Adoption Methodology
Methodology answers how to bring the AI operating model to life in an organization. The framework describes what the model is made of — principles, roles, entities, processes, portfolios, gates, and artifacts. Methodology connects those components into an adoption sequence: from diagnosing the current state and first initiatives to a managed portfolio with confirmed impact.
Why this section exists
Most companies have already run AI pilots. The gap is rarely technology — it is the lack of a managed loop: who decides, what evidence is required to move between stages, how to prioritize the portfolio, and when to stop an initiative with no impact.
Adoption methodology turns the framework from documentation into a working order of actions — without replacing the component sections, but complementing them with an adaptation route for a specific organization.
What this section covers
Topics will be published over time. For now, the structure and entry point are fixed; below is the logic the methodology follows.
From strategy to initiatives
How business challenges and strategic priorities become managed AI initiatives with owners, a value hypothesis, and a clear AI product route.
AI initiative portfolio metrics
Which indicators to track across the AI initiative portfolio: stage distribution, balance, priorities, and the link to the analytics layer.
Lifecycle and gates
How an initiative moves through the business funnel, where gates apply, what evidence is needed to advance, and when an initiative loops back to an earlier stage.
Value hypothesis and confirmation
How to state expected impact before delivery starts and how to confirm results after launch — so the portfolio rests on facts, not activity.
Delivery and launch readiness
How to assess readiness across data, architecture, integrations, support, and adoption before the next stage commitment is made.
Roles and decision rights
Who owns the outcome, risk, readiness, launch, and impact confirmation — and how to prevent unowned movement through the funnel.
How to read alongside other sections
| Question | Where to look |
|---|---|
| What is the model made of? | Operating model, AI product portfolio, AI initiative portfolio |
| How to implement the model in a company? | This section |
| What to use to run initiatives day to day? | AI Conveyor, Platform components |
| Step-by-step scenarios | This section → Playbooks |
| Templates and checklists | Artifacts |
Core idea of this section
Adoption methodology is not a separate "transformation project" — it is a managed transition from experiments to a loop where every AI initiative has an owner, evidence for decisions, and a measurable path to impact.