PM in AI-First Organizations by eBay AI Product Leader
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Main takeaways: -What is AI-first product management? -How is it different (and similar) to other kinds of PM roles? -Who are the key stakeholders in AI-first organizations? -What does the AI Product development lifecycle look like? How is it different from the software development lifecycle? -What are some example AI use cases and key success metrics? -What skills does an AI first PM need to master?
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PM in AI-First Organizations by eBay AI Product Leader
- 1. PM in AI-First Organizations by eBay AI Product Leader www.productschool.com
- 2. CERTIFICATES Your Product Management Certificate Path Product Leadership Certificate™ Full Stack Product Management Certificate™ Product Management Certificate™ 20 HOURS 40 HOURS 40 HOURS
- 3. Corporate Training Level up your team’s Product Management skills
- 4. Free Product Management Resources BOOKS EVENTS JOB PORTAL COMMUNITIES bit.ly/product_resources COURSES
- 5. 1 [Product Management in AI first Orgs] [ Yogesh Soni ] [ Píoduct Leadeí - AI]
- 6. Agenda 1. What’s AI, why AI, AI use cases 2. AI product lifecycle, stakeholders 3. PM responsibilities, skill sets, challenges 4. AI first orgs, becoming AI PM 2
- 7. 3 Backstory
- 8. Software development 4 Traditional programming Rules Data Output Humans manually code rules into software programs
- 9. What is AI ? 5 AI - training Rules a.k.a models Data Output Models are trained to learn from sample data and outputs
- 10. 6 AI - inference Model New Data New Output Model predicts the output when fed new data How is AI different ?
- 11. OK, so why do we care about AI? $13 trillion in economic activity across the world by 2030 * 1.2 percent of additional GDP growth per year through to 2030 Steam engine's boost to human productivity - 0.3 percent per year between 1850 and 1910 * Mckinsey report
- 12. AI is everywhere
- 13. Product Manager Problem definition Specify success metiics e.g False positives should be minimized Piecision > 99% Data scientist Model training Piepaie data Peifoim expeiiments Validate model Keep the model healthy and ietiain on an ongoing basis ML & data engineer Model production Deploy models and data pipeline Cieate model seivice & API endpoint Do pioduction testing , optimization and monitoiing Application developer AI Integration Build downstieam application, call ML APIs Explain ML output to end useis Piovide hooks to collect usei signal End users AI consumption Piovide signals and feedback thiough theii actions - e.g. fiequently maiking the emails as “not spam” Data analyst Data acquisition Supply laige dataset categoiized as “spam” oi “No spam” Monitoi model peifoimance and business metiics Spam filter - AI product development lifecycle
- 14. Product Manager Problem definition Specify success metiics e.g False positives should be minimized Piecision > 99% Spam filter - AI product development lifecycle
- 15. Product Manager Problem definition Specify success metiics e.g False positives should be minimized Piecision > 99% Spam filter - AI product development lifecycle
- 16. Product Manager Problem definition Specify success metiics e.g False positives should be minimized Piecision > 99% Spam filter - AI product development lifecycle
- 17. Product Manager Problem definition Specify success metiics e.g False positives should be minimized Piecision > 99% Data analyst Data acquisition Supply laige dataset categoiized as “spam” oi “No spam” Monitoi model peifoimance and business metiics Spam filter - AI product development lifecycle
- 18. Product Manager Problem definition Specify success metiics e.g False positives should be minimized Piecision > 99% Data analyst Data acquisition Supply laige dataset categoiized as “spam” oi “No spam” Monitoi model peifoimance and business metiics Spam filter - AI product development lifecycle
- 19. Product Manager Problem definition Specify success metiics e.g False positives should be minimized Piecision > 99% Data scientist Model training Piepaie data Peifoim expeiiments Validate model Keep the model healthy and ietiain on an ongoing basis Data analyst Data acquisition Supply laige dataset categoiized as “spam” oi “No spam” Monitoi model peifoimance and business metiics Spam filter - AI product development lifecycle
- 20. Product Manager Problem definition Specify success metiics e.g False positives should be minimized Piecision > 99% Data scientist Model training Piepaie data Peifoim expeiiments Validate model Keep the model healthy and ietiain on an ongoing basis Data analyst Data acquisition Supply laige dataset categoiized as “spam” oi “No spam” Monitoi model peifoimance and business metiics Spam filter - AI product development lifecycle
- 21. Product Manager Problem definition Specify success metiics e.g False positives should be minimized Piecision > 99% Data scientist Model training Piepaie data Peifoim expeiiments Validate model Keep the model healthy and ietiain on an ongoing basis Data analyst Data acquisition Supply laige dataset categoiized as “spam” oi “No spam” Monitoi model peifoimance and business metiics Spam filter - AI product development lifecycle
- 22. Product Manager Problem definition Specify success metiics e.g False positives should be minimized Piecision > 99% Data scientist Model training Piepaie data Peifoim expeiiments Validate model Keep the model healthy and ietiain on an ongoing basis ML & data engineer Model production Deploy models and data pipeline Cieate model seivice & API endpoint Do pioduction testing , optimization and monitoiing Application developer AI Integration Build downstieam application, call ML APIs Explain ML output to end useis Piovide hooks to collect usei signal End users AI consumption Piovide signals and feedback thiough theii actions - e.g. fiequently maiking the emails as “not spam” Data analyst Data acquisition Supply laige dataset categoiized as “spam” oi “No spam” Monitoi model peifoimance and business metiics Spam filter - AI product development lifecycle
- 23. AI generated original content AutoML - Build AI without specialized skills AI trends
- 24. Knowledge of AI ● Follows the industry trends ● Understands the state of art capabilities ● Learns from the experts in the team Business ● Clearly defines business outcome and metrics ● Translates the problem into “AI speak” ● Measures the business impact Prioritization ● Pick the right, impactful problems ● Breaks down problem in small chunks ● Right balance of short-term and research Data ● Understands the kind of datasets are needed ● Builds sound strategy for data acquisition ● Sets off a flywheel to improve products over time Communication ● Manages business stakeholder expectations, especially the iterative nature of AI products ● Explain complex ethical and legal implications AI Product Management Skills
- 25. Becoming PM in AI-first companies AI first company ● Company’s core products extensively use AI to provide great user experience ● Data is treated a first class citizen and used for competitive differentiation ● Company’s infrastructure, people, processes, and culture is aligned to maximize the use of data in AI apps Upskill to AI Online íesouíces - AI foí eveíyone Become AI fiíst píoduct leadeí Machine Leaíning (ML) - Digital and Classíoom Tíaining | AWS AWS AI technical Become an AI Píoduct Manageí
- 27. www.productschool.com Part-time Product Management Training Courses and Corporate Training