SAP customers struggle as migration costs climb and AI looms next

Budget constraints from S/4HANA cloud migrations

SAP customers are in a tough spot. The migration to S/4HANA, SAP’s flagship cloud ERP system, is supposed to represent progress, but it’s also draining budgets at an accelerating pace. The cost of transitioning legacy systems, integrating new environments, and maintaining operational stability is higher than many organizations expected. These projects are eating into funds that could otherwise fuel innovation, particularly in AI and advanced analytics.

The challenge here is structural. The data from the Americas’ SAP Users’ Group (ASUG) shows that these migration projects are now the single biggest driver of financial pressure. The implication for leaders is clear: resource allocation and timing matter more than ever. Companies need to rethink how they phase large-scale IT transformations while ensuring budget headroom for next-generation technologies that drive competitive advantage.

Forward-looking organizations should create rolling migration budgets that accommodate innovation initiatives in parallel. This approach supports advancing the enterprise system without stalling progress in high-growth areas like intelligent automation or data-driven decision-making. It avoids the all-too-common trap of spending heavily on the foundation while leaving too little for the structure that sits on top of it.

According to the 2026 ASUG Pulse of the SAP Customer survey, 61% of respondents in the Americas cited budget constraints as their top challenge, a jump of seven percentage points in one year. The same survey reported that 35% struggle to extract actionable insights from their data, and nearly half (48%) said integration remains a major hurdle. These figures highlight where most organizations are stuck: between necessary transformation and the financial realities of doing it right.

Marissa Gilbert, Research Director at ASUG, explained it directly: “From our research, it’s more so that S/4HANA projects are creating the budget pressures.” For executives, this means keeping transformation costs transparent and managing them holistically with innovation spending, not as separate budget lines but as an integrated strategy for modernization and growth.

Most SAP customers are in the early stages of AI adoption

AI is everywhere in the boardroom conversation, yet most SAP customers haven’t taken it beyond the test phase. The intentions are there, over 40% of enterprises are piloting AI, but the leap to operational deployment at scale is proving harder than expected. Many are building foundational knowledge, validating potential use cases, and preparing the data groundwork necessary for success. What’s holding them back is not a lack of belief in AI; it’s the practical realities of security, governance, cost, and internal expertise.

The joint study from ASUG, Microsoft, and Intel gives a clear picture. Out of 142 SAP member organizations surveyed, 41% are piloting AI, 39% are developing foundational understanding, but only 24% have deployed solutions operationally, and just 10% have reached enterprise-wide rollout. Barriers cluster around three main areas: security and privacy (32%), budget constraints (27%), and limited AI talent (27%). These are obstacles that can be solved but require intention and leadership alignment.

What’s concerning is that many organizations still evaluate AI’s return on investment without rigor. While 56% claim to measure AI ROI, only 18% use formal frameworks. The rest rely on informal methods or case-by-case analysis. That leaves a strategic gap between expectations and accountability. Sixty-one percent of companies say their primary goal for AI is cost reduction, yet only 31% consistently measure whether those savings are real.

For executives, this signals maturity challenges rather than technical ones. Scaling AI isn’t only about having the right algorithms, it’s about trust, process alignment, and financial discipline. Leaders must ensure tighter integration between AI experimentation and enterprise operations, governed by frameworks that measure business value beyond buzzwords.

Marissa Gilbert of ASUG noted that budget pressures from S/4HANA migrations could soon be followed by similar waves triggered by AI. That’s a call for structure. Companies that strategically plan AI investments, with defined ownership, transparent KPIs, and clear governance, will move faster and capture more lasting value.

Industry experts are divided over SAP’s AI readiness and the pace of customer adoption

There’s a split in how experts view SAP’s AI trajectory. Some see clear momentum toward adoption and integration; others argue that progress remains limited to surface-level pilots. These differing perspectives reflect the complexity of transforming enterprise systems that remain central to day-to-day operations worldwide. AI may be advancing fast, but when your systems run mission-critical functions like finance, manufacturing, and logistics, change is not something you rush.

IDC’s Group Vice President of Enterprise Software, Mickey North Rizza, presents the optimistic case. She points out that over half of organizations already have AI agents embedded in key workflows, while another 20.5% are scaling agent usage gradually. Her takeaway is that movement is happening at speed, especially at the workflow layer, where AI can augment traditionally manual processes. This shows that the early groundwork is being laid in ways that will expand naturally once trust and experience build across organizations.

Others see things very differently. Maribel Lopez, Founder of Lopez Research, warns that many SAP customers remain cautious because these systems power the heart of the business. Changing or augmenting them with AI can create risks if done without proper oversight and proven frameworks. Her view resonates with many SAP-heavy enterprises that prioritize stability and security over speed of adoption.

Adding a grounded, operational perspective, Chase Christensen, Segment CIO at Jabil, recognizes that SAP is moving faster than before, but emphasizes the need for stronger support systems. His comment that “give us the support rather than have us deal with a mix of systems integrators” underscores a practicality shared by many IT leaders, the need for SAP to simplify adoption and ensure consistent customer guidance from start to scale.

For executives, this divide among experts is a signal to proceed with balanced urgency. Rapid innovation without robust support structures and governance can lead to inefficiency. But moving too slowly risks being left behind as competitors integrate AI into their core workflows. Leadership teams should focus on disciplined experimentation with defined goals and predictive performance metrics, allowing for scale only when business outcomes justify it.

SAP’s new AI strategy emphasizes decentralization and user-driven adoption

SAP has started to pivot toward a more distributed model for AI integration. The company’s new approach empowers business users directly, giving them AI tools designed for immediate interaction and feedback. The goal is simple: enable adoption from the operational front lines. Jonathan von Rüeden, SAP’s Chief AI Officer, captured it clearly when he said, “We may have over-indexed as an industry on centralization.”

This strategy is now embodied in the Joule Desktop, a platform aimed at bringing SAP’s AI capabilities into daily business workflows. Instead of waiting for top-down integration, business units can start applying AI directly to their functions. Von Rüeden’s logic is that adoption should begin with use, and feedback from real users should inform system improvement over time. It’s a practical move meant to accelerate adoption by removing procedural bottlenecks.

However, this decentralization must be guided by structure. Without proper data governance, distributed AI experimentation can lead to fragmentation and risk. For executives, this means enabling controlled autonomy: allowing users freedom to innovate while upholding clear governance, security, and interoperability standards. That balance ensures that creativity inside the organization doesn’t compromise compliance or system integrity.

ASUG research supports this developing shift. While Microsoft still dominates AI tool adoption among SAP customers, 72% of respondents currently use Copilot, more than half plan to adopt SAP’s embedded AI features and Joule agents. This development shows the market’s openness to integrating SAP-native AI tools if they’re made accessible and intuitive.

Decentralization offers SAP an opportunity to close its adoption gap quickly. For corporate leaders, the message is to enable the workforce with these tools, provide oversight through standardized frameworks, and measure improvement not in isolated metrics but in how AI enhances agility and decision-making speed across the organization.

Hybrid environments persist as a long-term reality despite accelerating migration to the cloud

The pace of migration to SAP’s S/4HANA cloud is accelerating, but hybrid environments remain a core part of how many enterprises operate. According to ASUG, 56% of customers are already live on or migrating to S/4HANA, up significantly from 45% in 2024. Still, 28% continue to run hybrid setups combining cloud and on-premises systems. These figures make one thing clear: while the cloud transformation is real, enterprises are not abandoning established infrastructure overnight.

The persistence of hybrid models is driven by a combination of factors, integration complexity, regulatory requirements, and the need for continuity in mission-critical systems. Manufacturing, healthcare, and financial services are particularly tied to on-premises assets due to control and compliance needs. The result is a dual environment that must be managed cohesively rather than seen as a transitional inconvenience.

Marissa Gilbert, Research Director at ASUG, noted that hybrid models are expected to remain based on historical adoption patterns. In many organizations, large portions of operational data still reside in environments not yet fully optimized for the cloud. Migration, in this context, is not purely technical; it’s organizational, requiring coordination across business units and IT to align priorities and spending over multiple phases.

For executives, this is a strategic issue, not simply a technological one. Hybrid structures require integrated planning and flexible architecture that can handle cloud growth while maintaining the reliability and control of existing infrastructure. This means consistent governance frameworks, clear data-access models, and unified security protocols that cover both the cloud and on-premises layers.

ASUG’s data reinforces the shift toward cloud readiness: the share of customers planning to delay migration beyond two years fell from 22% in 2023 to only 9% in 2025. That decline shows increasing willingness to modernize, but also acknowledgment that hybrid operations will coexist with new systems for the foreseeable future.

For decision-makers, the takeaway is straightforward, deliver continuity and progress simultaneously. Maintaining hybrid environments effectively ensures stability while advancing cloud initiatives. The organizations that approach hybrid design as a structured component of digital transformation will maintain control over cost, compliance, and innovation all at once.

Main highlights

• Manage migration costs strategically: S/4HANA migration projects are driving the sharpest budget pressures for SAP customers, leaving limited room for innovation. Leaders should balance migration spending with investments in AI and data to sustain transformation momentum.
• Advance beyond AI pilots: Most SAP customers remain stuck in AI pilot stages, citing budget, governance, and skill gaps. Executives should formalize ROI frameworks and establish governance early to move from experimentation to measurable enterprise-scale outcomes.
• Bridge optimism and caution in AI adoption: Industry experts are split on SAP’s AI readiness, some signal acceleration, others warn of risk. Leaders should pursue a structured approach, scaling AI initiatives only when governance, security, and robust support are in place.
• Empower users through decentralization: SAP’s shift toward user-driven AI, exemplified by the new Joule Desktop, aims to speed up adoption and reduce IT bottlenecks. Executives should encourage this empowerment while maintaining strong governance and data controls across all departments.
• Plan for long-term hybrid operations: Hybrid environments persist even as cloud adoption increases, with 28% of SAP users maintaining mixed infrastructures. Leaders should design flexible architectures and unified governance models to ensure stability, compliance, and agility across both cloud and on-premises systems.