"I used to run AI projects just like I would ordinary features: spec, build, QA, launch. The calendar moved, the charts rose, and the team checked boxes. Then, the models kept changing after launch. Inputs shifted, users adapted, data aged, and the work kept going. After tripping over the same problem a few times, I realized my mindset, not my tools, was the problem. I was trying to manage probability with a deterministic frame."
"So, I tried something different: a portfolio-style operating model. Thinking along the lines of a good investor, I learned to size my bets, manage risk, and rebalance instead of pretending every model is a "done" project. The new AI-focused product landscape demands a new framework for building those products. In this article, I'll show you how I figured out an approach that works, and demonstrate how you can apply it too."
"Traditional product work rewards certainty. You're heard the questions before. Did we ship? Does it match the spec? Are there bugs? But machine learning produces distributions. Your job as a PM shifts towards shaping the odds and aligning expectations. Because of this, it's time for you to start thinking probabilistically, not deterministically. In simple terms: trade "Is the model right?" for "When is the model useful, at what risk, and for whom?""
AI projects require probabilistic thinking and a portfolio-style operating model. Treat models as assets: size bets, manage risk, and rebalance over time. Deterministic delivery fails as models shift after launch due to changing inputs, user adaptation, and data aging. Product managers should prioritize usefulness, risk tolerance, and target users rather than binary correctness. Explicit thresholds, trade-offs, and cadences for model classes enable measurable expectations and reduce launch drama. A portfolio approach applies investor-like sizing and risk management, shifting teams from one-off projects to ongoing lifecycle processes that accommodate model evolution and uncertainty.
Read at LogRocket Blog
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