Communicating AI product impact

This is the final lesson. Six weeks of building evaluation systems, running experiments, making ship decisions, and planning fixes. All of that work leads here. Now the results need to be communicated so stakeholders actually act on them. A common pattern: a team spends six weeks collecting evidence. Solid evidence. They know the numbers cold. Then they write a report. One report. They send it to everyone. Executives, product managers, engineers. Same report. And nobody acts on it. The exec skims it and asks a question the report already answered. The engineer cannot find the repro steps. The PM does not know what to prioritize next. The evidence was great. The communication killed it. That is the gap, and it is the most common gap in product data science. The hard part is not getting the data right. The hard part is getting the communication right. This lesson fixes that.