The Pilot phase focuses on learning fast with limited risk. Teams deploy AI capabilities in constrained environments to validate assumptions, understand adoption dynamics, and surface operational friction.
Successful pilots are not judged solely by technical performance, but by how well they integrate into real workflows and decision-making processes.
Pilots that do not explicitly prepare for operationalization often stall or die after initial success.
The AI Altitude Model (AAM) is a staged operating framework that helps organizations understand where they are in their AI adoption journey and what is required to safely progress from experimentation to full operational control and sustained value realization.
The end-to-end operating system that governs how organizations discover, deploy, adopt, and scale AI.
A structured model that maps AI initiatives from experimentation to sustained operational impact.
A structured method for identifying and prioritizing AI opportunities tied to real value pools.
A clear articulation of intent that aligns teams without prescribing tactics.
A continuous decision loop to observe, orient, decide, and act on AI performance in real operations.
A composite index that measures whether an AI initiative is ready to operate sustainably.
A structured network of internal champions that drive adoption inside the business.
.svg){kind=icon}