The Challenge

The pilot worked beautifully. A small team achieved impressive results with AI. Leadership said "roll it out to everyone." Six months later, adoption is patchy, results are inconsistent, and the team that made it work is exhausted from firefighting.

What worked with 10 engaged users breaks with 100 average users. The informal knowledge transfer that enabled the pilot doesn't scale. The edge cases that never appeared in testing are now daily occurrences.

The Approach

Scaling isn't deployment repetition. It's systematic capability building. The framework identifies what made the pilot work, which elements are transferable, and what infrastructure is needed to replicate success.

Effective scaling builds organizational capability, not just tool deployment. It creates the training, support, governance, and measurement systems that allow AI to succeed without depending on the pilot team's unique context.

Core Principles

  • Decode Pilot SuccessBefore scaling, understand WHY the pilot worked. Was it the technology, the team, the use case, or the support structure? Scaling amplifies everything. If you don't know what drove success, you might scale the wrong elements.
  • Systematize the HeroicsPilots often succeed through extraordinary effort that can't be sustained at scale. Identify the workarounds, the manual interventions, the informal knowledge sharing and systematize them.
  • Scale Support Before Scale DeploymentTraining, documentation, and support infrastructure must precede broad deployment. Scaling users faster than support capacity creates frustrated users who become resistant users.
  • Measure Adoption Quality Not Just QuantityCounting logins doesn't reveal capability. Effective scaling measures whether people are getting value, not just whether they're accessing the system.

Application Example

Management Consulting Firm: From 12-Person Pilot to 400-Person Deployment

Challenge: An AI research assistant pilot with one practice area achieved 45% productivity improvement. Firm leadership mandated organization-wide rollout within 6 months. Initial broader deployment saw adoption plateau at 23% with inconsistent results.
Application: Scaling framework revealed pilot success depended on one senior consultant who informally coached the team. Created "AI Champion" network with 35 trained coaches across practices. Built progressive training from basic to advanced. Established quality metrics beyond usage counts. 18-month deployment reached 78% meaningful adoption with documented productivity gains.

Implementation Scope

4-8

Assessment Phase

Weeks to decode pilot success factors and design scaling infrastructure

12-36

Implementation

Weeks for phased rollout with capability building at each stage

24-48

Optimization

Weeks for embedding capability and transitioning to business-as-usual