The GenAI revolution has a data problem.
Despite the excitement around enterprise AI, reports from Gartner and McKinsey reveal that over 70% of GenAI initiatives stumble—not because of inadequate technology, but because of poor data quality.
The promise of internal copilots depends on unlocking unstructured data: documents, emails, presentations sitting in corporate silos. But simply feeding these into a RAG system won’t cut it.
The real challenge? Creating “AI-ready data”—clean, contextually rich, and continuously updated. This means intelligent chunking, metadata enrichment, and robust data observability to prevent those confidently incorrect AI outputs we’ve all seen.
The shift is clear: we’re moving from model-centric to data-centric AI. The competitive advantage won’t come from the newest LLM—it will come from how well you prepare your data to feed it.
How are you preparing your unstructured data to truly unlock GenAI’s potential?
