Consulting · 03
AI adoption & roadmap
Find the AI use cases worth doing, prove them with pilots, and adopt them safely.
01The problem
Everyone is talking about AI. Few have a plan.
- 1.1
Pressure without direction.
Boards and customers ask about AI, but nobody has agreed what it should do for the business.
- 1.2
Pilots that go nowhere.
Experiments start with enthusiasm and stop without a measure of whether they worked.
- 1.3
Data that isn’t ready.
The information AI needs is scattered across spreadsheets, inboxes and systems that don’t talk to each other.
- 1.4
Risks no one has weighed.
Privacy, intellectual property, accuracy and regulation all matter, and each needs a decision.
02What we do
From idea to adoption, safely.
Start with a workshop, or go all the way to a governed rollout.
- AI-readiness assessment
- Where your data, systems, skills and policies stand today.
- Use-case discovery and ranking
- Every idea scored on value, feasibility and risk, so you start with the right ones.
- Pilots and proofs of concept
- Small, measured builds that show whether a use case pays off before you scale it.
- Data foundations
- The pipelines, quality checks and access rules that AI depends on.
- AI governance
- Policies, risk assessments and oversight aligned with ISO/IEC 42001.
- Team enablement
- Training so people use AI tools well, and safely.
03How it runs
From workshop to working pilot.
About eight weeks to a roadmap and a first measured pilot.
- 01Week 1
Discover
Workshops with leadership and the teams who’d use AI.
- 02Weeks 1–2
Assess
Data, systems, skills and risks, reviewed against the ideas.
- 03Week 3
Prioritise
Use cases ranked by value, feasibility and risk.
- 04Weeks 4–8
Pilot
Build and measure the top one or two.
- 05Ongoing
Scale
Roll out what works, with governance and training.
04What you get
A plan your board can read.
Written down, ranked, and backed by evidence from the pilots.
- AI-01
Readiness assessment
Data, systems, skills and policies today.
- AI-02
Use-case portfolio
Every idea, scored and ranked.
- AI-03
AI roadmap
What to do first, next and later.
- AI-04
Pilot results
What was built, what it measured, what it showed.
- AI-05
AI policy
Rules your team can follow.
- AI-06
Training plan
Who learns what, and when.
05Questions
What people ask us.
Do we need our own data scientists?
Not to start. Most valuable uses today build on existing models and tools. We’ll help you decide when in-house skills are worth hiring.
Is our data safe with AI tools?
It depends on the tool and how it’s set up. We assess each one for privacy, retention and access, and set rules your team can follow.
Which models or vendors do you recommend?
We’re vendor-neutral. We compare options against your use case, your data and your budget.
How do we measure success?
Each pilot has a measure agreed up front, such as hours saved, response time or accuracy, so the decision to scale rests on evidence.
Find the AI worth doing.
A short workshop gives your leadership team a shared view of where AI fits.