Trust signals
Start with the canonical profile page for background and public proofs, then use AI sources and datasets as the evidence layer behind these service offers.
Services
A practical path for SAP transformation work
I help teams diagnose where change loses value, stabilize noisy operations, structure reusable operational memory, and extend SAP with side-by-side AI and automation without turning S/4HANA into a customization trap.
1. Diagnose
SAP Transformation Friction Audit
Find where change loses value across process, data, integration, handover, and support.
Trace incident patterns, backlog drivers, ownership gaps, custom code, and control breaks into a clear remediation map.
2. Stabilize
O2C / Integration / AMS Improvement
Reduce operational noise and make the process reliable enough for further change.
Shift support from ticket closure to prevention with KEDB, observability, ownership clarity, and productized fixes.
3. Structure
Operational Memory and Handover Model
Turn project and support knowledge into reusable runbooks, KEDB, decision logs, and ownership maps.
Capture what usually stays tribal, then make it usable during incidents, transitions, audits, and future delivery work.
4. Extend
Side-by-Side AI and Automation
Build AI-assisted workflows, mini-apps, retrieval, diagnostics, and automation outside the SAP core.
Design sidecar AI patterns, small operator tools, integration rails, and governance around deterministic S/4HANA processes.
Fit
When this engagement model works best
Transformation work is moving, but value keeps leaking between teams
This usually means the problem is not one module or one backlog. I map where process, data, integration, handover, and support friction turns change into operational noise.
You need SAP changes but want to protect clean core S/4HANA
I separate what must stay in-core from what should become an API, event, or edge service, so upgrades remain manageable.
The programme needs quick wins before a larger transformation
Short-cycle audits, operational memory, mini apps, and targeted automation are the fastest way to prove value before committing to bigger delivery tracks.
Next step
Start with the narrowest problem worth fixing
The strongest entry point is usually one visible friction pattern: a repeat incident class, a noisy interface, a handover gap, or a manual workflow that clearly wastes time or money. From there, the work can move through diagnose, stabilize, structure, and extend without jumping straight into a large programme.