Scaling Blind Spots: The Hidden Risk in Customer Experience AI

If you build AI on the wrong customer insights, you don’t scale business results and improvements — you might bleed revenue and scale exclusion.

Image of 5 women on a panel, looking super smart, at the Women in CX Un Conference in Berlin, where my thoughts in this article were shared live.

Executives everywhere are racing to deploy AI in customer experience. The promises are tempting: faster service, lower costs, personalised journeys.

I'm not against any of that!

But there's a critical flaw in how most companies approach this transformation:

➡️ AI only scales the quality of the insights you feed it.

➡️ Most customer data today captures the wrong things.

➡️ Too often, AI hype is the tale wagging the dog: Companies roll out AI for efficiency because they can, not because it actually solves customer problems.

Don’t get me wrong, efficiency gains matter.

But only if they make the experience better, or at least don't impact it negatively, for the people paying you: your customers.

Take a common practice: after each call, you ask: "How would you rate Martin who handled your request?”

The customer gives Martin a high score because he was kind and professional.

But that same customer leaves the next month because the process Martin was forced to follow was rigid, slow, and frustrating.

➡️ Your surveys show 5-star ratings / your tNPS looks great
➡️ Your AI learns everything is perfect

🩸 Your revenue quietly bleeds out the back door

That's inside-out data: Asking what (you think) is important to you, never giving the customer a chance to tell you what might be important for them.

Your 5-star reviews are measuring employee performance while missing the system failures that drive customers away and never uncovering what actually drives customer decisions.

Here's what you should ask instead:

• "How could we have improved your experience today?"

• "What made this process harder than it needed to be?"

• "If you could change one thing about how we serve you, what would it be?"

These questions give customers permission to tell you about

-The rigid authentication process that took 10 minutes.

-The forms that had to be filled out three times.

-The system that couldn't handle their slightly unusual situation.

Martin gets 5 stars, but your process gets exposed for what it is: a revenue leak disguised as excellent service scores.

When you feed narrow feedback into AI, "Martin is great!" or tNPS is through the roof, you automate the wrong lesson.

But when you capture what actually frustrates customers you can build AI that fixes real problems instead of scaling fake success.

The Customers You’re Not Measuring (And Why That Matters at Scale)

Here's what we know for certain:

1 in 4 of your customers has a hidden disability.

Another 15% in Scandinavia have non-nordic backgrounds, some facing language barriers.

Add elderly users and those dealing with situational challenges, and you're looking at a massive portion of your market.

👉 These aren't edge cases.
It’s a significant share of your paying or potentially paying customers.

Ignoring them doesn’t just narrow your audience, it narrows your revenue base.

When you optimise your customer experience and AI for the "average" user, you're excluding real customers with real money who can't navigate your "efficient" systems.

The Double Exclusion Trap

Companies deploy AI that learns from data which already excludes huge segments of their market.

It's a multiplier effect: AI doesn't just maintain your blind spots, it amplifies them.

Your chatbot learns from successful interactions.

But what about the dyslexic customer who gave up because they couldn't spell "refrigerator" correctly?

The parent who abandoned the chat when your time-out feature kicked in?

Their struggles never make it into your training data.
Your AI never learns they exist.

👉 Put simply: you’re training AI to serve only the customers who least need help and leaving everyone else behind.

The Myth of the Standard Customer

Remember: AI systems that truly work aren't just more efficient, they're more flexible.

Predictive text, one of our most successful AI implementations, doesn't force users to spell perfectly. It adapts to how people actually type, catching errors and understanding intent even when the input is messy.

Real-time transcription turns meetings accessible for everyone, not just those with hearing impairments, but anyone in a noisy environment or reviewing content later.

These aren't "accessibility features."
They're good design that happens to work for everyone.

👉 And they’re not just nice-to-have add-ons. They’re features that expand your paying customer base instead of shrinking it.

How to Build AI That Actually Scales Revenue

1) Flip your data collection from inside-out to outside-in

Stop asking the questions that matter to YOU, ask questions that allow customers to tell you what matters to THEM. Ask what's making their experience harder than it needs to be. Use qualitative interviews and customer journey mapping to uncover the friction points your inside-out surveys miss.

👉 That’s how you uncover revenue leaks before they show up in churn.

2) Design for stress cases, not ideal flows

Your AI will face customers with dyslexia who can't spell product names, people with ADHD who lose focus when your process or explanations get overly complicated, autistic users who need clear, literal language and precise guidance, elderly customers who need more time, parents juggling kids needing the same, and thousands speaking your language as their second or third. Build systems that handle real human diversity, not textbook interactions.

👉 That’s how you scale inclusion into profit.

3) Shadow your frontline reps

They know exactly which customers your current systems fail. They've developed workarounds for your rigid processes. That’s where your real training data lives and it needs to be fed into your AI.

4) Make human escalation easy and data-rich

When customers need human help, that's not a failure, it's insight into where your AI needs to improve. Capture why they escalated, what the AI couldn't handle, and how the human resolved it.

👉 Every escalation is a free lesson on how to improve both your AI and your CX.

Your competitor is hoping you will stay focused on your 5-star review

You can feed your AI the same narrow data that's been hiding problems for years.
Your 5-star reviews will look great while customers quietly leave for competitors who actually solve their problems.

Or you can build AI that captures what really matters:

• the struggles,

• the friction,

• the reasons people give up.

The technology isn't the barrier anymore.

The question is whether you'll keep asking the wrong questions or start capturing the insights that actually drive revenue.

In five years, inclusive AI won’t be a differentiator it will be table stakes.

The companies adapting now will own the market while others scramble to catch up.

Your competitors are hoping you'll stay focused on those 5-star reviews.

👉 Because every customer you exclude today is a customer your competitor will gladly take tomorrow.