New SAP Ariba Spend Analysis and Insights Solution Now Available

Procurement is operating in a fundamentally different environment than it was even a few years ago. The function is still expected to deliver savings, manage suppliers, and support compliance. But those responsibilities now sit alongside a much broader mandate: helping the business navigate volatility, strengthen resilience, and make better decisions in shifting markets.

Even as procurement’s responsibilities have expanded, its ability to deliver on them is being scrutinized more closely than ever. The 2026 Economist Enterprise report, Procurement at a Crossroads: From Optimism to Realism, sponsored by SAP, found that confidence in procurement’s ability to collaborate effectively with the rest of the business fell from 90% in 2025 to 74% in 2026. That’s a significant drop in a single year, and it points to a gap between what procurement is being asked to do and what it’s currently equipped to deliver.

The same report also shows where organizations are focusing their investment. Category and demand management is expected to receive the second-highest level of digital investment among procurement disciplines over the next three years, behind only spend and performance analytics. That finding reflects a practical reality that trusted, timely, and complete spend data has emerged as a foundational requirement for procurement teams expected to deliver measurable value.

Turn spend insights into a strategic competitive advantage

Today, SAP is announcing the availability of SAP Ariba Spend Analysis and Insights, a new solution designed to help procurement leaders turn spend data into intelligent operations.

A unified foundation for spend data

SAP Ariba Spend Analysis and Insights helps give CPOs and their teams complete visibility into where and how the organization is spending. It can bring together spend data from across the business into a unified layer, drawing from SAP Ariba, SAP Fieldglass, SAP Concur, and SAP Cloud ERP, as well as non-SAP systems and external data providers such as market intelligence and economic indicators.

Rather than requiring teams to manually extract and prepare data from each source, the solution can handle that integration continuously, so users can focus on the insights themselves. Because it was built on SAP Business AI Platform, all data retains its original business context and relationships across applications, helping to create a trusted foundation for intelligent decisions and AI-driven operations. Spend data is classified to the United Nations Standard Products and Services Code (UNSPSC) standard and a customer’s own taxonomy, and enriched with corporate hierarchy information from Dun & Bradstreet on a weekly, soon to be daily, basis.

For many organizations, this represents a meaningful shift. Spend data is often spread across multiple systems, structured inconsistently, and difficult to reconcile—and by the time insights are ready, the conditions they describe may already have changed. SAP Ariba Spend Analysis and Insights changes where the work begins, so procurement teams can work from data that is already unified and actionable.

That foundation also matters increasingly for AI. According to the same Economist Enterprise report, 60% of executives identified digital transformation as procurement’s top strategic priority over the next 12 to 18 months, with more than half citing AI as the primary driver. One of the greatest barriers to harnessing AI is the lack of high-quality data. Clean, enriched, and classified spend data feeds into SAP Business AI, enhancing its ability to deliver better results over time through Joule Agents and other intelligent tools.

What’s included at launch

Several capabilities are available from day one, including:

  • Automated data ingestion from SAP-managed source systems—SAP Cloud ERP, SAP Ariba, SAP Fieldglass, and SAP Concur—with data accessible within minutes. Customer-managed source systems, whether SAP on-premise, non-SAP, or third-party data sets, are supported through flexible integration channels.
  • Unified, classified, and enriched spend mapped to UNSPSC v25 or your organization’s custom taxonomy at 95% accuracy. AI and machine learning models are trained to learn your business, support multiple languages, and are continuously refined with human intelligence built over more than 20 years, with SAP among the founding organizations in scaling enterprise-wide spend analysis. SAP Ariba procurement benchmarking helps customers compare performance against industry peers and identify where improvement opportunities exist.
  • Powerful analytics that help answer not just “Where did we spend?” but “Where should we have spent?” and “Where should we spend now and in the future?”—spanning cost savings, risk exposure, compliance, ESG, and productivity.
  • Joule can connect users to curated AI-generated recommendations and agents ready to execute within next-gen SAP Ariba solutions, helping teams move beyond reporting toward guided action while staying connected to the systems and workflows where decisions are made.
  • Native connectivity with SAP source-to-pay solutions can make it straightforward to convert analysis into category initiatives with SAP Ariba Category Management and drive action across other procurement workflows without the manual handoffs that typically slow execution.

An operating model discussion

Procurement’s mandate has expanded. Its data foundation needs to expand with it.

SAP Ariba Spend Analysis and Insights is designed to help procurement teams act with greater speed, advise the business with more confidence, and demonstrate value in terms that senior leaders can measure. This is not only an analytics discussion—it is an operating model discussion.

To learn more, join our webinar, “Rethinking Spend: The New Role of Spend Data in AI-driven Procurement,” on September 24, featuring Rick Gardner of The Hackett Group. The session will explore how trusted spend data and AI can help procurement teams make better decisions and drive greater business value. Register today to reserve your spot.

Organizations that invest in a clean, connected data foundation today will be best positioned to capture the full value of AI tomorrow. With SAP Ariba Spend Analysis and Insights, procurement can move beyond fragmented reporting and toward intelligent operations, using spend data not just to understand what happened, but to guide what happens next.


Callum Veness is senior director of Product Marketing for SAP Procurement & External Workforce.

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TabPFN-3.5 Plus Now Available in SAP AI Core for Instant Business Predictions

WALLDORF — SAP SE (NYSE: SAP) today announced availability of the new TabPFN-3.5 model from Prior Labs, an SAP company.

Capture business-wide AI value with speed and confidence

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

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

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

“Real-world data is rarely perfect. Datasets often contain complex relationships and varying conditions that make traditional machine learning difficult. TabPFN-3.5 is specifically built for these challenges, providing industry-leading accuracy and scalability for tabular data with less manual effort—making it the most effective tabular AI model in the industry to date,” said Philipp Herzig, Chief Technology Officer, SAP SE. “With TabPFN-3.5 and the SAP-RPT model family, customers get accurate predictions from labeled business data in minutes, with no training required. For us, tabular AI is not a supporting feature of the Autonomous Enterprise, it is the foundation.” 

For more detailed information and to learn how to get started on SAP AI Core, see the SAP Community blog, and view the TabPFN-3.5 Technical Report.

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

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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 SAP’s 2025 Annual Report on Form 20-F. 
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SAP BDC Helps Team Liquid Optimize Player Performance

WALLDORF SAP SE (NYSE: SAP) today announced that Team Liquid is using SAP Business Data Cloud to connect gameplay, wellness, biometric and operational data as it prepares for World of Warcraft’s Race to World First, which started August 18, 2026.

Playing the Long Game: Why Team Liquid’s Race to World First Runs on SAP Business Data Cloud

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

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

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

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

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

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

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Playing the Long Game: Why Team Liquid’s Race to World First Runs on SAP Business Data Cloud

In esports, success is often measured in milliseconds. A single decision can decide a match. Yet some competitions test far more than reaction speed and strategic execution. World of Warcraft’s Race to World First is one of those events.

SAP BDC Helps Team Liquid Optimize Player Performance

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

Performance analytics beyond the game

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

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

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

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

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

SAP Certification Marks 30 Years of Validating Skills for an Evolving World of Work

SAP is celebrating the 30th anniversary of its SAP Certification program, marking three decades of helping professionals across the SAP ecosystem validate their expertise and demonstrate their skills. Today, the focus is shifting from what candidates know to what they can actually do, reflecting how work is changing in the AI era.

Since its launch in 1996, the SAP Certification program has provided learners at different stages of their careers with a trusted way to validate their SAP solution skills, while helping organizations identify relevant expertise across SAP’s evolving technology portfolio. The numbers tell part of the story. In the first year alone, nearly 1,000 professionals earned their certification. Since then, hundreds of thousands of individuals have earned an SAP Certification each year as the program has expanded alongside SAP’s portfolio and global ecosystem. But the lasting impact of SAP Certification extends beyond the numbers. For three decades, it has evolved alongside the SAP portfolio, providing a trusted way to validate their expertise as technology and customer needs have changed.

Built to evolve

A defining characteristic of SAP Certification has been its ability to evolve alongside SAP technology. Since 1996, the program has grown from a handful of SAP R/3 exams to credentials spanning the full SAP portfolio. By the 2000s, new certifications covered SAP NetWeaver, SAP Business One, and SAP SuccessFactors solutions. When SAP S/4HANA launched in 2015, certification tracked every step of the transition. Each shift in the technology landscape brought a new wave of credentials, built to validate exactly the expertise the market needed most.

Access expanded, too, moving from SAP training locations to test centers around the world and, later, to fully remote delivery. Digital badges made credentials easier to share across professional networks and in the context of career opportunities. As cloud solutions introduced faster release cycles, the program evolved to match, introducing regular renewal assessments to help learners keep their skills and credentials current.

“Certification has been part of my SAP journey for many years. For me, it has always been about continuing to learn and making sure my skills evolve as SAP technology evolves. I have experienced the changes in SAP Certification firsthand—from on-site exams and webcam-proctored, multiple-choice exams to today’s scenario- and system-based assessments. From my first certification in 2008 to ABAP Cloud certification in 2024, Integration Developer in 2025, and SAP Generative AI certification earlier this year, continuing to learn and validate my skills has helped me take on new challenges and keep moving forward. Even after many years working with SAP, there is always something new to learn.”

Ricardo Resendiz, Software Engineer and SAP Integration Consultant at Deloitte

Certification reimagined

As SAP Certification entered its next chapter, SAP introduced a new approach to validating SAP expertise by expanding how skills are assessed beyond traditional multiple-choice exams to include practical, performance-based assessments. Candidates navigate real challenges in live SAP systems and can use AI tools during exams by design, not exception. It is certification that reflects the profession it is meant to validate.

As part of SAP’s commitment to equip 12 million people with AI-ready skills by 2030, SAP Certification can provide a way for professionals to validate the real skills they need for an AI-first world. Certification now measures how candidates solve real business challenges, not just what they know.

“For 30 years, SAP Certification has helped people demonstrate the skills they need to grow their careers and help organizations get more value from SAP technology. What has kept it relevant is our willingness to evolve. As AI changes how people work, certification must change with it, moving beyond what someone knows to validating what they can actually do.”

Diana Roesner, Head of Certification Transformation

Three decades after the first SAP Certification exams, the program continues to evolve alongside SAP technologies and customer needs. As the reimagined certification approach continues to take shape, SAP Certification remains focused on validating the skills needed today while preparing learners for the future of enterprise technology.

To learn more about SAP Certification, explore available certifications, or learn more about certification reimagined, visit SAP Learning.


Timo Schuette is global vice president for SAP Product & Solution Learning.

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When a Store Starts Thinking

In the heart of SoHo, every storefront competes for attention. During New York Fashion Week (NYFW), SAP is helping bring a fashion retail store to life.

Discover how Industry AI combines industry expertise, business data, and AI to turn insight into intelligent action

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

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

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

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

From display to decision

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

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

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

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

Co-innovation at the heart of industry

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

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

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

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

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

From industry expertise to intelligent action

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

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

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

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


Andre Bechtold is president of SAP Industries & Experiences and chief revenue officer of Industry AI at SAP.

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Beyond the Launch: Tangible Progress in SAP Services and Support for Real-World Impact

Earlier this year, we emphasized that business transformation is a continuous journey, not a static destination. It consistently translates innovation into tangible daily results. Today, we’re thrilled to highlight significant progress in the SAP Services and Support portfolio.

To better drive success, our unified service release model will continue to deliver expanded capabilities seamlessly. This update demonstrates the real-world impact of our strategy, especially as businesses increasingly embed AI into their core operations.

Realizing value: documented progress and strong customer adoption

Discover success plans and services that deliver the results your business needs to be future-ready

Our core mission remains to provide guidance that is simple, predictable, and directly connected to measurable business outcomes. We are delighted to see this approach gain substantial momentum. Hundreds of organizations have adopted our Advanced Success Plan and Max Success Plan, validating that deeper engagement leads to stronger, long-term results. Our customers’ success is our priority. We bring our entire portfolio together to help create a seamless experience, custom-fit to customer needs.

Empowering businesses with next-generation capabilities

To help organizations navigate in today’s dynamic market, we’ve focused our services on three strategic growth drivers:

  • Powering the Autonomous Enterprise: We help streamline processes and boost efficiency by pairing intelligent tools with expert guidance. This includes AI-powered capabilities like Joule for SAP for Me for proactive insights and agentic case resolution to help deliver faster, more intelligent support.
  • Enabling targeted business transitions: We deliver clear, structured pathways that help businesses confidently unlock SAP’s newest AI capabilities and innovations across specific lines of business. These tailored migration paths can simplify transition and integration processes.
  • Driving front-office impact with productivity AI: Our focus can deliver measurable productivity gains for front-office teams through deeply embedded AI capabilities, enhanced release guidance, and strategic engagement planning aligned with business outcomes.

Expanding and integrating support for every stage of the SAP journey

Our SAP Services and Support portfolio is streamlined into three success plans (Foundational, Advanced, and Max) with three supplemental offerings (development services, application management, and professional services), all designed to complement each other to support continuous adoption, innovation, and transformation at every stage of the journey. The focus is on clearly defining what each offering provides, when to use it, and the specific outcomes it can deliver.

Success plans: evolving as the primary engagement model

Our success plans continue to strengthen their position as they offer expanded solution area coverage and AI-powered capabilities. The Foundational Success Plan has full SAP solution area coverage, providing clearer pathways to Advanced Success Plan and Max Success Plan engagements. New expert-led AI services are now available, with enhanced SAP Build coverage to support AI maturity journeys. Furthermore, upgraded release guidance tooling helps improve planning consistency, and automated provisioning for Max Success Plan customers can streamline the engagement process.

The tangible impact of these integrated AI capabilities is already evident, with Joule for SAP for Me seeing strong adoption with nearly 200,000 users year-to-date, while agentic case resolution uses AI agents to help automatically handle and resolve support cases, working to reduce resolution times and minimize the need for manual escalation.

Development services: unlocking the full potential of SAP BTP

In the current release, our development services have been expanded to help unlock the full potential of SAP Business Technology Platform (SAP BTP).

New extensibility packages offer structured guidance and best practice frameworks for custom applications and integrations to enable faster delivery and consistent quality. Furthermore, all managed development engagements formally embed clean core principles, which help make custom solutions upgrade-safe and future-proof. In addition, AI-assisted tooling is now standard in eligible engagements, working to further reduce timelines and improve code quality.

Application management: sustaining continuity and performance

Application management, which handles the day-to-day operations and ongoing optimization of a customer’s live SAP environment, continues to evolve as a critical pillar to help ensure continuity and performance across SAP environments.

SAP enables customers to operate the Autonomous Enterprise with confidence by continuously governing, securing, and optimizing autonomous applications and platform services. AI-assisted monitoring and intelligent incident remediation help proactively identify risks, reduce resolution times, and improve operational resilience. Through an integrated adopt-to-operate approach, SAP can connect adoption, success planning, and application management into a seamless lifecycle experience, helping customers accelerate value realization, maximize business outcomes, and sustain transformation success.

Professional services: structured implementation and transformation

Our professional services can deliver targeted, one-time project expertise, including end-to-end implementations, complex migrations, upgrades, and system optimizations.

With the new release, SAP is simplifying its professional services portfolio to support transformation journeys by aligning with success plans and integrating proven expertise into scalable engagement models. Refreshed outcome-based services help expand coverage across SAP solutions and are designed to align with the Advanced Success Plan and Max Success Plan. Furthermore, formalized handoff protocols between success plans and professional services help foster seamless continuity of project context.

A partnership for continuous success

This unified portfolio release underscores SAP’s collective strength and shared commitment to help transform customers’ SAP experience with a streamlined support portfolio built for impact:

  • Modular choice: Easily scale services to match exact goals.
  • Ongoing optimization: Continuously unlock software potential with proactive guidance.
  • One connected system: Enjoy a frictionless, cohesive experience across the full suite.

Transforming your business shouldn’t be complicated. At SAP, we partner with you to turn complex transformation into measurable, lasting business results. Explore our newly evolved SAP Services and Support portfolio to see how it can help maximize value and drive your strategic goals forward.


Dr. Uwe Grigoleit is senior vice president of Customer Evolution & Portfolio at SAP.

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Lockheed Martin Advances Workforce Transformation with SAP SuccessFactors Solutions

BETHESDASAP SE (NYSE: SAP) today announced that Lockheed Martin has successfully gone live with SAP SuccessFactors solutions, including SAP SuccessFactors Employee Central, SAP SuccessFactors Employee Central Payroll, SAP SuccessFactors Recruiting and SAP SuccessFactors Onboarding, marking a significant milestone in the company’s workforce transformation journey.

Manage your entire workforce with AI-powered cloud solutions

The deployment brings together core human resources (HR) processes on a flexible cloud foundation designed to simplify HR operations, enhance employee experience and support a global workforce. 

A leader in aerospace, defense and advanced technology solutions, Lockheed Martin is modernizing its HR landscape by moving from legacy systems to a unified platform. The new environment provides a more connected experience for employees and managers while supporting greater consistency and operational efficiency across the enterprise.  

With almost 60% of its 123,000 employees being engineers, scientists and technologists, attracting, developing and retaining highly skilled talent at scale is critical to Lockheed Martin’s continued success. By creating a single source of truth for workforce data, Lockheed Martin is gaining clearer insight into its workforce, enabling more informed talent decisions. 

This transformation allows Lockheed Martin to accelerate its robust applied AI strategy across the workforce to help employees and managers access information more efficiently and unlock new insights across the employee life cycle. 

“Lockheed Martin is advancing an enterprise-wide transformation of exceptional scale and complexity, while meeting the rigorous demands of a highly regulated industry,” said Thomas Saueressig, Member of the Executive Board and Chief Customer Officer of SAP SE. “This milestone reflects Lockheed Martin’s commitment to modernizing workforce operations and investing in technologies that support employees, strengthen organizational agility and position the company for future innovation.”

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Top image courtesy Lockheed Martin

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 2025 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.  

From AI-Enabled Payroll to Autonomous Payroll: The Next Evolution of Workforce Trust

As AI becomes more deeply embedded across HR and business operations, one question continues to surface: how can organizations maintain employee trust while increasing automation? For payroll leaders, it’s an especially important question.

Payroll presents a unique paradox. It’s one of the business functions most suited to AI and automation because it’s highly structured, data-intensive, and compliance-driven. At the same time, it’s one of the most trust-sensitive functions in the enterprise because even small errors can have immediate consequences for employees’ financial well-being and confidence in their employer.

Payroll professionals often joke that nobody notices payroll when it goes right, and everyone notices when it goes wrong. Behind that observation is an important reality: every payroll run becomes a moment of trust between employer and employee. That trust has measurable consequences. According to new research* by SAP’s Future of Work Research Lab, 38% of employees worldwide have experienced a payroll error. These employees are significantly less likely to trust both their organization (13% lower trust) and their payroll function (17% lower trust). Trust declines even further with each additional payroll error experienced.

Replace fragmented HR systems by automating processes, simplifying tasks, and strengthening compliance

As National Payroll Week takes place across both the U.S. and the UK, we have an opportunity to recognize the payroll professionals who earn and maintain that trust every day, while reflecting on the transformation taking place across the payroll function itself.

As payroll teams navigate growing complexity, they’re being asked to do more with less while maintaining the accuracy, compliance, and reliability employees depend on. Employees increasingly expect faster, more flexible payroll experiences, including more frequent pay cycles and greater visibility into earnings and deductions, while tax laws, wage rules, leave requirements, and reporting obligations continue to evolve across countries, states, and local jurisdictions. At the same time, today’s workforce is more dynamic than ever, with employees changing roles, locations, schedules, and compensation arrangements in ways that can create downstream payroll impacts. Managing these changes manually is becoming increasingly difficult and raising the risk of errors, delays, and compliance gaps, positioning payroll not simply as an administrative process, but as strategic infrastructure supporting employee experience, compliance, finance, and business operations.

This combination of rising complexity and rising expectations is driving the next evolution of payroll: autonomous payroll.

Scaling trust in an era of complexity

Autonomous payroll is designed to help organizations navigate growing payroll complexity while maintaining accuracy, compliance, and confidence at scale. By combining AI, intelligent automation, integrated data, and real-time payroll monitoring, organizations can move beyond reactive payroll operations and proactively identify issues before they affect employees.

The goal isn’t to remove people from payroll, but to enable them to focus on higher-value activities by reducing manual effort and increasing visibility. Our global research suggests that employees do not see AI and human involvement as mutually exclusive. In fact, nearly half (49%) say they would trust AI in payroll more if they still had access to a human when AI fails. Additionally, 43% of employees say that the ability to request a human review of AI-generated outcomes would increase their trust in the use of AI in payroll. Trust is built not only through accuracy and efficiency, but also through the confidence that employees can escalate concerns, seek clarification, and access human support when needed.

Emerging capabilities such as AI-powered payroll agents and continuous payroll are helping organizations move from processing payroll to actively managing it. These capabilities can assess payroll readiness, detect anomalies, validate changes, and monitor workforce events across HR, payroll, and time data. By identifying how changes in roles, locations, schedules, compensation, or leave may affect payroll outcomes, organizations can anticipate impacts and resolve exceptions before they become costly errors. The result is improved reliability, stronger compliance, and greater trust across the workforce.

The shift is significant. Rather than finding errors at the end of a payroll cycle, organizations can increasingly investigate and resolve them throughout the cycle. But technology alone isn’t enough. As payroll becomes more autonomous, organizations must ensure intelligent systems operate with appropriate oversight, transparency, and accountability.

Automation requires accountability

As AI becomes more embedded in payroll operations, the conversation shifts from adoption to accountability. Trust becomes paramount, raising important questions about where AI can operate autonomously, where human review adds value, and where human accountability must remain visible.

Our global research shows that employee comfort with AI varies by payroll task. Employees are most comfortable with AI supporting calculation-heavy activities such as payroll calculations and anomaly detection, while they prefer human involvement for tasks requiring judgment, investigation, or employee interaction. Final decisions on payroll disputes remain the area where employees most strongly prefer a human. These findings reinforce an important point: the goal is not to replace human expertise, but to apply AI where it adds the most value while preserving human oversight where trust and judgment matter most.

The most successful applications of AI in payroll won’t simply automate tasks. They’ll strengthen trust by improving consistency, reliability, transparency, and the overall payroll experience employees depend on.

In other words, the future of payroll isn’t human or AI. It’s human expertise amplified by AI.

Building the foundation for Autonomous HCM

The implications of autonomous payroll extend well beyond payroll operations.

At SAP, we believe autonomous payroll is a key enabler of Autonomous HCM. Trusted AI experiences depend on trusted data, connected processes, and embedded intelligence, and payroll plays a critical role in making that vision possible.

Every hiring decision, compensation adjustment, promotion, workforce change, or organizational initiative ultimately affects payroll. As a result, payroll remains one of the most trusted and comprehensive sources of workforce data across the enterprise.

Realizing the full potential of autonomous payroll requires a unified foundation across HR, payroll, and time data. When organizations establish a single source of truth, they can reduce complexity, improve payroll accuracy, strengthen compliance, and enable AI to deliver more meaningful insights and recommendations.

This foundation is essential because AI cannot solve payroll challenges if it’s simply layered on top of fragmented systems and disconnected processes. Instead, organizations need connected data and integrated workflows that allow intelligence to operate across the entire workforce lifecycle.

When payroll, HR, and time data come together on that foundation, organizations can create more intelligent workforce experiences, make better decisions, and increase organizational agility.

Looking ahead

National Payroll Week is an opportunity to celebrate the professionals who keep one of the most important business functions running every day. It’s also an opportunity to recognize how dramatically that function is evolving.

For decades, payroll was viewed primarily as an administrative necessity. Today, it is increasingly recognized as a strategic capability that influences employee experience, compliance, operational resilience, and organizational trust.

The organizations that lead in the years ahead won’t simply process payroll more efficiently. They’ll create payroll operations that are more intelligent, more connected, and better positioned to support both employees and the business.

As payroll continues its evolution from a transactional function to a strategic business capability, the opportunity is not simply to automate existing processes. It’s to build payroll operations that can scale trust, resilience, and confidence in an increasingly complex world.

Because when payroll works, it does more than deliver pay. It helps build confidence in the organization behind it.

Discover how SAP SuccessFactors helps organizations simplify payroll, reduce complexity, and accelerate their journey to autonomous payroll.


*Data from a global survey of 1,576 full-time employees in July 2026.

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Sometimes the job of the FBI is not only to track criminals, but also to collect evidence, documents, photos, and witness statements.

AI’s Finance Challenge: Managing Token Spend Without Slowing Innovation

AI token spend is emerging as a new enterprise resource—one that finance must learn to forecast, allocate, and optimize against the value it delivers.

Generative AI is moving quickly from experimentation to essential infrastructure. Employees have woven it into routine daily tasks, and teams and applications are leaning on it more heavily every quarter. That growth comes with a new cost category finance wasn’t designed to handle: AI token consumption.

When AI is writing code and powering agents, token consumption can outpace traditional planning processes. Finance leaders suddenly find themselves in unfamiliar territory, needing to bring discipline to AI spending without becoming the function that kills adoption. Getting the balance right means treating tokens as an enterprise resource that must generate returns commensurate with its cost.

At SAP, we have been working through these questions firsthand. Here’s what we’ve learned from building, testing, and adjusting that framework.

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You can’t manage what you can’t see

Most finance leaders wouldn’t manage a major cost category from a single line item, yet that’s exactly where companies start with AI. Total spend matters, but it won’t tell you which teams, workloads, or usage patterns are driving that spend, or whether the consumption is actually producing useful outcomes.

Getting that visibility requires real collaboration across commercial, engineering, finance, and product teams. Finance brings the forecasting questions and accountability framework. Other functions bring the operational context that makes the numbers meaningful.

In our experience, repeated forecasting cycles generally improved our financial models as we layered in more operational detail around usage. The broader lesson: when a cost category is new and fast-moving, don’t wait for precision before acting. Start with enough transparency to make better decisions, then sharpen the model as patterns emerge.

Someone has to own it

Our most consequential insight was philosophical rather than financial. We learned that visibility alone isn’t enough and that consumption needs an owner.

A centralized AI budget makes early experimentation easy, but it also disconnects the people spending tokens from any financial accountability for them. As AI becomes more deeply embedded in business processes, that model breaks down.

This isn’t an argument for immediately charging back every LLM call with forensic accuracy. It’s an argument for managing AI consumption the same way companies manage other enterprise resources such as software, external services, and labor. Give decision-makers a clear picture of what their teams are consuming and what outcomes that consumption is expected to produce.

At SAP, allocating token costs to business areas has shifted the conversation from “How much are we spending?” to “What are we getting for this?” and “Is this the right place to invest more?” That’s a healthier conversation.

Token spend is not a technology line item. It is a strategic and operational investment decision. The goal is to make AI spending intentional, not just cheap.

Cost per token is the wrong scorecard

A large AI bill draws attention, but optimizing purely on cost can lead to exactly the wrong decisions.

The more useful question is the relationship between consumption and business impact. An AI tool that meaningfully accelerates software development, reduces repetitive work, or improves customer service will carry real token costs. Cutting that usage simply because the line item is visible could destroy more value than it saves.

We saw this firsthand. After rolling out AI developer tools at SAP, we recorded a mid-double-digit percentage increase in pull-request merge rates, a clear signal that development work was moving faster. The consumption was worth it.

Finance needs a paired view: cost metrics alongside value metrics. Governance without that view risks optimizing for cost at the expense of value creation.

Guardrails should target waste, not adoption

As usage scales, controls become necessary, but the right controls are surgical, not sweeping. When we examined consumption patterns in detail, three root causes of disproportionate spend emerged: power-user and automated-agent concentration, model misalignment, and tool proliferation.

Those are the areas where guardrails earn their keep. Controls matter because they focus on the sources of avoidable spend rather than putting a blanket brake on usage. At SAP, our response centered on three levers: token capping to prevent runaway consumption, model routing to better match capability and cost to the task, and tool rationalization to eliminate redundancy and concentrate investment where utilization justified it. That work helped contain a triple-digit-million-dollar financial risk while keeping adoption moving forward.

The principle is simple: remove waste, preserve productive demand.

From cost control to value governance

AI isn’t going to pause for the next planning cycle. Its capabilities, usage patterns, and economics will keep shifting, and the governance model around it needs to keep pace.

The work is not complete. The next step is to embed these practices into regular planning and reporting, assign clearer ownership, improve allocation, and build forecasting capabilities that can anticipate where costs are heading before they arrive.

Companies that do this well will still have an AI bill to pay. But they will have the transparency and accountability to tell the difference between consumption that is creating value and consumption that isn’t and direct investment accordingly.


Lukas Deutsch is chief controlling officer at SAP.
David Imbert is chief marketing officer for SAP Financial Management.

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