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Data-Driven Product Decisions With AI

5 min read
Tpm

Tpm

AI finds patterns. You decide what matters. Don't conflate correlation with strategy.

Data-Driven Product Decisions With AI

TL;DR

  • AI can surface patterns, anomalies, and suggestions from product data. It can't tell you which metrics matter or what to do about them.
  • Use AI for exploratory analysis and speed. You own the question and the interpretation.
  • Beware: AI will find "insights" that are noise. Your job is to filter for signal.

You have dashboards. Funnels. Retention curves. Maybe a data team. Now AI can query, visualize, and summarize. That's powerful — and dangerous. More data plus more automation can mean more confusion if you don't know what you're looking for.

What AI Adds to Analytics

Natural language queries:

  • "What's our conversion rate for users who signed up last month?" Instead of building a report, you ask. AI (or an AI-augmented analytics tool) generates it.
  • Saves time. Doesn't replace knowing what to ask.

Anomaly detection:

  • "Something changed." AI can flag dips, spikes, and unusual patterns.
  • Useful for monitoring. You still have to investigate why. AI doesn't know your product or your world.

Summarization and reporting:

  • "Summarize our key metrics this week." AI can draft. You review and add context.
  • Good for stakeholders. Don't let the summary replace looking at the raw data when it matters.

Hypothesis testing:

  • "Did feature X move metric Y?" AI can run the analysis. You framed the hypothesis. You interpret the result.
  • Correlation isn't causation. AI will give you numbers. You decide what they mean.

Where AI Can Mislead

Spurious correlations:

  • AI finds patterns. Lots of them. Many are random. "Users who logged in on Tuesday have higher retention" — maybe. Or maybe Tuesday users are different. You need to think, not just accept.

Metric inflation:

  • "Our engagement is up 20%!" Because you changed the definition? Because of a one-time event? AI might not flag that. You should.

Question bias:

  • You ask what you think matters. AI answers. If you never ask "what are we not measuring?" you'll never know. AI won't prompt you.
  • Periodically audit: What do we measure? What do we ignore? Why?

Over-reliance on dashboards:

  • Dashboards show what you instrumented. They don't show what you didn't think to track. AI can't invent new metrics. You can.

The PM's Role

  • Define what good looks like. Success metrics, north stars, guardrails. AI doesn't set those. You do.
  • Ask the right questions. "Why did this happen?" "What would we need to see to change our mind?" AI answers. You ask.
  • Connect data to decisions. A number is useless until you connect it to "and therefore we will/won't do X." AI might suggest. You decide.
  • Stay curious about the gaps. What don't we see? Qualitative feedback, support tickets, sales calls — data lives in many places. AI helps with structured data. You integrate the rest.

Manual process. Repetitive tasks. Limited scale.

Click "With AI" to see the difference →

Quick Check

What remains human when AI automates more of this role?

Do This Next

  1. Run one AI-assisted analysis — Ask a question of your product data. Get the result. List what you had to interpret or verify. That's your value-add.
  2. Audit your metrics — What do you track? What might you be missing? Add one "gap" metric or source.
  3. Create a "data → decision" ritual — Weekly or biweekly: "Here's what the data said. Here's what we're doing." Make the link explicit. AI helps with the "said" part; you own the "doing."