ISG Provider Lens™ SAP Ecosystem - SAP S/4HANA System Transformation - Large Accounts - U.S. 2026

For U.S. clients, the mandate has shifted from move to S/4HANA to modernize without disruption, reflecting a transition from reluctance to risk aversion

This ISG Provider Lens® SAP Ecosystem study assesses the competitive landscape for providers in the U.S. in 2025 by evaluating their capabilities, market positioning and execution across advisory, implementation and managed services. The research covers five quadrants: SAP S/4HANA System Transformation — Large Accounts; SAP S/4HANA System Transformation — Midmarket; SAP Application Managed Services; SAP Business AI and Business Technology Platform (BTP) Services; and Managed Cloud Services for SAP ERP.

Market context: why the landscape is shifting

U.S. enterprises face a deadline-driven push to exit SAP ERP Central Component (ECC) while maintaining financial discipline. Boards want modernization without disruption, prompting programs to be sequenced into smaller waves with milestone-based proof of value. Cloud economics, security expectations and audit demands are steering organizations toward standardized cores with composable extensions, avoiding the upgrade drag of heavy customizations.

SAP’s transformation strategy is increasingly bifurcated: large enterprises are deferring complex upgrades, while SMEs are being targeted with simpler greenfield implementations. SAP is struggling to convert its large ECC installed base to S/4HANA due to unclear incremental value, restrictive licensing and high sunk costs. As a result, SAP is pivoting to the midmarket and SME segments, where GROW with SAP enables faster deployments, lower sales friction and quicker revenue realization.

RISE with SAP is not being deprioritized technically, but it is commercially and behaviorally harder to sell, particularly for brownfield and bluefield upgrades. Many customers in nonregulated industries do not view the 2027 mainstream support deadline as urgent. Instead, they are choosing to sweat the asset, delay decisions, and consider extended support or third-party maintenance until 2030.

From a 2026-27 strategy lens, SAP’s pragmatic response is to win where it can — greenfield growth in the SME segment. Ecosystem partners are aligning around the GROW message, while upgrades remain complex, lengthy and politically difficult. The strategic implication is a two-speed SAP market: slowmoving large enterprises and fast-moving SME greenfield adoption.

Investments and risk management

Capital is available but tightly scrutinized. CFOs require clear ROI and defined payback periods, favoring selective modernization over big-bang rewrites. Decision-makers prioritize approaches that protect operations during cutover, reduce regression risk and simplify future updates.

Technology posture

AI has shifted from pilot to production. Practical gains are coming from AI-assisted code remediation, automated testing, incident triage and enterprise knowledge search. Meanwhile, BTP is becoming the default layer for integration, data, analytics and side-by-side innovation, keeping the ERP core clean and upgradeable.

Regulatory pressure and industry timing for S/4HANA migration

The urgency of SAP’s 2027 mainstream support deadline varies by industry. Regulated industries, such as life sciences and utilities, face heightened pressure due to stringent compliance requirements, including FDA, EMA and HIPAA, accelerating migration timelines. In contrast, manufacturing and other lessregulated industries are more likely to sweat the asset, deferring decisions and considering extended support options through 2030.

U.S. enterprise priorities: pragmatic modernization without disruption

● De-risk the journey to S/4HANA: For large enterprises, the mandate is modernize without disruption, not simply move to S/4HANA. Their approach has shifted from reluctance to risk aversion. ISG anticipates a shift toward bluefield or selective transformation because environments are too complex, integrated and risk-sensitive for full greenfield replacements.

● Prove value early and often: Every phase must connect to business KPIs, such as period-close cycle time, days sales outstanding (DSO) and inventory turns, not just IT milestones.

● Keep the core clean and innovate on BTP: Minimizing custom code in ERP and building extensions side by side on BTP can enable safer, more frequent releases and easier upgrades.

● Make AI practical: Enterprises should prioritize AI in delivery (code, test and documentation) and operations (triage, RCA and prediction) before automating business decisions.

● Industrialize run: Enterprises must adopt AIOps or SRE models, outcome-based SLAs, and FinOps disciplines for cloud ERP, including RISE and public cloud variants.

Consulting-led discovery as a starting point

Enterprises expect a consulting led discovery that links process baselines, data quality and the target operating model to a credible business case. They want a clear lane choice (greenfield, brownfield or selective data transformation [SDT]), a value office to track benefits, and an organizational change plan that prepares users for new processes, roles and UIs.

Industry-specific priorities and accelerators

U.S. clients want industry-ready accelerators, including process templates, compliance packs and reference architectures, to shorten time-tovalue and reduce design churn. Manufacturers prioritize shop-floor/warehouse execution and OEE; consumer and retail organizations focus on margin, replenishment and returns; and services-led firms seek project-toprofit visibility and strong experience layers. Sustainability and ESG appear more often in RFPs, typically starting with analytics-led reporting before full-module adoption.

Pragmatic AI adoption and orchestration

Interest in AI-enabled solutions is increasing, but the adoption of SAP’s Joule remains cautious due to questions about maturity, data safety/governance and measurable business outcomes. This is not a rejection; enterprises want credible AI orchestration, often backed by hyperscalers and integrated with ERP rather than relying solely on ERP vendor point features.

Provider dynamics: how the supply side is adapting

Consulting-first front end, factory-backed execution

Leading providers frontload consulting-led work, including value diagnostics, process intelligence and benefit modeling, to set lane choices and rollout plans. They then execute through repeatable factories for migration, conversion, testing and data, ensuring consistent outcomes across waves. Weaker approaches still depend on ad hoc and manual testing, which U.S. enterprises view as higher risk.

Operationalizing the clean core

Leaders operationalize clean core by design, not just in principle. They use BTP-based extension catalogs, maintain a keep/retire/refactor backlog for custom code, enforce whitelisted APIs, apply LCNC patterns (SAP Build), and integrate DevOps/ALM for versioning and traceability. The defining measure is the environment’s ability to take quarterly updates without disruption.

AI first delivery and operations

The differentiator is no longer we use AI, but where and how AI is embedded:

● In delivery: ABAP deconstruction and remediation, automated test case generation and documentation assistants

● In operations: ticket clustering, knowledge bots grounded in client artifacts, proactive anomaly detection, and self-healing runbooks with human approval

Providers that combine these capabilities with explainability and rollback plans will scale faster in compliance intensive environments.

Outcome-oriented SLAs for AI-enabled operations

SLA focus is shifting from activity metrics, such as response time, to business-aligned outcomes, such as order cycle time, periodclose variance and fulfillment accuracy. Contracts increasingly incorporate automationrate thresholds, first-time-right targets and continuous value sprints that convert prioritized backlogs into measurable benefits.

Quadrant-specific insights: how providers are delivering and adapting for U.S. clients

SAP S/4HANA System Transformation

Leading providers simplify the S/4HANA journey by offering well-defined transformation paths — greenfield, brownfield or SDT — aligned to client risk appetite, data quality and change readiness. They front-load a consulting-led discovery to align business outcomes, process gaps and data issues before design/build. Execution is supported by conversion and migration factories, prebuilt process templates, and automation for code remediation, testing and data migration. This approach shortens timelines and reduces dependency on large on-site teams while improving predictability and quality.

Proof points for enterprises

● A clear transformation lane recommendation with pros and cons, costs and timelines

● A data quality and harmonization plan detailing how master data will be cleaned, standardized and migrated

● A custom code assessment outlining what will be kept, retired or rebuilt on BTP

● Evidence of automated regression testing to reduce go-live and post-go-live risk

● Demonstrations of minimized downtime, especially for 24/7 operations

Frequent provider risks and gaps to monitor

● One size fits all recommendations instead of a tailored transformation lane

● Over customization that defeats the purpose of a clean core

● Polished presentations but weak data remediation capabilities, causing late-stage delays and defects

● Inadequate change management and training, leaving users unprepared for new processes and UIs

SAP Application Managed Services

Modern application managed services (AMS) providers have moved beyond traditional ticket handling, applying AIOps, automation and predictive monitoring to prevent issues before they occur. They are shifting to business-aligned SLAs, such as order cycle time and period-close timeliness, instead of legacy metrics like ticket-closure time. They incorporate continuous value sprints to deliver prioritized improvements on a regular cadence rather than in frequent releases.

Proof points for enterprises

● Clear breakdown of automation versus manual effort, especially for L1/L2 incidents

● Business aligned KPIs in addition to IT SLAs

● A cost-reduction roadmap leveraging selfhealing scripts, AI-powered analysis and proactive monitoring

● Evidence of FinOps practices for cloud ERP, including consumption visibility and cost optimization recommendations

● Real examples demonstrating incident reduction and measurable process outcome improvements

Frequent provider risks and gaps to monitor

● AIOps claims with predominantly manual triage and remediation

● SLAs focused on internal IT efficiency rather than business results

● Insufficient root cause elimination — temporary incident reduction followed by recurrent spikes

● No defined operating model for continuous value realization after go live

SAP Business AI and Business Technology Platform (BTP) Services

Leading providers position BTP as the innovation and integration layer, keeping ERP core clean while delivering modular extensions, analytics, data orchestration and AI agents. They develop upgrade-safe, sideby- side solutions, with structured governance for prompts, models and data. They also deploy AI to assist — not replace — business processes, with clear boundaries and human approval where needed.

Proof points for enterprises

● Catalog of BTP-based extensions with governance models and evidence of upgrade safety

● Demonstrations of AI use cases, for example, code remediation, test automation, ticket triage and decision support

● Data architecture showing secure, traceable data flows between SAP and non-SAP systems

● Approaches to accelerate analytics using Datasphere/SAC with reusable data models

● AI safety plan covering explainability, prompt governance and rollback procedures

Frequent provider risks and gaps to monitor

● AI or BTP capabilities that look good in demonstrations but lack lifecycle integration (DevOps, ALM and testing)

● Extensions built in ways that are not upgrade-safe and break during quarterly releases

● Over dependence on generic LLM behavior without grounding in enterprise data

● No defined governance for AI model/prompt changes

Managed Cloud Services for SAP ERP

Leading providers assume end-to-end responsibility for runtime resilience and security across hybrid, private cloud and RISE with SAP landscapes. They implement policy-as-code, automated compliance checks and routinely tested HA/DR playbooks. They standardize platforms and configurations to streamline upgrades, patching and incident management across multi-system SAP estates.

Proof points for enterprises

● Documented HA/DR drills with actual recovery times achieved versus targets

● Transparency into the provider’s security posture, including automated compliance checks

● Evidence of end-to-end observability correlating issues across infrastructure, integrations and SAP applications

● A defined model for cloud cost visibility and optimization, especially on hyperscalers

● Runbooks demonstrating automation for provisioning, scaling, refreshes and patching

Frequent provider risks and gaps to monitor

● Fragmented accountability across infrastructure, applications and integrations

● Limited transparency into cloud consumption with unexpected cost spikes

● Inadequate disaster recovery readiness, for example, plans untested under realistic failure scenarios

● Over reliance on manual monitoring that cannot scale to hybrid environments

Outlook for U.S. enterprises (12-24 months)

U.S. enterprises will keep favoring standardized routes to S/4HANA with measurable value checkpoints and provable clean-core controls. AI will become a platform norm, establishing automated test baselines, ABAP modernization copilots and L1-to-L2 triage — expanding only where audit trails and rollback are in place. GROW with SAP will proliferate across subsidiaries and carveouts, while RISE with SAP will underpin complex estates seeking cloud economics without losing risk control.

Sustainability and regulatory reporting will progress through analytics-first approaches before broader module rollouts.

Recommended actions for enterprises

● Select the transformation lane and secure industrialized delivery capabilities: Choose greenfield, brownfield or SDT based on data quality and risk appetite, and contract for conversion, testing and data factories that make execution repeatable across waves.

● Embed value realization into the contract: Tie acceptance to KPI improvements, not just go-live, and ensure automated regression testing and extension guardrails.

● Scale AI safely: Start with delivery and run assistants under human approval, and expand autonomy only where explainability, data controls and rollback mechanisms are proven.

Recommended actions for providers

● Measurable clean-core governance: Publish extension catalogs, code decommissioning/ refactoring roadmaps, and upgradesafe regression suites validated against quarterly releases.

● Industrialize AI: Embed AI across the SDLC and operations with human-in-the-loop controls and observable pipelines, and move beyond pilots.

● End-to-end accountability with financial transparency: Present one accountable operating model across advise/build/run, with clear FinOps and resilience metrics for business sponsors.

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