AI Adoption That Sticks

For organisations moving from pilots to actual adoption.

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 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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Trusted by

Proof

95%

AI adoption across the team

€55k/mo

Capacity freed for higher-value work

28%

Staff doing previously impossible tasks

Results from one client. Twelve months after a diagnostic engagement in early 2025.

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

If your organisation is like most, the board is now asking a version of this question: what is our AI plan?

That question is the right one. The risk is aiming for the wrong answer.

Many organisations try to "future-proof" for AI, as if there is a stable destination they can reach and hold. AI does not work that way. Capabilities keep compounding. New tools change what is possible, and each development creates new decisions for leaders, teams and technology owners.

The more useful goal is not future-proofing. It is building the organisational capacity to evaluate each change clearly and act with confidence.

That is the adoption layer.

The frame we use is the Three Circles: Leadership, People and Technology. These are not separate silos. They are three lenses for diagnosing adoption.

Leadership

Direction, prioritisation, budget, decision rights, sponsorship, and clarity on what success looks like.

People

Capability, confidence, time to practise, permission to experiment, and willingness to share what is working.

Technology

Tools, security, data quality, access, integration, and governance.

We work at the point where strategy, workflows, people and technology meet. This is not cloud architecture, model fine-tuning or custom infrastructure. Our work is to help you decide which workflows should change first, who needs what support, what governance fits your risk profile, and how you will measure whether AI is producing useful returns.

This work is led by Alastair McDermott, supported by senior technical expertise where the engagement requires it. That gives you a practical combination: commercial and organisational adoption experience, with enough technical depth to make sound decisions about tools, data, security and implementation constraints.

The first phase is diagnosis

Every engagement starts with a structured diagnosis.

We use a tool called RATES to score candidate workflows across five dimensions:

Repetitive
Annoying
Time-consuming
Error-prone
Scalable

Each workflow is scored from 0 to 2 on each dimension. The highest scores show where AI may create meaningful value.

The score is not the final answer. It is the start of a better conversation.

Once we have identified the strongest candidates, we test them against value, risk, feasibility, data readiness and internal sponsorship. That gives you a practical shortlist of where to begin, rather than a long list of AI ideas with no clear order.

The value of the diagnosis is not just the scoring. It is the clarity the process creates: which workflows matter, which teams are ready, what risks need to be managed, and where the first measurable gains are likely to come from.

What you leave with

By the end of the diagnostic phase, you have:

A prioritised shortlist of AI-ready workflows
A clear view of the leadership, people and technology gaps that could block adoption
A practical training and enablement plan for the teams involved
A governance model matched to your risk profile
A 90-day adoption roadmap with owners, measures and decision points

Who this is for

The board has asked the question. You need a credible answer.

You know AI matters, but you do not yet have a costed plan, a clear set of priority workflows, or a confident view of how AI fits into your team's work.

You do not need to chase every new AI release. You need a disciplined way to decide what matters, what is safe to adopt, and where it can produce measurable value.

That is where this engagement begins.

How we engage

Three ways to start

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

AI Adoption Launchpad

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

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

Annual Partnership

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

Formats, timelines, and investment are discussed on a Focus Call. A proposal follows within 48 hours.

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Looking for a lighter starting point?

If a full engagement feels like too big a step right now, Strategic AI for Leaders is a focused programme for senior leadership teams who want strategic clarity on where AI fits before committing to anything bigger. One-day default with two-day and half-day variants. It feeds directly into a Launchpad if you decide to go further. See Strategic AI for Leaders.

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?

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.

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

Team AI Adoption Programme AI Success Has Less to Do With AI Than You Think The AI Pilot Handbook

Last updated: April 2026