AI-Native Operations Leadership Checklist (One-Week Sprint)

Goal: In 7 days, replace ad hoc AI usage with one sanctioned, auditable workflow that has clear data boundaries, review rules, and rollback.

Day 1 — Pick the workflow (don’t boil the ocean)
- Choose one workflow already using AI informally: PR review help, on-call debugging, support reply drafting, internal doc Q&A.
- Write the failure you fear most (one sentence): data leakage, wrong action in prod, policy violation, customer harm.
- Name the executive owner and the operational owner (SRE, Eng Productivity, Security, or Product).

Day 2 — Tool and endpoint standardization
- Select the sanctioned surface for this workflow (e.g., GitHub Copilot for code, ChatGPT/Claude enterprise offering for text).
- Require SSO and offboarding support.
- Decide whether users can use personal accounts for this workflow (answer should usually be “no”).

Day 3 — Data boundaries (plain language)
- Define “forbidden inputs” for this workflow: credentials/private keys, customer data, incident details, unreleased financials, regulated data.
- Define “allowed inputs” explicitly (examples help).
- Publish a one-page policy + an approved tools list.

Day 4 — Review and accountability rules
- Decide what requires human verification (e.g., any production change; any customer-facing claim; any automated action).
- Update the team’s PR checklist or runbook to reflect this.
- Define who is accountable if AI-assisted output causes an incident (treat it like any other change).

Day 5 — Logging, retention, and audit trail
- Require logging of: prompt template/version, retrieved context sources, model name, output.
- Decide retention rule by policy (don’t invent numbers; align to your existing security posture).
- Ensure redaction strategy exists for sensitive tokens.

Day 6 — Evaluation harness (minimum viable)
- Create a small “golden set” of real queries/tasks for the workflow (10–30 is fine).
- Define pass/fail criteria that match risk (correctness, policy compliance, refusal behavior).
- Run the eval before any rollout; store results with the release artifact.

Day 7 — Rollout controls and kill switch
- Put the feature behind a flag.
- Stage rollout (internal users first; then limited cohort).
- Add a kill switch and a documented fallback path (what happens when the model is down).

Ongoing (weekly for a month)
- Review: usage patterns, near-misses, policy violations, regressions on the golden set.
- Remove exceptions instead of normalizing them.
- If teams ask for broader AI access, require the same artifacts: data boundaries, review rules, eval, logging, rollback.

Definition of Done
- One workflow is running on sanctioned tools with SSO.
- Data boundaries are published and enforced.
- There is an audit trail for prompts/context/outputs.
- There is a documented review step and a kill switch.
- A minimal eval harness exists and is used before changes.