[pmcgrathm]

As someone who is a product manager in the commercialized AI SaaS space, the most important pieces of feedback I would give a new PM here:

1)Don't let your -brilliant- colleagues try to force their -brilliantly complex- solution of a problem - clearly define market problems, and don't let the team try to go the route of trying to force fit a solution to a market problem. Market problems come first.

2)Frame the market problems appropriately for your ML/AI teams, and practice trying to frame the problem from a variety of angles. Framing from different angles promotes the 'Ah-ha' moment in terms of the right way to solve the problem from the ML side.

3)Don't commit serious time to a model before having a naive solution to benchmark against. Always have a naive solution to compare against the AI solution. 'Naive' here may be a simple linear regression, RMSE, or multi armed bandit/Thompson sampling.

[alexbeloi]

>Always have a naive solution to compare against the AI solution. 'Naive' here may be a simple linear regression, RMSE, or multi armed bandit/Thompson sampling.

This cannot be stressed enough, optimism bias will always push the scientist towards the 'more interesting/complete/new' method and model, but a seasoned practitioner will have the discipline to always establish a baseline (<1 days work).

[itronitron]

In my experience, simplifying a solution over time is the best way to ensure its adoption and long-term success. That applies both to UX/UI and the supporting computation/ML.