B (4:08)

Well, 71% of Americans believe that AI will cause massive job losses. Algorithms are getting smarter, faster, more capable every single day. My work puts me at the heart of this anxiety, where I bring AI applications to market for big tech companies and I help customers and businesses really take the potential of this technology further for their businesses. And through it all, I've seen brilliant professionals second guess themselves as AI gets smarter. But let me tell you this one fundamental truth about AI. AI is excelling at identifying patterns. It understands data. We humans excel at understanding what these patterns actually mean in this beautifully chaotic world of of human behavior. And even as these models and algorithms get stronger over time, this will stay true. Why? Because we understand things that cannot be quantified. Context, intent, unspoken emotions, cultural nuances. This depth of understanding comes from lived experiences that AI cannot replicate. Today, I'll share with you three stories from my experience to prove this point. That AI understands data and we understand experiences. And the key here is to not compete with AI, but to work with it while staying irreplaceably human. So how do we do that? Well, I was recently at a conference and met Sarah, a product manager. Her team has built an AI powered analytics dashboard that's telling them very clearly that 80% of their users are only using basic features and 20% are are using advanced features here and there. Now, Sarah looks at this data and she's like, okay, logically it makes sense, but she's questioning it. 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 gotta 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 the 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% 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. When he looks at the human element of this deal, he finds that.

A (8:56)

Not.

B (8:56)

The same people are showing up to these meetings. It's different stakeholders every time. And the responses in the emails have gotten vague and more corporate. AI is reading all of this activity as engagement, but really there's something else going on behind the scenes. He dug a little further and identifies that the customer is going through a restructuring. And three teams thought that they owned the decision to make this purchase. If Marcus didn't get into this human element of the deal, the deal would never happen. AI identified the activities. Marcus measured meaning in those activities. So the lesson to learn from this story is you need to read the room, not just the dashboard. Understand those micro expressions, the social cues in the room, the what are people saying? How are they nodding? We've all been in meetings where somebody says, that's interesting. Are they politely dismissive or genuinely curious? Well, our emotional radar knows that AI doesn't. I was with a friend recently. Her name is Priya and she works to use social media as a platform to help brands grow their revenue. Her AI tool is telling her to post fashion hack videos, those videos where you get a lot of fashion tips out for one of the brands. And she did that. And they saw great engagement, lots of follower growth. But when talking to the team, they identified that none of that follower growth and engagement on social media was leading to sales or revenue. They were building the wrong audience. They were attracting bargain hunters. That was exactly opposite of the person who would pay $200 to buy an ethically made jacket. This was what this brand makes. Now, AI was optimizing for followers and engagement. Priya knew they were making the wrong audience. So she flips the switch. She stops taking AI recommended content, instead starts building content that is showing sustainable cost of building these fashion items. She started showing stories of artisans that were making these clothes. Now AI in this case was optimizing for activity and engagement. Priya optimized for building a community. And they started seeing the sales skyrocket. So the lesson that we learn here is always pause and ask what is the story behind this data? And only we can do that. So if you see all these examples, there's one thing very common. The future doesn't belong to humans or AI. It belongs to humans that work closely with AI while staying irreplaceably human. Our ability to read the room, our ability to look at emotions, that is irreplaceable. Our ability to empathize with people, that's irreplaceable. So the next time you're feeling anxious about AI taking your job, remember that AI can identify patterns. Only we and you can identify the human behind it. Thank you.

A (13:28)

That was Priyanka Vergadia at TED Next in 2025. If you're curious about TED's curation, find out more@ted.com curationguidelines and that's it for today. TED Talks Daily is part of the TED Audio Collective. This talk was fact checked by the TED research team and produced and edited by our team, Martha Estefanos, Oliver Friedman, Brian Greene, Lucy Little and Tansika Songmar Nivong. This episode was mixed by Christopher Faisy Bogan. Additional support from Emma Tobner and Daniela Balarazo. I'm Elise Hu. I'll be back tomorrow with a fresh idea for your feed. Thanks for listening.

C (14:15)

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