Service
AI strategy consulting
A strategy is a set of decisions, not a set of intentions. This engagement produces the decisions: which use cases are worth the capital, what each is expected to return, what to build and what to buy, which model and vendor, what has to be true about your data first, in what order, and what governance the result requires.
The business problem
Most organisations do not lack AI ambition. They lack a ranking. Work proceeds on whichever use case had the most convincing demonstration, and because no threshold was agreed, nothing can be concluded either way.
The result is a portfolio of activity that cannot be defended or cancelled. McKinsey's State of AI survey finds most organisations still experimenting rather than scaling, and the UK government's AI adoption research found only about half of AI-using firms felt ready to scale.
Strategy work is what converts activity into a sequence with reasons attached. We wrote the long version of this argument in most companies don't have an AI strategy, they have activity.
When this is useful
The recurring situations that bring people to this engagement.
A budget without a plan
Funding approved for AI and no agreed basis for allocating it.
Competing internal proposals
Several departments with candidate projects and no common measure to rank them.
A vendor decision pending
A platform proposal on the table and no independent view of the alternative.
Pilots that will not conclude
Work in flight that cannot be scaled or stopped because success was never defined.
A board asking for a roadmap
A requirement to show sequence, cost and risk rather than enthusiasm.
Regulatory exposure
Use cases that may fall under the EU AI Act, and no view yet of which.
What we will do
Eight pieces of work. They are sequential, and each one narrows the next.
- Select the use cases. Identify the repeated decisions in the business, size each by volume, current cost and error rate, and discard the ones where a correct answer cannot be defined.
- Estimate business impact. Establish a baseline from data you already hold, then express the expected effect in a unit that appears in a management account.
- Decide build versus buy. Layer by layer rather than as one verdict, using the reasoning in build versus buy for enterprise AI.
- Select model and vendor. An evaluation method first, then a recommendation, so the choice can be revisited when a better model ships.
- Test data and infrastructure readiness. Where the inputs live, who owns them, what state they are actually in, and what it takes to reach them.
- Sequence the implementations. Ranked by expected value net of the work required, with the dependencies made explicit.
- Define governance requirements. Mapped to the NIST AI Risk Management Framework and, where relevant, the EU AI Act.
- Write the roadmap. Short, with thresholds, owners and stopping conditions rather than a multi-year Gantt chart.
What you will receive
Five documents, all of them yours, none of them longer than they need to be.
- A ranked use case shortlist with volume, baseline cost, expected effect and the main uncertainty for each.
- A business case for the recommended first build, including the pessimistic scenario.
- A build-versus-buy position by layer, with the reasoning stated.
- A model and vendor evaluation method, plus the current recommendation.
- A sequenced roadmap with thresholds, named owners, stopping conditions and a governance annex.
- An explicit not-now list: what we considered, rejected, and why.
Expected business outcomes
What the strategy is meant to make possible.
Capital allocated on evidence
Spend directed at the workflow with the best expected return rather than the best demonstration.
Fewer stalled pilots
Work that can be concluded, because a threshold and a stopping condition exist before it starts.
A defensible vendor position
A choice you can explain to a board and revisit without a rebuild.
Shorter time to first value
The smallest useful build identified up front, rather than discovered after two quarters.
Governance ahead of exposure
Requirements known before the system is live, which is materially cheaper than after.
A reusable ranking
A method for assessing next quarter's ideas, not just this quarter's.
How the engagement runs
Four to eight weeks, with a decision point you can stop at.
Frequently asked questions
Is this different from a readiness assessment?
The assessment is fixed-scope and produces a shortlist. Strategy consulting is broader: it covers sequencing, build versus buy, vendor choice and governance across a portfolio rather than a single first build.
How long does it take?
Four to eight weeks for most organisations. Longer than that and the strategy work becomes the project it was supposed to scope.
Do you produce a roadmap document?
Yes, but a short one. A ranked sequence with thresholds and owners is more useful than a multi-year plan that assumes model capability stays still.
Will you tell us which vendor to use?
We will give you an evaluation method and a recommendation, with the reasoning. We hold no reseller agreements, so the recommendation carries no margin for us.
What if our data is not ready?
That is a normal finding and it changes the sequence rather than ending it. Part of the work is establishing what state the data is actually in.
Who needs to be involved?
Whoever owns the numbers you want to move, someone who can speak for data access and security, and the people who do the work today.
Relevant insights
Start a conversation.
If you have a budget and a list of candidates, this is the engagement that turns them into a ranked sequence with reasons. Bring the list.