How to Pick Your First AI Pilot Project

Why Most AI Pilots Fail

Every executive has heard the AI pitch: “Transform your business with artificial intelligence.” But after the demo ends and the contract is signed, reality sets in. The pilot stalls. The data isn’t ready. The use case was too ambitious. Six months later, the project is quietly shelved.

The problem isn’t the technology. It’s the selection process. Teams pick AI projects based on what sounds impressive rather than what’s achievable and measurable.

“The best AI pilot isn’t the most exciting one. It’s the one that ships in 30 days and proves value.”

The Four-Factor Scorecard

Before committing to an AI project, score each potential use case across four dimensions:

1. Business Impact

What’s the potential value if this works? Consider:

• Hours saved per week/month
• Revenue influenced or protected
• Customer satisfaction improvement
• Error reduction rate

Score 1-5, where 5 = transformative impact on a core business metric.

2. Technical Feasibility

Can current AI tools actually do this reliably? Consider:

• Is this a proven AI capability (summarization, classification, Q&A) or bleeding edge?
• What accuracy level is acceptable? (80%? 95%? 99.9%?)
• Can you verify outputs before they reach customers?

Score 1-5, where 5 = well-established AI capability with mature tools.

3. Data Readiness

Do you have the data needed to train, test, or feed the system? Consider:

• Is the data digital and accessible?
• Is it clean, consistent, and well-labeled?
• Do you have enough volume for meaningful training/testing?
• Are there privacy or security constraints?

Score 1-5, where 5 = data is clean, accessible, and sufficient.

4. Risk Level

What happens if the AI makes a mistake? Consider:

• Customer-facing vs. internal-only outputs
• Reversibility of decisions
• Regulatory or compliance implications
• Reputational risk

Score 1-5, where 5 = low risk (internal use, easy to review, mistakes are reversible).

High-Scoring Use Cases

Based on our work with mid-market companies, these use cases consistently score well:

• Support ticket triage: Classify and route incoming tickets. High volume, clear categories, internal-facing, easy to measure deflection rate.
• Internal knowledge search: Let employees ask questions about policies, procedures, or product info. Reduces time spent hunting for answers.
• Meeting note summarization: Auto-generate action items and summaries. Low risk, immediate time savings, easy to verify.
• Document drafting assistance: Generate first drafts of SOPs, proposals, or reports. Human review before use, clear productivity gains.
• Data extraction from documents: Pull structured data from invoices, contracts, or forms. Reduces manual data entry.

Red Flags: Use Cases to Avoid (For Now)

Some use cases sound great but are likely to stall:

• Customer-facing chatbots (without human fallback) — high reputational risk
• Automated decision-making in regulated areas — compliance complexity
• Use cases requiring custom model training — data and expertise requirements
• Anything that needs 99.9% accuracy — current AI isn’t there yet

Save these for Phase 2, after you’ve built internal AI muscle with lower-risk pilots.

The 30-Day Pilot Framework

Once you’ve scored and selected a use case, structure a 30-day pilot:

• Week 1: Define success metrics (hours saved, accuracy threshold, user adoption)
• Week 2: Build or configure the solution using existing AI tools
• Week 3: Test with a small group, gather feedback, iterate
• Week 4: Measure results against baseline, document learnings

At the end of 30 days, you should have clear data on whether to scale, pivot, or stop.

Key Takeaways

• Score use cases on impact, feasibility, data readiness, and risk
• Start with internal-facing, low-risk use cases
• Support triage, knowledge search, and meeting summaries are proven starters
• Structure a 30-day pilot with clear success metrics
• Build AI muscle before attempting high-stakes projects

Conclusion

AI adoption isn’t about finding the most impressive use case—it’s about finding the right first step. Use the scorecard to evaluate objectively, pick a pilot you can ship in 30 days, and build momentum from proven wins.