🦾How You Can Leverage:

Who completes post-call satisfaction surveys?

Furious people.

That's it. Everyone else hangs up immediately because they have meetings, deadlines, actual work. Your customer satisfaction data systematically excludes the bulk of customers who had neutral or positive experiences.

So if you're measuring something like this, you’re measuring rage, not satisfaction.

Jim Palmer, Chief AI Officer at Dialpad, explained why this destroys strategy. You think you're understanding customer sentiment. Wrong. You're only capturing the most negative experiences while the silent majority never appears in your data.

Meanwhile, the richest intelligence your company generates vanishes the moment every sales call or team meeting ens. 

Nobody's treating conversations like the legit data goldmine they actually are. Companies are out hunting exotic new data sources while their actual competitive advantage evaporates daily into thin air.

So on today's episode, we broke down why voice is the last offline data set worth billions, how to measure customer sentiment without systematic bias, and why responsible automation means breaking your AI before customers do it for you. 

Companies ignoring this make million-dollar strategic decisions on fundamentally flawed data. Let’s get it. 

Your CRM is optimized.

Spreadsheets clean. Dashboards beautiful.

Cool. That's baseline now.

Jim explained what companies miss. The richest data you generate disappears after every customer call, sales conversation, internal meeting. He described this as bringing "the last offline data set online."

While competitors hunt for exotic data sources, your competitive advantage dissolves into nothing.

Because nobody captures it.

Voice conversations contain patterns structured databases never reveal. Humans communicate differently than they fill out forms. Way differently.

The strategic opportunity runs deeper than just recording calls.

Business conversations repeat constantly. Same customer questions emerge. Sales objections follow predictable patterns. Support problems cluster identically.

That repetition is gold.

When you fine-tune AI specifically for YOUR business context instead of using general systems, accuracy explodes. Jim shared how Dialpad's team published peer-reviewed papers on this. A specialized small language model trained on your actual business conversations absolutely crushes a giant general model trying to handle every possible scenario across every industry.

Try This: Map where substantive conversations happen at your company. Sales calls, customer support, strategy meetings, training sessions. Calculate what percentage you're capturing versus letting vanish forever. Below twenty percent? You're watching competitive intelligence evaporate while competitors build models trained on your industry's specific language patterns that compound every single quarter. 

Picture this.

Support call ends. Customer hears the request.

"Would you like to stay on two more minutes for our satisfaction survey?"

Most people hang up instantly. They got their answer. They're done. They have work.

Who stays?

The furious ones.

Jim described this perfectly as "horribly biased." Only customers angry enough to invest two more minutes pressing buttons actually respond. Everyone else with neutral or positive experiences vanishes from your data immediately.

Think about what this means.

You're making million-dollar decisions about customer experience improvements based on a sample that systematically excludes ninety-five percent of customers who weren't enraged. You're prioritizing product changes for your angriest users while the silent majority stays invisible.

That's catastrophic for strategy.

Jim explained how AI CSAT changes everything. Generate satisfaction scores on every single call automatically. No survey. No bias. Just actual sentiment across one hundred percent of interactions.

The silent majority finally shows up.

This reveals problems before they metastasize. It spots opportunities competitors miss because their metrics only capture emotional extremes.

When your customer satisfaction data excludes most customers, you're flying blind while thinking you see perfectly.

Try This: 

Pull your survey response rates. Compare them against total customer interaction volume. Five percent response rate? You're optimizing your entire customer experience strategy based on systematically biased data excluding everyone who wasn't angry enough to stay on the line. That gap represents flawed strategic decisions you're making right now.

Most companies test once.

Ship.

The they wait ….. and…. 

Disaster.

Jim emphasized red teaming. Adversarial testing where you actively try to break your AI before customers discover failures in production.

This isn't pessimism.

It's responsible automation.

You need to find edge cases where systems confidently generate wrong outputs while sounding completely authoritative. Because those edge cases WILL happen. Only question is whether you discover them during internal testing or after customer-facing catastrophes.

Companies avoiding AI disasters aren't the ones with perfect technology. They invested in evaluation frameworks first.

Jim explained how Dialpad doesn't run one smoke test and ship. They build test cases. Use other models to evaluate their models. Systematically identify where systems work versus where they fail spectacularly.

But here's what most people miss. Red teaming isn't just defensive. When you systematically test where AI fails, you simultaneously discover where it works exceptionally well.

Same process. Dual benefit.

The adversarial testing preventing disasters also reveals automation opportunities you never considered because you understand capabilities and limitations with precision. 

Try This: 

Spend one hour actively breaking your current AI implementations. Real adversarial testing with edge cases, unusual input combinations, scenarios mixing multiple variables unexpectedly. Document every failure mode. Find systems confidently generating incorrect information while sounding authoritative? You just identified production disasters waiting to happen.