Modern AI tools let product managers generate high-fidelity prototypes—things users can actually interact with. This new capability is powerful, but it amplifies some predictable patterns in human thinking that can lead us astray if we’re not deliberate about avoiding them.
Those thinking patterns are called cognitive biases. They’re systematic errors in reasoning that affect our decisions and judgments. We all have them. They’re shortcuts our brains take to process information quickly, but they sometimes point us in the wrong direction.
When it comes to prototyping, these mental shortcuts form a particularly vicious cycle: we anchor to a solution too early, seek confirming evidence, get emotionally invested in our polished creation, mistake polish for completeness, then underestimate what it actually takes to build it.
The result? Building the wrong thing while running over-budget.
But it doesn’t have to be that way. It’s possible to avoid or mitigate these biases so you can get the benefits of prototypes without the downsides.
Here are five biases that high-fidelity prototypes make worse—and what to do about them.
Anchoring Bias
Anchoring bias is our tendency to rely too heavily on the first piece of information we encounter when making decisions.
When PMs lead with a high-fidelity prototype, they anchor users to a particular solution and ask them to react to their interpretation of users’ needs rather than helping understand what users actually need. The conversation becomes about tweaking the solution instead of understanding the problem.
Stop. Start with the problem, not the solution you’re fired up about. Lead user conversations with questions about their current workflow, pain points, and desired outcomes. Show them nothing until you understand what they’re really struggling with.
Don’t start with solutions, start with problems.
Confirmation Bias
Confirmation bias is our tendency to seek out information that confirms what we already believe while ignoring evidence that contradicts our assumptions. And not usually consciously. Our brain just naturally focuses on the data that fits our beliefs.
Once teams anchor to a particular solution, it becomes tempting to run user tests designed to validate brilliant ideas rather than genuinely explore whether they’re solving the right problem. The polished interface makes the solution feel real, correct. PMs ask leading questions or interpret ambiguous feedback as positive signals.
Design your research to break your assumptions, not validate them. Write down specific hypotheses about user behavior and actively hunt for evidence that your prototype fails. Ask open-ended questions. Watch for confusion and friction, even when users claim to like what they see.
Don’t prototype to prove, prototype to learn.
Sunk Cost Bias
Sunk cost bias is our tendency to continue investing in something based on previously invested time, effort, or money, even when it’s no longer the best path forward.
As teams invest more effort into getting the prototype “just right,” it becomes harder to take feedback that says it’s the wrong thing to build. The more time spent perfecting interactions, tweaking visual design, and polishing user flow, the harder it becomes to hear that they might be building the wrong thing entirely.
Set learning milestones before you start prototyping. Decide in advance what evidence would make you pivot or abandon the current approach. Time-box your prototyping efforts. Step back regularly and ask: are we still on the right track?
Don’t fall in love with your first draft.
Precision Bias
Precision bias is our tendency to interpret detailed, specific information as more accurate or reliable than it actually is.
The realism of higher-fidelity prototypes signals to PMs and developers that the solution is well tested and ready to go. A prototype that looks and feels like a real product creates an illusion of completeness. The visual polish suggests the thinking is done, edge cases handled, technical challenges solved.
Treat your prototype as exactly what it is: a rough draft for learning, not a blueprint for building. Make it crystal clear to your team that the polish is just for testing. Document the assumptions and unknowns that still need resolving before development begins.
Don’t confuse looking real with being real.
Optimism Bias
Optimism bias is our tendency to overestimate the likelihood of positive outcomes and underestimate the time and effort required to achieve them.
Some biases are kryptonite for smart people. Optimism bias is one of those. Daniel Kahneman tells the story of a group of professors trying to estimate the work for a textbook on cognitive biases. They’re literally the experts, writing a book about this stuff. And, despite their expertise and careful thinking about the work, they underestimated the time to write the book by a factor of 8.
Smart developers can list all the things that’ll need to be done to go from a prototype to a real product. Nonetheless, the existence of the prototype inevitably leads to overly optimistic estimates for that work.
The way out of optimism bias isn’t padding your estimates. It’s a whole different kind of estimating, namely, reference class forecasting—comparing the new work to real work from the past. Instead of asking, “How long will this take?” we ask, “What past work is this most like? And how long did that take?” We recently did a couple of Humanizing Work Show episodes on reference class forecasting. Learn more here and here.
Don’t estimate from scratch, estimate from history.
The Way Forward
High-fidelity prototypes aren’t the problem—they’re powerful tools that can accelerate learning and communication. The trap is using them mindlessly.
Before firing up that AI coding tool, get clear on what you’re trying to learn. Are you testing a specific interaction pattern? Validating a core assumption about user behavior? Exploring technical feasibility? Let your learning objectives drive your prototyping strategy, not the other way around.
The goal with a prototype isn’t to build something that looks real. The goal is increase your odds of the end product solving real problems for real people. Sometimes that requires a high-fidelity prototype. Often, there are better tools to get the results you need.
You don’t have to figure this out on your own. We help Humanizing Work clients design good tests for their product ideas. Schedule a call with us to see if we’re a fit for you!
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