Business verification during development
The old model: build it, then verify. The new model: verify continuously, build iteratively.
The most expensive mistake in software development isn't building something badly — it's building the wrong thing well. Perfect execution of the wrong feature is a waste of time and money.
Traditional verification: build the feature, deploy it, wait for metrics, hope the numbers go up. By the time you know it didn't work, you've invested months.
AI-era business verification
Deploy to a segment, not everyone. Feature flags and targeted rollouts let you deploy to a small group and measure behavior in days. You don't need to be right the first time — you need to learn fast.
Real-time stakeholder dashboards instead of demo meetings. Stakeholders should see development progress continuously, not in scheduled reviews. When they can see what's being built as it's being built, misalignment surfaces early.
Customer interviews as a continuous practice. AI can summarize interview transcripts, identify patterns across conversations, and flag conflicting feedback. Make customer conversations weekly, not quarterly.
Business metrics as acceptance criteria. Not "the button works" but "click-through rate increased by X%." Define the business outcome the feature should produce before building it. If it ships and the metric doesn't move, it's not done — it's wrong.
The implication
Product managers need to define measurable business outcomes for every feature, not just functional requirements. "Users can filter by date" is a requirement. "Users who filter by date have 30% higher retention" is a business verification criterion. Build for the second one.