AI Strategic Consulting
An AI roadmap for companies that turns interest into working systems. We help you decide where AI actually pays off, then handle AI implementation end to end.
Overview
Almost every company has now run an AI pilot. Far fewer have anything in production, and the gap is rarely about capability. It is about picking a use case whose economics work, on data that is cleaner than assumed, in a workflow somebody is willing to change.
We start by finding out what is realistic for you this year, rank the candidates honestly, then build the one or two worth building. The measure is software your team uses on a Monday morning, not a demo that impressed a steering group.
Who this is for
- Companies with AI budget approved and no agreed place to spend it
- Teams whose pilots keep stalling before production
- Leadership who need a costed plan a board can actually approve
How we approach ai strategic consulting
The specific pieces of work a typical engagement covers. Scope is agreed up front — nothing here is a surprise line item later.
AI readiness assessment
Before any tooling decision: what your data actually supports, where the manual effort sits, and which teams are ready to change how they work.
AI roadmap for companies
A sequenced plan tied to business outcomes, not a technology wish list. Each step names its owner, its cost and the number it should move.
Use case selection
Most AI ideas fail on economics, not capability. We score candidates on value, data readiness and risk, then cut the ones that will not pay off.
AI implementation
We build the thing, not just the deck. Models, retrieval, integrations and the plumbing into the systems your team already uses every day.
Governance and safety
Access control, data residency, human review and an audit trail, designed in from the start rather than retrofitted before a compliance review.
Team enablement
How to use AI in your company day to day: working practices, prompt patterns and the judgement to know when not to use it.
From first call to measured result
The same sequence every time, so you always know what happens next.
Assess
Two weeks looking at your data, workflows and constraints. You get an honest read on what is realistic this year.
Prioritise
We rank the candidate use cases by value against effort, and agree the one or two worth starting with.
Pilot
A working system in production with real users and measurement attached, not a demo on sample data.
Scale
How to implement AI in your company beyond the pilot: rollout, governance, cost control and handover to your team.
Outcomes, not deliverables
A pile of artefacts isn't progress. These are the changes the work is meant to produce — and what we report against.
A plan you can fund
Costed and sequenced, so the board sees what the first phase buys before committing to the rest.
Fewer dead ends
The scoring step kills weak use cases early, before they consume a quarter of engineering time.
Systems in production
The measure is software your team uses on Monday morning, not a pilot that never left the sandbox.
Your team can run it
Documentation, training and ownership transfer, so you are not dependent on us to keep it working.
Common questions about AI Strategic Consulting
The things people ask before they get in touch. If yours is not here, ask us directly.
Where should a company start with AI?
With a task that is high volume, tolerant of a review step, and running on data you already hold. That combination is what makes a first project both useful and survivable. Customer support triage and internal document search are common starting points for good reason.
How long before an AI project reaches production?
The assessment takes about two weeks and the prioritisation another two. A first system in production with real users is typically three to four months from starting, depending on how much integration work the surrounding systems need.
What does AI implementation actually cost to run?
Model usage is usually the smaller part. The ongoing costs that surprise people are evaluation, monitoring and the human review step. We size all three during the assessment so the business case is not just the build.
Is our data good enough?
Often it is good enough for a narrower use case than the one you had in mind. The readiness assessment exists to answer this specifically, and it is better to know in week two than after six months of building.
Do we need to send data to an external model provider?
Not necessarily. Depending on your data residency requirements there are hosted, regional and self-hosted options, with real cost and capability tradeoffs. This gets decided during the assessment rather than assumed.
Thinking about AI Strategic Consulting?
Tell us what you are trying to change. If we are not the right fit we will say so, and point you somewhere better.
Looking at the wider picture?
AI Strategic Consulting usually sits alongside other work in Strategy & Consulting. Browse the full area to see what it connects to.