AI Integration Without the Hype
Practical lessons from integrating AI into real products. What works, what doesn't, and how to evaluate when AI actually adds value.
We're in the middle of an AI hype cycle. Every company is rushing to add 'AI-powered' to their product descriptions, often without thinking critically about whether AI actually solves a real problem.
Building LazyTeach taught me that AI is most powerful when it eliminates tedious, time-consuming work that requires some intelligence but doesn't require human creativity or judgment. Lesson planning is a perfect example—it requires understanding of pedagogy and subject matter, but much of it is formulaic.
The key questions I ask before integrating AI into a product: Does this task require consistency and scale? Is there training data available? Can we validate the output? And most importantly—does this actually solve a meaningful problem for users?
One mistake I see companies make is trying to automate things that shouldn't be automated. Just because AI can generate customer support responses doesn't mean it should, especially for complex or sensitive issues. The human touch matters.
Another pitfall is treating AI as a black box. Users need to understand what the AI is doing, why it's making recommendations, and how to override it when needed. Transparency builds trust, and trust is essential for adoption.
At SelectQuote, we explored AI for lead qualification and routing. The technology worked, but the ROI calculation had to account for the cost of errors. In insurance, a misrouted lead or poor qualification could cost more than the efficiency gains from automation.
My advice? Start small, measure rigorously, and focus on clear use cases where AI demonstrably improves the user experience. Don't chase the hype—chase real value.