Building applications with generative AI is a minefield. The allure of the technology often blinds teams to the fundamentals of product development. The technical aspects are often the easy part. The hard part is creating a product that users actually want to use. Here are the most common pitfalls, drawn from real case studies and personal experience.
The Hammer and the Nail
The first and most frequent mistake is using generative AI for everything. "Not everything is a nail," and generative AI is not a hammer. I once saw a team pitch an idea to use generative AI to schedule household energy usage to save money. They fed the list of energy-intensive activities and hourly electricity prices into an LLM to create a schedule. My question was simple: "How does this compare to just doing the laundry and charging your car after 10 p.m.?"
They never followed up. The simple, deterministic solution beat the expensive, unreliable AI. The goal was to solve a problem, not to use generative AI. Many teams forget this distinction, applying LLMs to tasks better solved by linear programming or basic logic.
The Product-UX Gap
The second major pitfall is assuming that if the AI works, the product works. At the other end of the spectrum, teams dismiss gen AI because their pilot users hated it. In every case I investigated, the issue wasn't with the AI, but with the product itself.
For example, a team building a meeting transcript summarizer initially focused on optimizing the summary length. Did users want 3-sentence summaries or 5-sentence summaries? It turned out they didn't care about the summary at all. They wanted action items specific to them.
Similarly, Intuit built a tax chatbot. Users didn't like it. After investigation, they realized users hated typing. Facing a blank chatbot, they didn't know what to ask. Intuit added suggested questions to click on, reducing friction and building trust. The AI components of AI products are often similar. The differentiation is the product experience.
The Hardest 20%
It took one team one month to achieve 80% of the experience they wanted. It took an additional four months to surpass 95%. The initial success made them grossly underestimate how challenging it is to improve the product, especially around hallucinations.
This is a painful lesson. The journey from 0 to 60 is easy, whereas progressing from 60 to 100 becomes exceedingly challenging. Getting from demo to production is a long, hard road.
Solutions
To avoid these pitfalls, start by asking if you even need AI. Then, focus relentlessly on the user experience. Incorporate human evaluation to supplement AI judges. And finally, be cautiously optimistic about timelines. Demos are cheap. Products are expensive.