An AI Governance Operating Model for an SAP Customer (AI CoE)
As of 2026-09-25
For SAP AI consultants and architects designing how a client governs AI at scale, after they can already discover use cases (M377) and triage one for risk (M375). Explains why an existing analytics CoE's structure is insufficient for generative AI; the two opposite failure modes (over- and under-centralisation) and the federated hub-and-spoke model that avoids both; the five structural components of an SAP-anchored AI CoE (platform layer, use-case registry, model governance layer, talent spine, literacy programme); how SAP's own Business AI Platform pillars (Build, Contextualize & Reason, Govern) and SAP AI Agent Hub's three governance questions map onto that model; central-team sizing thresholds (>5 domains, >50 power users, 4-6 architects); a three-category cost / five-category benefit ROI framework with a 90-day baseline discipline; and how to feed the use-case registry from the M375 ethics triage. Closes with a 90-day stand-up sequence, three exercises and a self-assessment.
What you will learn
- Explain why an existing analytics CoE's structure and staffing are insufficient for generative AI governance
- Diagnose whether a client's stalled AI programme suffers from over-centralisation, under-centralisation, or a missing structural component
- Design a federated hub-and-spoke operating model, sized correctly against the >5 domains / >50 power users threshold
- Map SAP's own Business AI Platform (Build, Contextualize & Reason, Govern) and SAP AI Agent Hub's three governance questions onto the five structural components of an AI CoE
- Build an ROI tracking structure (three cost categories, five benefit categories, 90-day baseline) that lets the CoE prove its own value
- Feed the CoE's use-case registry from the M375 ethics and privacy triage instead of running it as a parallel, disconnected process
Module overview
Who this is for. You have run a use-case discovery workshop (M377) and know how to triage a single use case for ethics and privacy risk (M375). This module is about the structure that has to exist before either of those activities can repeat reliably across an SAP estate: the organisational unit — or federated network of units — that owns standards, tooling, talent and governance for AI deployment. Most large SAP clients already run an analytics CoE that owns Datasphere and SAC; this module is about the AI CoE that absorbs generative AI into that structure without losing the data-governance discipline the analytics CoE took years to build.
Prerequisites
- M375 — SAP AI Ethics and Data Privacy in Delivery
- M377 — Running an AI Use-Case Discovery Workshop for SAP Clients
- Familiarity with how an existing analytics CoE (Datasphere/SAC/BW governance) typically operates at a large SAP client
Outcomes
- Diagnose, from symptoms alone, whether a client's AI governance problem is over-centralisation, under-centralisation or a missing structural component.
- Propose a correctly sized hub-and-spoke operating model with a named central team and a named first pilot domain.
- Explain to a client how SAP's own Business AI Platform and AI Agent Hub map onto the operating model you are proposing, using SAP's own vocabulary.
- Produce a 90-day stand-up sequence and a three-category/five-category ROI tracking structure for a named client scenario.
Full module available to members. The full module adds: the decision framework · the end-to-end scenario walkthrough · the KPI scorecard · the anti-patterns · the code blocks · the knowledge check · the diagrams.