AI Pilot Examples
Frameworks and examples for designing, running, and evaluating AI pilots — from defining success criteria to measuring results and making go/no-go scaling decisions.
Customer Support AI Pilot Design
beginnerA structured pilot plan for deploying AI in customer support, covering scope definition (which ticket types), success metrics (resolution rate, CSAT, cost per ticket), timeline, and scaling criteria.
Key takeaway: Successful AI pilots define 'what does good enough look like' before starting — without clear success criteria, pilots tend to expand scope and never reach a go/no-go decision.
Document Processing Pilot with Control Group
intermediateA pilot design that processes documents with both AI and manual methods in parallel, comparing accuracy, speed, cost, and user satisfaction to build a rigorous business case for scaling.
Key takeaway: Parallel processing pilots (AI alongside manual) produce the most convincing business cases because they control for seasonal and external factors.
AI Pilot Evaluation Scorecard
intermediateA structured scorecard for evaluating AI pilot results across technical performance, business impact, user adoption, operational feasibility, and risk dimensions with weighted scoring.
Key takeaway: Multi-dimensional pilot evaluation prevents common failure modes — a pilot can succeed technically but fail operationally or vice versa.
Sales AI Pilot with A/B Testing
intermediateA pilot design using A/B testing where half the sales team uses AI-assisted tools (lead scoring, email drafting, meeting preparation) while the control group uses traditional methods, measuring quota attainment and activity metrics.
Key takeaway: A/B testing AI pilots with half the sales team provides statistically robust evidence — anecdotal success stories do not convince CFOs.
AI Pilot to Production Transition Plan
advancedA framework for transitioning a successful pilot to production, covering infrastructure scaling, organisational change management, training, monitoring setup, and phased rollout with risk mitigation.
Key takeaway: The pilot-to-production gap kills more AI projects than pilot failure — plan the transition before the pilot ends, not after.
Patterns
Key patterns to follow
- Define clear, measurable success criteria before starting the pilot — not after results come in
- Include a control group or parallel processing to generate rigorous comparative data
- Plan the production transition before the pilot ends to maintain momentum
- Multi-dimensional evaluation (technical, business, operational, user) prevents false positives
FAQ
Frequently asked questions
Most AI pilots need 6-12 weeks to generate meaningful data. Shorter pilots risk not capturing enough volume for reliable metrics. Longer pilots risk losing organisational momentum. Define the minimum data volume needed for statistical significance and run until you have it.
Choose pilots that have high business value, good data availability, clear success metrics, a willing internal champion, and limited risk if they fail. Avoid piloting AI on your most critical or sensitive processes first — pick high-value but lower-risk use cases.
Industry data suggests 60-70% of AI pilots succeed technically, but only 30-40% transition to production. The gap is usually due to unclear business cases, insufficient change management, or pilot-to-production infrastructure gaps.
Typical AI pilot budgets range from £20,000-100,000 depending on complexity. This covers technology costs (API fees, infrastructure), consulting or development time, and the opportunity cost of internal participants. Keep it small enough that failure is acceptable.
Frame the pilot as a learning exercise with defined investment and clear success criteria. Show comparable pilots in the industry. Keep the budget small enough that it is a rounding error. Promise a clear go/no-go decision at the end with data to support it.
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