AI Product Management

The most dangerous phrase in product management today isn't "move fast and break things"—it's "we added AI." Across the tech industry, teams are shipping LLM-powered features with unclear value propositions, ambiguous success criteria, and no coherent strategy beyond checking an AI box on their roadmap.

As someone who's watched this pattern repeat across countless product launches, I've learned that managing AI features requires fundamentally different thinking than traditional product work. The probabilistic nature of LLMs, their unpredictable failure modes, and the gap between "technically impressive" and "genuinely useful" demand a more rigorous approach to scoping, measurement, and launch decisions.

Start with the Problem, Not the Technology

The first trap product managers fall into is reverse-engineering use cases around AI capabilities. "Our competitor added a chatbot, so we need one too" is not a product strategy—it's cargo culting.

Before touching an LLM, ask: What specific user problem are we solving? Could we solve it without AI? If the answer is yes, seriously consider that path. LLMs excel at tasks involving natural language understanding, generation, summarization, and extraction—but they're overkill for deterministic workflows that rules-based systems handle perfectly.

The best AI features emerge from genuine user friction. Customer support teams drowning in tickets? LLM-powered triage and response suggestions might help. Users struggling to find information in dense documentation? Semantic search could work. But slapping a chatbot on your homepage because it's 2025 won't move metrics.

Defining Success Beyond Novelty

Traditional product metrics often fail for AI features. Usage rates tell you nothing if users are frustrated. Time-on-page is meaningless when an LLM generates inaccurate summaries. You need a multi-layered measurement framework.

Task success rate should be your North Star. Can users actually complete their intended task using the AI feature? This requires qualitative research—watching real users interact with the feature and understanding where they succeed or abandon.

Accuracy and quality metrics matter, but they're deceptively complex. A 90% accurate LLM response sounds impressive until you realize that 10% error rate includes a catastrophic hallucination about your customer's account. Weight errors by severity, not just frequency.

Comparative metrics provide crucial context. Does the AI feature perform better than the existing solution? If your LLM-powered search returns worse results than keyword search, you're adding friction, not value.

Trust and adoption indicators reveal long-term viability. Are users returning to the feature? Do they rely on it or verify everything it produces? Sharp drops in engagement after initial trials signal fundamental trust issues.

The "Good Enough" Calculation

Here's the uncomfortable truth: LLM features will never be perfect. The question isn't whether they'll fail—it's whether they'll fail acceptably.

Your "good enough" threshold depends on failure cost and user expectations. A content summarization tool can tolerate occasional awkward phrasing. A legal document analyzer cannot tolerate missing critical clauses. Map your feature to this risk spectrum before defining launch criteria.

For lower-stakes features, 85% task success with graceful degradation might be shippable. For high-stakes applications, you might need 95%+ with robust human-in-the-loop workflows. There's no universal standard—only context-specific judgment.

Crucially, "good enough" includes the surrounding experience. Can users easily regenerate responses? Is there clear attribution for sourced information? Can they escape to human support when AI fails? The orchestration layer around your LLM often matters more than the model itself.

Roadmapping in the Age of Rapid Model Evolution

Traditional roadmaps assume stable technical capabilities. AI product management exists in constant flux. The model you built on might be obsolete in six months, or a new capability might suddenly make your ambitious feature trivially easy to implement.

Adopt a modular approach. Build abstractions that let you swap underlying models without rewriting your entire feature. Focus roadmap commitments on user outcomes, not specific technical implementations.

Plan in waves: ship a constrained MVP that solves one narrow problem exceptionally well, measure ruthlessly, then expand scope. This hedges against wasted effort if the approach doesn't resonate or if model capabilities shift dramatically.

The Value Discipline

The hardest part of AI product management isn't technical—it's maintaining discipline around user value. Every team feels pressure to "do something with AI." Resist features that are impressive demos but questionable products.

Before adding any LLM capability, complete this sentence: "This feature will measurably improve [specific user outcome] by [concrete mechanism], which we'll validate through [defined metrics]." If you can't, you're not ready to build.

AI is a tool, not a product strategy. The best AI product managers know when to use it—and when to ship something simpler that actually works.