AI readiness · Pillar 3 of 3

Data: no data, no AI.

AI can give a confident answer built on wrong information, and the decision that follows can be costly. AI that works for your organisation needs your organisation's own information: complete, owned, and safe to use. Find the data problems before you rely on its answers.

What data readiness means

AI needs information it can access, understand and use correctly. If company records are incomplete, inconsistent, out of date or poorly controlled, AI can produce answers that sound convincing and are wrong.

Data readiness means knowing what information exists, who owns it, and whether it is safe and suitable for the job you want AI to do.

Seven checks before AI relies on your information

Run them for one proposed use of AI at a time.

Source and owner
Where does the information come from, and which person is responsible for it?
Quality
Is it complete and correct? Are there gaps, duplicates or old figures?
Access and permission
Who may see it? May it be given to an AI tool under your own rules, your contracts and the law?
Freshness
How old is it, and how often is it updated?
Meaning
Do the AI and your people read each item the same way? A "customer" or a "sale" can mean different things in two departments.
Tracing an answer
Can a person see which records an AI answer came from?
Testing
Before you rely on it, test the AI on questions where you already know the right answer.

A fictional example

Why a supplier ranking is only as good as its records

An AI tool that recommends a supplier relies on current prices, delivery history and the conditions in each contract. Suppose delivery records are kept in one region only. The ranking will then favour suppliers from that region, and the recommendation will still look confident.

The fix is not a better AI tool. It is to know which records the ranking used, who owns them, and what is missing.

How AIR APAC helps

Data Review

A fixed-scope review of one planned use of AI. We check each source with the people who own it and give you a findings table, test questions and a list of fixes in order of priority.

See the Data Review

Data readiness briefing

Your team brings one proposed use of AI. We go through the seven checks together. Your team writes the list of what is missing and who owns each gap.

Training for the people who decide

Managers who act on AI advice need to ask where its information came from. Checking the evidence behind an AI answer is part of From Answers to Decisions.

Does AIR APAC clean, move or store our data?

No. In the Data Review, AIR APAC does not take copies of, clean, store or move your data, and neither the briefing nor the Data Review gives a grade or score. In the review, your staff run any check that needs records, in your own systems. If you need data work or software, Exeter Labs, the product company of AIR APAC, can do it under a separate agreement, or you can use any other supplier.

Which AI tools do the seven checks apply to?

Any AI tool that uses your organisation's own information: an assistant connected to your files, a search tool over your documents, or an agent that reads your records.

Delivery partners