# #246: Unlocking Value with Generative AI

Page: https://stenobird.com/podcast/data-futurology-leadership-and-strategy/246-unlocking-value-with-generative-ai
Text version: https://stenobird.com/podcast/data-futurology-leadership-and-strategy/246-unlocking-value-with-generative-ai.md
Podcast: [Data Futurology - Leadership And Strategy in Artificial Intelligence, Machine Learning, Data Science](https://stenobird.com/podcast/data-futurology-leadership-and-strategy)
Published: 2023-09-27T02:29:30+00:00
Episode link: https://podcasters.spotify.com/pod/show/datafuturology/episodes/246-Unlocking-Value-with-Generative-AI-e29r3nu
Audio file: https://anchor.fm/s/3fab060/podcast/play/76434622/https%3A%2F%2Fd3ctxlq1ktw2nl.cloudfront.net%2Fstaging%2F2023-8-27%2F37cbbcf5-cc6a-6390-fd30-ea45009f802a.mp3
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JSON: https://stenobird.com/v1/public/podcasts/data-futurology-leadership-and-strategy/episodes/246-unlocking-value-with-generative-ai
Duration seconds: 2655

## Resource

Moving beyond the generative AI hype requires a return to core product fundamentals and value-driven engineering. The discussion explores how to integrate LLMs into existing frameworks without sacrificing reliability or brand trust.

## Highlights
- Main idea: Generative AI is a powerful tool for augmentation, but it does not replace the need for robust data foundations and good governance
- Failure mode: Attempting to force LLMs to provide 100% consistent, deterministic answers can lead to 'squeezing' the model and reducing its utility
- Practical takeaway: Use 'eval-driven development' to quickly experiment, validate, and decide whether to scale or kill AI features
- Main idea: The success of AI integration depends on 'speaking to be understood,' ensuring technical and non-technical stakeholders share a common vocabulary
- Practical takeaway: Approach AI implementation like Amazon's 'working backwards' method—define the end-user value and press release before building the tech

## Topics

Generative AI, Product Strategy, Machine Learning, Data Science, AI Governance, Software Engineering, Commercial Value, Stakeholder Management

## Chapters
- 1:00 — Moving Beyond the Hype: An introduction to the current state of Generative AI and the importance of focusing on commercial outcomes rather than technological novelty.
- 4:20 — The Core of Value Creation: Discussing how the focus on extracting value from data remains constant, regardless of whether the technology is called ML, AI, or GenAI.
- 7:40 — AI as Ubiquitous Infrastructure: Comparing the future of AI to the adoption of electricity, where it becomes a pervasive, invisible layer of modern life.
- 11:20 — Foundations and Job Displacement: Evaluating the readiness of cloud and data sharing laws, while addressing skepticism regarding immediate large-scale job disruption.
- 14:30 — The Disillusionment Phase: Navigating the period where the initial excitement of new tools meets the reality of needing to redesign organizational frameworks.
- 17:50 — Managing Hallucinations and Expectations: Addressing the risks of LLM hallucinations and the necessity of managing stakeholder expectations regarding model accuracy.
- 21:20 — The Danger of Over-Constraining Models: How forcing models to be deterministic can lead to performance degradation and 'black box' drift.

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## Transcript

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