AI is quickly evolving from a set of powerful tools to a central component of the competitive enterprise. Specialized models, AI agents, and AI-native architecture will ensure that AI continues to embed itself into the very core of enterprise operations—with potentially powerful benefits.

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

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

1. New categories of AI foundation models unlock enterprise value

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

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

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

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

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

2. Software evolves toward AI-native architecture

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

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

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

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

3. Agentic governance becomes mission-critical

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

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

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

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

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

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

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

These advancements open the door to varied and entirely new modalities for users to work with enterprise software, as well as “no-app ERP” experiences. For example, to book a customer visit, a worker typically needs to open an analytics application to review the account, look in the CRM system to retrieve the customer’s address, and then navigate to another application to book travel, among other tasks. 

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

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

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

5. Deglobalization drives sovereign AI offerings

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

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

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

Executing on the 2026 AI themes

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

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

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

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

Embedded AI streamlines planning, operations, fulfillment and commerce to help retailers scale with speed, resilience and loyalty

NEW YORK — SAP SE (NYSE: SAP) today announced a new generation of AI-enhanced retail innovations at NRF 2026: Retail’s Big Show.

SAP continues to infuse AI into the DNA of every part of its retail solutions, reinforcing its suite-first strategy and helping retailers operate with greater intelligence, resilience and trust while delivering better experiences for customers everywhere.

“Retailers face a landscape where AI is no longer optional,” said Balaji Balasubramanian, President and Chief Product Officer for Customer Experience and Consumer Industries, SAP SE. “SAP provides one closed-loop, AI-enhanced retail operating system that ties planning, execution and engagement together. We put data and AI at the heart of retail, delivering speed, personalization and growth across every channel and segment.”

AI that turns retail data into actionable intelligence

The Retail Intelligence solution in SAP Business Data Cloud provides accurate demand and inventory planning, leveraging retailers’ data from across SAP software and third-party systems to drive profitable growth through actionable, real-time insights. Purpose-built for retailers and direct-to-consumer businesses, it will be generally available in the first half of 2026.

Harmonizing real-time data from sales, inventory, customers and suppliers, Retail Intelligence uses AI-generated simulations so planners can anticipate outcomes and optimize inventory. This improves forecast accuracy, reduces manual planning effort, lowers inventory costs and raises service levels. All this drives more seamless omnichannel engagements, which strengthen customer loyalty and enable growth without adding complexity for retailers.

“Retailers are seeking built-in, embedded AI solutions to help balance daily operations, future planning and agility to manage a dynamic market,” said Ananda Chakravarty, Vice President of IDC Retail Insights. “What sets SAP apart is the holistic nature of its approach, offering an agentic operating system that works in the background, connects data and orchestrates agents. SAP makes it an easy lift for retailers to achieve enterprise-wide intelligence, avoiding the complexity of many point solutions.”

AI that streamlines modern retail operations

Retailers must make fast, confident decisions across assortments, pricing and planning. To meet that need, SAP announced new AI-assisted assortment management capabilities, allowing planners to create, modify or retire assortments using natural language through the Joule copilot. This reduces the bottleneck on expert users, enabling faster responses to market shifts and freeing time for higher-value merchandising decisions.

SAP also introduced omnichannel sales promotions in sales orders, integrating the SAP Omnichannel Promotion Pricing solution with the SAP S/4HANA Cloud Public Edition, retail, fashion and vertical business solution. This enables advanced promotions such as bonus buys to be applied consistently across diverse channels, enabling a single source of truth for pricing and promotions in store and online, so retailers can deliver a consistent experience.

In addition, SAP is delivering deeper merchandising, segmentation and manufacturing support in the solution, tailored to fashion wholesalers and manufacturers. These enhancements provide the data and process foundation needed for AI-assisted fashion operations across the business.

AI that drives better customer engagement

As shopping journeys increasingly begin with AI assistants rather than storefronts or search engines, retailers need new ways to be present wherever buying decisions are made. SAP helps retailers connect products, pricing, inventory and promotions directly to AI-enabled discovery and shopping experiences, unlocking agentic commerce with its new storefront MCP server, part of the SAP Commerce Cloud solution.

Retailers can now make their storefronts intelligible to AI, driving shopping experiences not only on their storefronts but also on platforms such as ChatGPT. This creates a truly channel-less commerce experience, one where engagement, discovery and transaction happen more seamlessly across human and AI-assisted touch points.

AI that builds customer loyalty

As customer expectations rise and fulfillment networks grow more complex, retailers need confidence that every order will be delivered as promised, using AI solutions that provide proactive visibility and guidance to help keep operations running smoothly and at scale. And as brand visibility shifts in the age of agentic commerce, reliable and consistent shopping experiences are more important than ever to drive sustained customer loyalty and trust.

SAP announced Order Reliability Agent as part of the SAP Order Management Services bundle, planned for release in the second quarter of 2026. The new agent proactively identifies and resolves potential order issues, helping associates answer common questions about order status, stock availability and fulfillment risks before they impact customers.

By combining agentic autonomy with human oversight where judgment matters, these innovations from SAP drive insightful planning and improve operational efficiency, both enhancing the customer experience and driving profitable growth.

Visit the SAP News Center. Get SAP news via LinkedIn and Bluesky.

Media contact:
Mallory Kuno, +1 (425) 239-9362, mallory.kuno@sap.com, ET
SAP Press 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 SAP’s 2024 Annual Report on Form 20-F.
© 2026 SAP SE. All rights reserved.
SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of SAP SE in Germany and other countries. Please see https://www.sap.com/copyright for additional trademark information and notices.

NEWTOWN SQUARE —  SAP SE (NYSE: SAP) today announced the appointment of David Robinson as president of SAP North America. In this role, Robinson will oversee the company’s North America market units and lead sales operations across the broader Americas region. He will focus on driving customer success, accelerating business transformation, and helping organizations realize the full value of their SAP solutions.

“David’s deep understanding of our North American markets and strategic vision will accelerate SAP’s momentum in this geography as we continue to empower businesses with AI embedded in mission critical applications and powered by unified data to drive real business outcomes,” said Jan Gilg, Global President of Customer Success & Americas and member of the Extended Board of SAP SE. “With his appointment, we are bringing special focus and extra leadership capacity to SAP’s most important growth market.”

Robinson has 22 years of SAP experience and proven leadership across multiple business functions. Most recently serving as president and chief revenue officer for Cloud ERP and acting managing director for SAP U.S. Public Services, he has been instrumental in driving SAP’s and customers’ cloud transformation strategies. His SAP career includes leadership roles as senior vice president for RISE with SAP, global vice president and managing director, COO for Customer Success, and regional vice president for business finance in North America. Earlier in his career, he served as chief innovation officer for public services and led industry advisory, value advisory, and product engineering teams at SAP Labs.

Visit the SAP News Center. Get SAP news via LinkedIn and Bluesky.

Media Contact:
Victoria Dixon, +1 (703) 288-6020, victoria.dixon@sap.com, ET
SAP Press 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 SAP’s 2024 Annual Report on Form 20-F.
© 2026 SAP SE. All rights reserved.
SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of SAP SE in Germany and other countries. Please see https://www.sap.com/copyright for additional trademark information and notices.

For decades, BSH Hausgeräte GmbH has been a quiet force in kitchens worldwide, with brands like Bosch, Siemens, and Gaggenau in its portfolio. Now, the company is rewriting its own recipe—not for food, but for finance. In partnership with SAP, BSH is transforming its IT landscape to a cloud-first foundation, aiming to free employees from manual tasks and put insights at the center of decision-making.

In an interview, BSH Head of Governance, Methods, and Systems Heiko Schletz explained how the company is reshaping finance and why its move to the cloud is a critical ingredient to a successful AI-enhanced future.

Technology follows vision

Represented in more than 50 countries, BSH manufactures home appliances in 39 factories worldwide. Schletz’s team oversees group controlling and is responsible for ensuring that financial data flows smoothly from its core systems, such as ERP, all the way up to the consolidated group level. His team manages how financial data is structured and integrated across all global entities, ensuring it can be used effectively for company-wide reporting and decision-making.

BSH is working to bring accounting and controlling together into one integrated process, supported by real-time data and analytics. One principle is guiding this transformation: technology follows vision, not the other way round. As part of its transformation journey, BSH is embracing change by testing new technologies to support its vision.

To simplify reporting efforts, for example, BSH is currently piloting SAP Datasphere, SAP’s next-generation data management platform that can unify and govern all SAP data and seamlessly connect with third-party data.

A recent use case automatically connected accounting balances, controlling P&L data and market metrics in SAP Datasphere and delivering consolidated reports without spreadsheets and manual effort. “This shows where the journey is going—joining sources, bringing them together,” Schletz says.

Breaking down silos to empower AI

BSH’s long-term goal in the financial area is to get rid of silos between accounting, controlling, and treasury. Schletz envisions a parallel ledger architecture that supports both—legal entity and consolidated group views—enabling advanced analytics such as value-driver trees. By moving to SAP S/4HANA Cloud Private Edition, integrated with SAP Datasphere and SAP Analytics Cloud, BSH aims to create a single source of truth for finance spanning from subsidiary ledgers to group-level consolidation.

Schletz is convinced that with a cloud-based, synchronized toolset, his finance team can deliver the latest figures for decision-making faster and with less manual consolidation. “SAP’s AI evolution is running in the direction we also want to go,” Schletz explains. “The technology meets our vision and that’s why it’s a perfect fit.”

The company has relied on SAP solutions for decades, starting with SAP R/3 and now running SAP S/4HANA, SAP Business Warehouse, and SAP Analytics Cloud. The next milestone is cloud migration: RISE with SAP. “In the next two years, we go into the cloud,” Schletz says. “We want a synchronized toolset that gives us a holistic view.”

To get the most out of analytics and AI functionalities, BSH is currently consolidating and simplifying its comprehensive business application landscape. The company’s target is to move from six separate ERP solutions to one global SAP S/4HANA environment that covers all subsidiaries and geographies.

How to prepare an SAP S/4HANA transformation

Schletz’s advice for other organizations exploring the RISE with SAP journey is to start with a clear vision. “If you don’t begin with a concept that combines accounting and controlling, don’t start with SAP S/4HANA,” he says, noting that a finance transformation is not an isolated IT project—it requires alignment across logistics, sales, and customer service. “The SAP S/4HANA conversion is a cross-functional adventure,” he adds.

BSH’s journey is ongoing, but the direction is set. Cloud migration via RISE with SAP, integrated data, and a finance function designed for insight rather than manual effort. “We want the machine to do what it does best, so people can focus on creating value,” Schletz concludes.

SAP has announced a final five months transition period for the usage rights of its Compatibility Packs for SAP S/4HANA on premise, moving the expiration date from December 31, 2025, to the end of May 2026.

Compatibility Packs provide temporary usage rights, allowing certain classical SAP ERP functionalities to operate within SAP S/4HANA, aiming to ease the migration and maintain business continuity for customers moving from SAP ERP to SAP S/4HANA.

For most of the Compatibility Packs, the usage rights end on December 31, 2025, which has been extensively communicated to customers, partners, and user groups and is documented in SAP Note 2269324.

To counter for the fact that despite the extensive communication several customers still need some more time to manage this transition, SAP is offering this final transition period in an effort to provide customers with greater choice and flexibility.

Accompanying this extension, SAP will offer tailored programs for customers that are moving to the respective SAP cloud solutions that replace the compatibility pack functionalities. We encourage affected customers to contact their SAP representative.

Stefan Steinle is EVP and head of Customer Support & Cloud Lifecycle Management at SAP.