I’ve seen hundreds of technology pilot programs over my career. Most of them fail - not because the technology didn’t work, but because the pilot was designed poorly and produced ambiguous results.

Here’s how to run pilots that actually generate decisions.

The Typical Pilot Failure

The pattern is depressingly common:

A company identifies a promising technology. They engage a vendor, spin up a pilot, and test it in a limited context. The pilot runs for three to six months. At the end, results are mixed - some things worked, some didn’t, it’s hard to tell if the gains were meaningful.

The pilot team writes a report. Leadership reviews it. The conclusion: “interesting results, need more data before making a decision.” Another pilot is proposed. Repeat indefinitely.

Meanwhile, the organization has learned nothing actionable and made no actual technology adoption decision.

Why This Happens

Several design flaws cause pilot failure:

Unclear success criteria. The pilot starts without defining what success looks like. At the end, there’s no objective way to evaluate whether it worked.

Unrepresentative scope. The pilot tests the technology in a narrow context that doesn’t reflect real deployment conditions. Positive results don’t predict production success.

Insufficient duration. The pilot is too short to capture variability, edge cases, and the learning curve of users.

Wrong participants. The pilot uses enthusiastic volunteers rather than representative users. Results reflect early adopter success, not mainstream viability.

No baseline. Without measuring current performance, you can’t tell if the new technology actually improved anything.

Success theater. Everyone involved wants the pilot to succeed, so they unconsciously stack the deck through extra support, selected use cases, and optimistic interpretation.

A Better Framework

Here’s how to design pilots that produce real decisions:

Define Success Criteria First

Before the pilot starts, write down exactly what outcomes would justify deployment and what outcomes would justify killing the project.

Be specific and quantitative:

• “Reduce error rate from 4.5% to below 2%”
• “Achieve user satisfaction score above 7.5/10”
• “Process 100 transactions per hour with less than 2-second latency”
• “Demonstrate positive ROI within 18 months at scale”

Also define failure criteria:

• “Error rate doesn’t improve by at least 30%”
• “User satisfaction below 6/10”
• “Integration costs exceed $500K”

Get stakeholder agreement on these criteria upfront. This prevents goalpost-moving at the end.

Design for Representative Conditions

The pilot should reflect real deployment conditions as closely as possible:

Representative users. Include skeptics and average performers, not just enthusiasts.

Representative workload. Test with realistic variety, volume, and complexity of work.

Representative integration. Use actual connections to production systems, not mock integrations.

Representative support. Provide the same support you’d provide at scale, not extra handholding for the pilot.

If the pilot requires conditions that won’t exist at scale, you’re not testing whether the technology works - you’re testing whether the technology works under ideal conditions.

Establish Baselines

Before the pilot starts, measure current performance on your success metrics. Without a baseline, you can’t measure improvement.

This seems obvious, but many pilots skip it. “We know things are slow” isn’t a baseline. “Average processing time is 47 minutes with a standard deviation of 12 minutes” is a baseline.

Build in Learning Checkpoints

Don’t wait until the pilot ends to evaluate. Build in structured checkpoints - typically weekly or biweekly - where you:

• Review metrics against criteria
• Identify what’s working and what isn’t
• Decide whether to continue, adjust, or terminate early

If results are clearly positive after six weeks, you might not need three more months. If results are clearly negative, why continue?

Separate Testing from Evaluation

The pilot team is not the right group to evaluate pilot success. They’re invested in the outcome.

Establish a separate evaluation process:

• Someone not involved in the pilot reviews results against pre-defined criteria
• Evaluation happens before the team presents their conclusions
• Criteria-based assessment precedes qualitative interpretation

This prevents unconscious bias from shaping conclusions.

Plan the Go/No-Go Decision

Before the pilot starts, schedule the decision meeting. Identify who has decision authority. Clarify what happens next in each scenario - full deployment, expanded pilot, or termination.

Too many pilots end with “we’ll discuss next steps.” No. The decision should be an explicit deliverable of the pilot, not an afterthought.

Common Objections

“We need flexibility - we can’t know success criteria before we learn.” Fair point for genuinely exploratory work. But most corporate pilots aren’t true exploration - they’re evaluation of known technologies for known use cases. If you can’t define success, you’re not ready for a pilot.

“Representative conditions are expensive.” True. But unrepresentative conditions produce unusable results. A cheap pilot that doesn’t answer the question is more expensive than no pilot.

“Stakeholders won’t commit to criteria upfront.” This is a feature, not a bug. If you can’t get agreement on what success looks like, you’re not ready for a pilot. Better to discover that before spending months on a project.

“We can’t terminate early - we committed to the vendor.” Structure agreements to allow early termination. Pilots should have milestones and off-ramps.

After the Decision

If the pilot succeeds and you decide to deploy:

• Document what you learned about implementation requirements
• Plan the rollout with realistic timeline and resources
• Transfer knowledge from pilot team to deployment team

If the pilot fails:

• Document why, specifically and honestly
• Share learnings so others don’t repeat the pilot
• Thank participants for their time
• Move on without guilt - a clear negative answer has value

The point of a pilot is to reduce uncertainty about a decision. A well-run pilot that concludes “don’t deploy” is a success - it saved you from a bad deployment. That’s only possible if you designed the pilot to produce clear answers.

Design for decisions, not for activity. Your future self will thank you.