The Single Biggest Barrier to AI Adoption Isn't the Technology — It's This | Errol Gardner of EY

[00:00:00] [SPEAKER_01] Most organizations have embraced the cloud and have moved most of what's movable to the cloud.

[00:00:07] [SPEAKER_00] It seems like it's sort of morphing into a new industry. The single biggest impediment for any organization changing is usually something related to the human beings. It could be the leaders, it could be the sponsors, it could be middle managers, it could be the workers, it could be a combination of. There's huge anxiety in the workforce about whether A.I. effectively is going to displace their job.

[00:00:34] [SPEAKER_01] Okay, well, I'm interested in talking because I speak quite a bit to one of the other big consulting firms. I actually do some writing for them. And so I have a good sense of how they view this world, the new A.I. and particularly agentic world.

[00:01:00] [SPEAKER_01] And I thought this would be an opportunity to hear from you guys how you see the corporate landscape, the penetration of A.I. in production in large corporations and in particular agentic systems.

[00:01:19] [SPEAKER_01] Because, you know, I end up having a lot of startups on the podcast and they're all selling systems and they all say, oh, it's, you know, everyone's using our system. But, you know, when you get beyond an agent, you know, watching your email inbox, it gets more complicated. And I just, that's what I'm interested in.

[00:01:49] [SPEAKER_01] So usually I have the guests start by introducing themselves, giving a little bit of their background insofar as it's relevant for either consulting or A.I.

[00:02:05] [SPEAKER_01] And then maybe you can start by talking about A.I.'s view or thesis on what's happening with A.I. in the workforce or in the enterprise, I should say.

[00:02:22] [SPEAKER_00] Well, thank you for having me. It's great to be speaking to you about this, arguably the hottest topic in most corporate environments, government environments and many other organizations. So my name is Errol Gardner. I lead our consulting business here at EY globally. I've been doing that for about six years. I've been in this industry for over 35 years.

[00:02:49] [SPEAKER_00] So I've seen a number of different technology trends during that time and probably can look at A.I. in the context of many things that have been historically spoken about as changing the world very rapidly and then maybe give some context on that. But look forward to talking to you about all these different topics. And maybe just start in terms of how I see A.I. currently within the corporate environment

[00:03:19] [SPEAKER_00] and what businesses are doing and how they're responding to that. I think the first thing maybe just to highlight is actually the reality that change within large organizations is very difficult to execute. So anybody who references or pretends that to be otherwise, I think is either has never done it before

[00:03:47] [SPEAKER_00] or potentially maybe being slightly economical with what is achievable. And there are lots of very good and positive reasons for that. Organizations exist and those that are big and at scale generally have been around for quite some time. And they've got, you know, various mechanisms, barriers, arguably enablers that have made them successful,

[00:04:13] [SPEAKER_00] but also sometimes can act as barriers to them achieving change quickly. That can be lots of structures organizationally that get put in place, management structures, management systems. It can be people that work in the organizations. It can be leaders of a certain generation, mindset, work style, which doesn't necessarily align itself to new technologies and driving innovation and creativity.

[00:04:43] [SPEAKER_00] It could be a regulatory environment. There's a whole lot and a number of other factors that can very much come in the way of that. So I think fundamentally what we're seeing in the market at the moment, I think is a lot of talking up about the benefits that AI will bring to the corporate sector,

[00:05:09] [SPEAKER_00] what that looks like and what that could be, both productivity-wise, efficiency-wise, customer experience, driving value in different paradigms. The route to achieve that, though, has to go through all of those other elements that I've just spoken to and about. So I think you'll find the progress that's happening currently, as we're seeing with our clients,

[00:05:34] [SPEAKER_00] is very good in terms of people adopting, employees embracing AI, lots of development programs in terms of those with their technology skills, as well as those that don't have technology skills, understanding the art of the possible. How you then translate that into building capabilities that genuinely scale, be it across a whole value chain, even across a full business process,

[00:06:03] [SPEAKER_00] let alone then across geos, across different services and functions, we're still quite a way away from that. And when I say quite a way, I don't mean years and years. I just mean it's not happening like yesterday in the way that a lot of people are talking about, in my humble opinion.

[00:06:25] [SPEAKER_01] Yeah. And you're talking about AI broadly or about agentic AI?

[00:06:30] [SPEAKER_00] Oh, of course. I mean more in relation now to agentic AI. So that's a very good qualification, actually. So, I mean, obviously, AI actually has been around for quite some time. So, and different variants, and I know from your own experience and talking with many others, you know about those different elements. So, I think, but if you kind of talk about the slightly more modern versions of it,

[00:06:57] [SPEAKER_00] I think machine learning is used at scale in a number of different organizations across their processes and is driving efficiency and is well embedded in many different industries and processes across the board. If you think about gen AI, if you like, the kind of, I guess we were probably starting three years ago with the deployment of models, LLMs, and things of that nature.

[00:07:26] [SPEAKER_00] So, effectively just capturing information and processing it as fast as possible and converting it into texts, responses that effectively a human would put into place. I think that is being used quite extensively in a number of organizations for that purpose.

[00:07:50] [SPEAKER_00] So, as a way of be it using information that sits outside the organization and doing analytics around that, depending on the nature of the process, if you're doing research that's clearly widely available on the internet and using all the tools that can enable that. But then similarly creating private LLMs that would stay within the firewall of the organization, leveraging the data, knowledge, and insight within the organization itself

[00:08:19] [SPEAKER_00] to deliver a secure and protected way of protecting the corporate information within the four walls of the organization and using that as well. So, I think that is used, again, increasingly across business. But that's probably, again, not as pervasive as some may think that to be.

[00:08:44] [SPEAKER_00] And then the element I was speaking to, obviously, is everybody is now talking about agentic and probably have been for the last 12 months or so. So, I think that's the piece that arguably is genuinely transformational and impactful were it to be able to deliver.

[00:09:06] [SPEAKER_00] But I think when you start looking at agents replacing activity and activities that join up into a workflow, that join up into a process that are part of a value chain, that's the piece I think that is there's a lot of work. Because that is genuinely transforming an organization. That is genuinely not even just tweaking how they do things.

[00:09:31] [SPEAKER_00] You're essentially saying you have to rebuild how an organization works and operates. The challenge of doing that at scale is one that's ahead of us, not one that's behind us.

[00:09:46] [SPEAKER_01] Yeah. And I have a few questions, both on the Gen AI and agentic side. Is the adoption being driven or the experimentation with regards to agentic being driven from the bottom up?

[00:10:19] [SPEAKER_01] Is it really being driven from the top down, which I imagine would be a much slower process?

[00:10:33] [SPEAKER_00] Well, I think you could argue that both of those and other models are out there. And I think everybody's learning on this journey. So, I mean, obviously, as I mentioned earlier, with large corporates, they have checks and balances in place that take things like data privacy, obviously their own corporate data, but also their customer data, their employee data, and various other things.

[00:10:59] [SPEAKER_00] So they would have put mechanisms in place to make sure that information doesn't get out into LLMs in the broader world. So the challenge with that, of course, then is how do you control the employees that sit within an organization from leveraging tools that they may be more comfortable using in their private lives

[00:11:23] [SPEAKER_00] and moving corporate data into those tools in order to, oh, if only I could use it to do this bit of analysis or understand what the quick answer is to this particular problem. So that is a real and genuine issue, I think, that a number of organizations have to grapple with in terms of where the top down, bottom up meets.

[00:11:47] [SPEAKER_00] So if you have a workforce that is well-versed in the usage of Gen.AI tools, if you like, how do you control them not misusing data within an organization and putting it a little bit out there in the public domain? Because as you know, all these LLMs that sit more broadly on the internet are training models

[00:12:12] [SPEAKER_00] and using that data then and propagating that in a way that is not controllable from outside of the organization. So I think increasingly you will now find most major, or I would argue all major organizations now will put guardrails to prevent that from happening and will monitor usage by employees of any movement of data from their work laptops

[00:12:40] [SPEAKER_00] to platforms, environments that are not controlled in that respect. And some, therefore, may see if you're more of the innovative, creative side as an employee, you may see that as constraining your ability to innovate. So if you're using then the house tools and the house LLM and the curated private corporate LLM

[00:13:07] [SPEAKER_00] that is used by the organization, it may not be your favorite tool of choice. It might not be the one that you're used to using, and you might not like the constraint. And so that can become an issue. So I think you have no doubt both that adoption, if you like bottom-up, at the employee level, driving innovation, testing their own creativity and the boundaries of what they'd like to do,

[00:13:36] [SPEAKER_00] but probably with guardrails they're not so comfortable with. And then, as I say, from a top-down perspective, I think most organizations are encouraging their employees to embrace AI and become AI literates and do more L&D around this and understand how to use it, because I think people understand the benefits of employees being comfortable with it and enhancing their own personal productivity.

[00:14:04] [SPEAKER_00] But as I say, they'll do it with guardrails, which creates that, at some levels, some of that disconnect in the eyes of the employees in terms of, well, you're asking me to do one thing, and then you're putting constraints on my ability to do it.

[00:14:23] [SPEAKER_01] Yeah. Is this changing consulting? I mean, do you guys build, for example, as well as advise?

[00:14:35] [SPEAKER_00] Yes, absolutely. But to answer your first question, it is changing consulting, and I'm sure it will continue to change consulting. And I mean, the whole essence of the consulting industry, as you know, is essentially change. That is our, you know, primary function and objective, is to help organizations change.

[00:15:03] [SPEAKER_00] So, by definition, we're a business, we're an industry that also has had to evolve and change with every, you know, macro, as well as, in some cases, micro change that happens in the working world. And so, us changing is not particularly unusual in that respect. We've been through cycles with, you know, a lot of workloads move significantly offshore.

[00:15:32] [SPEAKER_00] So, you know, you have huge practices now that sit in India and various other parts of the world, which 30 years ago didn't happen. You've got the rise of in-house consulting operations within corporates that we've had to change and react to. So, there's a number of things that over time we've had to react to. And this is just the latest, which says probably I crystallize it in two or three different ways.

[00:16:02] [SPEAKER_00] One is we can't show up to clients and tell them about the benefits of how they could, if they deployed AI, change their business unless we're doing it ourselves. So, in the first instance, we have to show in our own business that we're at the front edge of deploying AI into what we do, how we operate. And, you know, so that's one.

[00:16:29] [SPEAKER_00] Secondly, the way we deliver services to clients, which obviously, interestingly, has also been through quite a significant transformation in the last five, six years. So, pre-2020, generally, this was an industry that if the client didn't see you sitting in their offices or their building or premises in some way, shape, or form, they didn't believe you were working for them. They thought you were doing something else.

[00:16:57] [SPEAKER_00] And obviously, with COVID, we've kind of completely changed that paradigm in terms of people physically being present with a client. But now, with AI, you get to a position of, okay, we're moving from a model where we measure consulting primarily by inputs as opposed to outputs. So, how many hours have you worked? How many days have you worked?

[00:17:23] [SPEAKER_00] What have you done in order to produce this deliverable, this outcome? You can now say, well, surely you used AI for that. So, it can't possibly have taken you as long. So, what does that look like? So, that presents both a challenge and opportunity for us in terms of how we operate because we can do things faster than we would have done historically. But we can also do more than we would have done historically as well

[00:17:50] [SPEAKER_00] and therefore achieve more as a consequence. So, it's completely changing us in terms of how we do service delivery. And then the other critical piece of what I think are, and probably our most fundamental role then, is how do we help other organizations move across that change as fast as possible? And what I'd say is the single biggest impediment for any organization changing

[00:18:19] [SPEAKER_00] is usually something related to the human beings. So, it could be the leaders, it could be the sponsors, it could be middle managers, it could be the workers, it could be a combination of. So, how do we help organizations deploy and embrace the technology? How do you think about and reimagine what the new world could be leveraging the AI, Gen AI and or an agentic workforce?

[00:18:49] [SPEAKER_00] But then how do you get the parts of the organization that need to make that happen bought in and aligned to making it happen? And it may be one for us to talk more broadly about later, but as you can imagine, we talk a little bit about the Workforce Reimagine study that we did.

[00:19:10] [SPEAKER_00] There's huge anxiety in the workforce about whether AI effectively is going to displace their role, their job, which is going to lead to resistance in the workplace to the deployment of enterprise-wide and enterprise-grade solutions.

[00:19:32] [SPEAKER_01] Yeah. And that's interesting when you say that, you know, in order to be a credible consultant, you have to, you know, go through the process yourself. Can you talk about that, how EY has adopted both Gen AI and agentic AI? Right. And, well, let's do that first because I have another question about how consulting has changed.

[00:20:03] [SPEAKER_00] Well, we obviously are an organization that employ a lot of people. So at the broadest sense for EY globally, we're about 400,000 people overall. That's the whole organization. And we very early made the decision to give as many of our employees as possible access to the tools in order to be able to leverage and use Gen AI within our environment.

[00:20:33] [SPEAKER_00] We, again, very early built a private LLM and moved a lot of our knowledge and information into that and gave people access to that with the controls that I've described so that they could experiment and do things with the information and data that we have within our organization. We also embarked on a huge learning and development exercise around, especially though, because we have a lot of people

[00:21:01] [SPEAKER_00] who've got technology skills natively, of course, but we have a number of people that don't have those skill sets and just encouraging as many people as possible to see AI. And especially if you think about it in the Gen AI context, it makes technology as easily accessible as you could possibly think.

[00:21:26] [SPEAKER_00] So to get it to stop thinking about the fact that this is a barrier or I'm not a technologist, I can't do this, to actually this is straightforward. It's as easy as using a search engine or something else in that context to get some of the information that you need. So we've done a lot of activity around giving people the tools and the platform, giving them the learning and development, and then asking them to do experimentation.

[00:21:54] [SPEAKER_00] And then also, I guess, top down is to say which parts of our business can we move fastest in terms of driving, you know, be it the way we do research, the way how we think about engaging with clients from a proposal perspective, for instance, or how we contract and think about consolidating that and making that a much faster process.

[00:22:20] [SPEAKER_00] To then, as I say, building solutions that are how we access knowledge that sits obviously anywhere globally, that we can now bring that into a much more accessible system to any consultant who's sitting anywhere and can bring that to the client as fast as possible. So a number of different elements of what we've done.

[00:22:42] [SPEAKER_00] And then, of course, there's big campaigns in terms of pushing that and encouraging the workforce to embrace that change. And as I say, to make it clear to our people that we need to show up differently in order to be credible in the marketplace. And that's not just in consulting. It's true of our tax business. It's true of our audit business as well in terms of what we're driving from that market perspective.

[00:23:10] [SPEAKER_00] So those are some of the things that we focused on. As I say, we've probably got about 90,000 technologists within our business who are part of helping to build and engineer the more sophisticated solutions around that. And also, we work with third parties, some of our ecosystem partners to drive and enable that as well.

[00:23:34] [SPEAKER_00] But that's the essence of how we've approached moving this forward within the organization.

[00:23:41] [SPEAKER_01] Yeah. And what about Agentec? Are you experimenting or have you adopted any Agentec workflows?

[00:23:51] [SPEAKER_00] Yeah. So, I mean, again, that's probably for the last 12 months or so. We've had a lot more focus on that. And so we've got ever-increasing number of agents, if you like, that sit within the business. It initially became a bit of a sport to try and count them. But now it's, you know, we're a big organization across many countries.

[00:24:16] [SPEAKER_00] It becomes a state you can't really count them in that sense anymore. And we look more qualitatively about what we're seeing. So, I think you mentioned earlier about in terms of looking at emails, about how you think about analyzing information, about updating yourself on new and information as it comes in on an hourly basis, could be a daily basis.

[00:24:40] [SPEAKER_00] But also deploying agents into tasks from a service delivery perspective, which are quite consistent. So, we've seen that a lot in our tax business that we've been able to do that in terms of producing returns and increasingly in the way that we deliver audits. That's a key element. And similarly, in the software development lifecycle, there are a number of efficiencies that we're driving from an agentic perspective in that as well.

[00:25:10] [SPEAKER_00] So, that's maturing. That's, as I say, we're our own corporate organization in that respect in terms of getting that and operating it at scale. But those are some of the areas that we're using agents across the business.

[00:25:26] [SPEAKER_01] Yeah. I can see this convergence. And maybe it's been going on for a long time. I don't know. But between, I mean, the consultants are now building systems.

[00:25:40] [SPEAKER_01] They're companies that build systems that, in effect, are consultants because they'll go to a prospective client, do an audit of their business, and say, well, this is where we can automate this aspect or this workflow.

[00:26:01] [SPEAKER_01] And then you've got the LLMs themselves, which increasingly have ingested all of the EY reports, all of the McKinsey reports. And, you know, there's a lot of public-facing information about the consulting industry's practices and outcomes. And you can turn to an LLM.

[00:26:31] [SPEAKER_01] I mean, I spoke to one company about, they're calling it decision intelligence. And they're building, in effect, a CEO co-pilot where the CEO would work alongside with this agentic system to explore the organization, you know, brainstorm, come up with ideas,

[00:26:58] [SPEAKER_01] and get information so that they could go prepared to their management or their board to propose changes. I mean, so do you see that kind of convergence that there's, you know, systems that are acting as consultants?

[00:27:24] [SPEAKER_01] They're startups that have products but then have a consulting side to get their products into the enterprise. And then consulting companies that now build products. It just, it seems like it's sort of morphing into a new industry.

[00:27:42] [SPEAKER_00] Yeah, I think there's a lot of what you say and there's a lot of both discussion about this. There's maybe some extrapolation that takes place around this topic. But I think that's part of, to your earlier point about the consulting industry changing.

[00:28:04] [SPEAKER_00] Arguably, the whole ecosystem is changing both what an organization, you know, what a corporation does and what the boundaries of that organization are. What the technology companies are doing, what the boundaries of their organizations are, and what startups are doing, as you say, and what established service providers or consulting organizations are doing and what they're doing. So I think at the margins, those things may be becoming a little bit more blurred.

[00:28:32] [SPEAKER_00] I'd also say that it's driving us to a point that the solution, if you like, the sweet spot, is probably a collaboration model between various actors that I've outlined and others in order to make the most compelling proposition. But I do think, and my kind of personal view around this is, there's a lot of talk around product companies becoming service companies,

[00:29:02] [SPEAKER_00] and service companies being embedded in software, and all of these types of things. And I think in reality, the process of change is fundamentally one that is embedded in a service organization. And how you implement technology and apply technology into a business in order to generate a business outcome and business value

[00:29:30] [SPEAKER_00] is something that the consulting organizations have many, many years of experience of doing. I don't think we're yet at the stage that you can codify that into a product, unless the product is something that's been arguably bought by an agent and or a non-human.

[00:29:54] [SPEAKER_00] So I think as long as we're an organization, an industry that is fundamentally helping and supporting human beings, helping people in corporations to deliver outcomes for their wider business, they will want to interact with people to do that. And similarly, if their workforce is made up of a blend of agents and people,

[00:30:22] [SPEAKER_00] you still have to make sure that the people are not acting as a limiter on the achievement of that overall business. So having and making sure that's the case is critically important. And then if you get to the dystopian world that says, yes, but maybe we'll operate touchless without any people, no humans in the loop, I think people need to deal with, that's a very interesting theoretical model. But tell me how that's going to work in practice.

[00:30:51] [SPEAKER_00] Because I think the reason technology exists is to make, hopefully, the human condition and human life better and corporate life better and enriching us, not in a monetary sense, but in terms of what we can achieve, as opposed to in a redundant sense.

[00:31:11] [SPEAKER_01] Yeah, yeah, good point. On agentic, where do you think we are in the enterprise writ large? Say, on a scale of 0 to 10, 0 is no agentic AI in the workforce.

[00:31:37] [SPEAKER_01] 10 is sort of maximal agentic use. I don't know what that looks like, but presumably we'll get there. I mean, do you think we're just at one or two on that scale? Are we closer to five? And how quickly do you see that moving?

[00:32:02] [SPEAKER_00] It's a great question that I can realize that the way I answer this could lead me as a hostage to fortune in many different ways. But, I mean, what I would say, if I look at how technology, as I say, has been deployed historically in the, and maybe I'll ask you a question, actually, if I'm allowed to do that. Where would you say cloud is today in the enterprise world on a 1 to 10 scale?

[00:32:29] [SPEAKER_01] Well, not being in the business, but from what I hear, I would say it's at 70 and 7 on a scale of 1 to 10 that most organizations, insofar as they can, because their regulations have prevented some stuff from moving to the cloud.

[00:32:52] [SPEAKER_01] But most organizations have embraced the cloud and have moved most of what's movable to the cloud. I don't know. Is that close from what you see?

[00:33:07] [SPEAKER_00] Well, I think it's a very difficult question to answer, but I would say it's less than 70, or less than 7 out of 10. And the reason I say that is not that it may be 7 out of 10 organizations have embraced it, but how wide is their usage of it in reality? So you said that most that could be moved to the cloud, whereas what's determining what could be moved? Because I think you'll probably find that varies by the same industry.

[00:33:36] [SPEAKER_00] Different companies will make a different determination of what could and shouldn't be moved. And the reason I use that as an example is that cloud's been around for 15 years, and it's taken that long to get to, let's even use your number of 7 out of 10. So, I mean, what I was going to say, which I think is validated by what you say, is we're at less than 1.

[00:33:58] [SPEAKER_00] And the reality is that there is so far for us to go to get even to 7 out of 10. I'm not saying it'll take 15 years, but it's going to certainly take longer than 15 days or even 15 months. And I think that for all the constraint points that I made earlier, it's much more difficult to do this.

[00:34:24] [SPEAKER_00] You could argue, actually, that cloud is almost a prerequisite to being able to do it as well. So, there's a codependency there in terms of making sure that you can do that. I mean, some would argue that's not necessarily the case. But I think that we are very, very early in terms of its adoption curve.

[00:34:48] [SPEAKER_00] Again, maybe you have a relatively large number of organizations, although I'd say that's still probably only 20%, who use it a little bit. I mean, in production, not playing with it. I mean, it's genuinely being used in a production scenario. But it will be at a very, very small element of their overall business. Yeah. So, I think we – now, that will be my view.

[00:35:18] [SPEAKER_00] And as I say, I'm a hostage to fortune, no doubt, in terms of committing to something so mathematical in describing it. But what I'd say to you is that that reflects the huge opportunity that's still ahead for organizations to get the real benefits of deploying this technology. And doing it in a safe way, doing it in a responsible way, given the impact it could have potentially on employees,

[00:35:45] [SPEAKER_00] doing it in a way that will generate and create new opportunities for employees in the future to work in a very different way, as opposed to purely looking at it from an efficiency perspective that may end up ultimately be a proxy for saying that we want to reduce our headcount. So, I think we're early. I genuinely think we're at less than one in that scenario.

[00:36:12] [SPEAKER_00] And I appreciate some of the hyperbole in the market would suggest that we're much further down. But in the enterprise space I'm speaking to now, I think we're still very much close to the starting gate.

[00:36:25] [SPEAKER_01] Yeah. And are you, from what you've seen of agents, either in experimentation or production at EY, do you think that the hype is going to be borne out? Or do you think, I mean, it could be that they can never, the researchers can never resolve the trust issue, can never resolve the reliability issue.

[00:36:54] [SPEAKER_01] And agents just fall to these, you know, watching your inbox rather than, you know, critical business processes.

[00:37:03] [SPEAKER_00] Well, I think the, if you think about the barrier of getting from experimentation, innovation to adoption at scale, and effectively deploying across workflows, not just single tasks or activity, that is quite a difficult hurdle to overcome.

[00:37:25] [SPEAKER_00] But I think it will, you know, you'll start to see many more organizations achieve that in terms of certain elements of their business and parts of their value chain, for sure. I think, you know, I go back to there are macro factors that are going to challenge, if you like, the train, the AI train that we all speak to and speak about.

[00:37:51] [SPEAKER_00] And so we just talked about cloud a little bit, if you like, and just going back to that, maybe just to zoom out a little bit from AI as a topic in its own right. As you know, there are a number of dynamics happening in the world today that are probably acting more as impediments to the fast adoption of AI globally

[00:38:20] [SPEAKER_00] compared to, if you think about in 2011, 2012, the globalization train that was assumed to be acting in perpetuity. So if you think about how the world's changed in the last five years and how we are very much more in a space where there's sensitivity around data and information

[00:38:50] [SPEAKER_00] from a nation state perspective and how much you can rely on critical infrastructure and industries being deployed and delivered from countries outside of the nation that you're sitting in. So sovereignty and directions around that being driven by governments, that's especially the case in Europe increasingly. But I think you're seeing it, well, it's arguably the case,

[00:39:18] [SPEAKER_00] it always has been the case in the US, but you're seeing it in many other countries around the world now who are worried about, we cannot end up being dependent on such a transformative technology without having some level of control in case something goes wrong. So that would be one. You then have the challenge of the narrative around the displacement of work

[00:39:47] [SPEAKER_00] that ultimately you say, well, who's going to act as custodian to protect employees from mass redundancy? Is a company going to do that? Are the technology firms going to do that? Or are governments going to have to do that? And I think you'll find ultimately that governments are going to have to lean in to make sure that doesn't happen, which is going to inevitably lead through regulation or peer pressure or taxes

[00:40:15] [SPEAKER_00] or whatever it may be that will disincentivize organizations from displacing people and replacing them with robots or agents or any other at scale in that respect. I think then you've got the other dimension of this ultimately is in many ways the balance

[00:40:40] [SPEAKER_00] the other way that says the world's moving to a state where our productive workforce and capability is declining. So especially in the Western world, we've got a declining working age population in most major countries and increasingly a burden falling on that working age population to manage the two ends of the spectrum, the babies and the old people who are nonproductive,

[00:41:09] [SPEAKER_00] if you like, in a way that you understand what I'm saying.

[00:41:13] [SPEAKER_01] Yeah.

[00:41:14] [SPEAKER_00] So how quickly can AI deliver the solution to solve for that productive capacity gap without displacing people en masse that you have the problem the other way? I think that is such a macro issue that in many ways there is no model, there's no platform, there's no empirical evidence of how you can solve for that.

[00:41:40] [SPEAKER_00] But I think a number of governments, be they transnational or national governments, are going to have to think about in terms of how do you manage this, which inevitably is going to put the brakes on some of the things and some of the technologies that are out there and the ability of organizations to adopt them quickly. The other one I'd just say to you as well is what's also interesting, of course,

[00:42:09] [SPEAKER_00] is that the value exchange that's happening at the moment between the technology industry, which is getting a huge amount of value uplift from the AI, if you like, and the AI lift that is the perception of that. And I don't mean just in stock market terms, and it's not about talking about a bubble or things in that nature,

[00:42:36] [SPEAKER_00] but the transition in many ways of workload and effort moving from traditional industries to technology firms doing things on their behalf. How sustainable is that in the longer term when essentially those companies, those traditional consumer companies, oil and gas companies, financial services and the like, how sustainable is that for those business models going forward?

[00:43:03] [SPEAKER_00] And that's another interesting dynamic that I think we'll need to watch going forward into the future as to where's the equity in that relationship going forward? And how do you kind of make sure that the technology sector doesn't become so powerful that it's controlling so many other sectors and or even replacing them

[00:43:32] [SPEAKER_00] in terms of the services that you see currently offered by other major organizations?

[00:43:37] [SPEAKER_01] Yeah. And, and, and I mean, as much as a gentic, maybe, uh, within, you know, speaking relatively, really in, in relative terms, uh, maybe far in the future. I mean, I, that could simply be years as opposed to decades, but, uh, I think that, uh,

[00:44:01] [SPEAKER_01] this whole workforce transition is going to have to be addressed by governments. I don't think, uh, through regulation of, uh, of, of, uh, hiring and firing practices itself, but maybe through taxation to provide universal basic income to, to people who've been laid off.

[00:44:26] [SPEAKER_01] But, you know, that, that gets very, uh, speculative, um, today when you're, two questions, one, uh, when customers coming to you, uh, can you break it down just very roughly, uh, how many are coming for traditional consulting, uh, help?

[00:44:53] [SPEAKER_01] I mean, you know, we're moving into this product line, we're transitioning away from that product line. How do we do that? How much of it is AI related, uh, generally, and how much of it is, uh, you know, what is a gentic AI? How can we use it?

[00:45:17] [SPEAKER_00] Well, I think again, without getting mathematical, I think the vast majority is still, um, solving traditional business problems. So we want to launch a new product. We want to buy an organization to lift our value. We want to get a better penetration to our customers, better customer experience. We want to drive productivity. We want, you know, more efficiency in our supply chain, whatever, all of those things are still driving our business. Probably what's different

[00:45:47] [SPEAKER_00] is the challenges coming increasingly as to, and we need to leverage AI to do that in a much more effective, efficient way. Uh, that, that the shore is the case. And, and we're seeing increasingly as organizations commit to the longer term technology projects that are non AI related. So it could be putting in, uh, you know, what are you call the systems of record changing their ERP platforms. Um,

[00:46:15] [SPEAKER_00] they're increasingly saying, okay, that might be a three-year project. Can you do a faster, but also then deploy AI to do it in a very different way, um, than you would have done it in the past. And they'll kind of build in, uh, almost savings expected from future developments in where AI is

[00:46:36] [SPEAKER_00] going in order to achieve that. So I think there's, there's more, um, more of a push to achieve through the traditional things that companies want to drive in terms of delivering business value in the traditional sense of the way you describe it, but to use that as a mechanism to, to lift, to drive,

[00:47:00] [SPEAKER_00] to get the benefits as a, of AI and its usage as quickly as possible. So that, that's probably more the way it's happening as opposed to people coming, clients coming saying, can you give us an AI thing or can you agentify our business without a context of, well, what are you agentifying it for?

[00:47:23] [SPEAKER_01] Yeah. Yeah. Yeah. Do you think that there are in, in most industries now, uh, agentic first startups that, uh, that are hoping to build, uh, businesses that, that, uh, compete with the majors and whatever industry they're in? Do you think that there will be the growth of new companies in many industries

[00:47:53] [SPEAKER_01] that are not only gen AI native, but the agent native, uh, and therefore will be faster, cheaper, uh, more productive than, uh, than the traditional leaders? I think you, um, there, well, the, the very

[00:48:15] [SPEAKER_00] simple answer to that is yes, no doubt. Uh, the, the, however bit that I would say is you kind of need to break that down beyond the generic into, let's say particular sectors where that's more likely to happen. Um, and, and, you know, maybe even parts of a business that are likely to be parts of the value

[00:48:40] [SPEAKER_00] chain of the business that you could actually, um, you know, you, uh, uh, uh, uh, uh, yesterday's traditional model, maybe you'd outsource that, but tomorrow's is actually somebody comes along and takes it away from you, uh, one of the startups that you're describing. So I think that's it. And obviously a gentic lends itself more to, um, industries which are more focused around processing

[00:49:07] [SPEAKER_00] information and, and where the product is not necessarily a physical product. It's, it's actually, uh, it could be data, it could be digital, it could be a service. Um, so for instance, financial services would be an obvious one, um, in that regard, but then you've obviously got other challenges in industries like that because they're heavily regulated and you can't just kind of jump

[00:49:33] [SPEAKER_00] in and, and start taking over bits of that without, uh, um, uh, kind of making sure you can adhere to a regulatory framework. So I think you for sure we'll see it, but the ability to do that at scale, again, both multi across multiple geographies, but also across multiple companies within a sector,

[00:49:56] [SPEAKER_00] uh, is going to be hard for a lot of the startups to, to execute on. Cause as I say, again, if you're a startup, you're startup startups generally in the technology space that are in a B2C world. So if they're operating in the consumer end of the spectrum, you can find a way to get to consumer adoption relatively quickly, getting through to an enterprise buyer is not straightforward.

[00:50:27] [SPEAKER_01] Yeah. Yeah, that's right. Uh, and, and I, I didn't mean to, to, to ignore the work re-imagined, um, report, uh, what, what were your general findings? I mean, I know we're running out of time, but, uh, with, with regard to AI's impact on the workforce. Well, as you can imagine,

[00:50:54] [SPEAKER_00] I, I touched on it earlier that the, I mentioned earlier that the, you know, the people dimension of, of, of the, uh, proliferation and success of AI, um, will be significant. And so, um, themes around anxiety of the existing workforce. So because a lot of it is just the hype that they read being, you know, online in social media and, and how things get amplified in a particular way,

[00:51:23] [SPEAKER_00] there's a concern as to what that will mean for their roles. There's the, another was around the gap of the learning and development that, that employees are getting to embrace some of the tools and be able to use that in their work environment and how they're being encouraged, um, around those things. But I think there was also a little bit, uh, which I think is for leaders to be very mindful

[00:51:49] [SPEAKER_00] of is to, is the vision for an organization as to how, um, how they want to embrace AI and what that means for the people within the organization. So not even necessarily then at a micro level saying, um, you know, an individual that works in a call center, but actually just more broadly, what's the philosophy, what's the vision, how, how important is it? And by, by getting people to,

[00:52:18] [SPEAKER_00] you know, adopt and use and leverage, um, AI in their day-to-day lives, they're the, the staff, the employees are getting a little bit worried that they're being moved into a scenario that, you know, a vision, a philosophy, an end game that they're not clear on in terms of what that looks like. So I think just increasing the communication from a leadership and sponsorship level and,

[00:52:44] [SPEAKER_00] and the C-suite and executives into the workforce to be clear about how they feel about that. What, what is, what are some of the guardrails they would think to in terms of the use and deployment

[00:52:56] [SPEAKER_01] of this will be really important. Yeah. Uh, and, and it seems you're, you're very focused on the impact on, on the worker, not so much on management. Uh, well, well, in that, in that study

[00:53:14] [SPEAKER_00] as well, you, it, well, I, I think the worker includes the management in that sense, but I, but, but I, I think the, if you think about it in terms of senior management, I think you also have a generational, um, issue in terms of what you typically find of course, is that younger workers are quite happy to embrace using the tooling and, and experiment and innovate. But oftentimes they're

[00:53:42] [SPEAKER_00] working for people who are of a different generation who do not necessarily embrace that innovation themselves personally, and will not necessarily reward those that, and encourage those that are driving that activity. So there's certainly a kind of workforce intergenerational issue, um, at play there, but I think it affects all levels in terms of potentially the impact,

[00:54:08] [SPEAKER_00] um, on, on their, you know, longevity within an organization. Yeah. Okay. We're, we're up to an

[00:54:17] [SPEAKER_01] hour. I mean, do you have a hard stop or, or should we, should we end it here? I don't have a hard stop, but I, um, but you're busy. Yeah, no, that's okay. It's just, it's the sort of thing I can talk

[00:54:34] [SPEAKER_00] forever. Okay. Let's anything you wanted to ask. You didn't get the chance to ask. Well, no, it's my,

[00:54:42] [SPEAKER_01] my mind works a little more slowly. Um, so, you know, things occur to me as we go along, but I think, I think we covered everything.

[00:54:52] Yeah. Yeah. Yeah.