AI Strategy & Consulting
Outcomes
A decision, not a deck
The output is a ranked plan with owners, budgets and success criteria. If the honest recommendation is to do less than you were planning, that is what you get.
Alignment across the leadership team
AI initiatives usually stall because functions disagree about priorities without ever surfacing the disagreement. A shared scoring framework makes the disagreement explicit and resolvable.
Build-versus-buy clarity
Much of what companies commission as custom AI is already available as a product at a fraction of the cost. Much of what they buy off the shelf will never fit their process. Getting this call right is often the largest single saving in the engagement.
A realistic view of the constraint
For most organisations the binding constraint is data quality, process documentation, or change capacity — not model capability. Naming the real constraint changes the plan entirely.
What we deliver
Opportunity map. Every candidate use case across the business, scored on value, feasibility, data readiness and change cost. Including the ones we recommend against, with reasoning.
Sequenced roadmap. A 12–24 month sequence with dependencies made explicit. Early items chosen partly for the capability they build, not only for their standalone return — the second project is always cheaper than the first, and the sequence should exploit that.
Build, buy or partner recommendation for each initiative, with the reasoning written down so it can be revisited when circumstances change.
Operating model. Who owns AI systems once they are live, how models get evaluated, how new use cases get proposed and approved, what governance applies. Most organisations have no answer to this, and it is the reason pilots never become programmes.
Capability and hiring plan. What you need in-house versus what is better sourced externally, and in what order to build the internal team.
Governance framework. Policy on acceptable use, data handling, human oversight, vendor assessment and model risk — proportionate to your regulatory exposure rather than copied from a template.
How it works
Weeks 1–2 — Discovery. Interviews across leadership and operational teams. We ask what is expensive, slow, error-prone or dependent on a few individuals. Those answers locate the opportunities more reliably than asking where people want to use AI.
Weeks 2–3 — Assessment. Data landscape, systems, process maturity, existing tooling and skills. We test feasibility assumptions where they are load-bearing rather than accepting them.
Weeks 3–4 — Prioritisation. Scoring workshop with your leadership team. Done together rather than delivered to you, because a ranking people helped produce is a ranking they will act on.
Weeks 4–6 — Plan. Roadmap, operating model, governance, capability plan. Presented as a working session, with the rejected options discussed as carefully as the recommended ones.
Six weeks is typical for a mid-sized organisation. Single-function scope can be done in three to four. Large enterprises with multiple business units take longer, and we scope that honestly up front.
Where this applies
Most useful for organisations with real operational scale, several competing AI ideas, and no agreed way to choose between them.
Least useful if you already have one clear, well-understood use case. In that situation, strategy work is a delay. Go and build it, and we will say so on the first call.
How we scope and price
Fixed scope, fixed fee, agreed before we start — we do not run open-ended consulting engagements. Cost is driven by organisational breadth (how many functions and systems are in scope), regulatory complexity, and whether feasibility testing requires hands-on data work. A single-function engagement is materially cheaper than an enterprise-wide one, and we will tell you which you actually need rather than defaulting to the larger version.
Frequently asked questions
The audit is technical and bottom-up: what your data and systems can support. Strategy is organisational and top-down: what the business should pursue and in what order. They complement each other and are often run together, but if you must choose one, start with the audit — it is cheaper and grounds everything else in reality.
Not necessarily. If one use case is obvious and self-contained, build it and learn. Strategy earns its cost when there are many candidates, shared infrastructure decisions, or a budget that has to be defended.
We build systems, so the conflict is real and we manage it by writing recommendations another firm could execute from, and by including build-versus-buy analysis that frequently concludes "buy". If a recommendation only makes sense if we deliver it, treat that as a warning sign — from us or anyone.
Then that is the finding, and the plan focuses on what would need to change first. This happens often enough that we consider it a normal outcome rather than a failure of the engagement.
Leadership for prioritisation and ownership decisions, operational people for how the work actually runs, and technical leads for feasibility. Roughly six to ten hours each across the engagement.
Specific enough to act on: named use cases, estimated costs, sequencing, owners, and a scoped first engagement. Not a maturity model or a slide describing the AI landscape.
More AI services
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Agentic AI Automation
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Generative AI Development
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