Join us for an insightful conversation with Kavita Ganesan, an experienced AI strategist who has built solutions for Fortune 500 companies like 3M and eBay. Kavita shares her journey in AI and provides practical frameworks for organizations looking to implement AI successfully, including her B-CIDS framework (Budget, Culture, Infrastructure, Data, and Skills) and guidance on evaluating AI pilots.

TOPICS DISCUSSED:

1. Kavita's AI Journey
From academic research at USC to practical implementations at eBay and 3M, Kavita shares how she developed her unique perspective as a "translator" between business and technical worlds.

2. The B-CIDS Framework
A comprehensive approach to AI readiness focusing on Budget, Culture, Infrastructure, Data, and Skills, with special emphasis on data and cultural readiness as foundational elements.

3. Data Readiness Challenges
The critical importance of digitizing paper processes, comprehensive data collection, and unified data warehousing across company branches.

4. Cultural Readiness and AI Literacy
Balancing enthusiasm and fear through company-wide AI literacy programs to enable better collaboration and understanding of AI risks.

5. Problem-First Approach to AI
Why business leaders should focus on identifying real business problems rather than forcing AI adoption without clear use cases.

6. AI Pilot Success Metrics
The three pillars of successful AI implementation: model performance, business outcomes, and user experience.

7. Recommended AI Use Cases
Sector-specific recommendations such as recommendation systems for e-commerce and content creation tools for marketing teams.

INSIGHTS:

- Data readiness and cultural readiness take the longest to implement and should be prioritized
- AI solutions should be built with production constraints in mind from the beginning
- Companies should avoid hiring data scientists without clear business problems to solve
- The costs and risks of third-party APIs need careful consideration in pilot projects
- Traditional machine learning tools are often more predictable and easier to implement than generative AI
- Business leaders should focus on problems first, then determine if AI is the appropriate solution

CONTACT INFO:

- Book: The Business Case for AI
- Website: kavita-ganesan.com

CHAPTERS
00:00 Introduction to data readiness challenges
00:59 Welcome and guest introduction
01:39 Kavita's background
02:25 Kavita's journey in AI
05:11 Introduction to the B-CIDS framework
05:54 Applying B-CIDS to mid-sized companies
09:18 The unique challenge of cultural readiness
10:41 Focus on business problems, not just AI
12:29 Common pitfall: hiring data scientists without clear problems
14:53 Why AI pilots fail
17:08 Three pillars of AI success evaluation
20:50 Recommended AI use cases
22:01 The value of different AI tools beyond ChatGPT
23:49 Ethical concerns and risks
25:40 Finding balance between innovation and risk
26:22 Closing thoughts

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