What are the key traits you look for in hiring AI Product Managers?

Key traits for AI PM is no different from other PM roles -- empathy for customer issues, ability craft / create / articulate problems and how we might approach the solution, industry and domain experience, and collaborative leadership to work with engineering. Willingness to learn or prior experience or understanding of AI/ML modeling challenges, and how they can be use in the context the industry / domain where it is applied is ofcourse a big plus. 

Top 3 traits that makes a Good PM a Good AI PM:

1. Understand foundational ML tech concepts and having used them to make product decisions. For eg: Statistical Regression, Causation vs Corelation, AUC, P/R, Features vs Labels, Feature distribution, Model Training, Model drift and auto training, etc
2. Aware of potential bias and fiarness need in ML solutions they have launched in the past. Having used model observability and interpretability to explain the model output for their product corner cases.
3. Ability to scale up product decisions going from single global configuration, to customized per user segment, to fully scalled 'personalized product expereince for each and every user'.

AI PMs are expected to have a broad understanding of ML lifecycle and knowledge of key ML trends in the industry. Ensure your product management basics are strong: Deep focus on customers, business priority, foster an experimentation culture. In this session, I also covered key traits of successful AI product managers: https://www.linkedin.com/video/event/urn:li:ugcPost:6929801568753971200/. Hope this helps!

The #1 trait I see the best companies look for now when hiring ANY senior product manager in an area where AI matters is a strong intuition and taste for how AI can be applied in a product.

Which comes down to having 1. knowledge of the fundamental AI concepts and 2. strong product sense in order to properly gauge opportunity/risks.

• Develop a strong intuition around how AI should and should not be applied in your product e.g.

  • Once your understand your user's JBTDs, Gen AI can be great to help your users quickly produce content first drafts, and summarize content stored in your app. e.g. summarize a meeting transcript, and generate an email follow-up draft to the customer. It's generally not a great tool for tasks like classification or tasks that require 100% accuracy.

  • Depending on the input/output, traditional ML models may be best, or different types of models e.g. NLP (text), or multimodal for a mix of text/images/audio/video.

  • Consider the fundamentally different ways to interact with AI features. Do your users prefer to interact with a dashboard for a certain task vs. a completely open-ended chat interface? Should your UI suggest and even limit what the user can ask the chat? Should your AI model output include a confidence score or be deterministic?

  • You may need different types of feedback loops in place for the model to learn and the feature to be useful (e.g. 'was this accurate?' button, real humans in the loop checking some results or labeling data, etc.)

  • One exercise to try is to observe your favorite AI features in products you use and try to reverse engineer how it was built and what the tradeoffs might have been.

• Understand and have a POV on your key levers when building AI capabilities: data quality, AI model training, accuracy, safety, security, latency, and infra etc.

  • Data quality is a critical input to how your model learns. Garbage (data) in, garbage (model) out.

  • The bar for accuracy, safety, and security differs widely depending on how sensitive the data being handled is, and how critical the task/workflow your AI feature is applied to.

  • e.g. Consider if your product automatically accepts/rejects credit applications vs. if it helps designers draft alternative iterations of a UI based on their first draft. The stakes are different - and AI model hallucinations are a bug in the former use case, but basically a feature in the latter.

  • Lastly, latency sounds like an engineering problem, but it's a critical topic to consider for an AI feature to be feasible.

  • e.g. Imagine you had to wait 10 more seconds for a new, AI-powered Google search to run but the results were 20-50% more relevant. Most users would not switch to use it for most searches. But some users for some use cases might (e.g. see Perplexity.ai 's success).

First and foremost, there are certain traits I value in any PM and I'd say these are the basic non-negotiables.

• Intelligence. You can't coach height in basketball and you can't coach raw smarts in PM

• Curiosity. Great PMs ask a lot of questions.

• Strong customer focus. You love talking to customers and learning.

• No jerks. I want humble, low ego people who enjoy finding out they're wrong because it means they learned something new.

For an AI hire, I touched on this in a few other questions but I want someone who has done the work and understands AI. People who know the difference between supervised and unsupervised learning, what a random forest is, how to measure the quality of a model, what LLMs are good for (and what they aren't), etc. Information is so democratized today it's easier than ever to learn this stuff before expecting to show up and add value as an AI PM learning on the job.