The Human-AI Handoff Is More Complicated Than Companies Think

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0:27David Rice: Most enterprise AI conversations start with the same question. What can we automate? But that is the wrong place to start. On today's show, I'm talking with Paula Goldman, Chief Ethical and Humane Use Officer at Salesforce, about the difference between designing for automation and designing for human AI collaboration. They're not the same thing. Automation asks what steps you can remove. Collaboration asks what work could become now that humans have a new capability available to them. The handoff between AI and humans isn't as clear as we like to pretend, either. When customers are angry, they want a person.

1:02David Rice: When they're embarrassed, they often prefer the AI. Blanket assumptions about where that line belongs are going to cost organizations trust in ways that are hard to recover from. Paula's core argument is that as AI becomes more powerful, judgment and relationship building become more important, not less. Productivity is an intermediate outcome. The more interesting question is what you do with the capacity that it creates. So today we're covering why starting with automation causes leaders to miss how human work creates value, how to think about the AI human handoff for your specific use cases, why every employee is now being asked to manage AI with the same lack of training managers once got, and what designing for human flourishing actually looks like in practice.

1:46David Rice: I'm David Rice. This is People Managing People. And if your AI strategy is organized around what you can automate rather than what work could become, this conversation will reframe the whole question for you. Let's get into it. Meet TeamFlect, the number one rated performance management and employee engagement platform built for Microsoft Teams. Trusted by over 1 ,000 organizations, easy to use, yet enterprise grade solution. Visit TeamFlect.com to learn more. Well, Paula, welcome to today's show. Thanks for having me. I wanted to start the conversation around like, you know, a lot of enterprise AI conversation.

2:23David Rice: It still begins with some version of, well, what can we automate, right? You kind of argue that designing for automation and designing for human AI collaboration are fundamentally different things. And if we start with automation as sort of the objective, what does that cause leaders to miss about human work and how it creates value? Thank you. I think you put that question really well. I think there's a few parts to it. one is just kind of the difference between a transformative outcome and a slight boost in efficiency. But it's like, we need to be asking, what does AI make possible for our businesses?

2:57How can we further delight our customers, our employees, and so on? But I think even at a more base level, like, I think the question is really about also recognizing where human interaction is fundamental to the success of an outcome, where the connection, for example, between someone that's answering the phone and someone that's called in is going to make the difference between like a really disappointed customer and a delighted one or where human coaching for an employee is going to make the difference between motivation and growth and not right and i think you know we can get into so much detail about it but i think we vastly underestimated why that's important i mean ai is so powerful there's so much we can do with it but the right question is how do we get people in AI working together well to get responsible outcomes and outsized outcomes, not just like where we plug in AI.

3:53David Rice: Part of our issue sometimes is that, you know, efficiency and value, they tend to be a little bit synonymous in our minds. But a workflow can become dramatically more efficient, well, at the same time worse at producing things that matter. Trust, learning, creativity, judgment, right? Automation tends to treat the existing workflow as sort of like the unit of analysis. Like what steps can we remove? Collaboration, I think, like asks a different question, which is like, what could this workflow become now that humans have a new kind of capability available to them? I think you said that very well.

4:25David Rice: I think this is particularly relevant for CHROs because a finance leader, they're going to see like 20 % capacity released. HR needs to ask sort of like what happens to the job, expertise, organizational system around that capacity. So I think this is like just one of the big things that we're kind of having to wrestle with right now. You've talked about designing the handoff between AI and humans, but that boundary, it's not as simple as AI handles routine work and humans handle complex work. We say that, but I don't think it's that clean. And sometimes people actually prefer AI for really highly sensitive interactions, while other people, they kind of seemingly simple moments depend enormously on human connection.

5:04David Rice: I'm curious, in your opinion, like, how should organizations determine where that boundary belongs instead of making just sort of like these blanket assumptions about it? And it's not that like that sort of truism, let, you know, let AI handle their team, let people handle the complex. It's not that it's wrong. It's actually a pretty good starting place. But actually, when you start looking at individual use cases, that's where it gets very interesting. Right. So like take customer service, for example. It is true, right? Like if AI has never seen a question before, it's probably and doesn't have any data on the answer to the question, probably shouldn't handle it.

5:39But there's other rules of thumb to like if something's highly regulated, you know, like 1-800-ACCOUNTAN, which does financial advice for small businesses, like if they get into like a regulated category like tax advice, they want a human to handle it. But then if you start thinking about customer emotion or emotional connection, that's where it gets really interesting, right? So tons of studies, tons of data that says when customers are angry, they generally want to talk to a person. when they're embarrassed, let's say like maybe it's a medical condition or just something that they don't want to feel like they're being judged for or something, there's a slight disposition for AI.

6:20And that's where I think really understanding those trends, those preferences, where people want to talk to people, where they don't, is really important because when you think about like just customer service, continuing this example, it's really important, not just as a matter of efficiency, but like this is where you keep your customer or you don't, right? And you don't get many chances for that. And that's why I think we're just starting to scratch the surface of how collaboration really works in practice, that paying attention to the human side of it is really important.

6:54David Rice: Yeah, maybe the distinction is not as simple as, you know, or it's not simple versus complex, but it's actually like, what do people want from this interaction, right? You mentioned that when they're angry, they want to talk to a human. I think also if they feel maybe embarrassment or fear of judgment, right? Where AI feels a little bit psychologically safer than a person just because we've had those like interactions over the course of our life where yeah, human beings, it felt weird, right? Or it made us feel uncomfortable. I'm kind of curious, because that suggests organizations need user research, not just process mapping before deciding where humans belong.

7:29David Rice: I wouldn't say like the boundary problem, it's not permanent either, right? People may initially want human reassurance and later become comfortable with AI or the reverse becomes the case. So it's kind of a question of choice. And I think we're using task complexity as the proxy for where humans add value. Do you think it's like emotional consequence is actually a better variable? I think they're both really important variables, right? And that people are ignoring the sort of human connection side of things, probably to their peril. I think they're both very valuable things to factor in. And I also think you're right, like things will change over time people may grow more used to interacting with AI and AI is obviously getting better and better and more and more powerful but you know what else could happen is that people value human connection more right that it may matter more that actually as consumers and employees use AI more and are kind of filtering out they use AI to sort of filter out what's coming in coming at them right marketing messages and so on that like will have fewer and fewer human touch points and they will matter much, much more, right?

8:37And so I think it's a little bit hard to predict how this is all going to play out. But having written the book, I just feel very, very confident that we are underestimating the importance of the human side of the interaction.

8:50David Rice: You told me this great story when we were talking before this about social workers who resisted an AI tool designed to save them from returning to the office after field visits. and then leadership saw that trip as like inefficiency right the workers saw it as the place where they process difficult cases together that feels like a much bigger lesson for chros to me because like how do you distinguish waste from the seemingly inefficient human interactions that actually impact trust learning judgment resilience the most right and that was a good example of where, you know, sort of really understanding that ritual of like what, you know, people are not only is that like you go into the field, you have difficult cases.

9:36Sometimes there are things you've never seen before. Sometimes there are things that just like maybe have a an emotional toll on you as a worker. You want to go back and process and learn together because because maybe your colleague has seen something before. This is a question of, it's not that AI can't help you take those notes or do that process, but how does it best both support both the efficiency and the collaborative learning that's taking place there? And that's where just like a small tweak of like really understanding what the goal is, not just to save time, it's to increase learning amongst the team, It's to get the best answers to serve the individual people who are being supported through these caseworkers and so on.

10:23It's a small tweak that could make an incredible difference. And in that case, the story I told you, it was like from a town council in the UK where that pilot just got rejected. And again, it's a small little tweak of like making it a bit more of a human-centric implementation that would have saved that and saved the efficiency and also all of the learning that went along with it.

10:46David Rice: We spent decades talking about friction as a bad thing and how to remove it, right? And now AI kind of gives us like this unprecedented power to actually do that. But I think what we're going to find more, and I talked with somebody a little over a year ago, an author, and he was talking about like, positive friction and how that is an actual thing, right? And so, like, the drive back to the office looked like transportation waste when you saw it on a process map, right? But in reality, it was peer supervision. It was the ability to decompress together, share informer knowledge. And so, like, I think we're going to find similar examples exist everywhere, right?

11:24David Rice: Junior employees, overhearing senior colleagues, the conversations that go on after meetings, you know, asking someone who sits by you, how they'd handle something. Like these interactions, they're invisible, but they're incalculable to like ROI and sort of employee development. So I think there's a connection to sort of rework there. I agree. Yeah, I agree. Actually, I think you made two really important points. So one was just about the value of kind of the different rituals that may take place within a work environment and really paying attention to them. But you also said something about the value of positive friction, which I really agree with.

12:02And at Salesforce, we call this mindful friction. Same concept that is really powerful or technology is really powerful. It can come up with answers in so many different domains. But there are places where the difference between a successful outcome and a failed outcome is a human stepping in with judgment. And so one of the things I spend a lot of time on every single day is where those moments where you really want to nudge someone to take a second look. It might be because it's a very consequential decision that AI is recommending, like, you know, in an employment screening context or screening someone for a loan or something like that, where you really want human supervision.

12:45that it might be just as simple as like, you really want someone to be engaged and that's going to make a big difference. You have to do it very, very carefully. But I think that that idea that friction is bad is not universally true, that there are places where we definitely need friction in the system.

13:04David Rice: I think I heard someone want to call this like load bearing inefficiencies, which I thought was just a funny way to say it. But I'm curious, does it fall to the CHRO to sort of identify which inefficiencies are, and we're saying inefficiencies in quotes here, because they may not be in the reality of things. Yeah, exactly. Does it fall to the CHRO to identify which ones are that before allowing AI sort of transformation to just remove it? This is such an interesting question. And let me start by saying, I think that the role of HR is so essential, so essential in helping all these different functions within a company use AI in a way that gets the best outcomes.

13:47And that means all kinds of things for the HR org, and all kinds of new types of responsibilities. I also think, though, in some ways, this question has to be devolved to every function where they know, you know, the marketing org really knows, like, where does taste making and, like, human authenticity really matter in the messages that we're creating? And where are the things where we can just ask AI to personalize a message 20 ,000 different ways for 20 ,000 different customers? And how do we find the right marriage of that? But it's both like this is what's super interesting is that all of a sudden AI is everywhere, right?

14:31It's not just like one or two apps that companies using. It's like, at least in the Salesforce context, it's deep and everything we're doing across every single function. And so you need people that are like experts at those functions to like really determine which are the hero use cases. And then you need HR leaning in and thinking about how do we make sure that human expertise is like really there and central on making for the best outcomes.

14:59David Rice: One implication of sort of what we talked about beforehand is that employees can't simply be the recipients of AI transformation, right? They have information about the work that people that sort of design these transformations maybe don't. And so organizations, they can't redesign every workflow by committee is the problem. So I'm curious, what does meaningful employee participation look like without slowing the transformation down to a point that executives become uncomfortable with it? Yeah, yeah. No, and I think that's a fair question. But I don't think, you know, one of the things I say in my book is workers should have a seat at the table.

15:36And I think that story that we talked about about social workers is a good example of why not doing it is counterproductive. But there are ways to do it where you're actually making it accelerate outcomes and anticipate problems before they actually become really, really big. So here's some ideas. These are like based on what I do, actually, on Salesforce and with my team. We do things like hackathons where we have, you know, employees coming up with ideas for how to use AI. One of my favorite examples of this is we had neurodivergent employees hacking how to use AI in Slack in such a way that they weren't feeling overwhelmed by all the different forms of information that they were getting flooded with.

16:20And then we end up using those ideas for focus mode in Slack. And so there's so many examples of like how involving people increases that type of innovation. But it's not just hackathons. It's asking for feedback in the application. It's involving people in the brainstorming about which apps are going to be really, really important. And it's listening to that feedback, right? Because the game, the name of the game is not just rolling out AI and saying you must adopt it. It's how people are actually using it in their day-to-day work to get better outcomes. And the truth is the people that are closest to it actually know how to do that.

17:00There's another story in the book that is a friend of mine, Amanda Ballantine, who used to run innovation for the AFL-CIO. She was telling me this story about going to a Stellantis factory, an automobile factory, where they had this partnership where workers on the shop floor were teaching other workers how to work with robotics. and because they had actually done it, they had actually worked with robotics. You know, one of the teachers she saw had created her own app for the students about how to troubleshoot the robot. That is born of real experience. That is helping actually drive efficiency.

17:38And it's an example of why you don't just roll out technology as a mandate from the top down. You really involve people in it and you'll get much better outcomes.

17:48David Rice: And I think like, you know, There's some hesitancy to do this, but I think everybody needs to keep in mind there's a big difference between employees having like a veto power versus providing design intelligence. And I think like frontline employees, they know about, I think about this because from my own point of view, I've worked in like hospitality, transportation, operations, all these different areas, right? Frontline employees that tend to know about sort of these workarounds or rules that don't quite appear in your SOPs a lot of time. They have like shadow workflows. No, that's exactly, that's exactly it.

18:22And there's sometimes when AI can sort of pick up on that tacit knowledge and help make it more available to people. And that's great. And then there's times where there's just all this stuff that is not going to come up. It's not explicitly codified. It's someone reading the room or it's someone really understanding how this has been done for years and figuring out how to do it. And that's why you want, you actually want that expertise, right? You want, that's going to get you much, much better results.

18:49David Rice: I would frame it less as the participation itself, almost less as consultation with employees and more like requirement gathering. Because there's an adoption argument, right? People are more likely to use a system when they recognize their reality in its design. A hundred percent. Yeah, that's a great way of framing it. Yeah. We see it across different types of technology, right? It's a legitimate attention to have. I guess my question for you would be like, how do you distinguish between legitimate frontline knowledge from resistance that's basically, I don't want my job to change? This is so important.

19:24And I'm really glad you asked this question. Here's what I want to say. We're living through such a big transformation in the way that we work. You know, change is hard. And again, but I actually think this is another reason why involving people and giving them a seat at the table and really understanding, or as you just said, sort of like requirement gathering makes for much better outcomes, is that like we're asking people to change. the way they work, right? It's easy to underestimate that there's a massive emotional curve that they go through and that there's knowledge that needs to be brought to bear there.

20:02And so how do you do this well? I actually think, you know, it's thinking about what, so one of the questions we talked about, what needs to stay human? And getting opinions about that, right? It doesn't mean that every single opinion dictates the outcome, but like really listening to that and really understanding where the place is where that kind of judgment and relationship building and so on, or just adjudicating edge cases makes a big difference. But involving people in that design of like, what should work look like, makes a really big difference. I just, we just did this with our own team.

20:37And, you know, I think there's a little bit of like, you get used to one way of working, and you get really good at it and then you're asked to change it. And that's hard. And people need to be like brought along and bought into it. And they need to see that actually it's gonna make the work better, right? And I think when they see that, it gets very, very exciting. But it is hard. It is change management.

21:04David Rice: Yeah, absolutely. There's another dimension to human AI collaboration. And I think like this, I'm seeing this more and more. as a talking point, something I've been talking to folks about on this show for over a year now, but something CHROs are going to have to wrestle with, right? And that is if we systematically hand certain cognitive tasks to AI, then we're not just redesigning jobs. We may be redesigning how people develop expertise and their ability to sort of shape what comes out of the workplace. So I'm curious, like, how should leaders think about what humans need to keep doing? Not because AI can't do it.

21:41David Rice: I think at some point it will probably do all things better than this, but because doing it is how people develop the judgment that they need to be successful in their roles. And I think there's a distinction here between can AI perform this task and should humans stop performing this task? Yeah, a hundred percent. Actually, I think you may, again, well, you always make lots of really great points, multiple points at once. So let's start with can AI do it versus should it, right? We went through some examples early in the conversation about that in the customer service context, other contexts as well, where the difference between can and should makes the difference between success and failure.

22:21Like I was just thinking from an HR perspective about nudge tech and technology that helps managers get more thorough performance reviews and give more thorough feedback. But there's a difference between AI helping to collect that information and a manager not only taking accountability for what that review looks like, but also delivering it because that, you know, this makes the difference between like motivation and not. That's why you're nudging someone to check in more often with their team versus having AI check in for you, like doesn't really work, right? So I think that can versus should distinction is really important.

23:02And then that question of developing expertise is really, really crucial, right? That's why we see so many new forms of, I mean, why there's a resurgence in conversation about apprenticeship, why there's a resurgence in conversation about training and skills and so on. I do think this question will look different in five or 10 years, right? Like, you know, No, you wouldn't have said like, think like just as an analogy, when the spreadsheet came out, right? You know, there was hand wringing about like people losing expertise about performing different calculations and so on. You still need to know the basics, but then you get sort of get used to a technology, you find ways of double checking the outcome, what knowing what you can rely on what you can't.

23:47And I think for that reason, we're also starting to see new roles emerge where it's like you're actually learning the basics by supervising AI, right? You're learning the basics by understanding, for example, if you were using AI in a sales context and you're seeing all these sort of inbound questions come in and AI sort of answering them, where is it working? Where is it not working? How can we improve it? So there's new ways of learning the business that are going to have to emerge because AI is changing how business gets done. Yeah, you know, we're of an age where like growing up, we had all these experiences where that built the expertise.

24:25David Rice: And I think like the thing that we've got to decide right now is that some tasks may need to remain deliberately human as training tasks, even though I know the AI can do it faster. but that then creates kind of an uncomfortable economic question because your cfo is ultimately going to come to you and go okay well who pays for your developmental inefficiency and that i think is going to be a bit of a tough one to answer but i don't as you said before it's not i think that's the wrong way of framing efficiency right we're talking about people learning how to ultimately how to manage AI systems.

25:01And there's so many examples about why that sort of management, that judgment, that oversight makes a massive difference for the outcome. So this is not about inefficiency. To the contrary, this is about you're going to make this massive investment in AI. You better have people who both know the business and know how to judge what tasks to delegate to EI and what not to, right? And when you have both of these things, the sky's the limit for what you can do. When you don't, you're kind of throwing money at a problem without solving a problem. So to my mind, this is actually a better way of thinking about the efficiency question.

25:38David Rice: Yeah, I agree. I think where my concern comes in is that, you know, we're talking about something like these are skills you develop because you may need them. They're going to be part of your work five years from now. But the folks that you had to convince that are used to thinking in quarters they're used to thinking in short-term gains there's a lot of short-termism in the market i do think some of that's changing i sometimes wonder about the challenge of convincing those folks that we've got to keep these in place because this will become increasingly important as this technology develops and we integrate it more and more yeah yeah i think you also conversely saw these examples where people were totally remaking different functions and then going back and saying, actually, humans have a gigantic role to play here, right?

26:23So it's like, I feel like we are learning this lesson. And I don't think these things are, I mean, I don't think these things are actually in tension with each other. That is to say, when you actually get the human role right, and when you actually teach people how to manage the technology that's changing how work gets done, you get better outcomes much faster.

26:46David Rice: You know, most AI business cases, they're relatively good at capturing sort of what disappears, right? You know, the hours saved, tasks automated, head count capacity created, that kind of thing. I think they're a little bit worse at measuring what might disappear with it, which is, you know, we mentioned that tacit knowledge transfer, the relationships, the judgment, mentoring, even those informal moments where people make sense of difficult work together, right? I'm curious, do you think our current definition of AI ROI systematically sort of undervalues that human side of work. It's changing, but yes.

27:22And that's why, you know, one of the trends that I really like is we're starting to measure AI ROI by outcomes, right? Versus just, so like, how many customer calls did it actually resolve versus just how many hours got saved or something like that? Those are two different ways of measuring the same problem. How many, if you're using AI for training, you know, did people's skills actually get augmented or not? If you're using it in, let's say, a legal setting, you know, and you're using it for research, like, are you actually getting a measurable improvement in the quality of the briefs that you're filing?

28:01Or are you, as we all know, getting a whole bunch of hallucinations that are getting filed and having your lawyer censored for that, right? Right. So again, when you change the way that you're looking at it to sort of the holistic metric, then you start to account for both what AI is capable of and what's not what it's not capable of and where you need people in the loop and double checking things or where you need actual human judgment or you need people actually learning the ropes while they're doing it, as you've talked about before. And so I think that's actually very positive. And I'm pretty confident that's where we'll end up is that we're going to be measuring more holistic business outcomes.

28:37And when you do that, you're going to be making better investments in the technology and you're going to have a clearer roadmap for where people's jobs should be evolving in a way that's exciting, like in a way that's empowering and gives people more power and more opportunity to do bigger and better things at work.

28:56David Rice: Yeah, I agree with you. I think we are moving away from, I've had a conversation several times recently, and I'm saying a conversation because it's the same thing, but it's the conversation about measuring usage. We're starting to see people go, that's not it. Because I think the thing that people are starting to realize is there are countable gains, but you're going to struggle with diffused losses with certain things. right so yeah like hours saved that you're going to measure that immediately but if you take like something like weaker judgment you may not be able to detect that for a year two years you know and there's like an asymmetry there where the benefit is booked now but the capability cost is deferred and that i think more and more people are getting turned on to that and be like okay we got i have to measure this differently.

29:50Or even like CSAT or something like that, right? So yeah, AI can answer all of your customer questions. That doesn't mean you're going to convert the deal or that your customer is happy with the service they got or whatnot.

30:02David Rice: And again, like AI is really powerful. I can do a lot of things that, and that's very, very exciting. You just want to measure it, measure it in the right way so that you're getting that right balance of where are the places where you really do want people stepping in. And why does that matter? Well, I guess that my natural follow-up question to that would be like, what would you actually measure? Because I just wrote a story recently called, Can You Measure Judgment? Because I've heard people say, well, you can't measure judgment. I'm like, well, actually, we've seen that there are certain things, there are signs and there are behaviors that you can measure, right?

30:38David Rice: So like, the problem I have with like, well, we can't, it's tough to measure that. It's like, well, then efficiency is going to win every argument. So what would you measure, I guess, if you were looking at this and thinking like, I want to be able to show the long-term impacts and sort of, if we're approving this huge transformation budget, what needs to maybe appear in that business case that isn't there today? Let's think about like from a customer perspective first, and we could think about it internally, but from a customer perspective, like, yes, as we just said, you could measure hours saved and you should, You should, but you should also measure customer satisfaction.

31:13You should measure customer attrition. You should measure, you know, how many customers are converting from the sales efforts, whether they're AI assisted and augmented or not. But notice that none of the things that I've just said and I could go on are new. These are holistic ways we measure our business right now. we're just kind of you know in this weird blip in time where we're you know obsessed about AI and so we're zeroing in on this kind of one intervention but when you really integrate it into the system and you look at those things then you realize it's a mix of strategies that you mean like you do want your customer to land on your website and be able to ask any question that they want about your product and get escalated to a like if it's a simple transaction go ahead like go transact online.

32:01If it's a more difficult one, escalate it to a person who might be able to close the deal and actually really talk the customer through what problem they're experiencing and help them reframe that problem, right? And so I guess what I'm trying to say is I don't think we need new metrics. I just think we need to apply them. We just need to be level-headed about how we apply them.

32:21David Rice: Yeah, no, I would agree with that. And you said something when we chatted before this that I really liked. He said, technology is powerful, but it's not the protagonist. We are. If we take that seriously, then the goal of AI transformation can't be, you know, simply just making people more productive, right? So what should organizations actually want humans to become better at as machines become more capable? Let me just take a step back for a second. Why did I write a book? I like I ask myself that question a lot because I believe my husband asks me that question all the time. And I will say, like, I was kind of getting upset because I had all these really smart friends, one of the celibacy to AI wave, I had all these smart friends reach out to me.

33:04They were so nervous. And they were saying to me, like, they didn't quite say it like this, but, you know, whether it was my friends reaching out to me or these board conversations, these customer conversations, this nervousness of like, do we still matter? Are people becoming obsolete? And it was so clear to me through all of my interactions with so many different customers, just like observing technology over time, that it was really the opposite was true, was that as AI becomes more powerful, that question of judgment and relationship building becomes more and more important. And that really what's happening is that we are all becoming managers of AI.

33:44We are going to be needing to manage. We are becoming turbocharged. And to be able to do that, we need to effectively manage the technology and understand what it's good at and not good at and then being able to step in. And so I think this sort of speaks directly to your question, because this isn't just about building a faster horse, right? It's about us imagining what's possible when you can name whatever it is in your role, when AI is able to personalize interactions at a hyperscale in marketing, or when AI is able to answer all those first-line questions that come in from customers, or in HR, when AI is able to give a chance to do that.

34:27all of your employees, all the information that they need about benefits at the tip of their fingertips, and you can change that effort to something else. What is possible, right? All of the things that I just said, that is going to become table stakes. What matters? Like, you need people that are in those roles that know how to leverage those efficiencies and then do something better for their customers or their employees. And that's why, like, it's so, so important that we have people that not only are fluent in AI, but are empowered in their judgment and empowered to think of new things. That's the moment that we're in, not a faster horse, a reimagined business, right?

35:10And that's why I'm so passionate about this whole conversation.

35:14David Rice: No, I agree. I'm hearing more leaders sort of become attuned to the fact that this is like an organizational design choice rather than just accepting sort of the inevitable consequence of this technology entering the workplace. And I'm hearing more people say, like, well, you know, it's not necessarily about everybody having the answer, especially leaders. Leaders are realizing, like, I don't have the answers. But a good leader can ask the right questions. And so, like, and then it's about judgment and sort of framing problems and building trust in your people so that they feel free to take responsibility, right?

35:48David Rice: And so, like, yeah, Yeah, productivity is an intermediate outcome, but I don't think it's like the end all be all. The more interesting question is, what are we doing with the capacity that this creates? That's exactly right. And by the way, productivity is very important. It's just really important. I'm not throwing shade at it. I'm just saying, like, you know, to get even in this intermediate stage, you know, we've seen all these AI disasters. Like, you need people overseeing it. You really need people to understand the boundaries of where I should step in and where people should. And also you need people that are empowered to reimagine things and to really keep their customers and their employees at the center of the transformation.

36:29And those are the companies that I think are going to win. I'm already seeing it.

36:33David Rice: I'm seeing like jobs getting redesigned. And I think managers are a great example of that, right? Like if AI can remove sort of the reporting and the admin work that goes along with that, the manager, then it becomes, you actually want something different from your manager. You're looking for them to become a better coach or to get more out of their direct reports. And this creates the opening for them to do that. But it also creates a different framework of like what you are actually looking for from a manager. So, cause I talk all the time about like, it's a little weird that we've had this thing in the last 50 years or a hundred years of work where like, oh, I was really good at my job, my individual contributor job.

37:12David Rice: So I became a manager and then like, I have no idea. It's not actually my personality type to coach. Right. So like now you can free it up. Like they don't have to be brilliant at this, but if they're really good at like sharing knowledge and helping people see a better way of doing things, that's your manager. they maybe weren't the best at doing the IC work, you know? No, that's, so first of all, what you just said, this is one of my pet peeves. And by the way, I think we're at this moment where all of a sudden we don't know it. We don't talk about it this way, but we're asking now almost every single employee to manage AI.

37:46Similar lack of training. We're just like, go do it. Right. It was kind of one of my motivations to write this book. But more broadly, again, what you just said is like a great example. I was thinking about my manager when I actually started writing this book. A couple years ago, I started having the idea for this book. My manager at the time was a man named John Stormer, and he didn't, I told him about this idea, wasn't part of my day job. He didn't say like, no, that's a bad idea. He didn't subtly redirect me. He just kind of quietly fed my imagination, send me podcasts, send me news articles, and kind of quietly conveyed this belief that it was important.

38:26And from that belief, this whole book blossomed and sort of gave me the confidence to put out this perspective in the world. And I think we underestimate how much that kind of connection really matters. Again, no one asked me to write this book, right? It came from a place of inspiration and connection that was nurtured by not only him, but so many different people in my community. And that's the kind of thing that we don't want to lose when AI becomes more and more powerful. That's the kind of thing we want to design into, actually make more, not only not lose, but make more prevalent. That's the kind of opportunity that gives me chills.

39:09Like that's what we want to go after.

39:10David Rice: We're in the same boat there. Well, Paula, this was a great conversation. I appreciate you giving us some of your time today. Oh, thanks for having me. And so thanks for asking such great questions. I loved it. Listeners, if you haven't done so already, you know, we would definitely want to go over and check out our AI signal. We're doing like an executive briefing every week with the one thing you've got to think about this week, whether that's attention, a question that you need to ask in your next leadership team meeting, a tool to assess the work that's going on right now. Be sure to sign up for that.

39:39David Rice: And until next time, think of the possibilities. We're redesigning work here.

39:46David Rice: Run performance reviews and bring employee engagement insights into your favorite AI tool. Connect TeamFlect to ChatGPT, Claude, or any LLM to check goals, write reviews, evaluate feedback, create tasks, and more, all through a simple chat. Try TeamFlect now for free.