Why Most AI Adoption Fails — and What the MAKIA Framework Does About It

Most companies don't have an AI problem. They have an adoption problem.

The tools are already in their hands. Subscriptions are paid. A handful of employees are quietly experimenting. Leadership has mentioned "AI strategy" in at least three meetings this quarter. And yet — nothing meaningful is changing. Workflows look the same. Decisions are made the same way. The promised productivity gains are nowhere to be found.

This is the gap I see again and again working with SMEs and business leaders: the distance between having access to AI and actually integrating it into how work gets done.

That gap is rarely technical. It's human, organizational, and strategic.

The MAKIA framework exists to help organizations cross it.

Why a framework, and why this one

There's no shortage of AI advice. What's missing is structure — a way to think about adoption that doesn't reduce AI to a productivity hack or inflate it into existential drama.

MAKIA is built around five dimensions that, in my experience, determine whether AI adoption takes root or fades out:

M — Meaning. A — Actors. K — Knowledge. I — Impact. A — Alignment.

It's not a checklist. It's a way of asking better questions before, during, and after you bring AI into an organization. Each dimension addresses a failure pattern I've seen repeated in companies of every size.

M — Meaning: why are we doing this?

The most common AI adoption mistake is starting with tools. "We need to use ChatGPT." "Let's try Claude." "Should we buy Copilot licenses?"

These are answers to questions nobody asked clearly.

Meaning is about strategic clarity. Why are we adopting AI here? What problem are we solving? What value are we trying to create? Does the initiative align with our culture, our mission, and the way we already work?

When Meaning is skipped, everything downstream becomes noise. Teams adopt tools without conviction. Pilots launch without success criteria. Six months later, leadership wonders why nothing happened.

A — Actors: who is this really about?

AI adoption is a human process. It involves fears, expectations, hierarchies, habits, and identities.

Actors asks: who is impacted? Who owns the change? Who needs support? Who's quietly resisting — and why?

The answer is almost never "everyone, equally." Some people see AI as opportunity. Others see threat. Some are already using it discreetly and don't want to be told they're doing it wrong. Others won't touch it because no one has shown them how.

Mapping these actors — and their motivations — is what separates an adoption strategy from wishful thinking.

K — Knowledge: do we actually know how to use this?

This is where most companies underinvest. They assume that because their teams can search Google, they can use AI.

Knowledge, in the MAKIA sense, isn't just technical training. It's the capacity to think critically about outputs, to design useful prompts, to evaluate quality, to recognize hallucinations, to understand when AI helps and when it gets in the way.

It's also the courage to experiment. AI literacy isn't built in a half-day workshop. It develops through repeated, safe experimentation — and through a culture that treats mistakes as learning, not failure.

Without this dimension, organizations end up with expensive licenses and unimpressive results.

I — Impact: is anything actually changing?

The least interesting question about AI is "how much time did it save?" The more interesting questions: is the work better? Are decisions sharper? Are customers noticing? Are people thinking more clearly?

Impact is about being honest. Measuring honestly. Looking at outcomes that matter, not just usage metrics.

Many companies optimize for visible activity — number of prompts, number of users — and quietly avoid the question of whether anything has materially improved. Impact pulls attention back to the work itself.

A — Alignment: does this hold together?

This is the dimension most organizations forget until it's too late.

Alignment asks: are our tools, our teams, our workflows, and our governance pointing in the same direction? Or do we have one team using Claude, another using ChatGPT, a third using something built in Sheets, with no shared standards, no prompt libraries, no oversight?

Alignment is what turns scattered experiments into organizational capability. Without it, AI adoption fragments — and when it fragments, it stalls.

What this framework is really for

MAKIA is not a methodology to be memorized. It's a lens to slow down and think with.

Most AI initiatives fail not because of the technology, but because organizations skip the human and strategic questions in their rush to deploy. The framework is a way to make sure those questions get asked — and answered — before the tools take over the conversation.

If there's one thing worth taking from this: AI adoption is not a tooling decision. It's a thinking decision. The organizations getting it right are the ones treating it that way.

That's where the real opportunity lives.