AI Leaders Podcast #84: The path to Human + AI Impact

Speaker A: It starts with ensuring you've got the right people and culture as the basis for achieving any sort of success and value and scale.

Speaker B: M welcome to Accenture AI Leaders Podcast. My name is Teresa Tung and I am our global data lead at Accenture and also your host for this episode on the role of people and the critical roles that we can all play as employees and leaders in achieving real value from AI. I'm thrilled to be joined by two experts in this space, and I'm going to ask each to introduce themselves. And let's start with Russ.

Speaker A: Hi, I'm Russ Smith from AstraZeneca. I am the Vice President of ERP Transformation Technology for the organization and it's a pleasure to be here.

Speaker B: Thanks, Russ. And our very own Accenture, since Nick, can you do the same?

Speaker C: Yeah. Great to be here. My name's Nick Tate. I'm the, uh, Tander workforce lead for Accenture in the uk that involves partnering with clients on working through this incredible, uh, change we're going through when it comes to AI in the workforce. Uh, the work that we do. Um, and I also haven't wear another hat, which is. I'm the lead for Accenture Achievement and AI Impact Initiative, which is really focused on some of the thorniest issues around adoption and competence and confidence in AI and really focused on the quality of adoption and roi. So it's really great to be here

Speaker B: and we're going to use your expertise for just that. So thank you for both of you. Uh, let's get right into it. I'm going to start the question with Russ. Many companies are stuck in pilot purgatory with AI, and few companies have achieved real enterprise value. Um, for companies who have achieved scale, scale and value, how do they do this?

Speaker A: Yes, it's a difficult one, but, um, I think you need to layer the answer to this question and start with the must have. So, for me, it starts with ensuring you've got the right people and culture as the basis for achieving any sort of success and value and scale and make sure people understand what's in it for them as well, and just really get them excited about the need for AI, how it adds value and how it will really make a difference. That, for me, is the start. Then you get to sort of cultural items, which is. Moving from pilot purgatory, as you say, into scale and value requires you to really focus on the best solutions and AI options that are available to you. And that's often quite hard because in. In many organizations, everyone likes to reinvent the wheel, right? It's not common culturally to value reuse. Right. We tend to reinvent rather than reuse. So getting your workforce and your teams to think about not just how do we invent new AI use cases, but perhaps take existing ones and take them to the next level. You have to iterate and continue to reuse and incentivize people for reuse and recognize people for reuse. And then you need to get to the last piece, which is what do you actually then want to scale? And for that you need to really start with the problem, not with the technology. What are the key things that you uh, are trying to solve with the AI and what is the problem that you can solve and can explain throughout the organization you're trying to solve with that? Then you get to the really tough bit, which is you've built it, you've solved your problem. But is anyone using it? Right. So to get value at scale, you've got to have adoption. And for adoption there's got to be sort of a frictionless experience. Everyone's got to understand what's in it for them, everyone's going to want to use it, it's simple to use, and away you go. Adoption's uh, great. Then somebody turns around and says, well, what's the value? And that's when it gets really hard. And I don't think there is necessarily a, uh, simple answer to this, but how do you measure the value to the organization? Now if it's a straight dollar output, fair enough, but you'll probably find that in most of the things you scale across an organization it's incremental productivity. For example, how do you actually measure that, uh, incremental productivity? It's not like you can say, well, I've saved X amount of hours that I've now used for something else because often the AI is embedded in your workflow. It's not a consistent end to end process. So you then get to the last stage which is measuring the value. So if you bring it back, I'll simplify it. People, culture, people understanding why they're doing it, solving the problem, making it easy to adopt so it goes at scale and then measuring in some way the value in a way that's understandable for the rest of the organization.

Speaker B: I really love the way that you broke it down. And it's really about use, usefulness for both the people as well as the business. If it's neither useful for the people or the business, none of this matters. And I think the way that we talked about it, it's not so Much a technology problem. And many AI conversations have fun focused so heavily on technology. And in fact, it's almost the easier part of all of this. So I think, Nick, I want to direct a bit more towards you. Right. So from your perspective, building on Russ's points, what role do talent, skills and ways of working play into turning this AI ambition into real value?

Speaker C: Yeah. What I really like about how Congress you articulated is it's really taking a product mindset. Um, you know, you're, you're falling in love with the problem, not the solution. I think at the m minute there are many solutions that we are, that we are idolizing. Um, and one of the issues is that those solutions are going to change very, very quickly. Actually far more quickly than people are going to be willing to or even able to adopt and adapt to. And, and that's a real issue that I see when I speak with, when I speak with clients. I think that if we want to build a world, and I think we are building a new world here, um, we need to be thinking about things where humans are really in the lead. But to do that, that means approaching things really holistically. And I think often in an old world with old money, you'd say here's a technology, here's some change, here's some training, go and use it and let's hope. And um, I think actually what you need to do is think of this far more holistically and iteratively in the same way that you'd scale a product, you scale competency within, within people. And I think we need to take a product mindset to people's development. So we need to think skills, we need to think mindsets, we need to think behaviors, we need to think culture, and we need to iterate on those as we go through that. That, you know, the approach that Russ, that Russ articulated. Um, because I think if you don't understand that, if you don't get into the head of kind of where what competencies need to be built now versus later, uh, you're going to probably solve for a problem that isn't right for where you are in the development. Um, and then I think the other thing is really around experimentation. So this technology changes when people get hands on keys and they start to really trust it and understand how it works for them. And um, it's the first time in history where we've had a technology that is symbiotic with how we learn because it learns with us depending on what aspect of AI you're talking about. So I Think you've got to create those feedback loops and train people to actually train themselves and use it every day in a way which is showing value incrementally. Not just these big bits of value, but actually how are you iterating the value that you're getting and how are you learning and reflecting on that. But sadly that's not happening. And certainly in the work that the research that we've done within Accenture, we run something called a Pulse of Change survey every year. So two and a half thousand, 3,000 executives, we know that 86% of organizations are planning to increase their AI investment. That's a big tick. But only 43% are planning to increase their investment in the workforce. And that ultimately is where you're going to get the value from. That's where you're going to get the scale value from. So we need to start to really get into, understand the delta of those things that need to be, that need to be addressed.

Speaker B: Yeah, I love the product mindset approach. It builds upon, Russ, what you were saying about usefulness both for the person using, uh, the tool as well as for the business, and then also some of the new roles when we're thinking about a product, um, as opposed to yet another technology project. I think, um, that's a very big pivot when we do that. It's not a one off case, but it's something that's meant to evolve and really think about putting that human first in what they're doing. Um, and how do you actually build that? I think along that lines, maybe that's why we're seeing the pickup so much more in these external facing tools that have been built with that product in mind. So I think, Russ, many organizations we're seeing employees adopt either unofficial or external AI tools much faster than the formal enterprise rollouts. What risks does that create and what can leaders realistically do to mitigate those risks without shutting down innovation risks?

Speaker A: Where do you start? Right, so let's start with the simple stuff. Um, when you're using those unapproved external tools, you are essentially exposing your company's data. That could be sensitive data, personal data, it could even be IP that keeps your company afloat. Right. It's, it's an incredible risk that I think can only really be addressed by education, people, people need to understand the risks. It's almost the opposite of what I said earlier on. What's in it for them? It's what harm could this do for our, uh, organization, for me and for my colleagues. Right. Because it's probably unintentional, but that leakage aspect outside of the company is, for me, probably one of the biggest risks. Now, the pressure on employees and workers isn't helping in this because I suspect many of the people listening to this are, uh, told, use AI. It will make you faster, more productive, more effective. Right. Which, which for the normal human being usually goes one of two ways. You normally go, well, if I don't do this, everyone else is, and therefore I'm going to appear to be worse. Right? The FOMO effect. And it will drive you to behaviors that maybe you wouldn't normally do if you had a bit more time. The second is a view that, well, if my boss says it makes me faster, it's got to be great, right? And if this one makes me even faster, isn't that great as well? So the expectations on the employees to utilize this tooling sometimes can create unintended circumstances. Now, the only way around that is to create a secure internal set of tools that are fully trusted, safe, vetted and available. Okay. If you don't want your workforce to use a tool that's outside, make sure there's a good tool inside. Yeah, that has to be the way of doing it. And you have to make it frictionless. You have to make it easy to use. Um, and again, there's education's coming into that as well. So you almost need to create a. Why would I want to use an external tool? I've got everything here and admittedly, depending on the size of the company, that may not be commercially viable. Not everybody needs a super, um, intelligent piece of AI for a very specific task. Most of us can probably do it with a secure internalized version of a very basic large language model. So I think it's really important that people understand the pace of change in AI and tooling. And it's important to recognize that you need to experiment and take risks and move fast. And whatever you do, don't pour concrete in your choice of tools because you're not going to be able to pivot and change when the next best tool comes along. So that, for me is very important to keep flexibility and ability to change. The other thing that needs to be considered in tooling as an organization is people have their favorites. It's a bit like, um, Android versus Apple. Yeah, if somebody likes Apple, don't waste time trying to get them onto a Samsung device. You're going to have the same thing with AI tooling. Some people love ChatGPT, some people love Gemini. There's more, many more out there. By the way. But once people find the one that they like, they tend to stick to it and gravitate to it. Um, so just recognize that I think is important because that change journey from moving from favorite tool A to less favorite tool B can be a very hard journey based on personal preferences.

Speaker B: Yeah, we need that two phased approach, I guess, a carrot and a stick. The carrot in terms of the good tools and rewarding that good behavior and a little bit of the, um, awareness of the risks that you're putting your company and yourself into by using these unapproved tools. Because as you were saying, a lot of it's so cool, right? When we see some of these new tools and it just. You're trying out, and I can see that you would just take the document that's available in front of you and you put it in and you get an answer. It's super cool. But also, as you were describing that niche and our mouth is dropped. Right? It was super scary how easy that is to leak something.

Speaker C: You know, like it's actually on companies to create amazing employee experiences and to have the wherewithal to do incredible work. And that changes, you know, what AI means at work. You know, it changes the notion of a, you know, a human plus an AI. First organization isn't just one where AI is available. It's in the process. It's actually that we're putting it at the forefront and we're making it really attractive for people to use so that they get really excited and they go back to it and that's. And they trust it more and they see more value from it. So I think there's a really interesting conversation, you know, when we think about employee value propositions as to what type of talent you want to attract or what type of workbench have you got for them to experiment with in the right way. Completely agree with that. Um, otherwise don't be surprised when people step away or go and find alternative routes, which we know is not where we want, where we want people to be, for sure.

Speaker B: So I think the tooling is part of it. But Nick, building on that point, just right now, reinvention is fundamentally about adoption and behavior change. So what should leaders be doing to enable this talent reinvention?

Speaker C: Yeah, it's a really interesting right, because I think fundamentally it's such a rich topic. And if we want to put humans in the lead of this new brave world, uh, with AI, it starts with leaders, and it starts with leaders kind of looking in the mirror. I think leaders jobs has been and Always will be to lead from the front and set the culture through their actions and their own behaviors. And that has a huge halo effect on the business. So the kind of, the role of leaders in this space at this time is utterly critical. Um, but I think it's a different type of leadership we actually need to see in this world moving forward. Um, leading in this world, I think is about doing and setting the right question and owning the outcome. Um, and I think it's a real, it's a time for real curiosity. You know, um, we're living in a world now where you can ask an AI any question and it'll come back with an answer. We can, we can argue whether the answers are good or not. And Russ, I agree with you that trust in the answers that we see is a very, is a pertinent skill. Everybody needs to learn very quickly.

Speaker B: Right?

Speaker C: But it's about questions and that starts with curiosity and a culture of curiosity in an organization. So I think leaders need to lead, um, and breathe and be comfortable with what they know, but also what they don't know and have dialogue with the teams and lead from the front, showing their own learning journey. And um, there's a real vulnerability in that in saying, I don't know how to use it, but this is what I'm trying to learn. They also need to take a real keen interest in what's holding their team back. So Russ, when you talk about the scale of AI products, um, and services, microservices within an organization, often the barriers of behavioral, because the context and the culture hasn't been set and we're second guessing what people need or want versus actually what they really want. Um, so, you know, a lot of the work we do within Human AI Impact Initiative is about understanding those blockers to scale. Um, and it's often because leadership isn't actually listening to what the teams want or need. They're presuming what it is because they haven't experimented it with themselves, they haven't learned themselves. They're not applying that, you know, to their teams. But I think, you know, taking an even bigger step back. You know, we're at this moment in time now where, where AI is no longer just. I don't think it is just a productivity tool. I think it's getting layered. You know, is a fundamental part of the operating system of a business and how it's going to run and how it's going to grow differently. So the question isn't really any longer whether AI is going to transform how companies work. It's actually fundamentally how companies are going to lead and organize around it. So there's really big questions I think for leaders at the minute as to how they drive adoption and it's happening at many different levels. But fundamentally I think it actually starts with themselves, um, which is maybe a bigger question to us.

Speaker B: So leaders should start with themselves. They should enable and give good tools that are good so people want to use them. Um, is there anything that they should be doing for the workforce that exists in um, enabling? I think there's a lot of people who really want to learn and try, but um, don't really know how to start.

Speaker A: I mean I can give you a very practical example. And this is, you know, this is something you can actually see in practice if you just go on LinkedIn quite often. And um, we created a enterprise education approach for AI and we rolled it out to every employee in the organization, every employee in the organization. And we tiered in different levels to effectively create basic understanding of AI. Then um, how could they use AI to become more productive then to more advancement of how they could use AI in a wider context using a series of tools that we made available internally to my, to my earlier point. And um, that has been extremely successful. Um, I think the last time I looked there was something like 60,000 employees that engaged at one or more of those levels, um, as well. So there is that real thirst and desire to learn about AI. Okay, that's the facts that are borne out in the numbers that we've seen. And as a result of that people are looking at how they can use the AI tools not just to improve what they do in a simple productivity way, but actually try and eradicate some tasks from their day to day grind so they can focus on higher value items. And I'm seeing that in reality from my colleagues. Um, I do it myself, I learn something new every day. Um, and it's pretty amazing what these tools can do. Even from say six, nine months ago, the thought that I can create a very simple AI agent with our tools that uh, overnight collates every message and email that I've received into a single message that I get at 8am in the morning. And it's prioritized it based upon my previous experience and interactions with those individuals. So it's sort of semi prioritized my take a look. See, you know, you couldn't have even thought of that a couple of years ago. But for me looking at that 8 o' clock in the morning, I know where to look and it makes me much More effective before we start. And that's just one simple use case. There's some pretty amazing stuff coming out as we speak in this area.

Speaker B: I think that's a great case in terms of personal productivity. I want to connect this with the topic you started with Russ on measuring AI value. At the beginning, that's how we begin. This has to have a value to the person and to the business. And, and that's a really key component for all of this. And yet it's one of the hardest and still unresolved for most organizations. Um, why is this so hard in practice? And what are some things we can do?

Speaker A: It's so hard because it's so new, is probably the simplest way I'd look at it. Um, and it's not like, uh, the AI is reproducing or replicating, um, or even replacing something that has a very clear value that you can just sort of pin on the wall as a, A saving or an indicator is often accelerating parts of processes, for example, that you've never really measured anyway. Because if you don't have a baseline, how do you know if you're measuring? And realistically, how many organizations measure every process that they do so they can do it before and after? The answer is very few. Right. At the end of the day, you may get some areas where, um, you get a fundamental breakthrough that you can quantify. But by and large, the AI tooling that I see in most prevalent use across the organization doesn't have any capability to measure anything other than usage. And usage does not equal value. There are not productivity measurement tools built into the AI that allow you to at least put a number on the value that it's given. You get to a point where you say, well, I must be getting value, I must be, because logically it makes me faster. But measuring it and turning it into value, I haven't seen that. Even for the most basic use case, which, you know, is prevalent in most companies I would say that are listening to this.

Speaker C: I think, I think it's an interesting thing though. Russ, just sorry to interrupt you, but in terms of sort of how and against the question around rollout, because if you're going to roll out all the tools to the same people in the same way, it's very difficult to A, B, C, D, E, F, G test what the difference or delta may, uh, or may not have been with it, I think the other thing as well, and you said this at the start, which I completely agree with, we're often using kind of Ferraris where what we might need is pogo sticks to run the race. And not all AIs are created equal. Um, there's also a role to say we're sticking in a lot of agentic sisters. We're not actually. What we really need is deterministic, good old fashioned RPA that probably isn't, you know, kind of forthright everywhere either. I do, I have to believe that you need to look at the quality of adoption. So how are people using it? And there needs to be probably some more qualitative feedback as to how it made people, you know, how it made people feel different at work or how easy it made things become. I think there's that and then I think there's something around connection, intelligence across an organization. So where and how are people connecting differently or agreeing? Because often the biggest disparity in large organizations, which, let's face it, are often very siloed, is they can't agree on the data or the information that they should be working towards. They don't have shared okrs, they're not aligned on the metrics that matter. And I think in time, what we'll start to see with these new systems in place is a more connected organization, which is by default simpler and easier to do business with, both internally and externally. But you're right, I think at the minute we're in this messy middle where we're trying to define value in old metrics, but we're looking at it in new money.

Speaker A: Yeah, you hit on a really interesting point there. And I went down this route myself. Um, qualitative feedback. Qualitative feedback in how the workforce feels they're using the tools and the values they're getting is more valuable than I think people realize historically because you tend to go for the hard facts and the hard numbers. But, you know, let me give you an example. Why would you not accept qualitative feedback that a tool is making your, your workers more effective if they tell you so? Well, at the same time, you probably run an annual survey to the same people saying, do you believe in our company strategy? And you believe it based upon that feedback. How is that different?

Speaker C: Right.

Speaker A: How is that different? The answer is it isn't. Um, and I ran some qualitative feedback on the adoption usage of said coding tools. Right? Because I couldn't prove it, but my gut said it was right. And we got about 85% of the team believed that it was improving them in a demonstrable way. And that for me was good enough. That for me was good enough to move forward because I, you know, I believe that the amount of work they were doing in volume, when you start adding it all up, uh, must pay for these tools, which are not particularly expensive. And that quality of things important, I feel, uh, often overlooked.

Speaker B: No, I was going to say I don't think we're mature enough yet to see the full value. Right. So the measures that you mentioned, adoption and the qualitative measures, it goes back to trusting your people. You've equipped the people with the tools, you've equipped them with the dangers and the possibilities. So the qualitative measure is probably the best you can get at the moment to see, uh, what's the impact in the future. Because I don't want to measure my developers by more lines of code. That's not the point. Right. You want to say it's better quality code and possibly a better product that they can deliver because they can do a lot more iterations and be much more adaptive in changes and updates and enhancements to what they're doing. Um, and some of that it's going to take a while to see. Right. People are looking for a very immediate couple months measure and then rollout and the qualitative and the adoption is much easier to get and you have to believe in that in parallel, then we should get some baseline numbers in what we're doing. So it could be in sales and sales effectiveness. You could get the feedback right away from the sales folks, just like we could in developers in software development. But then the actual impact on the numbers in sales or the, the better code and the software product and the use of the product that the software engineers were working on, that's what we want to measure on. And that's not a couple weeks measure. It's something that takes a little bit of a belief before we roll it out. And I think that's why we're not seeing that scale value for many companies yet, because they just haven't gotten there.

Speaker C: Yeah, but it's interesting though, because, um, Russell, you mentioned this, is that you've got AI turning up in process flows. But the challenge and the opportunity is how do you go right to left with your thinking? So you're focused on the outcomes, the new types of outcomes that are available now, versus identifying an old process or automating an old process. The question is, how innovative are you going to be with this technology and how bold are you going to be in terms of the impact on your organization? So I think there's, there's a big old question there around what are you using? Where and why and how and how are you going to measure those things? The other thing is that as things become, as AI become permeates of organizations more so I think you can have different types of metrics that matter. You're going to have effectiveness and efficiency of agents. You're going to have a new way to measure productivity between two different types of workforces versus just augmenting people. They're going to be working very, very differently. So I agree with you, Teresa, that the conversation around metrics and how do you scale the impact. We're nascent, but we need to start thinking about how organizations are organized to adopt and adapt to this technology in very, very different ways. So there's definite work to do there, for sure.

Speaker A: There's an interesting paradox you just put in my head then, Nick, which is if you're, if your primary goal is to prove and measure value, the easiest way to do that is to incrementally improve an existing measured process that by its very definition is not really what you want. You want AI reinventing processes, not improving them. And by the very nature of exploding a process and reinventing it, you don't have a baseline. So you can't measure value as simple as you end up with a scenario where you sort of encouraging incremental because you can measure versus truly revolutionary because you've got nothing to measure against.

Speaker C: But we've lived in a world which has been going where do I, you know, it's standard to fit, right? And I think that what, what this technology will do brilliantly is standardize lots of things and you'll end up with, uh, uh, the middle just kind of falling away. You know, you can have standardized products. So the question really for organizations are, and it's in people is where am I going to differentiate them? So how do I measure differentiation in how I'm using this technology, how that then shows up in my organization, how that then shows up externally in the market, you know, and I come from a products background actually. And the more differentiated a product is in the market, the more premium you can drive. So businesses doing this well, you know, organizations embracing it and taking that bold future, the reinvention mindset you're going to take are going to be driving to a premium, but they're going to have to really work out and understand where do we standardize and be fine with that focus. Amazing technology, amazing organizations, amazing people on how do you differentiate in the market. And that's a bold leap people are going to take for sure. And I love those sorts of Conversations, those are the conversations we're getting into now, which is great.

Speaker B: And that's why I thought that product mindset that Nick, you started in the beginning will be very helpful for as we think about the reinvention of the process, these products that truly disrupt a category is where we're at. You're really thinking about what is the impact again to the person, the employee, the user, the job to be done and then um, how you actually do it. Um, the product might not look anything like the previous generation because this is now um, AI and agent power, but you still know the usefulness. Right. That, that doesn't change something that makes my life better, um, and solves a key problem.

Speaker A: Yeah, Always start with the problem. Don't start with the technology. It's, it's a, it's an age old trap us technologies fall into. Um, shiny tech is always there. But start with the problem and with this you've got the building blocks to go after the problem. You know, in the past it was, you know, with package software and various bits and pieces, you almost had to bundle together a load of best of breed solutions to, to fix your problem. You've now got the most intelligent tool set or building blocks at your disposal. Start with the problem, tell it the problem and work your way towards the solution. Right. It's, it's a, it's a fantastic new world that we're in.

Speaker C: Yeah. This as well, that point around being actually able to articulate what problem it is you're trying to solve is a rare skill. Do you know what I mean? It's, I find it fascinating. We've lived in the world for the last 25 years where we have been optimizing for short text, text messages, news headlines, and now we're living in a world where context and large language and the use of facilitative language is incredibly important. A nuance in language is incredibly important and we haven't lived in that world. I did a degree in linguistics, so I geek out on this massively. Finally my degree was useful. Uh, but I think language is so important. People's facility with language now to really articulate and describe not only what the problem is, but what they're looking to achieve. Like what, what outcome are they really trying to drive? I think you can be incredibly creative and innovative and entrepreneurial in organizations now with this, with this technology.

Speaker A: It's a very strange world where no, uh, knowledge is a commodity rather than a skill. Right. And it's, it's the reality that if you know, uh, it's a bit like in the old world of networking, right. The people used to say, no, it's how you knew. Right. But who you knew meant you knew who to ask. Now we're in a world where you need to know what to ask and how to contextualize it. And that is a better skill than necessarily knowing all the knowledge because that knowledge is now a commodity. It's, it's uh, it's exciting and frightening I think in equal measure. But language as you say, is important and I think it's, it's an interesting shift you also see in a lot of the software markets at the minute now. Um, a lot of the providers are now putting a lot more value in semantics. The value of their semantics and the value of the logic in their product is starting to become a differentiator rather than the tech.

Speaker B: Yeah, because semantics is the meaning. Right. It's the meaning and the choice of what data to use, how to apply it. Given the scenario. I think that taste, right. It's not just the reference but the taste of knowing. This is an interesting problem, the opinion of how you could address it. Knowing which questions to ask and how do you have a taste as to which answers do I like and which ones I don't. I think that that opinionated, uh, sort of or scientific method, right. There's a, there's a way of, of looking at breaking the problem down and that's the new skill, not just knowing the reference.

Speaker C: Yeah, but there's a thing, isn't it around trust? Because I mean I would say this to people that it used to be when something looked bad, it was bad and now I think it'd look amazing and still be bad. And so there's this thing around kind of the abortees ask a great question, um, which has always been a leadership trait but it's also around questioning the answer that you get. And you can't take things on face value. Uh, when you get responses in these ways, I think that the skill is still critical thinking and lateral thinking. And if you look at something like the World Economic Forum and they do, you know, an annual skills report, you know, the grow, the growing skills are ah, empathy, leadership, kind of making connections with people, the human stuff becomes incredibly important. And I mean I've got, I've got three kids, you know, I went to my daughter's parents evening, she said to the teacher, what are you teaching my child? You know, what are you going to do? The thing is actually it's the growth of those skills, uh, kind of you Know the critical, the human elements is incredibly important to get the best out of this technology. You know, how do you amplify humanity kind of, um, human traits? This technology will force that out of you to make the most of it. It's a really interesting conundrum to solve

Speaker A: for and, and I think it's bringing to the foreseeing that's sometimes quite uncomfortable for some people, which is, you know, the best, the best leaders are the ones that are not afraid to ask for help and will ask for help. And this is very much, you know, brought to bear in the brave new world of AI because it's very easy to ask for help but not ask a human for help anymore. So there's an interesting conundrum in there, isn't there, somewhere? Right? You'd be quite happy to ask your AI how to do something, but would you ask the person stood next to you even if you knew they knew the answer?

Speaker B: So let's bring this back around so we. The role of the human is more important than ever to enable, value and at scale, AI, both to, um, drive adoption, but frankly to make the bigger change and to reinvent the process. And then the skills of the human and human empathy and um, reasoning and curiosity become more important as well. So I'm going to end with two, uh, asks for each of you. I want to have you each do two takeaways. And Russia, I'm going to ask you to go first. But for each of you, I want to give two takeaways, one for leaders and one for an employee who might just be starting early in their careers. What should each focus on today to be ready?

Speaker A: Let me start with a leader. I, um, would say to any leader out there, invest your time learning. So you know AI, and you view AI as more than just technology or tools, but more of a partner that helps you become a more effective version of yourself. And then make sure your team feel the same. Lead by example. Very, very basic leadership skill. And then if you go down to, I don't know, the um, an employee earlier in their careers. Now that's a long time for me. So, uh, you'll have to forgive me on that one. But it's about mindset and curiosity. Um, experiment, learn both from success and failure and get into a cycle of adopting the AI and the tools that, you know, work for you. But never get complacent. Continue to adapt and change because a new tool will be coming every two weeks, three weeks. So that would be my approach. Be hungry to learn and get the most out of the tools, but never sort of fix yourself in one place.

Speaker C: That's great for you. Uh, well, yeah, I'd say with leaders, um, and I agree with all the things that Russ said. Some of the still those things, um, I think it's about getting uncomfortable, actually. Um, I think you need to live in, sit in this discomfort, which isn't natural for people, but, you know, getting comfortable and, and get informed. Um, I agree with the learning and experimenting. Russ, you said something as we were having conversation on this was around, change up your sources, you know, swap your platforms, keep things fresh. I think you can as you need, you can rely on things. And actually, I think that's quite dangerous at the minute. You want to be exposing yourself to lots of different stuff in a completely safe and compliant way, obviously. And then I think the other thing is shift the narrative from productivity to value. I think that conversation around differentiated value, um, that's where leaders can really impact their organization and lead from the front. So I'd probably those things for leaders and then for early careers. Honestly, I don't think there's been, uh, a better time to be highly creative and bring the best of your ideas to an organization. And organizations are seeking to reinvent for a new audience with new expectations. Don't shy away from those conversations. You're going to represent those, you're going to enable them. You know, we're entering this highly entrepreneurial world. People are bringing, uh, people in. Early careers are bringing a whole new dimension and perspective to how work could get done. Um, and that's incredibly valuable to organizations right now to be the drivers of change. Um, so I think kind of speak up, get active, bring the best of your ideas, you know, and, uh, and I think smart organizations will listen to those for sure.

Speaker B: So whether you're a leader or a most junior employee, everybody has a really critical role to ensure, uh, we're adopting AI in a safe, secure and effective way. So thank you, Nick, and thank you, Russ. Uh, thank you all.