I’m proud to share that SAP has once again been recognized as a Leader in the 2026 Gartner® Magic Quadrant™ for Warehouse Management Systems (WMS)*. In our opinion, this recognition underscores SAP’s continued commitment to innovation in warehouse execution and reflects the confidence our customers place in SAP to run some of the world’s most complex, high-performance, and mission‑critical warehouse operations.

In today’s environment of ongoing disruption, rising customer expectations, and persistent labor and cost pressures, warehouse operations are more strategic than ever. We believe long-term recognition in this market requires more than point solutions; it demands scale, depth, and an orchestrated approach that connects warehousing with other supply chain functions.

A recognition we believe is built on scale, reach, and execution

We believe scale matters when it comes to warehouse management. Warehouse operations are increasingly complex and diverse, and organizations often operate networks that span regions, industries, and levels of automation. Warehouse management is not a one‑size‑fits‑all challenge, and global organizations need solutions that can support regional requirements while remaining consistent and integrated across complex networks.

SAP Extended Warehouse Management (SAP EWM) is designed to support organizations running SAP ERP or SAP S/4HANA as well as non-SAP environments. With customers across 24 industries, SAP EWM supports a broad range of warehouse complexity—from regional distribution centers to highly automated, high‑throughput operations. It enables organizations to standardize execution, improve visibility, and integrate warehouse processes with transportation, manufacturing, and broader supply chain operations.

SAP EWM continues to expand its innovation footprint with APIs for seamless robotics integration, as well as AI-assisted capabilities such as predictive labor demand planning and slotting. SAP’s Joule AI solution—embedded across applications—can further enhance SAP EWM by enabling natural-language interactions and supporting AI capabilities that operate both within workflows and in the background, helping organizations improve workforce efficiency and execution agility.

A portfolio approach to meet different warehouse needs

SAP’s warehouse strategy is built around choice and flexibility. In February 2026, SAP announced the general availability of SAP Logistics Management, an AI‑enabled, cloud‑native, microservices-architecture-based solution designed to complement SAP’s established logistics portfolio by supporting local, satellite, and mid‑scale logistics operations.

SAP Logistics Management helps unite warehouse execution and transportation planning in a single solution, supporting pick‑pack‑ship processes while enabling integrated freight coordination and carrier collaboration through SAP Business Network. Designed to work seamlessly with SAP Cloud ERP Private, the solution helps organizations connect localized operations with broader enterprise landscapes, improving coordination and real‑time visibility across logistics networks. Embedded AI capabilities, including support for Joule, can assist users with faster decision‑making and more intuitive interactions across logistics workflows.

Together, SAP EWM and SAP Logistics Management enable organizations to align technology investments with operational complexity, supporting mixed warehouse networks that include both highly automated facilities and smaller, distributed sites.

Platform strategy and supply chain convergence

Warehouse execution does not operate in isolation. SAP’s platform strategy connects warehousing with transportation, manufacturing, quality, maintenance, global trade, sustainability, and partner collaboration across the supply chain.

SAP EWM can be used with SAP Signavio solutions to support process analysis and continuous improvement. It can leverage standardized APIs across SAP ERP and SAP S/4HANA to connect with adjacent execution systems such as transportation management, global trade, environmental health and safety, and SAP Digital Manufacturing. This platform-based approach helps organizations move beyond siloed execution toward coordinated, end‑to‑end supply chain

Looking ahead

SAP remains focused on helping customers modernize warehouse operations while navigating broader ERP and cloud transformations. While organizations must carefully evaluate deployment models, cloud strategies, and long‑term road maps, SAP continues to invest in warehouse management solutions that scale globally, integrate deeply, and support evolving business requirements.

We believe that being recognized by Gartner as a Leader for the 12^th consecutive time reflects continued execution across SAP’s warehouse management portfolio and the trust customers place in SAP to manage critical logistics operations worldwide.

Read the full Magic Quadrant report from Gartner to learn more about why SAP is a Leader. Learn more about the capabilities of SAP Extended Warehouse Management.

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

*Gartner, Magic Quadrant for Warehouse Management Systems, by Simon Tunstall, Rishabh Narang, Federica Stufano, 29 April 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’s 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.

WALLDORF and AUSTIN — SAP SE (NYSE: SAP) and Dremio today announced that SAP has agreed to acquire Dremio, an open, high-performance data lakehouse platform built to accelerate agentic AI and expand SAP Business Data Cloud’s ability to combine SAP and non-SAP data to more effectively run analytical and AI workloads in real time.

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

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

“Enterprise AI doesn’t stall because the models aren’t good enough; it stalls because the data isn’t ready for AI agents,” said Philipp Herzig, CTO, SAP SE. ” Dremio eliminates that bottleneck. Combined with SAP Business Data Cloud, we can now take customers from raw, fragmented data to governed, AI-ready intelligence on a single open platform.”

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

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

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

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

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

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

About Dremio

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

About SAP

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Let me start with a simple experiment: Ask a generative AI tool to count the words in a document. It will likely be off by 10%.

In a blog post, that’s tolerable. In a financial disclosure, a regulatory filing, or a supply chain commitment, it is simply unacceptable.

Generative AI is statistical. Answers to enterprise level problems are a lot more deterministic. The distance between 90% and 100% accuracy is not incremental. In our world, it is existential.

In 2026, AI is no longer evaluated on novelty. It is evaluated on precision, governance, scalability, and business impact. As organizations move from pilots to scaled programs, five moments will define whether they capture lasting value or expose themselves to avoidable risk. I have seen these moments play out across every major market I oversee.

1. The governance moment: when agents become digital coworkers

The first moment arrives when AI stops being a tool and starts being an actor.

Agentic AI systems plan, reason, orchestrate with other agents, and execute workflows autonomously. They touch sensitive data and influence decisions at scale. If you are not already governing them as you govern your human workforce, you are exposing your organization to risk.

Agent sprawl will mirror the shadow IT crises of the past decade, but the stakes are categorically higher. Enterprises must establish agent lifecycle management, clear autonomy boundaries, policy enforcement, and continuous performance monitoring. Every board needs to answer three questions: Who is accountable when an agent makes the wrong call? How are decisions audited? When does the machine escalate to a human?

Geopolitical fragmentation compounds this urgency. Sovereign cloud, sovereign AI, and data localization are no longer theoretical concerns. They are regulatory realities in markets from New York to Frankfurt to Riyadh to Singapore. Governance in the age of AI is less about controlling risk at the edge and more about embedding deterministic control into probabilistic intelligence. That is a C-suite mandate, not an IT project.

2. The data foundation moment: when the last mile is the only mile that matters

The second moment is quieter, but it is where most enterprises will ultimately win or lose.

AI is only as reliable as the data and processes it operates on. Fragmented master data, siloed systems, and over-customized ERP landscapes introduce unpredictability at the worst possible moment: when AI provides a recommendation that affects your customers, your cash flow, or your compliance position.

Enterprise AI value will not come from generic large language models trained on internet-scale text. It will come from intelligence grounded in your enterprise data—orders, invoices, supply chain records, financial postings—embedded directly in your processes. Relational foundation models optimized for structured business data will outperform generic LLMs in forecasting, anomaly detection, and operational optimization.

The question every board should be asking is not only “What AI can we add?”, but also, “Is our data estate ready, or are we layering probabilistic intelligence onto fragmented foundations?”

3. The employee interaction moment: when the interface disappears

The third moment happens in your employees’ daily workflows, and it will accelerate faster than most organizations expect.

In 2026, we are moving from static application interfaces to generative user interfaces. Instead of navigating between systems, employees express intent: “Prepare a briefing for my highest-revenue customer visit this week.” AI agents orchestrate the workflows, assemble the context, and surface recommended actions.

But adoption is not automatic, and trust is not given. Employees will embrace AI teammates only when they are confident that outputs respect governance boundaries, reflect real business rules, and deliver measurable gains. Role-specific AI personas tailored for the CFO, the CHRO, the head of supply chain, built on trusted data and embedded in familiar workflows, are what will close the adoption gap.

Organizations that invest in AI-native architecture will accelerate ROI. Those that bolt AI onto legacy interfaces will struggle with trust, usability, and scale. This is a design decision with strategic consequences.

4. The customer moment: when intelligence becomes a competitive moat

AI proves its enterprise value most visibly at the customer edge.

Trained on your own data, your own policies, and your own interaction history, customer-specific intelligence compounds in ways that competitors cannot easily replicate. This is especially powerful in exception-heavy environments: dispute resolution, claims handling, returns management, service routing. AI that can classify cases, surface relevant documentation, recommend policy-aligned resolutions, and learn continuously from outcomes transforms these high-cost, high-friction processes into sources of competitive differentiation.

In 2026, your customers will not reward novelty. They will reward reliability, relevance, and responsiveness. Organizations that use AI to absorb complexity, without losing control over outcomes, will build moats that generalist tools cannot breach.

5. The strategy moment: when you decide how far to go

The final moment is the one that falls squarely on leaders.

AI adoption is not a single journey. It requires leaders to orchestrate three layers in parallel:

• Embedded AI: Persona-driven productivity gains built into core applications for immediate returns
• Agentic AI: Multi-agent orchestration of complex, cross-system workflows
• Industry AI: Deeply specialized applications co-developed to address the highest-value challenges specific to your sector

The trap is false sequencing: focusing only on embedded AI leaves value on the table and jumping to deep industry transformation without governance and data maturity multiplies risk. The organizations that will lead are those that align ambition with readiness and invest in clean core architecture, modern data foundations, and cross-functional AI ownership, while moving decisively from pilots to programs.

The leadership test

In 2026, the winners will not be those with the most AI features. They will be those who treat AI as a core operating layer, governed like a workforce, grounded in trusted data, tailored to employees and customers, and calibrated to the realities of their industry.

The gap between 90% and 100% is precisely where enterprise value lives. It is also where leadership is tested. The decisions you make in the coming months will determine whether AI becomes your most powerful source of durable advantage or your most expensive lesson in misplaced confidence.

This is the moment to move with precision.

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

For 50 years, if you weren’t a billion-dollar company, you could not afford to run your business with the same precision, depth, and intelligence as the world’s best.

The software itself wasn’t the barrier; the operational weight around it was: dedicated data centers, expensive hardware, annual upgrade cycles that consumed months of IT resources, and the specialist teams to keep it all alive.

SAP Business AI and SAP Cloud ERP have completely changed the economics of enterprise software. The heavy infrastructure disappeared into a subscription. A 200-person company can now run its core business processes as efficiently as a global enterprise, on a predictable monthly cost, without an army of IT staff.

AI accelerates this further. What took months of configuration and specialist knowledge can now be activated through natural language and intelligent automation. The deep industry expertise SAP spent 50 years encoding into its software is now accessible to businesses of all sizes.

“John Boos is a 137-year-old company, with 137 years of tech debt,” said Britt East, CIO at John Boos & Co. “To make matters more complex, we are growing incredibly fast. Every quarter is a record quarter! SAP Cloud ERP will be the backbone of our business in perpetuity, giving us a standard and scalable foundation to support growth while also unleashing our workforce with real AI use cases that make their lives a lot easier and the company as a whole more successful.”

The real value of SAP Business AI is that a midsize manufacturer in Stuttgart or a growing logistics company in Dallas could access intelligent business operations at speed and price point they can afford.

Won’t AI then replace software altogether?

Think of it this way: GPS system is genuinely intelligent. It calculates optimal routes, adapts to real-time traffic, and reroutes dynamically. But it is only as good as what backs it—the data underneath it, like accurate roads, turn restrictions, and governance for local speed limits, timeframes for live incident feeds and so on. Without the structured, maintained, trusted data layer, the intelligence has nothing to work with—it would confidently lead you off a cliff.

Software is not being replaced by AI. Software is becoming AI’s superpower.

With deep process and industry knowledge, semantically rich business data and enterprise-grade governance built in,  AI gets what it lacks on its own to deliver reliable, battle-proven, trustworthy, repeatable, and auditable results—every time. Agents are probabilistic. They predict, they infer, they move fast, and that is powerful. But it means that the more AI agents you deploy, the more valuable your underlying software systems become.

And the cost? Running a stack of AI tools adds up to significant infrastructure investment, fast. However, serious software companies, including SAP, have already embedded their AI directly into their platforms, and they often co-develop with leading AI providers, so you are not choosing between AI and SAP. You’re choosing SAP with AI already inside it.

“Many companies used to delay decisions because ERP felt too complex,” shared Tobias Siebler, CEO of FULCRUM Consulting Germany. “That has changed. With SAP Cloud ERP, you can start small, get live quickly, and still have a setup that grows with the business, including the current and new AI capabilities as they become available.”

The new stack: What this actually looks like

Imagine a shipping company that processes 10,000 orders a day. Traditionally, humans monitored exceptions, chased suppliers, and rerouted freight when things went wrong. Today, AI agents can scan the full order pipeline in real time, flag anomalies, draft supplier communications, and propose rerouting options—all within the governed environment of SAP’s supply chain data. Humans are irreplaceable in making the final call, but the agents do the legwork.

With Joule, work starts with what needs to be accomplished, not which system to open. Teams move from intent to execution in real time. Decisions are shaped by data, operational capacity, financial constraints, and customer demand.  AI agents handle coordination across workflows. People make the calls that matter. The whole process runs on the unmatched human ability to make decisions based on multifaceted considerations, supported by auditable, structured data.

That is the model. AI can’t replace the system. AI operates inside the system, supervised by humans and connected to real business data, constrained by real business rules and governance, delivering real business outcomes.

AI needs rich, structured, semantically meaningful business data to perform. SAP has 50 years of exactly that.

For fast-growing companies: SAP GROW Fast

Markets shift. Expectations evolve. Technology accelerates change. Naturally, our customers demand quicker and better results. SAP GROW Fast services are designed to help customers go live with AI-ready SAP Cloud ERP with speed and predictability. The deployment of finance and spend core capabilities for SAP Cloud ERP, as well as other SAP solutions on the way, can be done in months, not quarters. And from there, the business can expand into the rest of SAP Business Suite fast, all activated with AI from day one.

Companies taking advantage of SAP GROW Fast are gaining compound advantages with a platform that becomes more capable with every AI advancement that SAP and its partners embed into it. The companies that are waiting? They will be implementing what the leaders deployed today—three years from now.

The human element is not going away, it’s going up the stack

As we disrupt everything we do and work with AI to achieve better, faster business outcomes, SAP partners become key change agents. All around the globe, SAP partners are being enabled to extract business value quickly for our customers with the AI-ready SAP GROW Fast services. This is a step-by-step change into a world of AI-first business value adoption and should be leveraged by all our partners.

“Many organizations still assume that SAP is designed exclusively for large enterprises,” explained David Bayón Esporrín, go-to-market director of the Global SAP Practice at INETUM. “In reality, that perception no longer reflects today’s market. With SAP Cloud ERP, and especially with SAP GROW Fast, companies of almost any size can optimize core business processes and harness the power of AI to accelerate growth in a simple and cost-effective way.” (See the full video interview.)

We are living through a platform shift, not unlike the one the internet created. The businesses that thrive will be the ones that move with intention, combining the intelligence of AI with the governed, structured, operationally rich foundation that enterprise software provides.

The great equalizer is here. The only question is: How fast do you want to use it to your advantage?

Santina Franchi is president of the Corporate Segment at SAP.
Guido Beuningen head of AI and Public Cloud for the Corporate Segment at SAP.