Priyanka Vergadia

Sarah looks at this data and she's like, OK, logically, it makes sense.

But she's questioning it.

And this is the part I really love.

She didn't just trust the algorithm as is.

She picked up the phone and called their 20 clients that were their top clients and asked them why they're not using these advanced features.

Not to her surprise, she finds that they actually want to use these features, but they cannot find them because they are buried in some menu options and the documentation isn't clear as well.

Now, AI identified the pattern that people are not using advanced features, but it totally missed the why behind it.

Sarah's team goes in, rebuilds the entire experience, makes these features easier to find, and a few months later, the advanced feature adoption skyrockets.

AI saw the symptom.

Sarah diagnosed the disease.

Now, the lesson that we take away from this example is clear.

we got to question the question.

When AI recommends something, we need to ask why.

If we continue to do that, we will be successful.

On another occasion, I was working with a customer, Marcus, who is increasing sales efficiency using AI tools for their sales team, analyzing the data through emails and engagement.

And their AI tool is telling them that one of the biggest deals they have

has a 95 percent probability to close.

This was looking amazing.

The data was saying positive sentiment, lots of engagement.

But Marcus wanted to dig deeper and make sure that the deal happens.