How will GenAI impact enterprise data and AI strategy?
summary
Generative AI forces platform changes: enterprises must unlock vast unstructured stores (reported 70,000 Llama variants and lots of multimodal models) into searchable knowledge bases or graphs (examples include a 2,000‑document knowledge base and a 15‑use‑case retail hub), layer a semantic/matrix layer to support 60–70% text‑to‑SQL accuracy today, add LLM‑specific ops and guardrails, and use synthetic data to break cold starts — the concrete work is building pipelines, access controls, reasoning improvements and monitoring that actually make these use cases reliable.
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chapters (8)
- 0:00GenAI’s impact on enterprise data
- 1:07Adoption gaps in generative AI
- 3:59Adaptive data and AI strategy changes
- 5:04Unstructured data for enterprise knowledge
- 13:29Natural language analytics transformation
- 25:39Generative AI accelerates AI adoption
- 31:22Evolving AI platforms for GenAI
- 33:27GenAI long-term potential and challenges
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