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AIOps & AI-Augmented Engineering

AI that extends what your engineers can do, without replacing their judgment.

From AI-native IDEs like Windsurf, Cursor, and Kiro to foundation model platforms like Vertex AI and Amazon Bedrock, map where AI adds real value across every phase of the SDLC and where governance, cost controls, and human oversight are non-negotiable.

Discovery Questions

  • •Does the organization have a written AI adoption strategy with clear use-case boundaries?
  • •How are AI tools evaluated before broad rollout (pilots, evals, metrics)?
  • •What guardrails prevent teams from shipping unreviewed AI-generated code or content?
  • •How is AI tool spend tracked, attributed, and optimized?
  • •Is there a responsible AI policy covering bias, hallucination, and data privacy?
  • •How are AI capability gaps identified and addressed through training?
  • •What feedback loops exist to measure whether AI tools are improving outcomes?

Evidence to Collect

  • •AI adoption policy or strategy document.
  • •Pilot results and eval scorecards.
  • •AI spend dashboards.
  • •Training or enablement materials.

Implementation Patterns

AI Center of Excellence (CoE)

Stand up a lightweight CoE to own AI strategy, evaluate tools, and share patterns across teams.

Internal WikiSlack/TeamsOKR Tooling
Steps
  1. Define CoE charter: scope, membership, decision rights, and cadence.
  2. Maintain an AI tools registry with adoption status, cost, and use cases.
  3. Publish and iterate on AI usage guidelines and acceptable-use policies.
  4. Run quarterly AI retrospectives: what worked, what was wasteful, what to cut.
  5. Track AI ROI metrics: developer velocity delta, incident MTTR, test coverage gains.

Responsible AI & Governance Framework

Encode ethics, safety, and accountability into every AI initiative from the start.

NIST AI RMFEU AI ActInternal Policy
Steps
  1. Define data classification rules for what can be sent to external LLM APIs.
  2. Implement output review gates before AI-generated artifacts reach production.
  3. Require human approval for any agentic action with destructive side-effects.
  4. Log all AI API calls for auditability and cost attribution.
  5. Run bias and hallucination audits on models used in decision-making workflows.

Tips & Tricks

Learn from Ani's sleepless nights

Browse the full playbook

Battle-tested defaults from the platform playbook. Filter by layer, search, and steal the snippet over at the full playbook.