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SAP customers increasingly view S/4HANA migration as the foundation of modernization and AI as a potential value multiplier. But the available evidence does not show that every customer is ready for an AI-first transformation. The 2024 ASUG Pulse of the SAP Customer research found that moving to S/4HANA was the top named focus area, while interest in AI and machine learning rose sharply. At the same time, concerns about data, skills, cloud strategy, cost, and implementation risk remained substantial.
The practical lesson for CIOs is to manage S/4HANA, data quality, process redesign, and AI as a coordinated portfolio—not as one technology purchase.
What the ASUG survey actually found
The headline comes from the 2024 ASUG Pulse of the SAP Customer research. It surveyed 766 respondents, compared with 806 in 2023. That is a survey of ASUG respondents, not a census of all SAP customers, and respondents could select multiple priorities.
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In the 2024 results:
- 48% selected moving to SAP S/4HANA as a top focus area, up from 42% in 2023.
- 38% identified AI and machine learning among the technologies affecting transformation, up from 23% in 2023.
- 47% were reported as already using S/4HANA or having begun implementation.
- 69% expected to implement S/4HANA within two years.
- 62% were running or planned to run S/4HANA in the cloud: 40% in private cloud, 16% in managed cloud, and 6% in public cloud.
These figures measure different things. The 48% figure describes a priority; the 47% figure describes current use or implementation; and the 69% figure describes an expectation. None proves that a migration was completed successfully.
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Nor were S/4HANA and AI the only important transformation themes. CIO’s analysis of the research reported that 62% viewed data analytics and dashboards as significantly affecting transformation, while 57% said the same about cloud migration. Automation, integration, and process standardization were also central priorities.
Why S/4HANA became urgent
S/4HANA migration reflects both strategic ambition and practical pressure. SAP’s planned end of mainstream maintenance for Business Suite 7 at the end of 2027 has encouraged many ECC customers to make a platform decision. The specific date and available extensions should be checked against an organization’s SAP product, release, and contract.
The maintenance timetable is a forcing function, not proof of enthusiasm. Some companies will move because of support, security, compliance, skills, and vendor-road-map concerns rather than because they have already approved a compelling transformation case.
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Migration is not the same as transformation
A technical conversion can preserve the old operating model. A transformation program asks harder questions:
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- Which processes should be standardized globally?
- Which customizations are genuinely business-critical?
- Who owns master data and process performance?
- Which decisions require real-time information?
- What should be automated, and where must people retain approval authority?
An organization that carries every customization forward may complete a migration while retaining much of its technical debt.
Cloud is part of the decision—but “cloud” is not one model
The survey’s 62% cloud figure combines private cloud, managed cloud, and public cloud. Those options can differ materially in customization, extensibility, upgrade cadence, infrastructure responsibility, standardization requirements, commercial terms, and control.
| Model | Potential advantages | Key trade-offs |
|---|---|---|
| Public edition | More standardized processes and vendor-managed updates | Less tolerance for deep customization and greater dependence on SAP’s release model |
| Private edition or managed cloud | More flexibility for complex ECC estates and existing customizations | Higher migration complexity, cost, and risk of carrying technical debt forward |
There is no universally superior edition. Public cloud may suit organizations willing to adopt fit-to-standard processes. Private or managed cloud may be more practical for complex industries, but it can deliver less transformation if the old design is simply preserved.
Customer sentiment is also not uniform geographically. CIO reported that respondents in the DSAG community—Germany, Austria, and Switzerland—were substantially more critical of SAP’s S/4HANA cloud strategy: 13% expressed a positive opinion and nearly half a negative one. DSAG and ASUG represent different communities and geographies, so those findings should not be treated as contradictory or combined into one global customer view.
Common objections include perceived pressure to move from perpetual licensing to subscriptions, concern about customization limits, migration disruption, uncertain commercial terms, and doubts about public-cloud suitability for complex environments.
Why AI is appearing on the same roadmap
S/4HANA and AI are related, but they are not interchangeable priorities. AI systems need reliable, governed, contextual business data. ERP modernization can improve process consistency, data access, integration, and real-time analytics—the foundations on which useful AI depends.
Conversely, AI can strengthen the business case for modernizing an ERP environment. A company may want better forecasting, faster service, automated approvals, intelligent exception handling, or more useful operational dashboards. Those goals are harder to achieve when data is fragmented and processes are heavily customized.
The survey’s reported AI use cases were practical rather than limited to chatbots:
- Dashboards and analytics: 42%
- Customer experience: 22%
- Replacing manual processes with digital processes: 21%
- Integration between SAP and non-SAP systems: 21%
“AI” can refer to predictive analytics, machine learning, generative AI, conversational copilots, embedded ERP intelligence, or bounded autonomous workflows. Each has different data, governance, integration, and testing requirements.
SAP’s current product positioning connects unified data, business-process context, governance, Joule, and AI agents. That is SAP’s architectural and commercial thesis, not independent proof that every customer will achieve better results. SAP announced that Joule became available in S/4HANA Cloud Public Edition through the November 2024 release, subject to product and contract conditions. SAP later reported integrations with 13 applications out of the box and more than 130 generative-AI capabilities released across its cloud applications during 2024. These are SAP’s own product claims and should not be confused with independently verified customer ROI.
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The strongest counterweight to the AI headline is data reluctance. CIO reported that only 13% of respondents were currently willing to load data into a generative-AI model. Concerns included intellectual-property leakage, data organization, and the difficulty of combining structured, unstructured, and operational data.
Other barriers include:
- Confidentiality, data residency, and regulatory requirements
- Poor or contradictory master data
- Inconsistent process definitions and weak process ownership
- Unclear accountability for model decisions
- Hallucinations and incorrect recommendations
- Integration complexity and uncertain return on investment
- AI licensing and consumption costs
- Shortages of SAP, data, process, security, and AI skills
An S/4HANA implementation does not automatically create clean data, trustworthy semantics, or AI-ready workflows. Likewise, an AI pilot disconnected from authoritative ERP systems may produce a convincing demonstration without creating a durable business capability.
The skills problem is broader than hiring
The research found that 27% struggled to keep up with the pace of technology change, while 28% had difficulty finding internal candidates with the skills needed for new projects. S/4HANA skills were especially important, followed by AI, emerging technologies, and business-process management.
This is not simply a recruitment issue. Successful programs also require business process owners, data stewards, integration architects, cybersecurity specialists, change leaders, testing teams, and executives able to make trade-offs about standardization and risk.
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- Legacy pressure: Assess maintenance exposure, technical debt, security, custom code, and integration constraints.
- ERP decision: Compare public cloud, private cloud, managed cloud, selective transformation, two-tier ERP, and temporary ECC extension.
- Process and data foundation: Retire unnecessary customizations, define common processes, improve master data, and establish governance.
- AI experimentation: Start with controlled analytics, retrieval, assistance, or workflow improvements using low-risk data.
- Embedded AI: Evaluate application-specific features such as Joule against real business workflows and measurable outcomes.
- Controlled autonomy: Permit AI agents to execute bounded tasks only where permissions, audit trails, exception handling, and human oversight are established.
How CIOs should evaluate an S/4HANA-and-AI program
Migration is strategically justified when:
- The current ECC environment faces a support, security, or compliance deadline.
- Global process standardization is a business priority.
- Customization prevents faster change or reliable analytics.
- Real-time planning, reporting, or integrated operations matter commercially.
- The organization can fund data remediation, testing, process redesign, and change management.
- A target operating model exists before the technology selection is finalized.
An AI-first program may be premature when:
- Critical master data is incomplete or contradictory.
- Processes are undocumented or have no accountable owner.
- No measurable use case or baseline exists.
- Privacy, model-risk, and human-approval controls are undefined.
- The proposed tool cannot retrieve authoritative data or complete required system actions.
- The organization is still struggling to stabilize its ERP migration.
For each pilot, measure cycle time, error rate, cost, user adoption, decision quality, exception rates, and control performance. Scale only when integration and governance—not just model quality—are proven.
Commercial issues that can change the business case
SAP’s cloud and AI products are generally quote-based and contract-dependent. Buyers should separate software subscription costs from migration services, infrastructure, testing, data remediation, customization reduction, training, support, and renewal exposure.
Joule Base is described by SAP as included with eligible SAP cloud subscriptions, subject to compatible-product prerequisites. More advanced capabilities may require additional packages. SAP’s AI pricing material describes AI Units, which may be purchased annually and expire after 12 months if unused.
Before approving production deployment, model the number of users, workflows, records, business functions, frequency of use, consumption limits, renewal terms, data location, and exit costs. Verify availability by product, release, geography, data center, and contract rather than relying on a general marketing page.
Customers should also assess alternatives. Remaining on ECC temporarily may reduce near-term disruption but preserve deadline and skills risks. Selective transformation or a two-tier ERP can modernize chosen business units or processes. Independent data, analytics, or AI tools can operate around SAP as the system of record. Oracle Fusion Cloud ERP, Microsoft Dynamics 365, and Workday are credible alternatives for organizations willing to undertake a broader platform change, but suitability depends on industry functionality, integrations, implementation capability, data residency, and total cost.
What the survey means in 2026
The percentages above come from 2024 research. They show the direction of customer priorities at that time; they should not be presented as a newly verified 2026 snapshot.
Later SAP product announcements show that the vendor continued to connect cloud ERP, governed data, Joule, and AI capabilities. They do not establish broad adoption, successful deployment, or measurable ROI among customers. The central tension remains: S/4HANA may provide a modernization foundation, while AI may provide additional business value, but both depend on process discipline, trustworthy data, skilled teams, and commercially sustainable implementation choices.
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