Article Series
A four-part series on the product-side work AI features actually require, and the function most product organizations have not yet built.
For product managers, product leaders, CTOs, and engineering leads inside software companies who have AI features on the roadmap.
Every product team I talk to is being handed AI features by executives who assume the team can build them the way it has built everything else. It cannot, and this series is my honest attempt to explain why, without either apocalypse or hype. Four articles: one on why feasibility became a research question, one on why product’s quality bar now needs evidence product does not usually have, one on why the specification cannot come first, and one on the function that has to exist to hold all of that together. It is written for the people who have to build them.
Matt GenoveseFounder & Product Strategy Lead, Planorama Design
Product decides whether a feature is good enough to ship. With AI, answering that question suddenly needs evidence most product teams have no way to produce. Read this if you have been asked for a “ground truth dataset” and had no idea what to do with the request. Leave with the one question that tells you whether you are being handed an afternoon of annotation or a data science project.
The rule that says product owns what and engineering owns how has held for the whole history of software, and AI is the first thing to genuinely break it. Read this if you are trying to write requirements for an AI feature and the words won’t stay still. Leave with a specific artifact you now need to produce before the spec, a distinction hardware always carried and software mostly did not, and a cost asymmetry that makes skipping the upfront work more expensive than doing it.
After three articles diagnosing why AI features are hard for standard product teams, this one names the work that has to happen and shows why no role on your org chart currently owns it. Read this if you have caught yourself writing a job posting for a role that reads as one person doing three people’s worth of work, and suspected that person did not exist. Leave with a way to name the missing capability, an example of a real team doing this well under harder constraints than yours, and a decision framework for filling the gap without hiring the mythical unicorn.
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