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According to recent , more than half of marketers say fragmented or outdated data prevents them from acting in the moment.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 补谤别听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.

Early results in proof-of-concept applications of 麻豆原创鈥檚 robotics initiative, Project Embodied AI, demonstrate up to 50 percent reductions in unplanned downtime, up to 25 percent improvement in productivity, and significant reductions in operational errors across manufacturing, warehouse automation, and quality inspection.

These results are among the reasons why 麻豆原创 has expanded its Embodied AI ecosystem through partnerships with leading robotics companies and robotic enablement partners, as announced this week at 麻豆原创 TechEd. This builds on the recently announced collaboration with NEURA Robotics and NVIDIA to drive the future of physical AI.

The Embodied AI initiative extends the impact of into physical operations by making robots cognitive: able to autonomously execute complex tasks while understanding the broader business context in which they work. This empowers enterprises to faster adapt to changing operational environments.

麻豆原创 is uniquely positioned to deliver these innovations because of its decades of experience with business applications deeply integrated into the processes that power the modern enterprise. This allows 麻豆原创 customers to integrate robotics into the same business functions seamlessly, in a way no other company can. The result of our new robotics partnerships includes new proof-of-concept applications of embodied AI that demonstrate the business value and return on investment of our approach.

Cutting-edge experiments demonstrate measurable productivity gains

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

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

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

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

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

Transforming warehouse operations at Sartorius

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

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

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

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

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

Optimizing automotive production at Martur Fompak

, a global leader in automotive seating systems, teamed up with  and 麻豆原创 to explore how humanoid robots could transform field operations workflows.

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

Together the team is testing how cognitive robotics can support picking and packing operations at the company鈥檚 30 production plants across various continents and countries.

The early exploration connects Humanoid modular robots with 麻豆原创 solutions to execute workflows such as component retrieval, tray loading, and precise placement into production containers. 麻豆原创鈥檚 embodied AI agents provide context awareness around production orders and component variants.

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

Artem Sokolov, Founder and CEO, Humanoid

Initial findings demonstrate the value in automating repetitive and ergonomically demanding tasks, such as unpacking parts, handling trays, or supporting kitting processes. These experiments will be the foundation for a broader transformation in which humanoid robots participate in 麻豆原创-driven manufacturing environments and logistics processes.

Robotics company partners

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

AgiBot

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

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

.

ANYbotics

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

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

.

Booster Robotics

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

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

.

Galbot

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

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

.

Humanoid

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

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

Unitree Robotics

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

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

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Robotics enablement partners

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

Capgemini

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

Cyberwave 

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

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

HCLTech

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

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

KINEXON

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

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

Lighthouse

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

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

SinoSwissHub

SinoSwissHub is launching a regionally compliant, 麻豆原创-integrated orchestration platform for multi-robot fleets, with humanoids as the centerpiece.

鈥淲e don鈥檛 just connect robots to an 麻豆原创 system; we enable real-time physical data to reinvent processes and build adaptive, resilient value chains together with 麻豆原创,鈥� said Yuki Long, founder and CEO of and Aimbo Robotics.

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

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

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

Build custom agents on the most context-rich agentic platform

I鈥檓 excited to share the ability to build custom agents in will be generally available in December. New capabilities include AI-assisted agent design, system-triggered agents, extensibility of 麻豆原创-delivered Joule Agents, centralized enterprise-grade agent monitoring, support for Agent-to-Agent (A2A) protocol, and support for Model Context Protocol (MCP). Read our in-depth and to learn more.

鈥淛oule Studio鈥檚 agent builder connects 麻豆原创 analytics with merchandising systems, powering Accenture鈥檚 Optisell agent to surface at鈥憆isk inventory, recommend pricing actions, and simplify merchandise alert configuration — all without heavy custom development.鈥�

Catherine Nguyen, Global Lead for 麻豆原创 Business Group AI Strategy and Adoption, Accenture

For developers building pro-code agents, (麻豆原创 BTP) offers comprehensive support for popular open-source frameworks, like Crew.AI and LangGraph, and provides end-to-end identity, authorization, governance, and integration capabilities.

MCP support for is now available, providing with direct access to rich multi-model engines. This allows agents to be grounded in full data context: navigating relationships across customers and suppliers, understanding geographic dependencies through spatial data, and performing semantic searches through vector embeddings 鈥� all within a single in-memory engine. 

Additionally, 麻豆原创 HANA Cloud knowledge graph engine can now 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.

We鈥檙e also enabling agentic memory in 麻豆原创 HANA Cloud. With long-term memory, AI agents can persist context across long-running sessions and memorize past input and decisions, just like humans do, to become continuously smarter.

I鈥檓 excited to share several innovations, including our first enterprise relational foundation model (RPT), 麻豆原创-RPT-1, accompanied by a no-code testing playground environment and prompt optimizer service. New capabilities are continuously added to our , empowering developers to experiment with leading models and orchestration tools and scale AI development and productization across 麻豆原创 and non-麻豆原创 landscapes. .

Vibe on 麻豆原创 Build using the tools of your choice

New local give developers the ability to use agentic tools for development and preferred code assistants, such as Cursor, Windsurf, Claude Code, Cline, and OpenAI Codex — all while maintaining enterprise-grade governance and clean-core alignment.

An 麻豆原创 Build extension pack for Visual Studio (VS) Code is now available to simplify development of CAP, Fiori, UI5, and mobile applications. This makes it easier for VS Code developers to build faster and deploy apps on 麻豆原创 BTP. Looking ahead, we will publish our extensions on the Open VSX Registry to simplify the onboarding of these tools and provide similar native development experiences on other integrated developer environments (IDEs).

Increase velocity with 麻豆原创 Joule for Developers

, the best code assistant for 麻豆原创 development, empowers developers of all skill levels to build more efficiently by leveraging comprehensive, AI-infused developer tools to deliver precise, contextualized outcomes powered by purpose-built, 麻豆原创-centric AI models. This frees-up time to be more productive, creative, and proficient in accelerating ABAP, Java, JavaScript, and visual tool-based application development and automation of 麻豆原创 processes.

New enhancements to 麻豆原创 Joule for Developers, such as within and capabilities, help developers modernize legacy systems and meet enterprise-grade governance and security requirements. These improvements also boost productivity, enhance code quality, and support cloud transformation goals.

Developers working in can now use AI-assisted content creation to quickly generate comments, create workspace content, and summarize documents. AI responses can also draw directly from user-specific folders and workspace content, providing users with trusted insights tailored to their roles, while maintaining compliance and secure role-based document grounding. 

Looking ahead, new ABAP large language models (LLMs) trained on ABAP code and specialized for ABAP development will be released next year.

Connect everything to boost productivity

Developers build the bridges that keep businesses running. We鈥檙e making that work easier and faster with . The following innovations make API and agent-driven automation easier, ensure reliable end-to-end security with real-time monitoring, and boost developer productivity.

We are continuing to embed AI capabilities directly into 麻豆原创 Integration Suite. will now automatically provide targeted recommendations and intelligent healing to resolve common API anomalies such as error spikes, latency surges, or abnormal traffic patterns. Developers can also ask Joule to about the most used APIs to gain deep API insights and understand usage patterns.

MCP Gateway support will enable customers to expose custom APIs and integration flows that can be consumed by AI agents. This feature introduces the ability for customers to enrich custom agents built in Joule Studio or extend Joule Agents by integrating data from third-party and legacy 麻豆原创 systems, composed as MCP tools for smooth agentic consumption. This approach standardizes access, centralizes governance and security, and simplifies discoverability in the .

To jumpstart integration projects, you can access hundreds of out-of-the-box integration adapters as well as pre-built API, event, and integration content on .

Gain security and operations built in, not bolted on

麻豆原创 BTP provides developers with a unified environment to manage applications, secure data, and scale solutions seamlessly across 麻豆原创 and third-party systems. With core centralized application lifecycle, interoperability, security, and administration capabilities, developers can easily and quickly resolve errors and boost team productivity. Read the full list of enhancements in the .

Explore at your own pace

If you missed attending 麻豆原创 TechEd in person or virtually, please be sure to read the full list of announcements in the  and watch .

Wherever you鈥檙e at in your journey, there are easy ways to get started:

• Visit to start a
• Explore and new 
• Register for our upcoming on December 9
• Read more in “Introducing 麻豆原创 Snowflake, New Data Fabric Innovations for 麻豆原创 Business Data Cloud and 麻豆原创 HANA Cloud“

Michael Ameling is general manager and chief product officer of Business Technology Platform and a member of the Extended Board of 麻豆原创 SE.

BERLIN 鈥� At 麻豆原创 TechEd in 2025, (NYSE: 麻豆原创) brings AI deep into the development process to level up how developers build.

New AI-driven capabilities in the 麻豆原创 Build solution, an expanding data ecosystem and powerful Joule Agents empower developers to move from idea to impact with unprecedented speed and confidence. As AI transforms the nature of professional work, 麻豆原创 also pledges to equip 12 million people worldwide with AI-ready skills by 2030.

鈥溌槎乖粹�檚 announcements today give developers the tools they need to deliver at the speed of AI,鈥� said Muhammad Alam, member of the Executive Board of 麻豆原创 SE. 鈥淚nnovations across 麻豆原创鈥檚 unique flywheel of applications, data and AI put developers in the driver’s seat — where they belong.鈥�

Opening the Developer Ecosystem

麻豆原创 Build, the company鈥檚 flagship solution for enterprise application development and automation, now gives developers more freedom to build, extend and automate using the tools they love most.

For instance, developers who prefer agentic development solutions like Cursor, Claude Code, Cline and Windsurf can now use 麻豆原创 development frameworks with new 麻豆原创 Build local Model Context Protocol Servers. Visual Studio Code users will be able to access 麻豆原创 Build capabilities directly in their development environment with a new 麻豆原创 Build extension. This extension will also be made available later on Open VSX Registry for other development environments. 麻豆原创 and n8n also announced plans for an integration so Joule Studio agents and n8n agents can work together.

And with new agent building capabilities in Joule Studio, developers have the tools they need to extend 麻豆原创鈥檚 ready-to-use agents and build new agents grounded in 麻豆原创 business data and context that can act autonomously based on changing business conditions.

Putting Data to Work

Every intelligent application starts with trusted data. 麻豆原创 is giving developers more ways to put that data to work through 麻豆原创 Business Data Cloud.

The solution now connects with more of the data and AI platforms developers use every day. A new 麻豆原创 Snowflake solution extension for 麻豆原创 Business Data Cloud brings Snowflake鈥檚 fully managed data and AI capabilities directly to 麻豆原创 customers, giving them the flexibility to choose the right compute and storage for each data and AI workload, while maintaining governance, interoperability and business context. 麻豆原创 also announced a new 麻豆原创 Business Data Cloud Connect partnership with Snowflake. This complements existing integrations with Databricks and Google Cloud, giving developers more freedom to choose how they work with 麻豆原创 data.

With a new data product studio capability in 麻豆原创 Business Data Cloud, developers can turn raw data into ready-to-use assets known as data products that support analytics, AI and application development.

An expanded capability in the 麻豆原创 HANA Cloud knowledge graph engine can automatically generate knowledge graphs. This capability maps relationships across 麻豆原创 database tables, columns and data models, revealing how data fits together and why it matters. Developers will be able to see how their data connects across systems and uncover underlying business insights.

Bringing AI Autonomy to Life

麻豆原创 is evolving its AI portfolio to give developers the intelligence and orchestration power they need to take AI from insight to action.

麻豆原创 introduced its first enterprise relational foundation model, a new class of AI that predicts business outcomes rather than the next word in a sentence. 麻豆原创-RPT-1, or the first-generation Relational Pre-trained Transformer, can make fast and accurate predictions for common business scenarios like delivery delays, payment risk or sales order completion. 麻豆原创 launched a free playground environment for developers today.

New AI assistants in Joule coordinate multiple agents across workflows, departments and applications, bringing automation and autonomy to life. These assistants plan, initiate and complete complex tasks spanning finance, supply chain, HR and beyond. Today, 麻豆原创 introduces new agents built for technical users. For example, an agent for business process analysis will help teams understand how processes run, identify inefficiencies and uncover opportunities to optimize workflows and drive measurable improvements.

Lastly, as AI changes the nature of work for everyone, 麻豆原创 is pledging to equip 12 million people worldwide with AI-ready skills by 2030. 麻豆原创 will expand hands-on training and certification programs that integrate practical AI-ready tools, including through its partnership with online learning platform Coursera.

Visit the . Get 麻豆原创 news via  and .

About 麻豆原创

As a global leader in enterprise applications and business AI, 麻豆原创 (NYSE:麻豆原创) stands at the nexus of business and technology. For over 50 years, organizations have trusted 麻豆原创 to bring out their best by uniting business-critical operations spanning finance, procurement, HR, supply chain, and customer experience. For more information, visit鈥�.

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As the first step in a much broader playbook toward a next-gen, agentic case resolution workflow, 麻豆原创 is launching instant resolution and self-service tools to help make support smarter and more proactive. How do these offerings help in real-world support scenarios?

AI-accelerated instant resolution

Imagine a customer submits a routine ticket about a known error during a routine update. Instead of waiting in a queue, the customer gets an instant response from a specifically trained AI agent with a direct link to a relevant 麻豆原创 Note or 麻豆原创 Knowledge Base Article. It鈥檚 accurate, contextual, and fast. What鈥檚 more, it can reduce ticket volume and free up engineers to handle more complex or urgent problems. As a result, the customer resolves their issue in minutes with no back-and-forth or productivity loss.

Take another example of a user uploading logs after encountering a defect. An instant resolution tool like a smart log analyzer can review the data, immediately flag the root issue, and respond to the user with a link to the fix. What you get is near-instant technical diagnostics and zero-escalation resolution.

Instant resolution has clear, real-world benefits in customer support: faster answers, fewer escalations, and a better experience for users. But beyond the promise of speed and convenience, how effective is it really? What does the data tell us about the quality and reliability of AI-driven support at 麻豆原创?

Let鈥檚 take a look at a few metrics that shed light on the performance of our Auto Response Agent and the value it can deliver.

Agentic AI turns support from reactive to action-oriented, dramatically reducing time-to-resolution while enhancing accuracy. For example, the confidence rate of 麻豆原创鈥檚 Auto Response Agent is at 80%. In other words, to avoid wasting the customer鈥檚 time, this agent can deliver highly relevant solutions with a confidence score of 80%. This is a strong indicator of the quality our AI agents can offer.

First contact resolution (FCR) is a general customer service KPI and indicates that the case is closed after the first interaction either with humans or with AI agents. The FCR rate for cases that are answered automatically by 麻豆原创鈥檚 new Auto Response Agent is currently at 40%. This is in line with what human interactions achieve.

AI-enabled self-service

Before AI, self-service in customer support was mostly static and manual, like FAQs, basic help articles, and keyword-based search. While these knowledge base articles are extremely helpful, customers had to dig through generic content, hoping to find something relevant, often with little guidance or context. There was no personalization, no real-time assistance, and limited ability to troubleshoot complex issues on their own. It worked for simple problems but often left users turning to human support for detailed answers.

Instead of prompting users to search through static FAQs or documentation, AI dynamically surfaces the most relevant knowledge base articles, fixes, or guided workflows based on issue context, behavior, and history. Instead of a manual hunt, customers can take advantage of an intelligent, conversational experience鈥攐ften resolving issues before a ticket is even needed. The result? Fewer support cases, faster resolutions, and more empowered customers.

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. Structured knowledge and content allows us to build AI and AI agents with high confidence levels. Currently, 麻豆原创鈥檚 customer support addresses over 82% of issues via self-service.

AI and the evolving role of human expertise

This move to augment auto response with AI isn鈥檛 about replacing people. It鈥檚 about freeing up people so that they can focus on high-impact tasks that need creative thinking and human insight. 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 ensures that there鈥檚 no experimentation with customer cases that deserve hands-on attention from engineers and experts.

The road ahead

We move forward with a clear goal to achieve a support system that is faster, smarter, and more human because of its intelligent use of AI, not in spite of it. By augmenting first-touch support with agentic AI, 麻豆原创 has a blueprint for handling simple and complex issues at all levels of enterprise support.

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

However, the pace of change has made traditional approaches, like best-of-breed point solutions, no longer sufficient to help finance teams keep up.

That is why the future of finance technology should act as an orchestra, not disjointed soloists.

Finance teams require a unified experience where applications, data, and AI work seamlessly together. With 麻豆原创 Business Suite, these elements come together as one orchestra, enabling finance to operate in harmony and deliver outcomes that point solutions alone cannot achieve. This means not only faster close and stronger liquidity, but also tighter compliance and credible plans that hold up under change.

To strengthen collaboration across finance and bring innovation to the CFO鈥檚 office, 麻豆原创 unveiled Joule鈥檚 next stage as the AI force at the center of 麻豆原创 Business Suite鈥檚 value creation. A new generation of role-aware assistants in Joule are designed to partner with people in their specific business roles by tapping into the right agents behind the scenes for the job. For finance professionals, 麻豆原创 introduced the next round of automation support using agentic AI.

麻豆原创 is embedding AI-powered reasoning directly into finance processes to automate routine work and elevate the strategic role of finance with the following new agents:

• The Accruals Agent will calculate accruals and deferrals based on system data and present the accountant with a proposal for review, along with a detailed explanation of the calculation logic.
• The Cash Management Agent will automate daily bank statement reconciliation tasks and recommend opportunities for optimization.
• The International Trade Classification Agent will reason over product characteristics and trade regulations and act to classify goods for international shipping, recommending customs tariff numbers and commodity codes

By bringing automation to critical but repetitive finance processes, these agents underscore 麻豆原创鈥檚 commitment to equipping the office of the CFO with AI-driven innovation that strengthens resilience in an increasingly complex business environment.

Finance as the conductor of the enterprise

The role of the CFO has expanded far beyond closing the books or reporting quarterly earnings. Today, finance teams are expected to bring together data, processes, and people from the entire business to enable seamless collaboration.

With 麻豆原创鈥檚 AI-first, suite-first approach, applications and data flow seamlessly, allowing finance leaders to coordinate business operations with confidence. This unified foundation ensures that insights are not siloed, but instead shared across the enterprise, enabling faster and smarter decisions.

Take working capital management, one of the biggest pressure points for finance leaders: A recent of 480 CFOs found confidence levels were strong in almost all areas, including revenue, profit, and customer retention, but only 36 percent felt confident in their ability to achieve working capital targets. 麻豆原创 Business Suite can help organizations meet their targets by automating manual work and enabling actionable decisions — all by leveraging dynamic, autonomous, and action-oriented solutions in 麻豆原创 Business Suite.

When an organization manages incoming receivables, outgoing payables, as well as cash and inventory within 麻豆原创, that connected experience creates data that provides real-time visibility into liquidity through 麻豆原创 Business Data Cloud.聽 That data fuels 麻豆原创 Business AI to analyze situations, recommend actions, and even resolve issues like disputed invoices automatically, which would otherwise slow the cash collection process. And with the integration of Joule and intelligent agents, 麻豆原创 Business AI not only automates, it senses, reasons, orchestrates, and acts 鈥� turning insights into outcomes that free capacity and accelerate the business.

As AI agents take on more of the behind-the-scenes work, finance professionals can step fully into their role as the conductor of the enterprise. They can guide strategy with foresight, aligning every function in harmony and shaping outcomes that extend far beyond revenue or cost goals.

CFOs and their teams can then move beyond reactive decision-making toward shaping strategy in real time. This will position their office as a driver of growth, resilience, and innovation.

Looking ahead

麻豆原创 is laying the foundation for a new era of autonomous finance, where routine processes are automated, insights are delivered in real time, and finance teams are empowered to focus on strategy and growth.

These innovations mark just the beginning of 麻豆原创鈥檚 commitment to helping businesses navigate an increasingly complex financial landscape with agility, intelligence, and confidence.

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

This latest course will enable attendees to facilitate a new Joule Agent Discovery Workshop, guide workshop participants to identify appropriate use cases, and tailor the workshop format to the needs of different audiences.

But what are 麻豆原创 solutions for agentic AI? What do they stand for?

Joule Agents are AI systems that autonomously plan and execute multi-step workflows, collaborating to connect departments, speed up decisions, and streamline processes.

Discovering high-value opportunities for agentic AI

In the format of an 麻豆原创 Expert Lecture, this course introduces participants to the Joule Agent Discovery Workshop, a structured and collaborative method to identify high-value agentic use cases in an organization. Attendees will learn how to inspire and guide participants, prioritize ideas, and describe the selected opportunities in detail. The course also covers how to adapt the workshop to different timeframes, team sizes, and virtual settings. By the end, attendees will be able to guide participants in identifying where AI agents can make the biggest impact and lay the groundwork for their agentic journey.

In detail, learners will be able to: 

• Understand the purpose and structure of the Joule Agent Discovery Workshop and how it can be used to identify high-value agentic use cases
• Facilitate the workshop exercises, guiding participants from idea generation to prioritization and a detailed description of agentic use cases
• Adapt the workshop format to different team sizes, virtual environments, and timeframes to fit organizational needs

There are no prerequisites for this course, but experience with 麻豆原创 Design Thinking and workshop facilitation will be helpful. It is a good learning opportunity for a variety of roles such as support consultant, business user, and 麻豆原创 rookie.

The creative mind behind 麻豆原创 AppHaus methods and this learning journey

For many years now, Karen Detken, an expert user experience designer at the 麻豆原创 AppHaus, has worked in customer co-innovation projects and has gotten firsthand experiences and feedback when developing and hosting a variety of workshop formats with different methods and tools. Early on, the team decided to share these best practices and their tools and templates in the openly accessible .

When the topic of artificial intelligence arose and 麻豆原创 solutions started to include generative AI and large language models (LLMs) in their solutions, such as 麻豆原创 Business AI, followed by the latest step up with agentic AI, such as Joule Agents, the 麻豆原创 AppHaus team worked with customers on exploring appropriate business use cases to benefit from this very latest in technology. Based on these first experiences, the team started sharing helpful methods, as a co-innovation frontrunner, so that other teams, partners, and customers could drive their own exploration projects involving latest technologies.

For Detken, it is not only about enabling in and applying those technologies: 鈥淣ew technologies are developing very fast and are becoming widely accessible,” she said. “What is important is that we have a very clear picture of why we want to use the technologies. Because technology only has a value when you find the right purpose to use it. Customers and users need to be clear about the outcomes they want to have with that technology. This is the first thing you need to answer before using it. With the methods we provide, we intend to help people first understand what this technology can do for them, for the business, for the people.鈥�

This awareness and very conscious use of technology also includes the consideration of responsible and ethical guidelines that every new solution needs to follow (see 麻豆原创鈥檚 principles laid out in the ).

Bringing innovation and technology into the hands of people

The 麻豆原创 AppHaus team gets feedback from many different customer and partner teams. For the team of experienced co-innovation coaches, it is fulfilling to see workshop participants, along with attendees of enablement sessions, understand the new technology better. From this deeper understanding they help participants — along their — start generating ideas related to their business needs. They help them, as Detken puts it, 鈥渢hink of different ways how they can use AI to solve real problems.”

The latest 麻豆原创 Learning Journey for agentic AI is a compilation of helpful exercises to help customers and partners explore and approach this field of technology while discovering meaningful business use cases. In parallel and probably not that obvious at first sight, this new course testifies the openness of the team for novel applications such as using an avatar as speaker. It was built based on video recordings with Detken.

When asked about her view on agentic AI in contrast to generative AI, Detken describes it as follows: 鈥淕enerative AI uses an LLM as a kind of intelligent system or ‘brain.’ The same LLMs are used by an AI agent. The difference is that the agent can not only ‘think’ and use these large language models to generate content or analyze data and make decisions, but it also uses ‘tools’ or other applications to act upon these decisions or make changes autonomously. To put it as an example: with Gen AI, we only had the brain and now it’s the next step, we have the brain and the hands. Maybe in the future, we will have the entire body as well, which would probably be the robots.鈥�

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

Images courtesy of 麻豆原创 employee Viktor Georg

But as AI becomes more independent, new risks emerge. So, how can we navigate this next frontier responsibly? This is a question that we at 麻豆原创 do not leave to chance.

From tools to teammates

Imagine you’re buying a car. You expect it to meet all safety standards, regardless of where the component parts are built or how the car is assembled. The process behind the scenes does not change your expectation of safety. The same goes for agentic AI.

Agentic AI systems are more than tools; they are intelligent agents that plan, learn from experience, self-correct, and collaborate. They’re capable of orchestrating complex processes, making decisions, and even engaging with other agents or humans to achieve a goal. However, with this leap forward comes a new layer of complexity and risk.

Core capabilities and risks of agentic AI

Agentic AI systems bring powerful capabilities like planning, reflection, and collaboration, enabling them to tackle complex tasks autonomously. They can map strategies, learn from mistakes, use external tools, and coordinate with humans and other agents.

However, each strength introduces risks. For example, flawed planning can cause inefficiencies, reflection may reinforce unethical behavior, tool usage can lead to instability when systems interact unpredictably, and unclear collaboration can result in miscommunication and compounded errors. Balancing these capabilities with proper safeguards is essential for safe, ethical deployment.

Managing autonomy: balancing freedom with control

One of the most pressing challenges with agentic AI is managing its autonomy. Left unchecked, these systems can veer off course, misinterpret context, or introduce subtle risks without immediate detection. To address this, organizations must strike a careful balance between freedom and control.

We have learned that oversight should be calibrated according to risk. High-stakes domains like healthcare or human resources demand robust human supervision, while low-risk, routine tasks can tolerate greater autonomy. Also, continuous monitoring is essential; agentic AI systems, like any complex technology, require regular checks to ensure quality, compliance, and reliability.

A key element of this oversight is maintaining a 鈥渉uman in the loop鈥� approach, where human judgment is integrated into critical decision points, ensuring that automated actions remain aligned with human values and organizational intent.

This principle has been at the heart of 麻豆原创鈥檚 ethical AI approach from the beginning, reflecting our belief that AI should augment, not replace, human decision-making. To reinforce this, 麻豆原创 has introduced mandatory ethics reviews for all agentic AI use cases, ensuring that each deployment is scrutinized for ethical implications and remains aligned with our responsible AI principles.

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Building transparency and accountability

Transparency is not just a buzzword; it鈥檚 a foundational requirement for building trust in agentic AI. From the outset, during the design phase, it is crucial to classify AI systems based on the complexity and risk of the tasks they perform. This classification guides decisions about the necessary safeguards and ensures that mechanisms for human intervention are integrated from the beginning.

At runtime, transparency is maintained through explainability and traceability. Developers and end-users must be able to understand what the system is doing and why. Crucially, accountability must always rest with humans or legal entities, never with the AI itself.

Rethinking governance and regulation

Despite the emergence of agentic AI, there have been no new regulations specifically crafted for it. Existing laws and frameworks such as GDPR still apply and provide a solid foundation for governance. However, what has changed is the level of technical rigor required to remain compliant and ethically sound. Organizations must now adopt more robust processes. They need to analyze use cases with greater precision, apply risk-based controls that match the potential impact of the AI system, and ensure that ethical and legal standards are upheld through enhanced design practices and ongoing testing.

Designing with human values at the center

Agentic AI cannot be an excuse for lowered standards. At 麻豆原创, the stance is unequivocal: Even in autonomous systems, AI must meet the highest ethical benchmarks. This means embedding principles such as fairness, transparency, and human agency directly into the design.

Ultimately, all users should be equipped with the tools and understanding they need to supervise and, when necessary, intervene in the system鈥檚 behavior.

Building trust in a black-box world

Trust in AI doesn鈥檛 happen by default; it must be intentionally built and continually reinforced. One of the most effective ways to do this is by giving stakeholders the right amount of information. Too much detail can be overwhelming and counterproductive while too little fosters blind trust or fear of the unknown. The key lies in communicating clearly about the system鈥檚 capabilities, risks, limitations, and appropriate use. Empowering users to critically assess the AI鈥檚 behavior 鈥� and to know when to step in 鈥� is central to creating a safe, secure, and trusted AI environment.

Rethinking KPIs in the AI-augmented workplace

As agentic systems, like our Joule Agents, begin handling more tasks, human roles will naturally evolve. To keep up with this shift, organizations need to rethink how they define and measure success. This starts with investing in change management and upskilling programs that prepare employees to work effectively alongside AI. It also requires redefining productivity metrics, moving beyond task completion to focus on how well humans and AI agents collaborate. Success should be measured by how efficiently teams harness AI to unlock new levels of insight and innovation.

Building AI that builds trust

Agentic AI is not just another phase; it is a transformation. But like any transformative technology, success depends on how it鈥檚 built, governed, and used.

At its best, agentic AI amplifies human capabilities, accelerates innovation, and helps tackle challenges once considered too complex. But it also demands a new level of diligence, oversight, and ethical reflection.

The future is not just about building smarter agents; it鈥檚 about building responsible ones.

Learn more:

• The Evolution from Chatbots to Agentic AI Systems

Walter Sun is senior vice president and head of AI at 麻豆原创.

That is why 麻豆原创 is excited to announce our expanded partnership with Cohere, a leader in secure, enterprise-grade AI.

Together, we plan to bring Cohere鈥檚 powerful generative and advanced retrieval models to the 麻豆原创 ecosystem, starting with its model, and extending evaluations with , to enrich our product suite, playing an important role in powering agentic AI experiences.

These models are planned to be available alongside other leading AI models from 麻豆原创 as well as third parties in the generative AI hub in 麻豆原创 AI Core, with the intent to give customers more choice to build AI-powered solutions that meet their unique business needs.

Expanding 麻豆原创鈥檚 trusted AI model portfolio

is rooted in trust. Our customers expect and deserve AI that respects their data privacy, that it fits within their operational workflows, and that it understands the context and complexity of their industries. Cohere鈥檚 focus on security, efficiency, and enterprise applicability aligns perfectly with 麻豆原创鈥檚 approach to business AI and our generative AI hub.

Cohere Command models are lightweight, high-performing language models tailored for complex business tasks, with support for agentic workflows and multilingual operations. The Embed and Rerank models enable powerful enterprise search and retrieval capabilities, helping customers build accurate, context-aware RAG pipelines across structured and unstructured data.

Cohere models are designed to perform in production environments while respecting enterprise privacy requirements and compute constraints. And because Cohere shares our commitment to privacy-first design, these capabilities are built to serve even the most regulated industries, such as finance, healthcare, and the public sector.

麻豆原创: Launch partner for Cohere鈥檚 reasoning model

As part of the partnership, 麻豆原创 plans to be one of the聽first partners to offer Cohere鈥檚 upcoming reasoning model, a purpose-built, high-efficiency model designed to power agentic use cases.

We see enormous potential here. 麻豆原创鈥檚 vision for collaborative AI agents 鈥� capable of automating complex, multi-step tasks across systems 鈥� requires not just scale, but reasoning. Whether it鈥檚 helping consultants configure a system or enabling customer service to resolve cross-system issues, this next generation of AI requires models that can reason, plan, and act securely. Cohere鈥檚 reasoning model is built for exactly that.

We鈥檙e excited to partner with 麻豆原创 and bring its enterprise customers the latest security-first models and solutions from Cohere. We鈥檙e especially excited that 麻豆原创 will be one of the first partners to offer our upcoming reasoning model. 麻豆原创 and Cohere share a vision for practical AI innovation, and our collaboration marks an exciting milestone as we unlock new efficiencies and growth for global enterprises.

Martin Kon, President and COO, Cohere

Powering real-world applications across industries

With this collaboration, 麻豆原创 customers will be able to use Cohere models to solve pressing business challenges across industries, such as:

• Agentic task automation: Enable AI assistants that can take actions across enterprise tools and systems
• Multilingual RAG applications: Retrieve, rank, and summarize data from global policy manuals, compliance documents, or internal knowledge bases
• Secure document analysis: Understand long, structured, multimodal files like financial disclosures, M&A reports, technical manuals, or medical imaging
• Context-aware enterprise search: Improve search accuracy across unstructured content like emails, tables, or contracts

Customers will be able to easily access, test, and scale these models in production within 麻豆原创鈥檚 generative AI hub.

Expanding choice without compromise

With our partnership with Cohere, we are continuing to expand a growing ecosystem of AI capabilities that are open, secure, and business-ready. This partnership helps ensure that customers can choose the right model for their use case, while trusting that it meets 麻豆原创鈥檚 standards for quality, reliability, and compliance.

Together, 麻豆原创 and Cohere are enabling enterprises to harness generative AI with confidence, whether they鈥檙e building knowledge assistants, automating processes, or delivering new intelligent services to users.

To learn more about our approach to enterprise-ready AI, visit .

Walter Sun is senior vice president and head of AI at 麻豆原创.

This work is complemented by two additional areas of progress:聽first, the expansion of聽Google Gemini models聽in 麻豆原创鈥檚聽generative AI hub聽on聽麻豆原创 Business Technology Platform (麻豆原创 BTP); second, the use of Google鈥檚 video and speech intelligence capabilities to support聽multimodal retrieval-augmented generation (RAG)聽for video-based learning and knowledge discovery in 麻豆原创 products.

Together, these efforts reflect a shared commitment to deliver enterprise-ready AI that is open, flexible, and deeply grounded in business context.

Bringing AI agents together: laying the groundwork for interoperability

The future of work is agentic. Businesses are increasingly deploying AI agents that assist with real tasks 鈥� resolving customer issues, managing approvals, and collaborating across business functions.聽This is why 麻豆原创 is delivering a collaborative agent architecture with Joule聽to support cross-functional agentic workflows across 麻豆原创 Business Suite.

But for these agents to deliver real value, they cannot operate within a single vendor landscape. They must be able to collaborate across various platforms, securely exchange information, and coordinate actions across complex enterprise workflows.聽 This need for seamless interaction underscores why聽the represents a significant step beyond simple API integrations or enhanced tooling.

That鈥檚 why 麻豆原创 has joined Google Cloud and other enterprise leaders as a founding contributor to the new A2A protocol. This open standard is designed to ensure agents from different vendors can interact, share context, and work together鈥攅nabling seamless automation across traditionally disconnected systems.

Consider a customer dispute resolution scenario: a representative receives a billing inquiry via聽Gmail. Instead of toggling between tools, they can invoke聽Joule聽directly from the email. Joule, acting as an agent orchestrator, initiates a dispute resolution process, engaging another Google agent that connects to聽Google BigQuery, where relevant transactional warehouse data resides. Together, the agents validate the issue, retrieve insights, and recommend a resolution 鈥� without manual system switching, data reconciliation, or context loss.

This is the kind of cross-platform collaboration the A2A protocol is designed to enable: AI agents working together to accelerate business outcomes, reduce friction, and enable people to focus on more strategic work. It also reinforces 麻豆原创鈥檚 vision for聽Joule as an agent orchestrator聽working across enterprise workflows: interoperable, proactive, and deeply connected to business context.

Expanding access to Google models in generative AI hub

Beyond agent interoperability, 麻豆原创 is furthering its commitment to openness and flexibility by expanding access to聽Google models聽in the聽generative AI hub, a key capability of the聽AI Foundation聽on 麻豆原创 BTP.

Through the generative AI hub, customers gain enterprise-grade access to a curated portfolio of leading foundation models. That portfolio now includes Google Gemini 2.0 Flash and Flash-lite, which join the existing support for Gemini 1.5 models already available through the hub.

This expanded model choice gives customers the flexibility to build and extend AI-driven solutions using聽high-performance, low-latency models聽optimized for enterprise workloads 鈥� while staying within 麻豆原创鈥檚 secure, business context-rich environment.

By combining Google鈥檚 model innovation with 麻豆原创鈥檚 deep understanding of enterprise processes, we enable customers to apply generative AI in ways that are not only powerful, but also practical, trustworthy, and fully aligned with how businesses operate.

Unlocking multimodal understanding with Google Video Intelligence

As part of our continued collaboration with Google Cloud, 麻豆原创 is also advancing multimodal RAG, a highly requested capability among 麻豆原创 customers, especially for video-based learning content.

Multimodal RAG enhances information retrieval and generation by integrating multiple data modalities 鈥� text, images, audio, and video 鈥� into a single, structured process. This approach enriches knowledge sourcing and elevates how users interact with training and support materials.

To address the complexity of extracting meaningful insights from video content, 麻豆原创 leverages Google Video Intelligence for on-screen text detection across video frames, and Google鈥檚 Speech-to-Text API for accurate transcription of spoken audio. During the indexing process, these outputs are stored with corresponding timestamps, creating a structured foundation for retrieving relevant video segments with precision.

By grounding audio and visual content with time-aligned metadata, 麻豆原创 enables users to search and retrieve聽specific, contextually relevant moments聽within a video, making the learning experience more intuitive, accessible, and impactful.

鈥淎s agentic AI evolves, seamless handling of multi-modal data 鈥� text, voice, enterprise videos, and images 鈥� becomes paramount,鈥� said Miku Jha, director of AI/ML and Generative AI at Google Cloud. 鈥淭his introduces significant challenges for agent interoperability. An open protocol like A2A is therefore indispensable, providing the necessary framework and flexibility for agents to effectively communicate and collaborate across these diverse modalities. Multi-modality is not simply a capability; it is a foundational requirement driving the next generation of interconnected agentic systems.鈥�

This is another example of how 麻豆原创 is integrating Google鈥檚 AI capabilities into business-relevant scenarios, helping customers unlock more value from their unstructured content and elevate the way knowledge is delivered across the enterprise.

Shared vision for business AI

These efforts reflect a broader strategic alignment between 麻豆原创 and Google Cloud: a shared belief in AI that is open, composable, and grounded in real business context. Whether it鈥檚 shaping emerging standards for agent collaboration, providing choice through best-in-class models, or making unstructured content actionable, we are focused on helping our customers innovate with confidence 鈥� today and into the future.

To learn more about how 麻豆原创 and Google Cloud are shaping the future of enterprise AI, visit and explore to see these innovations in action.

Walter Sun is senior vice president and head of AI at 麻豆原创.

People were confiding their deepest thoughts to the chatbot and experts predicted that within a few years, conversations with chatbots would be indistinguishable from those with humans. It certainly took more than just a few years, but here we are at the edge of another stage of AI evolution.

Evolution of AI agents

Artificial intelligence has evolved significantly since 1966, advancing from basic rule-based systems to highly autonomous decision-making systems.

In the 1990s and early 2000s, relied on predefined keyword responses but lacked the ability to adapt to complex queries. By the 2010s, intelligent virtual assistants such as Alexa and Siri enhanced user interactions and introduced AI into everyday life through smart home integrations. In the 2020s, task-specific began to emerge, each tailored to perform specialized tasks. For instance, AI-driven personal finance assistants can analyze spending patterns and suggest savings plans, while AI-powered content moderation tools scan social media platforms to identify harmful content.

Looking ahead, autonomous AI systems are rapidly advancing. , composed of multiple independent agents, can collaborate to achieve a complex workflow beyond the ability of an individual agent. Tasks are coordinated between agents, as opposed to individual agents that often require human coordination and intervention between tasks. For example, in manufacturing, AI agents can independently optimize production lines, while in healthcare, AI systems are assisting in surgery by making real-time adjustments during procedures. Autonomous systems are also being deployed in logistics to manage inventory and optimize warehouse operations without human intervention.

Expanding capabilities of agents

Today, AI agents are like super-efficient digital teammates 鈥� smart systems equipped to perform tasks autonomously, learning from experience and adapting along the way.

罢辞诲补测鈥檚 agents have core capabilities like these:

• Planning: Agents go beyond executing single actions; they orchestrate processes, breaking down complex problems and mapping out efficient, step-by-step approaches.
• Reflection: Unlike traditional software, agents reflect their actions in real time and learn from mistakes. They self-correct and iteratively reason through the problem until they find the best solution. This capability allows them to handle more irregular, complex challenges, makes them more effective over time.
• Tool Usage: AI agents can use external tools 鈥� like calculators, APIs, databases, and even other AI models 鈥� to expand their capabilities, broadening the scope of tasks they can accomplish.
• Collaboration and Multi-Agent Interactions: Agents aren鈥檛 limited to working solo. They thrive in cooperative ecosystems, coordinating with other specialized agents and humans, leveraging their unique expertise to achieve a shared goal.

Why is AI agent innovation accelerating at this moment?

The answer lies in the remarkable advancements in foundation models. These models allow AI to handle complex data and produce outputs like code, text, or media that are tailored to specific tasks. They enable systems to think through problems deeply and autonomously, mirroring human cognitive processes.

For instance, the latest reasoning models like OpenAI’s o1 and o3 are game changers. They do not just perform tasks; they use real-time computing power to “think” and generate human-like outcomes. And the progress is mind-blowing: o3 scored over 80 percent on a human-like reasoning test while its predecessor, GPT-4o, scored only two percent on the same test just one year earlier.

With such rapid advances, AI agents are getting better at automating and enhancing business decisions, truly pushing the limits of what autonomous systems can achieve.

What makes 麻豆原创 unique in this space?

麻豆原创 Business Suite offers a unique advantage in the era of agentic AI. It is not just a collection of solutions; it’s a powerhouse for transformation that boosts continuous innovation. Here鈥檚 the simple breakdown:

• Applications: With , cloud ERP applications, 麻豆原创 Business AI, and 麻豆原创 Business Data Cloud come together to deliver exceptional business value 鈥� all powered by 麻豆原创 Business Technology Platform (麻豆原创 BTP). In this way, 麻豆原创鈥檚 business applications and technology platform aren鈥檛 siloed tools; they integrate processes end-to-end. This integration ensures that every action taken within these applications is based on trusted, business-critical data at its source.
• Data: All the data, whether from 麻豆原创 or other systems, is collected and unified in 麻豆原创 Business Data Cloud. This makes it a single, trustworthy source that breaks down data-silos and fuels advanced AI-driven insights. Because AI is only as good as the data you feed into it.
• AI: Joule helps employees coordinate intelligent agents to work together, breaking down barriers between functions and enabling real-time, company-wide improvements. Unlike others who might use AI in limited areas, 麻豆原创 integrates AI throughout your entire organization, enhancing efficiency and resilience on a large scale.

麻豆原创 is in a unique position to turn AI agent technology into business value given the breadth of applications and data we offer, allowing automation of tasks along all key business processes. Our domain knowledge is grounded in real-world business data and our process know-how is maintained in 麻豆原创 Signavio and 麻豆原创 Knowledge Graph. Bringing it all together, our unified entry point with our AI co-pilot Joule enables us to use AI agents to automate processes and augment decision-making.

Joule agents are collaboration experts

Thanks to our fully integrated approach, we are designing a system of collaborative Joule agents that work within and across the suite to support every business function, solving complex challenges and driving cross-enterprise productivity. Businesses don鈥檛 need hundreds of AI agents, just the right ones with the right skills, grounded in the right data, with the right guidance from 麻豆原创鈥檚 end-to-end business processes.

agents are available in all parts of the business, delivered out of the box with Joule and 麻豆原创鈥檚 suite of applications. This enables the transformation of entire business processes 鈥� end to end.

Agentic AI represents a transformative leap in business technology. By bringing together structured data management, seamless system integration, and advanced task automation, AI agents empower teams to operate with efficiency, accuracy, and agility.

麻豆原创 is taking the necessary steps for the next era of enterprise management by embedding systems of AI agents into 麻豆原创 applications, fueled by context-rich business data, helping customers to realize the full potential of 麻豆原创 Business AI.

Watch the video: 

Every day, your people spend too much time aligning data, decisions, and actions across functional silos. AI agents can help bridge these silos, so that core processes run flawlessly and the entire organization operates more efficiently.

However, capturing this opportunity is not about creating lots and lots of siloed agents across the enterprise that help reinforce more functional independent tasks. Instead, it鈥檚 about having the right agents, grounded in the correct business context and data, that can work together, supporting human collaboration and improving end-to-end processes.

At 麻豆原创, we have been investing heavily to deliver the full promise of . From the start, we鈥檝e architected 麻豆原创 Business AI with a Suite-first principle that ensures an integrated AI strategy that brings exceptionally more value with every skill, feature, or scenario added to our portfolio of applications and platform.

Joule, our generative AI copilot, provides one seamless integrated experience across the suite, providing a unified user interface across all business functions and more than 1,300 skills to perform work across the organization. You can ask any question or present any business problem, and Joule will work across every part of your business to solve it like no other solution in the market.

These investments set a strong foundation for realizing our vision for Joule agents — a vision we first shared at 麻豆原创 Sapphire in 2024. Joule agents are uniquely capable of working together and with business users in various roles to execute complex cross-functional processes with speed and reliability.

Watch the video:

With today鈥檚 announcement of 麻豆原创 Business Data Cloud, the foundation for Joule agents becomes even stronger, because AI agents are only as powerful as the data in which they are grounded.

麻豆原创 Business Data Cloud equips Joule agents with a single trusted data layer that breaks down data silos, unifying data across 麻豆原创 and non-麻豆原创 sources. With 麻豆原创 Business Data Cloud, Joule agents access the most complete and context-rich data sets, allowing them to reason more deeply and act with more insight to solve problems.

麻豆原创 Knowledge Graph, previously announced at 麻豆原创 TechEd in 2024, serves as the semantic bridge between Joule agents and 麻豆原创 Business Data Cloud. 麻豆原创 Knowledge Graph reveals the connections between data and processes, helping Joule agents find all the most relevant data to ground their decisions and actions.

While knowledge graphs are not a new concept, combining them with new advanced technologies makes them extremely powerful. 麻豆原创 Knowledge Graph is rapidly advancing to make 麻豆原创’s unique 50-plus years of business process expertise available to Joule agents. This process grounding further enables Joule agents to be aware of the context in which they operate and, therefore, to solve more challenging problems that involve multi-step processes spanning supply chain, procurement, finance, and more.

All these innovations turn our long-held AI agent vision into a reality, with more innovation to come, faster. Today, we announced the availability of a collection of ready-to-use Joule agents across finance, service, and sales, with more across the 麻豆原创 Business Suite portfolio in 2025.

The announcement includes the planned first quarter availability of a cash collection Joule agent previewed at 麻豆原创 TechEd in 2024. The cash collection agent will analyze disputes and work across finance, customer service, and operations to validate details and recommend resolutions. This Joule agent exemplifies the full promise of agentic AI 鈥� delivering new levels of operational efficiency by working cross-functionally to complete a complex, multi-step process that usually takes hours in just a few seconds.

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罢辞诲补测鈥檚 announcement also includes new ready-to-use Joule agents that advance efficiency across multi-step sales and service tasks. This includes a Q&A agent that continuously monitors opportunities and customer cases, proactively spotting questions and surfacing relevant answers from approved knowledge sources; a knowledge creation agent that automatically identifies novel case resolutions and creates structured knowledge articles that scale expertise across your organization; and a case classification agent that understands case context — for example, recognizing a tax-related inquiry even if the word “tax” isn’t mentioned — and correctly routes the case to the correct team.

This class of functionally focused Joule agents will become part of Joule鈥檚 collaborative agent architecture, making them available to team with other Joule agents to solve problems across cross-functional processes. For example, when the case classification agent identifies a customer billing dispute, it can route it to the cash collection agent, autonomously kicking off the dispute resolution multi-agent workflow. Through such , a dispute can not only be resolved in seconds but also within seconds of its receipt, further increasing process efficiency and delighting customers with unmatched response time.

In addition, 麻豆原创 also previewed a custom agent builder capability for Joule studio in 麻豆原创 Build. The new agent builder will make it simple for users such as citizen developers to create custom agents for their company鈥檚 unique business needs. A guided no-code workflow, informed by 麻豆原创鈥檚 business process expertise, helps ground custom AI agents in business processes and data, allowing them to solve problems through autonomous actions across a customer鈥檚 麻豆原创 and non-麻豆原创 applications. With the unique foundation provided by Joule, Joule agents, 麻豆原创 Business Data Cloud, and 麻豆原创 Knowledge Graph, the agent builder enables organizations to build powerful custom AI agents.

With Joule agents, Joule is not just an AI copilot, but becomes an AI orchestrator across all your organization. Joule can now adaptively assemble and orchestrate teams of agents — including out-of-the-box as well as customer鈥檚 custom-built AI agents — from multiple business functions to perform complex end-to-end processes. With Joule agents, teams work more seamlessly, work moves faster, and businesses operate more efficiently.

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Brenda Bown is chief marketing officer of 麻豆原创 Business AI at 麻豆原创.