Before the first AI project

AI Readiness Assessment

A structured review of where your data, governance and skills actually stand, and which AI use cases you can support today. You finish with a readiness scorecard and a prioritised roadmap.

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  • Data foundation
  • Data maturity
  • Use-case potential
  • Governance
  • Skills and infrastructure
Definition

What is an AI readiness assessment?

An AI readiness assessment is a structured review of an organisation’s data, infrastructure, governance and skills, carried out to establish which AI use cases it can realistically support and what needs to be in place first.

In many organisations the AI conversation starts well before the data is ready for it. Customer records sit in systems that were never designed to share them, ownership of the important datasets is spread across teams that each hold part of the picture, and nobody is entirely certain whether the numbers feeding a model are the ones the business would defend in a board meeting. None of that is visible while the use case is still an idea.

What tends to happen is that these constraints surface during the build, by which point the budget is committed and the delivery date is fixed. An assessment moves that discovery forward, while the options are still open and changes are still cheap. You get a documented view of what your current data can support, what needs work before anything is built on top of it, and which use cases are better left until later.

Scope

What we assess

Readiness is rarely a single number. These five areas tend to fail independently, so each is looked at on its own terms and scored separately.

01
Data foundation
Where your data lives, how it moves between systems, and whether the pipelines feeding a given use case are reliable enough to depend on.
02
Data maturity
A data maturity assessment across collection, quality, modelling and access: how consistently data is defined, who owns it, and how much manual repair happens before anyone can use it.
03
Use-case potential
Which candidate use cases are already supported by the data you hold, and which would need new collection, new consent or new integration before they become possible.
04
Governance and compliance
How consent, access control, retention and auditability are handled today, and where an AI workload would create obligations you cannot currently meet.
05
Skills and infrastructure
Whether your teams and your Google Cloud environment can run, monitor and maintain what gets built, once the people who built it have moved on.
The method

How it works

The assessment runs in three parts. We start with the systems: a working session with the people who run your platform, mapping where data comes from and what happens to it on the way through. We then take a small number of candidate use cases and test them against what that data can actually support. Finally we score each of the five areas and write up the evidence behind each score.

Most of the work sits on our side, so what we need from your team is a handful of sessions rather than a project. There is nothing to prepare in advance: an accurate picture of the current state is considerably more useful to us than a tidy one.

What you get

A scorecard and a roadmap

Readiness scorecard

Each of the five areas scored, with the evidence behind the score written next to it, so the result is something your team can argue with rather than a verdict handed down.

Prioritised roadmap

The work that needs doing, in the order that makes it useful: what unblocks the most use cases first, what can safely wait, and what everything else depends on.

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Next step

Then scope it in a Discovery Workshop

An assessment tells you where you stand. Once you know which use case to build first, the Discovery Workshop turns it into a prioritised backlog and a plan you can start against, in one week.