Is Your Data Ready for AI?
Check whether your documents and records are fit for AI use, and fix the problems that matter first
A taste of a lesson
We want an AI assistant that answers staff questions from our policies. Is our data ready?
Let's check rather than guess. Gather the policies the assistant would use and look at about twenty. For each, ask: is this the current version, are there older copies around, does it have an owner and review date, and should everyone be allowed to see it? Conflicting versions are the most common problem, because the assistant may quote the old one confidently. Score each policy red, amber or green. Exercise: pick one policy, such as annual leave, and count how many versions you can find on your shared drives.
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Explain the main dimensions of data readiness in plain terms
- Run a quick readiness check on one AI use case
- Separate blocking data problems from ones that can wait
- Assign owners and review cycles for key data sources
Lesson plan
- 1 Why data decides the outcome See how the quality of your own information shapes AI results. Start
- 2 The readiness dimensions Learn the main checks: availability, access, quality, currency, rights and ownership. Start
- 3 A quick readiness check Sample your sources and score them red, amber or green. Start
- 4 Permissions and privacy Make sure AI tools respect who may see what. Start
- 5 A prioritised clean up plan Fix blockers first and plan the rest in small steps. Start
Try asking
About this tutor
For managers, operations staff and anyone planning an AI project who suspects their data is messy. Most AI projects in business depend on the organisation's own documents and records, and many stall because those are out of date, duplicated, scattered, badly labelled or off limits. This tutor explains, without technical jargon, what data readiness means for common AI uses, how to run a quick readiness check on one use case, which problems block progress and which can wait, and who should own the fixes. You finish with a readiness checklist applied to your own project and a short, prioritised clean up plan that does not try to fix everything at once.
Reviews
4.7
3 ratingsSample
- Grace E.Sample
The red, amber, green check took one afternoon and gave my manager a clear picture of what to fix first.
- Ahmed R.Sample
Clear, no jargon, and it was reassuring that we did not need to clean everything first. The permissions lesson was eye opening.
- Dorota W.Sample
We found four versions of the expenses policy. Fixing that before our assistant pilot saved us a lot of embarrassment.
About the teacher
Risk, vendors, data and support: the unglamorous work that makes AI safe to rely on
9 tutors 493 lessons taught Sample
I teach the parts of business AI that decide whether a project survives contact with reality: data quality, vendor choices, contracts, security habits and customer support operations. My background is in IT operations and service management, which taught me to ask what happens when something goes wrong before asking what happens when it goes right. I teach with checklists, worked...
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