303: Execution Visibility: Why AI Doesn't Replace Jobs, It Replaces Tasks, with Kason Morris ၏ စာသားမှတ်တမ်းအပြည့်အစုံ

answer questions about talent. 00:02:32.719 --> 00:02:34.639 So just different types of questions. 00:02:34.639 --> 00:02:36.080 What skills do we need? 00:02:36.080 --> 00:02:37.680 How do we develop people? 00:02:37.680 --> 00:02:39.919 How do we move talent toward opportunity? 00:02:39.919 --> 00:02:44.960 You know, how do we prepare the workforce for what's coming next? 00:02:44.960 --> 00:03:00.560 And and that curiosity around helping people do more, you know, over time, as I've done that in various different industries, various different sectors, you know, I began to see a deeper problem, which is part of how and why you you found me. 00:03:00.560 --> 00:03:11.919 Um I think we're getting increasingly sophisticated at describing people through jobs, through skills, through profiles, through assessments, through data. 00:03:11.919 --> 00:03:18.800 Um, without really becoming as sophisticated in understanding work itself. 00:03:18.800 --> 00:03:27.439 And I'll I'll expand on this a little bit more and kind of brand home uh with why execution visibility is sort of my current narrative. 00:03:27.439 --> 00:03:39.599 Because we can see the org chart, you know, we can see headcount, we can see learning activity, but you know, you could even see outcomes sometimes. 00:03:39.599 --> 00:03:45.360 But what we could not always see is what happens in between those points. 00:03:45.360 --> 00:03:57.199 And I describe that as like what are the tasks, what are the decisions, what are the handoffs, what are the dependencies and the moments of judgment through which people actually create value. 00:03:57.199 --> 00:04:07.039 And now that we're in this AI era, like AI has is making that visibility gap really urgent and really, really important. 00:04:07.039 --> 00:04:19.759 You know, organizations are now being asked to decide what machines should do and what humans should continue doing and how roles should change. 00:04:19.759 --> 00:04:26.399 But you can't intelligently redesign work you don't adequately understand. 00:04:26.399 --> 00:04:34.480 And and that that realization is is what became this discipline and thinking around execution visibility. 00:04:34.480 --> 00:04:39.040 But the the starting point was always how do I help people do a little bit more? 00:04:39.439 --> 00:04:42.959 You know what I love about your execution visibility uh concept? 00:04:42.959 --> 00:04:48.160 When I first saw it, I was like, wait a second, that sounds almost like what I'm trying to do, but from a different perspective. 00:04:48.160 --> 00:04:55.040 You know, I talk about behavioral visibility after learning events and how that leads to results intelligence. 00:04:55.040 --> 00:04:57.839 And so we're like, whoa, we're on the same page here. 00:04:57.839 --> 00:05:07.199 And that's why I almost, in a sense, I'd like to call myself a student of yours because I've been reading up all your stuff, all the articles online about you, because it's really fascinating. 00:05:07.199 --> 00:05:19.120 But having said that, you know, did you have a specific experience somewhere where you realize organizations actually don't quite understand how work gets done? 00:05:19.120 --> 00:05:21.600 I'll give you an example from our last conversation. 00:05:21.600 --> 00:05:25.600 You said something about Salesforce and the COVID uh experience. 00:05:25.600 --> 00:05:29.120 I'm wondering if there's something in that story that you you could share. 00:05:30.639 --> 00:05:38.560 Yeah, um, you know, I would say this really became prevalent for me in the past few years of my executive work. 00:05:38.560 --> 00:05:57.759 Um, where for the technology company, I was asked to revamp and start to look at the the future of learning experience design uh right around COVID because traditionally the way folks would handle work or handle relationships or even sell was in person. 00:05:57.759 --> 00:06:01.839 And now we're we're we're dispersed and we're behind the screens. 00:06:01.839 --> 00:06:25.680 And it really got me thinking um a bit more about what are the skills and capabilities of my team today, you know, and and then now that we're going into a new way of trying to teach and engage sales leaders and and others, it's like, well, what what are the capabilities and and skills that we need for the future? 00:06:25.680 --> 00:06:42.639 Um and so as I started thinking about how to reorganize and restructure and and you know bring in the next wave of of capabilities, it it really got me thinking about that that problem that's in in plain sight, you know? 00:06:42.639 --> 00:06:53.360 And and it's like when you think about the traditional systems, like you know, most organizations understand their org charts far better than they understand how work actually creates value. 00:06:53.360 --> 00:06:58.000 And so as we were changing our work, that's where it got me really curious. 00:06:58.000 --> 00:07:09.519 And and and I started looking at uh elements behind the covers, like job architecture, um, you know, job profiles, job descriptions. 00:07:09.519 --> 00:07:16.959 And then I started looking at my talent as well and like who reports to whom, um, you know, what skills are listed. 00:07:16.959 --> 00:07:24.720 You know, you could look at the traditional stuff, like like whether they they completed certain training, um, whether business targets were were hit. 00:07:24.720 --> 00:07:39.600 But in looking at that, it it didn't necessarily tell me explicitly like where the critical decisions happened, you know, who was really good at resolving exceptions, like who could think on their feet and be really creative. 00:07:39.600 --> 00:07:45.040 Um, where were that where were the handoffs either accelerating execution or or slowing it down? 00:07:45.040 --> 00:07:54.560 Um where where does that that informal and formal expertise, like where does it exist amongst our our teams that were delivering this value? 00:07:54.560 --> 00:08:00.480

Because so much that's on paper is rarely the whole story of someone's capability. 00:08:00.480 --> 00:08:10.160 Um, but but our older mindsets were very much around, you know, this is the command and control, this is the order and the hierarchy of things. 00:08:10.319 --> 00:08:10.560 Yeah. 00:08:10.800 --> 00:08:24.160 And it and it forced me to think a little bit differently because the bottom line was the team was performing and producing, and we knew that work happened every day, but I couldn't really grasp it. 00:08:24.160 --> 00:08:44.799 And that's what that's what led me to thinking a little bit more about the the data behind it and the structures that we use and how can we build better data and better understandings around tasks, decisions, handoffs to drive the the the next era of of experience design for for the for the organization. 00:08:45.840 --> 00:08:53.519 You know, you uh have actually introduced, I I believe you're the only one who's introduced this idea of execution visibility. 00:08:53.519 --> 00:08:55.120 But correct me if I'm wrong. 00:08:55.120 --> 00:08:58.000 But what exactly is execution visibility? 00:08:58.000 --> 00:09:08.480 Let's assume someone's listening right now and they they want to understand how they could, you know, in a tangible way, how they could get their minds around execution visibility. 00:09:08.480 --> 00:09:09.840 What exactly is that? 00:09:10.559 --> 00:09:23.200 I would say the the simple, simplest possible explanation for what execution visibility is, is it's really about a better understanding of how work works. 00:09:23.200 --> 00:09:31.200 That's that's the simplest way that that I could I could put it, you know, because when you think about what's gonna happen, right? 00:09:31.200 --> 00:09:42.320 You know, the companies that are gonna do things well, they're gonna see how work actually creates value, and then they're gonna see how do we build the human capability behind it. 00:09:42.320 --> 00:09:50.320 So execution visibility is really all about how is value actually created in the work that we do. 00:09:50.320 --> 00:09:57.840 And if you go to many senior leaders today, you you you won't get a very clear answer or understanding around that. 00:09:57.840 --> 00:10:03.919 So we try to deconstruct and break that down uh through through this model and through this discipline. 00:10:03.919 --> 00:10:07.919 And then that allows us to answer, you know, other questions. 00:10:07.919 --> 00:10:13.039 Like once you understand how value is created, where do we use technology to create that value? 00:10:13.039 --> 00:10:16.480 Where do the humans sit in that value creation? 00:10:16.480 --> 00:10:18.399 How do we react to it? 00:10:18.559 --> 00:10:33.759 You know, no, no, actually, when you said that, I I was thinking of of a question that we've talked about before, which is around uh the use of AI in almost a cart before the horse uh analogy. 00:10:33.759 --> 00:10:45.440 You know, you you talk

about how you know everyone seems to be racing toward AI, we all know that, but you talk about how perhaps organizations are doing that before they understand the work itself. 00:10:45.440 --> 00:10:52.879 And and wouldn't that be dangerous if if you know you're you're putting a layer of sophistication over something you you barely understand? 00:10:52.879 --> 00:10:54.720 How exactly do you position that? 00:10:54.720 --> 00:10:56.879 I I I like the way you talked about that before. 00:10:57.519 --> 00:11:05.759 Yeah, I mean, I mean, to the point and and premise of of visibility, it's like thinking about the the work itself. 00:11:05.759 --> 00:11:14.720 Um so that's the task, the workflows, the decisions, the handoffs, and the dependencies that that create value. 00:11:14.720 --> 00:11:28.080 Uh, and then the to me, when you're able to do that, then the the the second place to making sure you you're putting the the technology in the right place is capability formation. 00:11:28.080 --> 00:11:37.759 So it's like, what are the experiences that actually allow people to develop expertise, judgment, leadership, confidence? 00:11:37.759 --> 00:11:48.240 So when you think about execution and visibility, it's sitting at the intersection of those two assets because I believe those are those are the two most important assets in work. 00:11:48.240 --> 00:11:55.840 The work itself, and then how you're developing your experts to actually make these decisions. 00:11:55.840 --> 00:12:08.080 And then only then are you starting to bring in the technology in a more meaningful um and and you know responsible way because that's where the discipline is. 00:12:08.080 --> 00:12:16.000 It's really all about the discipline of understanding how work creates value, how it builds capability, and how it develops human judgment. 00:12:16.320 --> 00:12:24.320 You know, one of the one of the big uh fear uh items out in the world is whether or not AI is replacing jobs. 00:12:24.320 --> 00:12:28.720 But you've said AI doesn't replace jobs, it replaces tasks. 00:12:28.720 --> 00:12:30.240 What do you mean by that? 00:12:30.799 --> 00:12:31.279 Correct. 00:12:31.279 --> 00:12:45.279 Because going back to the point around um understanding the work, if you're deconstructing work, you know, because in in the old way of thinking, our job titles were was the measure of value, was the the sort of container. 00:12:45.279 --> 00:13:01.840 And so now what what I'm saying, and what I think the the market is saying with with the AI sort of era, is now you have to think about work and of itself and breaking it down into tasks because what isn't what is AI really good at taking on a task. 00:13:01.840 --> 00:13:08.559 And and and if you don't break down the work into task, it'll do things, but it may not be doing the right things for you. 00:13:08.559 --> 00:13:33.919 So so that's where why I think when it comes to AI, uh it's really, really important to start to think about work in the way of of tasks and decisions, because once you're able to deconstruct it in that way, then you go into the question that I mentioned earlier of like, well, based on these tasks and decisions, which ones is AI really well suited for to do quickly? 00:13:33.919 --> 00:13:38.240 And that's gonna vary from function to function, company to company. 00:13:38.240 --> 00:14:03.679 And the problem that we run into right now is is is a bit of an AI arms race where everybody's bringing in all the cool technology, all the fancy things, and they're they're looking at like consumption, like, oh, people are adopting it, people are using it, but they don't really understand what they're using it for because they really don't understand the workflows and the processes and the behavior changes that they want to drive. 00:14:03.679 --> 00:14:08.639 Most organizations right now, in my opinion, are over-indexing for efficiency. 00:14:10.080 --> 00:14:10.960 Interesting. 00:14:10.960 --> 00:14:17.360 Um, you know, you also raise this idea of, and you've mentioned it here today already, human judgment. 00:14:17.360 --> 00:14:26.000 Uh, but in an AI world, you know, what do you see um as I mean, what which work develops human judgment? 00:14:26.000 --> 00:14:29.440 And how is AI either gonna help that or hurt that? 00:14:29.440 --> 00:14:31.360 What are your general thoughts on that? 00:14:31.919 --> 00:14:42.080 Uh it's it's such a uh an interesting um point and question and and debate because when you think about it, you know, expertise. 00:14:42.080 --> 00:14:44.879 How is expertise typically built? 00:14:44.879 --> 00:14:46.639 It's built through at bat. 00:14:46.639 --> 00:14:54.799 So you think about the analyst, the junior analyst, how do they become uh a senior analyst or how do they become an expert at what they do? 00:14:54.799 --> 00:15:04.399 They're analyzing papers, they're they're they're taking on low-risk, sort of repetitive tasks, they're getting feedback, they're going through different types of loops. 00:15:04.399 --> 00:15:09.360 And by by building the muscles over time, you do you develop context. 00:15:09.360 --> 00:15:27.440 So that's why when I say like the the ability to judge is so important, because now with with AI, you basically have uh a superpowered intern that knows the knowledge of everything, but they don't it doesn't necessarily know how to make the right decision with the output. 00:15:27.440 --> 00:15:31.440 And that's where that's where the human comes into play. 00:15:31.440 --> 00:15:45.039 So when you think about early career considerations and even just that whole expertise curve in general, you know, we're we're now starting to take away those opportunities with the emerging technologies. 00:15:45.039 --> 00:16:03.919 So I I would argue, and part of my thesis is that um organizations have responsibility now to, and even the learning functions have responsibility now to think about, okay, if we're taking these kinds of repetitive

and maybe lower risk tasks away that help build expertise, what are we replacing it with to develop expertise? 00:16:03.919 --> 00:16:06.960 How do we use AI as a partner in that? 00:16:06.960 --> 00:16:16.240 And that's where I see the future going is a shifting in that responsibility of like, yes, the business wants the outcome and wants the outcome faster and cheaper. 00:16:16.240 --> 00:16:36.000 But as I think of HR performance professionals and other professionals in the workplace, we have to start to think about like, hey, how do we use these tools to now simulate those moments that allow people to develop uh that that capability because you're gonna need a human in a loop to make those decisions and you're gonna need that accountability. 00:16:36.000 --> 00:16:43.840 And if we don't think about it pragmatically and and develop it with intentionality, there's gonna be a huge risk for for most organizations. 00:16:45.279 --> 00:17:02.960 You know, I think that whoever is listening right now, a leader in an organization, might want to hear some tips and tricks, if you have any, about how they should think about protecting things like human judgment while still embracing automation or AI in some form or manner. 00:17:02.960 --> 00:17:05.200 What are your thoughts on some advice? 00:17:05.599 --> 00:17:23.039 Yeah, uh I mean it I would say that you know, you you have to look at anything you're doing with AI, like like think about it with a holistic picture around, you know, what are our near-term opportunity gains? 00:17:23.039 --> 00:17:28.799 And then also, you know, I go through generally a series of four core questions. 00:17:28.799 --> 00:17:35.680 There are there are more, but when I think about a leadership team, I want them to confront these four questions. 00:17:35.680 --> 00:17:38.559 And I've mentioned them throughout what we've discussed so far. 00:17:38.559 --> 00:17:42.400 It's where understanding where the value is created. 00:17:42.400 --> 00:17:48.400 That's that's that's the first and and most important question, you know, as you think about AI. 00:17:48.400 --> 00:17:52.559 And then it's like which work should the AI perform? 00:17:52.559 --> 00:17:57.519 So just because it can doesn't mean that it should. 00:17:57.519 --> 00:18:01.200 And that's why you have to hold space and have that conversation. 00:18:01.200 --> 00:18:12.079 And then with the learning experts and those that you have around you, it's really once you break down work into tasks and things like we mentioned, it's like which work develops human judgment? 00:18:12.079 --> 00:18:19.039 So if you're a lawyer and you, you know, the elements of what you're doing, what are the things that actually help you become a stronger lawyer? 00:18:19.039 --> 00:18:23.359 If you're a project manager, what are the tasks that are helping you become a better project manager? 00:18:23.359 --> 00:18:36.240 So this is like sort of just the you know, pick your role, but just this,

you know, systematic steps to kind of start to deconstruct of these tasks which things are are are the most valuable for human judgment. 00:18:36.240 --> 00:18:47.200 And then last but not least, it's like now that we're gonna redesign, because it's not saying avoid technology, but it's saying now we're bringing in technology, you know, with some intentionality. 00:18:47.200 --> 00:18:58.559 How do we redesign this workflow in partnership with that technology without weakening the human capability or the organizational capability? 00:18:58.559 --> 00:19:14.960 And and I think that's the the crux of it is you know, once you understand which pieces are the most important for developing the experts, now you can make a very intentional decision as to where to put the AI and how the AI and the humans will partner. 00:19:14.960 --> 00:19:19.119 So that way you're not weakening that development piece for employees. 00:19:19.119 --> 00:19:21.680 Doesn't mean every company will do that. 00:19:21.680 --> 00:19:41.200 So this is why it's a it's that this is why I want to have these conversations and why this is so important uh uh to evangelize for, because the way I see it, and and I think in our previous conversation, I kind of gave that anecdote around in in a couple of years, every organization's gonna have the AI equivalent of the iPhone. 00:19:41.200 --> 00:19:45.279 We're gonna have all the stuff, all the infrastructure, all those things working. 00:19:45.279 --> 00:19:49.759 And then it's gonna it's gonna become full circle because what's gonna matter? 00:19:49.759 --> 00:19:55.519 Your talent and the capability of that talent and those leaders to make decisions with those outputs. 00:19:55.519 --> 00:19:56.880 And then guess what's gonna happen? 00:19:56.880 --> 00:20:00.319 You're gonna have a huge shortage because you're gonna have a demand and a need. 00:20:00.319 --> 00:20:09.680 And even if you look at World Economic Forum research and all the things that are leading up to sort of that 2030 narrative, it's all pointing in that direction. 00:20:09.680 --> 00:20:22.559 That some jobs will go away, but new jobs are gonna be created and the gap of behaviors and skills to actually fulfill those jobs is gonna continue to widen if we're if we're not thinking thinking about it. 00:20:22.559 --> 00:20:33.359 So I say it as like if you're taking away the the the old bottom rung and you're building a new rung, but you're not giving people the ability to step into it, you're gonna be in trouble. 00:20:34.079 --> 00:20:42.000 You know, this topic is uh very dear to my heart because in fact that's what the human side of leadership uh is all about. 00:20:42.000 --> 00:20:46.160 Uh, you know, we're inundated with technology, AI, and other things. 00:20:46.160 --> 00:20:51.920 Um, how do we hold on to some of those things that really make us successful that are human? 00:20:51.920 --> 00:21:04.160 Uh so I I think I I couldn't agree with you more, especially with the idea that the more AI gets involved in things, the less we re or the more we realize how important the human skills are, right? 00:21:04.160 --> 00:21:05.920 You know, it's sort of what you're saying. 00:21:05.920 --> 00:21:15.519 Now, on this note, you know, coming closer to my world, if you will, I have a question I wanted to ask you about the human side of leadership. 00:21:15.519 --> 00:21:18.880 Um, especially with respect to learning, right? 00:21:18.880 --> 00:21:23.279 And and and behavior in organizations, and of course, and results. 00:21:23.279 --> 00:21:29.839 You know, we know organizations spend billions of dollars every year teaching people things, right? 00:21:29.839 --> 00:21:37.599 And yet we're not really sure that they're uh turning those things into uh behaviors and into results. 00:21:37.599 --> 00:21:38.319 Yeah. 00:21:38.319 --> 00:21:47.119 From your lens, the world that you you are building things like ex you know, execution visibility, how do we address that? 00:21:47.119 --> 00:21:48.960 What do you think about this problem? 00:21:50.400 --> 00:21:59.519 I think it's it's one that uh the world we're in now is is forcing um us to kind of change and look at with some intentionality. 00:21:59.519 --> 00:22:08.240 Because when you think about the old way of working, you know, work is not simply where people produce. 00:22:08.240 --> 00:22:12.640 Work, part of work is where people become. 00:22:12.640 --> 00:22:23.519 And what I mean by that is this is where they test themselves, where they develop confidence, they receive feedback, they form professional identities, they discover what they're capable of. 00:22:23.519 --> 00:22:35.680 So now that you're bringing in the AI into it, it's like people are losing that comfort, losing that sense of self in like how do I grow? 00:22:35.680 --> 00:22:37.119 How do I develop? 00:22:37.119 --> 00:22:47.759 And I I care about human and organizational performance, you know, but I also care about what kinds of humans our systems of work are producing. 00:22:47.759 --> 00:23:05.839 So it's it's like, yes, with AI, a highly efficient organization can be created, but that could leave people with less agency, with weaker judgment, with no developmental pathway. 00:23:05.839 --> 00:23:11.519 Uh, and and to me, that's that's not a a well-designed organization. 00:23:11.519 --> 00:23:14.799 That's not a human-first organization. 00:23:14.799 --> 00:23:34.000 Um, so so these are the kinds of things that when I when I sit in the seat that I sit in, I think about deeply as as I build these operating systems that are data driven and and will will essentially leverage um the power of the data to to drive these these AI implementations. 00:23:34.000 --> 00:23:56.160 But as they come to to as they come to fruition, it's just having that that partnership across all parts of the employee journey, uh, including our learning functions, our talent acquisition functions, our development functions,

and and making sure that everyone has has skin in the game on on this this piece. 00:23:56.160 --> 00:24:05.759 It's like, yes, we can make the change, yes, we can reinvent, but why and so what, and then how are we keeping our human identities as a part of that? 00:24:07.759 --> 00:24:13.200 You know, as we uh obviously move into the future, we talk about the future of leadership. 00:24:13.200 --> 00:24:18.480 What's your view about that about you know what stays uniquely human? 00:24:18.480 --> 00:24:30.559 Like, you know, if AI becomes incre increasingly or really good at execution of tasks and different things, not that it's replacing humans, but where will where will humans shine? 00:24:30.559 --> 00:24:32.079 What becomes uniquely human? 00:24:34.799 --> 00:24:37.119 It's it's a powerful question. 00:24:37.119 --> 00:24:45.200 Um and I I can't say I don't have a crystal ball to kind of say exactly what that is. 00:24:45.200 --> 00:24:59.440 And and I think the the biggest idea that I I lean on at this point is the the judgment piece, and then also as we think about sort of the purpose of work itself. 00:24:59.440 --> 00:25:05.680 Like when one one thing that happened that I think we don't talk enough about is the the economics of this. 00:25:05.680 --> 00:25:12.559 Like, like essentially, if AI becomes more sophisticated, do people need to continue to quote unquote work? 00:25:12.559 --> 00:25:17.759 And then that begs the question of like, well, what is work in an AI era? 00:25:17.759 --> 00:25:30.960 And I would argue if we're keeping humans at the center of it, you know, the new barometers of work are not just the outputs, like the number of cods and widgets you produce, but it's about your ability to grow and develop. 00:25:30.960 --> 00:25:43.279 So it's like, how are you using these technologies to simulate scenarios where you're developing critical thinking and judgment and behavior change, and you're just doing you're just building those reps in that way. 00:25:43.279 --> 00:25:57.599 So when the time comes for you to make that uniquely human decision with the technology output, you're doing it better than the you know the next person, or you're doing it in the way that the the business is is looking for. 00:25:57.599 --> 00:26:00.799 And and that's how I I see it. 00:26:00.799 --> 00:26:02.240 I see what's uniquely human. 00:26:02.240 --> 00:26:08.160 It's not just becoming the old adage of the the learning organization. 00:26:08.160 --> 00:26:14.720 It's like how do we truly become like the the always learning, lifelong learning person? 00:26:14.720 --> 00:26:23.599 And and and we talked about this, I mean, in different capacities prior to the AI era, where it was like, oh, you're always be learning, you know? 00:26:23.599 --> 00:26:28.079 Uh, and and and now I think now there's gonna there's finally some teeth to it. 00:26:28.079 --> 00:26:29.839 Like, what does that really mean? 00:26:29.839 --> 00:26:42.000 You're you're now learning as a part of a function of being employable and of being viable in the future economics of the of the world. 00:26:42.319 --> 00:26:47.920 So you know, I I wrote down here a couple of really powerful quotes that you shared. 00:26:47.920 --> 00:26:53.119 One you said that um work is where people become. 00:26:53.119 --> 00:26:56.079 Uh, my friend, that is poetry. 00:26:56.079 --> 00:26:57.440 I love that. 00:26:57.440 --> 00:27:00.000 Work is where people become. 00:27:00.000 --> 00:27:02.559 You you gotta you gotta trademark that one. 00:27:02.559 --> 00:27:06.319 And then another one you mentioned was the the human first organization. 00:27:06.319 --> 00:27:07.680 That's the title of a book. 00:27:07.680 --> 00:27:09.279 I I think that's powerful. 00:27:09.279 --> 00:27:27.440 I think in all of these things, though, I I would love to know, let's say there's a CEO listening right now, or maybe any leader listening right now, if there was one thing you would love for them to take away or remember from this conversation, because there's a lot to unpack in in 30 minutes. 00:27:27.440 --> 00:27:29.119 I mean, you've done a lot of research. 00:27:29.119 --> 00:27:33.359 What would that one thing you would hope they would remember be? 00:27:36.559 --> 00:27:44.400 I would say that a task may be replaceable, but an experience may not be. 00:27:44.400 --> 00:28:22.720 So as leaders have these huge mandates to to figure out how to make AI work, you know, adoption of AI can can tell you the new thing that's present, you know, but when you start to think about why you're adopting it and what AI transformation actually is supposed to mean, you know, true transformation, I think, in this in this era of what we're doing is going to be about behavior change. 00:28:22.720 --> 00:28:33.920 Um, and and I think I think consumption of tokens and the adoption of technology, that that could be uh a potential illusion. 00:28:33.920 --> 00:28:54.559 Because as organizations deploy new platforms, complete trainings, report high usage, and we figured about our people, you know, employees are gonna preserve their old workflows and their old workarounds, and you're not gonna get all the value that you think you want out of this work. 00:28:54.559 --> 00:29:14.000 And this is why the discipline of visibility I think is so important and it being inclusive of not only how do we bring in technology, but how do we think about our people and continue to grow and develop them uh as we go through this transformation. 00:29:14.720 --> 00:29:27.200 Yeah, and and one of the things that I've told you that I get very excited about when it comes to our you know different paths is that for example, when you talk about behavior change, that's my world. 00:29:27.200 --> 00:29:44.880 You know, the world of of teaching people things and helping them change behaviors that push toward results and then having the behavioral visibility to recognize that it's actually changing, which to

me sounds very similar to some of the things you talk about in execution visibility. 00:29:44.880 --> 00:29:48.160 So maybe we both need to talk some more about these things, huh? 00:29:49.200 --> 00:29:56.240 It sounds like there's a there's a wide, a wide, wide world, and and I think things go are going so fast. 00:29:56.240 --> 00:29:59.519 Sometimes you have to go slow to go fast. 00:29:59.519 --> 00:30:11.440 And and this doesn't mean that like you have to change everything overnight in this mode of thinking, but maybe for one or two critical roles or critical pieces of work. 00:30:11.440 --> 00:30:31.119 See, I'm even trying to, you know, my own behavior of talking about the work, not just talking about a job title or roles, but the language of work and and being able to think about it in that way, whether you're a leader in learning or a leader in HR, um, really the language of work is what translates to the business. 00:30:31.119 --> 00:30:50.319 So the more that we can speak in that in that language and bring data behind it that that supports these ideas of execution, visibility, and breaking down tasks and determining where AI should live and determining what humans should do, that's that's gonna be be the magic. 00:30:50.319 --> 00:31:00.240 And and you just do it one critical piece of work at a time and learn from it, advocate for it, evangelize around it, and and go go from there. 00:31:00.240 --> 00:31:03.599 I I think we we try to boil the ocean. 00:31:03.599 --> 00:31:14.640 Um and if you just just step back with a little bit of intentionality, you can go slow and and go deep and then be able to go a lot faster long term. 00:31:15.200 --> 00:31:15.839 Love that. 00:31:15.839 --> 00:31:18.160 Uh K-Sign, I'm gonna give you the last word. 00:31:18.160 --> 00:31:33.599 If you could uh just share with us uh outside of LinkedIn, which I'm gonna have in the show notes, where people can find you uh and what new exciting projects or uh events or anything that you're looking forward to that we gotta come see you for. 00:31:34.400 --> 00:31:44.400 Yeah, um, I mean, for me right now as a as a practitioner, I am, I mean, I have a website that I'm refreshing um where you can learn about my work. 00:31:44.400 --> 00:31:49.200 I also have a newsletter which I just started on LinkedIn called Execution Visibility. 00:31:49.200 --> 00:31:51.599 And and what I'm publishing is field notes. 00:31:51.599 --> 00:32:01.839 So again, I'm I'm on the ground doing this work and and just bi-weekly sharing the lessons and the ways of thinking uh as a way to scale this. 00:32:01.839 --> 00:32:04.799 Uh you'll you'll find me speaking at at events. 00:32:04.799 --> 00:32:12.160 Um there are a few events in the fall that I haven't officially confirmed yet, but um I'll I will certainly be there. 00:32:12.160 --> 00:32:21.839 Um and then also if you want to learn a little bit more about my philosophies and my way of thinking, um, to what you mentioned earlier, I have

the book out on on Amazon. 00:32:21.839 --> 00:32:29.519 Well, my first book, which is more about the individual and and how they think about their their sort of life work, if you will. 00:32:29.680 --> 00:32:32.079 Um what is that what is that called again? 00:32:32.079 --> 00:32:33.680 Because I want to make sure I capture that. 00:32:33.920 --> 00:32:36.480 Oh, yeah, it's called life work revolution. 00:32:36.799 --> 00:32:38.000 Life work revolution. 00:32:38.000 --> 00:32:38.799 Got it. 00:32:39.119 --> 00:32:58.799 And and it it's think about it as as um all the things that I wish I knew 10 years ago as I've gone through the journey of reinventing, you know, my skill sets, my mindset, my stories, uh, and capabilities to to navigate, you know, various different sectors, and I've helped a bunch of other people do it. 00:32:58.799 --> 00:33:08.000 So it's full of stories and and just foundational, what I call like sort of future-proof principles that anyone could could provide. 00:33:08.000 --> 00:33:27.440 Um, but to kind of just close things out overall, like for those that would be listening to your to this podcast, you know, I think if you take it take away anything from this conversation, it's the idea that, you know, most organizations are currently in a position where they understand their art chart far better than they understand the work that actually creates value. 00:33:27.440 --> 00:33:34.160 And as we continue to talk about AI, understand that AI does not consume jobs whole. 00:33:34.160 --> 00:33:37.920 It changes tasks and decisions inside of them. 00:33:37.920 --> 00:33:47.039 And when we think about this moving to the human lens, the work is not only where people produce, you know, it's where people become. 00:33:47.039 --> 00:33:50.079 And that's how that's how I'm glad you you you called that out. 00:33:50.079 --> 00:33:57.279 And and even though some tasks may be replaceable, you know, the developmental spirit experience may not be. 00:33:57.279 --> 00:34:04.880 So when you think about that task, think about whether or not, hey, is this actually helping develop or grow the experts I may need for the future? 00:34:04.880 --> 00:34:12.719 Uh, and then last but not least, it's like, you know, you can't intelligently redesign work you can't adequately see. 00:34:12.719 --> 00:34:21.679 So these are the things that I just want people to to have in their heads as they navigate, you know, the transformations that they have to do. 00:34:21.679 --> 00:34:31.280 And if if anyone ever needs support or want wants to um have any questions, I I also keep a calendarly slot open to talk about these concepts. 00:34:31.280 --> 00:34:42.960 Um and I'm happy to speak about them, you know, with with team leaders, uh, or or do any kind of like you know, small group opportunities to just help educate. 00:34:43.519 --> 00:34:44.079 I love it. 00:34:44.079 --> 00:34:48.480 I love the execution visibility angle, and I love all the stuff you've shared. 00:34:48.480 --> 00:34:55.840 And I'm going to make sure we have the Life Work Revolution, uh, the Amazon link, uh, of course, your LinkedIn, and then your website. 00:34:55.840 --> 00:34:56.320 Yep. 00:34:56.320 --> 00:35:02.159 Heysan, it's been a pleasure, uh, just an honor to uh learn from you, listen to you, and talk to you. 00:35:02.159 --> 00:35:03.840 Thank you so much for being on the podcast. 00:35:04.239 --> 00:35:05.119 Thanks for having me. 00:35:05.760 --> 00:35:06.239 All right.