Pilot Purgatory

Here's the pattern I've watched play out dozens of times:

Months 1-6: Build impressive AI model. Validate with clinical data. Get excited.

Months 7-9: Create basic interface for pilot. Just enough to test the concept.

Months 10-12: Run pilot with 15-20 physicians. Get positive feedback. Celebrate.

Months 13-24: Try to scale to full organization. Adoption tanks.

What worked with 20 motivated pilot participants completely fails with 500 regular physicians.

Management blames: Change management. Training. Physician resistance. "They just hate new technology."

The real problem: The pilot interface was built for pilots, not for production.

Trap 1: Pilot Participants Are Not Your Users

Pilot participants volunteer. They're early adopters who love new technology. They're motivated to make it work. They'll forgive interface problems because they're excited to be part of something innovative.

Production users are mandatory. They're skeptical. They're busy. They resent workflow disruption. They're unforgiving of interface problems because they're just trying to get through their day.

An interface that delights Group 1 can actively repel Group 2.

Trap 2: Pilots Get White-Glove Treatment

During pilots: Dedicated support team. Fast bug fixes. Personal training. Constant hand-holding.

During production: Standard IT support. Slow bug fixes. Generic training video. Users are on their own.

A system that works beautifully with support fails catastrophically without it.

Trap 3: Pilots Optimize for the Wrong Metrics

Pilot metrics: Positive feedback from participants. Willingness to use again. Technical accuracy.

Production metrics: Daily active users. Time to complete task. Error rates. Whether people actually use it when nobody's watching.

If you're not measuring production metrics during pilot, you cannot predict production performance.

Before you declare pilot success, answer these honestly:

User Reality Check:

- Did you test with reluctant users, not just volunteers?

- Did you test with the busiest, most skeptical physicians?

- Did you test during night shifts and weekend coverage?

Support Dependency Check:

- Did you test without dedicated support available?

- Did you test with standard IT response times?

Data Reality Check:

- Did you test with messy, incomplete, contradictory data?

- Did you test with the edge cases that break everything?

Metric Reality Check:

- Are you measuring daily active users, not just "willing to use again"?

- Are you measuring time-to-complete, not just accuracy?

- Are you measuring usage when nobody's watching?

Most pilots score 0-3 out of 12. That's why 70% never make production.