Sectors we work in, and what changes in each
AI problems arrive in different shapes depending on the sector. A software business usually knows what it wants to build and needs help deciding what to leave out. A professional services firm already has demanding client work and needs a delivery model that can carry a change. A regulated business has the controls in place and needs a route through them that does not take a year. The method stays the same; the constraint rarely does.
How the work differs
SaaS and software businesses
Product teams here usually know what they want to build. The harder question is what to stop building, and how to sequence AI features that change the product rather than decorate it.
What usually blocks it A roadmap that has grown one feature at a time, with no single story about why customers stay.
Where we focus Product strategy, pricing and roadmap decisions, and AI features that carry real weight.
LegalTech and legal services
Document-heavy work with high review standards. AI earns its place where it removes reading and drafting effort, while every output is still checked by someone qualified to sign it off.
What usually blocks it Enthusiasm for the technology outpacing any agreed rule about what may be shared with it.
Where we focus Workflow selection, review rules and confidentiality, and document-heavy delivery models.
Financial services
Regulated environments where the explanation matters as much as the output. Readiness here is mostly about governance: what is approved, what is recorded, and who can show why a decision was made.
What usually blocks it Strong controls that make any change slow, and a backlog of ideas with no owner.
Where we focus Governance and review, use-case selection, and defensible measurement.
Professional services firms
The value sits in judgement and relationships, while the time goes into proposals, research and internal knowledge that the firm already owns somewhere. This is usually where the fastest return is available.
What usually blocks it Knowledge trapped in previous matters, inboxes and people who are already at capacity.
Where we focus Bid and proposal work, research over large document sets, and making internal knowledge findable.
Telecommunications
Large estates, complex operations and commercial pressure on cost to serve. Work here tends to be about choosing a small number of operational workflows and proving the numbers before scaling.
What usually blocks it Transformation programmes measured in activity rather than outcomes.
Where we focus Portfolio and product decisions, operational workflows, and benefit measurement.
Technology and platform businesses
Often the businesses with the most capability and the least clarity: plenty of tools running, no shared view of which ones matter. The work is usually about focus and commercial sequencing.
What usually blocks it Tooling adopted team by team, with no route to a shared way of working.
Where we focus Commercial sequencing, product portfolio, and treating AI as part of the product rather than a project.
What stays the same
The method does not change much between sectors, and that is deliberate. Start with one workflow and one named owner. Check that the material the tool will read is findable and current. Agree what may never be shared, and who reviews output before it leaves the firm. Measure the workflow as it stands before changing anything, so the comparison afterwards means something.
Engagements typically run six to eight weeks for a first project, which is usually long enough to know whether a use case should be scaled, refined or stopped. Coaching runs alongside where the pressure sits with a person rather than a process. Both start with the same conversation, and it costs nothing.
If your sector is not listed, it usually means the constraint has not come up yet rather than that the work is out of scope. The best next step is the AI readiness assessment or a look at our engagements.
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