– Notice of upcoming merger –

1. It is intended to merge emarsys interactive services GmbH (Local Court of Charlottenburg, HRB 118447) as the transferring company with 麻豆原创 SE as the acquiring company by way of a simplified intra-group merger. The transfer of the assets of emarsys interactive services GmbH shall take effect internally as of January 1, 2026, at 12:00 a.m. (鈥�Merger Effective Date鈥�). From the Merger Effective Date until the time of the dissolution of emarsys interactive services GmbH pursuant to Sec. 20 para. 1 no. 2 UmwG, all acts and transactions of emarsys interactive services GmbH shall be deemed to have been conducted on behalf of 麻豆原创 SE.

   麻豆原创 SE is the sole shareholder of emarsys interactive services GmbH as of the date relevant for the application of the group exemption provision under Sec. 62 UmwG, namely the filing of the merger with the respective commercial register and the respective date of registration. A merger resolution by the acquiring company 麻豆原创 SE is not required pursuant to Sec. 62 para. 1 sent. 1 UmwG. Consequently, it is also not necessary to convene a general meeting of 麻豆原创 SE to approve the merger. For the same reason, neither a merger report, a merger audit, nor a merger audit report is required, Sec. 8 para. 3 sent. 3 no. 1 lit. a), Sec. 9 para. 2, Sec. 12 para. 3, Sec. 60 UmwG.

2. The shareholders of 麻豆原创 SE are hereby notified of their right to demand the convening of a general meeting to vote on approval of the merger if the shares held by the shareholders making such a demand together amount to one-twentieth of the share capital of 麻豆原创 SE (Sec. 62 para. 2 sent. 1, and para. 3 sent. 3 UmwG).
3. A resolution by the shareholders鈥� meeting of emarsys interactive services GmbH approving the merger agreement with 麻豆原创 SE is not required, since, as of the date relevant for the application of the intra-group exemption provision of Sec. 62 UmwG 鈥� namely, the filing of the merger with the respective commercial register and the respective date of registration 鈥� the entire share capital of emarsys interactive services GmbH is held by 麻豆原创 SE, Sec. 62 para. 4 sent. 1 UmwG.
4. The following documents are available as of the date of this announcement:
   1. The between 麻豆原创 SE and emarsys interactive services GmbH.
   2. The annual financial statements and, where required, the annual reports of the companies who are parties to the merger for last three fiscal years:

      
      
      
      
      
      

麻豆原创 SE, April 24, 2026

The Executive Board

At a glance

• Current cloud backlog of 鈧�21.9 billion, up 20% and up 25% at constant currencies
• Cloud revenue up 19% and up 27% at constant currencies
• Cloud ERP Suite revenue up 23% and up 30% at constant currencies
• Total revenue up 6% and up 12% at constant currencies
• IFRS operating profit up 17%, non-IFRS operating profit up 17% and up 24% at constant currencies

Christian Klein, CEO:

鈥淲e had a strong start to the year, with Current Cloud Backlog growing by 25% and Cloud Revenue up 27% at constant currencies. This performance is supported by our momentum in Business AI as we are already delivering real outcomes for customers today. We are growing faster than the market and are gaining share as customers expand across our Suite and with our AI solutions. At Sapphire, we will show how we are taking the next leap forward.鈥�

Dominik Asam, CFO:

鈥淲e delivered a solid start to the year, supported by disciplined execution in revenue and profitability. At the same time, we have remained focused on managing our cost base and maintaining profitability as we navigate an increasingly complex and uncertain macroeconomic and geopolitical environment.鈥�

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 to 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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Marcus Winkler, +46 (6227) 7-67497, marcus.winkler@sap.com, CEST
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For more information, financial community only:
Alexandra Steiger, +49 (6227) 7-60437, alexandra.steiger@sap.com, CEST

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.

Across many enterprises, HR, time, and payroll systems have evolved through years of growth, acquisitions, and regional customization. The result is a patchwork of disconnected tools, duplicated data, and manual handoffs that quietly slow decision-making and increase operational risk. These systems may still 鈥渨ork,鈥� but they carry a hidden cost on productivity, accuracy, and confidence, as expectations on HR continue to rise and AI becomes central to how work gets done.

Fragmentation is the hidden bottleneck behind 鈥渟low鈥� decisions

The impact of fragmentation isn鈥檛 always visible, but it shows up clearly in how decisions get made.

When decisions stall, leaders often point to approvals, governance, or external constraints. In reality, much of the friction happens earlier, when teams reconcile data across systems before decisions can even begin.

According to the research, organizations with unified HR foundations gained faster access to trusted workforce information, generating insights 60% faster and creating new position listings 53% faster. Rather than adding tools, these organizations removed friction by eliminating manual validation, shadow spreadsheets, and repeated checks to confirm data accuracy.

As organizations look to AI to accelerate workforce planning, surface risks, and guide decisions, this foundation becomes even more critical. AI is only as effective as the data it can access and trust. In disconnected environments, AI inherits the same inconsistencies, delays, and gaps, limiting its ability to generate reliable insights and recommendations.

Consider a simple workforce planning decision like headcount approval. In a fragmented environment, HR pulls data from one system, finance validates it in another, and managers reconcile discrepancies in spreadsheets. What should take hours stretches into days鈥攏ot because the decision is complex, but because the data is.

With real-time, consistent workforce information, leaders can act faster and with greater confidence in their decisions. More importantly, unified data allows AI to move beyond reactive reporting to deliver proactive, decision-ready intelligence.

Most payroll errors aren鈥檛 human鈥攖hey鈥檙e structural

Disconnected systems don鈥檛 just slow work; they also increase errors.

When employee data, time records, and payroll information live in different places, every handoff becomes an opportunity for mistakes. Manual reconciliation and corrective actions become routine, especially during high-pressure cycles like payroll close.

Organizations with unified platforms see a clear shift. Payroll error rates drop by 64% and payroll cycles are completed 44% faster by eliminating data gaps and automating validation across connected processes.

This is where AI begins to shift from reactive to preventative. With unified data, AI can identify anomalies before payroll runs, flag potential compliance risks, and continuously learn from patterns across the organization. Instead of fixing errors after the fact, HR and payroll teams can prevent them altogether.

That structural shift changes the nature of work for HR and payroll teams. Payroll teams saw a 21% productivity increase, while HR teams improved productivity by 14%, as time previously spent tracking down discrepancies, correcting entries, and responding to escalations was redirected toward oversight, compliance, and continuous improvement.

Fragmentation quietly erodes trust and limits AI adoption

When systems are fragmented, trust erodes quietly. Employees lose confidence when pay errors occur or self-service tools don鈥檛 reflect their reality. Managers hesitate to act when dashboards conflict. HR teams become intermediaries between systems rather than strategic partners to the business.

Integrated HR, time, and payroll systems reverse this dynamic. Employees gain easier access to self-service tools, with 28% more employees able to directly access HR and time entry platforms. Managers benefit from real-time visibility into approvals and team data. And HR teams regain credibility as the source of accurate, timely workforce information.

Over time, this trust compounds. When people trust the system, they use it. Increased usage improves data quality, and better data strengthens decision-making.

This foundation becomes even more important as organizations scale AI across HR. Employees and managers are far more likely to rely on AI-driven recommendations鈥攚hether for career growth, scheduling, or compensation鈥攚hen they trust the underlying data. Without that trust, even the most advanced AI capabilities remain underutilized.

Fragmentation doesn鈥檛 just slow execution鈥攊t narrows what leaders believe is possible, forcing decisions to be shaped by system constraints rather than business needs.

The cost of standing still

The cost of fragmentation isn鈥檛 just operational; it鈥檚 financial, and it compounds over time.

Across organizations studied, the average annual quantified benefit totaled US$649,400 per 1,000 employees supported, driven by productivity gains, reduced errors, faster cycles, and better business decisions. Over three years,organizations achieved a 284% return on investment, with a payback period of approximately 15 months.

Beyond these quantified gains, there is a growing competitive gap. Organizations operating on unified platforms are not only more efficient, but they are also better positioned to embed AI across the entire employee lifecycle, from hiring and onboarding to development and workforce planning. Those still operating with disconnected systems risk falling behind鈥攏ot just operationally, but strategically.

The real risk isn鈥檛 innovation

Innovation draws attention because it鈥檚 new, visible, and often disruptive. Fragmentation, by contrast, builds quietly in the background until it starts to limit how the organization operates. But as organizations ask HR to deliver more鈥攂etter insights, faster planning, stronger compliance, and improved employee experiences鈥攖he limits of disconnected systems become harder to ignore.

Modern HR outcomes don鈥檛 come from layering new tools on top of outdated foundations. They come from reducing complexity, unifying data, and creating consistency across the most essential people processes. This is where platforms like 麻豆原创 SuccessFactors are evolving鈥攏ot just to unify core HR, time, and payroll, but to embed AI directly into the flow of work. By combining a trusted data foundation with AI-driven insights and automation, organizations can move from reactive operations to predictive, insight-led workforce management.

The question isn鈥檛 whether organizations can afford to modernize HR. It鈥檚 whether they can afford to limit the impact of AI by building on fragmented foundations.

AI doesn鈥檛 transform HR on its own; it amplifies what鈥檚 already there. And without a unified, trusted core, even the most advanced AI will struggle to deliver on its promise.

Learn how leading organizations are reducing fragmentation and building a strong foundation for AI by unifying core HR, time, and payroll with .

*

Lara Albert is chief marketing officer for 麻豆原创 SuccessFactors.

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

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

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

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

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The marketer鈥檚 reality: ambition outpacing execution

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

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

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

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

AI accelerating the engagement divide 

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

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

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

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

New model for engagement built on trusted enterprise data

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

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

At the heart of this partnership:

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

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

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

Kevin Ichhpurani, President, Global Partner Ecosystem at Google Cloud

From prompt to performance: how agents work together for marketing

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

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

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

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

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

For example, a marketer can prompt:

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

And from there:

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

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

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

Clear business outcomes for marketing teams

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

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

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

Beyond campaigns: continuous engagement at enterprise scale

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

For more information about Gemini Enterprise, visit .

Visit the . Get 麻豆原创 news via  and .

About Google Cloud

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

About 麻豆原创

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

Note to editors:
To preview and download broadcast-standard stock footage and press photos digitally, please visit . On this platform, you can find high resolution material for your media channels.

For customers interested in learning more about 麻豆原创 products:
Global Customer Center: +49 180 534-34-24
United States Only: 1 (800) 872-1麻豆原创 (1-800-872-1727)

For more information, press only:
Mallory Kuno, +1 (425) 239-9362, mallory.kuno@sap.com, ET
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.
Please consider our . If you received this press release in your e-mail and you wish to unsubscribe to our mailing list please contact press@sap.com and write Unsubscribe in the subject line.

鈥淩eoassunto provides dedicated support for reintegration,鈥� Proietti Pantosti said. 鈥淥ur goal is to significantly reduce the rate of reoffending among first-time convicts.鈥� As processes for reintegration are complex and time consuming, he had the idea to set up an offline, server-based AI tool to help inmates with job applications as well as an AI agent to automate the complex tax paperwork for companies that offer jobs for inmates. But he and his organization didn鈥檛 have the skills or funds to create an AI prototype to realize his concept.

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A community of changemakers

This is when came in, a global support community for young social entrepreneurs and a long-standing partner of 麻豆原创. In 2025, this organization established a for social entrepreneurs and NGOs to experiment with AI capabilities and implement AI tools and features to fix one challenge common to all social enterprises: a lack of helping hands in combination with a large volume of small, sometimes repetitive tasks.

According to Matthias Scheffelmeier, co-founder of ChangemakerXchange, young changemakers are tackling the most pressing issues of our time but are often stretched and under-resourced. 鈥淲e believe helping them mindfully and ethically adopt AI tools allows them to focus on their key expertise and therefore scale their impact in the world,鈥� he said. 鈥淭o address this, the ChangemakerXchange AI program provides customized support, in-person gatherings, and a public toolkit to help young entrepreneurs navigate AI.鈥�

As the longest-standing corporate partner, 麻豆原创 has supported social enterprise ChangemakerXchange for more than eight years. Beyond just financial support from the company, 麻豆原创 employees joined local cohorts of social enterprises, shared knowledge on AI, and brainstormed how individual ideas could be brought to life.

ChangemakerXchange initiated the Possibilists, a global alliance for youth innovation. on the needs and challenges young change makers face with AI. The study showed that while 65% use AI almost daily, 70% lack knowledge on how to navigate AI tools proactively for their purpose.

From environment to politics

Entrepreneurs in the European cohort of the ChangemakerXchange AI program cover environmental, social, and political projects.

One of them, Romania-based social enterprise Station Europe, aims to make democracy accessible, especially for young people from rural areas. 鈥淲e empower young people to engage in participatory democracy, embrace creative activism, identify and address disinformation campaigns, and design policy recommendations that reflect their communities鈥� needs,鈥� said Alin Gramescu, president & co-founder of Station Europe. To support these goals, the organization launched a collaborative platform in 2024 called that allows young people to explore new formats of political participation, taking them right into the heart of the policymaking process. Participants in hands-on workshops learn how to start from an actual issue or need and create a policy recommendation鈥攚ith AI clustering and processing workshop findings. This results in recommendations for government authorities based on the input of thousands of young people.

鈥淎I will help connect policymakers and young people. Within one year, we condensed more than 1,400 papers from over 80 workshops,鈥� Gramescu said. 鈥淥pening the platform to additional countries will exponentially increase the volume of data we will be dealing with.鈥� When asked for the value ChangemakerXchange added for his organization, he said 鈥淚 knew what I wanted to build to manage this content, but I needed the step-by-step technical guidance to make it happen.鈥�

Education as foundation for progress

Education is often described as the cornerstone of progress, and for Alexia von Salomon, concept & learning designer at Education Innovation Lab, this belief is her daily motivation.

鈥淔or me, education is the foundation for social innovation,鈥� von Salomon said. As a leader in educational transformation, she sees a lack of relevant future skills conveyed at schools in Germany and aims as high as transforming Germany鈥檚 education system.

Besides conducting workshops at schools, she creates learning experiences for teachers and pupils, like the learning platform 鈥渄igital sparks for the future.鈥� To scale reach, Education Innovation Lab focuses on self-guided learning platforms and train-the-trainer sessions for school teachers.

von Salomon uses AI to co-create and validate new concepts. 鈥淭his helped me to be more creative and think outside the box,鈥� she said. 鈥淯sing AI for early testing how minors would interact with learning content and tools reduces the iterations we need before actually conducting tests in schools.鈥�

She said that being part of the ChangemakerXchange program not only gave her the opportunity to get to know the right people in the tech industry, but to shift her perspective on AI and increase her use of AI tools. Her personal goal is to shape a future where learning is not just about knowledge, but about empowerment and transformation. 鈥淔rom my perspective, key skills for minors in a future influenced by AI will be creativity and critical thinking鈥攖o use the opportunity AI offers without suffering from the potential negative impacts,鈥� she said.

Serving business and society

More than 1,500 social entrepreneurs in over 130 countries are part of ChangemakerXchange鈥檚 global community. 鈥淭rue innovation happens when changemakers challenge the status quo and create solutions that serve both business and society,鈥� Scheffelmeier emphasized. For him, social enterprises are not just businesses, but catalysts for inclusive growth and sustainable impact. 鈥淏y combining technology with the vision of social innovators, we can scale solutions that address global challenges and build a future where profit and purpose go hand in hand,鈥� he said.

The problem is not the promise of AI. It鈥檚 the gap between intelligence in the system and connection in the moment. Customers are increasingly disengaging because intelligence is not being applied where it matters most.

Technology, particularly AI, has fundamentally changed what customers expect. They assume brands can recognize them across channels, understand context in real time, and anticipate their needs. When that doesn鈥檛 happen, the miss feels less like oversight and more like indifference. Timing is off. Service lacks continuity, and personalization stops at the surface, despite all the data behind it.

While many enterprises are trapped in siloed systems and disconnected data, consumer expectations are growing. Brands that don鈥檛 deliver the expected experiences are quickly abandoned.

In addition, global socioeconomic factors are increasing rapidly and unpredictably, challenging bottom lines and making customer loyalty more critical than ever鈥攁t a time when consumers are less loyal than ever.听

When economies falter, companies usually take one of two approaches. Some hunker down, cut costs and staff, and hope to survive. Others zero-in on differentiators like to drive growth and boost profitability.

The importance of CX for key metrics like churn, retention, loyalty, new sales, and competitive differentiation is well-established, so not investing in customer experience could be considered akin to saying you are willing to let those mission-critical metrics falter.

The following 15 takeouts from 麻豆原创’s highlight some of the most common CX pitfalls and opportunities.

1. 82% of consumers say a brand has disappointed them

Modern customers do not go quietly into the bad experience night. A whopping 82% of consumers say a brand has disappointed them, even when the product itself meets their needs. The issue isn鈥檛 the product or service; it鈥檚 the experience of purchasing and post-purchase care.

This is the essence of the 鈥溾��: the distance between what customers expect in the moments that matter, and what brands are actually delivering.

2. 60% do not pay attention to brands anymore and 48% care more about experience

Consumer attention in a difficult economy has shifted from logos and taglines to experiences that feel useful, contextual, and personal. So, what鈥檚 a brand to do when 60% of consumers say they simply don鈥檛 pay attention to brands and 48% care more about the experience than the product?

This is where CX outcomes become clear: engagement is no longer about shouting louder; it鈥檚 about showing up better and building experiences powered by unified data and intelligent orchestration.

3. Left unread: only 16% of customers skim email headlines, while 29% read one or two sentences

Consumer behavior in the inbox shows just how fragile engagement is:

• Most consumers only read the subject line
• Others will read one to two sentences before deciding whether to delete or engage further

Combined with the fact that 58% of consumers think most marketing emails they receive aren鈥檛 relevant, brands are staring down a massive relevancy problem. Sending more emails into the engagement abyss doesn鈥檛 solve this problem, but gaining a holistic understanding of your customers as individuals does.

4. 37% do not think brands personalize to their needs

For well over a decade we鈥檝e been talking about the importance of personalization, but today 37% of consumers believe brands don鈥檛 personalize engagements to their needs. Surface-level personalization鈥攏ames in subject lines, basic segmentation鈥攊s no longer enough.

This aligns with our assessment that 79% of companies have low or moderate CEM scores, meaning teams can access portions of shared data and deliver basic personalization, but coordination across marketing, sales, service, commerce, and product teams remains limited. Experiences often feel disconnected, forcing brands to rely on short-term tactics rather than building deeper relationships.

Consumers expect real-time, behavior-driven personalization based on context, intent, and history, not just boiler-plate persona buckets. Customers can see and feel investments in personalization and it matters.

5. 46% say customer service feels too impersonal, while 41% believe brands do not understand them as a person

Considering how much data brands collect, it鈥檚 striking that nearly half of consumers (46%) say customer service feels too impersonal.

Customers are asking a simple, and valid, question: 鈥淚f you have all this information about me, why isn鈥檛 my experience better?鈥� When data doesn鈥檛 translate into empathy and action, it starts to feel like surveillance, not service.

With 46% of consumers saying service isn鈥檛 personal, it should be no surprise that a nearly equal amount (41%) believe that brands don鈥檛 understand them as a person. However, 34% agree that AI can help brands better understand them and what matters most to them.

This presents brands with a real-time opportunity: use AI and data to close the perception gap. Instead of just predicting purchases, enterprises should also be anticipating customer needs and reducing friction.

6. 78% of brands say they deliver seamless cross-channel engagement, consumers disagree

Seventy-eight percent of brands say their engagement strategies offer seamless multichannel experiences with glowing outcomes like increased CLV, retention, and advocacy, but consumers are simultaneously reporting little emotional connection and frequent disappointment. In fact, 44% say that brand interactions feel less personal and more generic than ever before.

The takeaway: internal dashboards can create a if not tied directly to real customer sentiment and behavioral signals across channels.

7. 54% of enterprises cannot access and use real-time data, and 66% still rely on third-party data

Fifty-four percent of enterprises can鈥檛 access and use real-time data. On top of that, 60% suffer from 鈥渄ark data,鈥� which is information that鈥檚 collected but not used throughout the customer journey.

Without real-time, connected data, brands are mostly flying blind. AI, personalization, and omnichannel orchestration don鈥檛 fail because the ideas or execution are wrong; they fail because the foundations are.

Although privacy regulations and legislation are increasing while third-party cookies decline, a majority (66%) of enterprises are still heavily reliant on third-party data. Simultaneously, 55% say their data is too unstructured to use effectively.

The lethal combination of overreliance on external data plus underutilized internal data keeps brands from building strong, first-party relationships rooted in trust and value.

8. 78% of brands say AI is essential for customer retention in 2026

AI is everywhere, and 78% of brands view AI as critical to retaining customers in 2026. However, 66% report they can鈥檛 use AI to optimize campaign performance in practice, while many also note they can鈥檛 utilize real鈥憈ime AI optimization in day鈥憈o鈥慸ay campaigns.

A quick translation of the above stats: an AI strategy is crucial, but execution is lagging because of fragmented systems, poor data quality, and integration issues.

9. Only 30% share engagement data with a CX or CRM platform

Despite the collective agreement that a comprehensive customer profile is important, only 30% of brands share their customer engagement data within a CX or CRM platform. This means that most brands are attempting to deliver personalized experiences without having a unified engagement core.

If engagement data lives in campaign tools, service systems, commerce platforms, and ERP, but never gets connected via CX or CRM, customers will feel every fracture along their journey.

10. 30% of consumers have used AI agents that act on their behalf

AI is not just an enterprise capability; it鈥檚 also a customer behavior. Thirty percent of consumers say they鈥檝e used AI agents to make decisions and act on their behalf when buying from brands.

This is a game-changer when it comes to engagement. Brands are now engaging not only with humans, but also with AI buyers that ruthlessly and continuously optimize for relevance and value. If your systems can鈥檛 keep pace, AI will select your competitor whose systems are operationalized for success.

11. When it comes to customer engagement maturity, 79% of brands have yet to integrate data, systems, and teams across their business; only two in five decision-makers see their departments as actually coordinated

The Customer Engagement Maturity (CEM) scoring model assesses how well brands align people, processes, and technology to deliver cohesive, intelligent experiences. Looking at the 麻豆原创 Engagement Maturity Index:

• 16% of brands reside at low maturity
• 63% sit in the moderate middle
• 21% have high maturity

Despite year-over-year progress, most organizations are stuck in developing or evolving mode, able to execute campaigns but not orchestrate truly connected, enterprise-wide engagement. And leaders agree, with only two in five decision makers believing there is effective collaboration across departments.

12. Just 21% of brands are high-maturity, and they are gaining ground against their competition

High-maturity brands rise above the competition because they connect data and intelligence across marketing, service, sales, commerce, and operations. They use AI and automation to deliver personalized, omnichannel engagement in real-time, at scale.

And the maturity gap is becoming a performance gap. As top performers turn real-time intelligence into growth, the cost of competing with them rises for everyone else.

13. Personalized means personal: 58% of consumers respond positively to localized content

Personalization is more than a word or industry term. It means actually understanding and empathizing with your customer, including their regional traditions and social norms.

When engagement is done right, consumers respond:

• 63% say their favorite brand delivers seamless, connected experiences across mobile, web, and in-store
• 58% value localized content and product recommendations
• 55% appreciate highly personalized content
• 50% believe their favorite brand uses data to make interactions better

Customers aren鈥檛 against data or AI at heart. However, they are opposed to wasted data collection and bad experiences. It鈥檚 the job of brands to provide a great CX. If that job isn鈥檛 taken seriously, you can bet that other brands are willing to roll up their sleeves to fill the gap.

14. 77% of businesses plan to invest in AI-powered engagement in 2026

When it comes to the future state, 77% of businesses plan to invest in AI-powered customer engagement in 2026, and 76% are investing in omnichannel engagement technologies. At the same time, 29% say their top priority is connecting customer and stakeholder data across marketing, sales, service, commerce, and ERP systems.

The signal is clear: investment alone won鈥檛 close the Engagement Divide. The winners will be the brands that invest in connection鈥攐f data, teams, and systems鈥攏ot just in tools.

15. 15% say seamless integration will be the biggest driver of success

Lastly, and possibly most importantly, 15% of businesses believe seamless integration of engagement systems will be the single biggest driver of success. While that may sound like a small number, it captures a critical strategic shift: engagement is no longer a marketing problem or a channel problem. It鈥檚 an enterprise discipline that depends on unified data, coordinated teams, and embedded AI.

Artificial intelligence provides an evolving service for businesses. Employing cloud-based systems that can store, analyze, and route data will be the differentiator for brands in the marketplace.

Loyalty is transactional, and driven by great CX and a connected enterprise

Digital engagement has raised the bar when it comes to customer expectations, with more demands and a plethora of competitive choices if a brand doesn鈥檛 deliver.

It鈥檚 not a big leap to state that better customer experiences increase customer loyalty, which in turn leads to more purchases, augmented product utilization, and increased brand affinity and sentiment. And let鈥檚 not forget that an enhanced CLV lowers customer acquisition costs.

After all, loyalty is transactional and forged by the experiences customers encounter. In my conversations with customers across the globe, it鈥檚 clear that only the brands with truly at the heart of their operations will retain and grow their customer bases in the enterprises of the future.

That ambition relies on a technology foundation that can consistently deliver those experiences at scale. For British-founded luxury fragrance brand Molton Brown, moving from legacy systems to 麻豆原创 Commerce Cloud provided a high鈥憄erformance platform built for peak鈥憇eason resilience and continuous innovation. The impact was immediate: 100% uptime during peak trading, even as volumes surged to one order every three seconds during major events.

This kind of reliability is increasingly critical as the moments that shape experience and loyalty expand beyond owned channels. As product discovery shifts to social platforms and AI鈥憄owered assistants, consistent content and availability help the brand remain visible and trusted wherever customers engage. 麻豆原创鈥檚 evolving agentic commerce innovations are designed for this reality, keeping products discoverable, credible, and actionable across both human and AI interactions.

Ultimately, technology and AI are not the goal鈥攖he experience is. The brands that succeed will be the ones that use AI to show up more human, not less, turning insight into relevance and automation into trust.

The future of CX is for companies that operationalize intelligence across the enterprise鈥攃onnecting data, systems, and teams so AI can orchestrate experiences, not just analyze them.

Manos Raptopoulos is global president of Customer Success Europe, APAC, Middle East & Africa, and a member of the Extended Board 麻豆原创 SE.

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

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

From AI insight to AI in execution

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

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

Orchestrating the supply chain end to end with AI

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

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

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

New AI agents redefining planning, service, and operations

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

Manufacturing

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

Assets & services

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

AI agents advancing logistics execution

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

Aligning workforce, logistics, and assets in real time

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

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

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

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

Regulatory readiness and what鈥檚 next

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

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

See it live at Hannover Messe 2026

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

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

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

As an F1 Ambassador, H盲kkinen represents the values that define both elite motorsport and modern enterprise technology: speed, resilience, teamwork, and continuous improvement. His presence at 麻豆原创 Sapphire highlights the strong connection between high-performance racing and the intelligent, data-driven world 麻豆原创 helps its customers navigate every day.

Performance meets innovation

During his career in Formula One, H盲kkinen became known for his focus, strategic thinking, and ability to perform under pressure鈥攓ualities that closely align with 麻豆原创鈥檚 Services and Support organization. At the Services and Support Center, attendees can experience how these same principles help businesses run faster, adapt with confidence, and stay ahead in an increasingly complex environment.

H盲kkinen will share personal insights from his time at the pinnacle of motorsport, offering perspectives on decision-making in high-stakes situations, the importance of teamwork, and the role data and technology play in driving performance.

Event highlights

Visitors to the Services and Support Center can look forward to several exclusive opportunities:

• Experience H盲kkinen live in the Formula One Racing Simulator at the Race of Legends.
  • May 13 in Orlando at 1:00 p.m.
  • May 20 in Madrid at 11:30 a.m. and 1:30 p.m.
• Join theater sessions featuring voices from the Mercedes-AMG PETRONAS Formula One Team.
  • May 13 in Orlando with听Michael Taylor听at 11:30 a.m. ()
  • May 20 in Madrid with听Laura Goodrick听at 11:00 a.m. ()
• Take part in exclusive interviews听with H盲kkinen.
  • May 13 in Orlando at 10:30 a.m. ()
  • May 20 in Madrid at 2:00 p.m. ()

A unique experience with 麻豆原创 Services and Support

The Services and Support Center at 麻豆原创 Sapphire is designed as a hub for inspiration and interaction. With H盲kkinen on-site, visitors can expect engaging sessions, real-world insights, and hands-on experiences that connect motorsport excellence with business innovation brought to life by 麻豆原创鈥檚 Services and Support organization.

Join us in Orlando and Madrid

Whether attending 麻豆原创 Sapphire in Orlando or Madrid, this is a unique opportunity to experience one of Formula One鈥檚 legends up close鈥攁nd to explore how 麻豆原创 helps organizations achieve peak performance.

Don鈥檛 miss the chance to meet H盲kkinen at the Services and Support Center and discover how the mindset of a world champion can inspire your business transformation journey.

Get our sessions into your personal agenda:

• 麻豆原创 Sapphire 2026 Orlando 
• 麻豆原创 Sapphire 2026 Madrid 

The announcement was made at HANNOVER MESSE 2026, where they showcased PacXplorer, a high-tech packaging machine from Uhlmann that serves as both an industrial demonstrator and a development platform.

PacXplorer: Connected Production in Industrial Practice

Developed through collaboration within Factory鈥慩, the PacXplorer brings together digital twins, condition monitoring, smart services and interoperable production solutions within a collaborative data ecosystem. Factory鈥慩 is a lighthouse project funded by the German Federal Ministry for Economic Affairs and Energy as part of the Manufacturing鈥慩 initiative. Its objective is to establish a decentralized data space for the capital goods industry, enabling secure and interoperable data exchange across companies and industries for equipment manufacturers and operators alike.

The machine is integrated into 麻豆原创 system landscapes and operated live. It demonstrates how industrial data can be used in a sovereign, interoperable and cross鈥慶ompany manner not as a theoretical model, but in real production operations. This creates transparency regarding asset condition, utilization and performance while laying the foundation for new data鈥慸riven services.

Service as a Key to Resilience

The value of this approach becomes particularly clear in service operations. Where production, data and operations are tightly interconnected, service plays a decisive role in ensuring asset availability, productivity and stable customer relationships. One often underestimated lever is spare parts service: delays lead directly to downtime and economic losses, especially in volatile market and supply situations. At the same time, these processes remain heavily manual in many industrial companies.

麻豆原创 and Uhlmann are deliberately advancing the further development of this area. An AI鈥憇upported process assists throughout the entire workflow from handling incoming inquiries and clarifying missing information to identifying the correct spare part and generating quotations. The approach integrates into existing 麻豆原创 service and sales processes and is closely aligned with real鈥憌orld business operations. The objective is fast, reliable and scalable customer service.

鈥淭oday, industry is less concerned with cost optimization than with decision鈥憁aking under uncertainty,鈥� says Dominik Metzger, President and Chief Product Officer, 麻豆原创 Supply Chain Management, 麻豆原创 SE. 鈥淲ith 麻豆原创 Business AI and integrated production and service solutions, we move decision鈥憁aking directly into business processes. This allows companies to identify risks early, respond with greater flexibility and remain operational even under unstable conditions.鈥�

Rethinking Value Creation Together

The collaboration between 麻豆原创 and Uhlmann illustrates a fundamental shift in industry: resilience is not achieved through additional buffers, but through better, faster and more-connected decisions across the entire value chain. Companies that manage their production and service processes in a data鈥慸riven way can respond more flexibly to change and secure their competitiveness over the long term.

Beyond HANNOVER MESSE, 麻豆原创 and Uhlmann will continue their innovation partnership. Following the event, the PacXplorer will be operated at the S.Factory in 麻豆原创 Experience Center Walldorf, serving as a platform for customers, partners and co鈥慽nnovation to continuously advancing industrial transformation.

Visit the . Get 麻豆原创 news via  and .

Media Contact:
Dana Roesiger, +49 6227 7 63900, dana.roesiger@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.

Image copyright: 漏Uhlmann Group

AI alone is no longer a differentiator: What matters is where intelligence鈥痮perates鈥痠nside of a business. As demand volatility increases, fulfillment windows tighten, and customer expectations鈥痳ise,鈥痮rganizations need more than insights or task鈥痑ssistance. They need intelligence inside quotes, product content, service interactions, and campaigns, guiding decisions as they happen and continuously adapting as conditions change.

This shift lays the foundation for a new generation of executional AI, where capabilities evolve from supporting users to actively鈥痬onitoring鈥痜lows,鈥痑nticipating鈥痳isk, and over time acting as intelligent agents within core customer-facing processes.

With the Q1 2026 release of鈥� solutions, 麻豆原创 advances this shift by bringing AI closer to day-to-day customer-facing execution across sales, service, commerce, and engagement. Intelligence now operates closer to where outcomes are realized鈥攈elping organizations protect revenue, reduce friction, and deliver consistent, trusted experiences at scale.

Below, explore more of the highlights from the Q1 2026 release. And for full sub-solution details, see recaps for听,听,听,听, and听.

Optimize revenue streams with confidence

Revenue becomes more reliable when customer intent is captured early and executed consistently across sales and commerce workflows. The execution depends on speed and accuracy: accurate product information, relevant content, and seamless handoffs from inquiry to quote creation. When these are disconnected, teams face delays, manual rework, and missed revenue opportunities.

From customer inquiry to executable quote

• Email to quote with AI:鈥疉utomatically add SKUs from a deal using opportunity and email data with the Microsoft Outlook add-in for 麻豆原创 Sales Cloud. Users can choose to generate a quote, and the quote is quickly created in 麻豆原创 Sales Cloud in just a few clicks. After review, sellers can hit send; it is that easy.  
• Deep research: Accelerate account planning and reviews by synthesizing 麻豆原创 Sales Cloud and 麻豆原创 Service Cloud data with external market intelligence. For example, the deep research capability can deliver a detailed brief that can be used to better understand the account, their industry, and other crucial information like news and SWOT. Sellers will be able to engage prospects and buyers more effectively while customers will have more relevant and personalized information delivered.  
• Media attachments for product descriptions: Use AI to extract details from product documents, such as manuals, spec sheets, and PDFs, and automatically generate or enrich product descriptions in 麻豆原创 Commerce Cloud. This accelerates catalog updates and improves product data quality so that shoppers, search engines, and agentic commerce are rich with the most accurate product descriptions鈥攅nsuring product descriptions are detailed, differentiated, and discovered.

Delivering鈥痳别濒颈补产濒别鈥痵ervice at鈥痵肠补濒别

• Digital Service Agent handoff鈥痜or case creation: Connect every step of the service journey from conversational AI self-service to field resolution so service teams can resolve customer issues鈥痜aster and provide personalized service engagements that build trust.鈥疷sing conversational cues, Digital Service Agent鈥痵ummarizes intent identification for ticket creation while capturing essential information required for handoff to underlying solutions like 麻豆原创 Service Cloud.
• : Give service teams a single, real-time command center in 麻豆原创 Service Cloud, consolidating cases, tasks, and service orders into one view with visual workload insights so agents can prioritize faster, stay on top of commitments, and resolve more issues per day. 
• Retail Intelligence (麻豆原创 Early Adopter Care): Announced at NRF, Retail Intelligence provides one closed-loop, AI-enhanced retail supply chain planning environment that ties together planning, execution, and engagement. The result: human and agentic teams that don鈥檛 just execute tasks but reshape strategies, reimagine retail supply chain planning, and master autonomous growth and lasting differentiation.
  Learn more at the session.

Orchestrating engagement across the customer life cycle

Customer engagement spans browsing,鈥痯urchasing, fulfillment, and service across multiple channels. 麻豆原创 CX connects engagement directly to operational context.鈥�&苍产蝉辫;

• delivers鈥痯ersonalized, AI-personalized communications and interactions across every channel powered by connected customer and operational data all fully integrated across 麻豆原创. Teams鈥痗an鈥痙eliver consistent, intelligent engagement that builds loyalty and drives鈥痓usiness鈥痠mpact.

• :鈥疎xtend conversational analytics to SMS campaigns. A new data context model narrows analysis to the right dataset, returning faster, more precise answers to natural language questions, such as 鈥淲hat was SMS revenue last month?鈥�

• :鈥疨redictively鈥痠dentify鈥痗ontacts鈥痺ho are鈥痩ikely in the next鈥�30 days to engage,鈥痓ecome inactive, or remain inactive,鈥痵o marketers can target outreach鈥痺here it will deliver the strongest results.鈥��&苍产蝉辫;

Accelerate transformation鈥痺颈迟丑鈥痶丑别鈥痑dvanced success plan鈥痜or 麻豆原创 CX

To鈥痑ssist鈥痗ustomers鈥痮n their鈥痶ransformation鈥痡ourneys, 麻豆原创 launched the new Advanced Success Plan in the鈥�. This will鈥痟elp customers increase the value of individual applications, accelerate cloud transformation across 麻豆原创 Business Suite,鈥痑nd enable consistent adoption of new innovations and 麻豆原创 Business AI.

With expanded coverage with additional 麻豆原创 CX solutions, including and , the advanced offering is comprised of three powerful elements:

• Success expert: Regular 麻豆原创 expertise driving strategic customer outcomes
• Adoption guidance: Structured, AI-driven enablement accelerating adoption
• Activation and optimization services: Hands-on services to maximize performance and impact

Check out the鈥痺ebinar鈥痶o learn how the new service offering unlocks more of the transformative value of 麻豆原创 solutions:鈥�.

Intelligence where execution happens鈥�&苍产蝉辫;

With 麻豆原创 Customer Experience, AI moves beyond isolated鈥痑ssistance鈥痶o鈥痮perate鈥痙irectly within business execution flows. Intelligence is embedded where work happens鈥攊nside quotes, product content, service interactions, and campaigns鈥攈elping organizations respond in real time and deliver consistent customer outcomes at scale.

Learn more about 麻豆原创 CX in Q1鈥�2026鈥�&苍产蝉辫;

Read the 麻豆原创 Help documentation to get started with these new capabilities.鈥�&苍产蝉辫;

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

With the successful , 麻豆原创 has reached an important milestone. This achievement strengthens the foundation of the 麻豆原创 Sovereign Cloud portfolio in one of the most security-conscious markets in the world.

IT-Grundschutz confirms secure operation of 麻豆原创鈥檚 German data center facilities

IT-Grundschutz is the German Federal Office for Information Security鈥檚 (BSI) structured security methodology, and serves as a reference framework in public tenders and supplier assessments.

The certification on the basis of IT-Grundschutz confirms that the secure operation of the physical infrastructure of 麻豆原创鈥檚 German data centers has been positively assessed against Germany鈥檚 defined security requirements. It validates that physical protections, environmental safeguards, and facility-level operational processes meet BSI expectations.

In short: The secure facility operation of 麻豆原创-owned data centers in Walldorf/St. Leon-Rot, Germany, has been independently audited and confirmed against Germany鈥檚 national security methodology.

Strengthening one of 麻豆原创鈥檚 key sovereign delivery options: 麻豆原创 Cloud Infrastructure

The IT-Grundschutz certification strengthens one of 麻豆原创鈥檚 key sovereign delivery options in Germany: 麻豆原创 Cloud Infrastructure.

麻豆原创 Cloud Infrastructure is an Infrastructure-as-a-Service (IaaS) platform, operated in 麻豆原创-owned data centers and co-locations worldwide. In the Walldorf/St. Leon-Rot region in Germany, these data centers are owned by 麻豆原创, a German company, operated by approved personnel with the required security clearance, and designed for high availability, scalability, and stringent security requirements.

These data centers are designed to support GDPR-compliant data processing and to meet heightened regulatory and security requirements in Europe and Germany, including standards relevant to critical infrastructure and the processing of sensitive and classified workloads.

In three independent availability zones across separate data centers, interconnected via 麻豆原创-owned fibre infrastructure and using BSI-authorized German security hardware components approved for processing information classified VS-NfD, this foundation is complemented by certifications such as C5 Type II, KRITIS/NIS 2, TSI Level 3 (extended), ISO 22301, SOC 1 Type 2 and SOC 2 Type 2, SOX, EN 50600 and ISO/IEC 22237 (AC 3), and the German federal data center requirement catalogue.

On top of this, 麻豆原创 Cloud Infrastructure provides:

• An open鈥憇ource鈥慴ased, API鈥慺irst IaaS platform: Offering self鈥憇ervice provisioning, automation, and consistent resource management across deployment models
• A Kubernetes鈥慴ased cloud environment: Enabling cloud鈥憂ative workloads, container orchestration, and modern development patterns
• Open standards and proven open source technologies: Leveraging components used, developed, and refined for more than a decade in sensitive, large鈥憇cale environments
• Optimization for 麻豆原创 cloud services: Supporting aligned operations, integrated security, and efficient execution of 麻豆原创 workloads
• Support for 麻豆原创 and third鈥憄arty applications: Allowing 麻豆原创 and customer-specific workloads to run on one coherent, secure, and compliant infrastructure

麻豆原创 Cloud Infrastructure is an 麻豆原创-developed and 麻豆原创-operated IaaS platform for 麻豆原创 workloads and customer applications, ranging from global cloud scenarios to environments with high sovereignty and regulatory requirements, including an offering for the processing of classified information up to VS-NfD level in Germany. With the 麻豆原创 Sovereign Cloud portfolio, it enables both sovereign 麻豆原创 cloud services as well as the operation of customer workloads in a sovereign environment. At its core, it combines secure application operations with 麻豆原创 Cloud Infrastructure, which is designed for regulatory and operational control.

Sovereignty through choice and control with 麻豆原创 Sovereign Cloud

Digital sovereignty is frequently framed as a question solely of vendor origin, data residency, or the reduction of technical dependency. In practice, though, it is about demonstrable control. At 麻豆原创, we frame sovereignty across four interconnected capabilities:

1. Data sovereignty: 麻豆原创 stores data in local data centers or approved countries, avoiding unauthorized cross-border transfers and meeting critical infrastructure requirements.
2. Operational sovereignty: Sensitive operations stay local. Administration and maintenance are performed only by authorized personnel 鈥� either nationally approved personnel or nationals of an approved country 鈥� with the required security clearance.
3. Technical sovereignty: Control planes are hosted locally, with strict separation enforced through encryption or dedicated infrastructure.
4. Legal sovereignty: Governance stays aligned. Cloud providers must be based locally or in approved countries, and foreign authorities must mitigate ownership, control, and influence risks.

麻豆原创 Cloud Infrastructure meets these requirements. On this basis, data, operations, architecture, and legal control are brought together under clearly defined requirements.

Importantly, 麻豆原创 Cloud Infrastructure is embedded in 麻豆原创鈥檚 broader approach to offering customers choice in sovereign cloud. Different customers face different regulatory, operational, and transformation realities. Sovereign requirements cannot be met with a single model.

麻豆原创 Sovereign Cloud offers a range of delivery options to address different customer needs. Depending on specific requirements, customers can choose between the following options:

• 麻豆原创 Cloud Infrastructure: 麻豆原创鈥檚 IaaS platform is based on open-source technologies and is operated in 麻豆原创 data centers worldwide. Depending on the selected operating model, customer data processing and storage can be restricted to defined regions, for example, within the EU or exclusively in Germany, to meet specific data protection and compliance requirements.
• 麻豆原创 Sovereign Cloud On-Site: With 麻豆原创 Sovereign Cloud On-Site, 麻豆原创 provides and manages the full 麻豆原创 technology stack in a customer-designated data center, from hardware to 麻豆原创 Cloud Infrastructure and the 麻豆原创 Sovereign Cloud portfolio. It combines physical control on site with our operational expertise, for full autonomy while maintaining 麻豆原创鈥檚 support and compliance standards.
• Sovereign hyperscaler-based delivery models: 麻豆原创 partners with premium hyperscalers in specific markets to provide customers the ability to swiftly scale their resources based on their needs. This flexibility, paired with seamless integration, enables customers to innovate faster while maintaining operational efficiency.
• National sovereign cloud platforms such as Delos Cloud: For public sector customers in Germany, Delos Cloud combines hyperscaler technology with sovereign ownership and a nationally defined operating model, helping ensure regulatory alignment and clearly structured operational control.

麻豆原创 enables customers to select the model that aligns with their regulatory requirements, risk profile, and operational strategy.

Sovereignty is built, not declared

For customers, digital sovereignty is not a theoretical aspiration; it is an operational requirement that must function under real-world conditions. The IT-Grundschutz certification of 麻豆原创-owned data centers in Germany marks an important step in that direction.

As regulatory expectations evolve and sovereign requirements become more differentiated, 麻豆原创 continues to enable customers to choose the sovereign setup that aligns with their obligations and risk profile.

Sovereignty is ultimately measured by the ability to operate systems securely and reliably. With 麻豆原创 Cloud Infrastructure, that capability is deliberately embedded into the operating model.

Martin Merz is president of 麻豆原创 Sovereign Cloud.
Jonathan Bletscher is head of Global Cloud Infrastructure & Delivery for Global Cloud Operations at 麻豆原创.

麻豆原创 CEO Christian Klein and CFO Dominik Asam will host a financial analyst call to review first quarter results.

Media representatives may may listen in on the virtual analyst via on April 23, 2026, at 11:00 p.m. CEST/5:00 p.m. ET.

According to the World Economic Forum鈥檚 Future of Jobs Report, analytical thinking ranks as the top sought-after skill in the job market. Yet traditional teaching often struggles to connect theory with practice in a way that truly engages students.

It鈥檚 not a shortage of content or tools; rather, it is a gap in relevance and inspiration that leaves students disengaged from the very skills that could define their careers. Learning needs to be anchored in real-world data, meaningful contexts, and hands-on experience that sparks curiosity and excitement. The best learning doesn鈥檛 come from memorizing concepts, it comes from doing, especially when theory is tied to a topic that learners care about.

This is where , in collaboration with the NHL, comes in.

We have created a new Business Builders game that brings hockey into the classroom鈥攏ot just as a sport, but as a rich, real-world data environment for teaching analytics and critical thinking.

In this latest edition of games under the Business Builders umbrella, students take on the role of a hockey analyst responsible for identifying the factors that drive goal scoring using real NHL Game data.

鈥淗ockey is fast, dynamic, and full of rich data, a perfect environment for teaching critical thinking,鈥� said Brant Berglund, senior director of Coaching and GM Technology at the NHL. 鈥淟everaging a strategic partnership with NHL, 麻豆原创, and HEC Montr茅al, we鈥檝e created a pathway for universities to access approved NHL.com data for academic initiatives, without compromising the integrity of the League鈥榮 data.鈥�

Through our collaboration with the NHL, we provide a learning platform for educators that is full of authentic data students can relate to. The NHL generates just under 1.5 million data points per game, including about 120 shot attempts, 1,000 passes, and 5,000 puck touches鈥攔aw material for deep, practical analysis.

Business Builders can ignite an interest in STEM and help students build real data skills. The hockey-focused game was developed by the team at ERPsim Lab at HEC Montr茅al led by Prof. Pierre-Majorique L茅ger, as well as the support of academics from other universities. This reinforces a core principle of Business Builders: It is created by educators, for educators.

鈥淲hen the question feels meaningful, learners lean in, stay focused, and keep pushing forward,鈥� L茅ger explained. 鈥淔or students in sport management or business management, real sport data can also elevate the learning experience. It adds context, complexity, and constraints that traditional teaching methods cannot provide. Students learn to judge what truly matters, justify their decisions, and manage trade-offs. This develops professional judgment, confidence in analytics, and the ability to communicate strategy and decisions clearly. These skills translate directly to real careers in sport management and business.鈥�

Students explore questions such as:

• What sets top NHL scorers apart?
• Does shot angle affect scoring?
• Which NHL players lead in goals scored, and from what distances?
• Which shot speeds and shot types yield the highest goal conversion rates?

By analyzing these scenarios with 麻豆原创 Analytics Cloud, students learn to interpret visualizations, tell compelling data stories, and sharpen their data-driven decision-making and critical-thinking skills.

For professors, the platform is equally powerful. Business Builders supports active learning at scale by enabling educators to manage and evaluate larger groups more efficiently while gaining visibility into student engagement. This makes grading easier, supports discussion-based learning, and helps instructors understand how students interact with data.

By introducing a modern, meaningful learning experience鈥攆ar more dynamic than slides alone鈥攑rofessors bring real-world relevance into their classroom and elevate the impact of teaching.

麻豆原创鈥檚 role in this collaboration reflects its broader commitment to education, skills development, and preparing students for the future workforce. In a rapidly changing world shaped by AI, data, and digital transformation, access to practical learning tools that build real competencies is essential. Through the 麻豆原创 University Alliances program, 麻豆原创 works with educational institutions around the world to help bridge the gap between academic theory and real-world practice.

鈥淎ccess to business software and real data is essential for preparing students for the future,鈥� said Dr. Katharina Schaefer, head of Academic Partnerships at 麻豆原创. 鈥淲ith free learning platforms like Business Builders, we empower educators to bring enterprise analytics into the classroom and help students develop the skills that are increasingly in demand across industries, and in a world where data and AI define the competitive advantage. Today鈥檚 learners need more than conceptual understanding; they need practical experience with real software and real data to build confidence and readiness for work life.鈥�

One of the educators closely involved in shaping this new game is Prof. Olivier Caya from the University of Sherbrooke, who contributed his perspective as both a faculty member and practitioner.

鈥淲hat makes this experience so powerful is that it all happens in 麻豆原创 Analytics Cloud, the same solution used by thousands of organizations worldwide,鈥� Caya said. 鈥淭his creates a strong connection between what students do in the classroom and what they will encounter in professional practice. Students are not working with software detached from reality; they are developing skills with the same software used in real business environments.鈥�

The result is a learning experience that is fun, interactive, and relevant. Educators can stand out with a state-of-the-art platform that connects passion with pedagogy, while students gain highly sought-after skills using real software from a global technology leader.

Business Builders is provided free of charge to educators and students and includes access to 麻豆原创 Analytics Cloud. It is designed from beginner-friendly introductions to more advanced analytical challenges optimized for master鈥檚-level courses.

After using Business Builders, students can deepen their analytical knowledge through access to , a free learning platform that offers guided learning content, practice systems, and up to two 麻豆原创 certification exam attempts per year, helping them to boost their career opportunities even further.

Business Builders is about connecting passion with education. Together with academic and industry partners, 麻豆原创 is making analytical thinking tangible and memorable鈥攅mpowering the workforce of tomorrow with the skills that matter most today.

In this context, innovation is not about experimentation on the sidelines. It is about building the operational foundation required to remain effective, trustworthy, and competitive under pressure.

For many years, enterprise technology focused primarily on optimization, improving efficiency, reducing cost, and increasing speed. These goals still matter, but they are no longer sufficient for business success. Today, leaders must ensure that their organizations can adapt in real time, integrate seamlessly across ecosystems, and maintain control over their most critical data and processes.

This shift, from readiness for known scenarios to resilience in the face of the unknown, is what unites the 19 winners of the 2026 麻豆原创 Innovation Awards.

From technology adoption to institutional capability

Earlier this month, we announced the finalists for this year鈥檚 awards. Today, it is my privilege to recognize the winners, organizations that have moved decisively from ambition to execution.

What distinguishes these companies, public institutions, and organizations is not the novelty of the technologies they use, but the way they apply them. They treat business processes as strategic assets. They design for interoperability, transparency, and trust. And they understand that digital transformation is ultimately about strengthening institutional capability, not simply deploying software.

Across hundreds of submissions, the winning projects consistently demonstrated three priorities that are increasingly inseparable:

• Operational effectiveness, where strategic intent is translated into measurable outcomes
• Control and sovereignty, ensuring compliance, security, and ownership of data while operating at global scale
• Ecosystem orchestration, connecting partners, suppliers, and stakeholders to reduce fragmentation and increase responsiveness

By leveraging the depth and breadth of the 麻豆原创 portfolio, from cloud ERP and business AI to industry solutions and 麻豆原创 Business Technology Platform, these organizations have shown that stability and innovation are no longer mutually exclusive.

The 2026 麻豆原创 Innovation Award winners

The following organizations represent the action side of transformation. Each demonstrates how disciplined execution and a clear architectural vision can deliver tangible value for customers, employees, and society.

AI Excellence

Harnessing 麻豆原创 Business AI to fundamentally improve productivity and decision-making:

• Martur Fompak International

Cloud ERP Champion

Embracing cloud ERP as the backbone for long-term agility and scalability:

• PricewaterhouseCoopers (PwC)
• Vedanta Limited

Customer Experience Innovator

Redefining customer engagement through integrated, cloud-based business processes:

• IBM

Financial Futurist

Modernizing financial operations to increase transparency, resilience, and trust:

• Ministry of Defence of Ukraine

Industry Leader

Applying multiple 麻豆原创 solutions to transform business models, collaborate across ecosystems, and generate societal or environmental impact:

• Nestl茅 (Consumer Industries)
• Piller Blowers & Compressors GmbH (Discrete Industries)
• Prysmian (Energy and Natural Resources)
• City of Madrid (Public Services)
• Accenture (Service and Financial Services Industries)

People Experience Pioneer

Reimagining the employee experience through data-driven and human-centric transformation:

• Capgemini

Procurement Visionary

Using automation and insight to strengthen spend control and supplier collaboration:

• TASNEE (National Industrialization Company)

Services Superstar

Accelerating business outcomes through the effective use of 麻豆原创 services:

• Federal Tax Authority, United Arab Emirates

Supply Chain Catalyst

Building resilient, compliant, and transparent supply chains:

• Sasol
• Ericsson

Sustainability Hero

Leveraging data to enable responsible decision-making and support a more sustainable economy:

• ArcelorMittal SA

Technology Pathfinder

Solving complex business challenges with measurable impact using 麻豆原创 Business Technology Platform:

• Promocean The Netherlands BV
• Land O鈥橪akes, Inc.

Transformation Impact

Driving enterprise-wide change and strengthening long-term transformation capability:

• KPMG International

Recognition and responsibility

All of our winners have set a clear benchmark for what resilient, future-ready organizations look like in practice.

We look forward to recognizing their achievements at 麻豆原创 Sapphire, where customers and partners from around the world will come together to share insights, compare experiences, and continue shaping the ecosystems that underpin global business.

Looking ahead

The 麻豆原创 Innovation Awards are more than just a moment of recognition. They signal that digital sovereignty, operational resilience, and responsible innovation are achievable across industries, sectors, and regions. The organizations recognized this year demonstrate what is possible when technology choices are guided by clear priorities and long-term thinking.

May these examples encourage others to take the next step, moving with confidence from readiness to resilience.

Thomas Saueressig is Chief Customer Officer and Member of the Executive Board of 麻豆原创 SE.

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, our new user experience, is gaining momentum and driving significant impact for our customers. Customers are already efficiency, enhancing processes, improving , and .

Joule is now live across 35 solutions and will continue to meet our customers where they are: across the applications they use, with a firm understanding of their business context and data. That鈥檚 why in Q1 we are embedding Joule into more applications鈥攆rom 麻豆原创 Datasphere, where it can now execute tasks or explain specific functionalities, to 麻豆原创 Intelligent Clinical Supply Management, where users can use natural language to retrieve critical data and navigate to relevant applications.

Joule Agents, such as the Tender Analysis Agent, are boosting customer revenue growth by extracting critical requirements and flagging risks in complex documents. While project managers in 麻豆原创 S/4HANA Cloud Public Edition are saving time setting up projects with the new Project Setup Agent. Plus, there are many more agents to discover below.

Agents are becoming a key new user鈥攁nd enabler鈥攐f enterprise software, joining humans as the only other non-deterministic operators while simultaneously expanding enterprise software鈥檚 scope and usefulness. Our agents will continue to deliver trustworthy, repeatable, and auditable results every time.

We now have over 30 specialized agents and more than 2,500 Joule Skills. The agent-to-agent protocol means our agents work across 麻豆原创 and non-麻豆原创 systems. As the number of agents grows across both, 麻豆原创 AI Agent Hub already today provides customers with the essential infrastructure and guardrails to manage, govern, and discover agents in this new ecosystem.

Some highlights from Q1 2026:

• 麻豆原创 Joule for Consultants is a conversational AI solution that provides expert guidance on cloud transformations, drawing on 麻豆原创鈥檚 knowledge base. To improve trust and traceability, citations are now displayed in a dedicated side panel and can be grouped for clarity. Administrators can enable web search, allowing Joule to draw from public content while maintaining clear source attribution. For tailored answers to problems where the system may not have customer-specific documentation, consultants can now upload up to 10 PDF or text files directly into the chat. This is further enhanced by the inclusion of content from the 麻豆原创 Enterprise Architecture Reference Library, which provides more complete and accurate answers to complex queries. Get started here.
• 麻豆原创 Business AI for supply chain minimizes disruptions and simplifies planning. The Project Setup Agent allows project managers to rapidly establish new projects by drawing on data from past initiatives. 麻豆原创 Integrated Business Planning users can now generate complex formulas in Microsoft Excel with natural language. 麻豆原创 Digital Manufacturing can distill complex manufacturing issues into clear descriptions. Joule is also helping 麻豆原创 Integrated Product Development users create problem reports and requirement models with simple, natural-language commands. Explore more below.
• 麻豆原创 Business AI for finance offers greater efficiency and insight across critical processes. Joule now translates complex e-invoicing errors into plain language. The Dispute Resolution Agent automates root-cause analysis for invoice disputes, while payment advice processing significantly reduces document processing time. Unstructured data, such as PDFs, can now be automatically transformed into sales orders, and accountants can access natural language explanations for complex fixed asset calculations. Users can personalize their home page and easily understand system errors using natural language across 麻豆原创 S/4HANA Cloud Public Edition. Learn more below.
• 麻豆原创 Business AI for procurement and customer experience enhances the entire commercial journey with new capabilities. In procurement, automated statement of work (SOW) creation in 麻豆原创 Fieldglass reduces the time to define deliverables. The Catalog Optimization Agent means e-commerce managers can continuously improve product data quality. In retail, managers can get instant, conversational answers from Joule on order management data. There’s so much more to learn below.
• 麻豆原创 Business AI for IT and developers puts the latest tools and greater control directly into the hands of developers and data professionals. Joule is now generally available in 麻豆原创 Datasphere, enabling users to navigate the platform, get answers, and execute tasks using simple conversational language. The generative AI hub in AI Foundation continues to expand, offering developers access to the newest models, including OpenAI GPT 5.2, Gemini 3.0 Pro, Anthropic Claude Opus 4.6, and Claude Sonnet 4.6. Developers also gain greater power through enhancements such as advanced prompt optimization, metadata filtering, and declarative orchestration configurations in the prompt registry. Additionally, 麻豆原创 Document AI now offers more granular control with custom confidence thresholds and expanded document support. Dive into everything below.
• 麻豆原创 Business AI for industries delivers specialized intelligence to solve unique business challenges. Sales teams can accelerate their response process with the new Tender Analysis Agent, which automates the review of complex RFQ documents to improve win rates. Joule now works with 麻豆原创 Commodity Management to turn verbal or written negotiations directly into detailed draft deals. In life sciences, clinical supply professionals can use predictive analytics to reduce inventory waste costs, and Joule dramatically cuts information search time. 麻豆原创 Self-Billing Cockpit automates invoice data extraction from any format, significantly reducing manual processing time. Discover more for industries below.
• 麻豆原创 Business AI for business transformation management provides the critical insights needed to navigate and accelerate organizational change. Joule is now in 麻豆原创 Signavio, enabling natural-language searches that cut information discovery time. Business process model and notation simulations in 麻豆原创 Signavio provide clear, actionable summaries directly within process diagrams. Meanwhile, enterprise architects can leverage guidance in 麻豆原创 LeanIX to surface actionable insights directly from their architecture inventory, accelerating transformation execution and reducing the time to uncover them. Read more about transformation management below.

Joule

Joule, enhancements

User experience is improved by streamlining startup times and introducing cross-thread search functionality that lets end users find information across all conversation threads without manually checking individual histories. The document grounding capability has also seen a substantial upgrade, now supporting seamless integration with Google Drive.

To set up, see: , , and .

Furthermore, scalability has been greatly improved, as the system now supports up to 8,000 documents per pipeline, enabling large-scale data repositories to be processed and utilized efficiently.

For more information, see .

麻豆原创 Joule for Consultants, enhancements

Enhanced Citation Visibility
麻豆原创 Joule for Consultants has improved how citations are displayed for all identified sources returned by the product. Citations have been relocated to the right side in a dedicated panel for clearer visibility, and now also include public web search results when applicable (see below).

A new grouping feature has also been added, allowing citations to be grouped. This update provides users with a more transparent view of where information originates, strengthens trust, and improves traceability across all responses.

To see the sources and panel, click the sources button below each message; the panel will open on the right, showing all grouped sources.

Enable Web Search
Administrators can now enable/disable web search via the control panel for all assigned end users in 麻豆原创 Joule for Consultants.

When enabled, 麻豆原创 Joule for Consultants will consider public web content in its reasoning and cite relevant public sources in responses when they contribute to the answer. This enhancement gives organizations greater flexibility and transparency by enabling broader coverage of information while maintaining clear source citations for all sources used.

File Uploads in the Joule Message Input
End-users can now upload up to 10 files directly from the conversational message input box and reference them throughout the entire conversation.

Supported file types include PDF and TXT. Each file should be no more than 10 MB/600K characters; for PDFs, an approximation. A 100-page limit applies; if your file is larger, split it into multiple documents. Image files are currently not processed and will be ignored. We are working diligently to make this feature even more useful to end users. This enhancement enables richer, context-aware interactions by allowing you to incorporate your uploaded documents into its conversational responses throughout the session. Please be aware that the standard data privacy terms apply. See also the help documentation for additional information on the free user quota.

Content: 麻豆原创 Enterprise Architecture Reference Library
麻豆原创 Enterprise Architecture Reference Library data has been ingested and is now available for use in conversations. As more data is added, relevant portions may be included in 麻豆原创 Joule for Consultants鈥� responses, enabling more complete, accurate, and context-rich answers to user queries. Since 麻豆原创 Enterprise Architecture Reference Library content cannot be link-referenced, you won鈥檛 see the additional content listed under sources, even though it will be referenced.

Get started .

SECTION

麻豆原创 Business AI for supply chain

Project Setup Agent
Beta release

Project managers can now rapidly establish new projects by drawing on data from similar past initiatives. The agent bypasses complex interfaces and reduces reliance on the project management office (PMO) to facilitate the swift allocation of key resources needed to launch projects effectively. With a 10% reduction in project creation time, 16% faster resource allocation, and 30% less time spent reworking projects due to incorrect templates, teams can shift focus from operational coordination to improving project profitability and driving efficiency.

Get started .

麻豆原创 S/4HANA Cloud Private Edition, AI-assisted retrieval of equipment information in service management
General availability

Service managers using the AI-assisted retrieval feature in 麻豆原创 S/4HANA Cloud Private Edition gain a complete 360-degree view of customer equipment. The feature provides instant access to warranty information and a full history of service transactions, complemented by an AI summary and actionable recommendations. This allows service managers to more efficiently oversee service schedules, reduce potential downtime, and ensure customer equipment operates at peak performance.

Get started .

麻豆原创 S/4HANA Cloud Public Edition, AI-assisted input recommendations for returns order creation
General availability

Returns clerks can accelerate the creation of customer returns with data field recommendations powered by historical data. This feature analyzes past return documents with similar process variants to automatically suggest the most common input values and return reasons, minimizing manual data entry and reducing errors. Organizations benefit from a one percent reduction in data management costs and a five percent decrease in business and operations analysis expenses, enabling returns teams to process orders more efficiently while maintaining accuracy.

Get started .

麻豆原创 Integrated Business Planning, AI-assisted MRO inventory analysis
General availability

Inventory planners get a new analytical assistant in the MRO inventory analysis feature for 麻豆原创 Integrated Business Planning. The feature accelerates root cause analysis by generating clear, natural-language summaries that explain the key drivers behind recommended safety stock and reorder points. By translating complex calculations into understandable insights, this capability enables planners to reduce time spent analyzing inventory runs by 30%, leading to faster adoption of outputs and ensuring that inventory parameters align with strategic business goals.

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麻豆原创 Integrated Business Planning, add-in for Microsoft Excel, AI-assisted planning
General availability

Supply chain planners can now simplify their work with a new AI-assisted planning add-in for Microsoft Excel. Instead of manually creating complex formulas or formatting rules, which often require technical expertise, they can simply describe their needs in natural language, and the system automatically generates the correct syntax. This intuitive way of interacting with the system removes technical barriers and improves a planner鈥檚 efficiency by 10%, freeing them to focus on strategic analysis rather than implementation details.

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麻豆原创 Integrated Business Planning, AI-assisted system security check
General availability

Supply chain planners and security analysts gain a robust way to assess system configurations against established security recommendations. The feature evaluates compliance states and provides clear guidance on required adjustments, helping administrators identify and address potential gaps while aligning configurations with 麻豆原创 best practices. Organizations can expect a 27% increase in compliance with hardening guidelines and a 32% reduction in the effort required to meet security recommendations. This feature strengthens the protection of sensitive data and reduces the risk of security breaches.

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麻豆原创 Integrated Product Development, AI-assisted problem report creation
General availability

Maintenance engineers can simplify the creation of formal problem reports by leveraging AI capabilities in 麻豆原创 Integrated Product Development. By describing an issue in their own words to Joule, it intelligently extracts key details like the problem name, tags, and priority, and then automatically generates a structured report. This streamlined process dramatically reduces manual data entry and ensures all reports are consistent and compliant with organizational standards, improving overall efficiency.

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麻豆原创 Integrated Product Development, AI-assisted requirements model creation
General availability

Requirements managers now have a more direct path to creating requirement models within 麻豆原创 Integrated Product Development by using natural language commands with Joule. This feature allows them to initiate new models, specify names, and apply templates in a single step, completely bypassing the need to navigate through complex folder structures. This streamlined approach provides a much faster starting point for new projects and empowers users to begin their work immediately without requiring deep knowledge of the repository layout.

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麻豆原创 Field Service Management, AI-assisted automated scheduling analytics
General availability

Field service dispatchers and consultants can now access clear, on-demand explanations of auto-scheduling results that demystify complex system logic. The new feature interprets scheduling reports and translates technical scoring details into business-friendly insights, explaining why specific technicians were assigned, why alternatives were passed over, and why certain activities remained unscheduled. This transparency drives a 12.5% increase in dispatcher productivity and a five percent reduction in erroneous resource allocations, strengthening trust in automated decisions while significantly reducing analysis time.

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

Quality managers documenting complex manufacturing issues can now generate clear, objective, and structured descriptions with minimal effort. 麻豆原创 Digital Manufacturing for issue resolution offers description generation that refines rough initial inputs, removes bias and subjective language, and produces balanced, factual problem statements. With support for multilingual translation and enhanced clarity, organizations can achieve up to five percent improvement in quality engineer efficiency during issue handling and up to 10% reduction in errors throughout the problem resolution process.

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麻豆原创 Business AI for finance

Dispute Resolution Agent (for 麻豆原创 S/4HANA Cloud Public Edition)
Beta release

When invoice disputes arise, accounts receivable specialists need to act quickly without sacrificing accuracy. 麻豆原创 S/4HANA Cloud Public Edition introduces an agent that automates root-cause analysis, scanning invoices, sales orders, delivery records, pricing agreements, and tax rules to identify the source of discrepancies. The agent detects incorrect charges and recommends compliant solutions, such as credit memo creation, enabling finance teams to resolve disputes faster, minimize manual investigation, and cultivate stronger vendor relationships through transparent, efficient processes.

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麻豆原创 S/4HANA Cloud Public Edition, AI-assisted smart personalization of my home for applications
General availability

麻豆原创 S/4HANA Cloud Public Edition users can easily configure their home page with the most relevant applications through AI-assisted smart personalization. By describing their task in natural language, the system identifies the appropriate app, which can then be added to their home screen with a single click. This intuitive capability reduces the cost of personalizing the home page by 33%, shortens the learning curve for new users, and improves satisfaction by keeping frequently needed tools readily accessible.

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麻豆原创 S/4HANA Cloud Public Edition, AI-assisted error explanation
General availability

When encountering system errors, 麻豆原创 S/4HANA Cloud Public Edition users can turn to a new feature that generates clear, natural language explanations and resolution recommendations. This capability transforms cryptic error messages into easy-to-understand guidance, helping users of all experience levels quickly rectify issues and continue with their work. By reducing error resolution time by five percent, organizations benefit from increased productivity, improved data quality, and shorter training cycles for new team members.

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麻豆原创 S/4HANA Cloud Public Edition, AI-assisted sales order creation from unstructured data
General availability

Sales representatives benefit from a streamlined order creation process in 麻豆原创 S/4HANA Cloud Public Edition that handles unstructured data like PDF or image-based purchase orders. After uploading a file, 麻豆原创 Document AI automatically extracts the relevant information and proposes the data for a corresponding sales order request. This automation significantly reduces manual data entry, minimizes errors, and improves overall operational efficiency, allowing teams to process orders faster and enhance customer satisfaction.

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麻豆原创 S/4HANA Cloud Public Edition, AI-assisted processing of payment advices with 麻豆原创 Document AI
General availability

Accounts receivable clerks can accelerate their workflow using the 麻豆原创 Document AI-powered payment advice processing feature in 麻豆原创 S/4HANA Cloud Public Edition. The system automatically extracts payment amounts, references, and currencies from diverse invoice formats across multiple languages, with a self-learning capability that continuously improves recognition accuracy. Organizations implementing this feature can reduce document processing time by 70%, cut template maintenance time by 83%, and decrease value loss from manual processing delays by 40%.

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麻豆原创 S/4HANA Cloud Private Edition, AI-assisted fixed asset key figures explanation
General availability

Asset accountants gain clarity on complex fixed asset calculations through a new AI feature in 麻豆原创 S/4HANA Cloud Private Edition. The feature generates natural-language explanations that detail the origins of displayed values and how figures such as depreciation are calculated; for example, illustrating the impact of mid-year acquisitions with specific depreciation keys. This transparency reduces the effort required to analyze asset values, enables faster responses to asset-related questions, and helps mitigate compliance risks.

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麻豆原创 S/4HANA Cloud Private Edition, AI-assisted settlement rule proposal for asset capitalization
General availability

Overhead and asset accountants can now streamline the complex process of creating settlement rules for investment measures, eliminating the traditionally time-consuming, error-prone manual configuration. The solution automatically determines receivers, calculates percentages, and proposes feasible rules based on contextual data and user-defined instruction profiles. Organizations reduce the effort required to create full settlement rules by 50% while simultaneously improving accuracy in asset capitalization and enhancing overall operational efficiency across their financial processes.

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麻豆原创 Document and Reporting Compliance for 麻豆原创 S/4HANA Cloud Private Edition, AI-assisted electronic document error handling
General availability

Tax accountants navigating the growing complexity of e-invoicing mandates across multiple countries gain an easy way to decode technical errors without wading through intricate XML or JSON formats. Joule, integrated with 麻豆原创 Document and Reporting Compliance, delivers plain-language explanations of electronic document errors, enabling faster root-cause identification and more efficient resolution. Organizations get an 80% reduction in time spent understanding and resolving errors, dropping from 150 minutes to approximately 30 minutes. This results in faster processing cycles, reduced penalty risks, and improved cash flow.

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麻豆原创 S/4HANA Cloud Public Edition, AI-assisted error resolution for cost accounting
General availability

Operations managers in retail organizations can now access Joule via 麻豆原创 Order Management Services, enabling them to query order data and receive real-time, role-specific operational guidance across order processing, orchestration, sourcing, availability, returns, and fulfillment flows. Joule surfaces instant insights and recommended actions directly in the workflow, reducing the need to navigate multiple systems. This enables proactive intervention before issues escalate. The feature offers faster transaction access, improved responsiveness and accuracy, and lower operational risk, which support smarter, quicker decisions across the order lifecycle.

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麻豆原创 Business AI for spend management

Expense Report Validation Agent
General availability

Business travelers can enjoy a smarter, guided approach to expense report completion with an agent that proactively identifies missing items, prompts for necessary details, and clarifies confusing alerts throughout the submission process. By simplifying how users understand and resolve issues, the agent ensures accurate, policy-compliant reports with minimal effort required. This means a 30% reduction in time spent preparing and submitting reports, a 24% increase in first-pass approvals, and a noticeably improved employee experience that removes friction from the expense management process.

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Expense Pre-Submit Audit Agent
麻豆原创 Early Adopter Care

Expense report submitters can now catch receipt accuracy issues and policy breaches before hitting the submit button, avoiding the frustration of rejected reports and delayed reimbursements. This agent automatically reviews expenses during creation, surfacing compliance problems and offering smart suggestions for quick corrections. The agent uses a non-blocking design that keeps users in control of final decisions. Organizations benefit from a 10% decrease in sent-back expense reports, reduced rework for travelers, managers, and auditors alike, and a noticeably smoother reimbursement process that enhances the overall employee experience.

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Expense Automation Agent
麻豆原创 Early Adopter Care

Employees burdened by the administrative chore of creating expense reports can now delegate the heavy lifting to a Joule Agent. This agent automatically builds expense reports by aggregating transactions, populating custom fields based on contextual details and user history, and preparing everything for a quick review before submission. The outcome is up to 30%鈥� reduction in time on task for auto-generated expense reports. This offers a modern expense management experience that slashes manual data entry, accelerates the submission process, and frees employees to focus on high-value work rather than paperwork.

See the demo .

Concur Expense, AI-assisted configuration for audit rules
General availability

Expense administrators responsible for managing complex audit rule setups can now interact with their configuration environment in plain language, eliminating the need for deep technical expertise or tedious manual adjustments. This AI-assisted feature enables admins to search existing rules, create new ones, and receive real-time explanations simply by asking questions like “What rules apply to meals in France?”, delivering clear, actionable guidance instantly. The outcome is a 40% reduction in audit rule configuration effort, fewer support tickets, and empowered administrators who work with greater independence, accuracy, and confidence in maintaining compliance logic.

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Policy Navigator
麻豆原创 Early Adopter Care

Business travelers seeking quick answers to company travel and expense policies no longer need to sift through lengthy documents or wait for admin responses. Policy navigator in Joule allows employees to ask questions in natural language and receive clear, contextual guidance grounded in approved policies, whether planning a trip, in the middle of a journey, or completing an expense report. The result is in-the-moment policy clarity that prevents non-compliant spend before it happens, reduces support tickets, and empowers travelers to make confident, compliant decisions without disrupting their workflow.

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麻豆原创 Business AI for procurement

麻豆原创 Fieldglass Services Procurement, AI-assisted SOW deliverables creation
General availability

Procurement specialists can accelerate the development of their statements of work using the deliverables feature in 麻豆原创 Fieldglass Services Procurement. The feature analyzes the defined project scope and automatically generates precise, relevant deliverables that ensure tight alignment between buyer expectations and supplier commitments. By adopting this capability, organizations can reduce the time required to manually create SOW deliverables by 70% and cut the risk of poor outcomes by 50%, while fostering stronger collaboration during the negotiation process.

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麻豆原创 Business AI for customer experience

Catalog Optimization Agent
General availability

E-commerce product managers tasked with maintaining large 麻豆原创 Commerce Cloud catalogs gain an always-on agent that continuously reviews product descriptions, attributes, and translations against company quality standards. This agent pinpoints merchandising gaps and delivers actionable recommendations to enhance catalog accuracy, ensure consistency across languages, and improve product discoverability. The business impact is a 70% reduction in time to translate catalog data, 65% less time spent adding descriptions per asset, and a five percent reduction in data quality costs, all of which contribute to higher conversion rates and a more agile merchandising operation.

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

Key account managers in consumer industries can benefit from a streamlined, single-view promotion-creation experience in which simply naming a promotion automatically populates key fields. Drawing on master data, historical promotions, and learned preferences specific to each retailer, the system suggests dates, types, durations, and sell-in periods, then continuously refines its recommendations based on user edits over time. The impact is a 75% reduction in promotion setup time, 30% fewer data-entry errors and rework, and increasingly personalized suggestions that eliminate repetitive manual effort across promotion cycles.

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麻豆原创 Business AI for IT and developers

Joule Studio code editor and Joule Studio CLI

Building on the transformative capabilities of Joule studio low-code, 麻豆原创 is expanding the Joule studio family with two powerful new offerings designed to meet developers exactly where they work: Joule Studio code editor, a Visual Studio Code IDE extension, and Joule Studio CLI, a versatile command-line interface. Together, these tools deliver a unified, AI-assisted development experience that spans the full spectrum of development personas and preferences on Joule.

• Joule Studio code editor brings the intelligence of Joule directly into Visual Studio Code, the world’s most popular development environment, empowering pro-code developers with AI-guided scaffolding, contextual code generation, intelligent recommendations, and seamless integration with Joule, all without leaving their preferred IDE. 
• Joule Studio CLI extends this same power to the terminal, enabling developers and DevOps teams to automate project creation, manage configurations, execute deployments, and orchestrate CI/CD workflows through scriptable, command-line commands鈥攊deal for headless environments, automation pipelines, and teams that value speed and precision at the command line.

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Joule with 麻豆原创 Datasphere
General availability

Data professionals working within 麻豆原创 Datasphere can now accomplish informational, navigational, and transactional tasks through natural conversation with Joule. Whether asking how to use specific functionalities, retrieving details about a 麻豆原创 Datasphere instance, or switching system settings like language preferences, users receive instant answers with direct references to product documentation. Joule can even execute tasks directly from the conversation without requiring interaction with the standard interface. This direct execution reduces reliance on internal IT support and enables faster, more intuitive navigation throughout the platform.

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麻豆原创 Document AI, enhancements

Document level confidence
Customers now set confidence ranges for fields in the Schemas feature. When customers edit field settings, they can define their own thresholds for low, medium, and high confidence. These custom settings are reflected in the extraction results displayed for the relevant fields on the document details screen. See and .

Expanded Transportation Management
Customers can now use the Transports feature to export and import channels and workflows. See .

New schemas: business partner + delivery note for WM
The service plans embedded edition and premium edition now also support the standard document type, business partner document. See the list of supported document types in . Get started with 麻豆原创 Document AI, and .

Generative AI Hub in AI Foundation, enhancements

Metadata
Customers can now manage metadata for documents, collections, and chunks created with the Vector API to enable advanced filtering and organization of their content. For more information, see .

Retrieval API
Customers can merge and rank search results across multiple data repositories using the Retrieval API’s post-processing capabilities. For more information, see .

Prompt optimizations
Custom metrics are supported in prompt optimizations, enabling customers to define and optimize prompts based on their specific evaluation criteria. Only LLM-as-a-judge metrics with numerical or Boolean output types can be used in optimization tasks.For more information, see and . Customers can provide separate test and train datasets for prompt optimization. For more information, see .

Prompt registry
The prompt registry now enables customers to create and manage orchestration configurations declaratively, allowing them to version and track complex AI workflows alongside their prompts for better governance and reproducibility.For more information, see .

Secrets
Customers can now enter generic secrets using a form instead of JSON. The form appears in the Add Generic Secret dialog when you activate document grounding. A dropdown menu lets them choose the type of document repository. Depending on their selection, the remaining fields adjust dynamically, allowing them to complete the data. Some fields are already prefilled.If they prefer working directly with JSON, switch to the code view by clicking the 顒� icon. For more information, see .

New models available
New models are supported, including OpenAI GPT 5.2, Gemini 3.0 Pro, Perplexity Deep Research, and Anthropic Claude Opus 4.6.For more information on new and deprecated models, .

麻豆原创 Joule for Developers, ABAP AI capabilities, enhancements

New ABAP AI capabilities mean developers can expect a 20% reduction in time and effort to write ABAP/JAVA code, 25% reduction in time and effort to test ABAP/JAVA code, and 4.4% faster time to realized value.

This quarter, developers can now easily generate ABAP Unit tests for:

• Public, protected, and private methods of global ABAP classes
• Public methods of local classes within global class pools

See .

In addition, the documentation chat allows developers to interact with documentation on the 麻豆原创 Help Portal, providing context-aware answers and links to relevant documentation. This capability enhances productivity by offering quick access to related documentation directly within the development environment. See .

Finally, developers can now get AI-powered explanations of their ATC findings and code in the Custom Code Analysis/Custom Code Migration app. See and .

Get started .

麻豆原创 Business AI for industries

Tender Analysis Agent
General availability

Sales teams can elevate their tender response process with the Tender Analysis Agent, which automates the review of complex RFQ documents. The agent extracts critical product requirements, flags potential risks and policy gaps, and suggests optimized configurations tailored to customer needs. By reducing the effort to process incoming tenders by five percent and improving win rates, organizations can achieve measurable revenue growth while accelerating sales cycles and uncovering valuable cross-sell and up-sell opportunities.

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麻豆原创 Commodity Management, AI-assisted commodity work center
General availability

Commodity traders can transform how they capture and manage complex deals using the commodity work center in 麻豆原创 Commodity Management. Working alongside Joule, the feature converts verbal or written negotiations into detailed draft deals, automatically populating the numerous fields that traditionally require extensive manual entry. This enables traders to redirect their focus toward negotiating better commercial outcomes, while improving data accuracy and driving greater operational efficiency across their trading activities.

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麻豆原创 Intelligent Clinical Supply Management, AI-assisted predictive subject dynamics
General availability

Clinical trial coordinators seeking to boost their supply planning capabilities will find a powerful ally in 麻豆原创 Intelligent Clinical Supply Management. The predictive subject dynamics feature analyzes historical and real-time data to forecast patient enrollment trends and dropout rates, automatically generating insights that would otherwise require extensive manual analysis. This enables supply chain teams to redirect their focus to strategic decision-making, while reducing clinical inventory waste costs by up to two percent and improving demand forecasting accuracy across their trial operations.

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Joule with 麻豆原创 Intelligent Clinical Supply Management
General availability

Clinical supply professionals juggling multiple tasks and complex systems need quick access to information without disrupting their workflow. Together with Joule, 麻豆原创 Intelligent Clinical Supply Management delivers an intuitive, conversational interface that understands natural-language requests, enabling users to retrieve critical data and navigate to relevant applications effortlessly. This streamlined experience results in an 83% reduction in time spent on information searches, freeing teams to concentrate on higher-value activities and significantly boosting overall productivity.

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麻豆原创 Self-Billing Cockpit, AI-assisted document processing
General availability

Billing clerks managing self-billing workflows frequently encounter invoices arriving in a mix of formats鈥擡xcel, PDF, CSV, or text files鈥攐ften unstructured and spanning multiple languages. 麻豆原创 Self-Billing Cockpit addresses this challenge by leveraging intelligent document processing to parse and extract invoice data from virtually any format, converting it into structured payloads ready for automated billing. The result is significantly reduced time spent processing invoice line items, fewer customer-specific interfaces for integration specialists to build and maintain, and improved extraction accuracy through minimized manual intervention.

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麻豆原创 Business AI for business transformation management

Joule with 麻豆原创 Signavio solutions
General availability

Process analysts and optimization specialists working across complex organizational workflows require rapid access to diagrams, documentation, and performance metrics. 麻豆原创 Signavio solutions integrate with Joule to enable natural-language keyword searches across process diagrams, dictionary items, and help resources. At the same time, best-practice KPI recommenders guide users to the most relevant success measures. This intuitive approach delivers 50% faster information searches and navigation, ensuring teams make data-driven decisions with improved search quality and an enhanced overall user experience.

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麻豆原创 Signavio solutions, AI-assisted business process model and notation simulation insights
General availability

Process analysts leveraging 麻豆原创 Signavio can now access embedded business process model and notation simulations directly within their process diagrams, eliminating the need for fragmented tools and manual interpretation. Key metrics such as costs, cycle times, and resource utilization are automatically translated into clear, actionable summaries that highlight bottlenecks and opportunities for improvement. This streamlined approach reduces time to access process modeling insights by 50%, empowering teams to compare scenarios effortlessly and communicate findings to stakeholders with greater confidence and clarity.

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麻豆原创 LeanIX solutions, AI-assisted architecture guidance
General availability

Enterprise architects seeking to accelerate transformation initiatives can leverage 麻豆原创 LeanIX to surface actionable insights directly from their architecture inventory. The feature analyzes enterprise architecture data to identify opportunities and guides users through the workflows and tasks needed to efficiently act on recommendations. Organizations benefit from a 95% reduction in time to discover insights, 80% faster transformation execution, and a five percent reduction in value erosion from delayed action. Overall, this feature drives greater architectural productivity and more agile decision-making.

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Jonathan von Rueden is chief AI officer of 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.

With the听听1H鈥�2026 release,听we鈥檙e听deepening those connections across the HR lifecycle. This release focuses on听four听core priorities:听connected,听suite-wide听AI; unified experiences that adapt to how organizations work;听processes designed for clarity, accuracy, and compliance; and stronger foundations for skills and long-term growth.听Together, these innovations help organizations听anticipate听needs earlier, reduce friction in daily work, and move forward with greater confidence.听

Connected AI that works across HCM

AI in HR delivers the greatest impact when it works continuously across the entire workforce lifecycle鈥攏ot as isolated features, but as connected capabilities that share context and insight.

The 1H鈥�2026 release expands听suite-wide听agentic AI听across 麻豆原创 SuccessFactors solutions, helping听employees听get clearer answers, act sooner, and keep听work moving听across roles and responsibilities.听A connected network of听听now supports areas such as recruiting, workforce administration, payroll, learning, performance, and talent development鈥攚orking together behind the scenes to help听anticipate听next steps and surface relevant guidance.

This release also introduces a growing听workforce knowledge network, bringing trusted external expertise and research directly into the flow of work through Joule.听Teams can now access expert-backed global employment guidance and听research-driven听insights without leaving their workflows鈥攕upporting听faster, more听confident decisions.

To听further听support learning in the flow of work,听intelligent Q&A in听听now helps employees find information more easily. AI听can deliver instant,听context-aware听responses drawn directly from an organization鈥檚 learning content,听along with relevant links and resources,听so employees can get answers quickly without searching through courses or documentation.听

Unified experiences that adapt to how work gets done听

As HR听tasks听become more embedded in听day-to-day work, experiences need to feel intuitive, connected, and responsive听wherever work happens. In the 1H 2026 release,听麻豆原创 SuccessFactors solutions continue to unify experiences across the suite, giving employees, managers, and HR teams what they need听in听the moment.听

• Connected recruiting and onboarding:听Native integration between听 solutions, , and听听can bring AI-enabled听recruiting, core HR, and onboarding together into a single, continuous experience, helping hiring teams move faster while听maintaining听consistency from candidate through new hire.听

• Tailored experiences,听built faster:听The new听extensibility wizard听can provide guided, step-by-step support for creating custom extensions on听听(麻豆原创 BTP) directly within听麻豆原创听SuccessFactors solutions, making it easier to adapt experiences to unique business needs while preserving governance.听
• Simpler, clearer employee moments:听A redesigned, configurable 401(k) experience听in听听for U.S.听employees helps simplify enrollment and management by clearly explaining employer contributions and guiding deferrals and beneficiary setup, helping employees make informed decisions with confidence.听

Processes designed for clarity, accuracy, and compliance听

In the 1H鈥�2026 release, 麻豆原创 SuccessFactors introduces new capabilities that help organizations bring greater clarity and rigor to pay practices.

With听pay听transparency insights听in the , organizations can analyze compensation patterns and potential pay gaps, supporting transparent, data-driven pay practices in-line with evolving regulatory expectations,听including in the EU.听

Skills governance听for sustainable growth听

Preparing for听what鈥檚听next requires trusted, consistent skills data that organizations can rely on across HR, talent, and workforce planning.

In the 1H鈥�2026 release,听we are听strengthening听the听听with enhanced听skills governance, providing a centralized interface to help manage skills, apply governance standards, and ensure alignment across 麻豆原创 SuccessFactors solutions and partner applications. This helps organizations improve听skills听data quality, maintain consistency at scale, and make more confident,听skills-based听decisions.听

A connected foundation for the future 

This听release听reinforces听麻豆原创鈥檚 continued focus on an intelligent, connected HCM听foundation鈥攐ne designed to evolve with your organization and support confident decisions at every stage of work. By bringing together data, AI, and experiences across the HR lifecycle, these听enhancements help organizations reduce friction today while听laying听the groundwork for听tomorrow.

To explore what鈥檚 included in this release, check out the or watch the overview .

Bianka Woelke is group vice president and head of Application Product Management for 麻豆原创 SuccessFactors.

“In her role as chief people officer, Gina Vargiu-Breuer has laid important foundations for 麻豆原创’s continued success by strengthening how we attract, develop and lead our people,” said Pekka Ala-Pietil盲, Chairman of the Supervisory Board of 麻豆原创 SE. “We appreciate her energy and dedication and are convinced she will further drive 麻豆原创’s transformation into the age of AI.

“I am grateful for the trust of the Supervisory Board and look forward to shaping 麻豆原创鈥檚 transformation in the age of AI,” said Vargiu-Breuer. “This is about rethinking how we operate as a company: how we work, make decisions and deliver value to our customers. Together with my People & Culture team, we will continue what we started two years ago and turn this transformation into tangible business impact at scale.”

Vargiu-Breuer became a member of the Executive Board of 麻豆原创 SE in 2024. Prior to joining 麻豆原创, she served as Senior Vice President of Human Resources at Siemens Energy. Earlier in her career, Vargiu-Breuer held multiple roles at Siemens AG.

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To preview and download broadcast-standard stock footage and press photos digitally, please visit . On this platform, you can find high resolution material for your media channels.

Media Contact:
Bjoern Emde, +49听6227 7755107,听b.emde@sap.com,听CEST
麻豆原创 麻豆原创 Room;听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.

But new research from 麻豆原创 and Wakefield* suggests a different reality is emerging. AI isn鈥檛 making early talent irrelevant. Instead, it鈥檚 accelerating how quickly they become productive, reshaping the earliest stages of work, and raising expectations far earlier in the employee lifecycle.

According to the findings, 88% of CHROs say AI is making early-career talent role-ready faster. This acceleration raises the stakes on both sides. While organizations benefit from faster productivity and earlier impact, early鈥慶areer employees are entering roles with heightened expectations and fewer traditional learning buffers鈥攆orcing leaders to rethink how success is defined and supported from day one.

AI as an accelerator of readiness

Entry-level roles have long relied on repetitive, lower-stakes tasks that helped new employees learn how work gets done. Today, AI automates much of that foundational execution.

This shift is increasingly common: 79% of surveyed CHROs report that their early-career talent receives enterprise AI tools within their first month on the job. Additionally, 87% expect new hires to be comfortable with AI on day one or learn the tools immediately after joining.

With AI absorbing traditional tasks, early-career talent is stepping into meaningful work sooner鈥攁nd CHROs are already seeing the impact, with 56% reporting improved confidence and 55% citing increased productivity among those using AI.

This acceleration reflects themes we first explored in the 2025 麻豆原创 SuccessFactors Future of Work Predictions , where we examined how AI might reshape entry鈥憀evel roles. As foundational tasks continue to be absorbed by AI, the question becomes not whether early鈥慶areer roles will exist, but how organizations can redesign them to build capability in new ways.

When productivity accelerates, expectations follow

As early talent ramps faster, the expectations placed on them are rising just as quickly. Several structural factors are contributing to this shift: organizations are hiring fewer early-career talent, and those who do join are expected to take on more complex work earlier in their tenure. Our upcoming research from our makes this clear, as one research participant summarized, 鈥淓ntry level roles used to be focused on mundane tasks鈥攚hat should they do now? They bring an incredibly unique perspective; we want to hire early talent to challenge our norms and help us find better ways of working.鈥�

But with AI removing the mundane work, it may also remove many of the gradual, hands-on learning moments that once helped new hires build experience over time.

With these rising expectations, it鈥檚 easy to see how the cognitive load of entry level roles could increase substantially. CHROs report heightened performance pressure and increased mental effort as new hires try to keep pace with AI-accelerated work. Some researchers refer to this dynamic as 鈥�,鈥� the cognitive strain that comes from managing rapid, AI-driven workflow.

Together, these shifts create several risks for both employees and organizations:

• Shadow AI use rises: 56%of CHROs say early-career talent turns to unsanctioned AI tools when formal guidance is unclear. This behavior may reflect entry-level hires trying to keep pace rather than intentionally breaking policy.
• Inconsistent enablement creates talent risk: 44% of CHROs say uneven access to AI tools increases attrition risk, especially for early talent who may feel unable to live up to new performance expectations without tools to automate routine tasks.
• Foundational skills may erode: Even as AI boosts productivity, 38%of leaders worry early-career talent are not building long-term skills like communication, critical thinking, judgment, and collaboration. That concern is echoed in qualitative feedback from HR leaders as well. As one noted, 鈥淲e鈥檝e observed gaps in professionalism in business settings for entry鈥憀evel talent, from collaboration and stakeholder management [to] ownership and accountability.鈥�

Rethinking the first step into work

As traditional early鈥慶areer learning pathways narrow, organizations must now redesign how those learning moments happen. Our research points to several areas where HR leaders can intentionally strengthen the early-career ramp:

1. Build foundational skill development intentionally.

As repetitive tasks disappear, organizations have the opportunity to deliberately create new ways for early talent to build communication, collaboration, critical thinking, and decision-making skills. This can include structured, project-based experiences, clearer decision-making frameworks, and more frequent coaching that focuses on judgement and prioritization, not just task completion.

2. Design entry-level roles around higher-value work.

Early-career employees are capable of contributing more strategically when roles are designed with the right balance of scope and support. Redesigning entry鈥憀evel positions to include clear ownership鈥攕upported by explicit expectations, mentoring, and well鈥慸efined guidance for decisions and escalation鈥攈elps early鈥慶areer talent build confidence while managing risk.

3. Establish AI governance from day one.

Without clear guidance, early talent may struggle to understand how to use AI responsibly. Introducing AI expectations during onboarding, reinforcing role-specific best practices, and normalizing manager-led conversations about AI use can reduce shadow AI and build trust in new technologies early on.

4. Ensure equitable AI access across teams and managers.

As expectations rise, uneven access to AI tools can quietly increase workload pressure and stress for early-career employees. Providing consistent access, training, and enablement helps ensure new hires are equipped to meet accelerated demands without increasing burnout or attrition.

The bottom line

AI isn鈥檛 eliminating early-career talent from the workforce; it鈥檚 reshaping the path they take to become effective and increasing the value of the work they contribute. While entry-level roles may be fewer, expectations for impact are higher, placing greater importance on pairing AI fluency with strong human skills. For new graduates, developing both will not only help them land a job but also enable them to contribute quickly and build lasting capabilities.

When early鈥慶areer talent becomes productive sooner, companies can move faster, innovate earlier, and operate more efficiently, but only if that speed is matched with structure, coaching, and intentional development. Organizations that navigate this transition successfully will ensure early talent doesn鈥檛 just ramp up faster, but also builds the judgment, collaboration, and critical鈥憈hinking skills that AI can鈥檛 replace.

To stay on top of more upcoming research on the impact of AI on entry-level roles, visit our .

Lara Albert is chief marketing officer for 麻豆原创 SuccessFactors.

*The 麻豆原创 AI Talent Survey was conducted by Wakefield Research (www.wakefieldresearch.com) among 100 US CHROs (or CPO equivalent) at organizations with a minimum annual revenue of $500m where employees are using AI-enabled tools in their day-to-day responsibilities, between February 19th and March 2nd, 2026, using an email invitation and an online survey.

For 13 years, we have been shining a light on the people and organizations doing breakthrough work. Today, it鈥檚 my pleasure to announce the 2026 麻豆原创 Innovation Award finalists.

What distinguishes this year’s innovators is their ability to use 麻豆原创 technology to run, adapt, and innovate in the face of change and complexity. From using AI to unify financial and sustainability data to redesigning value chains for resilience and predicting risks rather than reacting to them, they are closing the “say-do” action gap and making innovation real.

Meet the 2026 finalists

The day we announce these finalists is always a highlight because we see how organizations are leveraging the 麻豆原创 landscape to make a tangible difference. This year, our finalists are setting benchmarks across 11 key categories:

• AI Excellence: harnessing 麻豆原创 Business AI to revolutionize processes and unlock new levels of productivity
• Cloud ERP Champion: embracing cloud ERP to future-proof operations
• Customer Experience Innovator: transforming businesses with cloud technologies to create standout experiences
• Financial Futurist: revolutionizing financial processes to deliver new business models and reduce risk
• People Experience Pioneer: reimagining the employee journey through digital transformation
• Procurement Visionary: automating procurement to manage spend for better control and greater value
• Supply Chain Catalyst: building strong, well-orchestrated supply chains that are resilient and compliant
• Sustainability Hero: harnessing the power of data to build an inclusive economy and shape a sustainable future (My personal favorite!)
• Technology Pathfinder: implementing 麻豆原创 Business Technology Platform to solve business problems with measurable impact.
• Transformation Impact: generating critical business value and strengthening transformation capabilities to remain resilient
• Industry Leader: leveraging multiple 麻豆原创 solutions to reinvent business models, collaborate across ecosystems, and create meaningful societal or environmental impact (Note: All entries are also automatically considered for the Industry Leader category, recognizing modernization within specific verticals from consumer industries to public services.)

The road ahead

To our finalists: congratulations.

You were selected by our distinguished panel of judges because you aren’t just comparing today against the peak of the hype cycle; you are building for the reality of the future. You have demonstrated case creativity, tangible outcomes, and the true spirit of the intelligent enterprise.

What comes next? The winners will be announced on April 14, 2026. Winners will receive a trophy and the option of a $1,000 charitable donation voucher or an admission ticket to 麻豆原创 Sapphire Orlando or Madrid, where we will hold the 麻豆原创 Innovation Awards reception.

But beyond the trophies, these finalists represent a signal to the market. They show us that while short-term risks are noisy and reactive, long-term value is built by those who integrate, automate, and adapt.

Let鈥檚 stay engaged, stay inspired, and keep closing the gap between saying and doing.

Sophia Mendelsohn is chief sustainability and commercial officer at 麻豆原创.

We believe this recognition reflects 麻豆原创鈥檚 continued focus on helping organizations run resilient, efficient, and increasingly intelligent transportation operations in a rapidly changing global logistics environment.

Magic Quadrant reports are a culmination of rigorous, fact-based research in specific markets, providing a wide-angle view of the relative positions of providers in markets where growth is high and provider differentiation is distinct.

We believe 麻豆原创鈥檚 placement as a Leader underscores our commitment to ongoing innovation across transportation, logistics execution, and supply chain orchestration.

Addressing today鈥檚 transportation challenges

Transportation operations are under constant pressure, from cost volatility and capacity constraints to sustainability requirements and rising customer expectations. Organizations need solutions that help them plan, execute, and adapt across increasingly complex networks while maintaining visibility and control.

and are designed to support these needs through a holistic, end鈥憈o鈥慹nd approach. By connecting freight procurement, planning, execution, and settlement on a single platform, 麻豆原创 helps organizations respond more effectively to disruptions, align transportation decisions with broader supply chain objectives, and support compliance with regional and industry鈥憇pecific requirements.

A platform built for complex, global operations

麻豆原创 Transportation Management supports organizations operating across multiple modes, regions, and industries. Built to scale with business growth, the solution is designed to support complex, global transportation networks while enabling standardization and process consistency across operations.

Customers across industries鈥攊ncluding consumer products, chemicals, agriculture, mining, retail, wholesale distribution, and industrial manufacturing鈥攗se 麻豆原创 Transportation Management to manage complex transportation networks at scale. Industry鈥憇pecific capabilities from 麻豆原创, such as support for automotive and mill and mining operations, along with integration with , help organizations address specialized requirements while accelerating time to value. Dedicated industry business units further tailor go鈥憈o鈥憁arket strategies and solutions to industry鈥憇pecific challenges.

Advancing transportation management with AI

Data-driven decision-making is increasingly essential for transportation operations. 麻豆原创 continues to invest in AI-driven capabilities that help automate processes, improve responsiveness, and increase productivity.

Recent innovations include AI-assisted use cases such as goods receipt processing, as well as the integration of conversational planning into transportation planning workflows. These capabilities are designed to help planners and operators work more efficiently by reducing manual effort and supporting faster, more informed decisions across execution and settlement processes. 麻豆原创 Joule for Consultants is another recent AI innovation that accelerates solution adoption by offering instant, expert-level guidance and best practice recommendations for solution configuration.

Supporting a connected logistics landscape

Transportation does not operate in isolation. 麻豆原创鈥檚 logistics portfolio brings together transportation, warehousing, and business network collaboration on a cohesive foundation.

This includes the recent general availability of , a new solution designed to support regional and local distribution operations by combining transportation execution, warehouse processes, and carrier collaboration in a single offering. 麻豆原创 Logistics Management complements 麻豆原创 Transportation Management by helping organizations extend standardized logistics processes to satellite locations and growing operations, supporting broader adoption while reducing complexity. 麻豆原创 Logistics Management can be deployed alongside 麻豆原创 Transportation Management to support multi-tier transportation operations, providing the right tool for each level of complexity.

麻豆原创 Transportation Management, together with other logistics solutions from 麻豆原创, helps organizations modernize their logistics operations in a way that aligns with their broader ERP and supply chain strategies as they progress on their transformational journeys via or .

Why organizations choose 麻豆原创 Transportation Management

Organizations choose 麻豆原创 Transportation Management to support complex transportation requirements across global and regional operations. The solution offers broad functional coverage, deep integration across the 麻豆原创 supply chain portfolio, and the flexibility to support both advanced transportation networks and evolving business needs.

With continued investment across 麻豆原创 Transportation Management, 麻豆原创 Logistics Management, warehousing, and 麻豆原创 Business Network for Logistics, 麻豆原创 remains focused on helping organizations operate resilient transportation processes while supporting both global complexity and localized execution models.

Explore how 麻豆原创 Transportation Management can .

Till Dengel is global head of Product Marketing for Logistics and Asset & Service Management at 麻豆原创.

*Gartner, Magic Quadrant for Transportation Management Systems, Brock Johns, Oscar Sanchez Duran, Manav Jain, 30 March 2026.

Gartner does not endorse any company, vendor, product or service depicted in its publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner publications consist of the opinions of Gartner鈥檚 business and technology insights organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this publication, including any warranties of merchantability or fitness for a particular purpose.
Gartner and Magic Quadrant are trademarks of Gartner, Inc., and/or its affiliates.

The 11th cycle of the Hasso Plattner Founders鈥� Award saw an evolution of the award itself. With a refined structure and this year鈥檚 strong thematic focus on AI, the award now recognizes achievements in two categories: Emerging Ideas, honoring visionary concepts that explore new architectural directions and long-term opportunities for customers and the business, and Scaling Innovation, celebrating innovations already delivering proven impact at scale.

The jury received a total of 254 submissions from all over the globe, from which 41 finalists representing nine different countries were chosen. Six teams made it to the final round, highlighting the breadth of innovation across teams worldwide.

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The winning teams were formally recognized and celebrated during a red carpet award ceremony on March 26 at 麻豆原创 headquarters in Walldorf, Germany. Executive Board Members Christian Klein and Sebastian Steinhaeuser introduced the finalists and announced the winners in the Emerging Ideas and Scaling Innovation categories, respectively. 

Emerging Ideas winner: 麻豆原创 Cognitive Twin Enterprise

The Emerging Ideas category honors bold thinking and visionary concepts that explore the future of enterprise software. This year鈥檚 winner, 麻豆原创 Cognitive Twin Enterprise (麻豆原创 CTE), embodies this forward-looking spirit by presenting a new way of how organizations plan, simulate, and execute in an increasingly complex and fast-changing world.  

麻豆原创 CTE introduces the idea of an ever-learning, AI-powered intelligence layer built on a continuously updated model of the entire organization. It unifies data, simulation, and AI on 麻豆原创鈥檚 foundation to help deliver guided autonomy across functions, supporting the shift from keyboard-centric SaaS to governed decision-making and agent-led execution. 

Acting as a constant observer of the business landscape, 麻豆原创 CTE evaluates an organization鈥檚 position against anticipated trends and potential changes. It runs what-if simulations and provides governed recommendations on 麻豆原创 applications and data across finance, spend, supply chain, HR, and customer experience, with selective, low-risk auto-execution and human-in-the-loop control for high-risk steps. By doing so, it can transform ERP into an AI-native system of foresight and elevates workforce intelligence. 

The solution helps organizations move from reacting after events occur to proactively and continuously testing scenarios, anticipating risks, and evaluating options. This provides the information they need to make critical decisions, allowing them to anticipate what鈥檚 next, shape it, and execute in a single, connected environment.  

鈥淲inning the Hasso Plattner Founders’ Award validated 麻豆原创 Cognitive Twin Enterprise鈥檚 vision,鈥� Natalia Aksakova, Strategy & Portfolio at Global Finance and Administration, says on behalf of the team. 鈥淚t reinforced that we are on the right path and gives us the momentum to bring the next era of ERP to life faster, with the ambition to help define how organizations operate in the years ahead.鈥� 

Scaling Innovation winner: 麻豆原创 Document AI

The winner in the Scaling Innovation category demonstrates how breakthrough innovation becomes truly transformative when it is embedded into everyday business processes and adopted at global scale. The 麻豆原创 Document AI solution can fundamentally change how organizations process and understand the vast volume of documents that power daily operations.

Across industries, enterprises continue to grapple with the rapid growth of unstructured data. Invoices, purchase orders, contracts, shipping documents, and many other business records still require significant manual handling in many organizations, creating bottlenecks, delays, and avoidable errors. The 麻豆原创 Document AI team addressed this challenge by bringing intelligent document processing directly into core business applications, enabling customers to automate document workflows seamlessly and securely.

What sets this achievement apart is not only the technological innovation but the scale of real-world adoption. The solution has become deeply embedded across 麻豆原创鈥檚 portfolio and is used by tens of thousands of customers worldwide to process billions of documents. By integrating advanced AI capabilities directly into existing workflows, the team has made automation accessible without the need for complex integrations or specialized expertise. This approach enables organizations to accelerate business processes, reduce manual effort, and improve the quality and speed of decision-making.

The award recognizes the team鈥檚 ability to translate research excellence and engineering innovation into measurable business impact. Their work demonstrates how embedded AI can move beyond experimentation to become a trusted and reliable component of everyday enterprise operations. By operationalizing AI responsibly and at scale, the team has helped strengthen 麻豆原创鈥檚 position as a leader in enterprise automation and intelligent applications.

Equally important is the long-term perspective behind the innovation. The continued evolution of document understanding capabilities, combined with growing adoption across 麻豆原创鈥檚 platform, illustrates how scalable AI can serve as a foundation for future innovation. The recognition celebrates not only the impact already achieved but also the momentum created for the next generation of intelligent enterprise processes.

鈥淲inning this award is a tremendous honor for our team,鈥� Tobias Weller, chief product owner and team lead, says. 鈥淚t validates years of hard work, close collaboration, and a shared belief in the transformative potential of AI to accelerate essential business processes and capture true business value for our customers.鈥�

Celebrating innovation across the AI spectrum

The Hasso Plattner Founders鈥� Award has long celebrated the people and ideas that drive 麻豆原创 forward. By recognizing both scaled impact and visionary thinking, the award highlights how innovation thrives at every stage of the journey鈥攆rom early exploration to global adoption. It underscores the belief that long-term success depends on both delivering value today and continuously reimagining what is possible.

At the ceremony in Walldorf, employees around the world came together or joined virtually to celebrate the winning teams and the many contributors who helped bring their ideas to life. Their work reflects the creativity, dedication, and passion that define 麻豆原创鈥檚 culture of innovation. As the company continues to advance its AI-driven strategy, this year鈥檚 winners demonstrate how teams across the organization are turning ambition into reality鈥攈elping customers run better, adapt faster, and prepare for the future.

The winning teams will be given the opportunity to pitch their project to the Executive Board in 2026. The projects will be recognized in the permanent Founders鈥� Exhibits in Walldorf and Palo Alto. In addition, the members of the winning teams will receive a personalized trophy.

A great way to break through that roadblock is to listen to leaders who jumped in early to use AI to transform outcomes. , a managing director of Finance in the Global IT division at Accenture, is one of those people.

The professional solutions and services company employs nearly 780,000 employees across 52 countries, who work with 350 partners to serve over 9,000 clients. The idea of transformation at Accenture鈥檚 scale might be intimidating to some, but not Lambert. He鈥檚 leading an听ongoing transformation听of Accenture鈥檚 finance function, which he calls 鈥渢he heartbeat鈥� of the company.

The results he鈥檚 achieved鈥� including saving the finance team a combined 57,000 hours annually by having AI generate narrative summaries for reporting鈥攕hine a spotlight on what鈥檚 possible. And he鈥檚 just getting started.

I had a chance to speak with him about how he became a leader in AI-driven transformation, and what others can learn from his achievements. This is a lightly edited version of our conversation

Q: As you know, innovating with AI is about reshaping how a business delivers value. But not every business leader is leading the charge. Some are watching and waiting. Why did you roll up your sleeves and decide to be on the forefront?

A: Taking a leadership position on AI is important to keep moving forward and shaping new services and capabilities. For example, across a company our size, even though we鈥檙e hyper focused on emerging technologies, we can find small problems across our technology landscape. There are processes and data living in different places and silos develop over time. Most large companies have this challenge. But those are valuable processes, and the business data we have is especially valuable. AI opens up new opportunities to bridge those gaps and deliver more end-to-end outcomes, so that our finance function can meet the growing business expectations of our stakeholders.

For many companies, the key to getting impactful results from business AI is to start with one function that鈥檚 central to business performance. Why was finance the right place for you to begin, and what did you want to achieve?

I always say finance is the heartbeat of our organization. I heard one of our global IT leaders use that phrase, and while it was inspirational, it also made me think, 鈥淟et鈥檚 not accidentally cause a heart attack for the organization.鈥�

Jokes aside; he was right. Your transactional and operational data flows through finance, and management decisions sit on top of it. Starting there gave us the ability to make end-to-end impact across processes that touch procurement, liquidity, forecasting, receivables, and more. And 麻豆原创 gives us a digital core where all that transactional data is harmonized.

The bottom line is that finance is the natural starting point if you want to move from reactive reporting toward more proactive, AI-driven insights that you can use to help move the business forward. So, we set out to unify data and transform finance processes in a way that scales across the whole value chain.

Cash and liquidity are so important in the finance function, and to an entire company. But managing it requires bringing together data, forecasting, and decision-making across many teams. How did AI help?

If finance is the heartbeat of a company, cash and liquidity are the lifeblood of your systems. Here鈥檚 a great example: Accenture engages in a lot of acquisitions, and we run operational cash in 50-plus countries, so it鈥檚 easy for decisions to default to historical, manual reviews. That鈥檚 what was happening at Accenture before a forward-thinking leader stopped by and asked if we could apply machine learning to the problem. Great leaders often ask great questions, and that one really got us thinking.

[AI] freed up 20% of our idle cash, which we could then move into global operations to fund acquisitions and strategic growth.

Eli Lambert

We took inspiration from retail: how stores treat inventory based on discounts and sales. If you treat cash like stock, you can apply those same learning models to figure out how much you really need to hold onto at any point in time. That鈥檚 how we built what we call 鈥淚ntelligent Cash.鈥� It brings all the business data together into a single data mart, a repository for structured data for a specific department or line of business, and uses machine learning to generate recommendations that our teams can act on.

AI is so good at this, and here鈥檚 what鈥檚 incredible: It freed up 20% of our idle cash, which we could then move into global operations to fund acquisitions and strategic growth. Now what used to take months, or even more than a year to build, we can now do it in days or weeks because 麻豆原创鈥檚 data cloud brings [麻豆原创] Datasphere, Databricks, and our machine-learning workloads into one place. The result is faster decision-making, better visibility, and much more accurate forecasting.

I love hearing about how you were able to use gains, delivered through strategic AI innovation, and then channel those gains into a high-value activity for the organization.听 I know you also worked on receivables, something that impacts cash flow and customer relationships. What pain points did you face, and how did automation and machine learning transform the process?

Receivables were highly manual compared to payables. Clearing was inconsistent, and reconciliation took a lot of time because payments often come incomplete or with partial data. Anyone who works in or near finance knows exactly what I鈥檓 talking about. So, we co-developed on the 麻豆原创 platform a machine-learning-based receivables solution. It more than doubled the automation rate for receivables processing and tripled automatic reconciliation, about a 300% improvement.

As part of that, we introduced high-confidence, one-click matching recommendations that reduce errors and cut down the manual work. We saw a seven percent uplift in auto-clearing with a cash application scheduler built on the 麻豆原创 platform that delivers matches about 77% faster. All of that adds up to a more efficient receivables process, improved cash-flow visibility, and better productivity for the team.

In a global organization like Accenture, reconciling financial data and surfacing meaningful insights can be a huge amount of work. You turned to generative AI to help, which is really smart. What led you to that approach, and how is it changing your team鈥檚 day-to-day experiences?

We were dealing with balance sheet reconciliations across 50-plus countries, and the process was decentralized. I know a lot of companies face this problem. So, first, we moved everything online. Then we brought in machine learning and generative AI to analyze cost categories, summarize data, and surface important shifts.

[Our] Intelligent Financial Advisor, built on the 麻豆原创 platform, can generate narrative commentaries that are so accurate that over 90% are simply approved with little or no revision. That鈥檚 saved about 57,000 hours globally. Our teams can focus on higher-value analysis instead of manual reconciliation.

Eli Lambert

We then deployed an Intelligent Financial Advisor built on the 麻豆原创 platform that can generate narrative commentaries that are so accurate that over 90% are simply approved with little or no revision. That鈥檚 saved about 57,000 hours globally, just in controllership work, and helped us move to a three-day global close instead of five. The insights come faster and clearer, and the teams can focus on higher-value analysis instead of manual reconciliation. It鈥檚 also helping create more consistent roll-ups across regions and letting us use our talent more strategically.

I鈥檓 hearing this theme of not only measurable business gains from outputs, but the ability to better allocate time from manual, rote tasks to ones that deliver far more value for the business. That also applies to planning and forecasting. How did you bring AI into that part of the finance function?

Our planning work had grown too complex. Remember, we鈥檙e a large-scale, multifaceted global business. So, we replaced old models with 麻豆原创 Analytics Cloud, which gives us multi-year planning models enhanced by AI.

We applied it first to merger and acquisition modeling, where accuracy really matters. It lets us model very complex data sets and helps our finance team collaborate more easily across the business. The results have been more accurate forecasts, reduced risk of errors, and much better collaboration between executives and practitioners. Early results were strong, and that encouraged us to expand AI use in planning more broadly.

What advice do you have for leaders who are not as far along in using AI to supercharge business results?

First, start with a high-impact function tied to real outcomes. Then focus early on data quality and harmonization; it鈥檚 the foundation for everything that comes after. Then get your cadence right and your team working together. Hone in on the use cases that really matter to you鈥攖he best vendors can help you identify those鈥攁nd make sure to get the help you need from those vendors and their partners.

Use AI to spur growth. At Accenture, we鈥檝e been able to use AI to save significant cash in one area, which we then invest in another, high-growth process鈥攁cquisitions in our case. That鈥檚 how you use AI to really rethink your business and move it to the next level.

Eli Lambert, on advice to other enterprises

As you go, take a crawl-walk-run approach: start slow then increase the pace of scale and adoption over time. Be sure to invest in change management and upskilling as you go to spur learning and adoption. And partner closely with technology providers and system integrators who鈥檝e been there before. That accelerates everything.

The final suggestion I have is to use AI to spur growth. At Accenture, we鈥檝e been able to use AI to save significant cash in one area, which we then invest in another, high-growth process鈥攁cquisitions in our case. That鈥檚 how you use AI to really rethink your business and move it to the next level. And that鈥檚 possible today in ways that were not, even five years ago. Seize that opportunity.

I couldn鈥檛 agree more with Lambert. AI really does provide an opportunity to re-imagine entire business processes for greater impact.

To keep exploring what鈥檚 possible, at Accenture. Then see more AI use cases in and across all your , including procurement, supply chain, manufacturing, and more.

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

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

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

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

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

Inspection robotics is about data

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

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

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

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

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

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

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

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

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

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

Treating robots as part of the workforce

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

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

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

Project Embodied AI in practice

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

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

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

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

Scaling safely and responsibly

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

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

A glimpse into the future

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

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

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

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

Top image courtesy of ANYbotics

Milan, Italy-based Campari Group is a global leader in the spirits industry. With a portfolio of more than 50 premium brands, including Aperol, Campari, Espol貌n, Wild Turkey, Courvoisier and Grand Marnier, the company markets its products in more than 190 countries and operates 24 production sites worldwide.

鈥淲e鈥檝e embarked on the RISE with 麻豆原创 journey to keep pace with innovations offered by 麻豆原创 Cloud ERP Private and embedded AI capabilities,鈥� said Jos茅 Silva, group head of IT at Campari Group. 鈥淭oday, we can reshape processes in line with business evolution, improve planning and make our supply chain more efficient鈥攅nsuring continuous product distribution worldwide. Moving to a centralized process model enables us to improve productivity and reduce TCO consistently.鈥�

The Campari Group go-live establishes an end-to-end digital architecture built on a core transformation backbone, embedded AI, data unification and IT landscape governance:

• At its foundation, Campari Group unifies finance, supply chain, marketing and human resources on the 麻豆原创 Analytics Cloud, 麻豆原创 Integrated Business Planning and 麻豆原创 Datasphere solutions as well as 麻豆原创 Business Technology Platform. This creates an integrated backbone for planning, analytics and application development.
• Embedded AI across 麻豆原创 solutions is enhancing both operations and employee experience. In 麻豆原创 SuccessFactors solutions, employees use the Joule solution and embedded AI to set and track goals, while 麻豆原创 Concur solutions automate expense matching. AI-assisted capabilities are streamlining order and payment posting, improving decision-making and optimizing operational costs.
• Campari Group is also implementing the 麻豆原创 Business Data Cloud solution to unify 麻豆原创 and third-party software data, delivering timely, contextual insights while preserving core business logic.
• Finally, Campari Group also adopted 麻豆原创 LeanIX solutions to map interdependencies between applications, processes and business owners, enabling more agile, informed decision-making.

鈥淐ampari is one of our best references in the food and beverage sector and is an excellent example of how 麻豆原创 solutions can transform organizational and production processes,鈥� said Carla Masperi, managing director, 麻豆原创 Italy. 鈥淏y combining cloud ERP with AI and data-driven planning, Campari is setting a new standard for digital transformation in the consumer products industry.鈥�

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Top image courtesy of Campari

Media Contact:
Raffaella Mollame, +39-340-7771644, raffaella.mollame@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.

Once closed, the acquisition will strengthen 麻豆原创 Business Data Cloud (麻豆原创 BDC)鈥攊ntegral for 麻豆原创’s AI-First and Suite-First strategy鈥攁nd accelerate the evolution of 麻豆原创 BDC to a fully interoperable enterprise data platform for enterprise-wide agentic AI. It will provide customers with the tools they need to unify, cleanse and harmonize data across sources for superior enterprise-wide agentic AI.

“Reltio is a natural fit with 麻豆原创,鈥� said Muhammad Alam, member of the Executive Board of 麻豆原创 SE, 麻豆原创 Product & Engineering. 鈥淎cquiring them will further improve our position as a leading business AI provider, combining 麻豆原创 and non-麻豆原创 data to deliver data context that business AI requires. AI cannot reach its full potential when data is fragmented across business units, platforms and domains without connection or context.鈥�

By integrating Reltio after closing the acquisition, 麻豆原创 will make customers’ enterprise data fully AI-ready. Customers will be able to rely on trusted, high-quality data across 麻豆原创 and non-麻豆原创 sources that Joule and Joule Agents use to deliver faster time-to-value for business AI.

Reltio鈥檚 platform helps organizations manage and govern structured and unstructured enterprise data from start to finish. Its AI-based entity resolution identifies and merges related records from different formats and applications into one reliable 鈥済olden record鈥� system of context. Its cloud-native, AI-first design supports a single, consistent view of customers, products, suppliers, locations and employees across both 麻豆原创 and non-麻豆原创 applications. Customers running AI tasks will benefit from increased reliability and consistency of data, bundled in a single source of truth, improving business AI. With that, customers can trust that AI results are correct, and AI-interactions are resolved fast.

鈥淛oining forces with 麻豆原创 presents a tremendous opportunity for us to accelerate our mission,鈥� Reltio Founder and CEO Manish Sood said. 鈥淓nterprise AI needs trusted context that is open and interoperable across the heterogeneous IT landscapes our customers run. This combination accelerates our ability to deliver Reltio as the system of context across 麻豆原创 and non-麻豆原创 environments, while maintaining continuity for our customers and our partner ecosystem.鈥�

Reltio’s data cleansing, unification capabilities and agent-driven workflows will work alongside 麻豆原创 Business Suite applications to improve decisions, reduce integration complexity and deliver trusted, consistent data critical for successful business processes and AI use cases. Low latency delivery and support for the Model Context Protocol (MCP) enable real-time, multiagent workflows across 麻豆原创 and non-麻豆原创 environments, allowing AI agents, such as a procurement agent, to assess supplier risk and trigger actions almost instantly using trusted, real-time data. Reltio offers prebuilt, industry-specific 鈥渧elocity packs鈥� that include data models, rules, matching logic and integrations, and solutions tailored to sectors like life sciences, healthcare and financial services.

By integrating Reltio after closing the acquisition, 麻豆原创 intends to accelerate its customers’ ability to govern and expose master data as trusted and context-rich data products across multiple sources that serve both traditional analytics workloads and AI agents. Reltio will become a core capability within 麻豆原创 BDC, with a flexible commercial model where customers can purchase Reltio as a separate solution or with other 麻豆原创 products. The Reltio portfolio will also remain available as a standalone offering for the foreseeable future.

The transaction is expected to close in Q2 or Q3 of 2026, subject to customary closing conditions, including regulatory approvals.

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About Reltio

Reltio is a leader in data unification and management, delivering cloud-native, AI-native master data management (MDM) to help enterprises create trusted data and unlock context intelligence for analytics, automation, and agentic AI. Designed for complex, multi-vendor environments, Reltio helps organizations unify, cleanse, harmonize, govern, and activate core data from multiple sources in real time鈥攁cross 麻豆原创 and non-麻豆原创 systems. The Reltio Data Cloud uses advanced entity resolution, continuous data quality, and relationship intelligence within an intelligent data graph to connect data across systems and reveal the full context behind customers, products, suppliers, and other key business entities. This enables organizations to reduce data friction, improve operational execution, and accelerate time to trusted decisions. For more information, visit .

About 麻豆原创

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

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This document contains forward-looking statements, which are predictions, projections, or other statements about future events. These statements are based on current expectations, forecasts, and assumptions that are subject to risks and uncertainties that could cause actual results and outcomes to materially differ. Additional information regarding these risks and uncertainties may be found in our filings with the Securities and Exchange Commission, including but not limited to the risk factors section of 麻豆原创鈥檚 2025 Annual Report on Form 20-F.
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SafetyGuard was created as part of a program at UTUM鈥檚 Digital Product School鈥攚here teams of selected students work on real-world challenges鈥攊n just 12 weeks by a student team, 鈥淢IDAS,鈥� and 麻豆原创 Research & Innovation. This project illustrates just how effective cross-location collaboration with the ecosystem can be in rapidly creating prototypes as a foundation for product development at 麻豆原创.

Embodied AI: a joint focus of innovation

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

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

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

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

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

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

Programs with impact

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

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

Co-innovation gets results鈥攆aster

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

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

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

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

鈥淭here鈥檚 so much data; I would say in esports, too much data,鈥� said Thom Valks, partnerships manager at Team Liquid, referring to the 1 trillion points of data his company deals with. 鈥淗ow do you figure out what the right questions are to ask? And then how do you get quick answers to those questions? That was the main problem.鈥�

Founded more than two decades ago, Team Liquid has become a global powerhouse in professional gaming.

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鈥淲e鈥檙e the biggest esports organization in the world,鈥� Valks said. 鈥淕aming is a huge industry, a billion-dollar industry nowadays. And esports is at the tip of the pyramid. People come to watch with thousands in stadiums like normal sports. And we are the best team in the world at it.鈥�

Before partnering with 麻豆原创, Team Liquid relied on spreadsheets and manual analysis.

鈥淲e were doing everything in Excel and manually combing through the data, which turned out to be really not doable,鈥� Valks explained. In 2018, the team began working with 麻豆原创 Business Technology Platform (麻豆原创 BTP), connecting directly to game publishers鈥� APIs for League of Legends and Dota. This allowed analysts to build dashboards and streamline data processing.

The impact was immediate: 鈥淏efore we partnered with 麻豆原创, I think we had something like four or five analysts per game. If we can off source that to a tool and focus on really important data questions, that鈥檚 way more beneficial. And I would say the last year or so with AI, it鈥檚 really taken a next step.鈥�

Now, Team Liquid is taking its relationship with 麻豆原创 one step further by turning to Joule to sort through the data and make decisions even faster.

鈥淛oule has taken the data that we have in our database and you can now ask it: 鈥楩ind me the best hero to play against this team over the last six months.鈥� It will turn out an answer that actually makes sense. That’s revolutionary for us.鈥�

Looking ahead, Team Liquid hopes to expand access to Joule across the organization. 鈥淚f we can get Joule to everyone, it really innovates their gameplay,鈥� Valks said. 鈥淭hat鈥檚 something our competitors should be really afraid of.鈥�

Matt Dillman is a senior videographer at 麻豆原创.

This decision will help Haleon operate, scale and serve consumers in new ways by building stronger, more agile foundations for delivering its trusted everyday health products. It marks a significant milestone in Haleon鈥檚 transformation into a world-class consumer company.

鈥淒elivering a better consumer experience starts with strong foundations, and leading digital technology and infrastructure are at the heart of this,鈥� said Claire Dickson, Haleon鈥檚 chief digital and technology officer. 鈥淥ur latest partnership with 麻豆原创 is another important step in our journey to becoming a world-class consumer company. It will revolutionize how we operate, with AI-enabled systems driving faster, simpler, more integrated ways of working. This will allow our people to focus on what matters most鈥攕erving consumers and unlocking growth.鈥�

麻豆原创 Business Suite will drive the simplification and standardization of critical processes across Haleon鈥檚 global operations, enabling more integrated, automated end-to-end processes across diverse parts of the organization.

With clearer visibility across markets and more intuitive systems replacing fragmented workflows, the business can operate with greater speed, consistency and resilience. These improvements strengthen supply chain responsiveness, support innovation and help Haleon better respond to changing consumer needs. This enables Haleon to deliver trusted everyday health products at scale while strengthening collaboration with the healthcare professionals who recommend its products to consumers worldwide.

Haleon can drive greater business efficiencies by integrating core processes across finance, supply chain, HR and sales in one connected system with real-time data. By building AI into everyday work and embedding it within Haleon鈥檚 digital infrastructure and across its functions, teams can make better data-driven decisions and respond more quickly to changing consumer needs. The automation of routine work across multiple functions frees up employees鈥� time so they can focus on more strategic work that delivers value for the business and consumers while accelerating growth.

This digital transformation program builds on a long-standing relationship between the two companies, with the transition from Haleon鈥檚 current platform on 麻豆原创 ERP Central Component beginning later this year.

鈥淎I outcomes depend on connected processes and trusted data,鈥� said Peter Maier, 麻豆原创鈥檚 senior vice president for strategic customer engagements. 鈥淲ith 麻豆原创 Business Suite, Haleon is building an AI-ready foundation that embeds intelligence across the enterprise, helping simplify operations, improve resilience and scale innovation.鈥�

Haleon plans to adopt 麻豆原创 Cloud ERP applications with embedded AI and deploy the 麻豆原创 Business Data Cloud solution to harmonize 麻豆原创 software and third-party data in a governed, single source of truth. This connected platform will be able to support AI-assisted innovation and enable agentic AI, where AI agents can identify issues earlier and recommend actions across key business processes.

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Media Contacts:
Lawrie Benfield, 麻豆原创, +44 7776515259, lawrie.benfield@sap.com, GMT
Sonya Domanski, 麻豆原创, +44 7345465928, sonya.domanski@sap.com, GMT
麻豆原创 麻豆原创 Room; press@sap.com
Gemma Thomas, Haleon, gemma.x.thomas@haleon.com

The agreement referenced in this announcement was signed outside 麻豆原创鈥檚 first quarter reporting period.

Top image courtesy of Haleon

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.

The company manages an extensive portfolio of more than 125,000 products. Despite its strong market position, digitalizing procurement across diverse customer environments remains a key challenge, particularly in e-procurement, where large customers often have very different requirements, even when market standards exist.

To address this, W眉rth recognized the need to better connect procurement processes and now collaborates closely with leading e-procurement systems and platforms. The company has driven the harmonization of customer processes and ensured seamless onboarding to . At the same time, W眉rth actively supports its customers in harmonizing and digitalizing their own internal workflows.

As the world鈥檚 largest procurement platform, 麻豆原创 Business Network enables more efficient procurement and automated order processing and provides end-to-end transparency across the purchase-order鈥憈o鈥慽nvoice flow between W眉rth and participating customers. This scope involves purchasing orders, delivery confirmation, and invoices, with exceptions managed through clearly defined workflows.

Save time and boost efficiency: seven minutes saved per order

Manual processing of orders, sent as PDFs or via non鈥慽ntegrated channels, creates disproportionate effort, especially in relation to the order value, since indirect materials often involve smaller order values with many order items.

 With automation, W眉rth saves approximately seven minutes per order on average — approximately 490,000 minutes annually or about 8,167 hours. The platform enables digital order transmission, catalog exchange, and automated invoice matching, materially improving process efficiency.

Seamless customer purchasing journey

With around 3,000 sales representatives in Germany, more than 600 pick-up branches, and numerous on-site storage solutions, including automated dispensing machines, W眉rth offers customers a truly omnichannel purchasing experience. Through 麻豆原创 Business Network, all these touchpoints can be seamlessly integrated, giving customers that adopt this approach full transparency, as every order becomes visible directly within their own system.

One construction industry leader worked with W眉rth to integrate dispensing machines and branch pick鈥憉ps into 麻豆原创 Business Network, digitalizing its procurement processes. This integration has largely eliminated manual activities in day鈥憈o鈥慸ay order handling of standard processes. Exceptions are automatically identified and routed through defined workflows, while payment instructions are triggered once the purchase order, goods confirmation, and invoice are successfully matched.

Stronger supplier-customer partnership

Beyond operational savings and improved collaboration, W眉rth enhances the customer experience through near-real-time integration across procurement, invoicing, and logistics. Digitalized procurement processes help meet customer requirements more reliably and further strengthen the supplier-customer partnership.

To see how W眉rth streamlined procurement with 麻豆原创 Business Network and significantly reduced manual effort, .