How Agentic AI is Changing Business and Leadership with Gary Crawford

In this episode of The Lucent Perspective, Rebecca Hastings speaks with Gary Crawford, an AI strategy expert and Founder of Owendale Advisory, a consultancy helping businesses adapt to the intelligence era.

With rapid AI advancements, including agentic AI, businesses face new challenges and opportunities. Gary shares insights on AI-driven organizations, leadership evolution, and talent strategies in a changing landscape. He explores AI’s impact on hiring, business transformation, and strategic decision-making.

Key Topics Covered

• What agentic AI is and how it differs from traditional AI.
• Why AI is moving from a tool to an autonomous workforce contributor.
• The impact of AI-driven automation on hiring and career development.
• How businesses should rethink talent strategies as AI expands.
• Why leadership teams must understand AI beyond the buzzwords.
• How agentic AI is shaping finance, healthcare, and software.
• Risks of delaying AI adoption and why hesitation is costly.
• The future of AI in business models and organizational structures.

Episode Highlights

[00:57] Introduction to Gary Crawford and his AI strategy expertise.
[02:33] What agentic AI is and how it operates autonomously.
[04:39] Why AI adoption is a business transformation, not just a tech shift.
[06:48] How AI is disrupting hiring and reshaping talent strategies.
[10:49] The risks of over-relying on AI for decision-making.
[15:08] How AI is transforming traditionally slow-adopting industries.
[22:53] Common misconceptions about AI and its business impact.
[31:12] Structural and cultural shifts businesses must prepare for.
[35:53] Why delaying AI adoption could leave companies behind.
[40:52] How leaders can strategically experiment with AI.
[47:31] Final thoughts on AI’s impact on leadership and strategy.

About Gary Crawford

Gary Crawford is the Founder & Chief Advisor at Owendale Advisory, where he helps businesses align AI opportunities with strategic goals. With a background in working with C-level executives, boards, and leadership teams, Gary specializes in AI transformation, business strategy, and executive coaching. His experience spans financial services, healthcare, energy, public sector, and retail, making him a sought-after advisor on AI-driven innovation.

📌 Connect with Gary:
🔗 LinkedIn
🔗 Owendale Advisory

Rebecca Hastings, Founder and Director at The Lucent Group

Connect on LinkedIn: https://www.linkedin.com/in/hastingsrebecca/  

Would you like to be a guest? Book a time to speak with Rebecca: https://calendly.com/rebeccahastings/discovery-call

The Lucent Group Ltd website - https://www.thelucentgroup.co.uk/

The Lucent Perspective website - https://thelucentperspective.com/ 

Rebecca has extensive talent and executive search experience supporting digital and technology businesses through complex changes and fast-paced scale-up periods. She works with businesses advising on C-level, technical, sales and commercial appointments, workforce planning, strategic talent management, recruitment processes and associated technology and employer brand development. 

Transcript

 00:05
Speaker 2
Welcome to the Lucent Perspective. I'm your host, Rebecca Hastings. I've spent over a decade working with executives in the tech sector and help successful companies build their leadership teams and scale. During my career, I've been lucky to have the privilege of learning from many exceptional leaders. In these conversations, you'll get perspectives from peers, be inspired and learn what it takes to become. One of the best is your chance to listen to experts talking about the challenges, solutions and the vital insights they've gained in their careers to date. 

 00:41
Speaker 1
The majority of organizations, the majority of leaders don't realize the extent of the shift that we're really on the precipice of now. We're really going to see some incredible changes. My expectation is we're going to see something in the scale of the industrial revolution on the agricultural revolution. 

 00:57
Speaker 3
Today, I'm thrilled to be joined again by Gary Crawford. Gary has been on the podcast before, but he is a globally recognized expert in AI strategy and emerging technologies. He's just launched Owindel, which is a consultancy dedicated to helping businesses stay ahead in today's landscape where things are moving super fast. We've had so many developments this week that I can't wait to discuss. Gary is going to use his decades of experience to help guide us through some of the complexities of digital transformation and how things like agentic AI are really transforming organizations once more. Gary, thanks so much for joining us today. I want to dive straight in. Businesses are talking about agentic AI all the time with me. Can you explain what it is and why this is so important? 

 01:49
Speaker 1
Yeah, it's great to see you again. Rebecca Gentiki really is a massive breakthrough. So until three months ago, I was really recommending to businesses that they shouldn't be thinking about taking human out the loop in any places. With AI, we just weren't really at that stage of maturity. But the breakthroughs over the last few months alone really start to change that. And you have Sam Altman at the beginning of this year saying that 2025 will be the first year where software actually becomes and AI actually becomes an employee within organizations. So the real thing with agentic AI is rather than the query response paradigm that we've had going on for some while with AI where you ask it something and it gives you a response. 

 02:33
Speaker 1
With agentic AI, you're starting to look at an autonomous system where you give it a goal and it's able to act freely towards that goal. So as it learns, as it explores, it can change its behaviors, it can replant it can adapt, it can. And come back to you with something that starts to represent a really valuable return on investment for your business. 

 02:54
Speaker 3
And you're absolutely right. Just. Well, obviously we're recording this the first week in February 2025 and deep research by OpenAI has just been released. 

 03:08
Speaker 1
Yeah, and this for me is really exciting. It's probably one of the best examples. It's not the only example, but it's probably one of the best examples of this exceptional intelligence. In this case the O3 model from OpenAI coming together with that ability to have autonomy and to be directed towards something. So some of the examples that we're seeing coming back from it really are quite exceptional. 

 03:31
Speaker 3
I haven't played with Deep Research, but I have played with the O3 model and it is definitely superior. But the quality of what it produces is streets ahead of where things were this time last year. And it is very believable that you are going to almost have AI as employees this year. I've heard people compare Deep research to a 25 year old MBA in your business. You know, half an hour of its work is an afternoon. And certainly I saw Sam Alton said that this is basically 1% of global productivity that it can do. 

 04:12
Speaker 3
I'm sure there's potentially some hype in that, but it is making companies that I'm speaking to really reassess what they need to hire when they are hiring, what skills are valuable, what experience is truly valuable is shifting and I think that plays to the advantage of people who are early career in some places and totally disadvantages them in other areas. 

 04:39
Speaker 1
Yeah, I think that makes sense. It's one of the things at Owondale Advisory that we speak about a lot is AI isn't just a technology and data challenge. It's very much a business challenge and it's really about business change and business transformation. You think of the strategic aspects of your business, you think about the operations, you think about the workforce, you think about how you engage and service your customers in various different ways. AI has to be seeded holistically through all of those things. And I think to be able to do that in a meaningful way, it means rethinking the boundaries of roles within the organization. 

 05:16
Speaker 1
So where perhaps the roles that we have today have been built up over quite some time with the software that we've had for a certain period and with the organizational structures that we've had, we probably now need to start unpicking which aspects of these roles do we want to continue being done by Humans, and perhaps even prioritized by humans, which other areas can we start to take apart and hand off to the silicon intelligence? And I think that probably speaks to what you're describing there. There will be some areas where perhaps certain aspects were part of someone who was fairly new to the workforce that they would have done before. Perhaps those opportunities aren't going to be there quite the same now, because let's face it, the models don't need coffee, they don't need to sleep, they're not going to take any sick breaks. 

 06:05
Speaker 1
But at the same time, I think that makes the role of the human actually ever more important. So if you think about dick research, perhaps coming up with an incredibly important market research paper and it does it in half an hour, a fraction of a time that a PhD student would be able to do that piece in and of itself might have some valuable insights. But until you actually start to hone that and turn it into something that's of value and it's contextualized for your organization, it doesn't really yet have that value. So people are going to have to really double down on the human aspects, the organizational aspects, the socio technical aspects of the roles, and then understand what can be given to AI effectively. 

 06:48
Speaker 3
And just because it's done that piece of work doesn't mean that you are not going to do anything. So for example, there was somebody in my team recently who went off and did about 100 page piece of research into something for me. And the value wasn't in me reading it was in them learning as they did it partially. But it was also in the fact that because they had really immersed themselves in this world and the technologies and the aspects that they were investigating for over a week, they were able to give me a really comprehensive and thorough overview in 90 minutes of something that would have taken me like a week previously to do. And I don't necessarily know if you're going to have the confidence in an AI doing that. 

 07:43
Speaker 3
And if you're going to have the kind of like, is the knowledge with you or is it with your AI is something that you're going to have to think about. So I mean there is potentially because the value isn't in the fact that someone's written all of that up, it's what that's meant and how their thinking has evolved during that time and what they've learned. And there's a lot of intangible stuff there. If you're listening to the Lucent perspective, you'll understand that hiring AI talent is complex and the market is ever changing. That's why we release a series of guides throughout the year for the UK and the US markets, reviewing technical and commercial talent trends in AI. 

 08:24
Speaker 3
Our guides go beyond salary lists and we share our top strategies for securing the best talent in each area, as well as insights into emerging market trends. If you'd like to register to receive our guides, please visit www.lucent-search.com salaryguide. Staying up to date has never been more important, so check out our guides at www.lucent-search.com salaryguide or click on the link in the description box. 

 08:58
Speaker 1
There's actually been some really interesting research come out over the last year which starts to look at the impact of artificial intelligence on new employees onboarding into an organization and it looks like you can take something like six months of experience and gain that within a two month period. So actually for that kind of upskilling knowledge enablement, onboarding type of thing, there are opportunities with that again, but I do still think it comes down to how we redesign and rethink those typical processes and our culture within the organization. So I couldn't agree more. The act of doing the research and focusing your thoughts and having the right questions as well as the information to answer, that's something that's deeply important for getting the humans to understand. Understand the context that you're trying to get there. 

 09:50
Speaker 1
There might be some interesting ways that we can bend around that with the collaborations between the carbon intelligence and the silicon intelligence. But yeah, I completely agree the planning is more valuable than the plan sometimes. 

 10:02
Speaker 3
And if you're listening to this and you're thinking, oh, I'm really worried there's not going to be a future for me in my career if you're early and you're starting out. One observation that I have been making over the last month or so as I've been working with some companies about what their teams might look like this time next year. There are people out there who have, you know, a premium salary because they've got a decade in their career of doing the right things. You know, maybe working a big bank, working in like a top tier consultancy and what they, you know, being attached to those logos is always going to have some halo effect and be a premium for them. 

 10:49
Speaker 3
But the reality is if 80% of what they did during those 10 years is no longer part of anybody's job and it's just something that an AI does, then that is devalued so there is a chance to actually catch up and accelerate if you are willing to develop depth of expertise and become, you know, very good at think and working alongside an AI, Because I think it is thinking alongside an AI tool. 

 11:18
Speaker 1
Yeah, I love that. One of the words that keeps coming back I've been discussing with a few people over the last few weeks is relevance. I think as we start to change everything about our working environments and our social environments based on this new technology, we have to start thinking about what is my relevance as an individual to the system, what is the team's relevance to the department, what's the department's relevance to the organization? What's your organization's relevance to the people who would have and will buy from you? So I think we have to rethink a lot of things and a lot of the logic that's been passed on for years, a lot of the wisdom that's been hard earned is going to change. 

 11:59
Speaker 1
I think a great example of that is a designer I was working with on a project fairly recently and were looking to embed artificial intelligence into the user experience to help with some suggestions. And some of those long standing pieces of wisdom such as minimize the number of clicks suddenly wasn't as relevant. For example, if you want to build a relationship with somebody, you don't minimize the number of times you talk with them. Instead you try to open up that opportunity, you try to interact more, you get to know them, you get to become more relevant, your conversations with them. And it's actually the same when you think about the user experience, ASPects with the AI. 

 12:39
Speaker 1
Minimizing the number of clicks to achieve an outcome actually loses a lot of opportunity for the AI to learn about that user and to foster that long lasting relationship. So yeah, I think there's a lot of things we have to rethink. Relevance in all different areas being one of those. 

 12:55
Speaker 3
And sales and marketing is definitely an area. There's loads of AI tools alongside recruitment, for example. And one of the things that I've noticed is you have to think about where the trust is going to lie. It's not just as simple as where the tasks will lie. There's certain areas of my business I'm really happy to use AI in. And there's other areas that I just think, oh, that's not something I want to do. And I think it's the same for job seeking certainly. 

 13:29
Speaker 1
Yeah, I think AI in those types of contexts is a great partner. You know, if I go back to the days when I used to be developing code, peer coding was a great way to Bounce ideas and to think through a problem, you know, I think you can almost think of AI a bit like that. You can bounce around ideas, you can try out new concepts, but when it comes to the hard task of writing something, I think you have to make sure that your genuine voice comes through. And there's probably a broader question in there about in an age of AI, how do you differentiate yourself from a marketing, in a marketing perspective or in a sales perspective, you know, if you're just chumming out what everyone else churns out from ChatGPT, you're probably not going to stand out. 

 14:11
Speaker 1
So there's some real hard miles that have to be put in there to understand where do we use it, where do we not use it? Yeah, it's not really always an easy answer. Sometimes that's about video like this. So people can see right now that Rebecca and Gary are having a conversation. We could put the transcript from this conversation out there and people would jump to the conclusion that it was AI. So even if you're not using AI within your business just now, you still need to think about people's perceptions because if they assume that you are, you're still going to fall foul of the same challenges that other people that are using IT are going to fall foul of. 

 14:50
Speaker 3
So can you share with us a real world example of how agentic AI is solving a business problem or has the opportunity to solve a problem in maybe an industry which hasn't traditionally embraced technology, but now is the time they can really accelerate? 

 15:08
Speaker 1
Yeah. Okay, so I think I'll start by giving one that isn't actually an industry example. And it's probably quite a controversial example, certainly from your perspective, Rebecca. But if you think about people who are out there just now in a difficult economy looking for the next role, that's a very challenging situation to be in. And if I understand it right, because statistics are something like for every 100 roles you apply for, you may get one interview. So there's a real scale issue for people to be able to get through the volume of applications that they need to get to that one opportunity to go in and meet somebody face to face. 

 15:49
Speaker 1
What's come about over the last few months is these multi application systems where people can upload their cv, they can answer a few short questions about what their interests are or what they're looking for in a role, and then they can step back from that process and allow these platforms themselves to automate the application process. So whenever LinkedIn, for example, says here's a new role at an organization, that platform could be hooked into that it can understand what the application process looks like and respond to it. Now, in some ways you could argue that allows the individual to scale their ability to apply, which is ultimately one of the key benefits of agentic AI is that ability to scale. But one of the reasons I quite like this as an example is it fundamentally breaks other parts of that recruitment process. 

 16:42
Speaker 1
It creates a huge amount of challenges as well. I think that's true for all areas of AI. Agentic AI is a little different. There are changes that we need to understand and there are opportunities that we can go after. But we need to realize that this is a fundamentally different system now and there are other challenges that we need to get over. What's your experience there? 

 17:01
Speaker 3
So I think it breaks the process on the candidate side and the client company side, for example. So this definitely is a great solution if you are a student looking for a summer job or if you want to, I don't know, work in an area where there's typically high volume recruitment happening, like contact centers or maybe, you know, construction and property labourers, for example. But it starts to get more challenging for you to use if you're an executive or if you're in a niche or senior role. The reason is you lose control about where your CV is going and AI is not discerning. It's a numbers game for it. It's not thinking, where would Gary like to work? Has Gary applied to them before? 

 17:53
Speaker 3
And you could end up in a situation where you deal with, you end up in the bottleneck which is the problem on the company side definitely. And you're lost there. And then they engage with, you know, someone like myself to go off and headhunt. But you've already applied so you've kind of like muddied the waters. But really it's an LLM application that maybe wasn't good enough. I. Or maybe was rejected because they knew that it was AI generated and had some kind of software in place to detect that. And that was one of their criteria for filtering out candidates, that there are instances where that kind of thing is probably happening right now on the corporate side of things for companies small or large, it's affecting everybody because of that volume of applications. 

 18:47
Speaker 3
And like genuinely, I know somebody in London, they had a thousand applications in one hour for a role. 

 18:55
Speaker 1
Wow. 

 18:56
Speaker 3
How do they provide a candidate experience? How do they, you know, there's obviously automation, acknowledging, rejecting things after a set period of time. In most people's systems. But how do you know you've actually got the best person you know, if you're not running a fair process, you can leave yourself open to risks around discrimination potentially, I don't know. But you do put yourself in a situation where nobody in certain talent functions can get anything done because all they're doing is managing applications and they're not thinking about the AI is taking away their ability to think about the stuff that adds real value. Like what is this job? Who is the Persona? How do we attract them? How do we assess people effectively? 

 19:44
Speaker 1
Yeah, I think that for me is a great example of why this has to be a challenge that is faced into by senior leaders of an organization. This is the whole reason why Ondale Advisory works exclusively with senior leaders. I think too often people assume that AI is a data and technology challenge and they want to hand that over to a technology department. It feels quite scary to think that actually this is a brand new emerging technology. And as a senior leader with very little technical background, I'm going to try and drive this initiative. But in reality it affects all aspects of the business. As I said before, it's a strategy, it's the operation, it's the workforce, it's the culture, it's how you engage your customers. So really it needs commercial and strategic leadership from the very top to understand what these things mean. 

 20:35
Speaker 1
And this is why probably one of my favorite things to do at Owendale is our intelligence briefings where we get in the room with the senior leadership team, sometimes with the board, and it's a closed door environment. We can talk about some important subjects to set the context, but then it's really an open table to ask anything from what does this mean for our corporate strategy all the way down to what does AI actually stand for and everything in between. These types of things like the recruitment challenge comes up all the time. And it's amazing really to hear how surprised people are about these things that many people in their organization are actually facing into struggling with. But they've never quite gotten that subject hasn't quite risen up to that senior level and it needs to be understood at that level. 

 21:20
Speaker 1
And then, you know, strategies have to be put in place that help the organization get through this shift towards an intelligence era. 

 21:27
Speaker 3
Totally. And there is a solution. Well, certainly I have a solution that I have worked hard to develop and written about. If anybody would like to know more about that, they can drop me a message anytime. I'm happy to share what I figured out, but it is literally Picking apart processes and thinking about which bits have to stay with people, which bits can you like actually leverage AI to do? And how do you make that all human? 

 21:58
Speaker 1
Yeah, yeah, I think, you know, possibly past the process you're describing. Value chain mapping tends to be an exceptionally important tool in that space as well. Because one of the things that AI allows us to do, and agenti is phenomenal for this, is it allows us to alleviate some of the human bottlenecks that we've just grown accustomed to and we just accept as the norm. So by starting to map out that value chain within the organization, and sometimes even with your organization, it gives a great opportunity to start to see where does the time go? Where are the real obstacles? Where can I remove a pebble and then reduce that water level in the system? 

 22:35
Speaker 1
Again, if humans are a bottleneck anywhere in your organization, that's a great place to expose the challenges that you've got and to start thinking about things like agentic AI, different forms of AI to alleviate some of that human challenge and help you scale become more efficient and faster to market. 

 22:53
Speaker 3
Other than thinking that this is a technology and data challenge, what are the other misconceptions you think leaders are having in pockets about AI and what agentic AI means for their business or generative AI? 

 23:11
Speaker 1
Yeah, I think there's a number of really key challenges. So that one that we've called out already I think is the primary one, mistaking it for a technology and data challenge, when really it's a business transformation challenge. I think another one is to assume that by allowing little isolated proofs of concept to be pushed ahead, that somehow you're suddenly going to become an AI enabled organization. That's simply not the case. You need to be able to look at the organization holistically and understand how all of these things align to your core organizational strategy. If you don't do that, you're only ever going to get siloed little pockets of things and you'll never get that critical mass that you fundamentally need to really shift ahead and get the benefits, whether there's benefits of scale, reduced cost, speed, anything like that. 

 24:03
Speaker 3
And you also risk actually automating rather than applying AI if you look at processes on a one off basis. 

 24:11
Speaker 1
Yeah, absolutely. I think another challenge is when organizations are scared of AI, what it means from a security perspective. Therefore they want to put policies in place that prevent people using it. And I'm sorry, for anybody who's listening to this podcast, I've got some terrible news for you. If you've put A policy in place to try and block AI. Your employees are working around it right now. All you've really done is push it underground so you're not garnering the learnings that are coming from the experiments that they're running. 

 24:44
Speaker 1
So I think being open minded and accepting that this is part of the future and being really forward facing into how do we start to work with this within our organization, how do we bring it in a manageable way, in a pragmatic way, but ultimately a safe way that's much better than attempting to block it at root. 

 25:06
Speaker 3
Yeah, that's not going to work because everyone's got a smartphone. Yeah, it's as simple as that. But you talk about some of the security concerns. I know something that has been in the news, obviously how deep seeking got their free model out so quickly and how impressive it is and what does this mean for security? Do you have any thoughts on that? Because I'll be completely honest, I haven't put it on my phone. I've not had time to think about it. I do have like a spare laptop that I use for anything that I'm like, what is the risk attached to this? Yeah, but I haven't deployed that yet. 

 25:47
Speaker 3
Do you have any thoughts on what the kind of like, almost like is there going to be like some kind of like global trade war rather than it being about tariffs, being about AI models, like where are we headed? 

 25:58
Speaker 1
What the team of Deepseak have done is very impressive, but it's not out with the realms of where we expected things to be going at this stage. I think the thing that really shocks people is it's a Chinese company that's done this, which is a first in this case and that's what's really shooking people up. But in reality it really is just following the trajectory that we're on. I think the big shift that we start to see is now a sudden response coming from the west. You see OpenAI starting to push more things out very quickly. So it does start to progress things that little bit faster. And there are all kinds of risks. 

 26:37
Speaker 1
The center for human tech, you know, if anybody wants to look further, do some great thought pieces on the challenges about race conditions around technologies that convey power to different organizations. So there's definitely risks of various different kinds that come from that. At the same time, I think something that's worth calling out is a large language model in and of itself doesn't necessarily evolve based on the information that's being put into it. It depends upon the application that is built around that and the additional logic that's built around that. 

 27:15
Speaker 1
So what I mean by that is you could go to hugging face, which if anybody's from a technology mindset you could think of as GitHub for large language models and you could pull down one of the Deep SEQ models, you could run it on your laptop and you could switch the Internet off and it would run perfectly well. What I mean by that is it's not sending information out to other places. You're not fundamentally changing what's contained within it. That's different to saying I'm going to interact with Deep seq's application where all of that information is going through the application and could be collected and stored and used to further train the models. That's a different thing. 

 27:54
Speaker 1
I think a lot of this again simply comes down to understanding how these models work, which is perhaps somewhat technological, but it's also very much a business challenge that needs to be understood. So Deep Seq the model isn't a massive concern. I think the acceleration for me from the competing organizations to one up each other, that's the bigger concern here. Or a lack of awareness of how to run these types of models locally in a completely safe and self contained environment. 

 28:32
Speaker 3
So if you're an executive, you need to understand how all these models work, not just know that they exist, not just have played with them. You need to actually know how they work or you're not going to be able to contribute to conversations around the board table or leadership team entirely. 

 28:49
Speaker 1
Absolutely. And it doesn't need to be a deeply technical conversation. Like I say, the majority of AI conversations for senior leaders are business conversations. There is a thin line of technical information that's essential, such as where is our data going? As a senior leader you need to be able to answer that question and frankly you should be able to answer that question out with the world of AI in the first place if you're responsible for the organization. So I don't think it's any different, but I think it just puts a new focus on it and really brings it back into the spotlight again. 

 29:23
Speaker 3
And if you are looking at the Deep SEQ developments and with any kind of fear, I think that you could just reframe that as quite inspiring. For me, one of the things I took from it is this is what you can do with a small number of people now like I think they say they and we have to take everything with a pinch of salt apparently. But they say they did this with 150 to 200 engineers. You know, they were obviously really ambitious, really driven, and I'm sure they were allowed to just get on with stuff and unencumbered with distractions potentially. So there is that leadership side there too. But addition, like you can do that in your business. 

 30:08
Speaker 1
Yeah, absolutely. I think it's worth calling out again the mobile phone here. You wouldn't say that it only costs 1200 places to build this mobile phone? No. We're standing upon generation of development and research and experimentation of different kinds. And it's not to take away from what Deep SEQ have done, but they are standing on the shoulders of giants and they're following a similar trajectory. I think what they've really proven is by continued on that trajectory because it's come from China, so it's not from the typical players that we've seen in this space. Really just goes to show that AI is going to proliferate. The costs are coming down, the cost to run these things is coming down. The capabilities of these things are ever increasing. 

 31:00
Speaker 1
That for me was one of the biggest things about Deep Seq was, you know, this really is going to get into all aspects of organizations, of life, of governance, everything. 

 31:12
Speaker 3
How do you see agentic AI in particular, though, transforming, you know, industries like software, finance, health, energy. Are there any things you see like we're not that far away from or are going to like, fundamentally shift? 

 31:27
Speaker 1
Yeah, I think we are. And I think the majority of organizations, the majority of leaders don't realize the extent of the shift that we're really on the precipice of now. I think over the course of this year and next year, we're really going to see some incredible changes. My expectation is we're going to see something in the scale of the Industrial Revolution or the Agricultural Revolution. In fact, some ligand experts actually talk about transformative AI tai now as more of an economic measure of AI rather than talking about kind of lofty Star Trek concepts such as AGI and superintelligence and things. Transformative AI is AI that has got an economic impact in the world that's equivalent to that of the Industrial Revolution. I suspect we're going to see that. 

 32:16
Speaker 1
And actually, if you think back to 2011, when Marc Andreessen says software is eating the world, I wouldn't believe that was ever actually the case. And that's easy for me to say in hindsight now looking at it, but all software ever really did was eat the storage and retrieval of information. It replaced probably one role, which was the gopher. Who would go for the files, store the paper in the center of the organization and bring them back to whoever needed them. But there's not been a huge amount really of reduction in team size. And actually in many cases we're seeing teams grow. So you look at a sales team, for example, you now have armies of salespeople whose role, you know, three quarters of the time is to update Salesforce. 

 33:01
Speaker 1
They're not really focusing on that deeply human role of connecting and building relationships and building trust. So I think one of the big changes that we start to see, and perhaps sales is a great lens to look at that through, is where we've got software as a service like Salesforce with loads of teams built up and actually spending most of that time focusing on the input and output of information. We're now going to start to see these pieces of software shipping with the intelligence to do the administration itself. So those two thirds of the time that salespeople spend on administrative overhead actually starts to disappear and they can start to refocus on the things that they're great at and really needs acumen touch such as building relationships with people that might actually buy. 

 33:52
Speaker 1
That's a massive structural shift for the organizations, It's a massive cultural shift for the organizations. And it's going touch every single department and every single level throughout the organization. 

 34:04
Speaker 3
Because you're not going to buy an agent, you're going to buy a team that work through a platform. It's not going to be one off. And I think people are used to thinking like of AI or software just delivering one task at a time. But agents will do a lot more with a lot more intelligence. 

 34:26
Speaker 1
But I think another interesting thing that comes from that, you make a brilliant point. There is, there has to be a change in the business model of the software as a service businesses, because if you're salesforce and you make money from every single license that you sell, then for a sales team potentially to shrink in size, that's not a good thing. You're reducing the number of licenses that you sell. So they're going to have to rethink the model in some way. So you start pushing into the remuneration line in the P and L and start charging in a slightly different way. So I think that's another aspect where we're going to see significant changes and changes that would be difficult to reverse. Once you start seeing that playing out differently in the financial sector. 

 35:11
Speaker 3
Certainly there are certain tools in the recruitment space, like certain applicant tracking systems that already price based on hypothetically the number of hires you plan on making in a year, which I suppose is value based pricing, it always leaves room for more interesting negotiation. So that's a skill that's going to increase in benefit. 

 35:35
Speaker 1
Yeah, absolutely. 

 35:36
Speaker 3
And what are the risks for businesses right now if they hang around and wait and see what happens? Because it's tempting, because there's so much of this unknown, it's kind of like, oh well, maybe I'll just wait six months and there'll be something else, it'll be sorted. 

 35:53
Speaker 1
For me, it is very tempting. And I think, although it's a confusing term, GPT, if I say GPT here, I'm talking about general purpose technology. There have been a small number of general purpose technologies in all of history. Things that really permeate all aspects of society and really cause an incredible change. Artificial intelligence looks like it is the next general purpose technology. It is going to get into all different areas. And actually if you look at the trend of adoption of general purpose technologies and each success of one, it shrinks quite considerably. And certainly the signs that we're seeing with artificial intelligence suggest that the uptake is going to be significantly faster than things like the Internet and things like computing and various different things like that. 

 36:44
Speaker 1
So the rate of change is fast, the rate of adoption is incredibly fast, the rates of breakthroughs appears to be accelerating. And actually, if we start to get to the point where some of these powerful models start to be able to adapt themselves and improve upon themselves, then you start to get to a fast takeoff scenario which could go really quickly indeed. So I think the big risk at the moment is for any readers, this is happening. This is absolutely going to happen. And if you're not fast at getting there, if you're not fast at adopting, you're very much going to be left behind for an organization. Imagine whatever type of organization you're in, if a competitor organization comes along and can do exactly what you do with zero employees, how do you compete with that? It's very difficult. 

 37:39
Speaker 1
I think the other aspect of this is fundamentally different. And I'll take an example, or maybe not the best example, but if you think of agile, we've been attempting to ADopt Agile since 2001 and many organizations have continued to struggle. Nonetheless, it's been a massive effort, there's been a huge amount of focus, but realistically, the board doesn't care if you're agile or not. What they want to know is, are you creating value? This is a big difference between something like agile, which was a core driver, or A core part of digital transformation. And artificial intelligence boards will care if you've adopted artificial intelligence because it has a meaningful impact in your top line and in your bottom line. 

 38:26
Speaker 1
So if the board are looking that closely at it and they're really driving that agenda, the chief executive absolutely has to get on board and understand how to drive that agenda. That's another aspect that's just going to continue to accelerate this. So you can't be left behind. You can't tell the board they're not getting the things that they want because there are other places that they can be. Yeah, this isn't an optional thing. So this is about how do you make quick inroads, experiment safely, experiment responsibly, understand truly what it means for your business. And again, this is where I come back to services like the Intelligence Briefing or the Horizon Scanner. Deeply understand what AI means for your business, what it means for your industry, your employees, for your customers, and then takes meaningful steps forward. 

 39:16
Speaker 3
And certainly there are organizations out there that are struggling to do this. I can think of a couple of companies that have basically set up new companies so that they can achieve this without the mess and the mindsets. And I suppose people have got empires that are going to be diminished. Getting in the way of solving things from a people perspective, that's really tough because you're going to have to put your trust in some people when their interests might not always be aligned with the organization. And you're going to have to think carefully about how you incentivize people moving forwards. 

 40:03
Speaker 1
Yeah, absolutely. And you're right, the Skunk Works approach is an approach and I think it has many challenges that comes with it. And I think many of us have lived through those challenges in different organizations. One approach that I particularly like actually is to combine John Habel's approach for scaling edges with Martin Fowler's insight into the Strangler Fig approach, which is really more of an evolutionary architecture in the computing side of things, but is very applicable to the organization as well and what you can do if you bring them together. So Hegel's scaling edges approach is really about identifying something within your organization, an area within your organization that isn't high priority. It's not got the board and the CEO and everybody's full time attention. 

 40:52
Speaker 1
It's not the highest priced thing that you're currently working on or the most strategic thing, but it's still important enough that it is meaningful change and meaningful improvement if you're able to make some traction with it. And then what you do is you get a very small team. They're not overly resourced, but you give them the freedom, the flexibility, the air cover to experiment and try new things in a different way that I have seen. I've been lucky enough to be involved in many global digital transformations and that approach of Hegel's is one of the best I've ever seen at starting to manage change. But when you combine that with Martin Fowler's approach for evolutionary architecture, really what Fowler says is if you're breaking apart a big monolithic system, then you start by recreating new small things inside little microservices. 

 41:46
Speaker 1
And then you can start to load, balance the traffic or the demand within the organization across into these new services and stop using the old ones. So you essentially strangle out the old system as you gradually, in a reduced risk and in a careful way, start to replace with these smaller services. So by combining those two approaches, by having something on the side that is able to experiment with people with the right mentality and the right focus and the right support, and then as that entity starts to become capable of doing things, switching things out from the organization's core operation towards these teams that are doing things in a new AI enabled way that is incredibly powerful and much more risk considerable. 

 42:32
Speaker 3
Thinking about the things you can take away and start thinking about right now, you need to get yourself up to date. And that's not just knowing the buzzwords. You need to understand how these models operate. If you're going to start deploying them at scale across your organization or making any radical changes, then you need to be like thinking about what is your organization of tomorrow going to look like? Who are the people that you are going to need in it? Because I truly think that skills are going to shift. What we are putting a premium on now might not actually be as important very quickly. And how are we going to manage all of that in our businesses? 

 43:12
Speaker 3
Because it does not make sense to be overpaying for some people and underpaying for people who maybe don't have as much experience but have got the right skills. And we could see that start to happen very quickly. And then you need to start getting these experiments happening within your business. 

 43:31
Speaker 1
Absolutely, yeah. And all of that has to happen within a good understanding of what do these changes mean for my industry? What do these changes mean for my business? What does it mean for my employees? What does it mean for my customers? Even to the extent of saying how are adjacent industries and other industries tackling similar problems in completely different ways so you have to have a firm grasp on what it means, the direction of travel, how it changes things, and then experiment around and within that framework. 

 44:04
Speaker 3
And I'm not sure you mentioned it, but one thing that I'm not hearing many people talk about at all is what this means for their suppliers and then what that means for them in the future. You want to think about your customers first and foremost or the people who work for you. That's completely understandable. But the reality is that costs or expectations are going to change and even procurement is going to shift. And we're living in a time where there's already been a lot of challenges with global supply chains. You could either make this better or worse. With AI, if you're in something like automotive or manufacturing right now, yeah, entirely. 

 44:48
Speaker 1
I think the change there is really quite substantial. So there's every chance now the suppliers that you've worked with for many years are disincentivized to look at the right changes for you as a business. It's human nature, it's organizational nature to survive. So there will be an awful lot of whitewashing, There will be an awful lot of misdirection. It's one of the reasons why Owindale focuses purely in the advisory space. We don't want to be shackled to sales targets in the execution space. We want to be able to look our customers in the eye and say, this is actually a really bad idea for you. You shouldn't reinvent Clippy or whatever example it is. 

 45:37
Speaker 1
Whereas some organizations are at the moment, unfortunately, in a position where they need to be hitting that revenue figure, they need to be driving forward, and that can get in the way of the right advice. I think the other thing that's quite interesting here is Kosi's theory of the firm and how the organization really is a way of coming together to reduce the cost of market trading. So we employ people because that is cheaper under certain circumstances than it is to go out and negotiate for new people to come and do perform a particular exhibit for you every single time. But actually, as we start to see the economics of labor change with AI being introduced, where the boundary of what you choose to do internally within your organization versus what you do externally to your organization could potentially change. 

 46:30
Speaker 1
So we may see organizations, for example, they start to go really deep in a particular area and outsource everything because there are other specialist areas supported by AI or companies supported by AI that can do things more effectively for them. Equally, you could see under certain circumstances, the alternative Happening where organizations are actually enabled to do more of the things that depend on the external market for within themselves, more like a vertical integration place. So, yeah, I think that's going to fundamentally change the boundaries and the collaborations with different partners is going to change what you choose to do internally and externally. But ultimately, I think this is a time where you need the right advice, the right guidance. I would strongly recommend that nobody outsources their strategy. You know, lots of organizations are saying, we will do your strategy for you. 

 47:22
Speaker 1
No, you need deep expertise injected at the right moment. And you need to own your strategy and own your destiny because this is an incredibly big change. 

 47:31
Speaker 3
And I also think that it's a time where you can afford to be a bit more ambitious and set bigger goals than you previously have. There's so much opportunity out there and you know, if you're in a strong financial position, you know you've got a lot of assets, you're cash rich. This is a great time to be investing in technology, but you could spend all your money in the wrong places very quickly if you do not feel like you are inform. You could waste things very fast. But the opportunity is there for you to really dominate a market where there hasn't been as much investment in technology or that just hasn't been a strong customer experience. AI can really boost that for you. 

 48:18
Speaker 1
Absolutely. I mean, over the years I've worked in a number of areas, financial services, automotive, in government, in healthcare, all areas where we've seen huge amount of wastage on digital transformations and various different upgrades and modernization projects. But I think the real opportunity here, or one of the big opportunities here with AI is you don't need to have all of that background in digital transformation. You don't need to be a modern technology player to actually come and join the party at this stage and leap from the competition. And in some ways, I think organizations that already have deep investment in certain areas are going to find it much more difficult to turn that behemoth around. So I would strongly recommend to organizations in any of those industries or others, don't be scared of AI because it sounds technical. 

 49:12
Speaker 1
In fact, if you don't have a technology capability, if you've never invested deeply in that space, this could be the very time for you to come explore this opportunity and actually take significant leaps forward that some of your competitors that on the surface look more mature, they simply aren't able to do. 

 49:30
Speaker 3
And you would probably be able to do it with fewer people. 

 49:32
Speaker 1
Absolutely, yeah. Faster. With fewer people, definitely. 

 49:37
Speaker 3
Things are always faster. With smaller teams. 

 49:41
Speaker 1
Absolutely, absolutely. Give me a small team of seven expert people working in the right way anytime over a team of 200 you can achieve incredible things. Moving at pace. 

 49:52
Speaker 3
Gary, thank you so much for your insights. For listeners who want to learn more, where can they find you? 

 50:01
Speaker 1
You can reach us@owandaleadvisory.com you'll find on there a range of different resources, including our recent report on Real world AI Lessons from the Leaders, which talks to some of the best in class cases that we see from financial services and from education and also from retail. 

 50:21
Speaker 3
And that is a really well curated resource. So I would encourage anyone that's curious about this to reach out, drop you a line and I'm sure you would share a copy of your insights with them. 

 50:34
Speaker 1
Absolutely, yes. 

 50:36
Speaker 3
Well, that's it for today's episode of the Lucid Perspective. Don't forget to subscribe. Share this with anyone in your network and if you've got any questions that you would like us to discuss or things that you're interested in knowing about AI now or in the future and what this means for technology people leadership, then get in touch. 

 51:00
Speaker 1
Thank you, Rebecca. 

 51:05
Speaker 2
Thanks for listening to welcome back to the Lucent Perspective. I'm Rebecca Hastings, founder and Director at the Lucent Group, a tech sector executive search and talent consultancy. If you enjoyed this episode, please subscribe, share it with others, post about it on social media or leave a rating and review. If you're a company looking to hire top technology leaders or you'd like to discuss your next move, please reach out to me on LinkedIn or send me an email to rebecca the listengroup.co.uk thanks again for listening today.