What is the one thing you advice an AI PM to have? How should they differentiate themselves amongst other PMs?

This will be anti-climactic, but my advice is "don't forget you're a PM." Stay laser focused on the customer, their pain and problems and solve the problem. AI is an incredibly powerful tool to solve customer problems, but that's all it is. A tool to solve problems. And it's not always the best tool. We have a beautiful new hammer, don't go around looking for unnatural nails. Work from problem to solution to tech (AI when appropriate), don't start with the tech and work back to a problem.

One key thing would be to have empathy for your fellow data scientists. When working with data scientists, remember that simply asking for Level of Effort won't suffice. Feasibility and effort are often exploratory in the AI realm. So, change the language you speak to foster successful collaboration!

Shared Understanding: As a PM, it's crucial to ensure that data scientists understand your goals and objectives. Clearly communicate what you're trying to achieve and the assumptions that need validation even before an AI model is developed.
Collaborative Exploration: Instead of seeking concrete estimates, foster a collaborative environment for exploration. Encourage open discussions, knowledge sharing, and joint problem-solving. Embrace the iterative nature of AI development to uncover insights and uncover the realm of possibilities.
By shifting the conversation and fostering collaboration with data scientists, PMs can bridge the gap between product goals and AI exploration. Embrace the power of shared understanding and collaborative exploration for AI success!

If I were to offer one piece of advice to an AI product manager, it would be to cultivate a deep understanding of both AI technologies and the broader business context in which they operate. This dual expertise allows AI product managers to bridge the gap between technical capabilities and business objectives, effectively translating AI innovations into tangible value for users and stakeholders.

My one piece of advice would be to be outcome-driven. Only a few models end up being deployed and adopted by the users, a lot of them don’t make it to the end or don’t deliver as expected. To ensure the efforts turn into outcomes, be thorough about the user needs and don’t focus only on the model performance but also make sure that the system and design from model output to user interaction is smooth. Learning to work well with data scientists and business stakeholders, and proactively solving the potential roadblocks are some ways to succeed as a Product Manager.