Bruno Aziza, former distinguished voice at Google, discusses practical applications of generative AI within the enterprise. Topics include: understanding generative AI, trust and data quality, financial impact and adoption, ethical considerations, identifying use cases, aligning enterprise architecture and AI strategy, and driving adoption in the enterprise.
44:38
forum Ask episode
web_stories AI Snips
view_agenda Chapters
auto_awesome Transcript
info_circle Episode notes
insights INSIGHT
Gen AI Is Probabilistic, Not Magic
Generative AI is probabilistic, not magical, and can produce incorrect answers quickly.
Treat it as a human-plus-machine tool that amplifies employee productivity when guided correctly.
volunteer_activism ADVICE
Begin With Data Quality Work
Start generative AI projects by fixing data quality, because models expose poor data quickly.
Use generative techniques to assist with labeling, cleansing, and identifying missing or corrupt records.
insights INSIGHT
Gen AI Shrinks Time And Boosts Proficiency
Generative AI adoption targets shrinking time to outcome, improving customer experience, and accelerating proficiency.
Nearly all proficiency levels produce higher-quality results with generative AI, especially for innovation tasks.
Get the Snipd Podcast app to discover more snips from this episode
In episode 806 of CXOTalk, we discuss practical applications of generative AI within the enterprise with Bruno Aziza, who was a distinguished voice at Google before joining CapitalG. The conversation explore the technical aspects, the importance of data quality, and the ethical considerations surrounding generative AI deployment. Be sure to watch episode 806 for a live, nuanced discussion aimed at elevating your strategic roadmap for enterprise AI. The conversation includes these topics: ► Understanding Generative AI in the Enterprise: A Deep Dive ► The Human-Machine Symbiosis in Generative AI ► Trust and Data Quality in Generative AI ► Financial Impact and Adoption of Generative AI ► Enterprise Trends and Use Cases for Generative AI ► Deploying Generative AI: Ethical and Practical Considerations ► “Trust and Data Quality are the Competitive Moat” ► Data Strategy is the Foundation of Generative AI Strategy ► How to Identify Use Cases for Enterprise AI ► How to Align Enterprise Architecture and AI Strategy ► What Does Data Corruption Mean for Generative AI? ► Deploying Generative AI: Ethical and Cultural Considerations ► How to Drive Adoption of Generative AI in the Enterprise ► Future Prospects: The Evolving Landscape of Generative AI and Enterprise Read the full transcript: https://www.cxotalk.com/episode/generative-ai-strategy-in-the-enterprise Subscribe: https://www.cxotalk.com Bruno Aziza is a Partner at CapitalG, Alphabet's (parent company of Google) independent growth fund. He is a seasoned operator who specializes in high-growth SaaS and enterprise software. Bruno has led product, marketing, sales and business development teams across all phases of growth, from startups to mid-size companies and Fortune 10 software leaders. Michael Krigsman is an industry analyst and publisher of CXOTalk. For three decades, he has advised enterprise technology companies on market messaging and positioning strategy. He has written over 1,000 blogs on leadership and digital transformation and created almost 1,000 video interviews with the world’s top business leaders on these topics. His work has been referenced in the media over 1,000 times and in over 50 books. He has presented and moderated panels at numerous industry events around the world. #enterpriseai #generativeai #cxotalk
CXOTalk