Why Most Companies Fail to See Bottom-Line Returns From Deploying AI Agents

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But if all you do is measure activity, not results, measure how many agents, not what they bring, then it serves no purpose for the company.

Maneet Ahuja (1:13)

Welcome. I'm Maneet Ahuja, editor-at-large at Forbes and founder of Iconoclast. And my guest today has spent four decades at the frontier of artificial intelligence. He's held senior leadership roles at Apple, Microsoft, and Google, chairs Cinevation Ventures, and leads ZeroOne.AI as founder and CEO. His bestsellers, AI Superpowers and AI 2041, change how the world thinks of this technology. And in his new book, AI Native, The Mandate to Transform Your Company, he issues a mandate to every CEO.

Dr. Lee, it's a pleasure to welcome you to Iconoclast. Thank you.

Dr. Lee, you argue that AI Transformation cannot be delegated to the CIO. It has to start from the top down, from the CEO rather. Talk to us about that.

Kai-Fu Lee (2:03)

Actually, I'm against the bottom-up approach, which is asking departments what budget do you need to implement AI agents and then giving CIO the budget to help the departments do a better job with finance, legal, customer service. That should be done, but it is not going to move the dial for the company.

AI is getting so smart, I'd say that they're like IQ 300 super intelligent beings, that when they enter the company, it is a terrible under-utilization to make them do customer service. They should help the company move the dial, make more money, make the CEO more effective, super power the CEO, and figure out ways to increase revenue, growth, profits. And also, in order to welcome these thousands of AI agents to the company, where AI agents will weigh out number of human workers, the organization chart has to change. The traditional hierarchy does not work.

And the number of people who know how to manage AI and take accountability, there's not enough of them. So there's a huge imbalance of people who just know how to manage people, people who don't know how to manage AI. And when AI does the bulk of the work, the organization chart should be much flatter. These are all steps that the CEO needs to make. And also, as the organizational chart changes, the institutional knowledge needs to remain. So these are all tough, tough, tough problems that no one other than the CEO can handle.

Maneet Ahuja (3:39)

So what is the clearest sign that a company is running an expensive AI laboratory rather than real commercial impact?

Kai-Fu Lee (3:47)

I would say about six to nine months ago, a lot of CEOs do talk to me.

I'm friends with many of them. And they want to show off, hey Kai-Fu, we're doing a lot of AI. Tell me what you're doing. Oh, we've implemented 700 agents and they're being used in this department and that. And then I would turn around and say, well, how much has it moved the dial for your bottom line?

Does it change your quarterly result? And comes that silence. They're like shocked. I'm supposed to do that. I thought just having the agents would do. But if all you do is measure activity, not results, measure how many agents, not what they bring, then it serves no purpose for the company.

Maneet Ahuja (4:26)

Well, you're still managing a workforce, right? You're just managing an AI workforce, right? So productivity needs to be looked at in a similar way, correct?

Kai-Fu Lee (4:37)

Yes, correct. But the issue is when you think about only AI enablement, not AI Native, what happens is everything works as before with human workers and then let human workers use AI. They can use chatbots to improve their writing and customer service and so on, and even when they do make an impact is a very, very small modest impact. McKenzie showed that only 6 percent of all the companies that use AI see a bottom line impact and even then the impact is not large. So it just means the CEO hasn't been driving it, as hasn't been driving using AI to change the bottom line.