AI Adoption That Sticks

For organisations between 15 and 150 people who have tried AI and are not yet seeing it pay back.

If you are reading this, you have probably already had the AI conversation internally. Someone has run a pilot. A few people on the team are using ChatGPT. There is a Copilot licence somewhere. And yet, despite all that activity, you cannot point to a single place where AI has changed how the organisation works.

You are not behind. You are in the same place as most 15-to-150 person organisations I talk to. The tools are not the bottleneck. The bottleneck is sitting between the tools and the team. That is where I work.

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The pattern

The pattern in almost every stalled AI adoption

Every stalled AI adoption I have examined has stalled for the same reason. It was not the technology. The technology works. It was that nobody had done the work of figuring out where AI belongs, who owns it, what good looks like, and how the team learns from each other.

Most organisations skip that work because it does not look like progress. Buying a tool feels like progress. Running a pilot feels like progress. Sending three people on a course feels like progress. Sitting in a room and properly mapping where your team's hours go - that does not feel like progress. So it does not get done.

Six months later, someone notices that the AI investment is not paying for itself, and the conversation turns to whether AI was overhyped. It was not. The investment was made into the third circle while the first two were still empty.

The approach

How we work at the adoption layer

I work at what I call the adoption layer - the part between the technology and the people. That means I am not selling you cloud architecture, model fine-tuning, or custom infrastructure. There are good people doing that work and I will happily refer you to them when it is needed. What I do is figure out which two or three workflows should change first, who needs which training, what governance structure matches your risk profile, and how to measure whether any of it is producing returns.

The frame I use is the Three Circles. Most AI failures sit in one of them:

Leadership - direction, prioritisation, budget, clarity on what success looks like
People - capability, confidence, time to practise, willingness to share what is working
Technology - tools, security, data quality, governance

Most AI vendors are selling into the third circle and quietly assuming the first two will sort themselves out. They do not. Adoption work moves all three at once, in the right order, with the right people in the room.

The first phase is always diagnosis

Every engagement starts with a structured diagnosis. We use a tool I have developed called RATES - we score candidate workflows on five dimensions: Repetitive, Annoying, Time-consuming, Error-prone, and Scalable. Each scored 0 to 2. The highest scores tell you where to start.

It is a deliberately simple framework. Scoring is the easy part. The hard work is the conversations the scoring forces you to have with your team, and that is where the real value of the diagnosis sits.

How we engage

Three ways to start

Every engagement starts with the diagnosis above. The format depends on where you are.

AI Adoption Launchpad - from €2,495

A short, focused engagement that takes you from "we should be doing more with AI" to a clear, costed adoption plan. We run a leadership workshop, survey the team's current AI use and confidence, identify the two or three workflows where AI gives you the biggest return, and produce a roadmap your team can act on.

Typical timeline: 3 to 4 weeks. You leave with a costed plan, a governance framework, and a training pathway.

Best fit: Organisations that know AI matters but have not yet got a costed plan. The board has asked the question. You need an answer.

Onsite Diagnostic Day - from €2,695

I spend a day with you on site. Morning is walking the floor, watching how the work happens, talking to the people doing it. Afternoon is a working session with the leadership team and a RATES-scored opportunity map of every candidate workflow we have identified.

You leave with a prioritised list of where to start, a clear sense of what the first build should look like, and the beginnings of an internal language for talking about AI in your specific context.

Best fit: Organisations with physical operations, multiple sites, or where the bottleneck is in how work flows rather than in any single role.

Annual Partnership - from €18,000/year

Once a first engagement is complete, the most useful thing we can offer is continuity. Quarterly strategic reviews, ongoing training, capability building, and access to me as your team's trusted AI advisor for the year.

Adoption is a capability you keep building. Capabilities build best with someone in the room asking the right questions over time. The compounding happens because each quarter builds on what the team learned in the previous one, rather than starting cold every time you decide to push further.

Best fit: Organisations who want a year of compounding capability rather than a single engagement.

What working together usually looks like

Most engagements run in two stages. The first stage is the audition - we run Phase 1, build the first solution or roadmap, and you see how the work actually goes. The second stage is the partnership, where we keep working together on a steady footing rather than starting a fresh proposal cycle every time something new comes up.

You don't commit to the partnership upfront. It's a conversation we have at the end of the first engagement, once you've seen the work and decided whether it's worth continuing.

Annual partnerships are an annual commitment with 30 days' notice from either side. If the work isn't producing, you walk. The first engagement is priced and scoped to deliver on its own terms - the partnership only makes sense if the audition has earned it.

What "saving time" actually means

When AI takes a few hours a week off someone's plate, that's capacity, not ROI. Capacity only becomes ROI when leadership decides where that time should go - billable client work, business development, deeper thinking, time off, or something else entirely. Most AI adoption stalls because nobody decides.

We make that decision part of Phase 1, so the time AI saves your team actually shows up in the business.

If you'd like the longer version of this argument, the article is here: Where is the ROI?

Proof

What this looks like in practice

Case study: an Irish engineering and environmental consultancy

We started with a diagnostic engagement in early 2025. Twelve months later, the team has 95% AI adoption, €55,000 a month of capacity freed up for higher-value work, and 28% of staff are accomplishing tasks the team previously considered impossible. Nobody was laid off. The work AI took over was the work nobody enjoyed doing in the first place.

95%

AI adoption across the team

€55k/mo

Capacity freed for higher-value work

28%

Staff doing previously impossible tasks

Alastair didn't sell us tools. He helped us figure out where AI actually belonged in our business. That's a different kind of consultant.

Director, Irish engineering and environmental consultancy

What success looks like

A successful adoption engagement leaves three things behind:

A leadership team that can have specific, grounded conversations about AI rather than abstract ones
A small number of working AI integrations in genuinely useful places, owned by the team
A clear sense of what the next year of capability-building looks like

If we get to the end of the diagnosis and any one of those three is missing, we have not done the work properly.

What happens next

The way to start is a Focus Call. It is a 25-minute conversation where I get a sense of your situation - where you are with AI, what you have tried, where the friction is, what success would look like. There is no charge, and there is no pitch.

If what I do is the right fit for what you need, I will send you a proposal within 48 hours with three options. If it is not the right fit, I will tell you that and try to point you somewhere useful.

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Related resources

Last updated: April 2026