AI strategy
Where AI pays off in your business, where it does not, and what has to be true before it can. Delivered as a prioritised plan, not a presentation about the future.
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The two ways companies get this wrong
The first is doing nothing, on the reasonable-sounding grounds that it is overhyped, until a competitor quietly halves the cost of something and the gap is structural rather than temporary. The second is more common and more expensive: starting with the technology rather than the problem. A pilot is launched because the board asked what we are doing about AI, it demonstrates well, it never reaches production because nobody identified whose job it changes, and the conclusion drawn is that AI does not work here. What did not work was starting from the answer.
How the assessment runs
We start with where time and money actually go. That means talking to the people doing the work, not only to management, because the frustrations that matter are rarely visible from above and the best candidates are usually processes nobody has ever described in a document. Each candidate is sized on three axes: the value if it works, the effort to build it, and whether your data can actually support it. That last one eliminates a surprising number of otherwise attractive ideas, and finding that out in a two-week assessment is considerably better than finding it out in month four of a project.
What you get
A prioritised list with an honest recommendation for each: build it, wait for it, or do not. Including the ones where a simple rule, a report, or fixing a broken process would deliver more than a model. Plus the unglamorous prerequisites: where your data quality blocks things, what governance you need before staff use AI on company information, and what the running costs would realistically be. If the conclusion is that you should fix your data and revisit in a year, that is what the report will say.
What you get
Starts with where time goes
Talking to the people doing the work, because the best candidates are rarely visible from a management deck.
Value, effort and data readiness
That third axis eliminates a surprising number of attractive ideas, and better now than in month four.
Including what not to build
Where a rule, a report or fixing a broken process beats a model. That answer appears more often than people expect.
Running costs modelled
Not just build cost. Token consumption and operational effort are what decide whether something survives.
Governance and policy
What staff may put into which tools, before somebody pastes a customer contract into a public chatbot.
A sequenced roadmap
What to do first, what depends on what, and what has to be true before the later items are possible.
Technologies
Frequently asked questions
Is our company too small for an AI strategy?
Small companies often get more from this, because a single well-chosen automation is a larger proportion of their capacity. The assessment scales down accordingly: for a company of twenty people it is days, not weeks, and it is usually focused on one or two processes rather than a portfolio.
What if you conclude we should not do anything yet?
Then that is the report, and it is a legitimate outcome that saves you considerably more than it costs. The most common version is that data quality or process documentation should be fixed first, which is unglamorous but is the actual prerequisite.
Who should be involved from our side?
Someone who can make decisions, and several people who actually do the work being examined. The second group matters more. Assessments conducted only with management consistently identify the wrong candidates, because the frustrations that are worth automating are not visible from there.
Find out where AI actually pays back in your business
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