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Course 2 of The AI Decision Course

Data foundations for AI

For leaders whose AI plans depend on data they have not checked yet: what data an AI project needs, what to fix first, and how to tell whether your data is an advantage.

18 short lessons. Each one is a five-minute read with a card your team fills in and three AI prompts to test it.

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Lessons in this course

  1. A better model won't fix data it can't see3-minute read
  2. Check that the examples exist before you promise the feature5-minute read
  3. Test whether your data is ready3-minute read
  4. The data only you have4-minute read
  5. Keep everything, or start from the question?4-minute read
  6. Connecting AI to your own data2-minute read
  7. Which documents are worth turning into data4-minute read
  8. Price the bad data, then settle who pays4-minute read
  9. Training a model when the right answers were never written down5-minute read
  10. Train on the conditions the model will meet5-minute read
  11. One central copy, or connect it where it lives?4-minute read
  12. Personalisation starts with one customer's whole history3-minute read
  13. Teaching AI what your terms and columns mean4-minute read
  14. When it is safe to let people ask your data for a number4-minute read
  15. Set how data problems get caught, then count who catches them first3-minute read
  16. When a rule changes, someone has to tell the AI3-minute read
  17. Extend the data platform you have, or buy a new one5-minute read
  18. When basic document search isn't enough, the question picks the fix6-minute read

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