AI Pilot Checklist

Pilots are where most enterprise AI effort goes and where most of it stalls, because they are designed to demonstrate rather than to measure. A pilot that cannot fail cannot succeed either; it can only be repeated. This checklist covers what a pilot must define before it starts and deliver before it ends. It complements how to run an ai pilot, why ai pilots fail, and ai pilot to production.

Who should use this checklist?

Business owners sponsoring a pilot, engineering leads running it, and leaders who will make the production decision at the end.

Is there a hypothesis with success criteria?

1. A hypothesis in the form: if we deploy X in workflow Y, metric Z will change by at least N.
2. Success criteria are measurable and agreed with the business owner.
3. Failure criteria are written; the pilot can produce a no-go.
4. The autonomy level under test is stated.

Reference: how to write acceptance criteria for ai.

Is scope bounded?

1. One workflow or one slice of it.
2. Users and volume defined.
3. Integration surface minimized to what the hypothesis needs.
4. Out of scope written down.

Reference: how to scope an ai project.

Is the baseline measured?

Metric	Baseline captured?
Cost per unit of work
Cycle time distribution
Quality and error rate
Volume by category
Human hours by role
Downstream effects relevant to the hypothesis

Reference: the AI ROI measurement framework whitepaper.

Are data and access arranged?

1. Data sources identified and accessible.
2. Historical data for the golden dataset located.
3. System access for integration granted.
4. Security and privacy constraints documented; provider terms confirmed.

Reference: the ai data readiness checklist.

Is evaluation built before automation?

1. A specification exists for the pilot scope.
2. A golden dataset is labeled by domain experts.
3. Metrics and thresholds are set from consequence.
4. Safety cases are included where the system reads untrusted content or acts.
5. The suite runs before the system touches the real process.

Reference: the AI evaluation and testing whitepaper.

Does the pilot run against the real process?

1. Shadow mode (compare without effect) or assist mode (humans approve) is used.
2. Real users participate with training and feedback channels.
3. Human oversight is designed, with reviewers who have authority.
4. Observability captures quality, cost, and latency from day one.

Reference: human-in-the-loop ai explained.

Are ownership and cadence in place?

1. A business owner and engineering owner are named.
2. Weekly reviews of evidence and friction.
3. Decision speed for scope questions agreed.
4. Stakeholders informed of what the pilot is and is not.

Reference: the ai project kickoff checklist.

Are exit criteria and the decision date set?

1. A decision date based on volume needed for evidence.
2. Go, no-go, and re-scope criteria written.
3. Production requirements listed so a go decision knows what follows: platform, governance, autonomy graduation, change management.
4. Budget for the pilot and an estimate for production.

Reference: the AI total cost of ownership whitepaper.

Is the commercial structure right for a partner-run pilot?

1. Paid and scoped, with acceptance criteria.
2. IP assignment and buyer-owned assets.
3. Data terms matching the data.
4. Exit criteria in the agreement.

Reference: how to structure an ai pilot agreement.

What must the pilot deliver at the end?

1. The specification with acceptance criteria.
2. The golden dataset and evaluation report.
3. Results against the baseline with the attribution method.
4. An integration and platform assessment.
5. A cost and value estimate for production.
6. A recommendation: go, no-go, or re-scope, with reasons.

Are the common pilot failures avoided?

1. No hypothesis; a demo dressed as a pilot.
2. No baseline.
3. Tuning on sample data, then meeting real data.
4. Running beside the process instead of inside it.
5. Extending indefinitely because no decision date exists.
6. Success declared on enthusiasm rather than metrics.

What happens when the pilot succeeds?

A successful pilot produces a specification, an evaluation report, a cost model, and a scaling plan with the controls production requires. The team that ran the pilot should carry into production, because context lost at that handoff is where many pilots quietly die despite meeting their criteria.

How FISTA Solutions runs pilots

FISTA Solutions runs pilots as evidence-producing engagements: hypothesis and baseline first, specification and golden dataset before tuning, shadow or assist mode inside the real workflow, weekly evidence reviews, and a decision on a pre-agreed date with a spec and evaluation report delivered regardless of outcome. Forward deployed engineers lead the pilot inside your organization, AI enablement supplies reusable platform components, and AI agents are scoped for production from the start. The record behind the approach is 150+ projects with 47% average efficiency gains.

To design a pilot that can actually decide something, message FISTA on WhatsApp, or read ai poc vs mvp for the distinctions between proof of concept, pilot, and minimum viable product.