AI Stages of Adoption (AISA)

The Problem with Traditional Adoption Models

Most technology adoption frameworks assume organisations progress uniformly through stages over time. Cloud adoption worked this way - IT led a sequential journey from initial projects through migration to reinvention, with the whole organisation moving broadly together.

AI doesn’t work like that.

When I speak with Chartered Directors and their Boards, I consistently encounter a disconnect between how they think about AI maturity and what’s actually happening in their organisations. The CEO declares “we’re well into our AI transformation journey” while marketing has been using AI for eighteen months, finance is still debating whether it’s relevant, and somewhere in the business dozens of employees are using ChatGPT for tasks nobody knows about.

This isn’t a failure of coordination. It’s the natural state of AI adoption.

A Different Axis: Investment, Not Time

The AI Stages of Adoption (AISA) framework deliberately uses investment rather than time as its progression axis. Investment means more than money - it encompasses the commitment of talent, data preparation, process redesign, and organisational attention required to move AI from experiment to capability.

This distinction matters because it explains why your marketing team might be optimising AI-generated content while your finance team is just beginning fraud detection pilots. Different functions face different investment requirements, risk tolerances, and competitive pressures. They naturally progress at different speeds.

Understanding this multi-speed reality is the first step toward governing it effectively.

The Five Stages

Experimenting

Organisations explore AI technologies, often without formal oversight. This is where shadow AI emerges - business units independently experimenting with tools like ChatGPT or Claude to solve immediate problems. There’s no formal strategy, limited budget allocation, and heavy reliance on consumer AI tools. The focus is on quick wins and proving what’s possible.

Adopting

After successful experiments, organisations begin formalising AI initiatives. Governance frameworks emerge. Budgets get allocated. Multiple departments show interest beyond the early pioneers. This is typically when an AI Centre of Excellence forms and the question shifts from “can we?” to “how should we?” - with emphasis on responsible implementation.

Optimising

AI becomes embedded in core processes with established governance, clear ROI measurements, and regular performance monitoring. Organisations refine their approaches, standardising best practices while measuring business impact systematically. The focus shifts to maximising value from existing implementations while identifying new opportunities.

Transforming

AI begins transforming how the organisation operates. Business processes are redesigned rather than merely enhanced. New AI-enabled products and services emerge. The organisation develops an AI-first mindset where AI capabilities inform strategic decisions rather than simply executing them.

Scaling

The organisation has fully embraced AI and focuses on scaling adoption across the entire enterprise and ecosystem. Mature governance frameworks enable rapid deployment. Innovation becomes self-sustaining. AI drives significant competitive advantage and often new revenue streams.

The Multi-Speed Reality

In practice, organisations typically have multiple AI initiatives at different stages simultaneously:

Marketing at Optimising (content generation)
Finance at Adopting (fraud detection)
Operations at Experimenting (predictive maintenance)
Customer Service at Transforming (AI-powered support)

Each function progresses based on its own risk tolerance, data readiness, technical capability, business urgency, and competitive pressure. Success in one area often catalyses adoption in others, but the progression is never uniform.

This multi-speed reality creates governance challenges that traditional oversight models don’t address. You can’t apply the same governance framework to a team experimenting with chatbots and a team transforming customer operations with autonomous agents. The AI Stages of Adoption provides the diagnostic foundation for adaptive governance that matches oversight to maturity.

Assessing Where You Actually Are

Most Boards overestimate their AI maturity by focusing on visible initiatives rather than systematic capability. The question isn’t whether you have AI projects running - it’s whether you have the governance, infrastructure, operational excellence, value measurement, and cultural readiness to progress sustainably.

I’ve developed the AI CoE Simulator to help Boards reveal their true multi-speed position across the organisation. It operationalises AISA into a practical assessment that maps each function’s actual stage rather than assumed progress.

Foundational

Introducing the AI Stages of Adoption - The original framework introduction, presented to Private Equity firms and portfolio company executives
Understanding the AI Stages of Adoption - A deeper exploration of AISA and how to use it to accelerate your organisation’s AI adoption