This model marks the next leap in tabular AI, giving organizations access to unmatched tabular AI predictions on structured business data without model training or tuning. For 麻豆原创 customers, TabPFN-3.5 Plus is now available in 麻豆原创 AI Core.

Critical enterprise business decisions,聽such as cash flow forecasting, payment delays or聽supplier risk scoring, run on structured, tabular data. While LLMs聽excel at language and knowledge,鈥痶abular foundation models聽(TFMs)聽are purpose-built for聽structured data and聽can accurately predict business outcomes based on tabular data such as payment delays, supplier risks, upsell opportunities, customer churn risk and more.聽

With TabPFN-3.5, 麻豆原创 customers can work with data as it exists in their systems. Missing values, mixed data types and inconsistent fields are handled by the model, returning聽accurate聽predictions without preprocessing.聽This聽newest model from Prior Labs鈥� family of tabular AI models聽uses in-context learning to make predictions from raw tabular data, handling columns with thousands of distinct values, such as product codes or customer identifiers, and mixed data types natively, without the trial-and-error configuration that traditional models require.聽TabPFN-3.5 Plus聽is the most聽accurate聽and scalable tabular foundation model available today, based聽on聽TabArena聽and聽BeyondArena, two聽external聽benchmarks designed to evaluate predictions over real-world data.聽

鈥淩eal-world data is rarely perfect.聽Datasets often聽contain聽complex relationships and varying conditions that make traditional machine learning difficult. TabPFN-3.5 is聽specifically聽built for these challenges, providing industry-leading accuracy and scalability for tabular data with less manual effort鈥攎aking it the聽most effective聽tabular AI model in the industry聽to date,鈥澛爏aid Philipp Herzig, Chief Technology Officer, 麻豆原创聽SE.聽鈥淲ith TabPFN-3.5聽and the 麻豆原创-RPT model family, customers get聽accurate聽predictions from labeled business data in minutes, with no training聽required.聽For us,聽tabular AI is not a supporting feature of the Autonomous Enterprise, it is the foundation.鈥澛�

For more detailed information and to learn how to get started on 麻豆原创 AI Core, see the , and view the .

麻豆原创 completed its acquisition of Prior Labs in July 2026, bringing one of the world鈥檚 leading TFM research teams into the 麻豆原创 family. Prior Labs will continue to operate as an independent entity, with 麻豆原创 having previously committed to invest more than 鈧�1 billion to scale it into a globally leading frontier AI lab for the structured data that underpins the world鈥檚 businesses.

Visit the . Get 麻豆原创 news via  and .

Media Contact:
Alex Vaught, +1 (206) 678-5712, alex.vaught@sap.com, PST 
; press@sap.com

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

For , Race to World First is a test of endurance as much as execution. The competition can require teams to compete continuously for weeks against some of the world鈥檚 most complex raid encounters, where fatigue, stress, recovery and coordination can influence performance while coaches still need to make decisions in real time.

That challenge has traditionally been difficult to solve because critical information often sits in separate systems. Gameplay metrics, combat logs, wearable-device signals, player wellness information and operational data can each provide insight. However, when analyzed in isolation, they leave coaches with an incomplete view of how player condition and in-game execution affect one another.

The solution helps Team Liquid bring those signals into a unified, AI-enriched analytics environment. Coaches, analysts and performance experts can view physical readiness, stress indicators, recovery metrics, scheduling context and in-game performance together, helping them identify patterns that are hard to detect across disconnected tools and spreadsheets.

The solution extends the connected data foundation with AI-powered recommendations. By analyzing trends across gameplay, wellness and biometric information, Joule can help coaches surface early warning signs and compare tactics against historical performance patterns. With Joule, coaches can also evaluate whether recovery breaks, role adjustments or strategy changes may help the team sustain precision during progression attempts.

鈥淎s we prepare for Race to World First, having player performance, health and game data in one place changes how we approach coaching and strategy,鈥� said Dr. Jesse Hart, Senior Director of Sports Science and Analytics, Team Liquid. 鈥溌槎乖� Business Data Cloud allows us to make decisions backed by data rather than intuition during one of the most demanding competitive periods of the year.鈥�

麻豆原创 and Team Liquid have collaborated since 2018 on esports performance, analytics and AI innovation, including solutions designed to support competitive preparation and real-time decision-making. 麻豆原创 Business Data Cloud marks the next evolution of that work by showing how connected data and AI can help teams turn fragmented information into action under pressure. The same challenge exists beyond esports: organizations across industries need trusted data, intelligent insights and human expertise working together so people can perform at their best when it matters most.

Visit the . Get 麻豆原创 news via  and .

Media Contact:
Martin Gwisdalla, +49 6227 7-67275, martin.gwisdalla@sap.com, CET
麻豆原创 麻豆原创 Room; press@sap.com

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

Unlike traditional esports tournaments that play out over a few hours, Race to World First pushes teams through days and sometimes weeks of continuous competition. Players face extreme cognitive load, limited recovery time, and the constant pressure to execute flawlessly against the most complex encounters ever designed in gaming. At that level, performance is no longer just about mechanics. It’s about endurance. And endurance creates a data challenge.

Performance analytics beyond the game

Over the last several years, Team Liquid has become one of the most innovative organizations in esports.

Together, we have built data platforms, advanced analytics capabilities, and AI-powered solutions such as Joule Agents that help players, analysts, and coaches access insights faster than ever before. As we discussed our next innovation journey with the team, one question emerged quickly: What if coaches could see not only how players are performing in the game, but also how they are performing as human beings?

Historically, game statistics, biometric measurements, and wellness data were often analyzed independently, limiting the ability to understand how they influence one another. Therefore, the next evolution of esports analytics is not collecting more data. It is connecting data.

For Race to World First, we helped Team Liquid bring gameplay analytics, player wellness information, and biometric signals from wearable devices together in 鈥攁nd relationships that were previously difficult to identify became visible.

This sounds simple, but it fundamentally changes how coaching decisions are made.

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One unified view of the entire team

For the first time, Team Liquid鈥檚 coaches, analysts, and performance experts can see how physical readiness, stress indicators, recovery metrics, and in-game performance interact鈥攊n one view instead of separate dashboards and spreadsheets.

During Race to World First, waiting is often not an option. Fatigue develops in real time, and physical and mental stress accumulate throughout the competition. With live data streams, Team Liquid’s coaching staff can now monitor conditions as they unfold and identify early warning signals before they impact performance.

AI that amplifies human expertise

Data becomes even more valuable when AI helps make sense of it. That is where comes into play. By connecting previously isolated datasets, Joule analyzes trends across gameplay, wellness, and biometric information simultaneously. Coaches receive proactive recommendations from the underlying data.

Imagine receiving insights such as: “Players鈥� reaction times are getting longer, and stress indicators are elevated. Consider a recovery period before the next progression attempt.” Or: “This tactic has historically underperformed with the current roster configuration. Alternative strategies have delivered stronger results under similar conditions.”

AI does not replace human expertise; it amplifies it by giving Team Liquid鈥檚 coaches access to information that would otherwise remain hidden across thousands of data points.

Proving ground for human performance

This collaboration represents something bigger than esports. Competitive gaming has become one of the world’s most sophisticated environments for exploring human performance鈥攊ts scale, intensity, and speed create a unique proving ground.

Team Liquid鈥檚 challenges are not so different from those organizations face in business: bringing fragmented data together, turning information into action, and helping people make better decisions under pressure. It is the question we started with, asked at a larger scale: How are people performing, not only within systems and processes, but as human beings?

Race to World First raises the stakes. By combining connected data in 麻豆原创 Business Data Cloud with AI-powered insights from Joule, we are exploring what becomes possible when technology helps organizations understand performance holistically鈥攎ost importantly, how to help people perform at their best when it matters most.

In esports, success will still be measured in milliseconds. Sustaining that precision for weeks is a different challenge, one determined by how well a team is supported by connected data, intelligent analytics, and a deeper understanding of the people behind the numbers.

Because in the end, championships are won by teams. Data simply helps those teams unlock their full potential.

Benjamin Blau is chief process and information officer at 麻豆原创.

At first glance, the space looks like a curated boutique. Clothing racks, soft lighting, and attentive staff set the scene. Pick up an item, and nearby displays can respond. Teams can also follow fitting-room activity and the sales floor in real time.

At the center is the Retail Innovation Lab by NYFW Collections and 麻豆原创, featuring fashion label RE/DONE. 麻豆原创 and N4XT Experiences, which operates NYFW Collections, built the lab as part of our . The store is open from September 11-30. For three weeks, visitors can experience connected retail in SoHo.

The new store is a progression of a three-day pop-up developed with fashion brand Public School New York in February. This season we are expanding the Retail Innovation Lab format to a full-fledged RE/DONE store, open to the public for three weeks. At the heart of it is a private “Command Center,” connecting the sales floor with the operations behind it. The Retail Innovation Lab doubles as an expansion of our 麻豆原创 Experience Centers, offering 麻豆原创 customers and prospects the chance to explore 麻豆原创 software and partner solutions in a real-life setting.

This approach reflects our co-innovation model, which aims to turn collaborative ideas into customer-facing experiences faster so visitors can experience the technology in action and understand its value firsthand. 麻豆原创, N4XT Experiences, and participating fashion brands contribute with their respective expertise. Together, we are developing a store concept that serves as a test bed for retail brands of various sizes.

From display to decision

Start your visit with a scan and opt in to a digital profile. As you browse, nearby displays can update with product details. A styling surface suggests complete looks and complementary pieces. Together, these features connect product discovery and styling in one experience.

In the fitting room, connected technology recognizes each item and size. An associate can respond quickly with another size or suggestion. When you’re ready, choose how to buy. Use your phone, ask an associate, or visit a traditional point of sale. Afterward, a personal digital wardrobe saves looks and keeps the experience going.

Behind the sales floor, the Command Center gathers movement, fitting-room, size, and sales signals. Teams gain a real-time view of customer activity and store performance.

Consider unexpected interest in one size: combined with inventory, merchandising, and replenishment information, that signal can reveal demand earlier. Teams can respond with greater precision. The store becomes a richer source of insight for assortment, inventory, and demand planning.

Co-innovation at the heart of industry

The lab also shows how our approach to co-innovation works. Customers and industry experts contribute their knowledge of operations, markets, and opportunities. 麻豆原创 brings more than 50 years of experience in business processes. It also brings the data, applications, and technology that connect them. Together, they ground innovation in the realities of retail and fashion.

In retail, that collaboration points toward autonomous commerce. Connected customer journeys and intelligent operations can work together within human-defined guardrails. The lab makes this direction tangible on the sales floor.

The same principle guides 麻豆原创’s approach to Industry AI, co-innovated with customers and industry experts. Their real-world knowledge gives AI context for industry processes and business decisions. AI can then understand what a signal means inside a business. It can also connect that signal to the surrounding processes.

That context differs by industry. In fashion retail, customer interactions can guide merchandising and demand. On a factory floor, production data can help teams improve operations. In consumer products, changing demand can inform planning and supply decisions. Each industry helps shape the AI built for it.

That’s the power of Industry AI. It brings an industry’s own knowledge into AI.

From industry expertise to intelligent action

Industry knowledge becomes more valuable when it moves businesses from insight to action. Companies can decide faster and orchestrate more processes across their operations.

That creates a path toward the Autonomous Enterprise. People define ambitions and guardrails; intelligent systems support them and execute more processes with speed, context, and precision. Human expertise stays central. It shapes the decisions and boundaries that move industries forward.

NYFW gives this collaboration a compelling stage. The lab shows how customer experiences and operations can connect in real time. Autonomous commerce brings that connection into focus for retail. Industry AI carries the same principle across industries.

Together, business knowledge, data, and AI create new ways for customers to act on insight.

Andre Bechtold聽is president of 麻豆原创 Industries & Experiences and chief revenue officer of Industry AI聽at 麻豆原创.

As one of North America’s largest commercial vehicle manufacturers, DTNA supports a vast ecosystem of dealers, fleets, service providers, and customers. Over the last decade, the company has steadily transformed its digital commerce capabilities, creating a foundation that has enabled growth, improved , and positioned the business for the next generation of innovation.

Building a platform for growth

DTNA’s digital commerce transformation wasn’t driven by a single project or technology investment. It was the result of a long-term, strategic commitment to improving how dealers and customers interact with the business.

The company’s journey began with a basic digital parts-ordering platform to help customers and dealers purchase parts online. While the experience was relatively simple, it helped DTNA establish digital adoption, connect key systems, and build relationships with its dealer ecosystem. Most importantly, it created the foundation for what came next.

As customer expectations evolved, DTNA recognized the need for a more modern and scalable commerce experience. The company invested in the 麻豆原创 Commerce solution, expanded digital capabilities, entered new markets, migrated to , and continuously enhanced the platform over time.

Rather than treating commerce as a one-time project, DTNA embraced a mindset of continuous improvement.

Transformation is about people as much as technology

Technology may enable transformation, but adoption determines whether transformation succeeds.

For DTNA, one of the biggest challenges wasn’t implementing new capabilities. It was helping a large network of dealers and customers embrace new ways of working.

“We spent those years pursuing adoption of the tool, really educating our dealer body and getting their buy-in to start using the tool and introducing it to their customers,” said Brenda King, IT manager for eCommerce and Catalog at DTNA.

That approach remains a cornerstone of DTNA’s strategy today. The company works closely with dealers, gathers regular feedback, and maintains strong collaboration between business and IT teams. According to King, that alignment has been critical to ensuring digital investments translate into business value.

The partnership extends well beyond project delivery. King emphasized the importance of working closely with business stakeholders to identify priorities, evaluate opportunities, and ensure technology investments align with business objectives. Rather than operating in silos, business and IT teams work together to shape priorities, guide investments, and continuously improve the customer experience.

Preparing for what’s next

Today, DTNA is exploring how AI can improve commerce experiences through capabilities like product recommendations, customer assistance, and guided buying experiences. But the company’s approach remains grounded in business value.

“We really look at how AI can help us achieve our business goals,” King said. “It’s not AI for the sake of AI.”

That perspective aligns with a broader trend highlighted in the . As organizations accelerate AI investments, many are discovering that successful innovation depends on strong foundations, clear business objectives, and the ability to connect technology investments to measurable outcomes.

The road ahead: Success built on a strong foundation

The company鈥檚 digital commerce business has achieved approximately 40% compound annual growth over the lifetime of the platform, while digital adoption and customer engagement continue to increase. Today, roughly 30,000 users interact with the platform every day.

Those results were not driven by a single initiative. They were built on years of investment in platform modernization, cloud migration, dealer collaboration, and close alignment between business and IT teams. These foundational investments created the flexibility needed to continue growing while preparing for future innovation.

“Our decision to move to 麻豆原创 Commerce Cloud was critical for us to continue growing,” said King. “It stabilized our infrastructure, gave us access to new capabilities, and created the flexibility we needed to keep evolving.”

DTNA’s experience offers an important reminder for organizations navigating their own transformation journeys: long-term success comes from combining innovation with the right foundation.

To learn more about DTNA’s transformation journey and how the company is preparing for the next phase of AI innovation, watch the webinar

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

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

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

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

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

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

Viable use cases in days, not weeks

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

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

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

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

The physical dimension makes embodied AI tangible

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

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

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

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

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

Bringing embodied AI to more customers globally

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

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

The top team鈥檚 rowers focused entirely on maximizing individual power. If the boat slowed, they rowed harder in isolation, disrupting the oars鈥� synchronized rhythm and creating water drag. Meanwhile, the junior rowers knew they were individually weaker, so they rowed in harmony.

Enterprises have faced countless variations of this problem: implementing systems that maximize productivity at the individual or team level but actively hinder the wider enterprise. Many organizations are experiencing something similar with AI today.

AI and sub-optimization

Sub-optimization is a systemic failure that occurs when the performance of a specific part of a system is maximized, inadvertently hampering the performance of the entire system. There are three intertwined themes: intensity, context, and prediction, which, taken together, explain how AI can sub-optimize an organization by making individuals and local systems stronger while straining the broader organization.

Research reinforces this disconnect between individual or even company-wide AI adoption and the value it delivers. shows near-universal enterprise AI adoption: 89% of respondents report regular AI use in at least one business function, but only 37% report an earnings before interest and taxes (EBIT) impact from AI at the enterprise level. Even worse: only six percent of companies can be categorized as high performers that already capture significant organization-wide value from AI.

What makes it so hard to move from AI adoption to measurable value capture? I believe there are three themes that influence a company鈥檚 ability to benefit its entire organization.

Intensity

AI tools often don鈥檛 reduce work; they intensify it. A found that employees who heavily use AI worked faster, took on a broader range of tasks, and worked longer hours, often without being asked. So, what appears to be higher productivity in the short run is actually silent workload creep and mounting pressure as employees manage new AI workflows and do more with less. that the most mentally taxing form of AI engagement was oversight; AI tools that require direct monitoring increased feelings of being overwhelmed by the volume of information at work.

It is easy to see why AI can feel intense: tasks that once required days can now be prompted into existence almost immediately. People start more things; they do more analysis and write more memos. However, like an eight-lane highway that suddenly narrows to a single-lane toll booth, individuals must still consume all this output. This bottleneck only compounds at the organizational level, as all employees produce more than ever, leaving both individuals and the organization as a whole struggling to keep up. Creation has scaled. Absorption has not.

The solution isn鈥檛 necessarily to use less AI, but to change where and how AI shows up. AI should understand user intent and surface the insights needed to answer the question, rather than generating static assets or requiring you to switch between different apps and systems.

If your question creates more things, it鈥檚 not helping absorption. 麻豆原创鈥檚 answer is , a central workspace across 麻豆原创 and non-麻豆原创 systems that uses AI agents to handle tasks.

Ask, 鈥淲hich stores run out of 65鈥慽nch TVs in the next 72 hours, and where is stock I can move?鈥� It will pull data across systems and orchestrate agents to act on the user鈥檚 behalf. In this case, 麻豆原创鈥檚 answer is autonomous action combined with a highly individual user experience for that specific situation, not more assets to be absorbed. If employees can avoid juggling systems and consuming assets, they can spend more time exercising judgment on actions that matter. This is how AI can alleviate intensity.

Context

Most AI systems understand the world, but not the enterprise in which they operate. There is a difference between a system of record鈥攖ransactions, master data, process logic鈥攁nd tacit knowledge鈥攅mails, chats, unwritten rules. And enterprises run on both. If AI only sees the system of record, its answers might be technically correct but contextually wrong because they don鈥檛 reflect the organization’s lived practice.

Even the most ostensibly basic questions require company context. Asking 鈥淲hich suppliers can I source coconuts from?鈥� requires knowledge of an organization鈥檚 process landscape across procurement, supply chain, compliance, finance, and other domains. This type of enterprise knowledge is usually scattered across process models, policies, chats, spreadsheets, and applications, so it鈥檚 tough to maintain. And even if they find it, agents cannot turn it into action without procedural knowledge of the involved people鈥攖he unwritten rules, decisions, and steps鈥攖hat make a process executable.

preview continuously captures institutional knowledge and makes it usable for both people and agents. It turns written inputs, chat inputs, process knowledge, policy guidance, and application logic into reusable building blocks that AI agents can consume. Blocks are captured once, governed centrally, and reused across the company. So when someone asks Joule Work about coconuts, the answer is driven by the company鈥檚 memory and reflects actual rules the process owners agreed upon鈥攆or example: “Only source from Brazil; others require formal exception approval.”

麻豆原创 Company Memory is not a one鈥憈ime implementation; it鈥檚 continuous. In this way, company knowledge behaves like infrastructure, ensuring agents act contextually, not just correctly, as policies and teams change.

Prediction

Business decisions are fundamentally prediction problems that rely on structured data. Most organizations use LLMs, which are great at unstructured data like text but for architectural reasons not so great at working with and generating the structured numerical data that underpins good predictions. Delay prediction, forecasting, anomaly detection, stock optimization, and credit risk are everyday operating questions that depend on structured, tabular data and forward-looking judgment.

Asking LLMs for reliable forecasts on enterprise tables is simply the wrong tool for the job. At the same time, traditional custom machine learning approaches are too slow for many real-time questions: after extracting data, sending it to specialists, and waiting weeks, the question often has changed by the time the answer comes back. This combination means predictive capabilities are either restricted to specialists or rendered inaccurate by generic LLMs; in either case, the organization鈥檚 decision-making is weakened.

Reliable forecasting and risk assessment should be a system property, not an individual hack. 麻豆原创-RPT-1.5 and TabPFN 3 are models that excel with tabular data and will integrate with Joule Work and 麻豆原创 Business Data Cloud for forward-looking questions directly on live tables.

麻豆原创-RPT-1.5 for 麻豆原创 data and TabPFN 3 for any tabular data are specialized prediction engines for structured data. They enable decision-makers working in the core systems to ask, 鈥淪hould I reroute volume? What鈥檚 the probability of on鈥憈ime delivery? What鈥檚 the cost delta across scenarios?鈥� and get answers grounded in real enterprise data.

Availability across the organization eliminates specialist bottlenecks and better equips the enterprise to handle uncertainty through prediction. This enables informed top-level decisions that really move the needle for a company.

AI for the benefit of the whole organization

AI has already proven it can make people more capable, but that does not automatically help the wider organization. AI shouldn鈥檛 be about optimizing isolated tasks; it should be about reshaping how work, knowledge, and decisions flow through the company.

Joule Work, 麻豆原创 Company Memory, and 麻豆原创-RPT-1.5/TabPFN 3 are great examples of how 麻豆原创 designs system-level capabilities. They offer a unified engagement layer, a living institutional memory, and a prediction engine for structured business data that elevate AI from individual-level hacks into a collective benefit for the enterprise.

This is AI that bridges the individual-to-institutional value gap, moving from simply getting AI into the company to generating value throughout the company.

Florian Kunzke is global director of AI Strategy at 麻豆原创.

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

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

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

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

Powering a global sporting goods business

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

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

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

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

One connected foundation for a connected customer experience

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

Built for the pace of sport

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

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

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

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

Alex de Minaur

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

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

For most enterprises, the integration environment they built over the last decade was never designed for what’s being asked of it now. Point-to-point connections, legacy middleware, fragmented tooling across dozens of systems: these were manageable constraints when the work was batch processing and scheduled dataflows.

Agentic AI changes the equation entirely. Autonomous agents need real-time access to data across the full application estate, consistent governance, and an integration layer that can scale without becoming a bottleneck. The organizations that have already modernized their integration foundation are finding that they have a meaningful head start.

麻豆原创 commissioned IDC to conduct an in-depth analysis of organizations using 麻豆原创 Integration Suite, including its advanced event mesh capability. 麻豆原创 Integration Suite is 麻豆原创’s flagship integration platform as a service, delivered on 麻豆原创 Business AI Platform, the unified foundation for 麻豆原创’s AI, data, and integration capabilities.

For this Business Value White Paper, IDC conducted in-depth interviews with eight organizations across manufacturing, consumer products, energy, fintech, healthcare, retail, and transportation 鈥� enterprises with an average of 48,529 employees and $14.53 billion in annual revenue, operating across the U.S., Germany, Denmark, India, and the UK. The results were quantified from actual outcomes, not a modeled composite.

What the IDC Business Value White Paper found: 麻豆原创 Integration Suite customers are achieving a 368% three-year return on investment with an eight-month payback, generating an average of $47,900 in annual benefits per integrated application, or $9.36 million per organization.

Numbers that matter

The IDC Business Value White Paper documents measurable impact across the full breadth of what integration touches in a modern enterprise:

• Integration speed and scale
  • 78% more application integrations
  • 41% less time to complete per application integration
  • 99% more application messages processed
• Operational reliability
  • 58% fewer unplanned outages
  • 35% faster to resolve integration errors
  • 19% fewer integration errors
• Business process automation
  • 49% more business processes automated
  • 21% efficiency gains for business process teams
  • 32% less time to onboard a business partner
• Developer and team productivity
  • 29% improvement in development team productivity
  • 31% faster development life cycle for new applications
  • 32% more efficient application integration teams

These aren’t projections. They are the average outcomes across eight real enterprises, documented through in-depth interviews by IDC analysts Shari Lava, group vice president for AI, Data, and Automation, and Matthew Marden, research vice president for Business Value Strategy.

More than an 麻豆原创 platform

One finding in this study deserves particular attention for customers and partners evaluating 麻豆原创 Integration Suite in mixed-vendor environments: on average, 64% of integrated applications in the study are non-麻豆原创, and 82% of integrations touch at least one non-麻豆原创 application.

麻豆原创 Integration Suite is not an 麻豆原创-only platform. It is the integration backbone for the full, heterogeneous application environment that modern enterprises actually operate, one where 麻豆原创 and non-麻豆原创 systems must work together reliably at scale.

One study participant described what that means in practice: “麻豆原创 Integration Suite is our main connection to the outside world. Every time anyone needs to connect to our 麻豆原创 systems, it goes through [麻豆原创] Integration Suite. It’s our main front door for API access, for data access, for collaborating with us, and for transferring data in and outbound into our 麻豆原创 systems.”

Built for the AI era

The timing of this study matters. Organizations are moving fast on agentic AI, deploying autonomous agents that need to orchestrate, monitor, and act across their entire application estate in real time. Integration is no longer a back-office concern; it is the operational layer on which AI-driven automation either succeeds or stalls.

As part of 麻豆原创 Business AI Platform, 麻豆原创 Integration Suite brings together API management, event-driven architecture through advanced event mesh, and AI-native capabilities 鈥� giving organizations the governance, observability, and real-time connectivity that agentic AI workloads require. That includes native support for MCP, LLM connectivity, and agent-to-agent orchestration, making it a ready foundation for autonomous enterprise processes.

Study participants described this directly. One said: “The real value of [麻豆原创] Integration Suite is the combination of its different capabilities: cloud platform integration, API management, and advanced event mesh. It’s the full set of these capabilities that makes it so important for us.”

Another captured the business case for reliability in AI-driven environments: “What we’re starting to see with 麻豆原创 Integration Suite is a level of reliability and standardization in the integrations we’re building, which enables positive levels of further scalability. We no longer have that kind of point-to-point integration, which had been a risk and a source of technical debt for us.”

Business case is clear

A 368% three-year ROI and eight-month payback is a compelling headline. But what sits behind those numbers is equally significant: enterprises can connect more of their application estate, automate more of their business processes, resolve failures faster, and onboard partners more quickly 鈥� all on a platform that is ready for the AI workloads they are investing in right now.

“We can innovate more with 麻豆原创 Integration Suite because it’s easier and the developers are happier working with the tool. It gives good results, so we continue to build on the foundations we have already created.”

That is the right foundation for what comes next.

.

Sid Misra is chief marketing officer of Technology Foundation for 麻豆原创 Business AI Platform at 麻豆原创.

Source: IDC Business Value White Paper, sponsored by 麻豆原创, The Business Value of 麻豆原创 Integration Suite (Doc #US54820926-BVWP, August 2026) , IDC Business Value Snapshot, sponsored by 麻豆原创, The Business Value of 麻豆原创 Integration Suite (Doc #US54820926-BVS, August 2026)

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

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

Collaboration for good

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

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

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

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

Turning memories into personalized games

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

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

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

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

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

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

AI with tangible human impact, not just productivity gains

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

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

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

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

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

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

The future of AI-enabled memory care

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

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

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

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

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

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

The honest answer is that most GTM teams don’t have an AI problem. They have a decision-flow problem.

I’m seeing this across some of the world’s largest enterprises. And my clearest takeaway is that the organizations pulling ahead are not doing the same GTM motion but faster. They’re doing a fundamentally different one.

Here are five moments in the customer journey where that shift is already showing up in results.

1. Segmentation: From demographics to business signals

Most GTM organizations still segment the way they always have based on characteristics like industry, company size, geography, and more. AI changes the input. Rather than asking who a prospect is, it asks what they’re signaling right now. This includes more nuanced signals like operational pressure points, purchasing patterns, and growth indicators embedded in their business data. Reps aren’t chasing more leads; they’re chasing the right ones, and that shows up in how many of those signals turn into real opportunities.

2. Engagement: Relevance replaces volume

Cold outreach reply rates have dropped to near-historic lows across enterprise sales. The answer can’t be to send more outreach. It must be to do smarter outreach. When AI has access to full business context such as what’s happening inside an account operationally, financially, and commercially, it can help teams generate outreach grounded in what matters to a buyer at that moment. That’s not personalization at the persona level. It’s relevance at the account level. What shifts is the quality of first contact. Response rates can rise while the time it takes for the first qualified meeting shrinks because the outreach reflects what a buyer is dealing with in the moment.

3. Deal execution: Clearing the invisible friction

This is the one that most organizations underestimate. In most enterprise deals, the seller isn’t the bottleneck. The system around the seller is. Approvals, pricing sign offs, quote generation, and contract routing are where time is lost and deals slip. This isn鈥檛 because the buyer hesitates, but too often because of internal complexity. AI agents can help eliminate this friction.

For example, Amadeus, working with 麻豆原创, deployed an autonomous agent that reconciles unstructured payment data, clearing around 40,000 incorrect transactions that previously required manual intervention. That kind of autonomous resolution doesn’t just reduce cost, it changes what the buying experience feels like from the customer’s side. Deals that stalled for weeks waiting on internal processes don’t have to anymore. 

4. Post-sale: Compressing time-to-value

The handoff from sales to post-sale is historically where value gets lost. Expectations set during the sale don’t always match what a customer experiences in the first 90 days. AI makes that gap visible and actionable in real time through an 鈥渁ccount brain.鈥� This can be thought of as a growing repository of context and knowledge around an account, which makes handovers much easier and, most importantly, independent of any single individual. This can shift time-to-first value and 90-day adoption rate: how quickly a new customer reaches their first meaningful milestone, and whether they’re using what they bought.

5. Retention and expansion: Proactive at scale

Net revenue retention is the most durable commercial metric and it’s the one most dependent on what happens after the sale. The historical challenge is scale. AI can have a big impact here. Continuous scoring of expansion-readiness and churn risk, triggered by behavioral and operational signals, means teams act on the right accounts at the right moment not after a customer has already made up their mind.

Expansion of net revenue retention is where the largest commercial upside in most enterprise businesses lives. Both are chronically underserved when customer success is working reactively, account by account, rather than across the full base at once. The organizations getting this right haven’t simply deployed more AI tools. They’ve been deliberate about where in the customer journey AI can add real value for customers.

At 麻豆原创, we’re applying these same principles to our own GTM organization. We’re investing in a model where a single AI-powered entry point connects our sales teams to a network of specialized agents spanning planning, outreach, quoting, content, customer engagement, and more. The goal is a shared intelligence layer built around each account to give our teams more context and consistency so they can drive even more value and better outcomes for our customers at every stage of the customer journey.

So to me, the right question isn’t “Where can we implement AI?”; it’s “Where does customer value stall because information, authority, and action are separated?”

That kind of clarity is what a more intelligent go-to-market organization can already bring. And it鈥檚 only a first glance of what else will soon be possible.

Jan Gilg is global president of Customer Success & Americas and a member of the Extended Board of 麻豆原创 SE.

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

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

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

Building momentum one mission at a time

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

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

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

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

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

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

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

Standardization creates the capacity to accelerate

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

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

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

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

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

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

Complexity demands faster, better-informed decisions

The scale of the Artemis program is difficult to overstate.

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

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

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

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

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

Autonomy works best when it expands human capability

NASA is already applying autonomous technology beyond administrative processes.

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

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

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

Partnerships turn ambition into capability

No single organization could accomplish the Artemis mission alone.

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

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

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

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

Trust is the ultimate operating system

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

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

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

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

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

NTT DATA is a $30+ billion global leader in AI, digital business and technology services, serving 75% of the Fortune Global 100.

The 麻豆原创 solutions will help NTT DATA replace multiple legacy HR systems with one unified platform for people data and processes, strengthening decision-making, employee experience and workforce planning. 麻豆原创 SuccessFactors solutions will serve as the system of record for people and talent data, working alongside NTT DATA’s existing employee service platform and specialist workforce planning tools. 麻豆原创 Business Data Cloud will connect this data with insights across other business functions.

鈥淭he initiative reflects NTT DATA’s view of talent as a strategic differentiator and AI as a capability that should be embedded across enterprise organizations,鈥� said Stijn Nauwelaerts, Chief People Officer, NTT DATA, Inc. 鈥淯ltimately, this is about creating an environment where our people feel empowered to do their best work, wherever they are in the world.鈥�

The deployment builds on a strategic partnership between 麻豆原创 and NTT DATA spanning more than 36 years, during which NTT DATA has collaborated as an 麻豆原创 platinum partner, global service partner and global reseller for 麻豆原创. In 2025, NTT DATA adopted 麻豆原创 Cloud ERP Private solutions to modernize its core systems.

麻豆原创 SuccessFactors solutions will now be deployed internally at NTT DATA over a 12-month period, with the company applying its own 麻豆原创 expertise to design and roll out the platform. This will create a single, authoritative source of HR data and processes, laying the foundation for faster, more consistent HR services across the organization. Leading its own implementation will also strengthen NTT DATA’s ability to guide clients through AI-driven HR transformation, with firsthand experience of the solutions it delivers.

鈥淣TT DATA is demonstrating how AI and cloud technology can redefine the employee experience,鈥� said Thomas Saueressig, Chief Customer Officer and Member of the Executive Board of 麻豆原创 SE. 鈥淲ith a unified, intelligent HR platform, the company will unlock new levels of productivity and scale a people strategy that supports a connected workforce worldwide.鈥�

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In an interview, 尝颈茅苍补谤诲 talks about the evolution of 麻豆原创’s proprietary programming language and ABAP platform, and discusses what the innovations announced at 麻豆原创 Sapphire will mean for customers.

Q: In our first conversation, you explained what ABAP is, what the future looks like, and why agentic AI will change the market. Let’s start from there and then dive deeper. How does ABAP interact with other development technologies in today’s 麻豆原创 landscapes?

Sonja 尝颈茅苍补谤诲: ABAP has been around for over 40 years and has been evolving ever since. With the era of agentic AI, we are now reaching the next stage of that evolution. The modern version of the development model is ABAP Cloud, which has been developed from the outset according to the guiding principles of openness and comprehensive support for the business logic.

We have robust, well-defined APIs that are interfaces to the outside world and offer a full environment with Open Data Protocol (OData) services, modern front ends, and an integration layer. This enables the development of modern web applications. This is in addition to 麻豆原创 Business Technology Platform (麻豆原创 BTP) extensions and an external system like 麻豆原创 Integration Suite, which can also be integrated.

We currently use Eclipse as a development environment. Starting in Q2 2026, ABAP development聽is聽also possible in Visual Studio Code, which is a milestone. The community was quite vocal in requesting this and it was an open door for us. Visual Studio Code is gaining ground and is currently the preferred IDE (integrated development environment) for many developers. Many AI extensions on the market being optimized for it first.

We have redesigned the architecture and are pursuing an open IDE strategy where we can also offer further IDEs for ABAP development. By opening it up, we now have a rich third-party ecosystem that we can leverage for AI, such as GitHub Copilot and Amazon Q.

In addition, we have published an ABAP MCP (Model Context Protocol) server that lets ABAP developers benefit from the entire AI tooling and ecosystem. This is a major, important step and an impressive example of interoperability. Furthermore, we can still rely on the strength of ABAP business logic. With this approach, we are combining the open IDE strategy with a high rate of innovation.

You mentioned the ABAP MCP server. What is that exactly?

尝颈茅苍补谤诲: MCP stands for Model Context Protocol and is an open standard for AI agents to interact with external tools and systems in a structured manner. It is a universal language that AI agents use to ask questions, trigger actions, and retrieve results.

The ABAP MCP server provides ABAP development capabilities based on this protocol. Any agent that supports MCP can interact with ABAP code and our ABAP systems in an intelligent, agent-driven approach. It is a new channel through which we can make our ABAP-specific capabilities available to the outside world.

The ABAP MCP server is an important building block for everything related to agentic AI and an important technical foundation for our new Custom Code Assistant for 麻豆原创 S/4HANA transformation. The ABAP MCP server for Eclipse is available as of Q2 2026.

What are the risks of using AI in 麻豆原创 enterprise systems and how do you address them?

尝颈茅苍补谤诲: The entire AI market is incredibly dynamic, fast-paced, and characterized by different interests. As such, we need to carefully examine which solutions are durable, robust, and trustworthy enough to run the world’s business processes. We want our solutions to remain secure, compliant, and true to everything 麻豆原创 stands for.

We must put our core mission at the heart of all decisions. ABAP platform is known for its ability to run large enterprise business. Our customers and partners trust this capability The AI solutions on the market do not qualify for this through their ability to build short-lived solutions, but instead by supporting our core capabilities. That鈥檚 why it helps to take a step back, look at the big picture, and make sustainable decisions, but also to remain open to revising past decisions in response to major shifts in the market.

The second point is the accuracy of the code. AI can generate seemingly plausible code that contains subtle errors. To counter this, we rely on a combination of human review and thorough agent testing, keeping humans in the loop at every critical step. AI agents handle the quality checks and validation, with developers making the judgement calls.

Another risk is the loss of business logic during the transformation. We want to help our customers migrate their legacy applications to modern solutions. We are developing custom code management agents for this purpose. The first was released in June. It is crucial for our customers and partners to retain their business logic during the transformation.

Lastly, security and data protection are central topics. AI models always need context to be effective. In the enterprise environment, this context can contain sensitive business data. That鈥檚 why we are taking a very careful approach: ABAP AI services only operate within established 麻豆原创 compliance and trust frameworks, for example. Customers always have control over what is shared and what isn鈥檛.

Let鈥檚 talk about 麻豆原创 Sapphire in 2026. What innovations from your area were presented?

尝颈茅苍补谤诲: 麻豆原创 Sapphire is also a very important conference for . In Q2 2026, we published the first release of the ABAP development tools for Visual Studio Code, initially in the ABAP Cloud scope including 麻豆原创 Fiori app development and with integration of GitHub Copilot and Amazon Q as AI solutions. Another goal is to support classic ABAP development in Visual Studio Code; additional ABAP object types will follow throughout the year. Also, as of Q2 2026, the ABAP MCP server for Eclipse and Visual Studio Code are generally available to connect third-party solutions, particularly AI-specific third-party tools, to the ABAP system.

We also delivered the first Custom Code Assistant in Q2 2026. The feedback is promising. This enables us to automate and accelerate code migration significantly. In this approach, all communication between legacy migration tools and new agents will take place through an engagement layer. Our overall migration strategy, which we presented at 麻豆原创 Sapphire, comprises seven different agent families across all phases of a migration project, from planning to execution.

Smaller customers and those with older system versions often found it difficult to access AI tools. What is changing here?

尝颈茅苍补谤诲: The barriers to entry are getting much lower. We are switching from users-per-month billing to a usage-based model. Billing will be according to actual usage, based on 鈥淎I units鈥� with individual prices. This will help make it easier to use our AI solutions. Customers will be able to better plan how much they want to spend on AI solutions.

In addition, as of Q2 2026, we introduced a side-by-side service that enables the use of all AI solutions, regardless of release. The availability of ABAP AI will be expanded to include 麻豆原创 S/4HANA Cloud Private Edition for all releases from 2021 and later. This enables us to support most of our customers with AI capabilities, even those that have older releases.

How do ABAP and AI agents fit into 麻豆原创鈥檚 longer-term product vision?

尝颈茅苍补谤诲: We are following the overall 麻豆原创 strategy, of course. It was apparent at 麻豆原创 Sapphire that agentic AI is the new theme. In the past, we had Infrastructure as a Service, Platform as a Service, and Software as a Service. Agentic AI solutions are now being added.

I like to use three concentric circles to describe our vision: ABAP Cloud is at the center, as the modern, clean-core variant of our language for long-term stability. The second circle is the ABAP AI layer: developer tools for code explanation, code generation, and ghost texting. The outer circle is comprised of AI agents, a network of specialized agents for complex and multi-step tasks such as transformation, migration, and quality validation. They can also take on development tasks, however, significantly reducing the required effort. This can free up time for decisions regarding business logic and architecture, as well as for verifying quality.

The circles coexist and reinforce each other. The fundamental orientation of the platform and the core of the solutions remain stable. Agents and AI skills complement this in the best possible way.

What milestones should customers and partners pay attention to?

尝颈茅苍补谤诲: There are four main milestones. The development environment for Visual Studio Code and the MCP server were released in Q2 2026. This enables the development of AI agents in an open IDE ecosystem. We released the first agent for the custom code migration strategy in Q2 2026, along with the extension of ABAP AI to include 麻豆原创 S/4HANA Cloud Private Edition release 2021 and later. A second agent for clean core transformation is planned for Q3 2026. And we will continue to develop multi-agent orchestration and add more agents to the portfolio in the course of 2026.

Do you have any other takeaways to share?

尝颈茅苍补谤诲: Development should be seen as an opportunity. It鈥檚 all about development and collaboration. ABAP and developers remain a strong team. AI will not change anything here. ABAP has a long track record of successful reinvention. AI is just the next chapter, taking over routine tasks and creating freedom for what creates value: knowing your business, making architectural decisions, providing high quality, and meeting security and compliance requirements.

Customers and developers can trust 麻豆原创鈥檚 AI to be implemented reliably: with a clear road map and high quality, security, and compliance standards that are critical for enterprise systems. We work closely with the community and our customers and partners. That is a key success factor.

鈥淲e started by fixing the basics,鈥� said Siani, Transformation and Technology director at Ipiranga, one of Brazil鈥檚 largest fuel distributors.

Ipiranga operates almost 6,000 service stations nationwide. Headquartered in Rio de Janeiro, the company has more than 6,200 B2B customers and 1,500 convenience stores. Ipiranga generates $120.7 billion in revenue, yet despite its size and market leadership, the company faced a critical problem: repeated project failures.

Working on the root cause

鈥淏efore we could start investing in new technology, we needed to take a good look at why some of our initiatives were not generating the expected value for the business,鈥� Siani said, speaking at the TAC Insights conference for in Toulouse. 鈥淲e needed a rigorous RCA.鈥�

RCA, or root cause analysis, is a structured approach to problem-solving that focuses on identifying the underlying causes of issues rather than just addressing surface-level symptoms.

鈥淚nstead of making new investments, we decided to dig deeper and identify the causes,鈥� she explained. 鈥淲hen we examined the company鈥檚 history, RCA confirmed that the high level of customization was limiting our ability to deliver at the required speed. Our inability to adopt the best technologies and functional best practices was directly impacting the company鈥檚 ability to evolve and drive business growth.鈥�

Speaking the language

From the outset, the Technology team recognized that the case for the ERP transformation rested on demonstrating how inconsistent processes and the lack of standardized best practices hindered the organization’s ability to respond quickly and remain competitive in the market.

鈥淲hen we went to the Board to secure the budget for the digital transformation, we didn鈥檛 start by talking about technology,鈥� Sian sharedi. 鈥淲e started by discussing what the business needed to achieve its strategic objectives faster. Then, we listed the obstacles preventing that progress along with the technology capabilities required to remove those barriers.鈥�

By translating technical challenges and opportunities into business language, Siani helped Ipiranga鈥檚 leadership understand that innovation without a strong foundation would not take the business to the level of efficiency required.

鈥淏y identifying these root causes, we were able to avoid a common trap,鈥� she explained. 鈥淚nstead of investing in new technology without fixing the foundation, we shifted our strategy. We refocused on聽 tools to process integration, governance, and operational discipline.鈥�

Getting approval for a big investment

Ipiranga partnered with 麻豆原创 to assess critical processes across operations, finance, and commercial operations.

“麻豆原创 brought in business experts to pinpoint how a heavily customized ERP system was slowing decision-making and limiting visibility and innovation,” Siani said. Rather than focusing on 麻豆原创 functionalities, they asked fundamental business questions: What does your financial process look like? How does your order-to-cash process work?”

Armed with these insights and a clear understanding of what was slowing decision-making, Siani returned to the board with a clear message: “Fix the foundation, or transformation will fail.”

As a result, she got approval to implement a new ERP system, driven by business value and ROI. What ensued was a massive integration effort, with a targeted go-live scheduled for December 31, 2026.

With support from Accenture and 麻豆原创, Ipiranga adopted a clean core strategy, ensuring minimal customization and long-term scalability. Overall, 104 legacy systems were analyzed, 64 systems will be integrated and 40 decommissioned, and over 750 interfaces are being built. Of course, all developments had to pass strict governance gates to guarantee the clean core.

People driving change

Technology may enable transformation, but people make it successful. Over 350 professionals from a variety of business and technical teams are involved in the ongoing project. Crucially, leadership played a direct role. With executive sponsorship and transparent communication, resistance to change has been minimal.

鈥淲e were careful not to impose change, but to explain the impacts clearly and discuss them with a multidisciplinary team,鈥� said the IT expert, who is also a rowing champion. 鈥淲e made sure to prepare teams early and embed change management at every phase.鈥�

The team prioritized initiatives with the highest return on investment (ROI) and paused non-essential projects. They also selected world-class partners鈥攊ncluding Accenture as the implementation partner and Amazon Web Services (AWS) as the hyperscaler鈥攖o help ensure a high-quality, successful transformation.

The company鈥檚 transformation is now in the middle of a critical milestone (SIT1). The expected ROI is over $40 million, but more importantly, the company has built something far more valuable than a new system.

鈥淲e will have a solid operational foundation, integrated, scalable ,and efficient processes, and a culture that understands how transformation goes beyond technology. Technology is simply the path,鈥� Siani said.

Ipiranga鈥檚 journey offers a powerful reminder that digital transformation is not about tools; it鈥檚 about assertive fundamentals. RCA can reveal issues technology alone cannot fix. It demonstrates that preparation is as important as execution, and that clean core strategies reduce long-term complexity.

“In effect, the path to successful transformation doesn鈥檛 start with innovation. It starts with clarity, discipline, clear goals that together determine the correct technology,鈥� Siani concluded.

Agentic AI builds on the economic potential of generative AI, which McKinsey has estimated could add US$2.6 to $4.4 trillion annually to the world economy. This represents the next stage of enterprise AI adoption: a shift from content generation to autonomous execution, and from isolated pilots to operational deployments.

Agent sprawl

That shift creates a new governance challenge. Agent sprawl occurs when AI agents are created, deployed, or connected across systems faster than the enterprise can inventory them, assign ownership, control permissions, monitor behavior, and optimize or retire them when they are no longer fit for purpose.

Underscoring this shift, found that 98% of companies have already deployed AI agents or plan to do so. But as adoption accelerates, governance is struggling to keep pace. According to the same report, less than half of the organizations surveyed have visibility into an inventory of AI agents.

The mechanics of agent sprawl are familiar to any technology leader who has navigated a wave of SaaS adoption. Individual teams, motivated by genuine productivity goals, deploy agents independently. Each one is designed for a specific task鈥攁 marketing automation agent, a supply chain monitoring agent, an HR onboarding bot鈥攁nd each works in isolation. Without a centralized platform or governance framework, the organization accumulates a fragmented landscape of agents that do not interoperate, cannot be audited consistently, and accumulate technical debt faster than they generate value.

, the average global Fortune 500 enterprise will have more than 150,000 AI agents in use, yet only 13% of organizations believe they have the right governance in place to manage those agents. Max Goss, senior director analyst at Gartner, told his audience at a London conference in April: 鈥淎s CIOs and IT leaders see an explosion of AI agents across their organizations, many are contending with an ungoverned sprawl of agents that expose their organizations to a range of risks, including misinformation, oversharing, and data loss.鈥�

He added: 鈥淢any organizations resort to blocking or restricting the use of AI agents, but this is not a long-term solution. If employees are unable to work in the sanctioned tools, they will likely go around the organization鈥檚 controls and start using shadow AI, which presents far greater risks. Organizations need to find a balance where they can govern agents and manage sprawl, but also safely empower employees to innovate with these tools.鈥�

Agents typically need broad, cross-environment permissions to function, but those permissions are rarely governed with the same rigor applied to human users. The risk posed by unmanaged or rogue AI agents in the enterprise is therefore real and growing.

AI agent security concerns

Publicly reported enterprise-security examples also point to agents leaking sensitive information or acting outside their intended scope, including cases where malicious instructions caused agents to bypass guardrails, delete production records, or trigger irreversible financial transactions.

The security concern is what registers most sharply with enterprise technology leaders. With chatbots and early generative AI, a security failure typically meant bad output: an inaccurate or inappropriate response that could usually be corrected after the fact. In the agentic era however, the consequences of an agent failure or security breach can be far more damaging because agents can take action, call tools, access systems, and initiate business processes.

That is why agent governance is no longer only an IT operations issue. It increasingly touches board-level concerns: risk ownership, regulatory exposure, data protection, auditability, operational resilience, and accountability for autonomous decisions.

The emerging AI governance platform

Leading organizations are beginning to treat agent governance not as a compliance overhead but as a strategic capability that determines whether AI investments compound as advantages or liabilities. As a result,  effective AI agent governance has quickly become a boardroom topic and is contributing to the emergence of a new platform category: the AI governance platform.

The category is still forming, but its purpose is becoming clear. Enterprises need a way to discover agents, understand what they do, control what they can access, verify whether they are compliant, and monitor how they behave in production.

麻豆原创 is one of the agentic AI pioneers in this emerging category. Through its 2023 acquisition of LeanIX, 麻豆原创 gained a foundation in enterprise architecture management. This has quickly become a recognized differentiator for 麻豆原创 AI Agent Hub鈥攑ositioning AI artifacts like agents, models, and MCP servers within the full architecture and business context of the organization.

麻豆原创 AI Agent Hub builds on this foundation as a command center for managing and governing AI agents and related AI assets across an enterprise, even when they come from different vendors and run on different systems.

As 麻豆原创 CTO Philipp Herzig explained on stage at this year’s 麻豆原创 Sapphire event, 麻豆原创 AI Agent Hub is intended to provide a governance layer of record for the enterprise agent ecosystem. 鈥淎gents are everywhere,鈥� he said. 鈥淪ome are great, some are not, and almost no one has a consistent picture鈥攏o central governance, no clear view of what each agent does, whether it adds value or whether it adheres to your policies.鈥�

He added: 鈥溌槎乖� AI Agent Hub changes that. One entry point and command center to discover, manage, and govern all AI agents, LLMs, and MCP servers in your landscape鈥攙endor-agnostic. [麻豆原创] AI Agent Hub allows you to discover all your agents in context: your landscape, your business processes. Once you have identified the right agents, you can control their risk and define architectural decisions or compliance rules.鈥�

A closing window

Given the pace of AI agent deployment, the window for implementing effective enterprise governance before a serious incident occurs is narrowing. For CIOs, CEOs, and company boards, the question is no longer whether to govern AI agents. It is whether governance gets designed into the architecture from the start or retrofitted after the first serious failure.

Organizations that treat agent governance as a strategic priority in 2026 will be better positioned to scale AI as a durable competitive advantage. Those that defer could spend 2027 cleaning up: in enterprise technology, the cost of speed without structure eventually gets paid. But with AI agents, the bill arrives faster鈥攁nd at greater scale鈥攖han anything that has come before.

While the power of technology is at its apex, so are expectations. And the gap between the two is where revenue goes to die.

Brands using AI to deepen human connection by freeing up sellers, marketers, and service agents to focus on empathy, trust, and complex problem-solving are pulling ahead. Those still bolting AI onto fragmented systems, or using it purely as a cost-cutting measure, risk falling further behind.

To help organizations address the rapidly changing factors impacting CX, we鈥檙e proud to debut our first quarterly 鈥淪tate of鈥� reports. Designed to pull together the latest research and data across sales, service, marketing, and e-commerce, these thought leadership pieces are available without a gate because we know that experience and expertise matter, and we want to share ours with you. (You can find links to the reports at the end of this post.)

The key takeaways from each report demonstrate that CX is no longer a single department’s job. Rather, it’s a cross-functional discipline, and the stakes for getting it wrong are higher than ever.

The cost of bad experiences is staggering

Let鈥檚 start with the number that should be pinned at the top of every leadership deck this quarter: bad customer experiences have put $3 trillion in global sales at risk in 2026, with consumers actively cutting back $2.1 trillion in spending and walking away entirely from $865 billion worth of it, according to Forbes data cited in our . That’s not churn. That’s customers voting with their wallets in real time.

And they’re not quiet about why. A striking 82% of consumers say a brand has disappointed them, and 60% admit they don’t pay attention to brands even when their product needs are being met. Translation: satisfying the transaction isn’t enough anymore. If the experience around it feels disorganized or impersonal, customers check out.

On the service side specifically, 75% of consumers say they’re put off by disorganized brands that pass them between multiple people or teams just to solve one problem, and 46% say service flat-out feels too impersonal.

The combustible combination of sky-high financial risk and low tolerance for friction is forcing every customer-facing function to rethink how it operates.

That’s the backdrop. Now let’s talk about what’s actually happening in each corner of the customer journey.

AI is everywhere, trust in it is not

Every function is racing to embed AI, with good reason. AI-driven traffic to U.S. retail sites is up 4,700% year over year, and almost 60% of consumers have already used AI to shop, as cited in our .

On the marketing side, notes that AI has moved from experiment to foundational: 33% of marketing leaders are using it for hyper-personalized engagement, and 31% say predictive insights and personalization have been a top priority all year.

Meanwhile the report points out that digitized suppliers leveraging AI are outperforming their peers on sales goals by a jaw-dropping 110%.

But here’s the twist that ties it all together: customers don’t actually love the AI they’re interacting with.

report underscores that point specifically:

• 79% of Americans say they strongly prefer human support over an AI agent
• 63% don’t believe AI can replace humans in service roles at all
• 89% believe brands should always offer the option to talk to a real person
• 81% believe AI is primarily being used to save the company money, not to improve their experience.

That’s the tension every function needs to sit with. AI is delivering real, measurable business value, but if customers believe that AI was deployed to cut costs rather than serve them, you might win efficiency but lose the relationship.

Trust is the currency, and it’s getting harder to earn

Each one of the reports circles back to the same word: trust. In marketing, 61% of B2B buyers say trust and credibility are the most important thing content can deliver, ranking above lead generation. Consumers are leaning on peer and creator trust more than brand messaging, too: 76% of brands report that sponsored content with creators now outperforms traditional advertising, and in social commerce, 45% of Gen Z shoppers say they’re more likely to trust a product once it goes viral.

On the e-commerce side, trust shows up as transparency. With tariffs pushing import costs up 15-30% across major categories, brands that explain price increases rather than quietly passing them along are building goodwill that pays off in retention. And in sales, the numbers show that your existing customers–the ones who already trust you–are an underused asset: 45% of revenue leaders are now focused on improving handoffs across marketing, sales, and service, while 39% are chasing expansion and upsell revenue instead of only hunting net-new logos.

The people problem behind the technology story

There’s one more thread that doesn’t get enough attention in the AI headlines: the humans delivering these experiences are stretched thin. Call center turnover is running 40-45% in 2026, spiking to 55-60% in high-stress sectors, while replacing a single agent can cost up to $46,000 when accounting for lost productivity.

As AI absorbs the easy tickets, what lands on human agents is the hard stuff: it鈥檚 complex, is emotional with high-stakes, and burnout is quietly eating away at CSAT and first-contact resolution scores. This matters because customer experience isn’t just a technology stack or a personalization engine. It’s fundamentally delivered by people; whether that’s a service agent handling an escalation, a seller navigating a buying committee, or a marketer trying to sound human in an AI-saturated feed. Protecting the people doing that work isn’t a wellbeing initiative separate from CX strategy. It is CX strategy.

Fragmentation is the enemy of good CX

If you weave every stream together, the state of CX has one clear directive: stop treating AI, data, and channels as separate initiatives owned by separate teams, and start treating the customer experience as the single thread that runs through all of them.

That means AI-shopping agents that use clean, structured product data. It means first-party data strategies that replace the crumbling third-party targeting most marketing was built on. It means service and sales teams that know exactly when to step back and let self-service work, and exactly when to step in and be human. And it means service organizations that give their people better tools instead of just more automation.

Future success won鈥檛 be shaped by the brands with the most AI; it will favor the brands that make customers feel understood, even as the experience becomes more automated. That’s not a technology bet. That’s a trust bet, and right now, trust is in short supply.

You can find our 鈥淪tate of鈥� reports here:

Jessica Keehn is chief marketing officer of 麻豆原创 Customer Experience.

The Netherlands-based, family-owned maritime group has been building vessels since 1927 and today operates internationally with more than 12,500 people and over 35 yards across six continents. With an ambition to become the world鈥檚 most sustainable maritime solution provider, Damen is combining its long-standing craftsmanship with innovation, digitalization, and operational excellence.

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After investing heavily in 麻豆原创 S/4HANA as its core ERP foundation, Damen is now focused on applying 麻豆原创 Business AI to extend the impact of that platform.

鈥淲e really would like to harvest the business value of that initial investment,鈥� said Han Coenraad, product manager ERP at Damen. 鈥淲e use AI, especially in our operational processes, to get things running more smoothly and support more data-driven decision-making.鈥�

Damen has enabled 麻豆原创 Joule for Consultants and 麻豆原创 Joule for Developers and is exploring conversational capabilities that allow employees to interact with 麻豆原创 systems and processes using natural language.

Looking ahead, Damen sees opportunities to use embedded and custom AI use cases to improve data quality, enhance operational processes, and help teams work faster and closer to customers.

One example is using AI to support parts sales engineers by reading customer documents from email and automatically transferring the right information into sales orders. The goal, Coenraad explained, is to 鈥渂uild vessels sooner, with lower costs and more customer satisfaction.鈥�

Inspired by 麻豆原创 Sapphire and the vision of the Autonomous Enterprise, Damen is ready for the next step: 鈥淭he strategy shift from 麻豆原创鈥攚e are really looking forward to it and we really embrace it. Let鈥檚 make that happen.鈥�

This year at 麻豆原创 Sapphire, we shared our vision for the Autonomous Enterprise — the next evolution of how businesses run, where AI agents execute critical workflows so people can focus on innovation, customer value, and business growth.

This vision comes to life through a reimagined Joule Work, evolving from an AI assistant into the central workspace for enterprise AI. We also introduced the 麻豆原创 Autonomous Suite, bringing AI agents and assistants across core business functions to execute complex workflows with human oversight. With 麻豆原创 Business AI Platform, customers and partners can build, manage, and govern AI agents. And by expanding Industry AI, we’re delivering AI grounded in deep business context and domain expertise to solve industry-specific challenges.

Customers are already benefiting. IT division, Bosch Digital, integrated 麻豆原创 Joule for Developers directly into their coding workflows. Developers saw a 20% increase in productivity using Joule to automate routine coding tasks and optimize code. Joule also generates test cases, speeding up unit testing by 15% to 20% and freeing senior developers for high-value tasks. , the country鈥檚 leading airport operator, defines safety thresholds, service levels, and playbooks, and its agent, Smart Network for Operative Winter (SNOW), executes them. The SNOW agent is a winter operations system that integrates real-time weather, runway, and operations/maintenance data to automatically orchestrate work at Patagonian airports. The agent has improved runway safety, cut direct costs by 16%, and reduced administrative effort by 90%.

built a tool using  so its clients can better handle international tax rules by developing and managing their own custom AI agents and solutions. This way, PwC鈥檚 clients can focus on strategy while AI handles tax. PwC鈥檚 tool helped one pharmaceutical company handle VAT on international transfers 60% more efficiently.

Another customer, , a global fashion retailer, used an AI agent, built on 麻豆原创 Joule, to cut HR process cycle times by 40% to 60%. The agent helps employees quickly handle HR transactions, such as leave requests and payroll queries, through natural language conversations. Reducing time spent on administrative tasks allows HR teams to focus on strategic talent management. These are just some of the customers getting value. There are many more.

Now let鈥檚 dive into the releases from Q2 2026.

Please note that this article covers only AI offerings released from April 1, 2026, to June 30, 2026.

Joule

Joule Work
麻豆原创 Early Adopter Care program (registrations closed)

redefines how people interact with and execute end-to-end business processes. As the user engagement component of the Joule solution, it moves the user experience beyond fragmented, transactional interfaces toward a unified, intelligent way of working across 麻豆原创 and non-麻豆原创 systems. Its dynamic workspace adapts to users’ intent, helping them focus on outcomes rather than spending time finding information. And because it can delegate execution to AI, users will no longer need to coordinate work across multiple application interfaces manually.

Joule Work will allow users to express in natural language what they want to accomplish, triggering to coordinate teams of Joule Agents that will surface the right insights and automate routine work across business domains and systems to achieve the goal. This happens in intent-driven, adaptive workspaces built in real time that keep teams focused on driving decisions and impact. Joule Work can help reduce manual handoffs, shorten cycle times, and enable teams to turn decisions into actions faster. A key function of Joule Work is to connect users with Joule Assistants, which are like smart teammates organized by function. These assistants use context to intuit people鈥檚 intent and act by coordinating the appropriate Joule Agents across the business. Joule Assistants understand organizations deeply and can automate complex tasks within and across functions, freeing employees to address more strategic work.

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Joule Work mobile app
General availability

Employees who use 麻豆原创 on the go can rely on the Joule Work mobile app to interact with 麻豆原创 applications in natural language on their smartphones or tablets. Joule is integrated directly into the app, so a simple chat can surface the latest figures, help complete approvals or maintenance tasks, and support work across areas such as sales, HR, and supply chain processes without having to navigate multiple mobile apps. On iPhone and iPad, users can even start by saying 鈥淗ey Siri, ask Joule in Joule Work,鈥� then speak their question, which is passed straight to Joule for a response. This gives organizations a single, mobile-enabled entry point to Joule capabilities and lets employees gain insights and act on tasks across their 麻豆原创 solutions using everyday language.

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Voice for Joule
麻豆原创 Early Adopter Care program (registrations closed)

A new partnership with LiveKit delivers intelligent voice for Joule, extending the experience beyond the keyboard and into settings where work happens. This partnership helps advance 麻豆原创’s vision for the Autonomous Enterprise. With LiveKit, 麻豆原创 customers can use real-time voice capabilities in Joule and access reliable, always-on conversational AI. This brings voice AI to a full range of roles, devices, and environments, putting Joule within reach of employees whose work is done away from a keyboard.

Enhancements for Joule
Multi-system support for 麻豆原创 S/4HANA Cloud Editions

Joule now supports connecting multiple 麻豆原创 S/4HANA Cloud Private Edition systems or clients and multiple 麻豆原创 S/4HANA Cloud Public Edition systems within a single Joule formation.

Work seamlessly across different 麻豆原创 S/4HANA environments through one unified Joule interface, increasing flexibility and efficiency for organizations operating multiple systems. Administrators enable this feature by configuring system-specific destinations with naming conventions, including additional systems in the Joule formation via System Landscape, and mapping system identifiers in the Joule Admin Center.

Developers can build custom capabilities that leverage data and functionality from multiple back-end systems. Business users access and execute processes across all connected systems naturally within their workflow.

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Autonomous SCM

麻豆原创 Digital Manufacturing, AI-assisted production engineering
General availability

Production engineers can analyze error logs to identify root causes and generate resolution instructions for production processes using 麻豆原创 Digital Manufacturing. The feature also enables engineers to extend production processes via script tasks generated based on natural language input.

Organizations can reduce error analysis time for production process errors by 20%, reduce error analysis time for connectivity errors by 20%, and cut the time to handle a production process or connectivity error from 4.5 to 3.6 hours — while improving operating time from 92% to 92.92%.

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麻豆原创 Digital Manufacturing, AI-assisted description enhancement
General availability

Quality managers can generate clearer and more structured initial descriptions of complex issues using 麻豆原创 Digital Manufacturing. By reducing bias and subjective language, a more balanced and factual representation of the problem at hand is created. Users can also refine and rephrase initial rough descriptions, facilitating more effective follow-up and thorough investigation, and translate descriptions into different languages.

This offers organizations an up to five percent improvement in the efficiency of quality engineers during issue handling and resolution, and an up to 10% reduction in errors during problem handling.

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Autonomous Finance

Project Billing Price Verification Agent
Beta release

Billing specialists can quickly identify mismatches between agreed prices and billing amounts using the Project Billing Price Verification Agent in the manage project billing application of 麻豆原创 S/4HANA Cloud Public Edition.

The agent identifies the relevant contracts and statements of work for the related customer project, extracts key pricing data, and compares them with the values in the project billing request. It highlights discrepancies, provides context, and suggests corrective actions.

Organizations can reduce time spent resolving price discrepancies by 75%, cut revenue leakage from undetected incorrect billing by 75%, and improve cash flow while reducing days sales outstanding by fewer billing cycle delays.

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麻豆原创 S/4HANA Cloud Public Edition, AI-assisted localized business data management
General availability

Accounts receivable managers can access and explore localized financial and logistics data through Joule using natural language, without leaving their daily workflows. The capability enables users to run complex reports using natural language instead of manual selection screens, and instantly filter, navigate, and explore results with AI-supported context awareness. This way, finance teams can reduce the amount of training effort required and increase productivity and confidence across the organization.

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Autonomous Spend

Expense Automation Agent
General availability

Expense Automation Agent helps employees who submit business trip expenses by creating a first draft of their expense reports. It automatically collects and adds transactions, fills in relevant fields using contextual information and past behavior, and lets employees quickly review and adjust before submission. Customers can reduce manual data entry, shorten report completion time by up to 30%, and allow employees to focus more on their core work.

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麻豆原创 Ariba Contracts, AI-assisted contract creation from documents and prompts
General availability

Contract managers who create and manage large volumes of agreements can use AI鈥慳ssisted contract creation from prompts to start new contracts more efficiently. The feature lets users enter a simple natural-language prompt directly in the contract creation flow, then proposes contract header fields in seconds for review and confirmation before finalization. Organizations can reduce the effort required to initiate contracts, provide a guided in鈥慶ontext experience, and build a scalable foundation for future AI capabilities while maintaining clear human oversight of each contract.

麻豆原创 Fieldglass, AI-assisted SOW worker role recommendations
General availability

Procurement specialists who manage statements of work can now define suitable worker roles more quickly. This feature applies generative AI to the SOW context, including scope, outcomes, and timelines, to propose relevant roles that users can review and refine. Organizations benefit from faster, more consistent SOW authoring, improved fit鈥憈o鈥憇cope, and clearer, better-governed worker role definitions.

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麻豆原创 Ariba Invoicing, AI-assisted multi-model data extraction
General availability

Procurement and accounts payable teams working in 麻豆原创 Ariba Invoicing can rely on multi鈥憁odel data extraction to capture invoice information more accurately. The feature leverages the latest large language models in the content extraction service to interpret and extract key invoice data, enabling a smoother capture process. Organizations gain a more reliable and efficient invoice processing experience, with improved data quality that helps reduce manual corrections and downstream errors.

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Concur Travel, AI-assisted policy rule generator
麻豆原创 Early Adopter Care

Travel program administrators who manage Concur Travel policies can set up and adjust travel rules more efficiently with the policy rule generator. By pasting existing policy text into an AI-based rule generator, they can automatically produce multiple rule classes and rules in a single step, then apply them via a guided wizard. Organizations save time on policy implementation, reduce configuration errors, and promote more consistent, compliant travel policies across their programs.

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Autonomous CX

Order Reliability Agent
Beta release

The Order Reliability Agent helps customer service and order management teams stay on top of order issues consistently. The agent continuously monitors orders in 麻豆原创 Order Management Services, detects risks such as failures or delays, and either takes automated corrective action or presents clear recommendations and root-cause insights for staff to review. Companies can cut the time spent analyzing and handling exceptional orders by around half. The agent can also reduce customer churn related to fulfillment problems by about 20%, helping create a more reliable order experience.

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麻豆原创 Revenue Growth Management, AI-assisted trade promotion creation
General availability

Key account managers who plan trade promotions in 麻豆原创 Revenue Growth Management can set up promotions more quickly. When they enter a promotion name in the relevant account context, the system proposes key details such as dates, promotion type, duration, and sell鈥慽n timing based on master data, historical promotions, and past user edits. Organizations can shorten promotion setup time by up to 75% and reduce data鈥慹ntry errors and rework by around 30%, improving both efficiency and consistency in promotion planning.

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麻豆原创 Revenue Growth Management, AI-assisted deal sheet generation
General availability

Key account managers can quickly turn promotion data into retailer-ready deal sheets. Starting from a single promotion, the feature fills in system-of-record fields, applies appropriate PDF or Excel templates, and checks that required information is present before the document is created. This helps organizations produce consistent, audit-ready deal sheets in seconds, reduce formatting and data-entry errors, and give account teams more time to focus on customer negotiations rather than document preparation.

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Joule with 麻豆原创 Order Management Services
Beta release

Operations managers and order management teams using 麻豆原创 Order Management Services can rely on Joule to handle everyday operational questions and tasks through simple natural language. By enabling conversational access to key data and actions across areas such as order processing, orchestration, sourcing, availability, returns, and flows, Joule provides real-time, role-aware guidance directly in the flow of work. Organizations benefit from faster access to relevant transactions and insights, can act earlier to prevent issues from escalating, and support smarter, more timely decisions that save both time and operational cost.

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麻豆原创 Engagement Cloud, email campaign duplication
麻豆原创 Early Adopter Care program

Marketing teams can duplicate existing email campaigns to speed up everyday execution. When a marketer copies a previous campaign, email campaign duplication carries over layout, branding, and technical settings, so they only need to update content such as copy or offers. This helps organizations reduce campaign setup time, keep branding and formatting consistent, and limit repetitive configuration work and related errors across channels.

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麻豆原创 Business AI Platform

Build

Joule Studio
麻豆原创 Early Adopter Care

The new Joule Studio gives product teams, architects, and developers a single place to extend, build, and integrate AI experiences from business intent through to production-ready solutions. It starts from the outcomes you want to achieve, uses your own processes and data for context, and connects out of the box across your application landscape. At the same time, it can generate product requirements and technical specifications from your company-specific context, apply eval-based, data-driven guardrails to AI coding assistants under 麻豆原创-managed enterprise controls, and remain open so you can work with third-party or 麻豆原创 models in the development environment that fits your needs.

There will be a migration path from the original Joule Studio to the new version to help customers transition without disruption.

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麻豆原创 S/4HANA custom code migration agent
General availability

ABAP developers and migration teams moving from 麻豆原创 ECC to 麻豆原创 S/4HANA can use the 麻豆原创 S/4HANA custom code migration agent to automate the complex, time-consuming process of migrating custom ABAP code from 麻豆原创 ECC to 麻豆原创 S/4HANA. Delivered as an agentic AI capability in 麻豆原创 Joule for Developers, ABAP AI, the agent runs 麻豆原创 S/4HANA readiness checks via ABAP test cockpit across entire custom code packages, interprets the findings, categorizes issues, and applies a mix of deterministic quick fixes and AI-based code changes with confidence scores, while recording all updates in transport requests for full traceability. High-confidence fixes are applied automatically, and lower-confidence proposals are added as comments for developer review, so teams retain control over final code quality while spending far less time on object-by-object analysis, freeing capacity for higher-value design decisions and overall migration governance.

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Contextualize and Reason

Generative AI hub, enhancements

The generative AI hub in the 麻豆原创 AI Core infrastructure integrates with hyperscaler-agnostic operations to improve accuracy and support enterprise-wide adoption of business AI.

Batch API enabling processing of high鈥憊olume foundational model (FM) workloads
Developers and platform teams working with 麻豆原创 AI Core can use the batch API to process high-volume foundational model workloads more efficiently. By submitting large collections of non-urgent AI requests as a single input file, they can run jobs asynchronously in the background. At the same time, 麻豆原创 AI Core writes results to an object store, ensuring real-time, fast, and responsive user experiences. This improves scalability for high-volume processing, simplifies the developer experience across different models and providers, and ensures fair, predictable throughput so that large jobs do not block smaller ones.

Inference observability service: centralized logging and feedback for generative AI workloads
Teams running generative AI workloads on 麻豆原创 AI Core can use the inference observability service to monitor, analyze, and systematically improve model outputs. The service centrally records prompts, responses, and key context with controlled overhead, lets developers and users rate the quality of each response, and attaches lightweight feedback. It also supports labels and filters so interactions can be easily discovered and exported as datasets for fine鈥憈uning, prompt engineering, and benchmarking. Organizations gain standardized transparency into prompt and response quality, reduce costs and effort by replacing ad hoc logging with a unified, compliant feedback channel, and accelerate iterative improvement using structured inference data stored in S3 or metadata-only mode, and managed via REST APIs for labels, feedback, and record retrieval.

Speech-to-speech
The availability of speech鈥憈o鈥憇peech (S2S) recognition helps agent and app developers build natural, end鈥憈o鈥慹nd voice experiences into 麻豆原创 applications.

New models available
New models are supported, including Gemini 3.1 Flash Lite, Claude Opus 4.7, GPT Realtime and Mistral Small, GPT 5.4, GPT 5.4-nano, and GPT 5.3-Codex.

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麻豆原创 Joule for Consultants, enhancements

Custom knowledge grounding connects 麻豆原创 Joule for Consultants to a separate grounding service that indexes organizational content via 麻豆原创 AI Core. It enables Joule to securely index and reference an organization鈥檚 methodologies, policies, templates, and delivery standards alongside 麻豆原创鈥慶urated knowledge. By grounding responses in company鈥憇pecific documentation, consultants can receive more accurate guidance that aligns with established ways of working across projects and engagements.

Expert workspace introduces personalized 鈥渆xperts鈥� that help tailor guidance for specific projects, domains, or workstreams. Context is retained across conversations, so users can switch between initiatives while preserving project-specific knowledge.

and .

麻豆原创 Document AI enhancements

Model selection
This new feature allows you to choose the large language model (LLM) used for document processing. The Default LLM reflects the best-performing model at any given time, while additional models such as Gemini 2.5 Flash and GPT-5 are also available. The list of supported models is updated frequently to ensure access to the latest advancements.

New standard document types
麻豆原创 Document AI workspace and OData V4 APIs now support three additional standard document types: learning certificate, order confirmation, and traffic violation notice. This expands the range of business documents that can be processed out of the box, reducing the need for custom configurations.

Configuration of document-level confidence ranges
麻豆原创 Document AI now supports configurable document-level confidence thresholds, making it easier to assess extraction quality at a glance. Custom confidence ranges 鈥� low, medium, and high 鈥� can be defined on the configurations tab of a schema version. When documents are processed, the overall confidence score is displayed in the document header with color-coding: red for low, orange for medium, and green for high confidence.

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麻豆原创 Domain Models

are AI models trained on 麻豆原创 domain knowledge, including code, data, metadata, business processes, architectural knowledge, and documentation. When customers initiate a query or want to create code, the models are designed to provide results firmly grounded in the 麻豆原创 context instead of relying on generic internet knowledge. Combined with context graphs and agents, the models bring deep 麻豆原创 knowledge to Joule, Joule Studio, Joule Agents, and 麻豆原创 applications.

麻豆原创 Domain Models will help:

• Create custom extensions in 麻豆原创 S/4HANA Cloud Public Edition and 麻豆原创 Ariba: Developers in Joule Studio can use specialized models for 麻豆原创 S/4HANA and 麻豆原创 Ariba to understand and generate clean core-compliant code from natural language.
• Query information in 麻豆原创 S/4HANA Cloud Public Edition and 麻豆原创 Ariba: Customers can use natural-language prompts in Joule to access customer data that is grounded in the underlying data models and the business context.

These capabilities will help create clean core extensions while preserving 麻豆原创 standards and governance. 麻豆原创 Domain Models are running under the hood of Joule and Joule Studio and are not directly exposed to customers.

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Govern

麻豆原创 AI Agent Hub enhancements

麻豆原创 AI Agent Hub gives organizations a single control pane for all AI agents, LLMs, and MCP servers across the enterprise. Featuring automated AI asset discovery across major platforms, including Microsoft, Google, AWS, and now ServiceNow and 麻豆原创 AI Core, alongside structured governance assessments and a verification badge that integrates directly with runtime solutions to control which agents and MCP servers are approved for use.

Looking ahead, we will expand into runtime observability and governance, identity and access control, agent-in-process mining, and workforce impact mapping to make the AI Agent Hub the central command center for AI governance at scale.

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Process Consulting Agent
General availability

Process owners, analysts, and operational leaders can turn process data into clear, practical insights without needing specialist analytics skills with the Process Consulting Agent. Users can ask questions in natural language, and the agent retrieves and analyzes relevant process information through a multi鈥慳gent system, returning structured findings along with suggested next steps. Organizations can cut the time spent searching complex data per artifact by up to 90% and reduce the effort to analyze, design, model, and monitor processes by around five percent, helping teams move more quickly from insight to action.

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Enterprise Content Research Agent
General availability

Enterprise architects and portfolio managers can quickly find and understand architectural information through the Enterprise Content Research Agent in 麻豆原创 LeanIX. By querying inventory data and related documentation across sources such as 麻豆原创 LeanIX, Confluence, and SharePoint, the agent highlights missing fields, supports gap analysis, and helps keep records complete and consistent, while leveraging MCP Server tools as needed. This reduces the time spent on informational searches and navigation, simplifies data management tasks, and supports stronger governance over architecture data across the landscape.

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WalkMe solutions, AI knowledge indexing
General availability

Digital adoption and enablement teams can use AI knowledge indexing to make internal documentation easily available to WalkMe鈥檚 AI services in a controlled way. The feature processes connected knowledge sources, such as web pages and files, extracts text content, and converts it into secure vector embeddings, enabling WalkMe鈥檚 contextual AI assistance to ground guidance in company policies, wikis, and procedures rather than generic models. Organizations can improve real-time compliance outcomes and see a 21% increase in procurement policy adherence, while giving employees faster, policy-aligned answers directly in their workflows.

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WalkMe solutions, AI knowledge referencing
General availability

Digital adoption teams and application owners receive app guidance that aligns with their company鈥檚 policies and standards with WalkMe鈥檚 AI knowledge referencing. When certain conditions are met, such as editing a specific field or completing a form, the feature retrieves relevant content from connected internal documentation so tools like AI SmartTips or chat can compare user input with best practices and provide tailored feedback. This allows organizations to anchor AI assistance in trusted company information and help employees access the right policy or governance details when needed.

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WalkMe solutions, pinned AI
General availability

Operations, HR, finance, and other business teams now benefit from on-screen assistance exactly where they enter data or complete tasks, using WalkMe鈥檚 pinned AI. By attaching AI smart tips to input fields and AI Launchers to specific elements, the feature provides contextual guidance in place, grounded in company knowledge sources, so users can continue their work without switching applications. Organizations can improve data quality across key forms and workflows, reduce errors and rework, and see measurable gains such as a 41% improvement in data quality.

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WalkMe solutions, on-demand AI
麻豆原创 Early Adopter Care program

Employees working across line-of-business applications can turn to WalkMe鈥檚 on-demand AI for quick answers or step-by-step support without leaving their current screen. Through a conversational in-app menu that travels with users across applications, they can ask questions, retrieve company knowledge, and trigger automations or Smart Walk-Thrus, keeping guidance and execution closely connected. Organizations benefit from faster access to trusted information and a measurable impact on quality, including up to a 41% reduction in time spent correcting ERP-related business tasks.

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麻豆原创 LeanIX solutions, AI-assisted enterprise architecture decision management
General availability

Enterprise architects and architecture review boards get faster, more consistent decisions with enterprise architecture decision management in 麻豆原创 LeanIX solutions. By providing context, such as transformation diagrams or landscape changes, they can ask the AI to generate a draft architecture decision entry that includes the relevant background, decision, and implications for stakeholders to review and approve. This reduces manual data extraction and authoring effort, streamlines collaboration on approvals, and helps ensure architecture decisions are documented and concluded in a timely, traceable way.

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麻豆原创 LeanIX solutions, AI-assisted fact sheet calculations
General availability

Enterprise architects and workspace admins using AI-assisted fact sheet field calculations in 麻豆原创 LeanIX solutions can quickly turn plain-language business rules into working calculations. When they describe the rule they need, the feature generates readable, commented code that is aware of their fact sheet types, fields, relations, and enums. Hence, calculations align with the actual workspace configuration. This helps teams move from a business question to a usable metric in minutes, increase self-service configuration, reduce reliance on JavaScript skills, and speed up the delivery and maintenance of calculated fields that downstream reports and views depend on.

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麻豆原创 LeanIX solutions, AI-assisted automation creation
General availability

Enterprise architects and workspace admins can transform plain-English workflow descriptions into working automations with AI-assisted automation creation in 麻豆原创 LeanIX solutions. When they describe the review, update, or governance flow they need, the feature generates the appropriate triggers, conditions, and actions with field mappings aligned to the current workspace configuration. This lets teams build and scale automations themselves, increasing EA productivity, reducing reliance on technical experts, and making it easier to keep key processes such as onboarding workflows and lifecycle checkpoints consistently automated.

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麻豆原创 Cloud ALM, AI-assisted document summary
General availability

Document summary helps project teams and engineers understand long documents more quickly. Within the documents application, users can trigger an AI-generated summary, review and edit it in a separate window, and then apply it as a persistent summary section in the document. This shortens the time spent manually reading and extracting key points, supports faster comprehension of complex engineering content, and enables quicker decisions without leaving the document workflow.

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麻豆原创 Signavio solutions, AI-assisted context analyzer, text-to-event matching, and sentiment analysis
General availability

The context analyzer helps process owners and analysts match free text with process objects, such as sales orders or purchase requisitions, to the corresponding process events in event logs. The feature links free-text records such as survey responses, feedback, comments, and tickets to the corresponding process events, so qualitative experience data appears alongside operational logs. This enriches process mining with unstructured text, reduces manual mapping work, and improves process analysis accuracy by around 30%, helping teams pinpoint bottlenecks and experience issues more effectively.

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Jonathan von Rueden is chief AI officer for 麻豆原创 SE.

*Disclaimer: This article provides estimated benefits. All calculations are estimates based on 麻豆原创 customer case studies, 麻豆原创 benchmarks, and other research. Actual benefits may vary and may be affected by additional factors not considered by this article. The information is provided 鈥渁s is鈥� without warranty of any kind, expressor implied, and in no event shall 麻豆原创 be liable for any damages whatsoever in relation with the use of this article. See Legal Notice on for use terms, disclaimers, disclosures, or restrictions related to this material.

As customer journeys become more connected and complex, gaps in execution can lead to inconsistent experiences, slower response times, and missed opportunities. The next frontier of customer experience is not generating more insights, but turning insight into coordinated action at scale.

This latest release of the solution portfolio helps organizations strengthen that foundation by connecting workflows across marketing, commerce, sales, and service鈥攅nabling more consistent, scalable execution across every customer interaction.

Explore the highlights of the Q2 2026 release. For full sub-solution details, see our recaps for the , , , , and solutions.

Turning customer intent into action

Customer interactions are becoming more conversational, connected, and immediate across channels and touchpoints. At the same time, organizations need faster access to information and simpler ways to take action鈥攚hether engaging customers, managing campaigns, or responding to changing business needs.

• Conversational AI shopping through a model context protocol (MCP) server: Enable secure integration between and AI agents that can guide or act on behalf of customers. AI assistants can query real-time product information, provide inventory updates, manage shopping carts, and complete transactions directly within chat or voice interfaces鈥攃reating more intelligent, conversational buying experiences beyond the traditional storefront. 

• Joule in 麻豆原创 Engagement Cloud (麻豆原创 Early Adopter Care): Bring 麻豆原创’s conversational AI directly into campaign workflows. Teams can ask product or campaign questions in natural language and get accurate answers without searching across multiple systems. They can also duplicate successful campaigns without starting from scratch, freeing more time for strategic thinking, creativity, and customer engagement.

• Rich communication services (RCS) in 麻豆原创 Engagement Cloud: Engage customers with rich, interactive messages supported by Google and featuring media, carousels, and action buttons within native mobile messaging experiences. Branded, verified messages help build trust and guide customers smoothly from discovery to purchase without requiring an additional application.

Scaling personalized engagement

Recognizing customer intent is only the beginning. As engagement channels expand, marketing teams need to respond quickly while delivering relevant, personalized experiences at scale. This requires frictionless campaign execution, timely insights, and the ability to tailor every interaction to each customer’s needs and preferences.

• AI-assisted content composer (pilot): Generate high-quality, on-brand campaign content in . Using Gemini models informed by audience, product, and campaign context, teams can quickly create and refine content variations so they can launch personalized campaigns faster and spend less time on manual content creation.

• Embedded audience builder: Enable marketers to access and activate rich data from directly within 麻豆原创 Engagement Cloud. With this capability, they can build advanced segments themselves without switching systems or waiting on data analysts. The precision and relevancy of omnichannel campaigns can be improved by combining behavioral, transactional, account, and profile data with operational data across the business.

Enabling consistent sales execution at scale

Success depends on turning insight into disciplined, repeatable actions that drive predictable revenue outcomes. As sales environments grow more complex, even small inconsistencies in data, priorities, or execution can undermine forecasts and cause opportunities to slip away. Acting with greater consistency and confidence calls for stronger data integrity, aligned behaviors, and clearer guidance.

• Agentic opportunity summary overview: Give sales teams the tools they need to quickly assess deal health. This capability in aggregates engagement signals, activity levels, and progress indicators into a real-time view, allowing teams to identify risks early, prioritize effectively, and maintain deal momentum.
• : Optimize sales velocity and help ensure the right product placement with retail execution enabled by intelligent, AI-enhanced processes that maximize revenue. Teams can improve visit planning and execution, harness insights to improve sales performance, and optimize interactions. For consumer products companies, this helps drive shelf availability, promotion compliance, and merchandising effectiveness across retail locations. Field teams gain greater visibility into store-level execution, enabling more consistent brand presence and stronger sell-through performance.

• 麻豆原创 Incentive Management: Improve sales team effectiveness by using the solution, which is part of solutions. It helps drive profitable behaviors that increase revenue and support business growth while providing real-time performance insights, dispute management, and motivating rewards. Teams can use flexible tools to streamline incentive compensation and quickly design, test, and launch sales plans. AI-supported recommendations are also available to guide organizations in optimizing plans, maximizing outcomes, and uncovering actionable insights.

• Consumer Products Intelligence (麻豆原创 Early Adopter Care program): Enable consumer product companies to turn the enormous amount of sales and trade data they generate into better decisions. It uses analytics and AI to help improve trade spend performance, increase sales revenue and margins, and reduce manual effort.
   

Standardizing service execution across the enterprise

Service teams are increasingly expected to deliver faster, more reliable support while managing growing complexity across channels and requests. Achieving this objective requires simplifying how services are accessed and helping ensure consistent processes across the organization.

• Self-service catalog: Allow employees to quickly find what they need without understanding backend processes. Through this guided, intuitive catalog for requests are automatically routed with the right context, reducing delays and improving resolution times.

• Content package framework: Leverage the framework for 麻豆原创 Enterprise Service Management to deliver rapid, scalable value across lines of business. Prebuilt, reusable configurations for case types, workflows, and catalogs help organizations deploy services more quickly while simplifying implementation across the business. With this approach, organizations can eliminate complexity, empower partners, and speed adoption. Content packages for HR service delivery will be coming soon. 
• Email editor in 麻豆原创 Service Cloud and 麻豆原创 Enterprise Service Management: Compose, edit, and manage customer communications more efficiently while maintaining high-quality service interactions. The modern, user-friendly email editor is built for an AI-first world.

• Creation of sales objects from customer hub: Let agents fully manage leads, opportunities, appointments, and sales orders directly from the service agent workspace of 麻豆原创 Service Cloud. This capability helps turn each customer interaction into an opportunity to deliver more value.

Accelerating connected order management

Turning insight into action requires connected systems that can adapt as the business evolves. As organizations expand order channels, fulfillment networks, and technology landscapes, they need integration and order management that can keep pace so teams can respond faster to change.

• Flow connector: Enables smooth data flow between the solution, other 麻豆原创 solutions, and third-party products. This predefined capability allows business users to configure custom business flows and integrations with minimal IT involvement, creating connected order management processes across the enterprise.

Execution at scale: the next customer experience advantage

As AI becomes embedded in daily operations, the differentiator shifts from insight generation to execution.

Our recently announced strategic partnerships with and Google Cloud help extend this execution-first approach by connecting AI-powered service, commerce, and engagement experiences directly to operational systems and business data. As a result, organizations can move from isolated interactions and insights to coordinated actions that drive faster resolutions, better customer experiences, and greater business impact.

Learn more about 麻豆原创 CX in Q2鈥�2026 

Read the 麻豆原创 Help documentation to get started with these new capabilities:

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

Those investments are critical. But they weren鈥檛 the finish line. They were the foundation. And today, they鈥檙e paying off in new ways as organizations adopt AI to drive measurable business outcomes.

But there鈥檚 another part of that foundation which is becoming just as important: data. The organizations best positioned to compound their value from AI are those that have invested in data foundations that give AI the trusted business context it needs to reason, recommend, and act.

Now, in conversations with customers across the Americas and around the world, I鈥檓 seeing a new pattern emerge. The discussion is shifting from where AI can be applied to what happens when intelligence becomes embedded into the core of how the business operates.

While every transformation is different, three common shifts keep coming up.

1. From AI use cases to intelligent business processes

The first wave of AI adoption focused on proving value. Organizations identified high-impact use cases, delivered measurable results, and built confidence that AI could make a difference.

That work isn鈥檛 finished. But increasingly, customers are asking how intelligence can become part of the business processes employees use every day. We鈥檙e already beginning to see what this looks like in practice.

For example, HR Path Brazil, a Brazilian company specializing in recruiting and managing talent for international firms, is using Joule embedded in 麻豆原创 SuccessFactors HCM to automate routine HR interactions. It is helping employees find the information they need faster while allowing HR teams to focus on more strategic work. The company has reported a seven percent reduction in standard HR support cases and two hours of聽HR support workload eliminated each week, which quickly adds up.聽It鈥檚 one example of how embedded and connected AI is becoming part of how work gets done.

2. From measuring AI use to measuring the business outcomes it creates

One of the biggest changes I鈥檓 seeing is how organizations define success. ROI is becoming a given and that is reflected in the data, especially in Oxford Economics research out just this week. It showed that organizations investing in AI expect to see an average return of 21% this year but increasing to 38% in two years. And as agentic AI continues scaling, it is projected to deliver $17.6 million in returns, more than quadrupling last year鈥檚 estimates (US$4.3 million).

This is allowing organizations to focus more on business outcomes from their AI. They are asking questions like can we shorten cycle times? Can we improve decision-making? Can we free employees to spend more time creating value? Can we become more resilient and responsive as a business?

This is an important shift because it changes the conversation from implementing technology to improving how the business performs.

3. From systems of record to the new operating system for the enterprise

The third shift is the one I believe will have the greatest long-term impact. For decades, enterprise software primarily captured transactions, standardized processes, and automated routine work. Now it鈥檚 beginning to help organizations anticipate change, recommend actions, coordinate work across functions, and increasingly execute routine decisions with human oversight.

That鈥檚 why I believe the Autonomous Enterprise represents more than the next phase of automation. It represents a new operating model for business.

Instead of people spending time connecting information across finance, supply chain, procurement, HR, and customer operations before deciding what to do next, intelligent systems can increasingly provide context, surface recommendations, orchestrate work, and help teams execute.

People remain firmly in control. But they鈥檙e supported by enterprise software that is becoming an active participant in how the business operates and executes, not simply a system that records what already happened.

Where we go from here

The organizations creating the greatest long-term advantage won鈥檛 just be the ones deploying the largest number of AI use cases. They鈥檒l be the ones that use those early successes to rethink how work gets done across the enterprise.

The journey to the Autonomous Enterprise won鈥檛 happen overnight, and it won鈥檛 replace the need for strong leadership, governance, or talented people. If anything, those become even more important.

We鈥檒l likely look back on today鈥檚 AI projects much the same way we now look back on the early days of cloud transformation; not as the destination, but as the foundation for a fundamentally new way of operating and innovating continuously.

The true winners from this shift will be the ones who continuously become more intelligent, more adaptive, and ultimately more autonomous.

Jan Gilg is a member of the Extended Board of 麻豆原创 SE and global president of Customer Success & Americas.

While the amount the average global business spends on AI increased slightly to US$28 million this year, the level of ROI from that investment has spiked. Globally, companies expect to drive ROI of 21% this year (US$6.3 million), up from 16% last year. That ROI is expected to grow to 38% in two years鈥� time (US$15.9 million).

Agentic AI is central to those ROI expectations. In the next two years, average ROI from agentic AI is expected to reach US$17.6 million, more than quadrupling from last year鈥檚 estimates (US$4.3 million).

These insights have been revealed in new global research, , which surveyed 2,600 business leaders across 13 countries.*

Commenting on the research, 麻豆原创 Chief AI Strategy Officer Sean Kask noted, 鈥淎I has moved from experiment to execution, and that鈥檚 beginning to show real returns. But there鈥檚 still a long way to go. Because AI that lacks context鈥攚hether that鈥檚 processes, data, or governance鈥攁t best creates activity without outcomes and at worst creates risk.鈥�

AI inching closer to enterprise maturity

While global investment in AI increased slightly from US$26.7 milion in 2025, there were significant changes in key markets. Investment increased significantly in Brazil, UK, Australia, and Germany, while leading markets like China and India saw funding decreases.

Today, almost a third of all tasks (30%) in the average business are supported by AI, a figure expected to increase to 48% in two years. Yet, while strategic investment in AI has almost doubled year-on-year to 17%, piecemeal approaches remain by far the most prevalent (41%).

Some of this may be a leadership problem. Under a half of companies have a dedicated AI leader responsible for AI adoption (46%), clear frameworks about AI development (52%), or even training on AI capabilities and risks (41%).

Yet, despite those challenges, 69% of businesses are satisfied with their current AI ROI, even though more than two-thirds are not convinced AI is achieving its full potential.

Some of this optimism is due to agentic AI, since over eight in 10 (83%) businesses say agentic AI has moderate to very high potential to transform their organization. Yet, it is still early days for the technology, with only three percent of businesses saying they are fully prepared for agentic AI, while the majority say they are either partially prepared or not prepared at all.

Global businesses meeting key AI challenges

Organizations are facing a range of challenges achieving ROI from AI, including data, workforce, and governance issues.

Data quality remains the biggest challenge for global organizations. The number of businesses that say they are data ready for AI dropped from last year, with 73% of companies revealing challenges with incomplete data. And that is impacting day-to-day work, with 79% of businesses experiencing rework, delays, or backlogs due to low quality AI outputs.

Similarly, businesses are managing the workforce impacts of AI. Almost eight in 10 businesses (78%) are either unsure or agree their company upskilling is not keeping up with the evolution of AI tools. And just one percent of respondents said AI will have no impact on their workforce planning. Meanwhile shadow AI use is increasing year-on-year, with 69% saying it happens at least occasionally.

鈥淭he next step in achieving value will be to integrate AI deeply with contextual data and processes,” Kask said. “But businesses across the world must understand AI often provides value that is harder to measure than expected, and risk that moves faster than most governance can keep up with. Businesses are quickly discovering that AI governance plays a foundational role in unlocking the value from AI.鈥�

Governance is a critical obstacle in the way of enterprise AI value. Just 12% of businesses say either their skills or their processes and frameworks are fully ready to govern AI effectively.

These issues may be exacerbated in an agentic future. Today, 38% of companies do not have a human-in-the-loop process for agentic workflows, 37% don鈥檛 have permission and access controls for agents, and only 44% have a registry of the agents in their business. This is critical, given more than two-thirds of businesses (69%) either agree or are unconvinced if they are deploying agents quicker than they can govern them.

Future of value from AI is the Autonomous Enterprise

鈥淩ealizing real value from AI is not going to be easy because it demands a new approach,鈥� Kask concluded. 鈥淏usinesses large and small will need to connect AI to the data and processes that run their organizations, and make sure it has the context and governance to drive trusted results. That鈥檚 what we call the Autonomous Enterprise. This isn鈥檛 a technical change; it鈥檚 a human one. Because you can only achieve real value if agents, processes, and people work as one.鈥�

*Australia, Brazil, Canada, China, France, Germany, Italy, India, Japan, Singapore, Thailand, United Kingdom, and United States.

External talent, including contractors, consultants, and project-based specialists, is becoming a core component of how work gets done rather than a stopgap solution.

Leading organizations are responding by treating external talent less as a short-term fix and more as a standing part of workforce strategy. With 74% of employers worldwide , workforce planning has become less about filling roles in sequence and more about maintaining access to critical capabilities. This shift moves organizations to a workforce model that can respond quickly, scale efficiently, and align with long-term business priorities.

End of 鈥渢emporary鈥� talent

External workers have historically been brought in to meet short-term needs, helping fill gaps during peak periods or support one-off projects. While that approach still exists, ongoing volatility has proven that this is no longer sufficient.

Demand signals change quickly, transformation is continuous, and new skill requirements emerge faster than internal teams can adapt.

In this context, external talent provides a clear advantage. It gives companies access to specialized expertise on demand, helps accelerate innovation, and supports operations without overextending internal resources. It also allows leaders to rethink workforce composition to better balance stability with adaptability.

This shift mirrors the recent shifts seen in procurement and supply chain functions, where visibility and cross-functional integration have become drivers of long-term success. Workforce strategy is moving in a similar direction.

You can鈥檛 manage what you can鈥檛 see

As organizations expand their use of external talent, visibility remains essential. Many companies still manage contingent labor in disconnected ways, which makes it harder to understand where talent is deployed, what it costs, and how effectively it is being used. Without that visibility, workforce decisions remain reactive.

When organizations can see how external talent is deployed across business units, geographies, and projects, they can plan with greater confidence. This level of insight also supports stronger governance by improving compliance, supplier performance, and consistency from sourcing to offboarding.

In practice, organizations that invest in visibility often see measurable improvements in efficiency, productivity, and decision-making speed. More importantly, they begin to treat external labor as a strategic lever rather than a cost center.

From reactive hiring to predictive planning

Visibility is essential, but the real opportunity lies in turning workforce data into actionable insight.

AI is playing an increasingly important role in this transformation. By analyzing hiring patterns, project pipelines, and market signals, AI can help organizations anticipate future talent needs instead of reacting to them. This is especially valuable in environments where workforce decisions need to balance cost, speed, and quality. For example, organizations can use AI to:

• Anticipate external talent needs tied to major initiatives, such as ERP rollouts or expansion projects, before staffing gaps affect delivery
• Identify where external specialists can help address immediate skill gaps while longer-term hiring continues
• Analyze market signals and workforce composition to help guide insourcing vs outsourcing strategies
• Flag bottlenecks and make corrections in onboarding, approvals, or assignment start times that delay productivity and increase costs

As organizations look to make external talent a more strategic part of workforce planning, technology becomes increasingly important. helps organizations gain greater visibility into their external workforce, connect talent data across the enterprise, and use AI-driven insights to make more informed staffing decisions.

By bringing together workforce planning, services procurement, and external talent management, organizations can better anticipate skill needs, improve agility, and align workforce investments with business priorities.

More connected approach to talent

One of the most important shifts underway is how organizations think about workforce composition. Rather than treating external and internal talent as separate categories, forward-looking companies are managing both as part of a single ecosystem.

This integrated approach offers several advantages. First, it more closely aligns with business goals. Leaders can allocate resources based on outcomes rather than employment type, ensuring the right skills are applied where they create the most value. Second, it improves agility. When workforce models are designed to flex continuously, organizations can quickly respond to changing conditions without disrupting operations. Third, it enhances the employee experience for both internal teams and external contributors by streamlining processes and making them more efficient.

Technology plays a key role in enabling this shift and provides organizations with the tools to manage external talent alongside internal workforce data, improving visibility and supporting more data-driven decision-making. Solutions such as 麻豆原创 Fieldglass help organizations bring greater transparency, consistency, and insight to how external talent is sourced, managed, and aligned to business needs. While no single solution defines success, the ability to connect data, processes, and insights is increasingly important.

Building resilience in an always-on economy

Business no longer moves in predictable cycles. Demand shifts quickly, priorities evolve in real time, and skills gaps can emerge faster than traditional hiring models can address. In that environment, resilience depends on staying adaptable while keeping work moving.

That is why external talent is becoming a more strategic part of workforce planning. With finding skilled talent becoming increasing more difficult, many organizations are looking for ways to maintain access to specialized capabilities as business needs shift. External talent can help teams move faster, bring in targeted expertise, and sustain progress on critical initiatives without overextending the core workforce.

For many organizations, this reflects a broader change in mindset. External talent has moved closer to the center of workforce strategy, especially in areas where speed, specialization, and adaptability matter most. How well organizations plan for and manage that talent will shape their ability to execute, compete, and grow.

Amber Roth is vice president of GTM for 麻豆原创 Fieldglass.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

A Structured Approach to ERP Transformation

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

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

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

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

Microsoft Azure as the Cloud Foundation

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

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

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

Building on a Longstanding 麻豆原创 Relationship

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

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

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About 麻豆原创

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

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When suppliers send order confirmations as PDF files, even minor discrepancies in price, quantity, or delivery dates can trigger significant manual effort within procurement. For Lemvigh鈥慚眉ller, one of Denmark鈥檚 largest wholesalers within steel, plumbing, heating and electrical products, this has long been a familiar challenge, consuming substantial time and resources.

The company has now tackled the very point where earlier automation initiatives often stalled. With a new solution based on several specialized AI agents, developed on 麻豆原创 technology and implemented in close collaboration with NTT DATA Business Solutions, supplier PDF order confirmations can now be read, interpreted, compared, and processed automatically鈥攄irectly against 麻豆原创 systems.

鈥淲e have previously tried both RPA and traditional automation approaches without really achieving the desired effect. The key difference this time is that we broke the task down into multiple independent AI agents, each responsible for a specific part of the process. Together, they now handle what previously required manual review,鈥� says Frederik Aakerlund, IT director at Lemvigh鈥慚眉ller.

10 weeks from idea to AI agents in production

The project originated with an e-mail from Jess Frederiksen, an AI鈥憇avvy project manager in Lemvigh鈥慚眉ller鈥檚 Market and Procurement organization. After successfully matching an order confirmation with a purchase order using ChatGPT as an experiment, he approached the IT director to explore whether this could be turned into a fully integrated system solution.

From the initial tests to production deployment, the entire project took just 10 weeks. According to Lemvigh鈥慚眉ller, this short implementation timeline was critical in allowing the solution to demonstrate tangible business value quickly and build internal support.

鈥淭his was not a long-running project. In 10 weeks, we moved from idea to AI agents in production, already delivering measurable value to our procurement officers,鈥� Aakerlund says.

Over time, Lemvigh鈥慚眉ller expects the solution to free up resources equivalent to three to four full-time employees. These resources will instead be redeployed to higher-value activities, including handling the most complex and exception鈥慸riven orders.

鈥淭he objective is not to reduce headcount, but to use our expertise more effectively. The AI agents take care of routine tasks, enabling procurement officers to focus on cases where their experience genuinely matters,鈥� Aakerlund adds.

More than 100,000 order confirmations automated

Each year, Lemvigh鈥慚眉ller sends approximately 175,000 purchase orders to more than 2,000 suppliers. While part of this volume is handled in a structured manner via EDI, around 60% of supplier order confirmations are still received as unstructured documents.

With the coordinated AI agents in place, the company can now automatically identify delays, quantity changes, and price discrepancies鈥攁nd respond significantly faster.

鈥淧reviously, when order confirmations were handled manually, it could take hours or even days before changes were reflected across the organization. Today, the AI agents update the data almost immediately, allowing customers to receive a much more accurate picture of deliveries far sooner,鈥� says Klaus Heinemann, head of 麻豆原创 ERP at Lemvigh鈥慚眉ller, who led the development together with the project team. 鈥淚n addition, we now identify price discrepancies before the final invoice is issued, saving time both for us and for our suppliers.鈥�

Multiple AI agents orchestrated in a single workflow

The solution is built around three cooperating AI agents, each with a clearly defined role in the process. One agent handles incoming e-mails and attachments, a second extracts and structures data from PDF documents, and a third compares the extracted information against purchase orders in 麻豆原创 to determine whether there is a match or a deviation.

As a result, complex and unstructured supplier data can be processed in a unified, automated workflow without requiring procurement officers to open and manually review lengthy PDF files.

鈥淲hat makes this solution robust is the interaction between the agents. Each agent is highly specialized, but they are orchestrated in a way that ensures the process flows seamlessly from start to finish,鈥� Heinemann explains.

During the project, the importance of master data quality also became increasingly clear.

鈥淚n areas such as Incoterms and other master data, we identified improvements that need to be addressed. This has been an important learning not just for this initiative, but for our broader work with AI,鈥� he says.

While it is still too early to measure the full impact on customer experience, error rates, or claims, expectations are that faster and more precise handling of supplier confirmations will, over time, lead to fewer surprises and significantly improved delivery transparency. Internally, the solution has been met with strong interest and curiosity among employees.

鈥淧rocurement officers clearly recognize the value of being relieved from the most tedious routine work. This has sparked a constructive dialogue about how technology can best support their day鈥憈o鈥慸ay responsibilities,鈥� Heinemann says.

Business AI with a clear business outcome

According to Lemvigh鈥慚眉ller, the investment is expected to deliver a return within a relatively short timeframe.

鈥淲e are talking about quarters rather than years when it comes to ROI. That is why it was essential for us to get the solution into production quickly and focus on processes with a clear and measurable impact,鈥� Aakerlund says.

For 麻豆原创, the project serves as a concrete example of how artificial intelligence can be embedded directly into core business processes rather than remaining a disconnected experiment.

鈥淢any companies talk about AI agents primarily in terms of automation. Lemvigh鈥慚眉ller demonstrates that the real challenge鈥攁nd the real opportunity鈥攍ies in coordination,鈥� says David Pontoppidan, head of AI at 麻豆原创 for the Nordics and Baltics. 鈥淚t is the orchestration of three specialized agents directly within the core process that makes this solution robust. This is also where many multi鈥慳gent initiatives fail, not due to limitations of individual agents but because of insufficient coordination. Lemvigh鈥慚眉ller has succeeded by anchoring the solution in its 麻豆原创 landscape, where data, business rules, and governance frameworks are already firmly established.鈥�

He continues: 鈥淚nnovation is not about company size. Lemvigh鈥慚眉ller shows that a Danish organization with short decision paths and a pragmatic approach to technology can move faster than many large global enterprises that are still in the planning stage. Ten weeks from idea to production is far from the norm, but perhaps it should be.鈥�

Designed for operations and scalability

The solution was implemented in close collaboration with NTT DATA Business Solutions, which was responsible for making the solution production鈥憆eady and fully integrated into Lemvigh鈥慚眉ller鈥檚 麻豆原创 landscape.

鈥淏y distributing responsibilities across multiple AI agents, Lemvigh鈥慚眉ller has been able to automate a complex process without losing transparency or control. This has enabled a fast and secure transition from pilot to production and ensures a more robust solution that can easily be expanded as new requirements emerge,鈥� says Kristian Dahl, 麻豆原创 UX manager at NTT DATA Business Solutions.

According to Dahl, the modular, agent鈥慴ased architecture was a key enabler in moving efficiently from proof of concept to live operation.

First step in a broader AI agent strategy

Initially, the AI agents have been deployed for selected supplier inboxes and business areas. However, Lemvigh鈥慚眉ller already sees significant potential in applying the same agent鈥慴ased approach across additional administrative processes.

鈥淭his is the first AI agent solution we have put into production. The experience has given us the confidence to consider similar approaches across other areas, including invoice processing and order management,鈥� Aakerlund concludes.

Ellen Vig Nelausen is a Nordic Integrated Communications Expert at 麻豆原创.

AI infrastructure as strategic asset 

Although infrastructure ownership is just one element of digital sovereignty strategy, European politicians and policymakers have argued that without domestic data centers, Europe risks dependence on U.S. and Chinese providers for critical AI capabilities.

This concern is echoed by some industry leaders鈥攑articularly those in finance and regulated sectors鈥攚ho increasingly view AI infrastructure as a foundation of economic security. Specifically, they argue that sovereign data centers enable companies to comply with stringent European regulations on data protection and AI governance. They say that locally operated infrastructure ensures that data remains under European jurisdiction, reducing exposure to foreign legal regimes and enhancing trust among customers and regulators.

Security and compliance imperatives 

European leaders also frame AI infrastructure as a hedge against geopolitical risk. They argue that dependence on external providers introduces vulnerabilities, whether through legal exposure, supply chain disruptions, or political tensions.

As Christian Klein, CEO of 麻豆原创 SE, noted at the 麻豆原创 Sapphire Madrid event last month, many European customers operate in the public sector or other highly regulated industries. 鈥淕eopolitical risk is a growing concern,鈥� he said. 鈥淲hat if sanctions suddenly block data flows across borders? Or if the latest LLMs can鈥檛 be deployed in certain regions?鈥�

Christine Lagarde, president of the European Central Bank, also highlighted this concern in her November 2025 speech titled noting that Europe must 鈥渁void single points of failure鈥� in critical areas such as data centers and compute capacity.聽聽

Proponents of sovereign AI infrastructure also argue that it can stimulate broader economic growth. Data centers often anchor the ecosystems of startups, research institutions, and industrial applications, enabling Europe to capture more value from the AI stack.

From a technical standpoint, proximity also matters. Locally sited data centers reduce latency and improve performance for AI applications, particularly those requiring real-time processing or integration with industrial systems.

But despite these perceived advantages, many European business leaders have urged policymakers to take a more moderate, nuanced approach towards sovereign data. Their concerns are not about the need for data sovereignty itself, but about how it is implemented鈥攑articularly the push to rapidly build new, domestically controlled AI data centers. They emphasize that that data residency (location) is only one element of the four standard pillars of a sovereign data strategy, which also include legal sovereignty (jurisdictional control), operational sovereignty (independent operations), and technical sovereignty (data control).

In discussions with policymakers, European business leaders from diverse sectors have been warning that reducing reliance on U.S. technology too quickly is unrealistic. This reflects a structural reality: Europe remains deeply dependent on non-European providers for cloud infrastructure, chips, and AI platforms.

Research from Swiss cloud provider Proton suggests that around 75% of publicly listed European companies rely on U.S. tech services, (primarily Microsoft and Google) for critical infrastructure, including e-mail, cloud, and software. Therefore, attempting rapid substitution risks disrupting operations without delivering viable alternatives.

Barriers and concerns

Even the most ardent proponents of sovereign AI infrastructure acknowledge that there are major practical barriers to building massive AI data centers in Europe, including energy. AI data centers are extremely power-intensive, and Europe already faces grid constraints, high electricity prices, and long permitting timelines.

Without significant investment in energy systems, some European business leaders warn that new data center projects risk delays, cost overruns, or cancellation.

Another concern is that infrastructure-focused, sovereignty-driven policies may distort markets. Critics warn that infrastructure subsidies could flow to less competitive domestic providers resulting in slower innovation and the misallocation of capital resources to politically driven projects rather than economically viable ones.

In this view, sovereignty risks becoming industrial policy for its own sake, rather than a driver of efficiency or innovation. But perhaps the most significant critique is that the focus on infrastructure may distract from a more pressing issue: AI adoption.

Europe has historically lagged in deploying digital technologies. Some business leaders, including 麻豆原创鈥檚 Klein, argue that the priority should be accelerating AI use across industries and point out that infrastructure alone will not drive productivity gains. Over-emphasis on the infrastructure component of sovereignty could slow deployment through added complexity and cost. As Klein has noted, focusing primarily on infrastructure is a mistake if it is at the expense of developing AI applications and software.

Europe, he said recently, should prioritize 鈥渃ode over concrete.鈥� At the World Economic Forum in Davos earlier this year, senior executives from major European firms, including Capgemini and Ericsson, also warned against an overly protectionist approach. They argued that excluding or limiting global providers would raise prices, slow tech adoption, and reduce competitiveness.  

The business view 

From a business standpoint, AI is rapidly becoming a general-purpose technology, and the costs of AI infrastructure directly impacts productivity. If European AI infrastructure is more expensive, European companies risk falling behind global peers.

While data residency and the other elements of digital sovereignty are essential for some businesses operating in sensitive and highly regulated sectors, the sovereignty debate in Europe risks oversimplifying a fundamentally global industry. As Henna Virkkunen, the European Commission鈥檚 technology chief, noted: 鈥淣obody can be competitive alone.鈥�  

Indeed, since AI development depends on globally integrated supply chains, including semiconductors, software, and talent, fully localized infrastructure may be neither feasible nor desirable.

Rather than building duplicative infrastructure to support AI development, Europe鈥檚 real competitive advantage may lie in its treasure trove of operational data鈥攁 resource that is often difficult to access because of overly restrictive regulation and data access rules, prompting growing calls for reform from business leaders across Europe.

Easing and standardizing data access rules would help European businesses tap into this resource and compete more effectively with international rivals as they move into the next phase of AI enablement鈥攖he .

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

Instead, they hit friction:

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

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

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

The agentic era is accelerating this shift dramatically.

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

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

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

The customer experience reality: ambition outpacing execution

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

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

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

A new model for customer experience built on trusted enterprise data

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

At the heart of this partnership:

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

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

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

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

Why this partnership matters

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

For commerce leaders:

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

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

For marketing leaders:

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

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

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

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

Unlocking new value for enterprises

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

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

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

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

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

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

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

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

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

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

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

Managing abundance

These are just some of the challenges facing KETRACO, , as Africa鈥檚 energy sector is undergoing a shift from a centralized power system to a more diversified, renewable-energy-based grid.

鈥淩enewable energy is abundant. The real challenge is how to manage, integrate, and stabilize it,鈥� Dr. Njogu Kimando, energy expert at KETRACO, said, speaking at the TAC Insights conference for in Toulouse. 鈥淭he energy transition is not constrained by capacity, but by our ability to manage complexity in real time.鈥�

Geothermal power generated in the Great Rift Valley provides about 40% of electricity in the region, making Kenya Africa鈥檚 largest geothermal producer. About 24% is generated by hydro power from rivers. The rest of the demand is met by wind power coming mostly from Lake Turkana, Africa鈥檚 largest wind farm, as well as solar. The fastest growing sector, solar is widely used in rural homes and businesses. Kenya has one of the highest household solar adoption rates in the world.

While renewables reduce costs, support climate alignment, and provide energy security, challenges include drought-induced water shortages, sun and wind variability and grid instability.

Lack of synchronized intelligence

In the traditional grid, power is generated at a few centralized plants, creating a stable source of supply that is easy to control based on demand forecasts. The renewables (REN) grid, on the other hand, fluctuates with the weather, requiring real-time monitoring, rapid balancing, and more dynamic system control.

The core challenge in modern power systems is not the absence of data, but the lack of unified, real-time visibility across fragmented systems. This lack limits the ability to make timely and coordinated operational decisions. 

鈥淲e鈥檙e constantly balancing supply and demand,鈥� Kimando explained. 鈥淲e have limited real-time visibility across generation sources, transmission assets, and demand patterns.鈥�

As renewables expand, KETRACO鈥檚 role has evolved from simply building and operating transmission lines to managing power flows in real time, coordinating variable energy generation, and ensuring grid stability and reliability. The company is relying on digital systems to accomplish these tasks.

Kimando outlined the company鈥檚 new, integrated smart grid infrastructure. Forming an end-to-end digital value chain, it functions as the digital twin foundation for the grid and links operational technology with enterprise systems and advanced analytics.

Data is captured by SCADA, an industrial control system for infrastructure and utility networks, and is securely routed through 麻豆原创 Business Technology Platform middleware to 麻豆原创 S/4HANA, which serves as the enterprise backbone. It is here that operational data is translated into structured business processes.聽

From data to decisions

鈥淲e鈥檙e relying on 麻豆原创 technology to transform that raw data into predictive, actionable intelligence,鈥� said Kimando, citing asset lifecycle management and outage reduction metrics as examples of ways to shift from reactive maintenance to predictive grid reliability. 鈥淒igital transformation is no longer a technology choice, but a strategic necessity. It鈥檚 a balancing game: values versus risks.鈥�

For KETRACO, the goal is to unlock the full value of renewable energy while avoiding the escalating risks of operating in a complex and dynamic power environment. Inaction leads to grid instability and operational inefficiency, underutilization of energy investments, rising costs, and exposure to regulatory and compliance risks. Action based on data analytics leads to improved financial efficiency and better CAPEX decisions. It also leads to enhanced operational resilience with reduced outages and faster system recovery.

鈥淭ogether, this strengthens our strategic positioning for the energy transition and ESG compliance,鈥� Kimando explained.

The next frontier

At KETRACO, AI is considered a capacity multiplier, enabling a crucial shift from resource-intensive grid expansion to intelligence-driven grid optimization. 

鈥淎I is helping us achieve more with the same workforce. We鈥檙e enabling engineers, not replacing them,鈥� the expert shared. 鈥淎utomation is enabling our people to focus more on predictability and decision making.鈥�

In addition, AI supports long-term sustainability goals because simulating scenarios before investing reduces errors and costs. It also enables self-optimized grid operations, reducing manual interventions and improving collaboration and integration among regional power systems and cross-border energy flows.

KETRACO鈥檚 role is to transmit electricity across Kenya and connect the country to the wider East African power market. Its importance is growing as Kenya has become a REN hub, expanding its geothermal, wind, and hydropower generation. Without its transmission infrastructure, much of Kenya鈥檚 renewable energy could not be delivered efficiently to consumers or neighboring countries.

In closing, Kimando summarized how digital transformation is changing the way power is managed, stabilized, and optimized: REN presents a system challenge, not a technological one. Technology must align to operations and strategy, control is achieved through visibility and integration, and partnerships accelerate scale and execution.

鈥淭he future grid will not be defined by how much power we generate, but by how intelligently we manage it,鈥� he concluded.

With the new data center in India complementing existing ones in Europe, the U.S., and Saudi Arabia, 麻豆原创 has dramatically strengthened the data federation capabilities of , linking together the procurement processes of businesses and their trading partners across the globe.

With this milestone, 麻豆原创 has delivered on a crucial promise made to a highly consequential set of customers in the Indian subcontinent and beyond. Later this year, the India data center will extend data federation capabilities for as well.

But what is data federation? And why is it so consequential for organizations in India and elsewhere?

Data federation assembles a unified view of data from disparate sources without physically moving or copying it. Trading partners can thus exchange goods and services across international borders while the data underlying those transactions remains stationary. By preserving the integrity of data where it resides, federation facilitates mission-critical operational processes on a global scale while achieving regulatory consistency with compliance requirements that arise at a national level. For example, if a buyer鈥檚 data resides in 麻豆原创 Business Network鈥檚 India data center but its supplier鈥檚 data sits in the U.S. data center, federation provides both parties with access to each other鈥檚 data without impinging on either nation鈥檚 policies governing the residency of that data. This becomes especially important as new legislation gathers pace around the world in response to rising concerns over individual privacy, national security and commercial sectors considered sensitive.

Enabled by , 麻豆原创 Business Network and its data federation capabilities are now built on the kind of modern, connected foundation required for the next era of cloud-based commerce. Beyond improved scale, resilience, and regional compliance, 麻豆原创 Business Technology Platform helps create the basis for agentic artificial intelligence, automation, richer analytics, stronger security, open extensibility, and seamless cross-麻豆原创 workflows and processes. The result is a network that can not only connect partners globally but also help customers to conduct business more intelligently, respond faster to change, and drive greater value from every interaction across procurement, supply chain, logistics, asset collaboration, and working capital management.

To collaborate with their trading partners on these and other core business processes, companies conducting business in India require a cloud-based platform that provides secure infrastructure for building resilient domestic supply chains while maintaining global connectivity, thus maintaining sensitive procurement, supplier, and manufacturing data within Indian jurisdiction. 麻豆原创 Business Network furthers these objectives with our newly launched data center in Mumbai, where data is localized in a manner compliant with (MeitY) guidelines, with a MeitY-empaneled cloud service provider.

While supporting compliance within an increasingly complex regulatory environment, data federation offers numerous technical advantages as well, especially in hybrid or multi-cloud environments. These benefits include cost efficiency, unified governance and security, and faster response times compared to accessing servers in distant regions. Yet the most valuable competitive advantage provided by data federation may be the most elusive: peace of mind for business leaders amid the ever-looming threat to supply chains posed by disruption.

To counter that threat鈥攚hether arising from geopolitical conflict, labor unrest, financial shock, natural disaster, or other upheaval鈥攂usinesses are turning increasingly to data federation and other cloud-based solutions to extend visibility and instill resilience across their operations and those of their trading partners. With the opening of our new data center in Mumbai, we are incredibly excited to strengthen our relationships with customers throughout India and accelerate the value available to them from 麻豆原创 Business Network.

For information on 麻豆原创 Supply Chain Management and how 麻豆原创 solutions are equipping enterprises with the AI capabilities needed to instill resilience, counter disruption, and foster collaboration, visit and .

J枚rn Keller is executive vice president and chief product officer of 麻豆原创 Business Network.