Why it matters now
Data is where many AI projects quietly fail. There are two traps: waiting years for a perfect data platform before trying anything, or ignoring data quality until a pilot breaks. I prefer the middle path. Let a valuable use case show you exactly which data matters, fix that, and keep building on the same foundations.
- Gartner forecasts that organisations will abandon 60% of AI projects unsupported by AI-ready data through 2026. 63% of organisations lack, or are unsure they have, the right data practices.Gartner, Lack of AI-ready data puts AI projects at risk, 2025 ↗
- 50% of CEOs say rapid investment has left their organisation with disconnected, piecemeal technology.IBM Institute for Business Value, 2025 CEO Study ↗
Common mistakes
- A multi-year data clean-up before any value is delivered.
- Ignoring data quality until a pilot fails in front of users.
- No clear owner for the data that matters most.
- Every team buying its own tools, so data ends up in more places.
What to do instead
- Fix data use case by use case. Let each valuable use case tell you which data it needs. Fix that data first.
- Name data owners. Give each important dataset an owner in the business who answers for its quality and access.
- Treat your own data as an advantage. Your proprietary data and processes are what competitors cannot buy. Protect them and make them usable.
- Agree a shared architecture. Choose a small set of platforms and standards so each new use case builds on the last.
- Check access early. Know who may see what before AI systems start reading documents and records.
Your first step this week
For your most important AI use case, ask the team which data it depends on, who owns that data and how good it is. One page is enough.
Related guides
- Should we build, buy or partner for AI?
- How do we get from AI pilots to scale?
- How do we govern AI risk and use AI responsibly?
All AI leadership principles and guides · 100 AI use cases by function