Why you should treat Prototypes as Decision-Making Tools


How to treat Prototypes as Decision-Making Tools

PUBLISHED

Apr 23, 2026

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​Dear Reader,​

You've probably heard the statement "With the friction of building nearing zero, it's even more important to have conviction about what to build and why" before. Truth is, this reduction in friction does not just apply to software in production. It also applies to the methods you use before that.

The comparison I see to "pre-AI" ways of working is that teams, which were good at or limited to qualitative interviews, tried to reduce the uncertainty around each Discovery question with, you guessed it, interviews. I call these dogmatic defaults.

A recent article by Ravi Mehta prompted (pun intended) me to revisit this idea - only this time with prototypes.

"Prototyping is fun — and it’s easy to get pulled into crafting the perfect thing. It’s critical to remember that prototypes aren’t the deliverable; they’re a tool for making better and faster decisions."

With everyone on your team (and in the boardroom) getting excited about prototyping with Lovable, V0 & Co., it's tempting to prototype every uncertainty you're trying to reduce. But that's dogmatic defaults all over again - just in a different color.

We have to remember what prototypes can help you learn:

  • Does this interaction feel right?
  • Does the flow make sense?
  • Can customers navigate this UI without getting stuck?
  • Do these two design directions trigger different reactions when people click through them?
  • Is this integration technically feasible before we commit engineering weeks to it?

They're also fantastic alignment tools since a visual idea can replace lengthy written and spoken discussions.

What prototypes can't tell you

They won't get you to informed conviction about scaled adoption or willingness-to-pay. Not because the tools aren't good enough yet, but because those questions require a different category of evidence.

Prototype reactions are attitudinal. Adoption and willingness-to-pay are behavioral. Someone saying "yeah, I'd use this" while clicking through your Lovable prototype in a 30-minute interview tells you almost nothing about whether they'll actually trade their time, attention, or money for it when the moment of truth arrives.

Humans are notoriously bad at predicting their own future behavior. No prototype, no matter how fast you produced it or how polished it is, changes that.

The underlying principle

Every discovery question deserves a method that matches it. The temptation with any new tool is to let the hammer pick the nail.

The question isn't "Should we prototype this?" The question is "What are we trying to decide, and what's the lightest, most honest way to get evidence for it?"

Sometimes that's a prototype. Sometimes it's a fake door. Sometimes it's a pricing page with a pre-order button. Sometimes it's still a good old interview.

The AI era doesn't make prototypes the answer to everything. It just makes them cheap enough to be the answer to more things than before. Which is why choosing when not to prototype is now a skill worth sharpening.

Thank you for Practicing Product,

Tim

Ways we can work together

1️⃣ Prepare for my next live webinar on May 7 by reading From Information to Evidence: How Context Informs Product Discovery Decisions

2️⃣ Order my book: Real Progress: How to Connect the Dots of Product Strategy, OKRs, and Discovery, which readers call "a practical guide you can return to again and again."

3️⃣ Join my next From Strategy to Discovery Workshop, where you learn how to make clear strategy choices, translate them into leading product goals, and understand needed Discovery actions before deciding what to build (with and without AI Assistance).

4️⃣ Learn about my training and coaching options for product teams, with a focus on creating strategic clarity, setting pragmatic goals, and implementing real-life discovery practices to reduce risk

If you consume one thing this week, make it this...

Synthetic certainty: the hidden risk of GenAI in Product Discovery

Entire products can now be assembled through prompts. The implied message is simple: if machines can produce artifacts, design expertise should become less necessary. But this conclusion comes from focusing on the wrong layer of the work. The visible change is faster production. The less examined question is how product teams arrive at understanding – how they interpret problems and behaviour before deciding what to build.

Who is Tim Herbig?

As a Product Management Coach, I guide Product Teams to measure the real progress of their evidence-informed decisions.

I focus on better practices to connect the dots of Product Strategy, Product OKRs, and Product Discovery.

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