterprise: a maturity journey that starts with intelligence based on trusted, transparent data; moves to optimization where AI is weighing trade-offs across cost, risk, and sustainability impact in real time; and progresses toward autonomy, where sustainability rules are embedded directly into enterprise workflows and executed within defined guardrails. Sustainability stops being a reporting activity and becomes a governing factor in how decisions are made. The architecture choices organizations make now will determine whether that automation can scale safely later.

From the tent to the big room

London Climate Action Week 2026 closed with an uncomfortable message delivered in 35-degree heat: the climate is not waiting for business strategies to mature. But a hopeful signal surfaced, too: the growth case, the nature case, and the technology case for climate action are converging, and each is being made in the language of returns, resilience, and competitive advantage.

The task for business leaders is to bring those cases inside capital allocation, procurement, and the data and systems where decisions are actually made. The organizations that thrive will be the ones that move the sustainability agenda into the big room, before the next heatwave makes the argument for them.

For more information on scaling sustainability for your business, visit .

Monica Molesag is global head of Sustainability Communications at 麻豆原创.

AI works when the foundation is right 

The energy and utilities sector is investing heavily in AI. Business听leaders worldwide are embracing artificial intelligence to increase efficiency, unlock new business models, and prepare for the energy transition. A successful proof of concept is often the first milestone鈥攂ut it marks only the beginning. The听real challenge听lies in scaling pilot projects across the entire听organization.听

In this context, the time and effort听required听for a full implementation听is听frequently听underestimated. Around six months are needed to build a robust data foundation. A further听12听months pass before initial results manifest in the form of a measurable return on investment. Large-scale rollout can take another three years. The reasons for this are manifold:听

• Unrealistic expectations: Many people use AI in their daily lives for simple tasks and expect similarly seamless effects in complex enterprise environments. 
• Legacy infrastructure: Historically grown system landscapes cannot be transformed overnight. 
• Regulatory complexity: In regulated industries such as electricity, gas, and water supply, compliance requirements are particularly high. They must be factored into every architectural decision from the very beginning. 
• Lack of AI-specific talent: What is needed are people who genuinely understand both the business and AI. This bridge between IT and the business side will become increasingly important in the future. 
• Organizational听change management:听Technology alone is not enough. Organizational transformation is and听remains听the decisive success factor.听

From AI hype to real value 

Building a new application is听only the first听step.听On the path to scaling, lifecycle management, identity and access management, security, compliance, and governance must all be consistently taken into account.听Release management, testing, and continuous improvement processes add further complexity.听鈥淭he听companies听that听invest in the right foundation today will benefit from AI to its full extent tomorrow,鈥� says Andre Bechtold,听president and听head of 麻豆原创 Industries & Experiences.听

For companies, this means overcoming fragmented data silos and developing an integrated data strategy. Legacy systems must be integrated into a modern data and AI platform on which AI models can genuinely create value. Torsten Welte,听head of Energy & Natural Resources Industries听at 麻豆原创,听summarizes听it as follows:听“AI is fundamentally transforming the energy industry. The business must understand what is technologically possible. And IT must understand what the business needs.”听

听can听provide听the听essential foundation for this. AI is already natively embedded in the suite in the form of Joule. This听can open up听concrete use cases for the energy industry:听in the area of asset management and predictive maintenance, utilities听can听proactively manage assets and grids before disruptions occur. The Utilities Customer Self-Service Agent, in turn, enables 24/7 self-service for customers and can reduce service costs by up to 90%.听

Distributed energy requires intelligent networking 

The topic of听distributed听energy听resources (DER) remains of听central importance. In the past, energy flowed in only one direction: from the power plant to consumers. In the future, it will be bidirectional. Consumers听that听generate their own energy will actively feed it back into the grid.听

DER听describes precisely听this principle: the generation of electricity through millions of decentralized resources such as solar panels, EV chargers, heat pumps, and battery storage systems听by听consumers and so-called听prosumers. These assets generate vast amounts of data. Their orchestration听represents听one of the key challenges of the energy transition.听

The 听solution听provides a platform听for听a听single source听of truth: technical assets, commercial contracts, and customer data are brought together in a coherent data model. This helps create the foundation for new business models such as smart tariffs, dynamic pricing, energy sharing, and demand response.

麻豆原创 consistently relies on a growing partner network built around its own data platform. Markus Bechmann,听global VP and听co-head听of听Industry Business Unit Utilities听at 麻豆原创, describes it this听way:听“Dynamic pricing and smart tariffs are no longer distant concepts.听They听are the business models听of听tomorrow. With 麻豆原创, energy providers already have the technological foundation today to seize these opportunities.”听

麻豆原创 Experience Centers: experiencing AI, not just discussing it 

To make AI tangible, 麻豆原创 Experience Centers offer visitors the opportunity to experience AI in real-world scenarios beyond classic demo environments. One central example is the 麻豆原创 Energy Park in Walldorf. Using real infrastructure on the campus, 麻豆原创 demonstrates how the company itself is implementing the energy transition. This includes e-mobility, intelligent asset management, and energy communities. 

A new chapter for the energy industry 

The 麻豆原创 for Energy & Utilities Conference in Toulouse has once again demonstrated that AI in the energy industry is no longer a topic for the future. However, the path from pilot project to company-wide transformation requires more than technological enthusiasm. To meet the challenges of the energy transition, what is needed鈥攁longside technological innovation鈥攊s a solid foundation of data, processes, and organization.

The objective of this collaboration is to digitalize procedures, improve efficiency and deliver better services to municipal employees and citizens in the areas of finance, revenue management and human resources.

The Madrid City Council has been working with 麻豆原创 software for two decades. It began in 2004 with the implementation of the first solutions in the areas of finance and HR, and in 2020 launched its public administration modernization project with the migration to the private cloud. This process is now advancing further with the adoption of the RISE with 麻豆原创 journey and 麻豆原创 Business Technology Platform (麻豆原创 BTP). The former is a comprehensive journey that combines the elements needed to migrate to the private cloud under a single contract: 麻豆原创 S/4HANA, infrastructure and managed services. The latter is the platform for integration, extension and application development.

A New Public Management Model

The adoption of these technologies represents a true revolution in the way municipal procedures are managed, from budgeting, execution and control of revenues and expenditures to the comprehensive management of human resources. This approach makes it possible to move beyond traditional models based on fragmented systems toward unified management with real-time information and digitalized processes.

The transformation has a particularly significant impact in the tax domain, as part of the project includes the integration of tax and revenue management solutions from 麻豆原创 into the city鈥檚 financial platform. This enables municipal revenues to be managed as a natural extension of the financial system, eliminating isolated developments and facilitating an end-to-end view of the full cycle, from taxpayer registration and assessment to collection and inspection. As a result, operational efficiency is improved while strengthening financial control and budget planning capabilities.

Currently, two-thirds of the City Council鈥檚 tax revenues are already managed within this environment, including Property Tax (IBI), the Urban Waste Tax for Business Activities (TRUA), Capital Gains Tax (IIVTNU) and the Terrace Tax (T2 2023). The next step will be to incorporate the Motor Vehicle Tax (IVTM) and the Economic Activities Tax (IAE).

The project has been developed using a phased methodology. During the first year, the City Council carried out a cleansing and harmonization of master data from its previous management systems (GIIM and +TIL), cross-checking identities with police databases, tax addresses with the Spanish Tax Agency (AEAT) and addresses with the municipal street registry. This process generated taxpayer 鈥淕olden Records鈥� and enabled, for example, an efficiency rate of 98.02% for Property Tax (IBI) in 2024. Data quality continues to be maintained for all new registrations.

According to Juan Corro, IT Manager of Madrid City Council (IAM), 鈥溌槎乖� technology offers us an extraordinary opportunity to accelerate our digital transformation and make the vision of a more efficient, innovative and citizen-centric local government a tangible reality. This project marks a paradigm shift: we are moving from managing paper files and isolated systems to managing information and processes in an integrated and intelligent way, with a 360-degree view. As a major capital city, Madrid has both the responsibility and the opportunity to position itself at the forefront of administrative modernization, serving as a benchmark for other municipalities.鈥�

Carlos Lacerda, Senior Vice President and Managing Director of 麻豆原创 Southern Europe, stated: 鈥溌槎乖� remains firmly committed to the Spanish public sector, which we have supported in its modernization processes for decades. This project is a benchmark for advanced digital administration and demonstrates how technology can act as a strategic enabler to simplify processes, integrate information and strengthen real-time data-driven decision-making, laying the foundation for a more agile, innovative and service-oriented public administration.鈥�

Benefits for the Administration and Citizens

The project is delivering benefits both in terms of internal efficiency and management, as well as citizen services:

• End-to-end process digitalization and a 鈥減aperless鈥� administration: The 鈥減aperless鈥� administration model has been consolidated, enabling the full digitalization of HR processes from start to finish. Requests are managed entirely through the municipal intranet. Internally, public employees can review and approve procedures with full traceability and in just a few steps, reducing processing times and errors caused by duplicate data. The result is a more agile, efficient and nearly 24/7 service that improves both the employee experience and citizen services.
• Operational efficiency and improved decision-making: Automation and AI capabilities integrated into the ERP system allow the City Council to significantly improve efficiency and productivity. Routine processes such as bank reconciliations and budget allocations are automated through rules and machine learning. In addition, the use of robotic process automation and services on 麻豆原创 BTP facilitates the automatic execution of repetitive tasks across systems. This reduces manual workload, minimizes errors and frees up time. Real-time analytics improve decision-making and, together with mobile and remote access to applications, enable more agile and flexible management.
• A more sustainable and efficient model: The implementation of RISE with 麻豆原创 enables the City Council to move toward a more sustainable and economically efficient IT model, based on subscription and pay-per-use principles. This approach reduces upfront investments, provides greater budget predictability and optimizes total cost of ownership. By scaling deployments according to municipal needs and paying only for required resources, the city improves responsible management of public funds while generating potential long-term savings.
• Greater adaptability and evolution: The City Council now has a flexible platform ready to evolve alongside technological, regulatory and social changes. The municipality will be able to align with national and European digital agendas, incorporate AI and advanced analytics capabilities, and evolve toward a smart administration model where data becomes a strategic enabler of better public policies.
• Continuous innovation: 麻豆原创 BTP is the innovation platform that integrates internal systems and third-party solutions, eliminating information silos. It also enables the rapid adoption of new technologies and responsiveness to changing needs and supports the City Council not only in modernizing processes but also in continuously evolving and launching innovative public administration initiatives.

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

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

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

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

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

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

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

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

The Autonomous Enterprise vision

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Exxon Mobil: Clean core and solid data foundation

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

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

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

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

Levi Strauss: AI at scale

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

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

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

Migration powered by AI

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

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

The world doesn鈥檛 break because of change

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

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

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

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

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

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

New AI sustainability agents

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

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

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

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

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

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

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

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

Only AI can deliver sustainability at scale

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

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

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

Enterprise autonomy entails gradual AI maturation:

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

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

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

Why 麻豆原创?

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

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

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

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

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

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

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

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

Autonomous Spend Management: From concept to reality

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

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

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

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

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

Why connected processes are critical

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

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

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

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

Expanding visibility into services spend and supporting adoption

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

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

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

The future of spend management

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

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

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

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

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

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

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

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

AI grounded in real operations

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

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

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

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

Joule Assistants across the supply chain

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

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

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

From assistants to autonomous agents

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

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

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

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

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

Where this shows up in practice

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

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

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

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

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

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

The path forward

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

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

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

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

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

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

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

The shift from periodic planning to continuous steering

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

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

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

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

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

Why governed data and connected planning matter

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

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

Continuous planning in practice

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

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

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

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

The future of finance is continuous

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

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

For more details, refer to the and the .

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

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

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

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

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

What Autonomous HCM means for HR leaders

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

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

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

Automate work with Joule Assistants

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

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

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

Reimagine the workforce with AI-driven planning

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

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

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

Elevate people through continuous upskilling

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

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

A new standard for human-centered Autonomous HCM

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

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

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

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

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

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

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

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

Aligning experience and execution

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

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

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

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

AI-driven discovery and engagement grounded in business reality

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

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

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

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

麻豆原创 Commerce Cloud innovations

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

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

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

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

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

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

Sales execution turns insight into action

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

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

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

Scaling trusted autonomous service

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

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

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

Industry AI in action

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

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

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

The next phase of customer engagement

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

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

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

The CX innovations and Industry AI scenarios highlighted here are planned for general availability in Q3 2026.
The capabilities announced as part of 麻豆原创鈥檚 Autonomous Enterprise run across 麻豆原创 Cloud ERP, including 麻豆原创 Cloud ERP Private.
Gartner does not endorse any company, vendor, product or service depicted in its publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner publications consist of the opinions of Gartner鈥檚 business and technology insights organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this publication, including any warranties of merchantability or fitness for a particular purpose.
Gartner and Magic Quadrant are trademarks of Gartner, Inc., and/or its affiliates.

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�