Скачать или смотреть How to Build an Enterprise-Ready AI Prioritization Strategy

In this episode of the AI Monetisation Podcast, Lexy Kassan, Lead Data and AI Strategist at Databricks, joins Binny Mathews to share how enterprises can build a practical, scalable, and enterprise-ready AI prioritization strategy.

Guest Background:
Lexy brings a rich and diverse background to the table. She started her career as a business analyst and evolved into a data scientist, risk analyst, and now a global thought leader in responsible AI. At Databricks, she works closely with CXOs to guide enterprise AI adoption, drive responsible AI frameworks, and reshape data governance and operational models.


What's Inside?

✅ AI strategy playbook: How to balance hype, risk, and value
✅ The new role of data foundations in enabling successful AI
✅ What "last mile AI" really means and why it’s business-critical
✅ Build vs. Buy: How to assess enterprise AI solutions smartly
✅ Responsible AI: From compliance to cross-functional accountability
✅ The evolving role of dashboards, agents, and embedded insights
✅ When NOT to use generative AI: A contrarian take


Key Takeaways:

1. AI success starts with robust data governance and business alignment.
2. Don’t over-engineer—sometimes simpler solutions outperform GenAI.
3. Shift from dashboards to actionable, workflow-integrated insights.
4. Prioritize use cases based on business value, not just technology excitement.
5. Responsible AI is everyone’s job—from IT to legal to business units.

This episode is essential listening for enterprise leaders, data strategists, and AI practitioners looking to drive real impact from their AI initiatives—without losing sight of cost, governance, or long-term value.


For full show notes, visit- https://bit.ly/4cE5hHG

🎧Check out this episode on Spotify- https://spoti.fi/42syD6Y


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TIMESTAMPS
00:00 Lexy’s role at Databricks
02:35 Shift from data to AI focus in enterprises
04:53 Evolution of data products and last-mile AI
07:42 Business users doing self-service analysis
10:06 Tools simplifying data science workflows
14:08 Early examples of agentic workflows
16:27 How AI strategy conversations start
18:49 AI use case prioritization in companies
22:41 Fine-tuning vs out-of-box model use
24:50 Expected vs actual AI project ROI
27:19 Pain points across the AI lifecycle
32:07 Responsible AI: When and how it comes in
35:07 Evolving self-service in last-mile AI
38:08 Lexy’s contrarian view on AI use cases
39:47 What triggers AI adoption in companies
43:14 Should every team have AI engineers?

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