How AI Is Changing Talent, Not Just Tasks: Rethinking Where Human Judgment Matters Most

Adi Ignatius (0:02)

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I'm Adi Ignatius.

Alison Beard (0:49)

I'm Alison Beard, and this is the HBR IdeaCast.

Adi Ignatius (1:00)

So every Thursday for the next month, we will be exploring how the rapid advancement of AI is changing what it means to be an executive. We'll look beyond the latest technology news and the cost of investment to better understand what AI means for you as a leader, for how you make decisions, and for how the fundamental structure of your organization is changing in lasting and unexpected ways.

Alison Beard (1:23)

First up today, we're considering the real impact of AI on the culture of organizations and what it means for talent management. It's about more than having a human in the loop and workforce restructuring.

Adi Ignatius (1:34)

And here to help us tackle that is Paula Goldman, Salesforce's Chief Ethical and Humane Use Officer. She also advises the US government on AI policies. She argues that while many organizations are focusing on how AI can make their workers more productive, reduce headcount, and create efficiencies, the deeper promise may depend on redesigning work to fully integrate human and AI talent together. Today, I'll talk to her about delegating decisions to AI, handling the employee resistance that's out there, and maintaining accountability all while trying to build trust. Goldman is the author of Manage the Machine, How to Harness Human-AI Collaboration at Work.

I want to explore the management choices and options that AI is creating, and maybe to frame this, you talk in the book about moving from human in the loop to human at the helm. Talk about that distinction.

Paula Goldman (2:36)

If you don't mind, let me back up and say that phrase human in the loop came from the Cold War, actually, it came from military, when all of a sudden technology could, for example, detect an incoming missile or something like that. And the obvious question was, okay, but who makes the consequential decision about this information? How do we create a system where people were making the consequential decisions?

That phrase, though, human in the loop got, I think, kind of misunderstood in this wave of AI. It kind of got framed like, okay, AI is going to draft and people are going to approve.

And that does not really work very well for every single thing when you're talking about AI agents. The whole point is that they're able to reason through lots of complex tasks and do lots of things all at once at incredible speed. And so instead, we need a system where we are putting the right things for human judgment at the right time.

That is what we mean when we say human at the helm.

We talk about that as a design principle for AI systems, but I actually think it's also a great metaphor for how we manage in the age of AI.

Adi Ignatius (3:48)

All right. So how should leaders decide about, I know there's no general rule on this, but what to give entirely to AI to run, to manage, what should be done jointly with humans, what should be fundamentally human? I mean, obviously it's case by case, but is there kind of a decision rule that can guide executives?

Paula Goldman (4:06)

I have to say, just starting by asking the question is a very big step forward. And because I think the early days of this wave has been a lot about just like, get people the licenses, give them a token budget. And all of a sudden now we're in this more strategic phase where people are asking, what actually is AI good at and where does it have flaws? And where do we want human judgment to carry the day? I think that the general stereotype that you hear, the general kind of received wisdom of like, AI can handle the routine and people handle the more complex things, like it gets you about 70% of the way there. But there are a lot of other things to take into account as well. And that includes customer preferences and emotions, employee preferences and emotions, questions where they may be sensitive topics that really only people could handle even if AI can.

And I think the richness is really in thinking about it from a disciplinary perspective. Like what does it look like in marketing or sales or service?