How To Become Generative AI Product Manager With No Experience

Summary

This video features Nicholas, Head of AI at Save the Children International, providing advice to aspiring AI product managers. He outlines three levels of AI implementation, from improving existing features to enabling entirely new products. Key skills include understanding AI user experience, knowing when to use off-the-shelf vs custom solutions, and making decisions based on business priorities rather than algorithms. Use cases to pursue involve improving poor out-of-the-box results through elaboration. Avoid replicating functionalities big tech companies will likely soon provide. Nicholas emphasizes the importance of testing AI-generated ideas and not fully trusting language model outputs.

Takeaways

• 😊 Nicholas is the Head of AI at Save the Children International, which operates in over 100 countries globally
• 👨‍💻 There are 3 levels of AI implementation in products: improving existing features, creating new AI-enabled features, and building entirely new AI products
• 🎓 The top 3 skills for AI product managers: understand AI UX and explainability, know when to use off-the-shelf vs custom AI solutions, and make pricing and scaling decisions based on data
• 📚 Good starter AI courses are Google Cloud, AWS, Azure courses to learn ready-made AI solutions, and deeplearning.ai's 1-hour Prompt Engineering course
• 😮 Generative AI models produce hallucinations - 100% fake outputs. Prompting is key to getting useful, trustworthy answers
• 💡 Good AI use case: personalized career next step advice by prompting with your background, goals etc. Bad use case: generic scheduling assistant - big tech will build this
• ⏳ Pursue AI use cases with bad off-the-shelf results but high reward with customization e.g career advice. Avoid short-lived surfing-the-AI-wave startups
• 🔍 Generative AI is good at finding risks, not guaranteeing safety. Ask it to find potential risks in ideas, not confirm there are no risks
• 📊 Improve AI solutions by feeding more specific information through prompting and checking if output quality improves
• 👍 Democratize AI via products customized to users with low AI literacy e.g technicians, using AI UX concepts like explainability

Q & A

What are the three levels of AI implementation in products?

The three levels are: 1) Use AI to improve an existing feature, 2) Create a new feature with AI that wouldn't be possible without it, 3) Build an entirely new product enabled by AI.

What does Nicholas recommend as a good starting point for learning about AI?

Nicholas recommends starting by learning what cloud providers like Google Cloud, AWS, and Azure have to offer in terms of off-the-shelf AI solutions. Their courses can provide a good foundation before specializing further.

What are the top 3 skills Nicholas identifies for AI product managers?

The top 3 skills are: 1) Understanding AI UX including explainability, 2) Knowing when it's better to use off-the-shelf vs. custom AI solutions, 3) Understanding AI tools and pricing models rather than algorithms.

How can generative AI be used to detect new markets?

It can generate ideas for new markets, then ask questions to validate the ideas and hypotheses behind them. Scrape the internet for answers, feed it all back into the AI, and ask if it's a good strategy to pursue.

What are some AI use cases companies should avoid pursuing?

Avoid generic use cases that would clearly be useful for many companies/domains, as large tech firms may quickly surpass internal efforts. Prioritize specialized use cases aligned to domain expertise instead.

What risks are there in relying too much on AI guarantees?

AI cannot guarantee answers are 100% correct or that there are no risks. It's better at finding risks if they exist rather than guaranteeing everything is safe. Need human oversight.

How can prompts be made more effective when using generative AI?

Elaborate prompts with more context-specific information lead to better, more tailored results. Can also use AI in a meta-prompting approach to help write better prompts.

What are some criteria to evaluate AI solution providers?

Look for trustworthiness, history in the problem domain, reasonable longevity expectations, and a focus on using AI for incremental improvements rather than just hype.

Where can I access recommended AI courses and resources?

The video description contains links to free AI courses recommended by Nicholas and Dr. Nancy. An upcoming AI product management course is also mentioned.

What were the key takeaways from Nicholas' advice?

Understand AI products across 3 levels, focus on AI UX, leverage cloud providers, avoid over-customization, elaborate prompts, seek niche use cases, ensure trustworthiness, and complement AI with human oversight.