Why AI is making your VOC program worse

We've got more customer data than ever before. Five times more than we had just ten years ago, according to the International Data Corporation.

AI tools are churning through millions of customer interactions, reviews, and conversations every single day.

Yet here's the thing that keeps me up at night: despite having all this information at our fingertips, most go-to-market leaders feel less confident about their decisions than they did a decade ago.

Sound familiar? You're sitting in front of a beautiful dashboard, looking at sentiment scores and NPS trends, but something feels off.

The data is there, the visualizations are crisp, but when it comes time to make that critical decision about product direction or customer strategy, you hesitate. You wonder if you're really hearing what your customers are trying to tell you.

This is the paradox we're living in. More data, less clarity. More automation, less confidence. And I think I know why.

The problem with how we built VOC

Let me take you back to how we used to do things. Voice of the Customer programs were built in a different era.

We relied on quarterly surveys, annual NPS scores, and post-mortem analyses. Sometimes we'd get customer feedback twice a year if we were lucky. The data was limited, but it was manageable.

We could read through every response, spot the patterns ourselves, and make decisions based on what we saw.

Then AI came along and promised to solve all our problems. Continuous monitoring! Real-time insights! Automated sentiment analysis! And technically, it delivered on those promises.

We now have access to customer feedback from Reddit threads, GitHub issues, support tickets, sales calls, and countless other sources. AI summarizes it all into neat little packages for us.

But here's where things went sideways. When AI looks at all this customer feedback, it does what it's designed to do: it looks for patterns. It creates averages. It smooths out the rough edges.

It takes thousands of unique customer voices and turns them into a single, sanitized summary.

The meat of the matter, those rough edges, those outlier comments, those subtle shifts in tone, gets lost in the process. What lands on your desk is a beautifully formatted dashboard that tells you customers are "generally satisfied" with a sentiment score of 7.2.

But what does that actually mean? What specific actions should you take based on that number?

What pricing model should you choose in 2026?

With AI now layered into most SaaS products, many are delivering value through usage, automation, and measurable outcomes all at once. That shift is pushing companies to rethink not just what they charge, but when and how pricing should evolve across the customer lifecycle.

GTM AllianceEleonora Berylo

What customers are really trying to tell you

Here's something I've learned after years of working with B2B and B2C companies: customers today communicate differently than they used to.

They don't always come right out and tell you what they're thinking. Instead, they leave signals: breadcrumbs that indicate their true feelings and intentions.

Think about your own behavior as a customer. When you're unhappy with a product, do you immediately fill out a feedback form? Probably not. You might start using it less frequently.

You might abandon your shopping cart more often. You might stop engaging with emails or skip those quarterly business reviews.

These are the signals I'm talking about: