The AI Learning Hub Journal

From Demo to Deployment: Why Pilots Stall

From promising pilot to deployed toolA promising pilota published result, or a vendor demonstrationValidated on local datasame task, your patients, your equipmentFits the actual workflowsomeone’s day gets better, not longerIntegrated with the recordno re-typing, no second screenMonitored in usesomeone would notice a changeDeployed and ownednamed owner, fundedNO LOCAL VALIDATIONNever checked on this population or settingTHE WORKFLOW DOES NOT FITRight answer, wrong moment, no time to use itNO INTEGRATION PATHLives outside the record; data gets re-enteredNO MONITORING PLANNothing would tell you if it quietly got worseUNCLEAR OWNERSHIPNo named owner, no budget, nobody to escalate toevery gap is where projects leakall five are organisational, not technical
Most tools are lost after the pilot succeeds — the science was never the narrow part of the funnel

The Demo Gap

A demonstration is a curated performance: selected cases, clean inputs, a presenter who knows where the edges are. Real deployment brings incomplete records, non-standard workflows, unusual presentations, and users who behave differently from the design assumption. The gap is not vendor dishonesty in most cases; it is that the demo is answering a different question. A demo shows that the capability exists. Deployment asks whether the capability survives contact with your data, your systems, your staffing, and your case mix. Treating a convincing demo as evidence about your institution is the most common category error in healthcare AI procurement, and it is the one that wastes the most money.

  • Demos establish that a capability exists — nothing about how it behaves on your data
  • Real inputs are incomplete, non-standard, and distributed differently from demo cases
  • The category error is treating a convincing demo as institution-specific evidence

The Integration Tax

Most of the cost and nearly all of the delay in healthcare AI is integration. The model has to receive data from record systems, imaging archives, and device feeds in the right format at the right moment; outputs have to land somewhere a clinician will actually see them, inside an interface they already use; identity, access control, and audit logging have to work; and none of this can destabilise systems the hospital depends on hourly. Legacy interfaces, bespoke local configurations, and vendor lock-in make each step slower than estimated. Teams that budget for the model and treat integration as a detail discover that the ratio runs strongly the other way.

  • Data in, output somewhere clinicians already look, plus identity, access control, and audit
  • Legacy interfaces and local configuration variation dominate the timeline
  • A tool that requires a separate login and a second screen will not be used, regardless of quality

Who Owns It on a Tuesday Afternoon?

Pilots are staffed by enthusiasts with protected time. Production is staffed by whoever is on shift. The unglamorous question — who monitors this, who is called when it behaves oddly, who decides to switch it off, who re-validates it after the vendor pushes an update, and out of whose budget — is what actually determines whether a pilot becomes a service. Institutions that answer it before the pilot tend to reach production. Institutions that defer it accumulate a portfolio of successful pilots that quietly expire when the champion moves on. Ownership is not a governance formality; it is the mechanism by which a tool stays safe after everyone stops paying attention to it.

  • Name the monitoring owner, the escalation path, and the person authorised to switch it off — before go-live
  • Vendor model updates require re-validation, and someone has to be responsible for noticing them
  • Pilots that depend on a single champion expire when the champion moves on
  • Ongoing cost and staffing belong in the business case, not in a later conversation

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