After years of working on AI/ML projects, I've learned that the hardest part isn't building the model—it's building something people will actually use. Too many AI products end up as shelfware, impressive demos that never make it into production or, worse, go live but get abandoned within weeks.

The Shelfware Problem

I've seen it happen countless times: a team builds an amazing ML model with 95% accuracy, everyone celebrates, and then... nothing. The model sits unused because nobody thought about how it would fit into existing workflows, or the predictions arrive too slowly, or the output format doesn't match what users need.

The problem is that we often start with the technology instead of the problem. We get excited about new techniques—transformer models, reinforcement learning, whatever's trending—and look for problems to apply them to. But that's backwards.

Start with the Problem, Not the Solution

The best AI products I've worked on started with a real, painful problem that people were desperate to solve. We didn't ask "how can we use AI here?" We asked "what's making people's lives harder?" and only then considered whether AI might help.

For example, one of our most successful projects came from watching analysts spend hours manually categorizing customer feedback. They hated it. We built a simple classifier that wasn't perfect—maybe 85% accurate—but it saved them hours every week. They loved it because it solved their actual problem.

Three Questions to Ask Before Building

Before starting any AI project, I now ask:

1. What happens if this doesn't work perfectly? If your model needs 99% accuracy to be useful, you're probably in trouble. Build for graceful degradation. Let users verify and correct predictions. Make it easy to fall back to manual processes when needed.

2. Will this fit into existing workflows? People won't change their entire process to accommodate your AI. Your solution needs to slot into what they're already doing, or make the change so obviously better that they'll adopt it immediately.

3. Can we measure the actual impact? Not model accuracy—real impact. Time saved, errors prevented, money made. If you can't measure it, you can't prove it's worth maintaining.

Ship Fast, Iterate Faster

The other lesson: don't wait for perfection. We shipped our feedback classifier when it was "good enough" and improved it based on how people actually used it. Turns out they cared way more about speed than accuracy for certain categories, which completely changed our optimization strategy.

Building AI products that people use isn't about the fanciest algorithms or the highest benchmark scores. It's about understanding real problems, fitting into real workflows, and proving real value. Everything else is just details.