Generative AI Capability Map

The capability stack that turns Azure OpenAI access into production GenAI — ML platform foundation, generative-AI operations, responsible-AI guardrails, and the model-governance discipline that scales beyond pilots.

BusinessCapabilityTechnology
Compass
  • Businesspersona, use case, outcome
  • Capabilitywhat the org needs to do
  • Technologythe technology choices
Guided journey · Step 1 of 4

ML Platform Foundation

ML platform foundation — registry, pipelines, deployment, monitoring. The substrate that takes notebooks to production.

~ 12 weeks

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Narrative intro

GenAI as a capability is not access to Azure OpenAI — access is table stakes. The capability is what turns pilot demos into production workloads with audit-defensible governance. This map names the four sub-capabilities: ML platform, GenAI operations, responsible AI, MLOps. The SKUs (Foundry, Copilot Studio, M365 Copilot) are the levers; the capability is the operating discipline.

Key takeaways

  • Access to Azure OpenAI is table stakes — the capability is everything around it
  • ML platform foundation is the substrate — registry, pipelines, deployment, monitoring
  • Responsible AI as build-time discipline is what makes scale defensible
  • Prompt flow and evaluation harnesses are the scale-beyond-demos pattern

Programme shape

Estimated duration
20–40 weeks
Estimated FTE
1 FTE ML platform lead + part-time security, governance, and responsible-AI partners
Spend tier
significant
Risk level
elevated

Most enterprises have GenAI pilots. The capability gap is what turns pilots into production workloads. Responsible-AI discipline determines whether scale is defensible.

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