Kyndryl’s Agentic AI Clean Field Could Change How SAP Teams Modernize to S/4HANA
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Key Takeaways
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Kyndryl's Clean Field approach offers a balanced modernization strategy for SAP S/4HANA transformations, selectively retaining valuable elements while eliminating obsolete custom code and workflows.
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By leveraging agentic AI, organizations can expedite decision-making and remediation processes, targeting chronic bottlenecks in SAP ECC-to-S/4HANA migrations, thereby reducing technical debt and manual effort.
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To maximize the benefits of this approach, SAP professionals should implement a custom code decision matrix and establish AI governance protocols to ensure successful long-term transformations.
SAP S/4HANA programs still get stuck in the same place: legacy custom code. This is not the glamorous part of transformation—just the stubborn reality of years of Z-objects, bespoke workflows, and temporary enhancements that became permanent. Now, Kyndryl’s new Agentic AI Clean Field approach aims directly at that bottleneck, proposing a middle path between starting over and dragging everything forward.
Why Clean Field Is Showing Up Now
Most SAP leaders already know the Greenfield versus Brownfield trade-off. While Greenfield reduces baggage but can feel like organizational amnesia, Brownfield preserves history but can also preserve bad habits. Kyndryl’s Clean Field approach is an attempt to selectively modernize—keeping what truly differentiates the business while retiring what simply accumulated.
The key here is the agentic idea: moving beyond AI that only recommends, to AI that can take goal-driven actions under governance. In migration terms, that translates into using AI to help identify custom objects, map dependencies, suggest or perform remediation paths, and accelerate decisions that typically take weeks of workshops and manual review.
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“Enterprises can no longer afford SAP transformations that are slow, rigid or weighed down by legacy complexity,” said Michael Bradshaw, Global Practice Leader for Applications, Data and AI at Kyndryl. “By combining our Clean Field approach and deep SAP expertise with agentic AI, we’re giving customers a faster, more disciplined path to SAP S/4HANA—one that reduces technical debt, requires less manual effort, accelerates outcomes and creates a digital foundation built for long-term innovation.”
The SAP Work This Targets
If this approach works in practice, it hits three chronic time-sinks in SAP ECC-to-S/4HANA migration programs:
- Custom code triage: Classifying what’s critical versus obsolete and what can be replaced by standard.
- Remediation throughput: Reducing the human-hours required to refactor, retire, or redesign legacy logic.
- Clean core discipline: Keeping the new system from inheriting the old system’s operating model.
From Code Janitor to Process Owner
The most practical implication for SAP professionals isn’t AI replaces consultants. It’s that roles tilt away from repetitive remediation work and toward higher-value decisions: process design, standard-versus-custom governance, and measurable outcomes such as cycle time, close time, on-time delivery, etc.
Teams that succeed will be the ones who treat AI-assisted remediation as a forcing function to clarify what the business needs—not what the old system happens to contain.
What This Means for SAPinsiders
Create a custom code decision matrix before tooling. Define categories such as keep, retire, rebuild, replace-with-standard, and attach owners in the finance, supply chain, and IT functions so AI output lands into a decision workflow, not a backlog.
Build an AI governance gate in your transport strategy. Require reviews for AI-remediated objects such as tests, approvals, and traceability, exactly like human changes, especially around FI/CO and Order-to-Cash objects.
Use clean core KPIs, not slogans. Track percent custom objects retired, extensions moved to side-by-side, and reduction in modification footprint release-over-release.
