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CXOTalk: Leadership, AI, and the Digital Economy
Generative AI Strategy in the Enterprise
Oct 6, 2023
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.
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Quick takeaways
- Generative AI in the enterprise requires a deep understanding of its probabilistic nature and should be seen as a collaboration between humans and machines, enhancing productivity and skills.
- Trust in data quality is crucial for successful deployment of generative AI, emphasizing the need for investing in data management and data quality as foundational steps.
Deep dives
Generative AI in the Enterprise
Generative AI is a hot topic, with tremendous interest and potential for innovation in the enterprise. Despite its popularity in consumer contexts, there are important considerations when using generative AI in a corporate setting. Firstly, it is crucial to understand that generative AI is probabilistic, not magic, and therefore not always correct. It works by completing information or sentences based on trained models. Secondly, successful deployment of generative AI requires orchestration of teams, with humans and machines working together. It is not a competition between the two, but rather a collaboration that enhances productivity and expands the skills of employees. Organizations should think of generative AI as an "Ironman suit" that empowers employees to achieve outcomes more efficiently. Additionally, trust in data quality is paramount when deploying generative AI. Poor data quality can expose flaws in the system, emphasizing the importance of investing in data management and data quality as a foundational step in adopting generative AI.
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