AI readiness assessment
Where LLMs and neural networks will genuinely help your business, where they will not, and what has to be true first.
Discuss your situationWhat it covers
- Use-case discovery across each department
- Data readiness: where your data lives, its quality and who can reach it
- Privacy, risk and governance
- A build, buy or wait view for each use case
- Team skills and readiness for change
How it runs
Workshops
Short sessions with each function to find where time and judgement are spent.
Score
Each use case scored on value, feasibility and risk.
Plan a pilot
A pilot for the strongest one or two, with measures agreed up front.
Roadmap
What to do now, what to prepare for, and what to leave alone.
What you receive
- A scored use-case register
- A pilot plan with success measures
- A data and governance checklist
Questions
Our data is not clean. Is it too early?
Usually not. Part of the assessment is showing which use cases work with the data you already have, and which need groundwork first.
Will you recommend a particular AI vendor?
Only where it fits. Some use cases suit a hosted model, some need an on-premise model, and some need no AI at all.
Other consulting work
An independent view on what to build, what to buy and what to leave alone, from people who also build.
Technology and systems audit →A clear, independent account of what your software, data and vendors are really doing for you, and what they cost you.
Business diagnostic →A short, structured look at how work really flows through your business, and the one or two changes that would matter most.