The Biggest AI Mistake is Treating Knowledge Like Data | Opinion
Summary
Many companies are investing a lot of money in artificial intelligence (AI), but they often treat knowledge like simple data. This leads to problems because the quality and management of organizational knowledge are very different from the handling of data, and poor knowledge management can cause AI to give unreliable results.Key Facts
- Companies spend billions on AI, focusing on bigger models, faster technology, and more data.
- The main problem is that businesses treat knowledge as if it were just data, which it is not.
- Knowledge includes documents like product manuals, support articles, and policies that are often scattered and poorly managed.
- Data governance is well established for financial, customer, and operational data, but not for written knowledge.
- AI systems rely on quality knowledge, and when knowledge is inconsistent or unmanaged, AI answers become unreliable.
- Many organizations have no formal system for managing knowledge or use mixed, fragmented approaches.
- Fragmented knowledge leads to fragmented intelligence from AI systems.
- Companies should focus on managing the most valuable content well, as that knowledge forms the best basis for AI use.
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