Enterprise integration has always been foundational work. But in the age of agentic AI — where autonomous software agents are being deployed to orchestrate business processes across sprawling, multi-vendor application landscapes — the stakes of getting integration right have never been higher.

For most enterprises, the integration environment they built over the last decade was never designed for what’s being asked of it now. Point-to-point connections, legacy middleware, fragmented tooling across dozens of systems: these were manageable constraints when the work was batch processing and scheduled dataflows.

Agentic AI changes the equation entirely. Autonomous agents need real-time access to data across the full application estate, consistent governance, and an integration layer that can scale without becoming a bottleneck. The organizations that have already modernized their integration foundation are finding that they have a meaningful head start.

SAP commissioned IDC to conduct an in-depth analysis of organizations using SAP Integration Suite, including its advanced event mesh capability. SAP Integration Suite is SAP’s flagship integration platform as a service, delivered on SAP Business AI Platform, the unified foundation for SAP’s AI, data, and integration capabilities.

For this Business Value White Paper, IDC conducted in-depth interviews with eight organizations across manufacturing, consumer products, energy, fintech, healthcare, retail, and transportation — enterprises with an average of 48,529 employees and $14.53 billion in annual revenue, operating across the U.S., Germany, Denmark, India, and the UK. The results were quantified from actual outcomes, not a modeled composite.

What the IDC Business Value White Paper found: SAP Integration Suite customers are achieving a 368% three-year return on investment with an eight-month payback, generating an average of $47,900 in annual benefits per integrated application, or $9.36 million per organization.

Numbers that matter

The IDC Business Value White Paper documents measurable impact across the full breadth of what integration touches in a modern enterprise:

• Integration speed and scale
  • 78% more application integrations
  • 41% less time to complete per application integration
  • 99% more application messages processed
• Operational reliability
  • 58% fewer unplanned outages
  • 35% faster to resolve integration errors
  • 19% fewer integration errors
• Business process automation
  • 49% more business processes automated
  • 21% efficiency gains for business process teams
  • 32% less time to onboard a business partner
• Developer and team productivity
  • 29% improvement in development team productivity
  • 31% faster development life cycle for new applications
  • 32% more efficient application integration teams

These aren’t projections. They are the average outcomes across eight real enterprises, documented through in-depth interviews by IDC analysts Shari Lava, group vice president for AI, Data, and Automation, and Matthew Marden, research vice president for Business Value Strategy.

More than an SAP platform

One finding in this study deserves particular attention for customers and partners evaluating SAP Integration Suite in mixed-vendor environments: on average, 64% of integrated applications in the study are non-SAP, and 82% of integrations touch at least one non-SAP application.

SAP Integration Suite is not an SAP-only platform. It is the integration backbone for the full, heterogeneous application environment that modern enterprises actually operate, one where SAP and non-SAP systems must work together reliably at scale.

One study participant described what that means in practice: “SAP Integration Suite is our main connection to the outside world. Every time anyone needs to connect to our SAP systems, it goes through [SAP] Integration Suite. It’s our main front door for API access, for data access, for collaborating with us, and for transferring data in and outbound into our SAP systems.”

Built for the AI era

The timing of this study matters. Organizations are moving fast on agentic AI, deploying autonomous agents that need to orchestrate, monitor, and act across their entire application estate in real time. Integration is no longer a back-office concern; it is the operational layer on which AI-driven automation either succeeds or stalls.

As part of SAP Business AI Platform, SAP Integration Suite brings together API management, event-driven architecture through advanced event mesh, and AI-native capabilities — giving organizations the governance, observability, and real-time connectivity that agentic AI workloads require. That includes native support for MCP, LLM connectivity, and agent-to-agent orchestration, making it a ready foundation for autonomous enterprise processes.

Study participants described this directly. One said: “The real value of [SAP] Integration Suite is the combination of its different capabilities: cloud platform integration, API management, and advanced event mesh. It’s the full set of these capabilities that makes it so important for us.”

Another captured the business case for reliability in AI-driven environments: “What we’re starting to see with SAP Integration Suite is a level of reliability and standardization in the integrations we’re building, which enables positive levels of further scalability. We no longer have that kind of point-to-point integration, which had been a risk and a source of technical debt for us.”

Business case is clear

A 368% three-year ROI and eight-month payback is a compelling headline. But what sits behind those numbers is equally significant: enterprises can connect more of their application estate, automate more of their business processes, resolve failures faster, and onboard partners more quickly — all on a platform that is ready for the AI workloads they are investing in right now.

“We can innovate more with SAP Integration Suite because it’s easier and the developers are happier working with the tool. It gives good results, so we continue to build on the foundations we have already created.”

That is the right foundation for what comes next.

Read the full IDC Business Value White Paper here.

Sid Misra is chief marketing officer of Technology Foundation for SAP Business AI Platform at SAP.

Source: IDC Business Value White Paper, sponsored by SAP, The Business Value of SAP Integration Suite (Doc #US54820926-BVWP, August 2026) , IDC Business Value Snapshot, sponsored by SAP, The Business Value of SAP Integration Suite (Doc #US54820926-BVS, August 2026)

Organizations today face increasing pressure to align workforce strategies with rapidly changing business needs. Whether planning for future skills, addressing talent gaps, or improving organizational agility, leaders need more than data. They need clear, actionable intelligence that helps them make informed decisions with confidence. 

As a result, people analytics is evolving from a reporting function into a strategic capability. By connecting workforce, skills, talent, and business data, organizations can gain a deeper understanding of their workforce and anticipate future talent needs.  

This evolution is helping lay the foundation for Autonomous HCM, where connected data and AI-powered intelligence help organizations make more informed workforce decisions. 

People analytics: a cornerstone of Autonomous HCM 

The future of HR isn’t simply about automating processes. It’s about providing leaders with the workforce intelligence needed to align talent strategies with business priorities. Industry research, including the IDC MarketScape: Worldwide People Analytics and Performance-Driven Workforce Planning 2026 Vendor Assessment, points to growing demand for solutions that bring together people analytics, workforce planning, performance data, and AI-powered insights. Together, these capabilities can help organizations move from reactive decision-making to a more proactive and strategic approach to workforce management. For HR leaders, that means spending less time gathering and reconciling information and more time focusing on actions that improve workforce performance, organizational agility, and business results. 

Connecting workforce insights to business outcomes 

At SAP, our vision for Autonomous HCM starts with connecting workforce and business data to create a shared understanding of people, skills, and organizational priorities. Through People Intelligence in SAP Business Data Cloud, organizations can bring together workforce, skills, talent, operational, and business data to gain a more complete view of their workforce, identify emerging opportunities and risks, anticipate future talent needs, and make decisions with greater context.  

This outcomes-based approach helps organizations answer critical questions like: What capabilities exist across the workforce today, and where are critical gaps emerging? What skills will be needed to support future business goals? How can talent be aligned more effectively to strategic priorities? Where are emerging workforce and retention risks? What actions can help improve workforce and business performance? 

From workforce intelligence to workforce action 

SAP is transforming its own approach to people analytics through People Intelligence. By bringing workforce and business information together, leaders can move beyond static reporting and better understand workforce trends, skills needs, and organizational priorities.  

Traditionally, acting on workforce insights has often been a manual and fragmented process. HR teams identify an issue, such as a skills gap or retention risk, and then coordinate across recruiting, learning, workforce planning, and business leaders to determine and execute the appropriate response. While analytics can help surface the problem, turning insight into action frequently requires significant time, effort, and cross-functional collaboration. 

The next evolution is connecting intelligence directly to action. As AI becomes more deeply embedded in workforce processes, organizations can move beyond identifying a workforce challenge to exploring potential responses and acting on approved decisions. For example, workforce intelligence could identify an emerging skills gap, help leaders evaluate different ways to address it, and connect those decisions to actions across hiring, learning, internal mobility, or workforce planning. 

Over time, AI agents will help accelerate this shift by connecting workforce intelligence with the actions needed to address it, helping organizations move more seamlessly from insight and decision to execution. 

The path forward for Autonomous HCM 

People analytics helps organizations understand what is happening. Workforce intelligence helps them decide what to do next. Autonomous HCM helps them act by connecting insights, decisions, and execution. 

The future of workforce management is not just about understanding workforce dynamics, but about helping organizations respond with greater speed, confidence, and precision. By connecting people analytics, workforce intelligence, and AI-powered execution, Autonomous HCM enables organizations to move beyond insight to action, creating a more adaptive, resilient, and business-aligned workforce. 

Learn more 

Explore the IDC MarketScape: Worldwide People Analytics and Performance-Driven Workforce Planning 2026 Vendor Assessment to learn more about the trends shaping the future of workforce intelligence, planning, and decision-making, and why SAP was recognized as a Leader. 

TrustRadius has announced its Top Rated awards for 2026, and SAP Customer Experience (SAP CX) is proud to share that SAP Sales Cloud, SAP Service Cloud, and SAP Commerce Cloud have each been recognized as Top Rated products in their respective categories. In a landscape crowded with vendor-sponsored rankings and paid analyst reports, TrustRadius stands apart. These awards are driven entirely by verified customer reviews: no paid placement, no analyst opinion.

To earn a TrustRadius Top Rated badge, a product must meet three stringent criteria:

1. At least 10 verified reviews submitted within the past year
2. A trScore of 7.5 or higher
3. A meaningful presence in its category

Every submission passes through TrustRadius’ fraud-protection verification process. That means these three wins are a direct reflection of what SAP customers experience every day, and they said it loudly and clearly.

What customers are saying

Across sales, service, and commerce, SAP customers consistently highlighted the depth of integration, reliability at enterprise scale, and the tangible business impact of AI-powered workflows. These aren’t features on a road map; they’re capabilities customers are using today, and the reviews prove it.

SAP Sales Cloud

“We had a comparison between SAP Sales Cloud, Microsoft Dynamics, and Sales Force Optimization. We selected SAP Sales Cloud for three main reasons: SAP Sales Cloud meets our requirements the best, especially in comparison with Sales Force; pricing is more attractive; and as we use multiple SAP products, the integration between different SAP products works well.” – Verified Reviewer
Read the full review here.

“SAP Sales Cloud is well suited for predictive analysis and strong integration with ERP, SAP S/4HANA. AI-driven recommendations for next best actions.”
Read the full review here.

SAP Service Cloud

“The enhanced service efficiency has unified agents’ workspace by consolidating emails, phone, and chats into a single control panel, eliminating the need for our agents to switch between systems. Also, AI-powered automation has reduced agents’ workload and increased efficiency by initiating automated ticket categorization and “next best action” recommendations.” – Chris Kithinji, Business Systems Analyst
Read the full review here.

“We can add an AI-based model for resolving customer queries without any human effort or intervention. It streamlines the customer support process by simply adding the automation.” – Kanika Rajpal, Technical Architect
Read the full review here.

SAP Commerce Cloud

“It is actually a cloud-based commerce platform, which is a package with lots of features, and we are using that for our Business to Business, Business to Customer, and even for Business to Business to Customer (kind of selling your product to another business and they are going to connect with the end customer) model. As it is very good to customize the features as per your need, and also we can integrate with other SAP products like SAP ERP, which is very beneficial to support the described model operation from anywhere.” – Ajay Thakkar, Software Engineer
Read the full review here.

“We switched to SAP Commerce Cloud four years ago. The software has simplified our e-commerce processes, particularly inventory management, payment processing, marketing, and general order management. Use of the software has enabled us to ensure a good customer experience across various channels. It has also enabled unlimited interaction between the customer service team and our clients, as well as third-party integration.” – Denis Bacarella, Digital Marketing Assistant
Read the full review here.

What’s next: agentic AI across the customer experience portfolio

Winning today’s recognition motivates us to keep pushing forward. SAP’s approach to AI goes beyond chatbots and dashboards.

As Balaji Balasubramanian, president and CPO of SAP Customer Experience, put it: “AI alone is no longer a differentiator. What matters is where intelligence operates inside of a business.”

That philosophy is driving a new wave of agentic AI innovation across all three award-winning products.

SAP Sales Cloud now features an email-to-quote capability through a Microsoft Outlook add-in that can automatically populate SKUs from deal and email context, helping to turn a seller’s inbox into a quoting engine. A new deep research feature can synthesize SAP Sales Cloud and SAP Service Cloud data with external market intelligence to help supercharge account planning.

SAP Service Cloud is rolling out an Agent Inbox that can consolidate cases, tasks, and service orders into a unified command center with live workload insights. A new Digital Service Agent handoff can intelligently summarize customer intent before ticket creation, helping to reduce friction and improve resolution speed.

SAP Commerce Cloud is embedding AI directly into catalog management, extracting product details from uploaded documents to help auto-enrich descriptions and improve product discoverability at scale.

Trust as a foundation for innovation

These TrustRadius Top Rated awards confirm what SAP has always believed: that great software earns trust through outcomes, not marketing. As we continue to evolve SAP Sales Cloud, SAP Service Cloud, and SAP Commerce Cloud with agentic AI capabilities, the voice of our customers remains our most important compass.

Learn more about SAP Customer Experience innovations here. Explore the TrustRadius Top Rated methodology at trustradius.com.

Artificial intelligence has entered a new phase. The conversation is no longer about whether organizations should adopt AI.

Across industries, businesses are already experimenting with AI to automate tasks, improve productivity, and create better customer experiences. The real question now is how do we move from experimentation to enterprise-wide transformation that delivers meaningful business outcomes?

At SAP, our answer is clear: the next wave of transformation will be driven by Industry AI. This belief is also reflected in the evolution of our own organization, as Customer Innovation Services evolves into Customer Industry Solutions. This is more than a name change; it represents an expanded mission and a recognition that the future of enterprise AI will be shaped by the combination of technology, deep industry expertise, engineering excellence, and customer-centric innovation.

The opportunity ahead is immense. Analysts estimate that generative AI alone could create between $2.6 trillion and $4.4 trillion in annual economic value globally, while global spending on AI is projected to exceed $630 billion by 2028. Yet, realizing this value will require enterprises to move beyond experimentation and deploy AI in ways that are deeply relevant to their industries.

For years, enterprises have pursued digital transformation through broad platforms and horizontal capabilities that could be applied across functions and sectors. AI has followed a similar trajectory. Large language models (LLMs) and general-purpose AI tools have demonstrated remarkable capabilities and unlocked entirely new possibilities. However, as organizations move beyond pilots and proofs of concept, one thing is becoming increasingly clear: generic AI can only take us so far.

“In the enterprise world, context is everything. The future of AI lies not in generic intelligence but in intelligence that understands industries, business processes, and how enterprises create value,” said Dominik Metzger, Global Head of Industry AI. “This is where Customer Industry Solutions plays a pivotal role, bringing together deep industry expertise, customer insights, and engineering excellence to bridge the gap between innovation and real-world business impact.”

A manufacturer seeking to optimize its supply chain faces challenges that are fundamentally different from those of a retailer personalizing customer experiences. A bank navigating regulatory requirements operates in a vastly different environment than a life sciences company accelerating research and development. Every industry has its own processes, data models, regulations, and ways of creating value.

This is precisely why Industry AI represents the next frontier of enterprise transformation. The Industry AI portfolio combines the power of AI with deep domain expertise and business context. It understands not only language, but also the nuances of industries and the realities of how businesses operate. It can address industry-specific challenges and deliver outcomes that are measurable, scalable, and relevant to the enterprise.

Building on our strong foundation of customer co-innovation, the Customer Industry Solutions organization brings together deep industry expertise, customer insights, and engineering excellence to accelerate Industry AI at scale. Importantly, we are also bringing together the strengths of customer innovation and forward-deployed engineering.

This combination is powerful. Customer innovation teams bring a deep understanding of business challenges, industry processes, and customer outcomes. Forward-deployed engineering brings the ability to rapidly build, deploy, and operationalize solutions in complex enterprise environments. Together, these capabilities enable us to bridge the gap between breakthrough innovation and real-world business impact.

Our role is not simply to help customers adopt new technologies. It is to work alongside them to address complex business challenges, rapidly translate ideas into solutions, and help move organizations from AI experimentation to enterprise-wide transformation.

Industry AI also changes the way innovation itself happens. The most valuable insights often emerge from solving real customer challenges. They come from understanding pain points on the ground, identifying opportunities to simplify complexity, and applying AI in ways that create tangible business value. This requires closer collaboration among customers, industry experts, engineers, and product teams than ever before.

This is another critical role that the Customer Industry Solutions organization will play. By working closely with customers across industries and regions, and by systematically capturing insights from the field, we can help inform future product development and accelerate the adoption of industry-specific AI capabilities at scale.

Every customer engagement becomes an opportunity to learn, refine, and build solutions that can benefit entire industries.

Sindhu Gangadharan is head of Customer Industry Solutions at SAP.

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.”

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