From Psychology to AI: Why Understanding People Makes Better Products

I studied psychology and cognitive science at Marist, not computer science. And honestly? It's been my biggest advantage building AI products.

The Missing Piece in AI Development

Most AI builders focus on what the model can do. Fewer ask what the user actually needs to see, understand, or control. That gap—between capability and usability—is where psychology comes in.

Three Insights from Cognitive Science

1. People Trust What They Understand

Cognitive load theory tells us that people can only process so much at once. When an AI system throws too much information at you, or hides its reasoning entirely, trust breaks down.

That's why WADL's agents always explain their decisions. Not with technical jargon—with simple, clear reasoning. "I escalated this because the data showed X, and you asked to be notified when Y happens."

2. Context Is Everything

Human memory is associative. We remember things in context, not in isolation. AI systems should work the same way.

The Deal Triage Assistant doesn't just score pitches. It learns what you've funded before, what you've passed on, and why. Over time, it gets better at surfacing deals that match your actual criteria, not just generic scoring rubrics.

3. Feedback Loops Drive Behavior

B.F. Skinner taught us that immediate, clear feedback shapes behavior. AI products should give users real-time signals about what's working and what's not.

In WADL's dashboard, every metric ties to a specific action. Not "AI performance is 85%"—but "your escalation agent saved your team 12 hours this week by catching these three issues early."

Design Thinking Meets Technical Execution

Understanding people doesn't replace technical skill—it enhances it. You still need to know how to build reliable systems, write clean code, and manage databases. But psychology helps you make better decisions about what to build and how to present it.

Building Addie: A Case Study

When I designed Addie (the AI matchmaking assistant), the hardest part wasn't the AI conversation design—it was making it feel emotionally intelligent without being creepy.

People want help, but they don't want to feel manipulated. So Addie's tone is warm but honest. She suggests, she doesn't push. She asks questions instead of making assumptions. That comes from understanding how people form trust with technology.

The Intersection of Creativity and Structure

My LinkedIn headline says it: "I've always been curious about how creativity and structure meet." That's psychology in a nutshell. Human behavior has patterns (structure), but every person is different (creativity).

Good AI products respect both. They have consistent logic and clear rules, but they adapt to individual needs and preferences.

Practical Takeaways for AI Builders

1. Show your work. Let users see why the AI made a decision.
2. Respect cognitive load. Don't overwhelm people with information.
3. Build feedback loops. Make it clear when something is working.
4. Design for trust. Transparency > magic.
5. Remember the user is human. Empathy isn't optional.

Conclusion

You don't need a psychology degree to build good AI products. But you do need to think about people—how they think, what they trust, and what actually makes their lives better.

That's the work. The AI is just the tool.