Seven properties that define AI-ready data
Every enterprise is racing to get its data ready for AI. Almost none agree on what that means. PSYCHIC defines it: seven critical questions, drawn from research across 1,000 enterprises, that determine whether AI can deliver real business value. It gives IT, data, security, and business leaders a shared framework to evaluate readiness, identify gaps, and act.
| The question to ask | Why it matters | ||
|---|---|---|---|
| Proprietary | Can AI access the unique data, knowledge, and context of your business? | Public models know the public web. Your advantage is the knowledge in your own files. | |
| Secure | Can AI use the unstructured data safely? | AI should see only what the person asking can already open. Anything less turns an answer into a leak. | |
| Yours | Do you maintain control over what happens with the data? | Data under your control never ends up in another copy, or in someone else’s training set. | |
| Current | Is AI working with the latest version of what is happening now? | When AI works from stale copies, its answers drift from what is true, and teams stop trusting it. | |
| Hybrid | Can you bring your AI to your data wherever it resides? | Knowledge lives wherever work happens. AI that sees one location gives partial answers. | |
| Indexed | Can AI find the right information, quickly and efficiently? | Without an index built for retrieval, AI scans, guesses, and spends tokens on partial results. | |
| Continuous | Can AI access and use the accumulated knowledge embedded in years of files and projects? | History is context. AI can answer how a document changed, not just what it says today. | |