For more than a decade, SAP customers have been wrestling with a familiar challenge: how to move from legacy SAP ERP Central Component (SAP ECC) environments to SAP S/4HANA without creating transformation programs that are prohibitively expensive, complex, or time-consuming.

Now, agentic AI could fundamentally change that equation, creating a different model for SAP transformation: one built around faster delivery, lower costs, and greater customer self-sufficiency.

That was the central argument put forward during a recent webinar examining what its participants described as “deshoring,” which uses AI agents to rethink work that organizations previously distributed between expensive onshore resources and lower-cost offshore teams.

For Stuart Browne, founder and CEO of Resulting IT, an independent SAP consultancy that spent the past seven years helping companies chart their journeys from SAP ECC to SAP S/4HANA, the opportunity starts with questioning assumptions the industry has accumulated over decades.

“The only way we deliver this in the future is by changing the way we’ve delivered it in the past,” he said. “So we’ve got to find new ways of accelerating the migration, and I think AI is probably the best bet for that.”

Why not faster, better, and cheaper?

The traditional technology transformation triangle says organizations might have projects that are faster, better, or cheaper, but generally only two of the three.

Browne challenged that premise by drawing an analogy to NASA’s “faster, better, cheaper” approach to missions. He argued that SAP transformations should similarly reconsider the assumption that improving one dimension requires sacrificing another.

“Why can’t you choose all three of these things?” he asked.

That question becomes increasingly relevant as companies confront the remaining volume of SAP S/4HANA migrations while simultaneously dealing with economic pressure, scarce SAP skills, and the complexity of global transformation programs.

Ranjeet Panicker, senior vice president and head of Business Transformation at SAP, framed the challenge around the complete life cycle of an SAP project, from initial discovery and analysis through design, build, and ultimately run.

At every stage, customers are asking the same questions: How can the project be completed faster? How can it cost less? How can my organization extract more value from the investment?

From offshoring to “deshoring”

For decades, mechanisms for reducing delivery costs have been moving work offshore. The economics were relatively straightforward as certain activities were moved to locations where labor costs are lower.

Browne argued that those savings can obscure another problem. While offshore resources may cost less, distributing work between offshore and onshore teams can increase the overall volume of work through handoffs, communication issues, rework, and coordination.

Agentic AI introduces another possibility. Instead of asking where human labor should be located, organizations ask whether some of that labor needs to be performed manually at all.

“Why can we not reduce the volume of work and reduce the cost of work?” Browne asked.

That question led to the concept of deshoring—replacing portions of location-based delivery with AI agents capable of performing or accelerating specific SAP transformation activities.

After analyzing roughly 180 typical activities involved in an SAP ECC to SAP S/4HANA migration, Browne’s research concluded that AI could potentially produce about a 60% cost reduction by compressing effort and allowing tasks previously requiring highly experienced people to be performed by less experienced workers augmented by AI.

Some of the most attractive candidates are highly skilled activities that consume significant amounts of time, including writing functional and technical specifications, performing fit-gap analysis, and analyzing custom code.

“If your run rate is a million a month,” Browne noted, “eliminating months from a program can dramatically change its economics.”

The 80% solution with humans handling the last mile

AI may be capable of producing what Browne characterizes as an 80% solution within hours. It means experienced people review, challenge, and refine that work rather than spend time manually producing everything from scratch.

“What AI produces shouldn’t be fully trusted without review,” he said. None of this means eliminating experienced SAP professionals. Instead, the emerging model redistributes where their expertise is applied.

The objective is not autonomous transformation. It accelerates the majority of the work while concentrating human expertise on the “final mile” that includes judgment, validation, design decisions, and other activities where experience adds the most value.

Custom code could be an early breakthrough

One area where Browne believes AI is already producing significant change is custom code analysis. Early SAP S/4HANA migrations often focused on getting existing customizations into the new environment and then determining how to remediate them. Agentic AI creates another option that determines whether the customization needs to exist at all.

According to Browne, AI can now analyze an entire custom code base, reverse engineer functional specifications, and determine whether the same business requirement can instead be met using standard SAP functionality.

Rather than migrating large amounts of legacy customization and addressing it later, customers could potentially understand their custom-code landscape and identify opportunities for fit-to-standard before even selecting a systems integrator.

“You can actually plan the fit-to-standard of your custom code before your SI’s have even been appointed,” Browne said.

The implication is significant because customers have already paid for standard SAP capabilities that may eliminate the need for some custom functionality while creating an environment that is simpler to maintain and upgrade.

SAP is building agents across the transformation life cycle

Panicker sees similar opportunities emerging across the broader SAP implementation life cycle.

The terminology of onshore and offshore itself may eventually become less relevant, he argued, because organizations will increasingly think about transformation work in terms of skills rather than locations. AI-led skills can be delivered through assistants and agents and applied across different stages of a cloud transformation.

SAP is targeting areas including system analysis, data management, custom code, configuration, testing, rollout, and project management.

Panicker described an assistant as a collection of agents supporting a particular topic area.

A system-analysis capability can examine the overall transition. Data-management capabilities can address data quality. Custom-code agents can support analysis, recommendations, and, in some scenarios, automated remediation. Configuration assistants can evaluate current and target states, while testing agents can help automate test scripts.

According to Panicker, these capabilities are connected with SAP Cloud ALM running on SAP Business Technology Platform, along with a data and knowledge foundation designed to provide the customer-specific context agents need. SAP’s overall objective, he said, is to reduce transformation effort by approximately 35%.

Testing could become the next frontier

Testing can consume substantial amounts of time, particularly in industries with security, compliance, or validation requirements. AI raises a more complicated question: How much of that testing can organizations eventually delegate to agents?

“If I can get the code to get remediated by an agent,” Panicker said, “can I allow an agent to do the testing and accept the testing?”

The answer will determine where humans remain directly involved in transformation workflows. Customers must decide not only if an agent can perform a task, but whether they have enough confidence in the agent’s knowledge, context, and guardrails to delegate responsibility for the outcome.

Context separates useful agents from hype

SAP programs generate enormous amounts of organization-specific information around architecture decisions, risk registers, test scripts, requirements, and other artifacts that evolve throughout a transformation.

Browne believes connecting AI to this continuously changing body of knowledge will be essential. “The ability to converse not with a static LLM, but to converse with that world as well, I think is what will make good agents even better,” he said.

Organizations may therefore need to rethink not only how they perform SAP work, but how they capture information. Programs traditionally run across Excel, Word, PowerPoint, SharePoint, and other repositories may increasingly need AI-native knowledge environments capable of providing agents with usable business and system context.

“That’s where I think this will be won or lost,” Browne said.

AI could also change who holds the knowledge

Perhaps one of the biggest changes involves something less technical: who possesses expertise. SAP implementations have traditionally depended heavily on experienced consultants and systems integrators. Customers frequently lack comparable knowledge, creating an imbalance that can make it difficult to challenge recommendations or independently evaluate major design decisions.

Browne believes someone with only six months of SAP experience could potentially move up the knowledge curve in weeks in ways that previously might have taken years.

Panicker agreed that access to knowledge is becoming far less constrained.

His own experience at SAP was initially concentrated in technical roles. Learning the business context surrounding that expertise often required finding someone willing to explain it. AI potentially allows professionals to move outside those traditional “swim lanes.”

Challenging the accepted SAP timeline

SAP customers and consultants have grown accustomed to transformations, migrations, and upgrades taking a certain number of months or years. Those timelines have become assumptions embedded into planning.

Both Browne and Panicker believe those assumptions now deserve to be challenged.

“It’s about compressing the time that these tasks make and connecting the decision-makers to be able to make better decisions more quickly,” Browne said.

Panicker similarly argued that organizations should stop accepting conventional project durations without asking what AI capabilities have been introduced to shorten them. “Why can’t we do this sooner?” he asked.

For Browne, the shift is already underway: “I’m not suggesting for one moment that we can press a button and deliver a whole program. Complexities around testing, change management, and design decisions remain.” But he believes customers should stop treating agentic transformation as something waiting over the horizon. “That world is here now,” he said.

Panicker’s message was equally straightforward: don’t wait on the sidelines: “Lean in, be curious about the technology. Ultimately, the sooner you can get to the outcome that you’re driving from a business standpoint, the more value you can bring.

At a time when many boardrooms are still asking whether AI will disrupt software revenue, go-to-market (GTM) leaders are asking a more practical question: how can AI help us find and keep more customers?

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

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

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

1. Segmentation: From demographics to business signals

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

2. Engagement: Relevance replaces volume

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

3. Deal execution: Clearing the invisible friction

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

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

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

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

5. Retention and expansion: Proactive at scale

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

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

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

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

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

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

At NASA, a moonshot is not a metaphor. It is an operating model.

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

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

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

Building momentum one mission at a time

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

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

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

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

“Each test flight in this program is going to inform the next steps of our mission,” Glaze said.

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

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

Standardization creates the capacity to accelerate

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

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

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

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

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

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

Complexity demands faster, better-informed decisions

The scale of the Artemis program is difficult to overstate.

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

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

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

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

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

Autonomy works best when it expands human capability

NASA is already applying autonomous technology beyond administrative processes.

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

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

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

Partnerships turn ambition into capability

No single organization could accomplish the Artemis mission alone.

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

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

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

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

Trust is the ultimate operating system

When I asked Glaze for her most important leadership advice, her answer was direct: surround yourself with smart people and trust them. “No one person can do these things,” she said. “They require thousands of people to achieve these amazing things.”

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

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

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

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

WALLDORF — SAP SE (NYSE: SAP) today announced that NTT DATA has selected SAP SuccessFactors solutions and SAP Business Data Cloud, integrated with Joule, SAP’s AI orchestrator, to power the next phase of its global People and Culture transformation.

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

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

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

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

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

“NTT DATA is demonstrating how AI and cloud technology can redefine the employee experience,” said Thomas Saueressig, Chief Customer Officer and Member of the Executive Board of SAP SE. “With a unified, intelligent HR platform, the company will unlock new levels of productivity and scale a people strategy that supports a connected workforce worldwide.”

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

Media Contacts:
Lawrie Benfield, lawrie.benfield@sap.com, +44 7776 515259, GMT
Sonya Domanski, sonya.domanski@sap.com, +44 734 546 5928, GMT
SAP Press Room; press@sap.com

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AI agents are writing code and translating legacy applications for use in the SAP cloud. Sonja Liénard, head of ABAP platform at SAP, explains what this means for the ABAP programming language and ABAP platform.

In an interview, Liénard talks about the evolution of SAP’s proprietary programming language and ABAP platform, and discusses what the innovations announced at SAP Sapphire will mean for customers.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Lastly, security and data protection are central topics. AI models always need context to be effective. In the enterprise environment, this context can contain sensitive business data. That’s why we are taking a very careful approach: ABAP AI services only operate within established SAP compliance and trust frameworks, for example. Customers always have control over what is shared and what isn’t.

Let’s talk about SAP Sapphire in 2026. What innovations from your area were presented?

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

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

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

Liénard: The barriers to entry are getting much lower. We are switching from users-per-month billing to a usage-based model. Billing will be according to actual usage, based on “AI units” with individual prices. This will help make it easier to use our AI solutions. Customers will be able to better plan how much they want to spend on AI solutions.

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

How do ABAP and AI agents fit into SAP’s longer-term product vision?

Liénard: We are following the overall SAP strategy, of course. It was apparent at SAP Sapphire that agentic AI is the new theme. In the past, we had Infrastructure as a Service, Platform as a Service, and Software as a Service. Agentic AI solutions are now being added.

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

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

What milestones should customers and partners pay attention to?

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

Do you have any other takeaways to share?

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

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

Despite rapid advancements in frontier AI models, many enterprise problems remain hard to solve. The challenge goes beyond just accessing better AI-generated suggestions.

It’s about making consequential decisions inside complex processes that require a deep industry specific context of data, regulations, human workflows, and processes.

For example, tasks like deploying and coordinating thousands of field technicians to restore energy grids after a storm and reduce unplanned downtimes or like keeping critically important production lines running and service levels high despite global supply chain disruptions involve many difficult decisions.

“These problems are incredibly hard to solve,” says Dominik Metzger, president of Industry AI at SAP, because solving them demands immense organizational change, especially in highly regulated industries.

Frontier LLMs are not enough

There is a growing realization among business leaders that solving these challenges requires more than just a powerful AI language model. In these situations, AI must do more than generate suggestions. It needs to act on insights and context to support the execution of business processes. This requires agentic AI that can work with trusted business data, apply industry-specific knowledge, and take actions in ways that are reliable, explainable, and useful to the people closest to the work.

AI is most valuable when it understands the context in which decisions are made. Manufacturers need to balance demand, production capacity, supplier risk, and quality requirements. Energy companies need to manage assets, safety, sustainability, and regulatory obligations. Life sciences companies need to innovate while meeting strict compliance expectations.

To help solve the most complex problems facing large enterprise customers, SAP is bringing together 50 years of deep industry expertise, leading AI engineering know-how, and customer-facing forward-deployed delivery capabilities in one organization to combine forward-deployed engineering with strong productization capabilities.

This enables SAP to move beyond custom AI solutions and build, productize, and scale end-to-end AI transformations for industry-specific business problems.

Think of it this way: the Autonomous Enterprise is SAP’s strategic direction, SAP Business AI Platform is its foundation, and Industry AI acts as a highly focused customer transformation offering, solving industry-specific challenges for individual customers to generate substantial business value.

What makes Industry AI different

The Industry AI portfolio is built on three differentiators: first, more than 50 years of SAP’s industry and process expertise across 26 industries; second, the richness of SAP customers’ data footprint and ontologies in an existing system of record; and third, a dedicated forward-deployed engineering (FDE) workforce to solve problems that do not have off-the-shelf answers, customer by customer.

Forward-deployed engineering embeds AI specialists, such as data scientists and AI builders, directly with customers to solve high-value, industry-specific problems, rather than relying solely on packaged software. Metzger explains that forward-deployed engineering involves “getting obsessed with the problems of our customers” and immersing SAP’s agentic AI developers in the challenges these customers face. “Working directly with customers, we will build, deploy, and scale Industry AI applications to deliver tangible business value for our customers,” he says.

Lessons learned by building these tailored solutions with selected customers will be productized as a standardized platform offering for many more customers to deploy and use. This model allows fast scaling and delivery, while also building up SAP’s platform and solution portfolio of high-value agentic solutions that are close to customer needs and current industry priorities.

Getting up close with customers

SAP’s decision to establish Industry AI as a focused business offering reflects the conviction that true value from agentic AI is created in close collaboration with the industry experts who face specific business challenges every day. This proximity is the fastest way to identify the most critical problems, develop and test practical AI solutions, and refine them based on real-world experience. Most importantly, it allows to deliver tangible business value and prove the impact of AI in practice.

This approach helps reduce the gap between a promising idea and a solution. On the one hand, teams can move more quickly from identifying a need to deploying a solution that delivers measurable value. It also ensures that what is built reflects real-world needs rather than assumptions made far from the customer environment.

For SAP’s enterprise customers, the promise of the Industry AI offering is not simply smarter software. It is a more practical path to the Autonomous Enterprise. Instead of asking teams to adapt to generic tools, Industry AI can help bring deep intelligence into the processes people already use and the decisions they already make, while staying connected to the business data, controls, and applications that keep organizations running.

Benefits

Examples of these benefits are easy to describe. An energy provider avoids costly downtime by identifying a likely spare part demand early and recommending relevant suppliers. A retailer adjusts inventory positions and trade promotions based on simulated scenarios and real demand signals before stockouts occur. A pharmaceutical manufacturer ensures the safe and reliable release of life-saving drugs through a highly precise, high-quality, and fully automated batch release process.

That matters because it can help organizations move faster, improve quality, and free employees to focus on higher-value work. It can also help companies turn industry knowledge into a lasting advantage, especially as AI becomes a larger part of how businesses operate.

For SAP, the formation of the Industry AI unit represents a strategic leap, combining industry-specific expertise, end-to-end offerings, and a value-based offering in a way that is clearly designed to differentiate SAP from competitors.

SAP’s differentiation from other AI platform providers and more traditional forward-deployed engineering companies is not simply about providing custom AI services: the focus is on turning industry-specific expertise into scalable, repeatable offerings that can be deployed across customers, creating a more sustainable and differentiated model for delivering AI value.

Specifically, the Industry AI offering is positioned as an all-in-one commercial package, including platform consumption and cloud services, solutions, forward-deployed engineering, and expert support, with pricing based on real customer business value and a single contract.

It is based on SAP’s unique deep industry-specific knowledge, business AI platform, and knowledge graph, enabling tailored processes for customers. In addition, SAP’s approach is grounded in customers’ business logic, decades of experience and enterprise grade governance, all implemented in standard products, differentiating it from some recently announced market offerings.

What comes next

As the World Economic Forum has highlighted, successful AI scaling depends not only on the technology itself, but also on practical changes to how people work, how decisions are made, and how organizations govern new capabilities.

SAP solves industry problems that generic AI—even if super powerful—cannot. Ultimately it is about enterprise and business process transformation, not AI deployment only. This then can redefine how AI transforms industries, by moving beyond isolated use cases toward autonomous, end-to-end agentic execution.

IDC has named SAP a Leader in the IDC MarketScape: Worldwide AI-Enabled Order Orchestration and Fulfillment Applications for Retail and B2C 2026 Vendor Assessment.

The recognition reflects SAP’s continued commitment to innovation in order management and the confidence customers place in SAP Order Management Services to run some of the world’s most complex, high-volume order operations.

Every order is an opportunity to keep a customer promise, earn lasting loyalty, and increase profitability. However, as customer expectations rise and supply chains grow more distributed, order management has become a complicated, siloed process. This is where SAP Order Management Services comes in and turns order management into enterprise execution intelligence.

SAP Order Management Services empowers businesses to run orders with intelligence, connecting demand, inventory, fulfillment, and financials in real time. With its cloud-native, API-first, composable architecture, the solution helps organizations to maintain operational efficiency and adapt to the changing business needs continuously.

Seamless, cohesive order management processes

Siloed, fragmented data and workflows are bottlenecks to delivering on customer promises. Organizations need connected order management processes throughout the entire order life cycle. SAP Order Management Services unifies data and processes into a single, cohesive experience with an intuitive interface that enables teams to monitor order processes, resolve exceptions, and tailor business flows to fit the way each organization operates.

From order capture to inventory visibility, fulfillment execution, and exception handling, every step of the order life cycle needs to be connected in real time. With SAP Order Management Services, every team member involved in order management can access a single source of truth, reducing errors and accelerating data-driven decision-making.

In addition, the Order Management Assistant empowers businesses with AI-driven actions and recommendations across the order life cycle. The assistant surfaces potential risks and gaps before they become customer issues and recommends next-best actions. Team members interact with the assistant in natural language, turning real-time order data into faster business decisions.

Composable order management

SAP Order Management Services integrates with SAP S/4HANA, SAP Customer Experience (SAP CX) solutions, the broader SAP ecosystem and partner solutions, connecting orders, inventory, pricing, and financials across the enterprise. Moreover, its cloud-native, API-first architecture enables businesses to easily adapt and expand new order channels, fulfillment nodes, and business rules as needed.

This modular approach gives organizations the flexibility to support a wide range of business models, from B2C to complex B2B, and to evolve their order management capabilities incrementally as the business changes.

Looking ahead

Being recognized as a Leader by IDC reflects the continued innovation of SAP Order Management Services. SAP is investing in expanded order management capabilities and deeper AI innovation to help businesses stay ahead of customer expectations and drive profitable growth.

To learn more, read the IDC MarketScape excerpt and explore the capabilities SAP Order Management Services provides.

Emilie Fournelle is head of Product Management for SAP Order Management Services at SAP.

Organizations often look at AI, automation, or new service tools as the starting point for service-led growth. In reality, service-led growth starts with a business model. Technology can help scale a service business, but it does not define how value is created, delivered, or monetized.

Service-led growth is one of the seven macro trends within Autonomous CX, SAP’s vision introduced at SAP Sapphire in 2026. At its core is a simple principle: every customer promise must be backed by the operational reality needed to deliver it.

Recent SAP research highlights a growing gap between customer expectations and the operational reality of service delivery. Disconnected handoffs, fragmented information, and pressure to adopt AI make service a business-model question, not just a technology one.

A practitioner’s perspective

More than twenty years ago, I was part of a global service organization that wanted to move beyond viewing service as a cost of doing business.

At the time, our primary objective was cost recovery. Service was necessary to support the product business, but it was not really seen as a business in its own right.

We started changing that by introducing new professional services and finding ways to monetize expertise we already had. Remote device monitoring, for example, allowed us to organize support across time zones and offer profitable after-hours services to customers who depended on continuous operations.

A few years later, I was asked a more fundamental question: could the service organization survive as an independent business?

To explore that question, we used an early version of what later became widely known as the business model canvas. We looked beyond service operations and examined the complete picture: our value proposition, customer segments, channels, activities, resources, costs, and revenue streams.

Looking back, that exercise taught me something that is still relevant today.

Service as a revenue driver is not a new idea. And it does not start with technology. It starts with a business model.

What do we mean by service-led revenue?

“Service as a revenue driver” is often used as if it means one thing. In reality, service can contribute to revenue in several different ways.

First, it can protect revenue. A customer whose issue is resolved quickly and professionally is more likely to renew, continue buying, and remain loyal.

Second, it can influence revenue. Service professionals often understand a customer’s operational reality better than anyone else. They may identify a need for additional services, training, upgrades, or new solutions, creating opportunities for commercial teams.

Third, it can generate revenue directly through premium support, professional services, subscriptions, remote monitoring, advisory services, or outcome-based offerings.

These ambitions are related, but they are not the same. Each requires different processes, skills, measures, and sometimes different operating models.

Not every service interaction should become a sales conversation. But every service organization should understand whether it is expected to protect, influence, or directly generate revenue.

Technology creates possibilities, not the business model

Technology has always played an important role in service innovation.

Remote monitoring reduced the need for on-site visits. Connected solutions made global support models possible. Customer and service platforms improved access to account, contract, equipment, and interaction data.

Today, Autonomous CX capabilities can expand these possibilities even further. Joule, embedded AI agents in SAP Service Cloud, and AI-assisted opportunity detection in SAP Sales Cloud can classify and route cases, summarize interactions, surface relevant knowledge, identify patterns, detect revenue opportunities, and increasingly automate routine requests. All within the context of a connected service and sales operating model.

Yet adoption does not equal usage, and usage does not equal value. Technology does not answer the most important questions:

• What value are customers willing to pay for?
• Which customers should we serve?
• How will the service be sold, delivered, and measured?
• Can it be delivered consistently, profitably, and adopted by employees and customers?

Customers are already drawing their own conclusions. In 2026, 81% believe AI in service is deployed primarily to save money, not to improve service. Seventy-nine percent still strongly prefer human support. These perceptions are not only a trust problem, they point to a business model problem. When the technology decision precedes the value decision, customers notice.

From ambition to execution

This is where many service-led growth initiatives struggle.

Ambition may be clear in the boardroom, while the organization underneath continues to operate as before. Service is still measured primarily on cost and case closure. Sales and service pursue different objectives. Customer information remains fragmented. Opportunities identified by service disappear during handovers. Employees are expected to adopt new behaviors without understanding why.

Turning service into a measurable revenue driver therefore requires more than enabling a new feature or deploying a new technology. It requires alignment between business objectives, processes, people, data, and technology.

The Advanced Success Plan version for SAP Customer Experience solutions is an expert-led engagement model that helps translate Autonomous CX into an executable plan. It does not replace business strategy or determine which services should be brought to market. Instead, it helps connect a chosen ambition to the processes, capabilities, and governance needed to make service-led growth measurable and repeatable.

1. Clarify the value intent

The first step is to define what service-led growth actually means for the organization.

Is the priority to improve retention? Increase renewals? Create opportunities through service interactions? Launch paid services? Improve profitability?

Through the value management expert session within the Advanced Success Plan for SAP Customer Experience solutions, stakeholders can align on business priorities, value drivers, and success measures.

The objective is not to create a long list of KPIs, but a shared understanding of the outcomes that matter most.

2. Connect the end-to-end process

Once the ambition is clear, the next question is how value will actually be created.

If a service professional identifies an opportunity, what happens next? Who owns the follow-up? Is the customer experience consistent from the initial interaction through fulfilment and invoicing?

Business process best practices and expert guidance can help identify gaps in ownership, handovers, and process alignment across service, sales, commerce, and supporting operations.

This matters beyond operational efficiency. SAP research shows that 45% of revenue leaders cite improving collaboration and handoffs across marketing, sales, and service as a current priority, and 39% are actively pursuing expansion, cross-selling, and upselling within existing accounts. Service teams often have valuable customer context, but the real question is whether the process exists to act on it.

3. Close capability and adoption gaps

Organizations can then assess which capabilities are needed to support the process. This could involve better access to customer information, improved knowledge management, analytics, automation, AI-supported recommendations, or opportunity management capabilities. Targeted expert services within the Advanced Success Plan for SAP Customer Experience solutions help connect SAP CX capabilities to desired business outcomes.

At the same time, employees need the right enablement, incentives, and confidence to adopt new ways of working. Without that, even the best-designed process remains a PowerPoint slide.

The cost of getting this wrong is real. Employees asked to adopt new behaviors without understanding why, or without the tools to support them, do not persist. When experienced people leave, they take institutional knowledge and customer relationships with them. Enablement is not a training exercise; it is a retention and continuity investment.

4. Measure, learn, and improve

Service-led growth is not delivered through a single project.

Recent research highlights several recurring priorities for service leaders: efficiency and time to resolution (48%), collaboration and handoffs across marketing, sales, and service (46%), first-contact resolution (40%), and AI-driven predictive insights (33%). These figures provide useful context, but the measures that matter for service-led growth depend on the value intent defined at the start. Progress should therefore be tracked against the agreed outcomes, whether these involve retention, renewals, service-generated opportunities, paid-service revenue, or profitability.

Ongoing governance and engagement planning helps organizations review progress, address gaps, and scale successful approaches over time.

Service-led growth is a business-model choice

Moving from cost recovery to service-led growth is not simply a matter of asking service employees to sell more. It is a business-model choice with implications for the value proposition, customer experience, processes, organization, skills, technology, and measures of success.

Twenty years ago, remote monitoring changed what service organizations could deliver. Today, AI is expanding those possibilities again. But the underlying challenge has not changed. Technology changes possibilities. Business models determine how that value is captured.

First, decide where service should create value. Then, build the operating model required to deliver that value consistently and profitably.

The Advanced Success Plan for SAP Customer Experience solutions can support that journey by helping organizations connect business outcomes with the processes, capabilities, adoption, and governance needed to turn ambition into measurable results.

Raf Dille is product manager for the Advanced Success Plan for SAP Customer Experience.
Tara Tracey is global product owner for the Advanced Success Plan for SAP Customer Experience.