Pratik Joglekar, writing for Smashing Magazine, on the core UX trap in AI products:
This is the risk at the heart of designing with AI today: probabilistic systems wrapped in deterministic interfaces. The AI offers a guess, the interface presents it as truth, and the user, or the organization, acts on it.
Humans are wired for deterministic thinking. We prefer to believe that past actions determine future outcomes. Flip a coin 999 times and get heads every time, the deterministic mind assumes the coin is rigged. The probabilistic mind accepts that the 1000th flip could still go either way. That second mindset is harder to hold onto, but it is exactly what designers need right now.
Products operate in complex, nonlinear environments, and AI is accelerating that complexity. When designers and product teams treat AI outputs as the answer rather than one of many possible answers, they build fragile experiences, and in some cases, like medical diagnostics or financial forecasting, genuinely dangerous ones.
That gap matters because the interface can make a confident-sounding answer look settled. The moment a chatbot, recommendation, risk score, or AI-generated summary hides the uncertainty behind the answer, it becomes a designed claim about how much the user should trust the system.
Joglekar’s practical advice is to treat the model as a signal generator, not a decision-maker:
What you receive is not truth. It is the most statistically likely outcome given the data available. Always ask whether past data meaningfully predicts future behavior. If additional context can improve the prediction, include it. Without context, the output is just one of many possible answers dressed up as the only one.
The trust piece lines up with a recurring AI product lesson: controllable over perfect beats confident and wrong. Joglekar again:
One of the hardest things for designers is making uncertainty understandable and actionable. When uncertainty is hidden, users treat AI outputs as facts. When it’s communicated clearly, trust increases.
Ranges, estimates, and confidence indicators go a long way. A delivery window of “Friday to Monday” tells the truth about variability without misleading anyone, whereas a specific timestamp that slips erodes trust every time. A face recognition feature that says “this looks like Pratik, is that right?” sets more honest expectations than one that just labels the photo with a name.
Communicating uncertainty does not weaken trust — it strengthens it. The goal is not to eliminate uncertainty but to design for it intelligently.
