How to Decide If A Heuristic Evaluation Is the Right Method

3. Making the case (if you need buy-in first)

A quick way to answer the most common pushback before you propose a variant. Create a one page document with the intent, early signals on problems with the funnel, and a few bullets of Q&A with the following answers to things they might say:

• Q: "We don't have time for research."

  • A: "This doesn't have to be a new research study. We can use an AI-assisted or human-led product trio evaluation that can be completed in as little as a day or two. Based on industry data, multiple evaluations typically catch 75 to 85% of usability problems in half a week."

• Q: "We don't have the budget."

  • A: "The human-led internal product trio or proxy stakeholder variants use staff time we already have. There's nothing to recruit or pay for. We anticipate getting the clarity teams need to act on known and unknown issues in this part of the product experience."

• Q: "We already know what's wrong."

  • A: "Then this effort will confirm it fast, uncover anything we've missed that's valuable, and turn scattered opinions into one prioritized, evidence-backed list everyone can act on."

• Q: "Let's just ship and see."

  • A: "Fixing usability issues after launch costs more in rework, support volume, and churn than catching them before."

4. Decision: Which evaluation type fits your situation?

Your options, at a glance

• AI-assisted internal evaluation. Same day. Fast pattern-matching against a static walkthrough. Misses context and real behavior. Treat findings as candidates to confirm, not conclusions. Layer on internal team review (product trio and/or cross-functional stakeholders) for clarity. If you already have customer research or behavioral data on hand, feed it in too, it sharpens where the pass looks, the same way any product data does.

• Human-led internal product trio evaluation. 2 to 3 days. PM, UX, and engineering ground the evaluation in real product context. No external customer perspective.

• Human-led internal proxy function evaluation. 2 to 5 days. After a short heuristics briefing, sales, support, data analysts, and other internal experts add proxy signal: how many customers they've personally seen hit each issue, and in what scenarios. No external recruiting needed.

• Human-led external ICP research evaluation. 3 to 5+ days. Thoughtful recruiting of ideal-customer-profile participants produces evidence-based findings, not internal opinion.

• Combination. 5+ days. The most robust option. Requires careful collation of findings by source (AI, internal, external) and clear storytelling to land them.

How to choose: two quick calls

Call 1: How much time do you have, and is internal confidence enough?

Call 2: Only if you landed on "need real customer validation" and are choosing between proxy functions and real external customers.

Question: Are you confirming an issue is real and how often it happens, or understanding why it happens?

Proxy functions answer the first question, they can tell you how many customers they've seen hit an issue and in what scenarios. Only real external customers can answer the second, proxies can describe symptoms, not reasoning.

One note on AI here: an AI-assisted pass built on uploaded flows and screens can't answer either question in Call 2 on its own. Getting it there would take a different setup, an agent with direct access to real usage data such as analytics, session recordings, or support logs, not just static screens. That's a more advanced build than what this guide assumes.

• Choosing speed when the decision needs validation. A same-day AI-assisted pass is a fine starting point, but don't let it be the final word on a high-stakes call. Schedule the validation step (whether internal product trio, internal proxy functions, or external customers) even if it comes later.

• Skipping proxy or external input because it's inconvenient, when the decision actually needs an outside perspective to increase confidence and be more effective and scalable.

• Treating existing usage data as a substitute. It tells you where to look. It doesn't replace looking.

• Reporting AI-assisted findings as if they include frequency, confirmed occurrence, or divergent use cases. A pass built on uploaded flows and screens can only flag candidate issues, not how often they happen. Say "identified as a possible issue," not "occurring for X% of users," unless the finding actually came from proxy signal, external research, or an agent with real usage data behind it.

• A Field Guide on how to conduct a heuristic evaluation is coming soon. Once you've picked a variant here, this upcoming guide will cover setup, execution, scoring, and prioritization.

• UX Sourcebooks leveraged in this Field Guide: Advanced Usability Checklist (full version and PDF).