The AI Pilot Preflight Send me Day 1

Free 5-day email course for healthcare AI teams

Build a healthcare AI pilot you can justify scaling.

Learn to choose a measurable use case, define meaningful human review, and plan for adoption beyond go-live. Five short emails, one practical decision each day.

Written by Zack Allen, applied AI and health tech builder.

See the five lessons

01 / A familiar situation

A working model still leaves decisions to get right.

For the people building, reviewing, and funding healthcare AI. If any of these sound familiar, start here.

Your pilot demos well. The value is hard to prove.

Learn to define the result and its owner before renewal, so you can turn “the clinicians liked it” into a number someone already reports monthly.

For clinical sponsors, innovation leaders, and founders.

Review takes weeks. Accountability is still unclear.

Learn to define what human review needs to catch, route projects by consequence, and understand where unapproved AI use is happening.

For clinical, governance, and privacy teams.

The model passed validation. Usage is fading.

Learn to assess adoption and cost beyond the pilot cohort, before you expand a tool nobody opens on a Tuesday.

For product, ML, and operational leads.

02 / The course

Five days. Five decisions.

One mistake a day: why it happens, what to do instead, and a decision to take back to your team. Open a lesson to see what it covers.

Day 1. The Demo Trap Define the result, metric owner, and stop condition before building.
  • Why demos reward breadth and workflows reward narrowness
  • The result sentence to write before you build a single thing
  • The measurement work you cannot go back and do
  • The kill criterion that keeps a pilot from lingering for years
Day 2. The Confident Lie Specify what reviewers check and how to measure what they catch.
  • How fluent output can slip past clinician review
  • The three parts a review process needs before it counts as a control
  • How to measure your real catch rate
  • When to make the system leave the field empty
Day 3. The Shadow AI Leak Understand unapproved usage and make the approved path easier.
  • What you lose when protected information leaves through a personal account
  • Why the approved tool loses to the browser tab that's already open
  • The survey method that gets staff to tell you what they're actually using
  • The usage metric that tells you the work is still happening in the shadows
Day 4. Governance Theater Match review to the consequences of failure.
  • The question to ask your AI governance committee that most can't answer
  • How to tier review by consequence instead of by technology
  • The turnaround commitment that stops teams routing around you
  • The project list you should be able to produce in an afternoon
Day 5. The Adoption Cliff Check adoption and cost per case before expanding.
  • Why your pilot cohort is the least representative group in your organization
  • The line item that kills year-two funding
  • The cost curve to model before you sign the enterprise agreement
  • Who to recruit for your second cohort, and what breaks for them
The Adoption Cliff Illustrative pattern; not measured data.

Eligible users actively using the tool (%)

03 / From Day 4 / Governance Theater

Give each project the review it needs.

When every AI project enters the same queue, low-risk work waits and high-risk work can pass without the scrutiny it needs. Start with a shared intake, then route by consequence.

Tiered Governance Conceptual comparison

One queue for everything

One queue for every project Low-risk and high-risk projects converge into the same narrow review queue. Low-risk High-risk One queue

Different consequences. The same wait.

Tiered by consequence

Same intake, different paths One intake splits into a short low-consequence path labeled 48 hrs and a longer high-consequence path labeled full review. These are illustrative review lanes, not a promise about every project. Low consequence High consequence Intake 48 hrs Full review Same intake, different paths

The question to take back to your team: What changes in your review when the consequences of an error change?

04 / Your guide

Written from both sides of the governance table.

I'm Zack Allen, an applied AI and health tech builder. I've spent ten years shipping ML to production through research teams, founder trenches, and regulatory purgatory across multiple medical domains.

I've been the builder waiting 11 weeks for approval and the reviewer watching persistence trump process. Now I run production-readiness audits for healthcare AI teams, reviewing use case, governance, and adoption against these same five failures.

I built the clinical note review tool that impressed everyone and saved nothing anyone could count. It took me two quarters to learn that praise is not evidence.

This course brings those lessons into the decisions you make before your next pilot scales.

05 / A few practical details

Before you start.

Who is this course for?

People building, reviewing, or funding generative AI in healthcare: clinical sponsors, health-system leaders, founders, product and ML leads, and governance teams. The focus is the decisions around a deployment, rather than model-building tutorials.

What arrives in my inbox?

Five short lessons, one per day. Each covers one mistake, why it happens, and what to do instead. The course is free, and you can unsubscribe anytime.

What happens after I sign up?

Check your inbox for a confirmation email and confirm your subscription to start the course. If you don't see it, check your spam or promotions folder.

What will I be able to take back to my team?

A set of questions to apply to your next AI project: who owns the metric, what reviewers check, where unapproved usage happens, how review is tiered, and whether adoption and cost hold up as usage grows.

The AI Pilot Preflight

Put your next pilot through preflight.

Five short emails to help you ask better questions before your next governance meeting, funding review, or rollout.