Enterprise adoption of generative artificial intelligence has progressed faster than the operational discipline required to sustain it. Most organizations now run one or more generative AI pilots, but a smaller subset has translated those pilots into compounding capability. This paper describes an em‐ pirical four stage maturity framework derived from leading the deployment of multiple production generative AI systems inside a large enterprise human capital management platform. Stage one is ex‐ ploratory pilots. Stage two is workflow embedded augmentation. Stage three is platform consolida‐ tion through reusable services. Stage four is governed organizational capability. For each stage we describe the operational signals, the dominant failure modes, the leadership decisions that deter‐ mine whether a team progresses, and the evaluative artifacts that distinguish a pilot from a capabili‐ ty. We also propose a unit economics measure (cost per resolved unit of work) that we found more predictive of sustainable adoption than the conventional token cost view. The framework is offered as a practitioner instrument, intended to help engineering leaders, technology executives, and AI governance professionals diagnose where their organization actually stands, independent of the mar‐ keting narrative around the work.
Prashanth Reddy Pasham (Mon,) studied this question.