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

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

From vision to execution

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

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

Click the button below to load the content from YouTube.

Always allow YouTube

Designed for better business outcomes

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

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

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

Joule Work

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

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

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

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

麻豆原创 Autonomous Suite

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

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

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

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

麻豆原创 Business AI Platform

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

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

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

Physical AI by 麻豆原创

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

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

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

Joule Studio: Extend, build, and integrate your agents

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

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

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

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

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

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

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

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

麻豆原创 Business Data Cloud

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

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

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

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

Governance

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

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

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

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

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

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

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

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

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

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

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

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

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

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

From enterprise intelligence to physical action

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

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

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

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

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

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

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

1. Business-driven physical action

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

2. Physical evidence into digital workflows

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

3. Enterprise-governed execution

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

4. Vendor and hardware agnostic

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

Physical AI at work with customers and partners

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

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

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

Intel: physical AI in autonomous warehouse operations

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

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

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

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

Holcim: industrial asset inspections

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

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

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

Start your physical AI journey today

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

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

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

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.

Held from April 20鈥�24, Hannover Messe is the world鈥檚 leading industrial trade fair.

On day one, Christian Klein, CEO of 麻豆原创 SE, stopped by the 麻豆原创 booth before joining German Chancellor Friedrich Merz and other industrial leaders on the center stage to discuss the importance of moving from AI ambition to real-world execution.

And visitors to the 麻豆原创 booth experienced that shift firsthand, following the production of the ginger shot.

Packaged in a neat blue box, the ginger shot was refreshing but that wasn鈥檛 the only takeaway. The real takeaway was how 麻豆原创鈥檚 new set of AI-powered manufacturing and supply chain innovations can deliver connected .

Supply chain orchestration

From AI and data and then using 麻豆原创鈥檚 agentic AI, visitors saw what supply chain orchestration looks like in practice. 麻豆原创 uses , trusted data, and applications to help manufacturers sense, analyze, and act in real time.

At the booth, visitors saw human operators interact with an ANYbotics robot through Joule using natural language to run live, remote field service inspections; Uhlmann鈥檚 high-tech glass-fronted packing machine, PacXplorer, in action opposite the CNC machine from DMG MORI that was creating spare parts for the PacXplorer; and, at end of the production cycle, AIMBO鈥檚 robot handling the picking and packing of the ginger shot. Both AIMBO and ANYbotics are part of 麻豆原创鈥檚 growing network of physical AI partnerships.

In addition to many tours held in German and English, day one also saw tours in Japanese, Chinese, and Portuguese鈥擝razil was the partner country at Hannover Messe 2026.

Equipped with headphones to block out the noise of the crowds at the booth, visitors heard how 麻豆原创鈥檚 AI can deliver trusted orchestration and smarter execution for and .

Live AI use cases demonstrate functions and benefits

Operations and insights use case

Here, visitors experienced 麻豆原创鈥檚 vision of supply chain orchestration. In this vision, supply chain orchestration acts as the nerve center of the enterprise. It uses external alerts such as natural disasters, port congestions, or supplier routes to optimize enterprise logistics and planning using agents.

Benefits can include faster response times with AI-assisted monitoring and automated alerts; improved decision-making with data-driven, operational decisions powered by integrated business AI capabilities; and seamless integration with end-to-end connectivity from supply chain planning through to manufacturing execution and quality control.

Top AI functions

• can assist with order release and real-time monitoring.
• A physical AI robot inspects hazards, analyzes inspection data, and identifies root causes.
• Supply optimization analysis helps summarize insights, analyze, and explain the time-series optimization planning run.

Smart production use case

DMG MORI demonstrated production at its CNC machine鈥攁s part of an end-to-end process鈥攆rom engineering to planning to production.

As the white robotic arm of the CNC machine silently moved the pusher spare part after the milling process, visitors learned about the benefits of integration, from design to tool management, CNC programs to as part of a seamless, integrated process. The production operator dashboard offers the operator on the machine AI capabilities and insights to operational and maintenance information.

The process then continues through to logistics execution with 麻豆原创 Logistics Management, which helps combine warehousing and transportation capabilities for smaller warehouses.  This features an AI-powered logistics assistant that can cut through the noise, automatically gathering, summarizing, and prioritizing critical shipment information. It can also provide real-time shipping prices, bringing to life trusted orchestration and smarter execution.

Top AI functions

• Joule with 麻豆原创 Logistics Management uses natural language to help streamline warehouse and transportation operations.
• can provide manufacturing information and support decision-making throughout the workflow.

Intelligent packaging use case

Uhlmann’s PacXplorer and 麻豆原创 highlighted a fully integrated, high-speed packaging line from 麻豆原创 S/4HANA, to 麻豆原创 Digital Manufacturing, down to Uhlmann鈥檚 automation layer to produce the packaged ginger shot. The ginger shots were moved away from the line by a mobile autonomous robot from Symovo. This use case showed visitors how 麻豆原创 supports regulated industries such as pharma and life sciences.  

Highlighted benefits include increased operational speed with higher throughput thanks to decreased order processing time, built-in regulatory compliance, reduced manual intervention, inventory transparency, and data integrity across the entire production chain.

Top AI functions

• Condition monitoring-led services can enhance asset uptime and service efficiency by combining AI-driven insights and seamless collaboration across the service ecosystem.
• AI-empowered flow analysis enables quick process modeling and engineering optimization.
• Intelligent exception handling is embedded in agent-driven processes.
• Joule’s integrated AI agents can support decision-making throughout the workflow.
• Joule can help power order and line insights.

Humanoid use case

At the final stop before getting their ginger shots, visitors watched an intelligent humanoid robot perform physical tasks at the end of the packaging line, bridging the gap between digital planning and physical execution, highlighting 麻豆原创鈥檚 Project Embodied AI.

Benefits of humanoids include increased operational speed with higher throughput due to a decreased order processing time; increased business uptime and cost efficiency especially in areas dangerous or difficult for humans; inventory transparency with real-time data integrity across the warehouse; and physical-digital alignment eliminating misalignment between planning and execution.

Top AI functions

• Joule and Joule Studio can enable robots to understand the physical world, make autonomous decisions, and learn from their environment for smarter operations.

More than a quick refuel

At the end of their visit, visitors got so much more than a quick refuel to slake their thirst. Following the creation of the ginger shot from recipe development and planning to production with mixing, filling, and packing, visitors came away with a clear understanding of how 麻豆原创 is connecting insight to execution with trusted orchestration and smarter execution. And, it is this trusted orchestration and smarter execution that is building the resilience every manufacturer needs in today鈥檚 world.

ANYbotics, a Swiss robotics company, has stepped into this space with a vision to shape a safer future for industrial inspection, one where robots operate as autonomous members of the inspection team, running inspection operations integrated into plant maintenance workflows. 

This vision is embodied in the company鈥檚 鈥淎NYmal鈥�: a four-legged inspection robot designed specifically for heavy industry.

Unlike general-purpose robotics platforms, ANYmal is engineered to operate in 鈥渂ig, dirty, dusty, and dangerous鈥� environments, says Nicole Zingg, director of Technology Partnerships at ANYbotics. Places where stairs, corrosion, heat, and unreliable connectivity are the norm, not the exception.

But hardware, Zingg says, is only one part of the puzzle that makes ANYmal indispensable to customers.

Inspection robotics is about data

鈥淲e build a hardware platform,鈥� Zingg explains, 鈥渂ut inspection robotics is really about data that is consistent and trustworthy.鈥�

ANYmal autonomously navigates industrial sites to collect data that goes beyond what a human can collect alone. Beyond just visual inspection, its sensors also collect multi-modal data, including thermal imaging, ultrasonic leak detection, gas concentration detection, acoustic anomaly detection, and more. The observations are fed into what ANYbotics calls 鈥渋nspection intelligence,鈥� which transforms the collected data into actionable operational insights. The result is higher uptime, longer asset lifecycles, and, most importantly, safer working conditions for humans.

ANYmal can make a huge impact on operations. One offshore wind customer, Zingg says, has used ANYmal to manage all inspections and has eliminated the need to send personnel to a remote platform for months. When human intervention was eventually required, ANYmal鈥檚 data from prior inspections made all the difference. The customer already knew exactly what was wrong, which expert to send, and what equipment to bring鈥攁voiding costly and risky trial-and-error site visits.

Yet for ANYbotics, delivering insights is not enough if those insights are not integrated in the software systems customers use.

鈥溌槎乖� is where ANYbotics needs to be native鈥�

Through extensive user research, ANYbotics discovered that many plant operators, maintenance managers, and field service teams already run their daily operations in 麻豆原创. Work orders, asset histories, performance trends, and decisions all flow through 麻豆原创 systems. 鈥淚f customers are using 麻豆原创, 麻豆原创 is where ANYbotics needs to be native,鈥� Zingg says.

Meanwhile, 麻豆原创鈥檚 Project Embodied AI was looking for robotics companies to partner with. The project focuses on extending the impact of 麻豆原创 Business AI into physical operations by enabling robots to autonomously perform complex tasks with an understanding of the broader business context.

It was clearly a perfect fit and has delivered advantages for both companies.

On the system side, a continuous, unbroken digital thread connects ANYbotics insights from industrial inspections to data in 麻豆原创 systems, helping inform key business and operational decisions across the organization.

For end users, embedding ANYmal directly into familiar 麻豆原创 workflows can also help ease adoption, since introducing robotics into already stretched industrial workforces can trigger anxiety. Concerns about job security, workflow disruption, and complexity are common, but embedding ANYmal directly into familiar 麻豆原创 workflows can help reduce that friction, Zingg explains.

Treating robots as part of the workforce

The first major integration point was聽. Rather than sending only human technicians, customers can now dispatch work orders directly to ANYmal as they would to any other field team member. The robot then autonomously executes inspection tasks, gathers data, and reports the results directly back into a company鈥檚 麻豆原创 system.

From there, the integration expanded into asset-related scenarios and is now moving toward broader enablement via 麻豆原创 Business Technology Platform (麻豆原创 BTP), with the goal of allowing robot-generated data to land wherever customers need it in their 麻豆原创 landscape.

The ambition is not to force humans to adapt to robots, but for robots to adapt to human workflows. 鈥淎NYmal has to put data in the 麻豆原创 system, just like human team members,鈥� Zingg notes. ANYmal becomes another worker in the same operational system of record.

Project Embodied AI in practice

This combination of ANYbotics robotic technology with 麻豆原创 bridges the gap between physical operations and enterprise applications and tangibly reflects the goal of Project Embodied AI.

On the 麻豆原创 side, AI agents operate on ANYmal鈥檚 robotic systems to execute physical tasks, such as safety inspections.

On the ANYbotics side, ANYmal is a physical object that moves through space, perceives its environment, and acts within real-world constraints. ANYmal uses 麻豆原创 historic and time-series data to inform decisions while at the same time remaining fully autonomous even in environments with no connectivity.

It鈥檚 important to note, Zingg stresses, that ANYbotics has control over ANYmal鈥檚 behavior and inspection execution, while 麻豆原创 has control over the business context such as work orders, asset data, or operational priorities. It is the 麻豆原创 business context that informs how ANYmal鈥檚 insights are consumed and acted upon while ANYbotics controls ANYmal鈥檚 physical interactions.

Scaling safely and responsibly

Today, more than 200 ANYmal robots are already in productive use worldwide, with inspection deployments in heavy-industry environments that would otherwise require constant human exposure.

Safety remains central to ANYbotics. Each deployment includes extensive testing and an on-site field engineer who helps ANYmal learn and validate its environment and trains customer teams on safe operational procedures. While ANYmal is built to work independently, humans remain firmly in the loop.

A glimpse into the future

As industries face labor shortages and aging workforces, undocumented expertise can all too often be lost. With autonomous inspection robots such as ANYmal, this knowledge is captured and turned into programs that can run day in and day out across multiple sites. The captured data flows into 麻豆原创 to become organizational intelligence that survives any workforce turnover.  

ANYbotics鈥� partnership with 麻豆原创 shows that this combination of robotics and enterprise software is moving swiftly from the experimental stage to real-world implementation.

In the future, industrial inspection will be powered by AI, not as disembodied dashboards or isolated machines, but as an integrated intelligent system where physical robots and digital workflows in 麻豆原创 systems operate as one.

In that future, robots like ANYmal are no longer novelties. They are coworkers, albeit mechanical four-legged ones, quietly extending human capability into places humans were never meant to go. These robots, together with 麻豆原创, are shaping for a future where dirty, dangerous, and dusty industrial inspections are being transformed into business insights.

Top image courtesy of ANYbotics

SafetyGuard was created as part of a program at UTUM鈥檚 Digital Product School鈥攚here teams of selected students work on real-world challenges鈥攊n just 12 weeks by a student team, 鈥淢IDAS,鈥� and 麻豆原创 Research & Innovation. This project illustrates just how effective cross-location collaboration with the ecosystem can be in rapidly creating prototypes as a foundation for product development at 麻豆原创.

Embodied AI: a joint focus of innovation

The objective of this particular prototyping project was to identify use cases in which robotics and AI could be seamlessly integrated into business processes. Based on its research and on user studies, team MIDAS pinpointed the reliable detection and documentation of safety risks as one such use case.

Safety inspections are essential in many industries, but they are often time-consuming and error-prone. SafetyGuard could change that: leveraging embodied AI, it makes inspections faster, more efficient, and more reliable, without increasing the workload for employees.

Embodied AI鈥攖he integration of artificial intelligence into physical robot systems鈥攚ill be a focal point of investment and development work at 麻豆原创 going forward.

SafetyGuard combines two technologies: modular robotics and AI-powered autonomy. In this prototype, robots, such as drones and humanoid systems capable of inspecting work environments without human intervention, are equipped with a specialized AI model that is trained, for example, to detect where protective equipment is missing and to automatically document safety-related incidents. What鈥檚 more, the robots can multitask. While they are carrying out inspections, they can transport materials and monitor machinery鈥攁n approach that combines efficiency gains with increased safety levels.

鈥淪afetyGuard demonstrates just how effective our ecosystem approach is. In this project, an 麻豆原创 team in Potsdam and a group of students in Munich joined forces and very quickly built a prototype that will have a real impact on product development,鈥� Tobias Riasanow, head of Ecosystem Development at 麻豆原创 Labs Germany, says. 鈥淚t鈥檚 the perfect example of what 麻豆原创 understands by 鈥榚cosystem development.鈥欌��

The project is also strategic to 麻豆原创 in that SafetyGuard addresses real-life industry requirements and complements 麻豆原创鈥檚 solutions for environment, health, and safety management. The prototyping approach that led to SafetyGuard could therefore become part of the 麻豆原创 portfolio in the future to help further reduce the time it takes to get from an idea to a product.

Programs with impact

To date, 13 prototypes have been developed for 麻豆原创 under programs run by UTUM, all in close collaboration with product teams at 麻豆原创 and directly related to their respective road maps. The programs offered by UTUM include:

• Innovation Sprint: Here, students have just one week to work on prototypes for solving specific user challenges. The most recent sprint, The Future of Retail with 麻豆原创 Joule, produced five such prototypes, including solutions for intelligent inventory optimization, virtual shelf monitoring, and retail assistance systems.
• Digital Product School: This 12-week program for students is designed as a platform for developing executable software prototypes. One prototype created out of this program was Carbon Data Exchange, which is now part of 麻豆原创 Sustainability Control Tower. Another is an application that makes it easier for developers to access the extensive documentation for 麻豆原创 Field Service Management. Instead of wasting valuable time searching, they can simply type their questions into a chatbot and receive helpful answers, including code examples and direct links to the documentation they need.
• Multistakeholder projects: Additional prototypes have been created in longer-term programs such as Digital Hub Mobility and Circular Republic. Programs like these bring together experts from companies, startups, and research institutions over several months and allow 麻豆原创 to test innovations in real-world customer and partner environments at an early stage in their development.

Co-innovation gets results鈥攆aster

The collaboration between 麻豆原创 and UTUM shows how quickly ideas can lead to tangible results when students, researchers, and 麻豆原创 teams work together to apply scientific approaches to real-life industrial challenges. SafetyGuard is an example of how embodied AI innovations can be created and how different groups of experts can benefit from one another.

鈥淪afetyGuard demonstrates the extensibility of 麻豆原创鈥檚 approach to embodied AI. 麻豆原创鈥檚 embodied AI layer integrates the business context and triggers actions across the suite. SafetyGuard鈥檚 AI-native, manufacturer-agnostic design is scalable across different use cases and enables multitasking, allowing, for example, robots to process safety incidents while performing other tasks such as industrial asset inspections. SafetyGuard is a proof point for 麻豆原创 for the added value of using cognitive robotics in safety scenarios.鈥�

Adelya鈥疐atykhova and Dr.鈥乽kasz鈥疧strowski, Initiators and Supervisors of the student challenge

This first appeared on the German 麻豆原创 News Center.

Its advanced refrigeration, air conditioning, and heat pump technologies keep supermarket shelves, hotel rooms, and hospital operating theaters at the right temperatures, whatever the ambient temperature is. Its compressors are essential for storing medicines, preserving perishable goods in shipping containers, and processing frozen foods. And if that isn鈥檛 impressive enough, its technology keeps ice hockey players gliding across the ice and breweries fermenting yeast for your beer.

The company is a longstanding RISE with 麻豆原创 customer and, like 麻豆原创, is constantly innovating its products to stay ahead. Christian Stenzel, vice president of Organization and IT at BITZER, has a clear vision for an 麻豆原创 strategy that prioritizes integration and rapid adoption of AI: 鈥淥ptimizing business processes is as important as product innovation at BITZER.鈥�

The 麻豆原创 Research and Innovation team is equally committed to keeping 麻豆原创 ahead by exploring new technologies and one team is currently dedicated to Project Embodied AI. Embodied AI combines artificial intelligence with a physical form, such as robots, that can perceive and act in the real world. Embodied AI agents take this a step further: extending the impact of into physical operations by making robots cognitive.

To explore potential use cases where cognitive robots could bring value, the Project Embodied AI team invited a select group of forward-thinking leaders and innovation professionals from 麻豆原创 customers to join its Physical AI and Cognitive Robots Exploration Council. And BITZER was one of them.

鈥淒emand-driven production is key in our business,鈥� said BITZER’s Stenzel, who immediately saw the potential value in using robots to meet demand fluctuations.

Running on (麻豆原创 BTP) and , already in place, BITZER already had the ideal software landscape to serve as a proof-of-concept test ground.

Before deployment, NEURA鈥檚 , one of Europe鈥檚 most advanced humanoid robots, was virtually trained for the pick-task use case on NVIDIA Isaac Sim software.

A new benchmark for intelligent automation

This proof of concept for Project Embodied AI sets a new benchmark for intelligent automation in warehouses, Stenzel said. The results highlight:

• Seamless integration: 麻豆原创 EWM connected directly with physical warehouse operations, no costly middleware required.
• True autonomy: Robots performed pick-tasks independently, demonstrating advanced task-level autonomy.
• Agility and flexibility: Robots could enable demand-driven production, operating 24/7 to meet shifting needs.
• Reliable processes: Orders of materials were automatically created, demonstrating how operational mistakes could be minimized.

A decisive step forward

Dr. Lukasz Ostrowski, head of Embodied AI and Robotics at 麻豆原创, heralded this proof-of-concept as a decisive step forward: 鈥淭he proof of concept at BITZER is great first step for experiencing firsthand how the impact of 麻豆原创 Business AI can be extended into physical operations. Further proofs of concept are planned as Project Embodied AI continues to assess the business value of embodied AI for customers.鈥�

Click the button below to load the content from YouTube.

Always allow YouTube

This month at , we announced a wave of 麻豆原创 Business AI innovations all built on the same technology foundation that powers our that we are now delivering to our customers and partners, allowing them to add even more value in the future.

We showed how the future of enterprise software is built on an AI-native architecture, powered by 麻豆原创 app, data, and AI foundation. With this approach, we are enabling a platform shift across the tech stack in a non-disruptive fashion, empowering developers to work faster and smarter using the frameworks and tools of their choice.

麻豆原创 HANA Cloud and 麻豆原创 Business Data Cloud: powering our AI-native future

麻豆原创 HANA Cloud is the database for 麻豆原创鈥檚 AI-native software architecture and the foundation of our broader data fabric strategy. At 麻豆原创 TechEd, we announced new AI capabilities for 麻豆原创 HANA Cloud that spur AI innovation.  

For example, Model Context Protocol (MCP) support for 麻豆原创 HANA Cloud is now generally available. This provides direct access to rich multi-model engines. Agents can be grounded in full enterprise data context: navigating relationships across customers and suppliers, understanding geographic dependencies through spatial data, and performing semantic searches through vector embeddings — all within a single in-memory engine.  

We鈥檙e also expanding 麻豆原创 HANA Cloud knowledge graph engine capabilities (Q1 2026) so customers can automatically generate knowledge graphs from 麻豆原创 HANA Cloud metadata. What used to take weeks of manual modeling can now happen automatically in minutes. But that鈥檚 not all. We鈥檙e also enabling agentic memory in 麻豆原创 HANA Cloud. With long-term memory, AI agents can memorize past inputs and decisions — learning and remembering just like humans — and become continuously smarter.

These advances show that 麻豆原创 HANA Cloud is truly powering an AI-native future. .

Bringing together the power of 麻豆原创 BDC and Snowflake

We are bringing the power of Snowflake together with 麻豆原创 Business Data Cloud (麻豆原创 BDC), calling it 麻豆原创 Snowflake. This partnership enables zero copy data sharing with Snowflake via 麻豆原创 BDC Connect.

Enterprises already using Snowflake today can leverage 麻豆原创 BDC Connect to integrate their existing instances of Snowflake with 麻豆原创 BDC, giving them seamless, real-time access to combined, semantically rich 麻豆原创 with non-麻豆原创 data in 麻豆原创 BDC. 麻豆原创 Snowflake will be made generally available in Q1 2026, and 麻豆原创 BDC Connect for Snowflake in H1 2026. Find more information here.

麻豆原创-RPT-1: a new category of AI models

One of our most exciting announcements at 麻豆原创 TechEd was the launch of our first enterprise relational foundation model 麻豆原创-RPT-1, pronounced: 鈥渞apid one.鈥�

Businesses run on structured data. But large language models (LLMs) struggle with a general understanding of table structures and associated semantics. This requires the use of machine learning, or 鈥渘arrow AI,鈥� for tasks like classification, regression, and more. But classical machine learning necessitates training a model on each task, which easily can lead to hundreds of separate models.

麻豆原创-RPT-1 puts them all into one single, pre-trained model that understands relational business data and predicts business outcomes. Unlike language, image, or video models, 麻豆原创-RPT-1 accurately predicts business based on tabular data such as payment delays, supplier risks, upsell opportunities, customer churn risk, and more.

We believe that 麻豆原创-RPT-1 is a super capable foundation model today. It provides up to 2x better prediction quality compared to narrow models and 3.5x better prediction quality as compared to LLMs. .

麻豆原创-RPT-1 comes in three versions. 麻豆原创-RPT-1-small is for super-fast predictions and 麻豆原创-RPT-1-large is for highest accuracy. Both will be generally available in Q4 2025 in the generative AI hub in AI Foundation. 麻豆原创-RPT-1-OSS is the open-source version, available in Hugging Face and GitHub.

You can test 麻豆原创-RPT-1 today with your data or our use case data samples via no-code UI or via API in the new 麻豆原创-RPT-1 playground, an intuitive and interactive space to test for free and open to everyone and .

We are continuously adding new capabilities to AI Foundation and models to the generative AI hub, empowering developers to experiment with orchestration tools and leading models to scale AI development and productization across 麻豆原创 and non-麻豆原创 environments. For example, Perplexity is now generally available in the generative AI hub, so users can correlate business data with external data from the internet. Evaluation Services and Prompt Optimizer, in close collaboration with NotDiamond, are now also generally available in AI Foundation, freeing up users to adopt the most appropriate model for their use cases without the need for rewriting prompts. .

Digital sovereignty made in Germany, for Europe

Digital sovereignty is becoming increasingly important, reflecting the need for regional AI services that align with local regulations, standards, and values. As an example, Europe will benefit from its own strong, trustworthy infrastructure to support innovation, data protection, and ethical AI.

AI Foundation, including various models and all the services we offer, is already available on our own cloud infrastructure. As a next step, we are expanding our 麻豆原创 Cloud Infrastructure offering in our 麻豆原创 data center in Walldorf, Germany, to Deutsche Telekom through the Industrial AI Cloud project, providing secure, high-performance infrastructure for AI innovations across public institutions, defense, and society. 麻豆原创 delivers 麻豆原创 Cloud Infrastructure, 麻豆原创 Business Technology Platform, and applications 鈥� including our AI Foundation with frontier AI from Mistral, Cohere, and others 鈥� on Telekom鈥檚 Munich data center. Both companies uphold the highest standards of data protection, security, and reliability.

This marks a milestone as more European companies join the Industrial AI Cloud project, advancing applied AI across Europe with trusted, business-embedded solutions that unlock the full potential of industry data. See the announcement here.

Enabling customers to build, extend, share, and orchestrate AI agents

To help manage Joule Agents and Joule skills, we have introduced the concept of AI Assistants 鈥� role-based AI teammates, accessed through Joule 鈥� like a financial assistant that brings together agents for cash collection, treasury, and more. We will provide AI Assistants in Joule for every core business role, offering our users an agentic experience like never before.

Out-of-the-box Joule Agents are powerful, but we know that every company has unique requirements. We believe AI should adapt to users鈥� systems, not the other way around, so we are enabling them to use Joule Studio to extend 麻豆原创鈥檚 pre-built agents with custom fields, tools, and reasoning logic while retaining all the deeply grounded integration capabilities 麻豆原创 provides. Joule Studio also provides low-code tools to build custom agents that integrate with all other Joule Agents, Joule skills, and 麻豆原创 BDC.

Using a low-code approach, users can build Joule Agents visually with natural language and drag-and-drop. But we also want to meet the needs of developers who want ultimate flexibility. Our pro-code approach gives developers the freedom to build agents using the agentic framework of their choice 鈥� for example, LangGraph, CrewAI, Google鈥檚 Agent Development Kit, and more. 麻豆原创 Cloud SDK for AI now supports agentic development, ensuring these pro-code agents can be seamlessly integrated and giving developers the best of both worlds: deep integration and full flexibility.

No matter how you want to build agents, an important question is how to integrate them into the larger ecosystem beyond 麻豆原创. We鈥檙e making Joule Agents fully compatible with the agent-to-agent (A2A) protocol soon, so agents can discover and collaborate with each other.

A2A exposes rich semantics describing an agent鈥檚 capabilities, allowing both 麻豆原创 and third-party agents to work together seamlessly. We are collaborating with partners 鈥� AWS, Google, Microsoft, ServiceNow, and more 鈥� to standardize this protocol for full interoperability. This capability will allow Joule to orchestrate tasks across multiple agents, both 麻豆原创 and non-麻豆原创, increasing automation and productivity across the enterprise. Read more here.

To manage and govern agents across the enterprise, is now generally available, providing centralized control of 麻豆原创 and non-麻豆原创 agents. In addition, is available now for tracing agent actions, benchmarking against KPIs, and identifying bottlenecks or opportunities for agents to further improve business.

No 麻豆原创 TechEd without ABAP news

The ABAP journey continues with 麻豆原创-ABAP-1, which will be available in the generative AI hub in Q4 2025. Trained on ABAP code, it is designed to build ABAP AI use cases, enabling developers to build smarter, custom AI solutions in modern ABAP code. .

In addition, ABAP Cloud development is coming to Visual Studio (VS) Code. The new ABAP Cloud extension for VS Code delivers a streamlined, file-based development experience with built-in AI assistance. Powered by an ABAP language server, it will initially support 麻豆原创 Fiori UI service development and expand to additional ABAP Cloud scenarios over time. This brings ABAP development into the same environment where developers already build with UI5 and CAP. General availability is planned for Q2 2026. .

What鈥檚 next: embodied AI and quantum

麻豆原创 TechEd is always an opportunity to look to the future. This year, that future includes not just humans, but also autonomous devices, including humanoid robots.

By integrating Joule Agents natively with robots, 麻豆原创 is bringing business logic into the physical world, enabling a wide range of autonomous devices to operate with enterprise context. We highlighted our strategic partnerships with robotics companies and system integrators to serve customers like Sartorius, Bitzer, and Matur Fompak, demonstrating how our expanding physical AI ecosystem enables robots to understand business processes and execute complex tasks autonomously.

Early proof-of-concept deployments show Joule successfully integrated with 麻豆原创 business applications and autonomous systems across asset performance, logistics, field services, and warehouse operations. While still in the pioneering stage, these implementations illustrate how 麻豆原创 is extending Joule to serve both human users and autonomous devices, shaping the future of enterprise AI.

Read more about the partnerships and implementations here.

AI is a new compute paradigm that changes everything. But there is another compute paradigm on the horizon: quantum computing. It鈥檚 early days, but 麻豆原创 is driving the future of enterprise computing with a vision to help businesses get ready for quantum computing.

麻豆原创 is not building quantum hardware; instead, we are focusing on creating quantum algorithms for business applications. These solutions are simple to deploy 鈥� on when needed, off when not 鈥� and are designed to be hardware-agnostic, collaborating with partners such as IBM to ensure seamless integration without re-platforming. This approach will enable organizations to unlock operational efficiency and drive better business results at enterprise scale.

I couldn鈥檛 be more excited about what鈥檚 next for our customers鈥� future as we bring 麻豆原创鈥檚 AI-native architecture to life.

Philipp Herzig is CTO of 麻豆原创.

Across industries, operational tasks often require real-time judgement and adaptation to constantly changing conditions. Traditional automation falls short here, as physical execution remains disconnected from business logic.

麻豆原创 addresses this gap with its , which extends the impact of into physical operations. By enabling robots to act with business awareness, they can autonomously execute complex tasks while interpreting real-time enterprise context. This allows organizations to adapt more quickly and reliably to dynamic operational environments.

麻豆原创 is uniquely positioned to deliver these innovations due to decades of experience building business applications deeply embedded in the processes that power the modern enterprise. This allows 麻豆原创 to integrate robotics into business functions seamlessly, and in a way no other company can.  As a result, autonomous robots operate as an extension of enterprise systems鈥攏ot as isolated automation鈥攅nabling consistent, context-aware execution in dynamic environments.

To explore how embodied AI can support your operations, contact us . Current capabilities available for productive use or testing span a range of warehouse and operational scenarios, including picking and placing, asset inspection, quality inspection, and safety inspection.

The following examples illustrate these capabilities across real-world use cases.

Enabling autonomous warehouse execution at Bosch

One of the many strategic initiatives and are working closely is in the area of , where Bosch and 麻豆原创 is collaborating with to explore how physical AI can transform warehouse automation. In a joint proof of concept, 麻豆原创鈥檚 embodied AI capabilities and Aimbo鈥檚 Industrial Embodied AI deployment middleware enabled Aimbo鈥檚 humanoid robot, Anigon, to execute warehouse tasks based on real-time operational context from 麻豆原创 Extended Warehouse Management (麻豆原创 EWM), including delivery data, order priorities, and storage locations.

This initiative combined 麻豆原创 S/4HANA-driven workflows directly with autonomous robotic execution at a European Bosch facility. Trained and deployed by Aimbo in 10 working days, a single Anigon unit equipped with the universal dual grippers provided by Bosch was rapidly integrated to handle intralogistics workflows across three distinct bin configurations and three workstation layouts, creating a bi-directional data loop with 麻豆原创’s embodied AI connected to 麻豆原创 EWM. It successfully demonstrated how enterprise software, AI, and humanoid robotics can work together to enable more adaptive, scalable, and resilient industrial operations.

While the initiative remains in an exploration phase and was validated in dedicated beta and test system environments, the collaboration highlights the strong potential of embodied AI for intelligent warehouse automation and future enterprise-scale industrial AI applications.

“This achievement demonstrates what鈥檚 possible when bold collaboration meets enterprise innovation鈥攃onnecting AI, robotics, and 麻豆原创 to shape the future of autonomous operations. Together, we are moving physical AI from experimentation to enterprise-scale industrial AI with real operational impact.”

Gamze 脰zt眉rk, Head of CoE ERP Product Excellence, Bosch Digital

Optimizing warehouse inspections at Vodafone Procure & Connect

麻豆原创, together with 听补苍诲听, is demonstrating how embodied AI and humanoid robotics can help address operational inefficiencies and safety risks in warehouse environments. The collaboration highlights聽麻豆原创鈥檚 role in connecting robots to end-to-end warehouse processes and business context, helping enable more intelligent, responsive and traceable operations.

In a scenario test at 鈥檚 warehouse in Duisburg, Germany, 麻豆原创鈥檚 embodied and Accenture鈥檚 physical聽AI capabilities enable humanoid robots to perform inspections and monitor operations using real-time business context from 麻豆原创 systems, including inventory data, task priorities, and storage locations.聽The聽pilot聽demonstrates how robots can identify damaged or misplaced items, detect aisle obstructions, and feed findings directly back into 麻豆原创 systems for closed-loop traceability聽and faster operational response.

By reducing reliance on manual inspections and improving operational consistency, the collaboration shows physical AI can enhance both efficiency and safety in dynamic warehouse operations. Learn more about this collaboration聽.

Scaling vehicle validation at Mahindra & Mahindra

, a global automotive and industrial group, collaborated with 麻豆原创 to target a high-volume, error-prone process: manual validation of vehicle identifiers.

Here, 麻豆原创’s embodied AI uses 鈥檚 visual AI, Amalgamation AI, to cross-check real world observations to 麻豆原创 records, enriched by application data and context engine numbers, VIN plate, and Engraved Chassis Number, enabling fast, scalable vehicle validation.

This approach has the potential to increase throughput by up to three times, reduce manual verification effort by about 67%, and achieve data extraction accuracy of 95%-99%.

Optimizing material handling at Martur Fompak

, a global supplier of automotive seating systems, teamed up with 麻豆原创 to address throughput bottlenecks and limited shop floor responsiveness caused by manual material handling.

For this scenario, 麻豆原创鈥檚 embodied AI orchestrates a robot to remove materials across a live automotive manufacturing environment, guided by real-time business context from , including physical attributes and warehouse logistics.

“By assigning repetitive, non-value-adding, and physically demanding tasks to robots, we reduce physical strain and potential injuries while enabling our people to focus on safer, more meaningful, and higher value work that drives productivity and innovation.鈥�

脰zge G眉l艧ah Geydirir, 麻豆原创 Logistics Modules & Operational Excellence Manager at Martur Fompak

Early findings indicate throughput and work order completion improvements of up to five times, along with improved inventory accuracy.

Read the full customer success story and see the 2026 麻豆原创 Innovation Award recognition .

Advancing spare parts inspection at Tetra Pak

, a global leader in food processing and packaging solutions, partnered with 麻豆原创 to address warehouse bottlenecks and risk of errors in spare parts inspection processes.

This proof of concept shows 麻豆原创’s embodied AI and replicating parts of Tetra Paks’ 麻豆原创 Extended Warehouse Management inbound process in a lab, where a humanoid robot verifies the received items and packs them into plastic boxes for further processing. These results can be automatically recorded back into 麻豆原创, closing the loop between execution and system updates.

An 麻豆原创 expert estimates that this end-to-end automation will improve stock and quality accuracy by 10%鈥�20% and build in-house expertise in advanced technologies for continued productivity gains.

Reducing costly storage errors with autonomous inbound processing at 础谤莽别濒颈办-尝骋

, a global manufacturing joint venture, partnered with 麻豆原创 to address a costly and persistent issue with incorrect storage placement in inbound logistics. At the 础谤莽别濒颈办-尝骋 facilities, such errors lead to millions of euros in annual losses through rework, inventory discrepancies, and production disruptions.

In this ongoing proof of concept, 麻豆原创鈥檚 embodied AI enables a humanoid robot to execute and record inbound storage tasks guided by real-time context from (麻豆原创 EWM). Context-enriched 麻豆原创 EWM tasks are routed through 鈥檚 fleet management system, allowing the robot to scan barcodes, transport materials, and provide confirmations with 麻豆原创-level traceability.

This approach has the potential to reduce manual intervention and eliminate placement errors at the source, improving inventory accuracy and end-to-end traceability. By preventing rework and downstream production disruptions, it can significantly lower inbound handling costs while improving overall process quality.

麻豆原创 runs 麻豆原创: scaling autonomous logistics execution in 麻豆原创鈥檚 own warehouse

麻豆原创 partnered with , an innovative robotics startup, to validate embodied AI in a live warehouse environment, demonstrating how AI-driven robotics can enhance operational efficiency.

In this proof of concept, 麻豆原创鈥檚 embodied AI uses real-time business context from to select, prioritize, and orchestrate warehouse tasks through Cyberwave鈥檚 platform. Cyberwave validates tasks in digital twin environments before live deployment, while robotic policies are trained and improved using real-world data, simulation, reinforcement learning, and vision-language-action models.

This demonstrates the readiness of 麻豆原创鈥檚 embodied AI to support autonomous execution in real warehouse operations while maintaining a closed loop between robotic execution and 麻豆原创 system updates.

Cutting-edge experiments at BITZER demonstrate measurable productivity gains

, a leading name in refrigeration, air conditioning, and heat pump technology, teamed up with 麻豆原创 and NEURA Robotics to revolutionize warehouse logistics. In a recent pilot proof-of-concept, BITZER鈥檚 warehouse became a testing ground for one of Europe鈥檚 most advanced humanoid robot, , which was able to perform pick-tasks on its own in real time.

The tasks are selected by embodied AI agents. The process also integrates 麻豆原创鈥檚 business logic from through (麻豆原创 BTP). Prior to its warehouse deployment, 4NE1 was trained virtually using NVIDIA’s Isaac Sim software. This ensured the robot was fully prepared for real-world operations. By integrating embodied AI into warehouse operations, BITZER could reach 24/7 utilization and a high level of responsiveness.

The technology can add to human expertise, stepping in during demand fluctuations and peak periods. It also complements regular shifts with flexible and scalable support. This ensures operations remain agile and efficient, even under varying workloads. Thanks to a single source of truth, orders can be expanded or cancelled in near real time, as robots execute changes almost instantly. This improves reaction times and enables BITZER to maintain high service levels while optimizing the use of resources.

鈥淲e are excited to join the Embodied AI initiative with 麻豆原创 and NEURA. We believe this collaboration will enhance our operational efficiency and drive innovation in our processes.鈥�

Christian Stenzel, Vice President of Corporate Organization and IT, BITZER

Transforming warehouse operations at Sartorius

Imagine stepping into a warehouse in which intelligent machines work side-by-side with humans. The first proof of concept for embodied AI at shows it is possible and marks a milestone in the journey to next-level logistics.

鈥淪o far, like many others, our focus was on fixed automation, in which specialized equipment handles only a single task. Now we鈥檙e making automation intelligent, and far more dynamic, to help us navigate a fast-moving world.鈥�

Steffen Dietz, Manager of Business Process Management in Operations and Supply Chain, Sartorius

The proof of concept, also delivered through the partnership with 麻豆原创 and NEURA Robotics, demonstrates how cognitive robots can support manual workstations in an advanced warehouse environment. Here, the humanoid robot 4NE1 was trained with Sartorius products in NEURA Robotics鈥� lab. The solution builds on an 麻豆原创 S/4HANA migration and 麻豆原创 Extended Warehouse Management (麻豆原创 EWM) rollout in May, which established the foundation for leveraging the latest capabilities from 麻豆原创.

The result boosts efficiency and enhances operational resilience. 鈥淲e鈥檙e really happy to help spearhead this new age together with 麻豆原创 and NEURA,鈥� Steffen Dietz, manager of Business Process Management in Operations and Supply Chain at Sartorius, shared.

Robotics company partners

Building upon these successes, 麻豆原创 announced the following additional partnerships:

AgiBot

Agibot creates general-purpose embodied robot products and an application ecosystem. The company delivers a complete product portfolio and deploy across all major application scenarios.

“Our 麻豆原创 partnership transforms industrial automation by combining humanoid capabilities with enterprise intelligence that understands business context,” said Peng Zhihui, founder of .

.

ANYbotics

ANYbotics provides a full-stack autonomous inspection solution that combines autonomous robotics with inspection intelligence.

“Integrating this continuous flow of inspection intelligence with 麻豆原创 makes operations not only autonomous but truly intelligent, where issues are predicted, understood, and prevented before they affect production,鈥� said Dr. P茅ter Fankhauser, CEO and co-founder of .

.

Apptronik

makes AI-powered humanoid robots designed to collaborate thoughtfully with humans, initially in critical industries such as manufacturing and logistics, with future applications in healthcare, the home, and beyond.

Today, Apptronik鈥檚 flagship robot, Apollo, excels at flexible work in dynamic environments, supporting industrial tasks like warehouse picking, placing, and material handling while adapting to changing demand on the floor. With 麻豆原创鈥檚 business context guiding every action, these humanoid robots can execute warehouse tasks with the right priorities at the right time so automation stays aligned with real operational needs.

Booster Robotics

Booster Robotics provides T1 humanoid robots for warehouse operations and field maintenance.

“Our humanoid platforms, with 麻豆原创’s intelligence, creates an adaptive automation foundation that understands business processes and operational context,” said Cheng Hao, CEO of .

.

Boston Dynamics

delivers mobile robots for industrial inspection and safety monitoring, enabling autonomous patrols, anomaly detection, and rapid incident response across large-scale facilities.

By connecting robots鈥� real-time site awareness with 麻豆原创鈥檚 enterprise intelligence, organizations can turn inspection data into safer operations and faster, more informed decisions.

Galbot

Galbot’s fully autonomous, general-purpose humanoid robots have been deployed across a wide range of applications, including industrial, logistics, retail, and healthcare sectors. Powered by proprietary vision-language-action models, the Galbot G1 autonomously performs complex tasks such as precise parts sorting, industrial bin handling, and end-to-end pharmacy operations. These models enable Galbot robots to rapidly adapt to dynamic environments, ensuring high precision and efficiency even in challenging real-world conditions.

“Our collaboration with 麻豆原创 marks a key milestone in transforming how robots understand and operate within enterprise environments. By integrating business context awareness into our robots, we’re creating automation that seamlessly adapts to shifting operational priorities in real time,” said He Wang, founder and CEO of .

.

Humanoid

Humanoid offers reliable HMND 01 humanoid robots that provide cost-effective industrial automation and warehouse solutions, with modular designs that enable configurations for logistics operations, asset monitoring, and scalable field service applications.

“麻豆原创’s AI platform gives our robots intelligence to adapt and scale with enterprise needs, which creates flexible automation,” said Artem Sokolov, founder and CEO of .

RobotEra

develops humanoid robots designed for enterprise operations and, together with 麻豆原创鈥檚 embodied AI, enables robots to execute end-to-end 麻豆原创 EWM workflows鈥攆rom material verification and visual inspection to exception handling鈥攇uided by real-time business context.

By integrating 麻豆原创鈥檚 operational intelligence with RobotEra鈥檚 humanoid robotics platform, customers can enable automation that understands warehouse priorities, handles exceptions, and completes 麻豆原创 EWM tasks with enterprise-grade reliability.

Unitree Robotics

Unitree Robotics provides advanced quadruped Go2 robots for warehouse navigation and asset inspection, plus G1 humanoids with human-like dexterity for logistics operations, alongside industrial B2 models for outdoor facility maintenance.

“麻豆原创 embodied AI agents will revolutionize enterprise autonomous operations from warehouse management to predictive maintenance across facilities,” said Wang Xingxing, CEO and founder of .

.

Robotics enablement partners

麻豆原创 also introduced the following robotics enablement partners to connect humanoid and mobile robots, optimize intralogistics, streamline inspections, and orchestrate physical assets enabled by 麻豆原创 Business AI and automation technologies:

Accenture

partners with 麻豆原创 to advance physical AI for enterprise operations, helping organizations connect digital business processes with autonomous execution.

The collaboration brings 麻豆原创鈥檚 process and data intelligence together with Accenture鈥檚 expertise in robotics, digital twins, and operational transformation to support use cases such as inspection, risk detection, and workflow optimization.

Aimbo AG

delivers the industrial embodied AI deployment Middleware that connects enterprise software with robotic execution. The company鈥檚 vision is to build robust, closed-loop system capabilities spanning models, data, evaluation, and deployment.

True value for industrial leaders lies not in a powerful model alone, but in a reliable system that integrates deeply into the factory鈥檚 operations, continuously creating production value and improving with every cycle.

Capgemini

Capgemini explores the value that can be derived from the convergence of advanced technologies such as agentic and multi-agent AI systems, humanoid robotics, reinforcement learning, spatial computing, real-time 3D environments, and conversational AI. and 麻豆原创 are jointly exploring physical AI to help organizations gain a competitive edge.

Cyberwave 

Cyberwave聽connects 麻豆原创 systems to the physical world through its physical AI platform, which integrates robots, sensors, and digital twins into enterprise workflows.

鈥淭ogether with 麻豆原创, Cyberwave turns enterprise data into coordinated physical action鈥攂ridging the gap between digital intelligence and real-world operations through physical AI,鈥� said Simone Di Somma, founder of .

FairConsult 24|7

partners with 麻豆原创 to deliver embodied AI-enabled warehouse execution, combining 麻豆原创鈥檚 business context-driven task intelligence with FairConsult 24|7鈥檚 fleet management system to orchestrate enterprise-connected robotic operations.

Together, they unite 麻豆原创鈥檚 real-time operational priorities with fleet-level control, enabling robots to execute warehouse tasks with transparency, governance, and measurable performance.

Fujitsu

brings physical AI and visual intelligence capabilities through its Amalgamation AI offering, enabling intelligent image processing and data extraction that can be deployed to edge devices including cameras and robots.

Together with 麻豆原创, Fujitsu’s AI-driven visual capabilities integrate with 麻豆原创’s agent-based workflows to capture and validate real-world inputs鈥攕uch as vehicle identifiers in automotive inspection鈥攄irectly within 麻豆原创 systems for downstream business processing.

HCLTech

HCLTech provides automation expertise through its AI Force platform and 麻豆原创 integration capabilities, leveraging in collaboration with 麻豆原创 to accelerate generative AI-led robotics solutions.

“Our collaboration with 麻豆原创 enables cognitive robotics to seamlessly integrate with enterprise systems, transforming business operations through automation,” said Vijay Guntur, CTO and head of Ecosystems at .

KINEXON

KINEXON brings physical AI to day-to-day material flow management, helping customers scale mixed-fleet operations with a vendor-agnostic orchestration platform for autonomous mobile robots (AMRs), automated guided vehicles (AGVs), and manual vehicles.

“Our collaboration with 麻豆原创 infuses business-driven agentic reasoning into real-world material movement planning and execution, maximizing utilization and throughput,” said Dr. Alexander Huettenbrink, co-CEO of .

LG CNS

, a South Korea鈥揵ased IT and AI transformation company with presence across Asia, partners with 麻豆原创 to deliver AI-driven enterprise transformation.

Combining 麻豆原创鈥檚 business context and application intelligence with LG CNS鈥檚 PhysicalWorks, a robot training and multi-vendor robot orchestration and control platform, the collaboration, starting with PoC initiatives, enables AI-driven automation and optimization using real-time data across business processes.

Lighthouse

Lighthouse transforms business complexities into streamlined digital solutions, leveraging expertise across 麻豆原创 Intelligent Asset Management, 麻豆原创 Business AI, and 麻豆原创 BTP. 

鈥淓mbodied AI has huge potential for use cases, including asset and site inspection, health and safety, and quality inspection to deliver more resilient, flexible operations. We see major customer needs today, such as hazardous environments on offshore platforms in the oil and gas industry, utilities, and transportation,” said Urs Gehrig, managing director of Business Development at .

valantic

and 麻豆原创 are partnering to bring embodied AI to real-world operations. By merging 麻豆原创鈥檚 context-driven orchestration with valantic鈥檚 integration expertise, they enable high-precision, automated asset inspections. This collaboration transforms insights into immediate, coordinated action, ensuring safer operations, stronger compliance, and reliable asset performance. It’s embodied AI, made practical.

Through these strategic alliances, 麻豆原创 continues to lead the evolution from traditional robotic tools to those that empower autonomous operations, informed by deep business context.

To explore how 麻豆原创 technology makes proofs of concepts possible in robotics, explore the . To get involved in 麻豆原创’s Embodied AI initiative, .

Dr. 艁ukasz Ostrowski is head of Embodied AI and Robotics at 麻豆原创.