The 90-Day AI Automation Pilot Checklist

Phase 1: Discovery (Days 1-21)

Before writing any code, you need clarity on what you're automating and why. This phase is about alignment, not action.

Week 1: Problem Definition

• Map the current workflow — Document each step, who does it, how long it takes, and where errors occur. Use actual observations, not assumptions.
• Quantify the pain — How many hours per week? What's the error rate? What's the downstream cost of mistakes?
• Identify the bottleneck — Which single step, if automated, would have the biggest impact?

Week 2: Success Criteria

• Define 2-3 measurable KPIs — Examples: processing time, error rate, throughput, labor hours saved.
• Set baseline measurements — You can't prove improvement without knowing where you started.
• Agree on "good enough" — What level of accuracy or speed would justify scaling?

Week 3: Feasibility & Scoping

• Assess data availability — Is the data you need accessible, clean, and in a usable format?
• Identify integration points — What systems need to connect? Who owns them?
• Define the pilot boundary — One workflow, one team, one location. Don't boil the ocean.

Phase 2: Build (Days 22-60)

This is where most teams want to start. Resist the urge. The discovery work above is what makes this phase efficient.

Week 4-5: Core Development

• Build the minimum viable automation — Focus on the happy path first. Handle edge cases later.
• Establish human-in-the-loop checkpoints — Where should a human review before the AI proceeds?
• Set up monitoring — You need visibility into what the system is doing from day one.

Week 6-7: Integration & Testing

• Connect to real systems — Use staging/sandbox environments where possible.
• Run parallel processing — Let the AI work alongside humans, comparing outputs.
• Document failure modes — What happens when things go wrong? Build graceful fallbacks.

Week 8: User Training

• Train the operators — Not just how to use it, but how to spot problems and escalate.
• Create runbooks — Step-by-step guides for common scenarios and troubleshooting.
• Identify champions — Who on the team will own adoption and feedback?

Phase 3: Validate (Days 61-90)

The pilot isn't done when the code works. It's done when you can prove the business value.

Week 9-10: Controlled Rollout

• Go live with guardrails — Start with 10-20% of volume, human review on all outputs.
• Collect feedback daily — What's working? What's surprising? What needs adjustment?
• Iterate quickly — Small fixes based on real-world feedback.

Week 11-12: Measurement & Decision

• Compare to baseline — Are you hitting the KPIs you defined in Week 2?
• Calculate actual ROI — Not projections. Real savings based on pilot data.
• Make the go/no-go call — Scale, iterate, or sunset based on evidence.

Common Failure Modes

I've seen pilots derail for the same reasons repeatedly:

1. Scope creep — "While we're at it, can we also..." No. Stick to the original scope.
2. No baseline — You can't prove 40% improvement if you didn't measure the starting point.
3. Wrong workflow — Piloting on an edge case instead of a high-volume, high-pain process.
4. No champion — Someone on the operations team needs to own success.
5. Perfect is the enemy — 85% accuracy that ships beats 99% accuracy that never leaves dev.

What Comes Next

If your pilot succeeds, the next step is a production rollout plan. That's a different playbook—one that covers scaling, monitoring, and organizational change management.

If your pilot fails, that's valuable too. You learned something without committing to a full deployment. Analyze why, adjust, and try again with a different workflow.