In my last post, I looked at Customer Effort Score as one way to make the Disability Tax more visible in product design. But you don’t have to wait for your next research study to start looking for it. You may already have years of evidence sitting in your customer research archive. The problem is knowing what to search for.

Customers rarely say:

• “Your product is creating a Disability Tax.”

They say:

• “I always go to the website for this part.”
• “I know that button doesn’t work.”
• “I usually have my husband do this.”
• “I have to try it a couple of times.”
• “I figured out a way around it.”

Those comments are easy to miss when the original study was focused on something else.

Don’t search only for accessibility

A search for accessibility, screen reader, or keyboard will find some useful material. It will also miss a lot.

The Disability Tax often shows up as behavior. Look for customers switching methods, repeating steps, asking for help, avoiding parts of a product, spending too much time on something simple, or describing routines they’ve developed to work around the product.

One phrase I’d pay particular attention to is: “I always…”

• “I always open this in another browser.”
• “I always start from this page.”
• “I always have to zoom out first.”

That may be a customer describing a habit. It may also be a workaround they’ve repeated so many times that it no longer feels unusual.

Use AI to look for the pattern

If your organization has an approved AI search or research tool, you can use it to look across transcripts, notes, and previous usability studies.

The important part is telling the system to search for evidence of effort, not simply mentions of disability.

Here’s the prompt I’d start with:

Prompt

Role

You are a UX researcher and accessibility analyst reviewing customer research documents, interview transcripts, usability studies, and research notes.

Goal

Find evidence of product-induced customer effort, with particular attention to experiences that may represent a Disability Tax.

I am looking for moments where customers had to do extra work to complete an ordinary task, including cases where they eventually succeeded.

Also identify cases where excessive effort caused someone to abandon the task.

Look for behaviors such as:

• Workarounds: The customer developed another way to accomplish the task.
• Device or channel switching: The customer moved between an app, website, browser, device, mouse, keyboard, phone support, or another method because the first approach didn’t work.
• Seeking assistance: The customer asked a family member, colleague, support agent, or another person for help.
• Repetition or recovery: The customer retried steps, reentered information, backed up, restarted, or recovered from repeated errors.
• Avoidance: The customer avoids a feature, control, or part of the experience because they expect difficulty.
• Excessive time or navigation: A routine task required substantially more time, steps, searching, or navigation than expected.
• Assistive technology friction: The customer describes problems involving a screen reader, magnification, keyboard navigation, voice control, captions, switch access, or other assistive technology.
• Abandonment: The customer gave up or moved to another channel because completing the task required too much effort.
• Habitual workarounds: Pay particular attention to phrases such as “I always,” “I usually,” “I know to,” “I have to,” or “the way I do this is.” These may describe workarounds that have become routine.

For each example, report:

• Source: file name, study, participant ID, and timestamp if available
• Customer goal: what the customer was trying to accomplish
• Exact customer quote
• Extra effort: what additional work the customer performed
• Effort type: workaround, switching, assistance, repetition, avoidance, excessive time, assistive technology friction, or abandonment
• Task outcome: completed, abandoned, or unknown
• Disability or assistive technology evidence: state exactly what evidence exists
• Likely product barrier: describe only what the evidence supports
• Confidence: high, medium, or low based on the strength of the evidence

Important constraints

Do not assume someone has a disability because a task was difficult.

Do not infer a specific disability from behavior.

Keep the customer’s exact words separate from your interpretation.

General usability problems should still be reported, but label them as general customer effort when there is no evidence connecting them to disability or assistive technology.

Do not force examples into the Disability Tax category.

If there is not enough evidence to understand why the customer experienced extra effort, say so.

After reviewing the individual examples, group repeated patterns by customer goal or product journey. Identify places where multiple customers describe similar forms of extra effort.

What to do with the results

Start by reading the original passages. AI is useful here because it can help us find needles in a very large haystack. It shouldn’t decide which needles matter.

Look for repeated behaviors. Look for a workaround that appears across several studies. Look for a task that customers technically completed but repeatedly described as tiring, confusing, or unnecessarily complicated. And look for old problems.

One of the interesting things about searching an archive is that you may discover a Disability Tax that customers have been describing for years. Nobody grouped those comments together because each study was asking a different question.

That’s the opportunity.

We already collect a tremendous amount of customer evidence. We may not need another metric or another study before we start learning from it. Sometimes we need to ask a different question of the research we already have.