AI Pilot Programme Template
A structured template for designing, running, and evaluating an AI pilot programme. Guides you from hypothesis definition through pilot design, execution, measurement, and go/no-go decision-making. Ensures your pilot produces actionable evidence, not just a demo.
Overview
What's included
Pilot Hypothesis & Objectives
Pilot Hypothesis & Objectives
Pilot name: Sponsor: Pilot lead: Start date: End date: Duration: weeks
Hypothesis
Complete this statement: "We believe that using AI to [action] will result in [outcome] for [user group], which we will know by measuring [metric]."
Business Problem
What specific problem are we solving?
Pilot Objectives
| # | Objective | Measurable Target | Priority |
|---|---|---|---|
| 1 | Must have | ||
| 2 | Must have | ||
| 3 | Nice to have |
Success Criteria
| Criterion | Target | Measurement Method | Data Source |
|---|---|---|---|
What are we NOT testing?
Pilot Design
Pilot Design
Scope
| Dimension | Detail |
|---|---|
| User group | users from [team/department] |
| Process/task | |
| Data scope | |
| Geography | |
| Duration | weeks |
Control vs Pilot Comparison
| Control Group | Pilot Group | |
|---|---|---|
| Size | users | users |
| Process | Current process (no AI) | AI-assisted process |
| Metrics tracked | Same as pilot | |
| Selection criteria |
Technology Stack
| Component | Choice | Rationale |
|---|---|---|
| AI service/model | ||
| Integration method | ||
| Data pipeline | ||
| Monitoring |
Resource Requirements
| Resource | Allocation | Duration |
|---|---|---|
| Pilot lead | % | Full pilot |
| Technical resource | % | Weeks - |
| Business users (pilot group) | hours/week | Full pilot |
| Data/analytics | % | Weeks - |
Budget
| Item | Cost |
|---|---|
| AI tool/API | £ |
| Development | £ |
| People (time) | £ |
| Total | £___ |
Evaluation & Go/No-Go
Evaluation & Go/No-Go Framework
Data Collection Plan
| Metric | Collection Method | Frequency | Responsible |
|---|---|---|---|
| Daily/Weekly | |||
| Daily/Weekly | |||
| End of pilot | |||
| User satisfaction | Survey | Weekly + end | |
| Usage / adoption | System logs | Daily |
Weekly Check-In Template
| Question | Week 1 | Week 2 | Week 3 | Week 4 |
|---|---|---|---|---|
| Are users actively using the AI tool? | ||||
| Are we on track against success criteria? | ||||
| What issues have emerged? | ||||
| What adjustments are needed? |
Pilot Results Summary
| Success Criterion | Target | Actual | Met? |
|---|---|---|---|
| Yes/No | |||
| Yes/No | |||
| Yes/No |
Go/No-Go Decision Framework
| Decision | Criteria |
|---|---|
| GO — Scale | All must-have success criteria met; positive user feedback; costs within budget |
| GO — Iterate | Most criteria met; identified improvements needed before scaling |
| PIVOT | Hypothesis partially validated; significant design changes needed |
| NO-GO | Success criteria not met; costs exceed value; user adoption too low |
Recommendation
Decision: Go — Scale / Go — Iterate / Pivot / No-Go Rationale: Next steps if GO: Lessons learned:
Instructions
How to use this template
Define a clear, testable hypothesis
Your hypothesis should be specific enough to be proven right or wrong in the pilot timeframe. Vague hypotheses produce vague results.
Keep the scope deliberately small
Limit to one team, one process, and one AI capability. A focused pilot produces clearer evidence than a broad one.
Establish a control group
Comparing AI-assisted performance against a control group provides much stronger evidence than before/after comparisons alone.
Collect data continuously
Do not wait until the end to measure. Weekly check-ins catch problems early and allow course corrections.
Make the go/no-go decision objectively
Use the predefined success criteria and decision framework. Avoid sunk cost bias — a failed pilot that generates clear learnings is valuable.
Watch Out
Common mistakes to avoid
FAQ
Frequently asked questions
Most AI pilots run for 4-8 weeks. This is long enough to see meaningful patterns and short enough to maintain momentum. Very simple pilots (e.g. testing a chatbot) can run for 2-3 weeks; complex pilots (e.g. AI-assisted decision-making) may need 8-12 weeks.
Enough to get statistically meaningful results, typically 15-30 users in the pilot group. For quantitative metrics, consult a statistician to determine the sample size needed for your desired confidence level.
A 'failed' pilot is not wasted if it produces clear learnings. Document why it failed (wrong use case, data quality, user adoption, technology limitations) and use those insights to inform the next initiative.
Use off-the-shelf or low-code AI tools for the pilot wherever possible. The goal is to test the business hypothesis, not the technology. Invest in custom development only after the pilot validates the use case.
Plan the transition before the pilot starts. If the go criteria are met, you should have a clear path to production: infrastructure scaling, full security review, user training for all target users, and production support setup.
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