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Where It Goes Wrong
Tools bought before a use was agreed. Training that was a demo. Output nobody trusts enough to send. No rule about what can go into a model. Enthusiasm concentrated in one person.
Most teams are well past the trial stage and still not getting much back. AI consulting is the work in between: deciding where it genuinely helps, teaching your people to work it well, and setting the rules that let you use it without exposing the business.
Buying an AI tool takes an afternoon. Changing how a team works takes longer, and that is where all of the value actually sits.
Almost every business we meet is already using AI somewhere. Very few can point to what it has changed. The gap is rarely the model. It is that nobody decided what it was for, and nobody was taught to work it.
Dragon Horse calls this being AI Aviators. AI is an instrument, and instruments do not fly themselves. Our job is to make your people good at flying yours.
AI consulting is advisory and enablement work. We are not selling you a platform and we do not need you to buy anything new to start.
Every engagement works through the same four questions, answered against your actual work rather than against a generic maturity model. Where AI helps here. How your people work it well. How you run it without creating risk. And how the capability stays once we are gone.
Nobody announces that they have stopped using the AI tool. They just quietly go back to the old way, and six months later the licence renewal arrives and someone asks what it was for.
Tools bought before a use was agreed. Training that was a demo. Output nobody trusts enough to send. No rule about what can go into a model. Enthusiasm concentrated in one person.
A small number of uses chosen deliberately, people trained on their own work until they are fluent, a clear rule about what is allowed, and a named owner who keeps it alive.
We do not arrive with a platform to sell or a maturity model to fill in. We start with what your team spends its week doing.
We sit with each function, map the real work, and mark the tasks worth handing over. The list of what to leave alone matters just as much.
Working sessions on live tasks, in the tools you already pay for, until people are fluent rather than merely willing.
A short written policy your team will actually follow: what data is allowed, what always gets human review, and who signs off.
Everything documented and owned by a named person inside your business, with a review a quarter later to see what actually stuck.
Your team's work, function by function, with the tasks worth handing to AI identified, sized and put in order.
An honest read on what you already own, what is worth adding, and what to cancel. We take no commission from any vendor.
How to ask, how to check, and when to stop and do it yourself. The difference between a team that uses AI and one that is good at it.
Client data, confidentiality, disclosure and approval. Written plainly enough that people follow it without being chased.
What you will measure, what counts as working, and when to stop something that is not. Agreed before the pilot, not after.
Sessions run by function rather than by tool, so each team learns AI against the work they are actually accountable for.
The measure of good AI consulting is what your team can still do a quarter after the engagement ends.
Effort concentrated on the handful of uses that pay, instead of spread across pilots that quietly lapse.
People using it on live work by the end of a session, not agreeing in principle to try it later.
A written rule on data, review and disclosure, so you are not relying on everyone's individual judgement.
Playbooks and working practice owned by your team, not knowledge that walks out with a consultant.
The right answer is different for a marketing team than for a finance team, which is why sessions are run by function rather than as one all-company briefing that suits nobody.
We sell no platform and take no commission, so the advice is not shaped by what we would like you to buy. We use AI every day across a working agency, which means the guidance comes from running it, not from reading about it.
Where a few people are getting a lot out of it, most are not, and nobody has written down which is which.
Where the budget question has arrived and there is no agreed view on what it should buy.
Where client confidentiality or a regulator means the guardrails have to come before the enthusiasm.
Workflow development builds systems for you. Consulting decides what is worth building and makes your people capable of using it. Many clients do consulting first and build afterwards, once they know what is worth automating.
Usually yes, and that is the most common starting point. Having access is not the same as being good at it, and the gap between an average user and a fluent one is very large.
Frequently. Some work should stay human because the judgement is the product, and some should stay human because the risk is not worth the saving. Naming those is part of the job.
The people doing the work, not only the people responsible for it. Sessions run by function, so each team works on tasks they are actually accountable for.
Let us look at what your team is doing with AI now, what it should be doing, and what would have to change for that to happen. You do not need to buy anything to start.
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