Human Intelligence in the Age of Agentic AI: Navigating Work and Leadership Transformation

What happens when AI doesn't just create, but acts autonomously? Kieran Gilmurray, CEO of Kieran Gilmurray Company and Limited & Chief AI Innovator, takes us beyond generative AI into the realm of agentic systems that can plan, adapt, and operate with minimal human guidance.

TLDR:

• Generative AI creates content while agentic AI takes action and makes decisions autonomously
• Knowledge is becoming freely accessible, challenging the traditional model of hiring and training junior professionals
• Organizations face strategic choices between automating roles or augmenting human capabilities
• The human-technology partnership remains essential, with AI handling routine tasks while humans develop uniquely human skills
• Leaders must learn to manage hybrid teams comprising both human and digital labor
• Emotional intelligence, curiosity, and communication skills become increasingly valuable as technical tasks are automated
• Organizations need adaptive mindsets and structures to thrive in an environment where competitive advantages may last months rather than years
• The goal should be creating AI-native intelligent businesses with people at their core, not replacing humans

As knowledge becomes freely accessible through AI for mere dollars per month rather than years of training, organizations face profound questions about their workforce structures. 

• Do we still need junior staff when AI can perform their tasks? 
• How do we balance human and digital labor? 

Kieran shares a striking example of how a business professor compressed what would have been 12 weeks of team research into just 2.5 hours using a customized AI system, producing results the client couldn't believe weren't human-generated.

Despite these capabilities, Kieran emphasizes that the most successful approach isn't replacement but augmentation: "Great people plus great technology equals an even greater result." 

While AI handles routine tasks, humans should develop their uniquely valuable skills—emotional intelligence, curiosity, cognitive flexibility, and communication.  Leaders must learn to orchestrate hybrid teams of humans and digital workers, setting appropriate metrics and creating meaningful career paths in this new environment.

The most forward-thinking organizations will move beyond simply adding AI to existing processes and instead fundamentally reimagine how they deliver value. These "AI-native intelligent businesses" will combine intelligent processes, people, and technology to create frictionless experiences for customers. As technology becomes easier to copy and competitive advantages shrink from years to months, an organization's true edge will be its ability to build adaptive teams that combine human creativity with technological capabilities.

Ready to navigate this rapidly evolving landscape? 

Listen now to discover how you can thrive alongside AI rather than be replaced by it.

Chapters

• 0:00 Introduction to Agentic AI
• 3:09 Distinguishing Agentic AI from Generative AI
• 8:50 Impact on Junior Roles and Knowledge Work
• 16:10 Augmentation vs Automation Strategic Choices
• 21:30 Team Dynamics with Digital Labor
• 27:30 Leadership in an AI-Native Organization
• 32:02 Creating Adaptive Organizations for Future Success

Transcript

Ross Dawson: 0:00

When it comes to who we hire. That opens up the question now do we have Gen AI and a Gen Tech labor and do we actually need as many juniors Now? Someone's going to have to press the buttons for the next couple of years and any foresighted firm is going to go. Do you know something? This is great, but people plus technology actually makes a better answer. I just not might need as many.

Kieran Gilmurray: 0:20

Welcome to the Amplifying Cognition podcast. I am Ross Dawson, a futurist and entrepreneur fascinated by the unlimited potential of the human mind. In this podcast, we learn from amazing people how to think better and build better organizations in our massively accelerating world. We explore what's possible, how to augment ourselves and, ultimately, who we can become. The Amplifying Cognition Podcast began its life as the interviews for my book Thriving on Overload, uncovering the specific techniques that inspiring people like Tim O'Reilly, nir Eyal and Kathy Hackle used to make sense of a world of unlimited information. I renamed the podcast Amplifying Cognition to bring in other themes, such as AI augmentation. However, my core interest and I would argue just about the most important thing today is how we can achieve better cognition in an accelerating world. I think Thriving on Overload is even more relevant than when it came out, and it is the foundation of all of my work today. You can check it out if you're interested, including free chapters, at thrivingonoverloadcom.

Kieran Gilmurray: 1:23

In this episode, I speak with Kieran Gilmurray. Kieran is CEO of Kieran Gilmoury Company and Chief AI Innovator of Technology Transformation Group, works as a keynote speaker, fractional CTO and in delivering transformation programs for global businesses. He's author of three books, most recently Agentic AI. He's been named as the top thought leader on generative AI, agentic AI and many other domains. In this conversation, we talk about agentic AI, the idea of software labor restructuring roles in businesses, ai, native intelligent businesses essentially, how humans and AI work together in the future business. Stay tuned for a wonderful conversation with Kieran Gilmurray. Kieran, it's fantastic to have you on the show.

Ross Dawson: 2:12

Absolutely delighted, ross Brilliant, to be here. Thank you so much for the invitation, by the way.

Kieran Gilmurray: 2:18

So agentic AI is hot, hot, hot. Ingentic AI is hot, hot, hot, and it's now sort of these new levels of how it is. We have these autonomous or semi-autonomous aspects of AI. So I want to really dig into. You know I've got a new book out on Ingentic AI, particularly looking at the future of work. I particularly want to look at work, so you know where I'm applying cognition. So I want to start off just by thinking about, first of all, what is different about agentic AI from generative AI, which we've had for the last two or three years, in terms of our ability to think better, to perform our work better, to make better decisions. So what is distinctive about this layer of agentic AI?

Ross Dawson: 3:09

I was going to say Ross, comically nothing if we don't actually use it, because it's like all the technologies that have come over the last 10, 15 years. You know, we've had every technology we have ever needed to make work more efficient work, more creative work, more innovative, to get teams work more efficient work, more creative work, more innovative, to get teams working together a lot more effectively. But let's be honest, technology's dirty little secret is that we, as humans, very often resist, so I'm hoping that we don't resist this technology. You know, like like the others, we have slowly resisted in the past, but they've all come around to make us work with them, but this, this one, is subtly different. So when you say, look, agendic AI is another artificial intelligence system. The difference in this one, if you take some of the recent what I describe as digital workforce or digital labor, go back eight years to look at robotic process automation, which was very much about helping people perform what was meant to be end-to-end tasks. So, in other words, the robots took the bulky work, the horrible work, the repetitive work, the mundane work and so on, all vital stuff to do, but not where you really want to put your teams, not where you really want to spend your time and usually all of that mundaneness sucked creativity out of the room. You ended up doing it most of the day, got bored and then never did the innovative, interesting stuff.

Ross Dawson: 4:28

Agenda is still digital labor sitting on top of large language models, and the difference here, as is described, is that this is meant to be able to act autonomously. In other words, you give it a goal and off it goes, with minimal or no human intervention. You can design it as thus you know, or both, and the systems are meant to be more proactive than reactive. You know they plan, they adapt, they operate in more dynamic environments. You know they don't really need human input. You give them a goal, they try and make some of the decisions and the interesting bit is you know there is, or should be, human in the loop in this. You know a little bit of intervention, but the piece here on, like RPA that was, you know, rpa1, I should say not the later versions, because it's changed is its ability to adapt and to reshape itself and to relearn with every interaction. If you take it at the most basic level, you look at a robot under the sea trying to navigate. You know to build pipelines. In the past, it would get stuck. A human intervention would need to happen. It would fix itself. Now it's starting to work itself out and determine what to do.

Ross Dawson: 5:35

If you take that into business, for example, you can now get a group of agentic agents, for example, example, to go out and do a, an analysis of your competitors. You can go out and get it to do deep research. Another agentic agent to do reach deep research, mckinsey, bcg or something else. You can get another agent to bring that information back, distill it, assemble it. Get an agent to create it, turn that into an article. Get another agent to proofread it. Get another agent to pop it up onto your social media channels and distribute it, and get another agent to basically SEO, optimize it, check and reply to any comments that anyone's making.

Ross Dawson: 6:15

You're sort of going here, but that feels quite human. Well, that's the idea of this. Now we've got generative AI, which creates. The problem with generative AI is that it didn't do. In other words, after you created something, the next step was well, what am I going to do with my creation? Agentic AI is that layer on top where you're now starting to go. Ok, not only can I create, I can decide, I can do and act and I can now make up for some of the fragility that exists in existing processes where RPA would have broken. Now I can sort of go from A to B to D to F to C and if suddenly G appears, I'll work out what G is. If I can't work it out, I'll come and ask a person Now I understand G and I'll keep going forever in a day.

Ross Dawson: 6:58

Why is this exciting? Well, or interesting, I should say. Well, used right, you know, this can now make up for all the fragility of past automation systems, where they always got stuck and we needed lots of people and lots of teams to build them, whereas now we can let them get on with things. Where it's scary is that you know now we're talking about potential human level cognition. So therefore, you know what are teams going to look like in the future? Will I need as many people? You know? Will I be managing as a leader managing, you know, agentic agents plus people? You know agentic agents can work 24-7. So am I, as a manager, now going to be expected to do that? Its impact on, you know what type of skills, in terms of not just leadership, but digital and data and technical and everything else. So there's a whole host of questions, as much as there is new technology here. Ross.

Kieran Gilmurray: 7:49

Yeah, yeah, absolutely. And so I mean those are some of the questions that I want to ask you, the best possible answers we have today and you know, in your book you do emphasize you know this is about augmenting humans. It is around, you know how it is. We can work with the machines and how they can support us, and human creativity and oversight being at the center. But the way you've just laid out, there's a lot of what is human work which is overlap from what you've described.

Kieran Gilmurray: 8:18

So, just at a first step, thinking about individuals, all right professionals. At a first step, thinking about individuals, all right professionals, knowledge workers, and so they have had. You know there's a few layers. You've had your tools, your excels, you've had your assistants, which start to go and do tasks when you ask them, and now you have agents which can go through sequences and you know flows of work in. You know knowledge processes. So what does that mean today for a knowledge worker who's starting to have? You know flows of work in, you know knowledge processes. So what does that mean today for a knowledge worker who's starting to have? You know whether enterprise starts to bring them in or they say, well, this is going to support it. So what are the sorts of things which are manifest now for an individual professional in bringing these agentic workflows to play? What are the examples, what are ways to see how this is changing work?

Ross Dawson: 9:09

Yeah, well, let's dig into that a little bit, because there's a couple of layers to this. If you look at what AI potentially can do through generative AI, all of a sudden, you know, the question becomes why would I actually hire new trainees, new labor, on the basis that, if you look at any of the studies that have been produced recently, then there's two roles, two setups. So let me do one, which is actually we don't need junior labor, because junior labor takes a long time to learn something, whereas now we've got generative AI and other technologies and I can ask it any question that I want and it's going to give me a pretty darned good answer. And therefore, rather than having having you know three and four and five years to train someone to get them to a level of competency, why don't I not just put in a genetic labor instead? It can do all that lowish level work and I don't need to spend five years learning. I immediately have an answer. Now that's still under threat because the technology isn't good enough yet. It's like the first scientific calculator version. You know, they didn't quite work. Now we don't even think about it. So there is a risk that all of a sudden, agentic ai can get me an answer, or generative ai can get me an answer that previously would have taken six or eight weeks.

Ross Dawson: 10:26

Let me give you an example. So I was talking to a professor from chicago business school the other, and he went to one of his global clients and normally the global client will ask about a strategy item. He would go away, him and a team of his juniors and equals would research this topic over six or 12 weeks and then they would come back with a detailed answer where the juniors would have went round, done all the grunt work, done all the searching and everything else, and the seniors would have distilled it and off he went. He's actually written a version of a GPT and he's fed it to pass strategy documents and he fed in the client details. Now, he did this in a private GPT, so it was clean and clear, and in two and a half hours he had an answer Literally his words, not mine. He went back to the client and said there you go. What do you think? By the way, I did that with generative AI and agentics and they went no, you didn't. That works too good. You must have had a team on this. And he said literally not. And he's being genuine, because I know the guy put his reputation on it. So all of a sudden, now all of those roles that might have existed could be impacted.

Ross Dawson: 11:35

But where do we get then the next generation of labor to come through in five and six and 10 years time? So there's going to be a lot of decisions need made as to look. We got Gen AI, we potentially got a Gentic AI. We normally bring in juniors. Over a period of time, they gain knowledge and, as a result of gaining knowledge, they gain expertise and, as a result of gaining expertise, we get better answers and they get more and more money. But now all of Gen AI is resulting in knowledge costing nothing.

Ross Dawson: 12:04

So where you and I would have went to university, you know, let's say, we did a finance degree that would have lasted us 30 years career done, tick.

Ross Dawson: 12:11

Now, actually, you know, gen AI can pretty much understand, or will understand, everything that we can learn on a finance degree, plus a politics degree, plus an economics degree, plus, plus, plus, all out of the box for $20 a month, and that's kind of scary. So when it comes to who we hire, that opens up the question now do we have Gen AI in a Gen T labor, and do we actually need as many juniors Now? Someone's going to have to press the buttons for the next couple of years and any foresighted firm is going to go. Do you know something? This is great, but people plus technology actually makes a better answer. I just not might need as many. So now, when it comes to the actual hiring and decision-making as to how I'm going to construct my labor force inside of an organization, decision-making as to how I'm going to construct my labor force inside of an organization, that's quite a tricky question, if and when this technology Gen-AI and agentics really ramps through the roof.

Kieran Gilmurray: 13:03

I mean, I think these are fundamentally strategic choices to be made, as in, crudely, it's automate or augment, and you could say, well, all right, first of all just say, okay, well, how do we augment, automate as many of the current roles which we have? Or you can say, well, I want to augment all of the current roles we have, junior through to senior. And there's a lot more subtleties around those strategic decisions and reality is some organizations will be doing those two extremes and a lot in between 100%, and that's their question doing those two extremes and a lot in between A hundred percent and that's their question or potentially, at the moment it's actually.

Ross Dawson: 13:38

Why don't we all comment currently? Because the technology isn't good enough to replace, and it isn't, it still isn't. And no, I'm a fan of people, by the way, don't get me wrong. So anyone listening to this should hear that I believe great people plus great technology equals an even greater result. The technology, the way it exists at the moment, is actually and you look at some research out from Harvard, Ethan Mollick, hbr, microsoft, you name it, it's all coming out at the moment says if you give people Gen AI technology, of which a genetic AI is one component, I'm more creative, more productive and, oddly enough, I'm actually happier. It's breaking down silo, it's allowing me to produce more output you know, between 10 to 40 percent but more quality output, and and and and and and. So at the moment it's an augmentation tool, but we're training, to a degree, our own replacements. Every time we click a thumbs up, a thumbs down, every time we redirect the agentics or the gen AI to teach it to do better things of the machine learning or whatever else it is, then technically we're making it smarter, and every time we make it smarter.

Ross Dawson: 14:42

We have to decide oh, my goodness, what are we now going to do? Because previously we did all of that work. Now that for me has never been a problem because for all of the technologies over the decades, everybody panicked that technology is going to replace us. We've grown the number of jobs, we've changed jobs. Now this one, will it be any different? You know potentially.

Ross Dawson: 15:00

And why I say potentially. Is you and I never worried? And and our say, our audience, too much, when you know our ea was potentially automated when the taxi driver was augmented and automated out of a job when the factory worker was augmented out of a job. Now we've got a decision, particularly when it comes to so-called knowledge work, because, remember, that's the expensive bit inside of a business the $200,000 salaries, the $1 million salaries. Now, as an organization, I'm looking at my cost base going. Well, I might actually bring in juniors and make them really efficient, because I can get a junior to be as productive as a two-year qualified person within six months and I don't need to pay them that amount of money. And or, actually, why don't I get rid of my seniors over a period of time? Because I just don't need any.

Kieran Gilmurray: 15:47

Taking a very quick break. This podcast is just one facet of our work to amplify human cognition. If you're interested in thinking better in a world of overload, using AI to augment yourself, finding like-minded thinkers or improving your organization's performance, just go to amplifyingcognitioncom. You'll find a wealth of free resources and useful tools. Now back to the show. Yeah well, those are the things that some leaders will do, but I mean, it comes back to the.

Kieran Gilmurray: 16:18

You know, the theme of amplifying cognition in the sense of you know, the real nub of the question is, yes, you can sort of say, all right, well, now we are training the machine and the machine gets better because it's interacting with giving more work and so on, but it's really finding the ways in which the nature of the way we interact also increases the skills of the humans. And so John Hagel talks about scalable learning. In fact, peter Senge used to talk about the organizational learning, and that's no different Today. We have to be learning and so saying, well, as we engage with the AI and, as you rightly point out, we are teaching and helping the AI to learn we need to be able to build the processes and systems and structures and workflows that, where the humans in it are not static and stagnant as they use AI more, but they're more competent and more capable.

Ross Dawson: 17:12

Well, that's the thing we need to do, ross. Otherwise, what we end up with is something called cognitive offload, where now, all of a sudden, I'll get lazy, I'll let AI make all of the decisions, and, over time, I will forget and not be valuable. For me, this is a question of great potential with technology, but the real question comes down to then okay, how do we employ that technology? And, to your point a second ago, is what do we do as human beings to learn the skills that we need to learn to be highly employable, to create, you know, be more innovative, more creative using technology?

Kieran Gilmurray: 17:46

Right, I'm answering the question you just asked 100%, and this is literally the piece here. Well, but that's the question. So do you have any answers to that?

Ross Dawson: 17:55

No, of course, Of course. Well, mine is it's so. You know, for me, ai will be absolutely. And AI is massive, and let me explain that because everybody thinks it's been around. You know, if we look at generative AI for the last couple of years, but AI has been around for 80 plus years. You know, it's what I call an overnight, an 80 year old overnight success story. Everybody's getting excited about it.

Ross Dawson: 18:18

Remember, the excitement is down to the fact that I can now interact with, or you interact with, technology in a very natural sense and get answers that I previously couldn't. So now, all of a sudden, we're experts in everything across the world and if you use it on a daily basis, all of a sudden, our writing is better, our output's better, our social media is better. So, you know, the first bit is just learn how to use and how to interact with the technology. Now we mentioned a moment ago but hold on a second here what happens if everybody has, you know, uses it all the time. The AI has been trained. There's a whole host of new skills. Well, what will I do? Well, this, for me, has always been the case. Do Well, this, for me, has always been the case. Technology has always come. There's a lot less saddlers than there are software engineers. There might be a lot less software engineers in the future. So, therefore, what do we do? Well, my one is this All this has been the same, regardless of the technology. Let technology do the bits that technology is really good at Offload to it. You still need to understand or develop your digital, your AI, your automation, your data literacy skills. Without a doubt, you might do a little bit of offloading, because now we don't actually think about scientific calculators. We get on with it. We don't go into Amazon and automatically work out all of our product sets, because it's got a recommendation engine. So therefore, you know, let it keep doing all its stuff.

Ross Dawson: 19:27

Whereas, as humans, I want to develop, you know, greater curiosity. I want to develop what I would describe as greater cognitive flexibility. I want to use the technology. Now that I've got this, how can I produce even better, greater outputs, outcomes, you know, better quality work, more innovative work. You know, and part of that is now going okay, let the technology do all of its stuff. Free up tons of hours, because what used to take me weeks takes me days. Now I can do other stuff. Like you know, wider reading. I can partner with more organizations, I can attempt to do more uh, more things in the day, whereas in the past I was just too busy, uh trying to get the day job done. You know the other bits.

Ross Dawson: 20:04

I would be saying companies need to develop emotional intelligence in people, because now, if I can get the technology to do the stuff, now I need to engage with tech. But I'm, more importantly, I'm now freed up to work, cross silo, to work across businesses, to bring in different partner organizations and, statistically, only 36 percent of us are actually emotionally intelligent. Now, ai is an answer for that as well, but emotional intelligence should be something I would be developing inside of an organization, a continuous innovation mindset, and I'd be teaching people how to communicate even better. Notice, I'm letting the tech do all the stuff that tech should do regardless. Now I'm just over-indexing and over-amplifying the human skills that we should have developed over the last 10, 15, or 20 years.

Kieran Gilmurray: 20:45

Yeah, so, probably to your point, this comes about people working together, and so I think that was certainly one of the interesting parts of your book is around team dynamics. So there's a sense of yes, we have agentic systems. This starts to change the nature of workflows. The workflows involve multiple people. They involve AI agents as well. So, as we are thinking about teams, as in, multiple humans assisted by technology, what are the things which we need to put in place for effective team dynamics and teamwork?

Ross Dawson: 21:22

Yeah. So look, what you will see potentially moving forward is that mixture of, you know, agentic labour working with human labour, and therefore, you know, as from a leadership perspective, we need people, that we need to teach people to lead in new ways, like, how do I apply agentic labour and human labour and what proportion? You know, what bits do I get agentic labour to do, what bits do I get human labor to do? You know, again, we can't hand everything over to technology. You know, when is it that I step in? Where do I apply humans in the loop? You know, when you look at agentic labor, it's going to be able to do things 24-7. But as people, we physically and humanly can't. So you know, when am I going to work, what is the task that I'm going to perform? You know, as a leadership or as a business, well, what are the kpis that I'm going to measure myself on and my team on? Because now, all of a sudden, my outputs potentially could be greater, or I'm asking people to do different roles than they've done in the past, because we can get, you know, agentic labor to do it. So there's a whole host of what I would describe, as you know, current management consideration because, let's be honest, like when we introduced erp, crm, factory or something else, it just changed the nature of the tasks that we perform.

Ross Dawson: 22:30

So this is thinking through, you know, where is the technology going to be used? Where should we not use it? Where should we put people? How am I going to manage it? How am I going to lead it? How am I going to measure it? So these are just the latest questions that we need to answer inside of work.

Ross Dawson: 22:44

You know, and again from a skills set perspective, you know, from both the leadership and getting my human labor team to do particular work, or how I onboard them, how do I develop them. What are the skills that I'm now looking for when I'm doing recruitment? What are the career paths that I'm going to put in place now that we've got human plus agentic labor working together? You know, those are all conversations that managers, leaders and team leaders need to have and strategists need to have inside of businesses. But it shouldn't worry businesses because, again, we've had this same conversation for the last five decades. It's just been different technology at different times where we've had to suddenly reinvent what we do, how we do it, how we manage it. So what are?

Kieran Gilmurray: 23:23

the specifics of how team dynamics might work in using a Gen-TKI in a particular industry or in a particular situation, or any examples. So let's make this groundless.

Ross Dawson: 23:40

Yeah. So let me ground it in physical robots before I come into software robots, because this is what this is software labor, not anything else. When you look at how factories have evolved over the years. So take Cadbury's factory in the UK. At one stage Cadbury's had thousands and thousands of workers and everybody ended up engaging on a very human level managing people, conversations every day, orchestration, organization, all of the division of labor. Stuff happened. Now when you go into Cadbury's factory it's hugely automated, like other factories around the world. So now we're having to teach people almost to mind the robots. Now we far less people inside of our organizations and hopefully, to God, this won't happen in a what I describe as knowledge worker park. But we're going to teach people how to build logical as knowledge worker park. But we're going to teach people how to build logical, organized, sequential things. Because to break something down into a process, to build a machine, it's the same thing when it comes to software labor. How am I going to break it and deconstruct a process down into something else? So the mindset needed to actually put software labor into place varies compared to anything else that we've done.

Ross Dawson: 24:45

Humans were messy. Robots can't be. They have to be very logical pieces. You know, in the past we're used to dealing with each other. Now I'm going to have to communicate with a robot. You know that's a very different conversation. It's non-human. It's silicon, you know, not carbon. So how do I engage with a robot? Am I going to be very polite? And I see a lot of people saying please, would you mind doing the following no, it's a damn robot, just tell it what to do.

Ross Dawson: 25:09

My mindset needs to change. So if I take, you know, in the past, when I'm asking someone to do something, I might say give me three things. Or can you give me three ideas? Now I've got an exponential technology where my expectations and requests of agentic labor are going to vary. But I need to remember I'm asking a human one thing and a bot another. Let me give you an example. I might say to you, ross, give me three examples of well, that's not the mindset we need to adopt when it comes to generative AI, I should be going to give me 15, 50, 5,000, because it's a limitless vat of knowledge that we're asking for.

Ross Dawson: 25:42

And then I need to practice and build human judgment to say, actually, I'm not going to cognitively offload and let it think for me and just accept all the answers.

Ross Dawson: 25:50

But I'm not going to have to work with this technology and other people to you know, to develop that curiosity, develop that challenging mindset, to suddenly teach people how to do deeper research, to fact check everything that I'm being told to understand when I should, you know, use a particular piece of information that's been given to me and hope to God is not biased, non-hallucinated or anything else, but it's actually a valuable knowledge item that I should be putting into a workflow or a project or a particular document or something else.

Ross Dawson: 26:18

So, again, it's just working through. You know, what is technology, what's the technology in front of me, what's it really good at, where can I apply it? And understanding that, where should I put my people and how should I manage both, and what are the skills that I need to teach my people and myself to allow me to deal with all of this potentially fantastic, infinite amount of knowledge and activity that will hopefully autonomously deliver all the outcomes that I've ever wanted, but not unfettered and not left to its own devices ever. Otherwise, we have handed over human agency and team agency and that's not something or somewhere where we should ever go the day we hand everything to the robots. We might as well just go to the care home and give up.

Kieran Gilmurray: 27:01

Yeah, we won't be doing that soon. So around that, let's think about leadership. So I mean, I've alluded to that in quite a few. I mean a lot of it has been really talking about some of the questions or the issues or the challenges that leaders at all levels need to engage with. But this changes, in a way, the nature of leadership. As you say, we've got digital labor as well as human labor. It's an organization that has different structure. It impacts the boundaries of organizations and the flows of information and processes across organizational boundaries. So what is the shift for leaders? And then, in particular, what are the things that leaders can do to develop their capabilities for a somewhat different world?

Ross Dawson: 27:51

Yeah, it's interesting. So I think there'll be a couple of different worlds here. Number one is you know we will do what we've always done, which is we'll put in a bit of agentic labor and we'll put in a bit of generative AI and we'll basically tweak how we actually operate. You know, we'll just make ourselves marginally more efficient, because anything else could involve the redesign and the restructure of the organization, which could involve the restructure and the redesign of our roles, and as humans, we are very often very change resistant. Therefore, I don't mind technology that I understand and I don't mind technology that makes me more productive, more creative, but I do mind technology that could actually disrupt. You know how I lead, where I actually fit inside of the organization and something else. So for those leaders, there's going to be a minimal amount of change and there's nothing wrong with that. That's what I call the taker philosophy, because you go taker, maker, shaper, and I'll walk through those in a second, which is I'll just take another great technology and I'll I'll be more productive, more creative, more innovative, and I recommend every business does that at this moment in time. Who wouldn't want to be happier with technology, doing greater things for you? So go box number one and therefore the skills I'm going to have to learn not a lot of difference, just new skills around ai, you, you know. In other words, understanding. You know bias, hallucinations, understanding cognitive offloading, understand where to apply the technology and not and by not I mean very often people put technology at something that has no economic value waste time, waste money, waste energy, get staff frustrated and something else. So those are just skills people have to learn. It could be any technology I've said.

Ross Dawson: 29:24

The other method of doing this is almost what I describe as the COVID method. I need to explain that statement. When COVID came about, we all worked seamlessly. It didn't matter. There was no boundaries inside of organizations. Our mission was to keep our customers happy and therefore it didn't matter about the usual politics, the usual silos or something else. We made things work and we made things work fast, silos or something else. We made things work and we made things work fast.

Ross Dawson: 29:50

What I would love to see organizations doing and very few do it is redesign and re-disrupt how they actually work. And I'm sitting there going. It's not that I'm doing what I'm doing and I've now got a technology. Where do I add it on? As you know, two plus one is equal to three. What I'm sitting going and saying is how can I fundamentally reshape how I deliver value as an organization, and working back from the customer will pay for a premium for this and therefore, if I work back from the customer, how do I reconstruct my entire business in terms of leadership, in terms of people, in terms of agentic and human labor, in terms of open ecosystems and partnerships and everything else to deliver in a way that excites and delights? So, if we take the difference between bookstore and Amazon, I never, or rarely, go into a bookstore anymore. I now buy Amazon almost every time, not even thinking this.

Ross Dawson: 30:42

If I look at AI, you know they're what I describe as Uber's children. You know their experiences of the world and how they consume are very different than what you and I have constructed. Therefore, how do I create, you know what you might call AI native intelligent businesses that deliver in a way that is frictionless and intelligent, and that means intelligent processes, intelligent people using intelligent technology, intelligent leadership, an organization to deliver on its promise to customers, to gain a competitive advantage, and those competitive advantages will be less and less. I can copy all the technology quicker. Therefore, my business strategy won't be 10 years, it possibly won't be five, it might be three or even less. Won't be five, it might be three or even less, but my winning as a business will be my ability to construct great teams, and those great teams will be great people plus great technology to allow me to deliver something digitally and intelligently to consumers who want to pay a premium for as long as that advantage lasts, and it might be six months, it might be 12 months, it might be 18 months. So now we're getting to a phase of almost fast technology, just like we have fast fashion.

Ross Dawson: 32:08

But the one thing we don't want to do is play loose and fast with our teams, because ultimately I still come back to the core of the argument is that great people who are emotionally intelligent, who've been trained to question everything that they've got, who are curious, who enjoy working as part of a team in a, in a culture and that piece needs to be taken care of as well, because if you just throw robots at everything and leave very few people and what culture, are you actually trying to deliver for your staff and for your customers?

Ross Dawson: 32:35

But how do I get all of this to work, to deliver in a way that you know is effective, is affordable, is operation efficient, profitable, but with great people at the core who want to continue being curious, creating new and better ways of delivering in a better organization, not just in the short term, because we're very short termist. How do I create a great organization that endures over the next five or 10 years? By creating, you know, flexible labor and flexible mindsets, you know, with flexible leaders, organizing and orchestrating all this to allow me to be a successful business. Change is happening too quickly these days. Change is going to get quicker. Therefore, how do I develop an adaptive mindset, adaptive labor force, an adaptive organization that's going to survive six months, 12 months and maybe, hopefully, to God, 16 months plus?

Kieran Gilmurray: 33:22

Fantastic, that's a great way to round out. So where can people find out more about your work?

Ross Dawson: 33:42

Yeah, look Fantastic. That's a great way to round out is where I publish far too much stuff and give far too much stuff things away for free. But I have a philosophy that says all boats rise in a floating tide, so the more we share, the more we give away, the more we benefit each other. So that's going to continue for quite some time. I have a book out on the Gentic AI. Again, it's been given away for free, ross. If you want to share it, please go for it, sir as well. You know, as I said, let's continue this conversation, but let's continue this conversation in a way that isn't about replacing people, but it's about great leadership, great people and great businesses that have people at their core, with technology serving us, not us serving the technology.

Kieran Gilmurray: 34:21

Fabulous Thanks so much, Ciarán.

Ross Dawson: 34:24

My pleasure. Thanks for the invite.