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

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

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

From AI-first to AI-native

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

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

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

The foundation behind the Autonomous Enterprise

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Supply chains are entering an era of permanent disruption

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

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

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

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

Companies are shifting from optimization to AI-enabled orchestration

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

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

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

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

Resilience is now defined by decision velocity

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

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

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

Trust and governance are the biggest barriers to scaling AI

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

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

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

The next frontier is the Autonomous Enterprise

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

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

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

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

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

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

Building the autonomous supply chain

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

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

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

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

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

The bottom line

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

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

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

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

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

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

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

Preparing the next generation of AI agent builders

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

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

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

Collaboration with educational institutions globally

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

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

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

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

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

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

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

Building the workforce of the future

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

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

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

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

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

What sets the Advanced Success Plan apart

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

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

1. AI鈥憄owered customer experiences

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

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

2. Hyper鈥�personalization at scale

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

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

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

3. Unified customer data and breaking down silos

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

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

4. Omnichannel commerce and B2B digital transformation

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

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

5. Customer retention over acquisition

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

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

6. Service as a revenue driver

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

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

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

7. Navigating digital transformation complexity and skills gaps

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

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

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

Measurable outcomes

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

Getting started

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

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

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

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

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

New AI sustainability agents

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

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

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

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

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

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

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

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

Only AI can deliver sustainability at scale

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

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

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

Enterprise autonomy entails gradual AI maturation:

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

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

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

Why 麻豆原创?

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

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

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

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

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

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

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

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

AI grounded in real operations

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

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

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

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

Joule Assistants across the supply chain

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

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

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

From assistants to autonomous agents

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

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

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

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

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

Where this shows up in practice

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

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

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

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

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

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

The path forward

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

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

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

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

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

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

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

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

Aligning experience and execution

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

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

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

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

AI-driven discovery and engagement grounded in business reality

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

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

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

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

麻豆原创 Commerce Cloud innovations

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

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

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

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

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

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

Sales execution turns insight into action

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

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

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

Scaling trusted autonomous service

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

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

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

Industry AI in action

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

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

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

The next phase of customer engagement

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

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

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

The CX innovations and Industry AI scenarios highlighted here are planned for general availability in Q3 2026.
The capabilities announced as part of 麻豆原创鈥檚 Autonomous Enterprise run across 麻豆原创 Cloud ERP, including 麻豆原创 Cloud ERP Private.
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�

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

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

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

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

麻豆原创 Agent Hub: Command center for agentic governance

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

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

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

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

New AI capabilities

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

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

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

Looking ahead to a new paradigm

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

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

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

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

Business transformation never stops

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

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

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

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

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

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

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

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Build faster with intent-based development

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

When triggered, Joule Studio:

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

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

Vanitha Ponnusamy, Sony

Develop agentic solutions your preferred way

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

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

Harness best-in-class partnerships: n8n and Vercel

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

Vercel for blazing-fast, custom digital experiences

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

n8n for visual workflow orchestration at enterprise scale

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

Deploy enterprise-ready agents securely

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

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

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

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

Bring agents into the flow of everyday work

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

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

Suraj Gahalyan, Accenture

Joule Studio: 麻豆原创 Business AI Platform in action

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

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

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

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

Get started today

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

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

• Learn more at

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

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

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

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

Why this matters to 麻豆原创 customers

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

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

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

The real enterprise challenge: Trusting agents that act

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

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

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

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

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

NVIDIA OpenShell: The foundation

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

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

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

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

Enterprises expect clarity on questions such as:

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

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

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

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

1. Enterprise-driven runtime requirements

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

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

This includes shaping requirements around:

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

2. Co-development of OpenShell capabilities

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

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

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

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

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

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

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

Raising the bar for enterprise agentic AI

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

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

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

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

Partnership opens new pathways for enterprise data migration with 麻豆原创 AI-supported tooling complemented by Palantir鈥檚 AIP for data migration scenarios to simplify an expedite digital transformation for 麻豆原创 customers, with Accenture as a co-innovation partner for joint customers

This year at 麻豆原创 Sapphire, 麻豆原创 and Palantir announced an expansion of their strategic partnership focused on delivering new data migration capabilities designed to help enterprise customers succeed in the AI era and realize 麻豆原创鈥檚 vision of the Autonomous Enterprise.

The enhanced partnership is designed to facilitate joint customers鈥� cloud migrations, with the most complex data migration scenarios moving quickly, securely, and confidently through their business transformation journeys.

AI embedded across the migration life cycle

The expanded partnership builds on 麻豆原创鈥檚 broader agentic migration strategy, uniting 麻豆原创鈥檚 deep expertise in enterprise applications and 麻豆原创 Business AI with Palantir鈥檚 AIP to deliver AI-driven data migration capabilities that accelerate timelines, secure cost efficiencies, and fundamentally transform how organizations operate. 麻豆原创 customers can now leverage Palantir鈥檚 AIP for data migration scenarios alongside the agent-led toolchain from 麻豆原创, which includes business transformation tools and the new migration and modernization assistants to accelerate their transformations to 麻豆原创 Cloud ERP.

鈥淭o turn the vision of the Autonomous Enterprise into reality, organizations need trusted partners to help them transform their core operations and unlock the power of business data and AI,鈥� said Christian Klein, CEO of 麻豆原创 SE. 鈥淭ogether with Palantir, we are enabling customers to move to the cloud with speed and confidence through complementary capabilities that accelerate innovation across the enterprise.鈥� 

鈥淲e are proud to partner with 麻豆原创 and Accenture to bring the power of advanced AI and data migration to the world鈥檚 most important operations. This partnership is designed to help customers realize the full value of their data, accelerate cloud migrations and AI adoption, and build more resilient and efficient operations,鈥� said Alex Karp, co-founder and CEO of Palantir Technologies. 

Accenture as global strategic services partner

Accenture plays a key role in bringing this joint effort to life as the first global strategic services partner for this initiative, helping their clients translate these capabilities into large-scale business-led programs. Together, 麻豆原创, Palantir, and Accenture can help joint customers identify acceleration opportunities across 麻豆原创 and non-麻豆原创 systems sooner, achieve faster time-to-value, and drive continuous innovation by fundamentally redefining the approach to 麻豆原创 Cloud ERP migrations.

By embracing AI from day one, organizations can automate migration analysis, planning, remediation, testing, and impact assessment鈥攎oving from tracking project timelines to creating measurable business value at every stage. 

鈥淚n today鈥檚 world, speed to value is critical for our clients,鈥� said Julie Sweet, chair and CEO of Accenture. 鈥淲e are excited to co-innovate with 麻豆原创 and Palantir to help our clients accelerate their journey to ERP modernization with 麻豆原创, which is the foundation for reinventing core operations and using AI to achieve new performance frontiers.鈥� 

New 麻豆原创-validated deployment options

麻豆原创 is making Palantir AIP for data migrations scenarios available as an 麻豆原创 Endorsed App on the 麻豆原创 Store, and soon as an 麻豆原创 Solution Extension, establishing a trusted, 麻豆原创-validated path for accelerating complex data migrations, including migrations to 麻豆原创 Cloud ERP. The new 麻豆原创 Solution Extension unites 麻豆原创鈥檚 deep expertise in mission-critical business processes and semantically rich data with Palantir AIP to securely accelerate data migrations for 麻豆原创 customers. With the new offering, customers can gain faster insights and more intelligent, data-driven business outcomes.

Together, are redefining 麻豆原创 data migrations, including ERP modernization through complementary solutions, transforming a traditionally complex migration process into an effort that enables faster value realization. 

Availability 

Palantir AIP for data migration scenarios is now available as an 麻豆原创 Endorsed App on the 麻豆原创 Store. The 麻豆原创 Solution Extension is planned to be generally available to 麻豆原创 customers in Q3 2026.

Jan Gilg is a member of the Extended Board of 麻豆原创 SE.

The acquisition helps customers make their 麻豆原创 and non-麻豆原创 enterprise data AI-ready and will provide customers with the tools they need to unify, cleanse and harmonize data across sources for superior enterprise-wide agentic AI.

Visit the . Get 麻豆原创 news via  and .

Media Contacts:
Aim茅e Leabon, +1 (646) 799-3277, aimee.leabon@sap.com, EST 
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麻豆原创 麻豆原创 Room; press@sap.com

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

Terms of the deal were not disclosed. The transaction is still pending regulatory approval.

Most enterprise AI projects fail to deliver value not because of the AI itself, but because the underlying data is fragmented, locked in proprietary formats and stripped of the business context that makes it meaningful. The result is a familiar and costly pattern: pilots that cannot scale, slow integration of new data sources, duplicated engineering work and compliance risk when organizations cannot explain how an AI-driven decision was reached. Dremio helps eliminate that data fragmentation and integration friction. The acquisition will complement the 麻豆原创 Business Data Cloud and 麻豆原创 HANA Cloud offerings to ensure seamless data integration across 麻豆原创 and non-麻豆原创 data with high performance and low cost to accelerate AI-ready context and time-to-value for AI.

“Enterprise AI doesn鈥檛 stall because the models aren鈥檛 good enough; it stalls because the data isn鈥檛 ready for AI agents,” said Philipp Herzig, CTO, 麻豆原创 SE. ” Dremio eliminates that bottleneck. Combined with 麻豆原创 Business Data Cloud, we can now take customers from raw, fragmented data to governed, AI-ready intelligence on a single open platform.”

With Dremio, 麻豆原创 Business Data Cloud will become an Apache Iceberg-native enterprise lakehouse that unifies 麻豆原创 and non-麻豆原创 data to power agentic AI at enterprise scale. Apache Iceberg is the industry-standard open table format, and 麻豆原创 Business Data Cloud will natively support it as its foundation. This means no data movement or format conversion will be necessary. 麻豆原创 and non-麻豆原创 data can coexist on the same open foundation, with federated analytical reach across every enterprise data source, combined with 麻豆原创 HANA Cloud鈥檚 in-memory engine for real-time transactions and operational performance.

The Dremio lakehouse platform is set to vastly improve the economics of enterprise analytics. It is serverless and elastic, scaling up automatically when demand spikes and scaling back down when it subsides, meaning no fixed capacity to provision and no performance ceiling when it matters most.

With Dremio, 麻豆原创 will deliver a universal, open catalog built on Apache Polaris and the open Apache Iceberg REST Catalog API. It serves as both the discovery and semantic layer of 麻豆原创 Business Data Cloud, giving every connected engine 鈥� 麻豆原创 or non-麻豆原创 鈥� a single point of access to unified business context: meaning, relationships, access rights and data lineage. This catalog will form the foundation of the 麻豆原创 Knowledge Graph, embedding business relationships, organizational hierarchies, regulatory classifications and cross-system lineage as native properties.

Dremio has been a leading steward of open-source projects at the heart of its platform: Apache Iceberg, Apache Polaris and Apache Arrow, and 麻豆原创 is fully committed to continuing to invest in and prioritize these contributions.

The transaction is expected to close in Q3 of 2026, subject to customary closing conditions, including regulatory approvals.

Visit the . Get 麻豆原创 news via  and .

About Dremio

Dremio is the Agentic Lakehouse: the only Iceberg-native data platform built for agents and managed by agents. Every knowledge worker and AI agent gets instant, governed access to enterprise data through any LLM or tool of their choice. Federated queries reach any source without ETL pipelines. An AI Semantic layer adds business context so every agent draws from the same source of truth. The lakehouse manages itself, running clustering, optimization, and compaction autonomously. The result: trusted insights that drive better business outcomes, without the infrastructure complexity or overhead. A lead contributor to Apache Iceberg and co-creator of Apache Arrow and Apache Polaris. Trusted by Shell, TD Bank, Michelin, and thousands of organizations worldwide.

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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This document contains forward-looking statements, which are predictions, projections, or other statements about future events. These statements are based on current expectations, forecasts, and assumptions that are subject to risks and uncertainties that could cause actual results and outcomes to materially differ. Additional information regarding these risks and uncertainties may be found in our filings with the Securities and Exchange Commission, including but not limited to the risk factors section of 麻豆原创鈥檚 2025 Annual Report on Form 20-F.
漏 2026 麻豆原创 SE. All rights reserved.
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Li茅nard is an information scientist and business informatics professional who joined 麻豆原创 in 2012. As senior vice president and head of ABAP platform at 麻豆原创, she is responsible for ABAP and all matters related to ABAP platform. In this role, she is also the head of ABAP AI and thus globally responsible for the latest developments and innovations in this domain.

In this interview, she discusses ABAP, the role of AI in development, how agentic AI will transform legacy applications, and what’s next.

Q: What is ABAP, exactly? How would you explain it to someone who might have heard of it but doesn鈥檛 really know what it is? And why is ABAP so important for enterprise software?

A: ABAP has a very long history at 麻豆原创. It is the company鈥檚 first and only proprietary programming language and turned 40 in 2023鈥攁n unusually long run in the fast-changing world of software.

What sets ABAP apart from other programming languages such as Java or C++ is that it was specifically designed for building and optimizing the business applications that large enterprises rely on every day. Among its many features is a high level of abstraction, which makes it very easy for developers to write or extend business software. It also reduces complexity because security concepts, authorization checks, and quality controls are already embedded in the language. This allows developers to focus entirely on the business logic鈥攖hat is, on the tasks they want the program to perform.

Over the years, ABAP has evolved to keep pace with how companies deploy software. The newest version is ABAP Cloud, which has a restricted language scope and is designed to support development in what 麻豆原创 calls a 鈥渃lean core.鈥� This is essential for running our cloud products. Enterprises still operating in a non-cloud environment can use ABAP Cloud to prepare the code in their on-premise systems or in 麻豆原创 S/4HANA Cloud Private Edition in such a way that it can also be run in the cloud.

Beyond its role as a programming language, ABAP is also a platform. ABAP platform is the foundation that underpins all of 麻豆原创’s core solutions, from older installations such as 麻豆原创 ERP Central Component (麻豆原创 ECC) to on-premise solutions, 麻豆原创聽S/4HANA Cloud Private Edition, and 麻豆原创聽S/4HANA Cloud Public Edition.

Q: Will ABAP continue to play a crucial role for 麻豆原创 customers?

A: Yes, both in terms of the programming language and the platform ABAP is still highly relevant. The programming language looks very different to the way it did 40 years ago of course鈥攂ecause we have continuously refined it over the years鈥攂ut it still forms the backbone of 麻豆原创鈥檚 core ERP solutions and extensions. There are roughly five million registered ABAP developers worldwide today, with around two million actively developing.

Through ABAP Cloud and our dedicated ABAP AI team, ABAP has evolved into a modern development language for business solutions. I don’t know of any other programming language that covers this scope. It is used globally. Almost all the world鈥檚 100 largest companies are 麻豆原创 S/4HANA customers, and underneath it always runs ABAP platform.

Q: How will AI shape ABAP development going forward?

A: For me as head of ABAP platform, this is one of the questions that intrigues me most. AI has completely disrupted the technology market. This of course also impacts the 麻豆原创 developer portfolio and how we customize and extend our solutions. We have therefore invested in AI-powered efficiency tools, such as a chat assistant that explains code on the fly. Another is 鈥済host texting,鈥� a feature that generates code suggestions while the developer types.

In the coming years, AI agents will be able to generate code鈥攊ncluding at the scale demanded of large enterprises鈥攁nd even build entire solutions. We believe that the next wave of AI will not just assist programmers but take on many of the routine tasks they perform today.

A crucial question for 麻豆原创 is: how can we leverage AI to translate legacy code into modern code without losing the underlying business logic that makes each system unique? A lot of our customers are still operating older solutions, including those based on 麻豆原创 ECC. So, we need to provide a clear migration strategy and the right tools to simplify and accelerate their move to the cloud.

That’s why we’re currently developing a service that will work for everyone鈥攔egardless of which system version they run. The aim is to bundle all of 麻豆原创鈥檚 ABAP AI capabilities into a single offering that can boost developer efficiency and allow custom code to be migrated. Ideally, this service will be agent-driven鈥攁s 鈥渁gentic AI.鈥�

Q: What is agentic AI?

A: Agentic AI works with so-called 鈥渁gents.鈥� Agents have specialized capabilities, can communicate with each other, exchange results, and thus solve highly complex tasks together. How they collaborate varies based on the complexity of the use case.

Most approaches involve an 鈥渙rchestrator,鈥� a lead agent that manages other agents to complete a particular task. The orchestrator does not have to call on the individual agents in a fixed order鈥攔ather, its greatest strength lies in intelligently combining the agents in dynamic, adaptive networks.

So, it鈥檚 no longer just about making human developers more efficient. When agents are powerful enough, they can build entire applications and thus take on part of the developer’s tasks. In our case, agentic AI can support the very complex task of transforming code, accelerating it significantly and reducing complexity.

This approach relies on different agents that focus on different aspects of the task: for instance, one agent specializes in explaining custom code; another makes code changes; and a third estimates the effort of a transformation project. When these agents collaborate, that’s when the real magic of agentic AI happens.

AI will radically change the role of developers. Despite continuing to set the direction, they will increasingly focus on business logic rather than on the coding itself. They will work with the code generated by AI systems, checking that it is correct, secure, and aligned with the problem they鈥檙e trying to solve. Thought leadership, however, will remain firmly with people. Developers will continue to decide what matters and communicate their instructions to AI through good prompts. The entire AI domain is extremely dynamic and evolving at astounding speed. Powerful solutions are already available today, so this isn’t a distant vision鈥攊t’s already upon us.

Q: How do customers benefit from agentic AI?

A: Agentic AI will deliver significant value in transforming legacy applications and custom extensions into cloud solutions from 麻豆原创, and thus the latest ERP versions. In February 2026, we extended our existing custom code management app with AI features that help developers understand what the code is doing and what changes are needed to future-proof it. And, of course, AI also provides recommendations on how the code can be extended. In the future, we will complement all this with agents. However, this will take some time, as we refuse to compromise on quality and security.

We are also investing in the developer experience with ABAP platform to make it as easy to use as possible. Here, agentic AI will help reduce the complexity that has built up over decades of development.

Q: Should we be worried about security?

A: No, we deliberately allow sufficient time before any release to make sure that quality and, above all, security meet a high bar. Don鈥檛 worry: AI won鈥檛 take control and generate or integrate solutions unilaterally or unchecked. Humans will remain in charge every step of the way and will always have the last word when it comes to ensuring that code complies with our standards.

Q: Where are we now and what鈥檚 next?

A: ABAP AI tools aimed at boosting developer productivity have been available since February 2025, and we are now building agentic AI in the ABAP context. However, it鈥檚 early days and agentic AI still must prove itself in practice. As I see it, though, it will transform the market.

As part of our road map, we released , a custom-trained, specialized AI model, on the generative AI hub in early January 2026. This model is specifically designed to explain ABAP program code.

Next, we plan to make all ABAP AI tools available as an independent side-by-side service. In a subsequent phase, we will transition the use cases embedded in those tools to agents.

In addition, we are expanding our cloud-based ABAP development into additional development environments (IDEs), especially ABAP development tools for Visual Studio Code. So, the team will also tap into the AI tools available there as part of our push toward agent-driven development.

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

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聽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聽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.聽 This聽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

• 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. 聽

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.

Instead, they immediately encounter friction. They鈥檙e asked to repeat information they鈥檝e already shared across multiple channels and departments. Then they see an offer for the item they just purchased, rather than something similar or new. And when they encounter an issue down the line, customer support doesn鈥檛 recognize their history.

Micro moments like these do not feel minor to customers anymore. They feel inexcusable. Customer expectations have changed faster than most brands can keep up. Customers now assume brands know who they are, what they need, and what鈥檚 happening right now. And they expect brands to act on that knowledge instantly.

At the same time, businesses are embracing a new era of AI. Dubbed “agentic AI,” it represents a paradigm shift where AI doesn鈥檛 just analyze or recommend products, but increasingly plans, decides, and acts through a network of agents. This creates a massive opportunity for customer experience (CX) leaders today, in particular marketers, who, according to McKinsey, are leading in AI adoption amongst business functions. But it also raises the stakes.

Because when AI moves faster than your data, systems, and processes, it exposes everything that鈥檚 broken. That tension鈥攂etween rising expectations and disconnected reality鈥攊s exactly what 麻豆原创 and Google Cloud are addressing together.

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The marketer鈥檚 reality: ambition outpacing execution

According to recent , more than half of marketers say fragmented or outdated data prevents them from acting in the moment. Insights arrive too late. Activation requires manual stitching across tools. And even the best strategies stall before they ever reach customers.

It is clear that most organizations genuinely want to deliver great customer experiences. But fragmentation is what stands in the way of delivering connected, meaningful engagements.

On one side: Customers expect effortless, relevant, and real-time experiences. On the other hand, organizations still operate with fragmented data, siloed teams, and delayed insights.

Our latest reveals that customers are increasingly frustrated: 45% say brands can鈥檛 keep up with changing expectations, and 44% say interactions feel less personal than before.鈥�

AI accelerating the engagement divide 

The disconnect between what customers feel and what businesses believe is the “.” Customer signals live across disconnected systems. Data arrives late or without context. Execution happens separately from insight. And while customers feel this friction immediately, many companies do not realize how disconnected their experiences truly are in their customers’ eyes. Now, AI is accelerating this divide.

Agents can generate content, launch campaigns, and optimize engagement at unprecedented speed. But when those agents act on incomplete, outdated, or fragmented data, they only exacerbate inconsistency and poor customer experiences.

When talking to our customers, it鈥檚 clear that there is no shortage of ambition when it comes to AI. In our research, 78% of brands say AI will be integral to their customer retention efforts this year. But only 46% of brands can connect their data in a way that is accessible to power AI sustainably.

The real challenge for CX leaders today is ensuring that AI has the right foundation: trusted data, unified context, and direct connection to execution.

New model for engagement built on trusted enterprise data

麻豆原创 and Google Cloud are expanding their partnership to enable a fundamentally different approach to marketing execution, one grounded in trusted enterprise data and real-time signals, accelerated with multi-agent coordination, and delivered at scale through 麻豆原创 and Google鈥檚 customer engagement solutions.

麻豆原创 provides both operational truth for elements such as inventory, orders, and fulfillment status, and deep customer knowledge across customer experience interactions. Google Cloud brings additional real-time signals and analytics, along with advanced AI. Combined, they create a shared, real-time understanding of the customer, grounded in business and situational context.

At the heart of this partnership:

• 麻豆原创 Business Data Cloud (麻豆原创 BDC) connects semantically rich data across the enterprise with AI to enable real-time insights and drive personalized interactions grounded in business context. This includes 麻豆原创 Business Data Cloud Connect for Google BigQuery.
• Google BigQuery unlocks real-time signals across the Google ecosystem, such as geolocation, weather, and rich analytics, through bidirectional, zero-copy data access with 麻豆原创 BDC, while ensuring enterprise-grade governance and security.
• 麻豆原创 Customer Experience applications provide the real-time behavioral context 鈥� customer profiles, transactions, orders, service interactions, and consented engagement data.
• 麻豆原创 Engagement Cloud activates enterprise data and AI insights and predictions to securely orchestrate real-time, personalized interactions across the entire customer life cycle.

With these innovations, marketers can finally move from insight to execution automatically.

To realize the full potential of agentic AI, businesses need their systems to speak the same language. By uniting 麻豆原创’s enterprise data and customer engagement platform with Google Cloud’s AI, we鈥檙e enabling marketers to move beyond simple automation to multi-agent orchestration, driving dynamic campaigns that reason and adapt to market shifts in real time.

Kevin Ichhpurani, President, Global Partner Ecosystem at Google Cloud

From prompt to performance: how agents work together for marketing

Another critical element of this new execution model is agent interoperability. Gemini Enterprise acts as a central hub for multi-agent coordination, enabling  customers鈥� agents to securely exchange context and take action across platforms. Meanwhile, Joule acts as the engagement layer within 麻豆原创 applications, executing tasks, orchestrating campaign and content workflows, and optimizing marketing outcomes. Working together, 麻豆原创 and Google are enabling true multi-agent orchestration connected to trusted enterprise data.

Within this broader CX transformation, 麻豆原创 Engagement Cloud is where agentic intelligence becomes operational for marketing teams. It is the environment where enterprise signals, generative media, and AI agents translate into real customer interactions and automated lifecycle journeys.

Advanced generative capabilities powered by Google Gemini models, for example, Nano Banana 2, introduce new agentic skills that help CX teams dynamically generate messaging, imagery, and campaign variations. Through assistants and agents in Joule, these capabilities become embedded directly into marketing workflows, allowing brands to adjust tone, localize content, and respond instantly to changing conditions.

It is not just content generation and personalization that are being rewired. With unified data context and interoperable agents, mobile messaging can turn into immersive conversational experiences with Google Rich Communication Services (RCS) and advertising audiences, and creative, which can continuously evolve based on real-time performance and business signals, transforming campaigns into intelligent, self-optimizing systems.

And through this multi-agent network, marketers will not need to build every step of a campaign manually. Instead, they define the goal, gain more time to focus on strategy and creativity, and let agents handle the rest.

For example, a marketer can prompt:

• 鈥淚ncrease repeat purchases from customers in the last 30 days.鈥�
• 鈥淢aximize customer lifetime value while reducing campaign operational costs.鈥�

And from there:

• Joule Agents coordinate content production, grounded in customer and enterprise data, understand business context, customer history, and constraints
• Google鈥檚 Gemini Models and agents generate creative variations, messaging, and channel-specific content
• Agents collaborate across 麻豆原创 and Google Cloud to personalize, activate, and continuously optimize campaigns in real time across engagement channels and media networks

This is more than a data integration. It鈥檚 a leap forward for AI agents that can collaborate naturally and execute seamlessly. By combining 麻豆原创 Business Data Cloud Connect for Google with interoperable AI agents across 麻豆原创 and Google, we鈥檙e giving organizations a path from AI experimentation to AI-empowered customer experience at scale. Marketers can spend less time on manual tasks and more time shaping the customer journey.

Balaji Balasubramanian, President and Chief Product Officer, 麻豆原创 Customer Experience and Consumer Industries

Clear business outcomes for marketing teams

By enabling a network of interoperable AI agents and grounding them in enterprise data and shared context across 麻豆原创 and Google, organizations can achieve measurable outcomes, including:

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

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

Beyond campaigns: continuous engagement at enterprise scale

While marketing is a natural starting point, this is just the beginning. Customer engagement does not live in one system or team. Engagement spans commerce, service, sales, supply chain, and operations. A brand promise made in a message must be fulfilled by inventory. A personalized offer depends on pricing, availability, and delivery. And a single customer service interaction can shape the future of customer loyalty and lifetime value.

This multi-agent model is designed to support high-value use cases across the 麻豆原创 Customer Experience portfolio, laying the foundation for an AI-driven customer experience powered by trusted, unified, real-timedata.

In an AI-driven world, customer experience goes beyond any single interaction鈥攊t’s defined by every touchpoint a customer has with your company.

Customers can deploy Joule Agents in 麻豆原创 CX Solutions to build, launch, and optimize marketing campaigns

Gemini Enterprise acts as a central hub for agents to take action across 麻豆原创 and Google Cloud platforms

LAS VEGAS 鈥� (NYSE: 麻豆原创) and Google Cloud today announced a new partnership that will help marketers put AI agents to work at scale.

Through new integrations between the 麻豆原创 Engagement Cloud, 麻豆原创 Customer Experience (麻豆原创 CX) and Joule solutions and Gemini Enterprise, joint customers can now deploy agents that securely access unified data stored across both ecosystems to execute complex marketing strategies based on high-level goals defined by the user.

Together, 麻豆原创 and Google Cloud provide a unified foundation for data and AI agents to operate across both ecosystems. Gemini Enterprise will act as a central hub for data integrations and multi-agent coordination, allowing agents to take action across a customers鈥� 麻豆原创 and Google Cloud solutions. These integrations will be supported by the 麻豆原创 Business Data Cloud Connect solution for Google and BigQuery, which enable bidirectional, zero-copy data access between the two platforms, with enterprise-grade security and governance. Capabilities across both Gemini Enterprise and agent gateway APIs from 麻豆原创 will allow customers鈥� agents to more securely exchange context, trigger actions and optimize outcomes across platforms, enabling true multi-agent orchestration.

The integration allows marketers to prompt an agent within 麻豆原创 Engagement Cloud with a clear objective like, 鈥淚ncrease repeat purchases from the last 30 days,鈥� or 鈥淢aximize customer lifetime value while reducing campaign operational costs.鈥� An agent, like a Joule Agent, will handle the end-to-end process鈥攆rom content personalization to visualization to conversational engagement.

鈥淭his is more than a data integration; it鈥檚 a leap forward for AI agents that can collaborate naturally and execute seamlessly,” said Balaji Balasubramanian, President and Chief Product Officer, 麻豆原创 Customer Experience and Consumer Industries. 鈥淏y combining 麻豆原创 Business Data Cloud Connect for Google with interoperable AI agents across 麻豆原创 and Google Cloud, we鈥檙e giving organizations a path from AI experimentation to AI-enabled customer experience at scale. Marketers can spend less time on manual tasks and more time shaping the customer journey.

鈥淭o realize the full potential of agentic AI, businesses need their systems to speak the same language,鈥� said Kevin Ichhpurani, President, Global Partner Ecosystem at Google Cloud. 鈥淏y uniting 麻豆原创鈥檚 enterprise data and customer engagement platform with Google Cloud鈥檚 AI, we鈥檙e enabling marketers to move beyond simple automation to multi-agent orchestration, driving dynamic campaigns that reason and adapt to market shifts in real time.鈥�

According to from 麻豆原创 Engagement Cloud, more than half of marketers say fragmented, outdated data prevents them from acting in the moment. 麻豆原创 and Google Cloud are helping remove that roadblock by unifying data and letting AI agents turn insights into action. Using Joule with 麻豆原创 Engagement Cloud, campaigns can move from planning to activation automatically without manual stitching across tools.

Customers will benefit from autonomous campaign generation, optimization and continuous improved performance. Businesses will achieve faster speed-to-market, lower operational overhead and always-on optimization that drives higher ROI, while giving teams more time to focus on strategy and end-to-end campaign execution.

While marketing is the first example, and will be available to customers in H2 2026, this multi-agent orchestration model is designed to support high-value use cases across the 麻豆原创 CX portfolio, laying the foundation for AI-driven customer experience, powered by trusted, unified real-time data and interoperable agents.

For more information about 麻豆原创 Customer Experience solutions, visit .

For more information about Gemini Enterprise, visit .

Visit the . Get 麻豆原创 news via  and .

About Google Cloud

Google Cloud offers a powerful, optimized AI stack 鈥� including AI infrastructure, leading models like Gemini, data management capabilities, multicloud security solutions, developer tools and platform, as well as agents and applications 鈥� that enables organizations to transform their business for the Agentic Era. Customers in more than 200 countries and territories turn to Google Cloud as their trusted technology partner.

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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To succeed, manufacturers need more than incremental improvements or siloed optimizations. They need to orchestrate their operations end to end with connected processes and trusted data, so they can respond faster to change, operate more efficiently, remain compliant, and continue to grow鈥攅ven as disruption is constant.

At Hannover Messe 2026, the world鈥檚 leading stage for industrial transformation, 麻豆原创 will introduce a new set of AI鈥憄owered manufacturing and supply chain innovations. These innovations help companies ensure business continuity by orchestrating people, processes, and technology across their extended enterprise, turning volatility into an opportunity for resilience, efficiency, and customer impact.

From AI insight to AI in execution

For years, manufacturers have invested in analytics and dashboards to improve visibility. But visibility alone does not prevent disruption. What鈥檚 required now is AI embedded directly into core business processes, where intelligence can analyze alerts, reason over business impact, and provide real-time solutions to resolve issues. With agentic AI, companies are now able to go a step further: automating the right actions for the best outcomes, with humans remaining in the loop wherever critical decisions are required.

麻豆原创 is operationalizing AI at industrial scale by embedding AI agents directly into supply chain and manufacturing workflows and contextualizing them with trusted enterprise and network data. Built on harmonized industrial, transactional, and network data, these agents can move beyond analysis to real鈥憈ime prediction and execution, working to deliver resilience, regulatory readiness, and measurable customer impact from day one. Creating tangible ROI is what matters most鈥攚hether by reducing unplanned downtimes, scrap, and rework, or by increasing quality and ultimately production output.

Orchestrating the supply chain end to end with AI

At the center of 麻豆原创鈥檚 focus at Hannover Messe is .

麻豆原创 helps manufacturers connect processes and data not only across internal teams, but also across company boundaries鈥攚ith suppliers, logistics partners, and service providers. By using AI agents to connect design, planning, procurement, manufacturing, logistics, service, and asset management鈥攁nd by integrating seamlessly with ERP and line-of-business systems鈥斅槎乖� helps break down silos that slow decision-making and increase operational risk.

This new agentic orchestration is powered and governed by a portfolio of intelligent applications that act, not just analyze, enabling faster, more coordinated responses without sacrificing quality, control, or growth.

New AI agents redefining planning, service, and operations

At Hannover Messe 2026, 麻豆原创 will showcase AI agents that help manufacturers and operators reduce time to value, stabilize operations, and improve service levels amid ongoing disruption. As a precursor to broader announcements planned for 麻豆原创 Sapphire, these agents demonstrate how agentic AI delivers practical benefits across all supply chain domains. Here are a few examples:

Manufacturing

• Production Master Data Agent helps automate and optimize the creation and maintenance of production master data. By leveraging the bill of materials, the agent can generate production routings鈥攊ncluding operations and work centers鈥攁nd help ensure components are correctly assigned across the production process. This helps reduce manual effort, accelerate production setup, and keep production data accurate as requirements change. General availability is planned for Q2 2026.
• Production Planning and Operations Agent enables planners to release production orders using natural language while automatically validating material availability, capacity, and scheduling constraints. Joule provides recommendations鈥攕uch as alternative components or rescheduling options鈥攖hat planners can review and approve, reducing manual work and keeping production aligned with real鈥憌orld conditions. General availability is planned for Q2 2026.

Assets & services

• Field Service Dispatcher Agent can improve service responsiveness and asset uptime by dispatching the right technician based on skills, location, asset condition, and priority鈥攄riving faster resolution and better workforce utilization. General availability is planned for Q2 2026.
• Alert Processing Agent can enrich operational alerts using past incidents, resolutions, and contextual signals and recommend clear, data鈥慸riven actions to help teams resolve issues faster and improve operational reliability. General availability is planned for Q3 2026.
• Asset Health Agent analyzes time鈥憇eries health indicators to assess and summarize the current and projected health of individual and multiple technical objects. By forecasting when assets are likely to become critical and alerting users in real time, the agent supports condition鈥慴ased maintenance and helps minimize downtime while ensuring asset availability. General availability is planned for Q3 2026.

AI agents advancing logistics execution

• Material Reservation Agent helps ensure materials are available when and where needed by automating reservation creation and maintenance based on business rules鈥攔educing delays, improving inventory accuracy, and optimizing working capital. General availability is planned for Q2 2026.
• Outbound Task Orchestration Agent can protect customer service levels by detecting and resolving picking and packing issues in real time, orchestrating corrective actions to support on鈥憈ime, accurate delivery. General availability is planned for Q2 2026.

Aligning workforce, logistics, and assets in real time

Operational resilience also depends on synchronizing people with all other resources as conditions change.

With , skills, certifications, availability, and labor rules are aligned with real-time operational demand so workforce plans can adjust automatically as production changes.

In logistics, , together with the new solution, helps organizations reduce transportation costs, accelerate warehouse execution, and improve delivery performance. Using conversational interaction with Joule, order managers can prioritize fulfillment while automatically accounting for availability and scheduling constraints.

Asset and quality operations also benefit from embedded intelligence. AI-assisted anomaly detection and alert processing in helps teams identify risks earlier, prioritize actions, and reduce unplanned downtime. In parallel, 麻豆原创 Document AI can automate the , improving throughput, data quality, and compliance at scale.

Regulatory readiness and what鈥檚 next

As regulatory requirements tighten, 麻豆原创 is expanding support for Digital Product Passports as part of , aligned with the EU鈥檚 Ecodesign for Sustainable Products Regulation (ESPR). These capabilities help manufacturers create ESPR鈥憆eady product records capturing environmental impact, material composition, repairability, and recyclability data. General availability is planned for Q2 2026.

Expanded 麻豆原创 Business Network capabilities also deliver built鈥慽n e鈥慽nvoicing compliance and data-residency support, enabling secure partner collaboration, synchronized logistics, and improved delivery performance across global networks.

See it live at Hannover Messe 2026

Taken together, these innovations reflect a shift from reactive management to intelligent execution鈥攚here AI is embedded directly into the processes that keep manufacturing and supply chains running today while laying the foundation for the next wave of innovation that will be unveiled at 麻豆原创 Sapphire.

Visit 麻豆原创 at booth F08 in Hall 15 at Hannover Messe 2026, April 20鈥�24, to see how AI-infused orchestration, embedded AI agents, and end鈥憈o鈥慹nd supply chain applications are redefining manufacturing.

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

Now, however, many economists and policymakers view AI as a potential catalyst for reversing that slowdown. AI鈥攅specially the rise of generative AI and AI agents鈥攊s widely expected to shape the next phase of productivity growth in advanced economies, including those in the U.S. and Europe.

The key question for business leaders is not whether AI will matter, but how large the productivity gains will be, how quickly they will materialize, and which region will benefit most.

Productivity growth

The (OECD) estimates that AI could raise annual labor productivity growth in advanced economies by roughly 0.4 to 1.3 percentage points, depending on adoption intensity and sector exposure. These gains would be meaningful because even an additional half percentage point of annual productivity growth compounds significantly over a decade.

However, the OECD and other economists stress that outcomes depend heavily on complementary investments in digital infrastructure, workforce training, and organizational change, rather than on technology alone.

Between 1995 and 2019, U.S. labor productivity grew at 2.1% annually compared to one percent in Europe. This disparity arose in part because companies in the U.S. invested more aggressively in information, communications, and technology while those in Europe were constrained more by regulatory and other factors.

Expectations for AI-driven productivity gains remain generally stronger in the U.S. than in Europe. suggests that widespread adoption of generative AI could raise U.S. labor productivity growth by around one to 1.5 percentage points per year.

Several structural factors support this view. The U.S. has a deep technology ecosystem, global leadership in AI research and venture capital, and a large, digitally intensive services sector, including finance, professional services, and IT, where generative AI tools can be rapidly deployed.

Agentic AI

In both Europe and the U.S., AI agents represent a particularly important development. Unlike earlier automation tools that handled isolated tasks, AI agents鈥攍ike Joule Agents from 麻豆原创鈥攁re designed to plan, reason, and execute multi-step workflows. For example, an agent might manage customer service tickets, draft responses, query databases, escalate issues, and update systems鈥攁ll with limited intervention.

In knowledge-based industries, this kind of workflow automation could significantly raise output per worker. But rather than replacing entire occupations, AI agents may reduce time spent on repetitive administrative and 鈥渓ong-tail鈥� tasks, enabling workers to focus on higher-value analysis, strategy, and interpersonal activities.

Despite stories about failed corporate AI projects, which can typically involve bolt-on or stand-alone AI pilots rather than a more integrated, holistic approach, recent evidence from the U.S. suggests that productivity gains are already emerging in some sectors. For example, financial institutions have reported significant efficiency improvements in back-office operations through AI deployment.

Similarly, experimental studies in professional services show that generative AI can increase output quality and speed, particularly for less experienced workers, effectively narrowing skill gaps within teams.

European outlook

The outlook for productivity gains in Europe from AI is more mixed. According to a recent the medium-term gain in productivity from the AI alone would vary considerably across countries, and for Europe as a whole would be rather modest: about 1.1 percent cumulatively over five years.

But with pro-growth reforms, the IMF suggests that much bigger gains are possible over the longer run. Like the OECD, the IMF emphasizes that regulatory frameworks, labor market structures, and the pace of technology diffusion will strongly influence outcomes.

Several structural differences shape Europe鈥檚 trajectory and the size of what has been called the 鈥淎I growth dividend.鈥� First, AI adoption among small and midsize enterprises (SMEs), which form a larger share of the European economy than in the U.S., tends to be slower. Second, Europe鈥檚 digital market remains more fragmented across national boundaries, languages, and regulatory systems, which can complicate scaling technology platforms. Third, the European Union has taken a more precautionary regulatory approach to AI governance. While this may reduce certain risks, it could also dampen short-term productivity gains if compliance burdens slow deployment.

Europe鈥檚 strengths

That said, Europe has strengths. It leads in advanced manufacturing and industrial engineering, sectors where AI-driven optimization, robotics, and predictive maintenance can raise capital productivity. In these areas, AI agents embedded in industrial systems could significantly enhance supply chain efficiency and reduce downtime.

In addition, as 麻豆原创 executives have pointed out, Europe has an enormous repository of structured business and manufacturing data, which is essential for reliable and effective AI systems as well as trust in AI Agents.

If AI adoption accelerates in manufacturing and energy systems and if European companies seize the opportunity to build advanced AI agents and apps using their business data, Europe could see much more robust medium-term productivity gains. As an example, 麻豆原创’s internal use of AI tools has already significantly improved its own developer productivity.

Labor flexibility

A critical factor in both the U.S. and Europe is labor market adjustment. Historically, the U.S. labor market has demonstrated greater flexibility, with higher rates of job switching and occupational mobility. This flexibility may facilitate faster reallocation of workers into AI-complementary roles, amplifying productivity gains, though this could be offset by more effective existing workforce retraining.

As the (BIS) has noted, AI鈥檚 productivity effects are unlikely to be automatic. Productivity gains from AI depend on complementary investments in skills, management practices, and digital infrastructure. The BIS warns that without these, AI tools may produce only marginal efficiency improvements.

The historical lesson from past general-purpose technologies, such as electricity and IT, is that productivity surges occur only after organizations redesign processes to exploit new capabilities and take a holistic rather than piecemeal approach toward implementation.

No AI bubble

While some investors have expressed concerns about an AI bubble, total AI spending in the U.S. is still below one percent of GDP. Joseph Briggs, senior global economist at Goldman Sachs, notes that this is well below historical infrastructure cycles. For comparison historical infrastructure investments such as IT spending, railroads and canals typically represented between two and five percent of GDP.

Like these previous investment waves AI, particularly agentic AI, is likely to generate significant productivity growth and a corresponding boost to GDP in those regions and sectors that seize the AI opportunity.

The past few years have taught us that disruption is the new normal. Whether it鈥檚 global conflicts, raw material shortages, or sudden demand spikes, supply chains need to pivot faster than ever. That鈥檚 why this year, the conversation isn鈥檛 about incremental improvements鈥攊t鈥檚 about reimagining processes with intelligent technologies that anticipate, adapt, and act autonomously.

From complexity to clarity: how agentic AI changes the game

Agentic AI is reshaping supply chains, and we鈥檙e already seeing real value in practice:

• Supplier onboarding in hours, not weeks: Companies can drive substantial efficiencies by collaborating more deeply with their suppliers, such as material or logistics providers. AI agents autonomously validate supplier credentials, check compliance, and integrate them into your network, cutting onboarding time by up to 50%.
• Predictive maintenance and service that prevents downtime: Instead of reacting to failures, AI agents monitor equipment health and trigger proactive service, reducing unplanned outages by 30%.
• Autonomous disruption handling: When short-term disruptions or opportunities arise to demand or supply levels, AI agents evaluate events and alerts, model scenarios, and drive action while keeping humans in the loop. If critical inventory needs to be shifted for example, agents place orders automatically, optimizing stock levels and reducing lead times by 25%.

These aren鈥檛 distant possibilities鈥攖hey鈥檙e real scenarios already piloted by 麻豆原创 Supply Chain customers. AI isn鈥檛 replacing people; its amplifying human decision-making, freeing teams to focus on strategy rather than firefighting.

Why this matters: analyst rankings tell the story

麻豆原创 solutions underpin strategies that earned recognition in major 2025 analyst reports, including:

• IDC MarketScape for Multi-Enterprise Supply Chain Commerce Networks*: 麻豆原创 was named a Leader for enabling real-time collaboration and orchestration across global ecosystems with 麻豆原创 Business Network.
• **: 麻豆原创 was positioned as a Leader for innovations like predictive maintenance and agentic AI through Joule.
• ***: 麻豆原创 was recognized as a Leader for integrating planning, manufacturing, and logistics with advanced analytics and AI.

These accolades aren鈥檛 just badges of honor. They validate the trust our customers and partners place in 麻豆原创 and the impact we deliver together. They also reinforce a critical truth: supply chain excellence is now a boardroom priority.

Sustainability: from obligation to advantage

Sustainability isn鈥檛 just a compliance checkbox; it鈥檚 a competitive edge. More than 25% of global emissions are already taxed or regulated by trading systems. Circularity and carbon accountability have become core KPIs for supply chain leaders because responsible practices deliver measurable benefits. Meeting environmental, social, and governance (ESG) standards lowers regulatory and reputational risk, while optimizing logistics for lower emissions often translates into fewer miles traveled and reduced fuel costs. At the same time, customers and investors increasingly favor brands with transparent sustainability metrics, making it a powerful differentiator in the market.

麻豆原创 solutions help companies measure emissions, enable ESG compliance, and embed sustainability data deep into operational decision-making for procurement, logistics, dispatching, and planning. This turns sustainability into a lever for growth rather than a reporting exercise. In fact, companies recognized in Gartner鈥檚 rankings often cite sustainability as a driver of resilience and profitability. When businesses can prove carbon accountability and circularity, they鈥檙e not just meeting regulations, they鈥檙e building trust and unlocking new market opportunities.

Looking ahead: our 2026 roadmaps

In 2026, our priorities center on enabling supply chains that are more intelligent, more connected, and more resilient. We are deepening our investment in agentic AI to support end-to-end value streams such as integrated business planning, sales and operations execution, digital manufacturing, and logistics execution. The goal is to bring AI directly into processes where decisions are made so planning becomes more predictive and execution becomes more automated. Over time, organizations will entirely redesign workflows and decision-making processes for the true step-change in agentic AI.

We are also advancing our capabilities in supply chain orchestration. As global supply chains operate increasingly across networks, companies need a coordinated layer that unifies planning, procurement, manufacturing, and logistics with partner ecosystems. This year, we will continue strengthening how our solutions identify risks in the n-tier network of complex supply chains by synchronizing data, prescribing decisions and actions across the enterprise and the broader network, and helping customers manage disruptions end鈥憈o鈥慹nd with greater speed and clarity.

Finally, we remain focused on data excellence. Reliable, harmonized data is essential for AI-driven decisions and for orchestrating the supply chain. In 2026, we are continuing to enhance master data consistency, improve network-wide data quality, and support AI鈥憆eady data models that help ensure our customers can trust and operationalize their insights at scale.

Together, these areas form the backbone of the innovations we are delivering this year, with a clear aim of helping customers move from reactive operations to intelligent, proactive orchestration. But technology alone isn鈥檛 enough. The real magic happens when we collaborate with our customers and our partners, turning complexity into opportunity.

The takeaway: 2026 is about action at scale

The supply chain landscape is evolving faster than ever. Agentic AI, sustainability, and intelligent automation aren鈥檛 optional, but essential. Companies that embrace these technologies to truly evolve how they operate and take even complex decisions will lead in resilience, efficiency, and responsibility. Those that hesitate risk falling behind in a world where adaptability is the ultimate competitive advantage.

Don鈥檛 wait for disruption to force your hand. Build the capabilities now that will carry you through uncertainty and position you for growth. Learn more about 麻豆原创鈥檚 AI-powered solutions .

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

*, November 2025, IDC #US53010225
**, December 2025, IDC ID # US52977525
***, October 2025, IDC ID# G00826212

Agentic AI was everywhere at NRF, emblazoned across the booths of technology exhibitors and the focus of many of the daily conference sessions. The message was simple: retailers face a major upheaval as consumers switch from traditional browser-based search to AI-enabled product discovery.

Consumers are rapidly adopting AI agents to help them find, compare, and, increasingly, buy products鈥攖his while many brands are still optimizing for search engines and are quietly disappearing from the models driving the next generation of product discovery.

鈥淎gentic commerce鈥攕hopping powered by AI agents acting on our behalf鈥攔epresents a seismic shift in the marketplace,鈥� McKinsey, the strategic management consultancy, noted in a . 鈥淚t moves us toward a world in which AI anticipates consumer needs, navigates shopping options, negotiates deals, and executes transactions, all in alignment with human intent yet acting independently via multistep chains of actions enabled by reasoning models.鈥�

This, as speakers and panelists at the NRF conference acknowledged, isn鈥檛 just an evolution of e-commerce; it鈥檚 a rethinking of shopping itself, in which the boundaries between platforms, services, and experiences give way to an integrated, intent-driven flow through highly personalized consumer journeys that deliver a fast, frictionless outcome.

As the McKinsey report noted, the stakes are high. By 2030, the U.S. B2C retail market alone could see up to US$1 trillion in orchestrated revenue from agentic commerce, with global projections reaching as high as $3 trillion to $5 trillion.

This means all the participants in the retail chain, from brands and retailers to logistics and payment service providers, will need to adapt to the new paradigm and successfully navigate the challenges of trust, risk, and innovation.

To help retailers address the immediate challenges posed by the shift to agentic commerce, 麻豆原创 argues that three steps are necessary: first, restructuring web-page product data to be machine-readable; second, adding semantic summaries for LLM reasoning; and third, tagging products by the problems they solve, not just their attributes.

麻豆原创 announced a series of AI-enhanced retail innovations at NRF 2026, including a new storefront model context protocol (MCP) server that enables retailers to make their digital storefronts intelligible to AI and the new AI-native Retail Intelligence solution in 麻豆原创 Business Data Cloud that leverages data from across 麻豆原创 software and third-party systems to help provide accurate demand planning, improved forecast accuracy, and lower inventory costs to drive more seamless omnichannel engagements.

麻豆原创 Customer Experience has also unveiled a recently that can be combined with the聽, creating one conversational AI that can handle the entire journey from product discovery and transaction to post-sales support.

These moves reflect a recognition that that LLMs have become a legitimate shopping channel, and that product discovery is moving from search engines to AI recommendations.

This shift challenges years of SEO and brand building. To stay relevant, 麻豆原创 believes retailers must take an AI-first approach and have strong, connected data that helps agents understand products, predict demand, and respond quickly. Without this strong data foundation, brands will be at risk because if customers get poor recommendations and errors in pricing, trust can disappear fast.

Although some early agentic AI adopters in the retail sector are already seeing the benefits of agentic commerce, many global retailers are still ill-prepared for the holistic transformation they need to succeed in this new retail environment.

As McKinsey noted in a separate , 鈥渨hile most retail merchandising teams have invested in automation tools聽and experimented with AI, 71% of merchants say that AI merchandising tools have had limited to no effect on their business so far.鈥�

鈥淭he challenge,鈥� McKinsey said, 鈥渙ften lies less in the technology than in how it鈥檚 integrated and used. Systems remain fragmented, data is too messy to use to deliver useful recommendations, and adoption is uneven: 61% of respondents say that their organization isn鈥檛 at all or is only slightly prepared to scale AI across merchandising.鈥�

Onstage at NRF, Andre Bechtold, president for 麻豆原创 Industries & Experience, also emphasized that retailers should prepare now for agentic commerce and noted that simply “bolting on” AI tools to existing systems is not enough.

鈥淩etailers are operating in an environment defined by volatility鈥攖ariffs, margin pressure, supply chain disruption, and customers that expect real-time, hyper-personalized experiences everywhere,鈥� Bechtold said during a discussion with Gymshark, the workout apparel retailer. 鈥淎t the same time, boards and investors are asking a tougher question than ever before: what outcomes are we actually getting?鈥�

鈥淭he challenge,鈥� he said, 鈥渋sn鈥檛 a lack of innovation. In fact, most retailers have plenty of tools, pilots, and point solutions. The real issue is that disconnected technology doesn鈥檛 translate into resilient growth. That鈥檚 why the conversation is shifting. It鈥檚 no longer about isolated AI use cases or shiny new features. It鈥檚 about whether AI and data are embedded across the business鈥攃onnecting supply chains, finance, merchandising, and customer engagement鈥攊n ways leaders can trust.鈥�

Echoing the same point, Thomas Saueressig, member of the Executive Board of 麻豆原创 SE, Customer Services & Delivery, commenting in a this week about a PwC survey of global CEOs that found that companies rarely achieve lower costs or higher sales through the use of AI, emphasized that AI only contributes value when consistently embedded in business processes. 聽鈥淎s long as AI runs alongside the core business as an isolated project, the effects remain limited,鈥� he said.

Our vision for agentic commerce is bold. In , we showcase a future where humans and AI agents collaborate to drive intelligent recommendations, proactive operations, efficient business processes, and deeper customer relationships. While this vision points forward, 麻豆原创鈥檚 focus is firmly grounded in helping retailers take practical steps today. This isn鈥檛 about flashy demos of a distant future鈥攊t鈥檚 about building the foundation now for how consumers will buy and retailers will sell in the years ahead.

Unlike traditional AI systems that respond to prompts, agentic systems act on intent. They learn from preferences, make proactive recommendations, and can complete transactions on a shopper鈥檚 behalf. These agents are increasingly becoming the starting point of the buying journey, reshaping how brands compete for visibility, trust, and loyalty.

This evolution introduces both opportunity and risk. As AI agents mediate more interactions between brands and consumers, retailers must rethink how they capture intent, transact with agents, and deliver post-purchase experiences that reinforce trust.

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Discovery is moving from search to assistants

Historically, product discovery revolved around search engines, marketplaces, and brand-owned storefronts. That model is shifting quickly. Answer engines and AI shopping agents are becoming new entry points for commerce鈥攐ften before a shopper ever visits a retailer鈥檚 site.

Like marketplaces before them, AI agents introduce a new layer between brands and customers. The difference is speed and autonomy. Agents don鈥檛 just surface options; they reason, decide, and act.

For retailers, success is no longer about ranking on a page. It鈥檚 about ensuring products are visible, understandable, and trusted by machines that influence purchase decisions on behalf of humans.

At NRF, 麻豆原创 expanded its agentic commerce vision with the announcement of the storefront MCP server for 麻豆原创 Commerce Cloud, planned for Q2 availability. The storefront model context protocol (MCP) server can enable channel-less commerce by allowing businesses to safely and reliably engage with multiple AI agents鈥攚hether embedded in a retailer鈥檚 own experiences or originating from third-party assistants like ChatGPT or Perplexity.

The storefront MCP server helps merchants surface products and can enable buying across channels for both people and machines. It鈥檚 the first of many steps 麻豆原创 is taking to help customers fully participate in agentic commerce by supporting MCP, ACP, UCP, and other emerging agentic protocols.

Product content becomes the currency of visibility

In an agent-driven world, product content is no longer just marketing鈥攊t鈥檚 operational infrastructure. AI agents cannot recommend what they cannot interpret. Every attribute, image, specification, availability signal, and proof point directly impacts whether a product is surfaced, compared, or selected.

This is where generative engine optimization (GEO) is evolving. Optimization must now serve two audiences: humans and machines. Product data must be structured, consistent, and enriched, so AI agents can confidently represent it to shoppers.

The in helps transform how merchants manage product data at scale. It can clean catalogs, enrich attributes, standardize details, fill gaps, and support multilingual content using real-time data. The agent can scale to catalogs with more than 10 million items, helping teams improve content 70% faster, increase data completeness by 5%, and reduce maintenance effort by 63%.

With AI-ready product data as its foundation, retailers can better match shopper intent, optimize merchandising by channel, and improve pricing and delivery decisions with precision.

Payments must evolve for autonomous commerce

As buying journeys fragment across devices, channels, and agents, payments must become more flexible and nearly invisible. Consumers expect to pay how and when they choose, including through agent-initiated transactions.

New payment rails like FedNow, RTP, and stablecoins are enabling faster, lower-cost transactions, while wallets and bank-based payments continue to converge. Networks such as Visa and Mastercard are already preparing for autonomous commerce by allowing consumers to set spending limits and controls for AI agents.

For retailers, the priority is delivering frictionless, secure payment experiences that integrate seamlessly into agent workflows.

The can enable this flexibility through a no-code, low-code approach. Its headless, extensible architecture helps support diverse payment methods, ensure compliance through automatic updates, and integrate natively with 麻豆原创 Commerce Cloud鈥攚orking to give retailers agility without sacrificing control or scalability.

Returns become a strategic intelligence engine

Returns are one of retail鈥檚 biggest challenges. According to IHL Group, global returns have surpassed US$1.9 trillion and are growing faster than sales. What was once a cost center is now a strategic differentiator.

The next phase of returns management is defined by intelligence. AI enables 鈥渒eep, reject, or return鈥� decisioning based on loyalty history, behavioral signals, margin impact, and lifetime value. Returns data becomes a feedback loop that improves forecasting, product quality, and merchandising decisions.

Complete, connected data is essential. 麻豆原创 can deliver this through native integration between 麻豆原创 ERP and 麻豆原创 Commerce Cloud, creating a single source of truth across inventory, costs, and transactions. found that organizations using both platforms achieved up to 80% lower TCO, up to 90% productivity gains, and 105%鈥�245% revenue uplift from hyper-personalized experiences.

can extend this foundation across the full returns journey, helping to orchestrate centralized rules, guided returns, real-time inventory visibility, and faster refunds鈥攖urning returns into a loyalty-building growth lever rather than a revenue drain.

Commerce is detaching from the storefront

As predicted at the end of 2025, AI agents are taking on more shopping tasks, pushing commerce beyond traditional storefronts. A shopper may simply state an intent and let an agent handle research, selection, and checkout.

Discoverability now depends on structured, trustworthy signals鈥攔eviews, ratings, social proof, and consistent data that agents rely on to evaluate quality and brand credibility.

Retailers must move beyond transactional efficiency to deliver connected, personalized experiences across every touchpoint. Loyalty programs must reward engagement, not just purchases. Inventory visibility, accurate delivery promises, and proactive issue resolution become table stakes.

can enable retailers to design adaptive loyalty strategies for this new environment, personalizing rewards and offers based on real-time behavior鈥攚hether purchases happen through traditional channels or AI agents. These insights can then feed transactional agents, helping to improve relevance and outcomes across the journey.

Operational reliability remains critical. 麻豆原创 Order Management Services help unify order, inventory, fulfillment, and POS data, while agentic innovations like the Order Reliability Agent can proactively resolve fulfillment issues before they impact customers.

Trust is the core retail responsibility

As agentic systems influence more of commerce, trust becomes the most valuable asset retailers can protect. Consumers must trust that their data, preferences, and payments are secure and governed responsibly.

Retailers and commerce providers increasingly act as AI trust custodians, balancing intelligence with deterministic constraints and governance. On-site AI can scale associate expertise and personalization while preserving brand integrity and customer confidence.

Commerce is becoming an ecosystem of intelligent interactions鈥攚here discovery, payments, fulfillment, and returns are connected by agents acting on behalf of shoppers and businesses alike.

The winners will be those who align product intelligence, flexible payments, data-driven returns, and trust across every touchpoint. Agentic AI can make commerce more personal, efficient, and scalable鈥攂ut only for those who build the right foundations today.

To learn more about how 麻豆原创 Commerce Cloud is powering AI-driven commerce, visit .

Kollen Glynn is global head of 麻豆原创 Commerce Cloud for 麻豆原创 Customer Experience.

Since the launch, has empowered organizations to streamline operations for increased efficiency, reduced manual workloads, and untangled multi-channel complexity. With this approach, businesses can deliver on customer promises with seamless customer experience. This momentum has also been recognized in the market, as 麻豆原创 Order Management Services was named a Leader in by IHL Group for its robust capabilities and enterprise readiness.

, a leading German home improvement retailer, is already seeing the benefits. With 麻豆原创 Order Management Services, Hornbach connects digital and physical stores with full visibility into day-to-day transactions, providing omnichannel retail experience at scale to its customers.

However, the retail landscape is evolving continuously. While profitable growth is critical to businesses, earning and sustaining customer loyalty now is becoming more important. Ahead of the curve, 麻豆原创 has heavily invested in expanding capabilities in the 麻豆原创 Order Management Services bundle to help retailers deliver on customer promises with intelligence, scalability, and adaptability, leading to boosts in customer loyalty.

At NRF 2026, 麻豆原创 is unveiling new and enhanced capabilities that power retailers to not only operate more efficiently but also achieve loyalty-led growth through every order.

AI in 麻豆原创 Order Management Services

Joule in 麻豆原创 Order Management Services: 麻豆原创鈥檚 AI copilot, Joule, is now available in 麻豆原创 Order Management Services. Access order-related data, analysis, and insights through conversations in natural language and visual display.

Order Reliability Agent: Accelerate operational efficiency with the Order Reliability Agent in 麻豆原创 Order Management Services. Proactively mitigate and resolve any potential issues and gaps, such as stock discrepancies or process bottlenecks, to help ensure every order is fulfilled seamlessly and to boost customer loyalty.

AI-assisted copy generation and translations: Create promotional copy in seconds and translate it into any language with AI assistance, helping to reduce manual workload and accelerate time-to-market.

UI enhancements

Workflow-optimized UI: The enhanced and unified UI in 麻豆原创 Order Management Services can deliver a consistent user experience across order, inventory, and fulfillment operations. Teams can now work faster, reduce training time, and maintain full visibility across every step of the order lifecycle.

Watch the 麻豆原创 Order Management Services  to get a closer look at the AI capabilities in action. Visit the 麻豆原创 booth at NRF 2026, January 11 鈥� 13, to learn more about 麻豆原创 Order Management Services and catch an in-person demo.

Emilie Fournelle is head of Product Management for 麻豆原创 Order Management Services at 麻豆原创.

To navigate AI鈥檚 evolution, organizations need to understand that it鈥檚 no longer just a question of “What can AI do?” but “How do we set our organization up for success with AI? How do we build for it? What problems do I solve with which models? How do we govern it?”

Looking ahead to five critical themes that will define enterprise AI in 2026, these present both opportunities and challenges for organizations. Let’s dive in.

1. New categories of AI foundation models unlock enterprise value

Advances in generative AI stem from breakthroughs in 鈥渇oundation models,鈥� massive neural networks trained on vast amounts of data that can be adapted to a wide range of tasks.

Large language models (LLMs) were the first wave of foundation models at scale. General-purpose LLMs, trained on the equivalent of all the text on the internet, opened the door to many value-adding use cases, including summarizing documents, writing code, and powering applications like ChatGPT and Claude. Over the last few years, we have already seen the foundation model approach applied to other domains, such as video creation and voice.

In 2026, specialized foundation models optimized for specific data types and domains will power the high-value enterprise AI use cases. Video generation models have already shown that models grounded in real-world physics data can reason about scenes and physical dynamics. Emerging world models demonstrate that simulating the physical world unlocks new possibilities in simulation, synthetic training data, and digital twins. Vision-language-action models demonstrate that robot-specific foundation models can generalize to new tasks and environments, enabling the transformation of web-scale knowledge into real-world actions in logistics and manufacturing.

In the enterprise domain, a similar shift is underway for structured data found in databases and transactional business software. While LLMs are impressive across many enterprise use cases, they cannot handle tasks like numerical predictions, such as inferring a delivery date or supplier risk score. However, work on relational foundation models shows that training on structured datasets鈥攆or example, data in tables, rather than generic text or images from the internet鈥攃an deliver high predictive accuracy without the tedious feature engineering and training required in classical machine learning. This means organizations can deploy predictive models in days, not months. Recent launches of relational foundation models, such as 麻豆原创-RPT-1, Kumo, and DistilLabs, highlight how new models can directly support use cases like forecasting, anomaly detection, and optimization across ERP, finance, manufacturing, and supply chain scenarios.

In 2026, these specialized models are expected to scale to deliver superior performance and economics for structured business tasks, surpassing general-purpose LLMs and state-of-the-art machine learning algorithms. These models will emerge as the workhorses behind high-value enterprise tasks.

2. Software evolves toward AI-native architecture

AI has seen various approaches create value over the decades, from the first rules-based expert systems to probabilistic deep learning and the recent explosion in generative AI. In 2026, organizations will shift from enhancing existing AI applications and processes to AI-native architectures, which will fully realize the promise of modern AI.

AI-native architecture adds a continuously learning, agentic intelligence layer on top of deterministic systems, enabling applications to become intent-driven, context-aware, and self-improving rather than being statically coded around fixed workflows. Agentic systems will still only be as good as the context layer they can reliably retrieve and ground on. Here, organizations should invest in truly comprehensive, semantically rich knowledge graphs that provide a scalable source of context, making AI-native software dependable and self-improving.

Enterprise applications will increasingly be built natively around AI capabilities, featuring user experiences designed for multi-model, natural language interaction; AI agents reasoning through complex processes; and a foundation managing foundation models, services, and a knowledge graph capturing semantically rich business data.聽AI-native architecture also enables more employees to create apps鈥攕uch as smaller, ad-hoc productivity applications鈥攊n a matter of minutes without straining IT.聽

AI-native architecture builds on, and even requires, established SaaS principles and investments in modern cloud applications. The technical term for combining probabilistic, adaptive AI models with deterministic systems of record is called neurosymbolic AI. It brings together AI鈥檚 best capabilities to adapt with reliable, governable, and deterministic processes. Next-gen applications will not just have AI bolted on; they鈥檒l be built around AI at their core. This means combining reasoning, business rules, and data to deliver insights and automation seamlessly. Imagine ERP systems that proactively flag anomalies, recommend actions, and even execute workflows autonomously鈥攁ll while staying aligned with company policies and regulations.

3. Agentic governance becomes mission-critical

Over the past two to three years, generative AI has introduced a wave of value-added use cases. These use cases were largely based on users sending a prompt to a model, receiving a response, and then interacting with the model again.

Last year saw the start of the next wave of innovation: AI agents capable of planning and iteratively reasoning through multi-step tasks, including selecting tools, self-reflecting on progress, and collaborating with other AI agents. These advanced AI agents promise to tackle complex business processes that were previously immune to automation, such as analyzing myriad documents, records, and policies to or .

However, the proliferation of AI agents, many of which handle critical tasks and sensitive data, demands the development of new capabilities. Agentic governance will emerge as a critical capability as organizations deploy hundreds of specialized AI agents. The “agent sprawl” challenge will mirror previous shadow IT crises, but with higher stakes given agents’ autonomous decision-making capabilities.

Forward-thinking enterprises will establish comprehensive governance frameworks addressing five dimensions: agent lifecycle management (version control, testing protocols, deployment approval, retirement procedures); observability and auditability (agent inventory, logging, reasoning paths, and action traces); policy enforcement (embedding business rules, regulatory constraints, and ethical guidelines into agent execution); human-agent collaboration models (defining autonomy boundaries, approval requirements, and escalation pathways); and performance monitoring (tracking accuracy, efficiency, cost, and business impact).

The organizational shift will prove profound鈥攆rom viewing AI as an independent tool to managing agents as digital coworkers requiring onboarding, performance reviews, and continuous improvement. HR and IT functions will collaborate on “digital workforce management” as organizations treat agentic governance as seriously as they do traditional workforce oversight.

4. Intent-driven ERP and generative UI emerge as a new user experience

Consumers are becoming increasingly familiar with computer interactions requiring prompts in natural language, voice, and even images and gestures. At the same time, generative AI鈥檚 ability to create text, graphs, code, and HTML on the fly is improving rapidly. In parallel, AI agents enable users to simply express their intentions, allowing the agent to determine how to work toward achieving that goal.

These advancements open the door to varied and entirely new modalities for users to work with enterprise software, as well as 鈥渘o-app ERP鈥� experiences. For example, to book a customer visit, a worker typically needs to open an analytics application to review the account, look in the CRM system to retrieve the customer鈥檚 address, and then navigate to another application to book travel, among other tasks. 

In 2026, we will see 鈥済en UI鈥� experiences increasingly surface via digital assistants, relieving users from the need to navigate between multiple applications and perform manual tasks. With time, AI will allow the user to simply express the intent: 鈥淧repare a trip to my customer with the most leads.鈥� From here, an AI agent will plan out the steps and required systems, interacting with the user to confirm travel details while dynamically generating analytical graphs and briefing material in the window. As AI agents develop stronger calculation and prediction tools, users will be able to “speak to their data” more naturally, with agents making data-based decisions in the background. To be clear, interactions with agents will extend far beyond a chat box; organizations will enjoy rich visualizations, complete workflows, and the ability to build hyper-personalized apps with just a few commands.

The user interface will not disappear. No-app ERP experiences and autonomous agents require the same foundational substrate that humans rely on for their daily work: structured workflows, security, governance, and business logic defined in business applications. The difference is that agents consume these primitives programmatically at scale, not only through a GUI, and humans can interact with these agents via natural language without ever needing to open the application.

These capabilities will usher in a new paradigm for human-AI collaboration and productivity in the workplace. Personalized experiences and adaptive workflows across applications and data sources will lower adoption barriers. This ability to focus solely on achieving a user鈥檚 intention, regardless of the interaction modality and underlying systems, will drive return on investment (ROI) in AI and enterprise software.

5. Deglobalization drives sovereign AI offerings

AI sparked debates about digital sovereignty among nations due to AI鈥檚 potential impact on everything from scientific discovery and national security to economic productivity and even culture. Events in geopolitics, such as supply chain disruptions caused by tariffs and war, have only intensified the urgency that many nations and organizations feel to become digitally sovereign.

Digital sovereignty has two broad definitions. First, digital sovereignty is an information security designation governing data storage and access, such as U.S. FedRAMP and German VSA, required to process sensitive governmental data in a 鈥渟overeign cloud.鈥� Second, and more broadly, sovereignty refers to the provenance of physical assets, intellectual property, legal jurisdiction, and services along the cloud stack. For example, does an application utilize an AI model created in Europe, the U.S., or China, and is the data center geographically isolated?聽

The high stakes, geopolitical uncertainty, and complexity of 鈥渟overeign AI鈥� will lead enterprises to increasingly demand AI and cloud solutions that are simultaneously cutting-edge, flexible, and fully sovereign. This intensifies the shift from globalized one-size-fits-all cloud to regionally compliant, AI-powered enterprise platforms. At the same time, governments will continue to refine their national AI strategies to invest in areas along the stack where they can compete and create value.

Executing on the 2026 AI themes

In 2026, AI is poised to move from a supporting tool to a fundamental pillar of the enterprise. This shift is driven by a convergence of defining trends鈥攊ncluding increasingly capable agents, generative UI, and AI-native architecture鈥攖hat push AI from the application layer and into the very core of business operations.

Organizations that thrive will be those that recognize this shift and build an enterprise that is purpose-built for AI: establishing robust governance to manage a new, collaborative workforce of humans and AI agents; embracing gen UI to lower adoption barriers and an intent-driven user experience that helps employees interact naturally; seeking out specialized foundation models that are precisely tuned for enterprise use cases to drive business value; and, finally, building applications natively around AI that combine reasoning, business rules, and data, delivering proactive insights and automation.

However, in 2026, organizations will still need high-quality, connected data. Data siloes severely limit the effectiveness of AI. As mentioned, AI-native architecture requires established investments in modern cloud applications that harmonize data across the entire business鈥攂ecause unified data means AI鈥檚 outcomes are more accurate and relevant.

Jonathan von Rueden is chief AI officer at 麻豆原创 SE.
Walter Sun is senior vice president and global head of AI for 麻豆原创 Business AI at 麻豆原创.
Sean Kask is vice president and head of AI Strategy for 麻豆原创 Business AI at 麻豆原创.

As companies race to implement this new technology, they鈥檙e discovering it鈥檚 not all as smooth as envisioned. Following a recent , high costs and fuzzy business value are creating speed bumps. As it turns out, the technology itself isn鈥檛 the problem, it鈥檚 how and why we use it. Like any shiny new gadget, AI agents only matter when they solve real problems that make people鈥檚 lives easier. So, what kinds of issues are they good at tackling? And how do we make sure we鈥檙e designing systems that serve actual humans and are not just chasing the latest tech trend?

That鈥檚 where things get interesting: deciding when you truly need an agent, how much freedom it should have, and what challenges and tasks it鈥檚 meant to address all while ensuring it genuinely helps people, instead of just ticking the 鈥渨e use AI鈥� box. The real magic happens when humans and agents team up, working side by side for the best results. How do we make the most of this human-agent partnership?

If you鈥檙e looking for a practical way to get started, the 麻豆原创 AppHaus Joule Agent Discovery and Design workshops offer a hands-on approach to help tackle these exact questions. With a blend of human-centered design methods, these workshop formats put people first, working to ensure agentic systems aren鈥檛 just flashy but genuinely useful.

Want to try it out for yourself? Here鈥檚 how you can run your own workshops and define impactful agentic systems.

A toolkit to build human-centered agentic solutions

The Joule Agent workshops are offered as two different formats, each designed to guide you through a different stage of building effective agentic systems: the Joule Agent Discovery workshop and the Joule Agent Design workshop. Together, these workshops provide a hands-on, human-centered path for creating AI agents that can truly deliver value.

First stop: Joule Agent Discovery workshop

The Joule Agent Discovery workshop is a structured approach to uncover the most valuable opportunities for agentic technology. It focuses on real-world challenges and identifying where automation can make the biggest impact. In two to three hours, participants dive into questions such as: What specific inefficiency or challenge needs solving? What could be automated? Who would benefit most from automation? What needs to be achieved with the automation? How complex and variable is the problem at hand?

The workshop also introduces participants to agentic technology and examines how much the selected challenges would benefit from it. By the end of this workshop, participants identify one or more high-value use cases that are well-suited to agentic technology. This helps ensure that efforts are focused on meaningful improvements rather than adopting technology for its own sake.

Second stop: Joule Agent Design workshop

Next is the Joule Agent Design workshop, which brings together those closest to the process鈥攅nd users and business experts鈥攖o define the details of the agent: its responsibilities, required skills, and how it will collaborate with people. The workshop follows a practical structure:

1. Define the focus area: Clarify what target users need to achieve within the selected process and identify which aspects would benefit most from automation.
2. Identify tasks to delegate: Use the metaphor of 鈥渉iring a super-specialist鈥� to decide which responsibilities should remain with people and which can be assigned to agents. Exercises help determine how many agents are needed, the risks of automating certain tasks, and where consistency versus autonomy is required.
3. Describe the super-specialist job: Draft a job description for each agent, outlining necessary skills and responsibilities.
4. Instruct the super-specialist: Define the instructions or workflow, including information requirements, decision points, and where human involvement is needed.

By the end of the workshop, each agent is described in detail, including its tasks, required knowledge and tools, and an initial set of instructions. This forms the foundation for configuring the agent鈥檚 system prompt.

The workshop material also offers guidance on structuring the system prompt based on the gathered information, ensuring a smooth transition from workshop insights to practical implementation. The entire process is designed to be completed in a single day and can be conducted virtually in Mural.

Learning how to run these workshops

To help ensure that anyone can confidently run these workshops鈥攏o advanced degrees or secret codes required鈥攁 set of self-paced courses are available and can be completed at the individual pace of the learner:

• : This course offers a comprehensive introduction to the Joule Agent Discovery workshop. It provides a clear, step-by-step guide, explains the exercises in detail, and gives practical advice on how to facilitate effective sessions. You鈥檒l gain the skills to identify agentic opportunities and successfully lead your team through the process.
• : This short webinar also centers on the Joule Agent Discovery workshop, but it specifically highlights a practical method for assessing the agentic potential of automation ideas. Consider it your quick reference for making informed decisions about automation.
• : The latest addition to our curriculum, this course walks you through the Joule Agent Design workshop step by step. It covers each exercise, shares real-world examples, and offers facilitation tips. You鈥檒l also learn how to adapt the workshop format for various time constraints and organizational needs.

All the resources required to facilitate these workshops are freely available on the website. Learners can simply visit the site, explore the materials, and start their agentic journey with confidence to turn ideas into new useful, human-centered AI solutions.

Karen Detken is an expert user experience designer at 麻豆原创 AppHaus.

Seismic shifts are not new for retailers鈥攂ack in 1999, e-commerce was still an afterthought. By 2000, everything changed as retailers went all-in on digital.

In聽2025,聽we are聽living in yet聽another pivotal year.聽This holiday season might feel familiar as you scroll through deals, compare brands, and race to beat shipping deadlines. But beneath the surface, something far more transformative is happening.聽The year 2025 will聽likely be聽the last consumers shop as they do now.听听

Agentic AI is reshaping commerce by making shopping faster, smarter, and effortless. Discovery is moving from people browsing their favorite brands to intelligent orchestration. Instead of opening 10 tabs to hunt for the right deal, shoppers will simply ask: 鈥淔ind me the highest rated black, puffy winter coat, size 10, under $200 that ships in two days.鈥�

The agent will handle the rest鈥攕canning thousands of options, validating reviews, confirming delivery timelines, even factoring in loyalty perks. That future isn鈥檛 tomorrow; it鈥檚 already here, and by next holiday season, most shopping journeys will begin, evolve, or end with AI agents.

While this type of shopping creates convenience for shoppers, it creates a challenge for retailers that have focused on brand campaigns and poured millions of dollars into advertising to be the 鈥渂rand of choice鈥� in the discovery process. Decades of investment into SEO, paid traffic, and brand recognition are losing their edge. While not abandoning these strategies entirely, they must evolve for the AI-first world.

However, there is something that hasn鈥檛 changed over the course of decades: the need to create loyal customers who make repeat purchases and give the greatest share of their wallets. This, too, is more challenging than ever. In fact, 72% of consumers report that this holiday season they will only that consistently meet their needs in the moment.

Creating customer loyalty in the age of agentic commerce means conquering two critical fronts:

• Optimizing for discoverability: Agents will favor retailers that make buying seamless.
• Creating customer loyalty post-purchase: With discovery being augmented by AI agents, humans will now give their ongoing loyalty based on post-purchase experiences. On-time delivery, easy returns, and rewards that feel personal are the new battleground for brand equity.聽 And with agents learning from human behavior, exceeding shopper expectations post-purchase can ultimately impact a brand鈥檚 likelihood of being recommended in the discovery phase.

The question remains: how do we move from esoteric AI conversations to practical strategies?

Discovery and loyalty: How to win in the age of agentic AI

• Make your catalog agent-ready: Treat AI as a new kind of shopper. Ensure product feeds are rich, structured, and machine-readable, complete with attributes, use-case-driven descriptions, real-time pricing, and accurate inventory. Clean, structured product data is now the foundation of intelligent discovery.
• Create solutions, not just SKUs: AI-driven traffic behaves differently. Design bundles, add-ons, and value stacks that solve specific problems and allow agents to match shoppers with outcomes, not just product lists.
• Build trustworthy, accessible information: Operationalize trust by surfacing verified reviews, transparent pricing, sustainability details, and clear return policies. Make this data accessible through well-structured APIs, not scraping, so agents and humans see the same reliable truth.
• Let prediction power personalization: Use unified data and AI to predict what customers want before they act, enabling real-time next-best-actions across email, SMS, push, in-app, and other emerging channels. This predictive intelligence turns fragmented campaigns into that deliver higher engagement and revenue.
• Make loyalty the thread that ties every experience together: Loyalty is no longer a program. It鈥檚 a relationship. Use every interaction to tailor meaningful, emotional moments that adapt, remember, and feel consistent across channels in order to help convert agent-driven traffic. Then, use personalized exclusives and perks to foster high-value relationships with those new customers.
• Deliver on your promises, every time: Eighty-eight percent of customers leave a brand after one bad experience. That鈥檚 why operational reliability is the new loyalty. Bring order, inventory, payments, and fulfillment into alignment, so customers receive what they were promised, when they were promised. Loyalty now begins at checkout.
• Prepare for the new return economy: Agent-driven buying makes it easy for consumers to purchase first and decide later. Set clear limits to protect margins and reduce friction in the returns journey because a seamless return can build more loyalty than the purchase itself.

麻豆原创 is already helping brands prepare for this future with AI-enabled technologies across , , , and .

A brand already building for the future

Global sports brand . Historically reliant on seasonal campaigns, Mizuno wanted a more sustainable way to engage its diverse customer base across 10 product categories and multiple channels. Mizuno unified its customer data and used AI to create personalized journeys, turning one-off interactions into long-term relationships.

The results speak for themselves:

• 52% year-over-year (YoY) increase in active customers
• 62% increase in revenue from premium customers
• 35% increase in customer win-backs
• 33% increase in the number of orders

麻豆原创: A partner built for scale, stability, and growth

As customer behavior evolves and AI reshapes what鈥檚 possible, one thing remains constant: 麻豆原创鈥檚 commitment to helping brands win their biggest commercial moments. This year鈥檚 holiday results make that clearer than ever. We鈥檙e not just helping brands plan for peak season鈥攚e鈥檙e helping them execute it with precision, intelligence, and confidence.

Nearly 20% YoY growth in total messages sent underscores the trust brands place in 麻豆原创 Emarsys to deliver at scale. Mobile and emerging channels surged鈥攊n-app (+61%), SMS (+32%), push (+27%), and inbox (+91%) all saw significant YoY gains鈥攁s brands met customers exactly where they were browsing and buying. Omnichannel maturity accelerated with brands using a richer mix of channels to create connected, high-value experiences across every stage of the shopping journey. And with 100% uptime and flawless reliability, teams executed independently and confidently, even during their highest-volume moments.

Paired with exceptional commerce performance, the story becomes even more compelling: brands used more intelligent engagement to guide shoppers toward higher-value purchases (+18% YoY average order value) and ultimately drove substantial YoY revenue growth (+40% gross merchandise value)鈥攁ll powered by a that delivered uninterrupted performance with 100% uptime through the holiday shopping rush, ensuring we鈥檙e here for our customers when it matters most.

This is what partnership looks like: scale, intelligence, reliability, and results so brands can focus on creating exceptional customer experiences, not managing technology.

Looking ahead

The year 2025 will be remembered as the last holiday season where brand mattered more than the overall experience.

This year, and 48% of shoppers would support brands bringing more AI into their buying experience. This sets the stage for growth in 2026 as AI agents deliver relevance, trust, and immediacy, making shopping simpler, smarter, and more satisfying for people everywhere.

The brands that win won鈥檛 be the ones shouting the loudest. They鈥檒l be the ones using 麻豆原创 to be most discoverable, dependable, and unforgettable.

By anticipating needs and creating better, personalized journeys, AI will enhance every stage of commerce. And 麻豆原创 is here to make that future happen.

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

From decades of customer support defined by reaction to calls, tickets, or queues, to the evolution of proactive support with pre-AI digital platforms, to the current AI-powered ecosystem that is redefining how support teams strategize, operate, and deliver resolutions. AI-enabled support anticipates needs, predicts failures, and delivers instant, seamless resolutions at scale.

And most importantly, this shift is as transformational as it is technological.

Keeping pace with transformation

As customers navigate complex and ambitious transformation projects, whether it鈥檚 moving to the cloud, scaling AI, or modernizing complex operations, there is always a quiet mandate: systems supporting critical business processes must run smoothly because the costs of downtimes have never been higher.

For businesses, uninterrupted operations are non-negotiable. 麻豆原创鈥檚 AI-driven support can anticipate issues before they arise, helping to ensure critical processes run smoothly, even during high-volume peak events. 麻豆原创 uses 麻豆原创 Business AI to help prevent issues proactively, working to ensure a smooth experience by avoiding system outages, platform scalability issues, data overloads, or service overloads. During the peak sales event of Cyber Week 2024, 麻豆原创 achieved 100% uptime for 麻豆原创 Commerce Cloud customers. As the Cyber Week 2025 numbers come in, we already have delivered 100% uptime and improved GMV for global sales events like Singles Day (GMV reached 鈧�7,108.72M, or +180.2% YoY, with 6,315.99K orders, or +46.4% YoY) and El Buen Fin (GMV hit 鈧�12,341.70M, or +13.18% YoY, and 10,385.74K orders, or +32.24% YoY).

Scaling self-service with AI

Structured knowledge and curated content enable 麻豆原创 to build AI and AI agents with high confidence levels. Today, over 82% of customer issues are addressed via self-service. This allows users to get instant resolution to issues or bridge knowledge gaps they face during the use, implementation, and continuous improvement of 麻豆原创’s solutions.

AI in instant response and resolution

When it comes to delivering instant response and resolution in customer support, the impact of AI-integrated services is remarkable. When 麻豆原创鈥檚 Auto Response Agent is highly confident of the solution, based on the underlying data and knowledge, it can deliver highly relevant solutions that can save customers significant time and effort. Additionally, the first contact resolution rate for cases answered automatically by the agent is at par with what human-human support interactions achieve.

Supporting 麻豆原创 Business AI

麻豆原创 Business AI supportability is all about making AI real for customers through the right systems that drive successful adoption. As 麻豆原创 delivers AI capabilities across its portfolio, we enable customers to have the right support when they encounter issues in early deployment.

As customers scale AI across their organizations, we have concrete processes and tools to help support them, so they can deploy new AI with the utmost confidence. For example, the Incident Solution Matching service is integrated with 麻豆原创 Joule for Consultants, allowing efficient support information retrieval and helping to eliminate the hassle of searching through vast amounts of 麻豆原创 documentation.

Empowering support engineers with AI

AI is not just transforming customer outcomes, it鈥檚 also transforming how our engineers and experts deliver precision and speed, freeing them from logistical tasks so they can focus on support requests that need specialized attention. Thanks to 麻豆原创鈥檚 AI-integrated self-service offerings, we鈥檙e able to instantly resolve customer issues four out of the five times they come to us.

AI-powered solution recommendations in self-service can eliminate the need for at least 10% of the cases being created. This is a big win for human-generated knowledge being delivered by AI-generated tools. Every third case gets submitted with an AI-recommended product component for optimal routing and faster processing.

In 麻豆原创鈥檚 multi-location, multilingual, global setup, standardized communication is key. Around 10% of responses by support engineers take advantage of 麻豆原创鈥檚 AI-assisted language optimization services.

There鈥檚 more. We have agentic case resolution, AI-assisted creation of 麻豆原创 Knowledge Base Articles, and automatic error categorization, covering use cases that help our engineers deliver their best work with greater accuracy and higher quality.

And, of course, 麻豆原创 runs its own products and solutions, serving as a first reference for our customers. As Dr. Benjamin Blau, 麻豆原创鈥檚 Chief Process and Information Officer, puts it: 鈥淭his is ‘麻豆原创 runs 麻豆原创’ in action. As customer zero, we validate every AI innovation in real-world complexity before it reaches you. We鈥檝e architected this multi-agent AI on our own 麻豆原创 Business Technology Platform, including the 麻豆原创 AI Core foundation, and our service and support data lake. Agentic case resolution is a blueprint for enterprise-grade, responsible AI, proving the power and maturity of the 麻豆原创 Business AI portfolio, empowering customers with faster resolutions for an elevated experience.鈥�

Will AI replace support teams?

Short answer: No.

To elaborate, let鈥檚 take the example of an AI agent that automatically responds to customers. 麻豆原创鈥檚 instant response and resolution are only activated when the system is very confident with its response. Our commitment to the relevant, reliable, and responsible use of AI helps ensure that there鈥檚 no experimentation with customer cases that deserve hands-on attention from engineers and experts. The legacy of trust that 麻豆原创 has earned over 50 years of industry leadership, which is also trusted by 90% of Fortune 500 companies, drives this rigor applied to AI.

What does this mean for our engineers? Any move to augment our work with AI is not about replacing people. It鈥檚 about freeing time, energy, and creative space to focus on high-impact tasks that need critical thinking and human insight. AI amplifies human expertise. Customers benefit from this blend of machine intelligence and human insight, ensuring every solution is relevant and responsible.

It鈥檚 also important to highlight that 麻豆原创 is a growth company. The use of technology helps us deliver what customers expect from support teams and build ongoing knowledge that feeds AI systems for intelligent decision-making, also meeting the future demands of AI-augmented support.

Yes, the world is witnessing role reductions across the industry with the adoption of AI in business workflows, but we also see the emergence of critical new roles that help us navigate the current reality. How many of us had heard of AI trainers or carbon accountants 15 years ago?

These are exciting times for innovation. 麻豆原创鈥檚 partnerships, such as our collaboration with Databricks and Snowflake, empower developers to turn business data and AI into real business outcomes.

We鈥檙e truly at the crossroads of innovation and transformative tools that can turn imagination into impact. 麻豆原创鈥榮 Chief Technology Officer, Philipp Herzig, summarizes it perfectly: 鈥淎I is transforming business at every level, but it鈥檚 people who turn transformation into progress. With 麻豆原创 Business AI, we鈥檙e combining the best of human ingenuity and machine intelligence to deliver impact that matters.鈥�

Stefan Steinle is executive vice president and head of Customer Support & Cloud Lifecycle Management at 麻豆原创.

The autonomy of AI agents in decision-making and innovative problem-solving is what makes this disruptive technology so attractive to companies, yet also one that demands focus and clarity.

Organizations need to understand where it is appropriate to deploy AI agents while ensuring they behave as intended: staying within their operational scope, using only authorized data, and maintaining compliance with the company鈥檚 guidelines and regulations.

If in the world of espionage, agents would normally operate in the shadows,聽in the corporate world, companies need to ensure that AI agents operate in plain sight. Their behavior should be transparent, measurable, and always under control.

As organizations scale their use of agentic AI, the specter of a new challenge emerges: understanding where exactly in their processes agents should be applied, if they are acting the way they are supposed to, and what their impact and cost really are. After all, AI agents are a business resource like any other, and should always be governed by an organization鈥檚 leadership, in alignment with that organization鈥檚 goals.

This is where agent mining capabilities in 麻豆原创 Signavio solutions come into play, bringing visibility, accountability, and continuous optimization to the growing ecosystem of AI-driven process transformation.

The problem: invisible autonomy in enterprise AI

AI agents become more autonomous — and sometimes more ambiguous — as they learn and evolve. Their decisions happen in milliseconds, driven by complex reasoning that can appear as an invisible black box to end users.

Without transparency, organizations risk blind automation, which means agents operate efficiently on paper but unpredictably in practice, potentially introducing errors, inefficiencies, or unnecessary costs.

At the same time, agents operating visibly allow organizations to see and learn from the agents鈥� behavior, potentially revealing new approaches, creative solutions to existing problems, or unexpected positive outcomes.

The solution: AI Agent mining

Agent mining enables organizations to understand and optimize the behavior of AI agents operating across their business processes. Agent mining in 麻豆原创 Signavio solutions provides the ability to:

• Trace agent behavior to see how agents make decisions, navigate process steps, and adapt to different contexts
• Analyze impact and measure the agents鈥� influence on key metrics like process time, accuracy, and compliance
• Monitor cost by tracking computational or LLM-related expenses, helping ensure cost-efficient performance
• Benchmark performance by comparing outcomes of agent runs to identify areas for refinement

But it鈥檚 also more than just a monitoring tool. AI agent mining is an intelligence layer for the AI workforce. By transforming invisible actions into measurable insights, it empowers organizations to:

• Increase transparency into agent behavior and decision logic
• Control operational costs by understanding and optimizing LLM usage
• Ensure compliance through auditable decision trails
• Continuously improve performance by learning from live agent execution
• Measure true business value of AI automation, not just efficiency metrics

Agent mining from 麻豆原创 Signavio can extend from Joule Agents built by 麻豆原创 to third-party or custom-built AI agents, offering a unified lens across an organization鈥檚 AI landscape.

A broader vision: AI agent excellence

Unless we鈥檙e watching the Mission: Impossible franchise, no one wants to see rogue agents on the loose, which is why agent mining is just one of four pillars under 麻豆原创 Signavio鈥檚 comprehensive approach to AI agent excellence. The pillars that help ensure intelligent agents are not just deployed, but deployed intelligently, include:

• Agent discovery: Identifying the right processes and opportunities where AI agents can drive the greatest impact
• Agent context: Providing agents with the right process knowledge and compliance parameters to act responsibly and efficiently
• Agent mining: As outlined above, observing and analyzing how agents actually behave in operation
• Agent value impact: Quantifying the business value that agents deliver, such as efficiency gains, cost savings, or improved customer experience

Together, these pillars ensure that organizations not only automate processes but continuously learn from and improve their performance. The world is not enough; it鈥檚 critical to have both working in sync.

As 麻豆原创 Signavio General Manager Dr. Gero Decker shared, 鈥淎I agents represent a fundamentally new paradigm, and a key question remains: How should we perceive them? Should we view AI agents as advanced technical constructs or as non-human humans?聽It’s a complex question that organizations must address as they deploy AI agents at scale.”

鈥淎t 麻豆原创 Signavio, we are preparing for the future by focusing on the organizational and process dimensions of agentic AI adoption,” he said. “Our AI agent excellence approach aims to ensure that AI agents are integrated seamlessly, efficiently, and compliantly into the broader enterprise. In this rapidly evolving landscape, no company can afford to stand still. Our vision is to be an engine for innovation and reinvention, helping companies grow and adapt.鈥�

麻豆原创 Signavio solutions can empower organizations to ensure their AI agents are not only effective but also transparent and accountable, paving the way for smarter, more strategic automation — in other words, AI agent excellence.

Lucas de Boer is a Global Marketing program lead for 麻豆原创 Signavio.

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.