AI and Product Management

Webinar: How to Be an AI Product Manager by Facebook AI Product Leader, Natalia Burina

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Key Summary

• AI Product Managers need to identify business problems that AI can solve, envision strategic solutions and bring them to life.
• There are different types of AI PMs, including ones focusing on products, platforms, AI research, and building AI responsibly.
• AI shifts software development from a deterministic process to a probabilistic one, making it essential for PMs to understand and manage the trade-offs.
• Some practical skills for AI PMs include: understanding how to use rapid innovation tools, knowing the various categories of problems that machine learning can solve, and comprehending the AI product pipeline.
• AI should be developed responsibly, ensuring it is fair, private, robust, explainable, accountable and reliable.
• AI PMs need to identify the right problems AI can solve, understand the technology's potential, align and track the right business metrics, and understand the potential harms of the technology.
• AI should support business metrics and AI metrics should relate to greater business goals, requiring engagement with all stakeholders to define suitable metrics.
• An AI PM should foster an experimental culture, taking calculated risks and being willing to learn from failures, as AI rewards those willing to do so.
• Barina's tips for success include telling a compelling story, preparing a six-month plan to stay focused, and using Andrew Bosworth's cold start algorithm when starting a new job.

Webinar: AI/ML Product Management by Uber Sr PM, Kai Wang

• Explains how machine learning customizes the Uber experience, such as determining the best driver, pickup location, delivery time, and ensuring transaction safety.
• Talks about AI and machine learning product management, including the definition, types, and the skills required for AI product managers.
• Discusses the differences between AI products and traditional software products in terms of defining success, project and risk management, as well as the needed technical understanding of AI and machine learning.
• Different types of AI products include platforms/frameworks, AI applications addressing specific use cases, and applied machine learning products utilized in daily life such as Google Search, self-driving cars, and digital assistants like Siri.
• According to Kai, 10% of AI product managers work on machine tooling, 20% work on AI services, and the majority focus on applied machine learning.
• Importance of having a fallback plan for when AI models fail was stressed, reminding AI product managers to prepare for wrong predictions.
• Emphasizes remaining user-centric while being technically proficient and understanding the needs and behaviours of users

Panel Discussion on The Future of AI in Product Management

• The panelists highlighted the use of AI in various sectors, including the restaurant industry and medical field.
• They identified the need to correct existing biases in AI data sets to prevent further embedding of such biases.
• They believe AI technology should be accessible and usable across different departments in an organization.
• The panelists suggested product teams should explore AI and understand its potential for solving customer problems and enhancing their work.
• They foresee AI as a service and believe AI integrations will become a substantial part of the tech industry in the next five years.
• The panelists spoke about the need for AI to complement human skills, rather than replace them.
• They cautioned against over-promising on AI capabilities, emphasizing it should be seen as a tool for efficiency and problem solving, not a replacement for human roles.
• The panelists called for a realistic approach to AI adoption, leveraging human strengths alongside AI capabilities.

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