Your team just shipped three features this week. Last quarter, that would have taken a month. AI tools turned your engineers into feature factories. Your designers generate variants in minutes. Your PMs prototype without waiting for engineering resources.
Everyone’s celebrating velocity. Who’s checking if you’re solving the right problems?
Creation velocity isn’t validation velocity
Recent research shows contradictory results on AI’s impact. Some studies report significant productivity gains, others find developers actually slow down when using AI tools. The pattern emerging is clear: AI accelerates code generation, but delivery stability often decreases and quality concerns rise.
You can ship faster. But are you learning faster?
The real gap isn’t speed. It’s the mismatch between how quickly you can build and how quickly you can validate what to build. As I explored previously, AI amplifies senior strategic judgment while commoditizing tactical execution. Without that senior judgment directing what to build, teams become feature factories.
Most teams obsess over who builds features (PMs? Engineers? Designers?) and how fast they ship. Almost nobody asks whether those features matter to customers.
The warning signs aren’t dramatic. In B2B, users don’t immediately churn. Instead, adoption stays low. Features exist but go unused. Customer success teams field more “why did you build this?” questions. Renewal conversations get harder six months later.
Traditional product-market fit frameworks assume you’re testing hypotheses at a measured pace. You build, you measure, you learn, you iterate. AI tools break that rhythm. You can generate and ship features faster than your instrumentation can tell you if the previous batch worked.
The path-of-least-resistance problem
Teams pick AI use cases based on what’s readily available, not what aligns with strategic goals or delivers meaningful customer impact.
You have a new AI tool that generates UI components. So you build features that need UI components, whether or not those features solve real user problems. The tool shapes the roadmap instead of customer needs shaping tool selection.
Amazon’s principle of working backwards matters more now, not less. Start with customer needs, then figure out what to build and how to build it. Most teams do the opposite: start with what AI tools can do easily, then justify it with assumptions about customer value.
Research on AI project failures consistently points to the same issue: too much attention on technical capabilities, not enough on actual market needs. Most AI projects fail not because the technology doesn’t work but because teams built the wrong thing quickly.
Speed as tactic, not strategy
The companies navigating this well treat speed as a tactic, not a strategy. They use AI to accelerate after they’ve validated direction, not to spray features and hope something sticks.
This requires different metrics. Not just “features shipped per sprint” but “validated learning per sprint.” Not just “time to market” but “time to meaningful user engagement.” Not just “productivity gains” but “impact per unit of effort.”
Product managers still balance the same three dimensions: user needs, business viability, and technical feasibility. What changes is the first pillar. Understanding user needs now means distinguishing between what users actually need and what AI makes easy to build.
AI can help with validation too. Synthesize user interviews at scale. Analyze support tickets for patterns. Enable rapid prototype testing. But the craft of knowing what to validate and interpreting results still requires human judgment. That’s where the gap lives.
The test that matters
Ask what you learned this week, not what you shipped.
If the answer is “we shipped five features,” you’re measuring activity. If the answer is “we validated that users struggle with X and confirmed that approach Y doesn’t solve it,” you’re measuring learning. Fast teams don’t ship more, they learn faster.
AI tools are incredible for accelerating learning. Generate multiple variations, test them quickly, and see what works. But only if you’re testing the right problem.
Speed is a compounding advantage when paired with direction. Speed without direction just gets you lost faster.
Even when you’re building the right features at the right pace, there’s another bottleneck most teams ignore. AI can generate code quickly. Someone still has to ensure it’s production-ready.