AI Capabilities Guide for Platform Teams
AI adoption is not one capability. It is a stack of capabilities that must mature together: development workflow, platform guardrails, observability, and team operating model.
Capability layers
- Assist — copilots help with local code generation and refactoring.
- Automate — repeatable tasks move to templates, workflows, and policy checks.
- Augment decisions — telemetry and AI insights suggest actions with context.
- Govern at scale — security, compliance, and quality controls are default, not optional.
How to avoid stalled AI rollouts
- Pair every AI feature with an operational owner.
- Track DORA metric impact for every major AI workflow change.
- Keep golden paths short: one command to run, one dashboard to verify, one rollback path.
- Instrument agent and copilot workflows so failures are visible.
Practical scorecard
Rate each area from 1 (ad hoc) to 5 (reliable):
- Workflow integration (IDE + CI + deployment path)
- Observability coverage (metrics, logs, traces, alerts)
- Guardrails (tests, policy, rollback safety)
- Team enablement (docs, runbooks, onboarding)
- Business impact (DORA movement, incident trends)
Revisit the score monthly and prioritize the lowest scoring domain first.
If you need the delivery baseline first, start with the DORA primer. Then use the observability primer to improve signal quality.
Run this yourself: GitHub repo link