WEBVTT - This is what AI is actually doing to your brain, with Gabriella Rosen Kellerman 0:00:01.000 --> 0:00:04.360 It's late afternoon and you've got three AI tools running 0:00:04.440 --> 0:00:09.760 at once, browser tabs, multiplying, and your brain just stops working. 0:00:10.680 --> 0:00:16.640 You're not burnt out, but something is definitely wrong. Gabriella 0:00:16.840 --> 0:00:20.600 Rosen Callaman is a leader at Boston Consulting Group, and 0:00:20.640 --> 0:00:24.239 she actually decided to study that feeling and give it 0:00:24.320 --> 0:00:29.639 a name. AI brain fry. Brain fry is not the 0:00:29.680 --> 0:00:33.000 same as burnout. It doesn't follow the same rules, and 0:00:33.320 --> 0:00:36.080 the things that organizations are doing to fix it, well, 0:00:36.200 --> 0:00:41.440 some are actually making it worse. In this conversation, Gabriella 0:00:41.560 --> 0:00:45.120 and I get into who is getting hit hardest, why 0:00:45.320 --> 0:00:48.200 using more than three AI tools at once is where 0:00:48.240 --> 0:00:51.800 productivity falls off a cliff, and what managers are doing, 0:00:52.000 --> 0:00:56.840 often unknowingly that is adding fifteen percent more mental fatigue 0:00:56.880 --> 0:01:01.200 to their teams. This episode really changed how I think 0:01:01.240 --> 0:01:04.319 about my own AI use, and I hope it does 0:01:04.400 --> 0:01:07.200 the same for you. Also, just a quick heads up, 0:01:07.280 --> 0:01:08.960 there's a little bit of background noise at the very 0:01:08.959 --> 0:01:11.480 start of this episode. Record it early so it clears 0:01:11.520 --> 0:01:12.920 up quickly to stick with us. 0:01:19.080 --> 0:01:22.800 Welcome to How I Work, a show about habits, rituals, 0:01:22.840 --> 0:01:26.760 and strategies for optimizing your day. I'm your host, doctor 0:01:26.800 --> 0:01:27.760 Amantha imber. 0:01:32.040 --> 0:01:36.800 Gabriella. Your article all about brain fry for HBr went 0:01:37.400 --> 0:01:41.880 completely nuts? Can you tell me what is brain fry? 0:01:41.959 --> 0:01:43.679 For those that have no idea. 0:01:43.520 --> 0:01:46.720 I'll start by giving you the definition that we used 0:01:46.760 --> 0:01:49.760 in the study, and then I'll break it down kind 0:01:49.800 --> 0:01:54.280 of in layman's terms. So we define it as mental 0:01:54.320 --> 0:01:58.800 fatigue caused by excessive use or oversight of AI tools 0:01:59.360 --> 0:02:03.360 beyond what one's cognitive capacity. And think of that as 0:02:03.480 --> 0:02:07.639 like a strain of your brain from over using AI 0:02:07.840 --> 0:02:09.880 in mentally intense ways. 0:02:10.080 --> 0:02:13.120 And how is that different from just burnout? 0:02:13.600 --> 0:02:16.560 Let me start by actually explaining how we came to 0:02:16.600 --> 0:02:19.600 do this study to begin with, which was we were 0:02:19.639 --> 0:02:23.360 really interested in what is the relationship of burnout to 0:02:23.480 --> 0:02:26.680 the use of AI tools at work? And it turns 0:02:26.680 --> 0:02:32.000 out in the literature that it's very unclear. So some 0:02:32.080 --> 0:02:34.680 studies suggest that when you use a lot of AI 0:02:34.720 --> 0:02:38.519 tools at work, it makes the workload lighter, it gives 0:02:38.600 --> 0:02:42.239 you more free time, and it decreases burnout. And then 0:02:42.360 --> 0:02:45.560 other studies suggest that when people are using more AI 0:02:45.600 --> 0:02:49.559 tools at work for a variety of unclear reasons and mechanisms, 0:02:49.560 --> 0:02:52.960 that may be increasing burnout, and it even happens to 0:02:52.960 --> 0:02:57.359 be the case that there's a whole literature studying radiologists 0:02:57.720 --> 0:03:02.280 and whether radiologists in part particular have increased or decreased 0:03:02.280 --> 0:03:05.720 burnout because of the use of AI tools. So we 0:03:05.720 --> 0:03:10.000 were very interested to study it, and in particular, as 0:03:10.040 --> 0:03:13.080 you may be aware, there is more and more usage 0:03:13.120 --> 0:03:16.760 of multi agents at work, meaning you're not just using 0:03:16.760 --> 0:03:19.560 one AI tool at once, you're using multiple agents at once. 0:03:19.960 --> 0:03:23.080 Sometimes the agents are working with each other. And what 0:03:23.160 --> 0:03:27.160 we found was that indeed there's this way of using 0:03:27.200 --> 0:03:31.000 AI very intensively. We're really trying to keep up with 0:03:31.080 --> 0:03:33.480 these tools and use a lot of them at once, 0:03:33.520 --> 0:03:38.400 and it's very cognitively intense, and it produces this experience 0:03:38.520 --> 0:03:41.760 that we call AI brain fry that does not have 0:03:41.840 --> 0:03:45.880 a relationship with burnout, doesn't increase burnout, doesn't decrease burnout. 0:03:46.160 --> 0:03:49.160 And then we did see that totally different type of 0:03:49.480 --> 0:03:53.080 AI use, which is using a I to replace repetitive 0:03:53.120 --> 0:03:57.960 tasks at work, decreases burnout. But we hopefully also started 0:03:58.000 --> 0:04:01.720 to advance the conversation about the extent to which burnout 0:04:01.720 --> 0:04:05.920 as a construct is no longer sufficient to help us 0:04:06.040 --> 0:04:11.000 understand the domains of strain that will be raised in 0:04:11.080 --> 0:04:12.720 the new era of AI work. 0:04:12.880 --> 0:04:16.920 I find that's so interesting the distinction. Now in your research, 0:04:17.040 --> 0:04:19.760 you look at the different types of roles or different 0:04:19.839 --> 0:04:25.560 functional areas within an organization, and interestingly, marketing people had 0:04:25.720 --> 0:04:27.839 the highest rates of brain for it. I think it 0:04:27.880 --> 0:04:31.760 was twenty six percent. What's going on there, Like, how 0:04:32.000 --> 0:04:35.080 are marketing people different from say, finance people. 0:04:35.400 --> 0:04:39.680 Yeah, so it's a great question. The first thing I'll 0:04:39.720 --> 0:04:44.320 say is that it's possible that marketing is on the 0:04:44.360 --> 0:04:46.880 higher end, but maybe there will be other roles that 0:04:47.000 --> 0:04:50.720 spake higher in future studies. What we did see in 0:04:50.760 --> 0:04:53.800 general is that roles that are more operationally heavy, roles 0:04:53.800 --> 0:04:56.839 that are more technically heavy, seem to be spiking higher 0:04:56.880 --> 0:05:00.960 than other roles. And indeed, in marketing there is a 0:05:01.000 --> 0:05:04.200 lot of operational lenses that are brought to the use 0:05:04.200 --> 0:05:10.679 of AI, things like monitoring content, looking at optimizing content particularly, 0:05:10.800 --> 0:05:15.760 and like search engine optimization, you know, question and answer optimization. 0:05:16.680 --> 0:05:18.599 Marketing is one of the domains where a lot of 0:05:18.600 --> 0:05:22.160 the roles have been most disrupted by AI in terms 0:05:22.200 --> 0:05:26.440 of the specific skills and responsibilities. So in a way, 0:05:26.440 --> 0:05:29.400 it wasn't surprising to us to see that. I just 0:05:29.440 --> 0:05:33.080 want to be cautious in how we extrapolate from that 0:05:33.240 --> 0:05:35.400 to the level of certainty that we can get to. 0:05:36.000 --> 0:05:40.240 So your research found that it's about AI oversight specifically 0:05:40.279 --> 0:05:44.320 as opposed to using AI, say for automating or you know, 0:05:44.480 --> 0:05:50.840 offloading repetitive tasks. Can you explain what AI oversight is 0:05:51.000 --> 0:05:54.640 specifically like instead of day to day layman's terms. 0:05:55.200 --> 0:05:58.320 Yeah, so it means quite a lot of things. But 0:05:58.800 --> 0:06:02.520 if you've ever inn with an AI agent, you'll notice 0:06:02.520 --> 0:06:05.080 that you're going to give it a prompt, it's going 0:06:05.120 --> 0:06:08.000 to give you something back. You're going to respond to that. 0:06:08.640 --> 0:06:13.200 You are directing the agent at work. That agent might 0:06:13.240 --> 0:06:17.920 be responsible for actually delivering value, let's say some analysis 0:06:18.080 --> 0:06:21.839 or delivering code. Right, So, to the extent that you 0:06:22.120 --> 0:06:26.560 are then responsible for the output of that agent or 0:06:26.560 --> 0:06:30.960 that tool, you are overseeing it, and your domain of 0:06:31.040 --> 0:06:36.039 responsibility now extends to that agent. In a way, you 0:06:36.080 --> 0:06:39.159 could say that you are managing that agent as a manager. 0:06:39.200 --> 0:06:42.560 It's different type of management, but it is true that 0:06:42.640 --> 0:06:49.039 your sphere of accountability has increased. The activities include decision making, 0:06:49.400 --> 0:06:57.080 error correction, feedback, prompts, monitoring all kinds of interactions that 0:06:57.160 --> 0:07:00.880 are required in order to oversee that agent. 0:07:01.360 --> 0:07:04.760 And so in terms of the language around agents, so 0:07:04.800 --> 0:07:09.640 that could just be Chetchjpat or co pilot or Claude 0:07:09.760 --> 0:07:13.520 or Gemini as opposed to true a gentiki. 0:07:13.200 --> 0:07:17.040 Those are agents. So many of those it could be 0:07:17.160 --> 0:07:20.560 those things. It could be agents that are running other 0:07:20.640 --> 0:07:24.320 agents and you are responding to sort of the orchestrator layer. 0:07:25.160 --> 0:07:28.080 Sometimes some of the folks in the study talked about 0:07:28.560 --> 0:07:31.800 agents that are running longer processes and then sort of 0:07:31.840 --> 0:07:34.520 feeding back to them and they then have to go 0:07:34.600 --> 0:07:37.120 through and figure out what may have gone wrong at 0:07:37.160 --> 0:07:41.400 various points in the process, so unwinding it versus the 0:07:41.800 --> 0:07:45.400 more conversational agents where it's more one step at a time. 0:07:46.120 --> 0:07:49.520 But yes, all of those things could be under the 0:07:49.640 --> 0:07:51.720 umbrella of oversight and monitoring. 0:07:51.920 --> 0:07:54.960 And it was interesting because you looked at how many 0:07:55.600 --> 0:08:00.760 AI tools increase productivity, and then there was this productivity 0:08:01.000 --> 0:08:05.160 cleif with productivity tanked after I think it was about 0:08:05.280 --> 0:08:09.880 three AI tools. Why is two or three AI tools 0:08:10.440 --> 0:08:12.800 the optimal number? And what happens after that? 0:08:13.200 --> 0:08:15.840 For those who haven't seen the study, what we did 0:08:15.960 --> 0:08:18.440 was we asked people how many tools are you using 0:08:18.480 --> 0:08:22.040 at once, and it was open ended. Many people said one, 0:08:22.200 --> 0:08:26.080 many people said two, one person said twenty five, and 0:08:26.120 --> 0:08:30.920 then everything in between. And then we you know, we 0:08:30.960 --> 0:08:33.640 asked all kinds of other questions, including things like how 0:08:33.720 --> 0:08:37.040 much has AI increased your productivity at work? It happened 0:08:37.080 --> 0:08:39.520 to be the case there was a really interesting relationship 0:08:39.520 --> 0:08:45.040 between those two particular items, such that this self reported 0:08:45.120 --> 0:08:50.440 productivity increased through AI increased with multiple use of tools 0:08:50.480 --> 0:08:54.040 from one to two, from two to three, but then 0:08:54.200 --> 0:08:59.439 beyond three that self reported increased decreased, the point being 0:08:59.720 --> 0:09:03.840 there is diminishing returns beyond about two to three tools. 0:09:04.280 --> 0:09:06.880 It may vary by person. By the way, our brains 0:09:06.880 --> 0:09:09.480 are not all the same. I think it's important also 0:09:09.520 --> 0:09:12.440 to not like this is timestamped to January of twenty 0:09:12.440 --> 0:09:15.240 twenty six and to the generation of tools that we 0:09:15.400 --> 0:09:18.400 have right now. Oftentimes, what is a mean to work 0:09:18.440 --> 0:09:21.040 with these tools all at once is the tools have 0:09:21.280 --> 0:09:25.160 processing times, and so maybe it's the case that in 0:09:25.200 --> 0:09:28.080 the windows, where one tool is processing, you can go 0:09:28.120 --> 0:09:30.760 to another tool and turn your attention and then it's 0:09:30.800 --> 0:09:34.520 sort of like a triangle of rotation between the tools, 0:09:34.800 --> 0:09:37.959 and if there's faster processing times, maybe you can't do that. 0:09:38.040 --> 0:09:41.040 Another thing that didn't make the cut of the study 0:09:41.080 --> 0:09:43.079 of what we were able to fit into the publication 0:09:43.760 --> 0:09:46.760 that I'd love to share is that we saw a 0:09:46.840 --> 0:09:52.160 similar pattern of the peak at three for the sophistication 0:09:52.640 --> 0:09:56.280 of the users of those tools. So we have at 0:09:56.320 --> 0:10:01.680 BCG a categorization of AI sophistication that's really just about 0:10:01.880 --> 0:10:05.080 how long you've been working with AI tools, how well 0:10:05.240 --> 0:10:09.320 you know how to work with orchestrateor agents and autonomous layers, 0:10:09.400 --> 0:10:13.680 and it helps us see how do we learn to 0:10:13.720 --> 0:10:17.640 get better with the tools over time and therefore say 0:10:17.720 --> 0:10:21.480 things about like what's the limitation of someone's experienced curve 0:10:21.559 --> 0:10:24.800 or learning curve versus the tools, let's say, or some 0:10:24.920 --> 0:10:28.679 interaction between human and AI that's more fundamental. And what 0:10:28.720 --> 0:10:31.160 we see is that it seems to be that the 0:10:31.200 --> 0:10:34.920 most sophisticated users tend to prefer to be around three. 0:10:35.920 --> 0:10:39.360 So there are the folks who are hanging out, you know, 0:10:39.400 --> 0:10:42.440 maybe at the twenty five that I mentioned are probably 0:10:42.480 --> 0:10:45.640 not the most sophisticated users are probably folks who are 0:10:45.679 --> 0:10:48.520 still a little bit earlier in their learning curve. Obviously, 0:10:48.520 --> 0:10:52.440 they're not the earliest, but there's some wisdom that's coming 0:10:52.480 --> 0:10:56.400 into play around. It's not about like maxing out the 0:10:56.520 --> 0:10:58.959 number of tools you can use at once. It's really 0:10:59.000 --> 0:11:02.480 finding that sweet spot of productivity where it feels good 0:11:02.480 --> 0:11:05.600 to your brain. You have that feeling of mastery and 0:11:05.679 --> 0:11:09.079 yet you're getting that boost from the tools with the productivity. 0:11:09.320 --> 0:11:12.800 I think about my own use of AI, and it's 0:11:12.840 --> 0:11:15.160 really interesting, you know that you point out, Okay, this 0:11:15.240 --> 0:11:18.640 study is time dated to January twenty twenty six. And 0:11:19.000 --> 0:11:23.559 depending on what I'm asking my AI tools to do, 0:11:23.760 --> 0:11:26.719 and I'm generally more of a claud user, although I'll 0:11:26.760 --> 0:11:30.520 sometimes go into chat TOPT for certain things. Is that 0:11:30.600 --> 0:11:34.360 you know, processing times do vary. Like if I'm asking 0:11:34.880 --> 0:11:37.199 the AI tool to do some deep research on a 0:11:37.280 --> 0:11:42.680 topic and meanwhile I might have another tab or task 0:11:42.840 --> 0:11:45.959 open that is, perhaps I'm working with it to edit 0:11:46.040 --> 0:11:50.120 some content. Then I know for me, and I mean, 0:11:50.160 --> 0:11:53.920 I know how bad multitasking and context switching is for 0:11:54.120 --> 0:11:57.520 my brain as a psychologist, but I find myself doing 0:11:57.559 --> 0:12:01.079 it quite a bit. When I'm using the AI tools 0:12:01.120 --> 0:12:04.840 to help augment or save time on whatever it is 0:12:04.880 --> 0:12:09.079 that I'm doing, I'm curious is that something you can 0:12:09.120 --> 0:12:13.280 relate to in your gabriella And I'm wondering what behaviors 0:12:13.280 --> 0:12:16.319 have you changed around how you're interacting with AI since 0:12:16.360 --> 0:12:17.199 doing this study. 0:12:17.760 --> 0:12:22.280 One fascinating thing in the in the qualitative descriptions of 0:12:22.320 --> 0:12:25.080 brain fry, there was a lot of task switching that 0:12:25.240 --> 0:12:28.280 was mentioned as we ask people what does brain fry 0:12:28.440 --> 0:12:32.439 feel like and when have you experienced it? A lot 0:12:32.480 --> 0:12:35.960 of discussion of you know, I'm bouncing between browser tabs 0:12:36.040 --> 0:12:39.040 and what it feels like to go from tool to tool. 0:12:39.760 --> 0:12:43.880 I think that element of AI is very much connected 0:12:43.920 --> 0:12:46.760 to the broader issue of digital overload, which you know, 0:12:46.800 --> 0:12:50.760 we've been talking about for a couple of decades now. 0:12:51.040 --> 0:12:56.920 So for me taking breaks having like true no digital 0:12:57.000 --> 0:13:01.679 slots and I do at least twenty for hours breaks 0:13:01.760 --> 0:13:05.640 from the digital once a week. I think having breaks 0:13:05.800 --> 0:13:08.920 at work where I do phone calls instead of zoom 0:13:08.920 --> 0:13:12.120 calls and I'm away from my computer and just talking 0:13:12.160 --> 0:13:16.000 and doing the audio is really really helpful and restorative. 0:13:16.840 --> 0:13:20.040 I think right now where there is like a feeling 0:13:20.080 --> 0:13:24.720 of excitement and such deep engagement with the AI tools 0:13:24.960 --> 0:13:27.600 and a feeling of like, look at this magic that's 0:13:27.720 --> 0:13:31.480 very hard to pull away from. So the more we 0:13:31.559 --> 0:13:35.560 can be aware of the fact that just being engaged 0:13:35.600 --> 0:13:39.000 in this hyper busy way with our computers, with any 0:13:39.080 --> 0:13:42.199 kind of digital tool, there are brain networks we can't 0:13:42.240 --> 0:13:45.600 access when we operate that way, and knowing to take 0:13:45.640 --> 0:13:50.320 those breaks and take those digital detox moments, you will 0:13:50.400 --> 0:13:53.280 feel different, right, It feels different in your brain and 0:13:53.320 --> 0:13:57.360 your body when you step away from these devices phones too, 0:13:57.840 --> 0:14:01.720 like that the audio is fine, but the internet, the email, 0:14:01.800 --> 0:14:04.440 that's all part of the same thing is a very 0:14:04.480 --> 0:14:07.120 important part of my routine as well. 0:14:07.320 --> 0:14:12.280 Now you give some recommendations for what leaders and managers 0:14:12.520 --> 0:14:17.600 can do for their teams that are presumably using AI 0:14:17.840 --> 0:14:22.040 quite frequently. What are sort of the top ways that 0:14:22.560 --> 0:14:26.160 managers can improve this brain for our situation? 0:14:26.680 --> 0:14:31.000 Yeah, thank you for asking. One of the most useful 0:14:31.040 --> 0:14:34.760 parts of this study was we were able to demonstrate 0:14:35.440 --> 0:14:42.040 these strong predictive relationships between manager behaviors, team practices and 0:14:42.160 --> 0:14:47.480 organizational factors and mental fatigue. And these factors were all 0:14:47.520 --> 0:14:51.280 AI related. So at the manager level, the two most 0:14:51.360 --> 0:14:55.480 important factors that we found on the one hand, when 0:14:55.560 --> 0:15:01.200 managers spend more time answering their employee questions around AI 0:15:02.240 --> 0:15:07.840 that predicted fifteen percent less mental fatigue for the members 0:15:07.880 --> 0:15:12.040 of their team, which is a huge number. And the 0:15:12.080 --> 0:15:15.760 way I tell myself the story of that is that 0:15:16.280 --> 0:15:20.880 this is a manager who's leaning into the inner personal time, 0:15:20.960 --> 0:15:25.040 the human relationships, the care and the support for the team. 0:15:25.480 --> 0:15:28.120 You know, in the best case scenario, these tools are 0:15:28.160 --> 0:15:31.280 giving us more time for that. They're giving us more 0:15:31.320 --> 0:15:35.320 time to feel connected, to feel that sense of belonging 0:15:35.360 --> 0:15:39.720 in community, and we're experiencing them together as a team. 0:15:40.480 --> 0:15:43.560 When the manager is showing up that way, that is 0:15:43.680 --> 0:15:48.080 having a sense of you know, slve, it's really showing 0:15:48.120 --> 0:15:51.040 this decreased effect on the mental fatigue of the team. 0:15:52.000 --> 0:15:55.080 And then the inverse of that came through as well, 0:15:55.680 --> 0:15:58.560 in the sense that there was a five percent increase 0:15:58.760 --> 0:16:02.640 in mental fatigue of the team. When the manager is 0:16:02.640 --> 0:16:06.480 setting an expectation that the employees should just be learning 0:16:06.600 --> 0:16:09.560 how to use the tools on their own. So when 0:16:09.640 --> 0:16:13.000 this is framed as like a solo mission, go out there, 0:16:13.280 --> 0:16:16.120 do this on your own, it's not about us doing 0:16:16.160 --> 0:16:19.680 this together, there is an increase in the mental fatigue 0:16:19.680 --> 0:16:22.080 that the team members will experience. 0:16:22.440 --> 0:16:25.720 I find that so interesting. Now, how about some of 0:16:25.760 --> 0:16:28.760 the company messages that I'm certainly hearing, Like a lot 0:16:28.760 --> 0:16:31.680 of organizations sprout like you know, AI is going to 0:16:31.720 --> 0:16:35.840 make you more productive? Like what do those messages do 0:16:36.320 --> 0:16:41.040 to brain fry burn out, having higher intentions to leave 0:16:41.080 --> 0:16:46.080 your company? Like what messages should leaders be sending about AI? 0:16:46.520 --> 0:16:50.680 The message that had the strongest impact in terms of 0:16:50.840 --> 0:16:56.520 decreasing burnout in particular and mental fatigue was sending a 0:16:56.560 --> 0:16:59.640 message from that the company cares about work life balance. 0:17:00.400 --> 0:17:03.560 So this was a case where it actually is not 0:17:03.640 --> 0:17:07.639 a message about AI. It's really a cultural message of 0:17:07.680 --> 0:17:12.120 the value that's placed on having a workplace and an 0:17:12.160 --> 0:17:16.960 employee base that is fulfilled, that has a sense of 0:17:17.720 --> 0:17:20.439 that balance between who they are as a person and 0:17:20.480 --> 0:17:23.520 who they are as a professional. Having that come through 0:17:23.640 --> 0:17:28.080 as a strong positive pays off dividends in terms of 0:17:28.240 --> 0:17:31.920 the pros and the engagement and the productivity that we're 0:17:31.960 --> 0:17:34.760 seeing in the data. In terms of the AI story, 0:17:35.680 --> 0:17:38.280 what we did see in the study, which is important 0:17:38.320 --> 0:17:41.480 to know and important to take forward, employees who felt 0:17:41.560 --> 0:17:45.160 the sense that their workload was going to increase dramatically 0:17:45.240 --> 0:17:49.679 because of AI were more vulnerable to this mental fatigue, 0:17:50.480 --> 0:17:53.800 and there could be lots of reasons for that, but 0:17:54.000 --> 0:17:56.880 the goal shouldn't be to tell the story that your 0:17:56.920 --> 0:18:00.159 work's going to get heavier because of AI. And I 0:18:00.160 --> 0:18:04.800 don't think organizations are intentionally telling a story like that, right. 0:18:04.960 --> 0:18:08.719 It's more about how do we emphasize the positives that 0:18:08.800 --> 0:18:11.680 will come for AI. How do we emphasize the sense 0:18:11.760 --> 0:18:16.200 of there is a lot of creativity that can open up. 0:18:16.240 --> 0:18:19.560 There's a lot of new domains of building that you 0:18:19.640 --> 0:18:21.919 can do as an individual. There's a lot of lift 0:18:22.080 --> 0:18:25.119 that you can get through this that you things you 0:18:25.359 --> 0:18:28.800 have not been able to do before. Lean into those 0:18:29.000 --> 0:18:33.960 domains of it, and really watch out for unintentional undertones 0:18:34.280 --> 0:18:38.560 of this is going to lead to that increased work 0:18:39.200 --> 0:18:42.720 which people, when they hear, will be more disposed to 0:18:42.760 --> 0:18:43.840 that mental fatigue. 0:18:43.920 --> 0:18:47.399 I think that's so interesting because something that like I 0:18:47.440 --> 0:18:50.960 certainly hear the wellbeing message from ladies and I even remember, 0:18:51.200 --> 0:18:52.800 I mean, this is going back a couple of years 0:18:52.840 --> 0:18:55.919 ago when AI was I guess you know, when it 0:18:55.920 --> 0:18:58.440 was pretty early in the journey for a lot of businesses, 0:18:58.440 --> 0:19:01.800 and I remember speaking to achieve people officer at a 0:19:01.880 --> 0:19:05.200 big global firm, and they'd been copying a lot of 0:19:05.240 --> 0:19:08.959 flak in the media for having very high workloads and 0:19:09.000 --> 0:19:13.960 having very high levels of burnout. And I remember talking 0:19:14.000 --> 0:19:16.520 to her about her strategy and she said, well, AI 0:19:16.600 --> 0:19:20.080 will solve that because we'll implement it, we'll train people, 0:19:20.560 --> 0:19:23.760 and then they'll be able to do essentially I'm paraphrasing 0:19:23.880 --> 0:19:28.000 a normal forty hour work week, and I really see 0:19:28.160 --> 0:19:30.320 that kind of a story playing out. In fact, I 0:19:30.359 --> 0:19:32.800 can't even think of a client that we're working with 0:19:33.000 --> 0:19:37.639 or an organization that I've read about that has implemented AI. 0:19:37.880 --> 0:19:41.640 They've trained people, and now there's like there's hard data 0:19:41.920 --> 0:19:46.280 to show that employees are working less hours, Like, have 0:19:46.400 --> 0:19:51.520 you seen any examples of anyone that's doing this well 0:19:51.560 --> 0:19:55.280 as opposed to just saying the right things, because I 0:19:55.359 --> 0:19:56.720 feel like there's a bit of a gap there. 0:19:56.880 --> 0:19:59.120 Yeah, you know, I think what's coming through and I 0:19:59.200 --> 0:20:02.000 have seen this in real life as well, but I 0:20:02.080 --> 0:20:04.520 think it's coming through more in the data In a 0:20:04.560 --> 0:20:09.080 way that I can stand behind statistically, is that there 0:20:09.200 --> 0:20:13.240 is this cluster of use that's really about replacing these 0:20:13.280 --> 0:20:17.240 repetitive tasks, these tasks that people don't want to be doing, 0:20:17.960 --> 0:20:21.199 and the people who are doing that and choosing to 0:20:21.359 --> 0:20:25.600 use the time in ways that are creating enjoyment, positive 0:20:25.640 --> 0:20:29.119 experiences at work, more connection. That's what we suffer, this 0:20:29.200 --> 0:20:33.800 particular population in our study who had that decrease burnout. 0:20:34.560 --> 0:20:38.320 I hear it when people talk about, for example, even 0:20:38.880 --> 0:20:42.400 folks who do a lot of interviews, whether it's customer 0:20:42.440 --> 0:20:45.879 interviews or media interviews, and they talk about not having 0:20:45.920 --> 0:20:49.960 to transcribe anymore and the extent to which they can 0:20:50.000 --> 0:20:52.720 really just focus on the conversation and lean into that 0:20:52.840 --> 0:20:55.800 and enjoy it and it doesn't have to suck hours 0:20:55.800 --> 0:20:58.880 of their time. Those sorts of tasks and the way 0:20:58.920 --> 0:21:01.400 people talk about what means to have that time back 0:21:01.440 --> 0:21:04.760 and have that depth of interaction. Those are the stories 0:21:04.800 --> 0:21:08.320 that I think about and I think are where I'm 0:21:08.359 --> 0:21:12.159 seeing the really big lift and so much low hanging 0:21:12.200 --> 0:21:16.040 fruit by the way across organizations to find those moments 0:21:16.080 --> 0:21:21.119 and those stories and that left and I think sometimes 0:21:21.240 --> 0:21:25.000 you know, our job in helping organizations is really identify 0:21:25.080 --> 0:21:28.159 those moments where the lift is going to come from 0:21:28.640 --> 0:21:32.680 finding that work that can be easily automated and where 0:21:32.720 --> 0:21:37.280 the time given back will energize, right versus like be 0:21:37.400 --> 0:21:39.960 filled with something that you know will deplete. 0:21:40.080 --> 0:21:41.879 Now, you might think of brain fry as just a 0:21:41.920 --> 0:21:45.000 personal problem, but that is not the way to think 0:21:45.040 --> 0:21:48.480 about it. It is also a leadership one. Coming up, 0:21:48.560 --> 0:21:52.399 Gabriella gets into what it actually looks and feels like 0:21:52.440 --> 0:21:55.520 when you're in it, how to build self awareness around 0:21:55.560 --> 0:21:58.320 your own cognitive limits, just like the way a runner 0:21:58.400 --> 0:22:02.480 loans their breath, what leaders can do right now to 0:22:02.640 --> 0:22:06.520 check in on whether their team is suffering from it. Plus, 0:22:06.600 --> 0:22:09.439 we also get into the ten twenty seventy rule that 0:22:09.480 --> 0:22:13.880 most organizations are completely ignoring when it comes to AI adoption. 0:22:17.560 --> 0:22:20.200 If you're looking for more tips to improve the way 0:22:20.280 --> 0:22:23.480 you work can live. I write a short weekly newsletter 0:22:23.560 --> 0:22:26.720 that contains tactics I've discovered that have helped me personally. 0:22:27.119 --> 0:22:30.200 You can sign up for that at Amantha dot com. 0:22:30.400 --> 0:22:36.720 That's Amantha dot com for. 0:22:36.680 --> 0:22:41.760 People listening that feel like I reckon. I am experiencing 0:22:41.840 --> 0:22:45.959 brain fry every single day. Like, what are the first 0:22:46.040 --> 0:22:49.160 thing or things that you would suggest that they immediately 0:22:49.200 --> 0:22:49.840 start to do? 0:22:50.040 --> 0:22:53.520 Okay, So the first thing is I got the funniest text, Actually, 0:22:53.520 --> 0:22:56.400 my husband got the funniest tax from a friend who 0:22:56.840 --> 0:23:00.520 had read our piece on brain fry and was like, 0:23:01.000 --> 0:23:03.720 I'm kind of loving my brain fry right now. Like 0:23:04.359 --> 0:23:07.240 I'm just like, I know I'm having brain friend kind 0:23:07.280 --> 0:23:09.960 of loving He had like all his cloud browsers open 0:23:10.040 --> 0:23:12.000 and he was like building a website and he's like, oh, 0:23:12.000 --> 0:23:13.919 I'm kind of loving it. So I want to just 0:23:14.000 --> 0:23:17.040 recognize that, like these tools can be really engaging and 0:23:17.080 --> 0:23:19.240 exciting and often we get to the point of brain 0:23:19.320 --> 0:23:22.560 fry because we're enjoying ourselves, right, So it can be 0:23:22.600 --> 0:23:24.840 really engaging to see like how much you can build 0:23:24.880 --> 0:23:27.840 and how fast, And so the point is not to 0:23:27.880 --> 0:23:31.480 deprive ourselves of the excitement and the engagement. And to 0:23:31.600 --> 0:23:34.200 my friend, if you're listening, like you go enjoy yourself 0:23:34.240 --> 0:23:38.440 and you know, don't deprive yourself of that. The goal though, 0:23:38.560 --> 0:23:42.240 is really like this is giving us opportunity to develop 0:23:42.400 --> 0:23:46.359 acute self awareness of our own intelligence as we come 0:23:46.440 --> 0:23:49.400 to meet this new alien intelligence. 0:23:50.119 --> 0:23:50.439 Right. 0:23:50.520 --> 0:23:52.919 So I really think it's like this is a deep 0:23:53.200 --> 0:23:56.080 self awareness that we can develop of our own brains 0:23:56.680 --> 0:24:02.000 that's brought on by trying to understand artificial intelligence and 0:24:02.200 --> 0:24:05.439 starting to understand just as when you go out to 0:24:05.600 --> 0:24:08.360 go for a run, you start to learn your breath, 0:24:08.440 --> 0:24:10.399 you start to learn when am I out of breath? 0:24:10.640 --> 0:24:13.159 When do I need to slow down? Those are the 0:24:13.160 --> 0:24:16.280 things we have to understand with our own brains as 0:24:16.280 --> 0:24:20.760 we're using these tools. We want to operate in an optimum. 0:24:21.359 --> 0:24:24.119 We don't want to make mistakes at work, right, And 0:24:24.160 --> 0:24:26.200 that was one of the things in our study that's 0:24:26.240 --> 0:24:28.919 at stake clearly with this is we need to be 0:24:28.960 --> 0:24:31.440 able to work sustainably. We need to have our attention 0:24:31.560 --> 0:24:34.399 and our working memory intact for the whole day, for 0:24:34.480 --> 0:24:37.240 all the tasks that we have, for the task outside 0:24:37.240 --> 0:24:39.119 of work, for our families, right, we want to be 0:24:39.160 --> 0:24:41.760 in good shape for all of those things. How do 0:24:41.800 --> 0:24:44.480 we pace ourselves? How do we start to feel that 0:24:44.560 --> 0:24:47.520 sense of brain fry coming on? Take a break, do 0:24:47.640 --> 0:24:51.239 something else, have a digital detox, Start to figure out 0:24:51.280 --> 0:24:54.000 how many tools can you reasonably work with at once. 0:24:54.359 --> 0:24:57.280