AI-Native Leadership Operating System (ALOS) — 30-Day Rollout Checklist

Purpose: Help founders, engineering leaders, and operators scale AI-assisted work safely. This checklist assumes you already ship software regularly and want to add AI copilots/agents without losing reliability, compliance, or cost control.

WEEK 1 — Define accountability and risk tiers
1) Publish a 1-page AI Use Policy: what’s allowed, what’s prohibited (PII rules, secrets handling, customer comms, regulated decisions).
2) Create 5 risk tiers (Tier 0–4): Internal → Assist → Customer-facing → Regulated → Autonomous actions.
3) Assign owners:
   - One executive sponsor (final escalation).
   - One “model gateway” owner (routing/logging).
   - One security owner for AI exceptions.
4) Add RACI to every new AI workflow: Responsible (builder), Accountable (approver), Consulted (security/legal), Informed (support/sales).

WEEK 2 — Put governance into infrastructure (not meetings)
5) Stand up tracing/logging for all AI calls: prompt version, model, tool calls, retrieval sources, output hash, user/workspace ID.
6) Implement a basic policy gate:
   - Block secrets in prompts.
   - Block PII to non-approved models.
   - Enforce retention settings and approved vendors.
7) Create a “decision log” template attached to PRs: what changed, why, expected impact, rollback plan.

WEEK 3 — Build evals and release lanes
8) Create an eval set (20–50 cases) for each Tier 2+ workflow: happy paths + adversarial inputs.
9) Add CI thresholds for AI workflows: minimum pass rate, maximum eval cost, latency budget.
10) Define release lanes:
   - Tier 0: team lead approval.
   - Tier 1–2: PM + Security review.
   - Tier 3: Legal/Compliance sign-off.
   - Tier 4: two-person rule + sandbox + explicit rollback.

WEEK 4 — Control cost and operationalize learning
11) Build a cost model per workflow: cost per successful task, not cost per token.
12) Add guardrails: rate limits, per-workspace caps, routing to smaller models by default, caching for repeated queries.
13) Create an incident taxonomy: hallucination, prompt injection, data leakage, model regression, tool misuse.
14) Run a weekly “Eval–Ship–Learn” review (30 minutes):
   - Cost/latency deltas
   - Top 3 failures with examples
   - Any policy near-misses
   - Planned changes and owners

Success criteria (by day 30)
- 100% of production AI workflows have an owner, risk tier, and logging.
- Tier 2+ workflows have automated evals in CI.
- You can answer: which model ran, what data it saw, what tools it called, and who approved the workflow.
- AI spend is allocated to a team/product with a monthly budget.

If you only do three things: (1) risk-tier your workflows, (2) add tracing + evals, and (3) assign a single accountable owner per production endpoint.