Every sector has a different AI reality.
The underlying challenge is often the same. But governance, pace of change, workforce readiness and accountability structures differ in every sector — and that shapes how the work gets done.
Local Government
Democratic accountability, constrained budgets and a workforce spanning digitally confident to cautious. Governance isn't optional here — it's the starting point. Mike has led AI adoption at Cabinet Member level inside a council.
One new unitary authority redesigned citizen services from scratch. On vesting day, residents experienced one council that worked — not five sharing a name.
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Central Government & Public Services
NHS trusts, central government departments, police, housing associations and universities share the governance characteristics of local government — public accountability, procurement constraints, union considerations — at different scales and with different mandates. The AI opportunity is significant and largely untapped.
A public services organisation redesigned how citizens accessed services. Any language, any channel, any hour. Trust went up, not down.
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Financial Services
Regulation means AI has to be embedded carefully — into controlled workflows, with auditability, and governance that satisfies FCA and model risk standards. The opportunity is significant. So is the cost of getting governance wrong after the fact.
A wealth management firm's compliance register went from permanently behind to fully current. The next FCA visit produced zero findings on policy currency.
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Professional Services
Law firms, consultancies and advisory practices face a particular challenge: AI changes the economics of knowledge work, but client confidentiality, professional liability and quality assurance add governance complexity that most AI programmes don't address.
A mid-size law firm's utilisation improved 15% and write-offs dropped 20% — because knowledge stopped being locked in partners' heads.
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Technology & SaaS
Technology companies adopt AI tools faster than most — but fragmented adoption without a coherent operating model creates shadow AI, duplicated spend and governance gaps. Mike's Verimatrix transformation is a direct reference: engineering productivity, support automation and sales enablement delivered inside a global SaaS company.
A SaaS engineering team gained roughly an hour per developer per day. Cycle times dropped 25%. Zero IP incidents — because every AI-assisted line was tracked.
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Small & Medium Organisations
Smaller organisations often have clearer AI opportunities and fewer layers of complexity — but less capacity to pursue adoption properly. Engagements are right-sized: a focused diagnostic, a narrow pilot, or fractional AI leadership for a team that doesn't yet have that expertise in-house.
A fifteen-person company grew revenue 35% and expanded into a new market — because the operational bottleneck was removed, not just managed.
Explore this sectorWhat stays the same across every sector
The sector context changes the constraints. The underlying work — identifying genuine opportunities, testing them in real operations, and embedding what works — stays the same.
Start with real workflows
AI adoption that isn't grounded in how work actually happens produces pilots that work and programmes that don't. Every sector has a different workflow reality.
Governance is a design problem
Every sector has a different governance environment — FCA, democratic accountability, professional liability. Well-designed governance enables adoption in all of them.
Evidence before commitment
The Radar model exists because organisations make better decisions when they start with honest assessment. That applies whether you're a council, a bank, or a ten-person firm.
Not sure where you fit?
If you're not sure which sector framing applies to your organisation, that's a fine starting point for a conversation.