How AI Is Changing Talent, Not Just Tasks: Rethinking Where Human Judgment Matters Most
HBR IdeaCast
In this episode of HBR IdeaCast, hosts Adi Ignatius and Alison Beard discuss the profound impact of AI on organizational culture and talent management with
Key takeaways
- Paula Goldman suggests that AI should be managed as a 'teammate, but not a human one.'
- The concept of 'human at the helm' focuses on placing human judgment at the right time rather than just having humans approve AI drafts.
Main topics
- Human-AI collaboration models
- Redesigning entry-level roles
Notable quotes
AI needs to be managed as a teammate, but not a human one.
Conclusion
The episode serves as a guide for executives to look beyond efficiency
Transcript preview
Speaker 1 (0:02) This podcast is sponsored by Dataiku Agent Management. Enterprises have a system for tracking and approving the software they use. But when it comes to AI agents, most of them can't say the same. Take control of your agentic workforce with one view across every platform and tool, showing what's driving revenue, what's drifted, and who signed off on it. Visit dataiku.com slash HBR. D-A-T-A-I-K-U dot com slash HBR. Speaker 1 (0:47) I'm Adi Ignatius. Speaker 2 (0:48) I'm Alison Beard, and this is the HBR IdeaCast. Speaker 1 (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. Speaker 2 (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. And Speaker 1 (1:34) here to help us tackle that is Paula Goldman, Salesforce's Chief Ethical and Humane Use Officer. She also advises the U.S. 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. Speaker 1 (2:23) I want to explore the management choices and options that AI is creating. And, you know, 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. Speaker 2 (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. 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. Speaker 1 (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, you know, obviously it's case by case, but is there kind of a decision rule that can guide executives? Speaker 2 (4:06) I have to say, just starting by asking the question is a very big step forward. 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 there may be sensitive topics that really only people could handle, even if AI can. And I think the richness is really in kind of thinking about it from a disciplinary perspective, like what does it look like in marketing or sales or service? Speaker 1 (5:09) Yeah. And embedded in all of this is how we think about AI. And it's, I don't know if this is metaphorical or real, but, you know, is it software? Is it a coworker and a teammate? I mean, how do we really think about that? Yeah. Well, Speaker 2 (5:26) actually, I think that's a super fascinating question. People get very upset when we anthropomorphize AI. It's somewhere in between. I like to say AI needs to be managed as a teammate, but not a human one. It's a collaborator, but it's not a human one. But there are a lot of skills of management that actually really do apply to AI. And I argue we are all going to need to know. how to manage it because it is a collaborator and it's going to be critical. We're all managing multiple AI agents in our job or will be. So that's kind of the central imperative. Speaker 1 (6:03) As companies look at these options and these potentialities, I don't know where we are in the sort of wave of AI adoption or exuberance or disappointment, but Speaker 2 (6:13) CEOs Speaker 1 (6:14) still feel pressure to show an AI productivity payoff. Speaker 2 (6:18) Yeah. Speaker 1 (6:18) I guess the question is, is there a risk or an opportunity cost? in treating AI primarily as a cost-cutting technology. Speaker 2 (6:26) Productivity and efficiency are really important, right? I don't think anyone would argue with that. And I don't think anyone would argue that that is a key benefit of AI. The question is, what happens when you take that too far and you ignore the other goals? Productivity and efficiency is not the only goal of one's organization or business, right? So you could think of lots of examples. of where if you only take that into account and you don't take the so-called human side into account, you end up with worse business outcomes. So take customer service. This is arguably one of the places where AI has the most product market fit. I don't want to wait on hold for an hour to get an answer about whether I can get a refund for something. But there's lots of evidence that says when people are angry, they want to talk to a person. When people are embarrassed, they want to talk to AI. Some people just want to talk to a person, even if AI could answer a question. Let's say there's like my friend who works in health care was saying when someone has a new cancer diagnosis, her company makes sure a person schedules that first appointment. That's not because AI is not capable of it. It's because there's something to preserve there. And that's, I think, where the analogy breaks down is that you don't want to lose a customer or have a terrible customer experience because you've extended it too far. Speaker 1 (7:47) So you said something earlier that we should be aware of anthropomorphizing AI. But the fact is, AI is so anthropomorphized. It is, yeah. When we deal with ChatGBT, it adopts a kind of overly friendly language. Speaker 2 (8:02) Yeah. Speaker 1 (8:02) You know, I've heard people say, What you don't want to do is try to fool people. Speaker 2 (8:05) When Speaker 1 (8:06) you cross a line and you're trying to fool people or the result is that you have confused people, that's a real no-no. Do you agree with that? Is that a risk? Speaker 2 (8:14) I think that is a risk and it's worth paying attention to. I think the risk is different for different use cases, right? So it's a more severe risk when you're talking about AI companions than it is for customer service, but still a risk. I also think... There's other reasons that you want to make