Navigating the growth frontiers of generative AI
summary
Five-step roadmap claims to take enterprises from siloed data to scaled generative AI by unifying the data estate, adding responsible-AI guardrails, and industrializing deployment. Specific tools and patterns cited: Amazon Bedrock, vector databases and knowledge graphs, synthetic-data via GANs, and RAG and LangChain for retrieval; a large airline pilot produces 50+ use cases into production. Accelerators and LLM‑ops automate prompt libraries, model selection (open vs proprietary), monitoring, and bias/privacy controls; several industry pilots report usable production deployments.
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chapters (16)
- 0:00Introduction to generative AI
- 1:45Five-step generative AI approach
- 3:11Generative AI strategy and architecture
- 4:15Unifying enterprise data for gen AI
- 5:18Integrating existing platforms
- 6:02Enterprise readiness and RAG
- 7:07Responsible AI and governance
- 8:10Contextualizing and enriching gen AI
- 9:57Tailoring gen AI for industries
- 10:48Industrializing gen AI development
- 11:33Key gen AI use cases
- 13:00Enterprise applications and automation
- 14:25Getting started with gen AI
- 15:50Data foundation and AI flywheel
- 16:33Impetus services and roadmap
- 17:36Summary and Q&A
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