Having worked at the intersection of AI engineering and product management, I have observed a common pattern: brilliant AI models failing to deliver business value because of poor product thinking. Here is how to bridge that gap.
Start with the Problem, Not the Technology
It is tempting to start with “let us use GPT-4” or “we should build a RAG system.” Instead, start with a clear problem statement. What user pain point are you solving? What business metric will improve? The best AI products are invisible—users do not care about the underlying technology.
Design for Failure
AI systems will make mistakes. The question is not if, but when. Great AI products anticipate failures and design graceful degradation paths. This means clear confidence indicators, easy escalation to humans, and transparent limitations.
Measure What Matters
Traditional product metrics like DAU and retention still matter, but AI products need additional metrics: accuracy, latency, cost per query, and user trust indicators. Build dashboards that surface these early.
Iterate on Data, Not Just Code
In traditional software, you improve by writing better code. In AI products, you often improve by curating better data. Build feedback loops that capture user corrections and edge cases.
The best AI PMs understand both the technical possibilities and the human needs. Bridge that gap, and you will build products people actually want to use.