AI excels at clerical tasks like transcription and basic analysis. However, it lacks the business context to identify strategically important, "spiky" insights. Treat it like a new intern: give it defined tasks, but don't ask it to define your roadmap. It has no practical life experience.

A UK startup has found that LLMs can generate accurate, simulated focus group discussions. By creating diverse digital personas, the AI reproduces the nuanced and often surprising feedback that typically requires expensive and slow in-person research, especially in politics.

While AI efficiently transcribes user interviews, true customer insight comes from ethnographic research—observing users in their natural environment. What people say is often different from their actual behavior. Don't let AI tools create a false sense of understanding that replaces direct observation.

The most effective way to use AI in product discovery is not to delegate tasks to it like an "answer machine." Instead, treat it as a "thought partner." Use prompts that explicitly ask it to challenge your assumptions, turning it into a tool for critical thinking rather than a simple content generator.

When asked to describe a user process, an LLM provides the textbook version. It misses the real-world chaos—forgotten tasks, interruptions, and workarounds. These messy details, which only emerge from talking to real people, are where the most valuable product opportunities are found.

The primary danger of AI in product management isn't technical failure but the abdication of critical thinking. Over-relying on AI summaries of user feedback means missing the crucial 'color' and context. Leaders risk losing their direct connection to the customer's voice by outsourcing their thinking to an LLM.

LLMs are technically non-deterministic systems designed to guess the next most probable word, not verify facts like a calculator. This inherent design means they will confidently produce incorrect information, making human verification indispensable for high-stakes business decisions.

AI is great at identifying broad topics like "integration issues" from user feedback. However, true product insights come from specific, nuanced details that are often averaged away by LLMs. Human review is still required to spot truly actionable opportunities.

The most reliable customer insights will soon come from interviewing AI models trained on vast customer datasets. This is because AI can synthesize collective knowledge, while individual customers are often poor at articulating their true needs or answering questions effectively.