Redefining Roles: Why AI is About Repurpose, Not Replacement

Daniel Taylor: Welcome to Managing Potential, the podcast where we go behind the scenes with the world's most impactful leaders. And if you're looking to turn talent into performance, you're in the right place. Hi, I'm Daniel Taylor and I'm a coach and management and leadership developer. And I have big news this week. Firstly, one that you can tell straight away. Managing Potential is now a video podcast. And it's still available on all the usual platforms Spotify, Amazon Music, wherever else, but we're also going to be on TikTok as well. So, watch out for that. And all you need to do is search for managing potential across all the platforms, and you will find me. Now, this is the start of series two. And in this thing, in this particular series, I'm doing things a little bit different. The interviews are still here. They are absolutely still here. We've got an absolute Brilliant roster of speakers all ready to talk to you about management and leadership and share all of their insights. But before that, I'm going to start off with something called managing potential insights. And this is where I mix in some different ideas, concepts, thoughts, reflections, all on a particular topic where I can really deep dive. And today I'm starting on redefining roles and why AI is about repurposed and not. Replacement. And this has all come out of my time at the CIPD Festival of Work, which was held last month at London's Excel Exhibition Centre. And there was an absolute real buzz around the whole exhibition, but one particular topic dominated the whole agenda. Yes, of course, it was AI. And what was really fascinating this year compared to previous years was almost like a shift in tone. In past years, there was almost like Worry about robots are taking our jobs. But instead, the focus was much more positive, firmly on how leaders and particularly line managers, maybe like yourself, can guide teams through transition. So, the core takeaway from the festival is that jobs aren't actually disappearing due to AI. Instead, roles are being redeveloped and repurposed, and it's not about displacement. But particularly about repurposing. That's what was really, really crucial. Now, this is not me just lulling myself into false sense of security, being a bit naive, but it is backed up by data and for full transparency. One of the key reports is from Anthropic. Now, this is the company behind Claude, which is an NAIA system. And obviously, there's gonna be a bit of bias, I guess, or they are clearly gonna be projecting a particular agenda. But what was really interesting about their recent groundbreaking reports, which is the Labour Market Impacts of AI report, and this is where they analyzed real world usage and data and found that there was no systemic increase in unemployment, particularly in the highly AI exposed occupations. And that is a real surprise. Now, although Anthropocritics report did find some early warnings, and this is crucial. What they found was entry-level jobs weren't necessarily being there, particularly in these AI-exposed roles, and what they would finding that organizations and and particularly companies were weren't necessarily firing people, but they were changing how they fill tasks, changing how they they reshape and and and look at work, and that was really interesting. Now, I do have two examples for you, and the first one. Is quite a high profile one, and that is AI at IKEA. Now, IKEA had decided to introduce an AI customer service assistant, brilliantly named Billy, and I have a Billy bookcase here right behind me. Now, what they wanted this AI agent to do is look at all the commonly found questions, all the the calls to their call center, and in doing so they were able to reduce the calls and By 47%. Now, some organizations would at that point go, do you know what? We're gonna reduce the call center. We're gonna cut it. Let's downsize that call center. But in fact, IKEA did the absolute opposite. What they did is looked at the remaining 53% and looked at the calls that AI couldn't resolve. And what they discovered, the customers were looking for advice on room layouts, the colour, the matching, the style. And they realized that those inquiries required a human, a human for opinion, taste, empathy, and contextual judgment. All things the AI agent couldn't necessarily offer. So what did they do? IQ then reskilled 8,500 front-facing customer service representatives and they repurposed them into remote interior design consultants. Now, what's really interesting about this is they took a cost center. A massive cost to the business and turn that into a massive new revenue stream. Now that is fantastic. And that is how you turn AI or use AI to really elevate human capital. Now, this is not a one-off. I wanted to double-check this and see if there were other avenues, and there's lots of case studies that are starting to come out. One really interesting one was with Airbus, a big global US, European aerospace organization. And what they did they really embedded AI skills into their competency framework, in their training, in their upskilling, and what they did across thousands of employees to look at AI, particularly around things like data analysis and daily tasks. And in doing so, they were able to pivot in order to focus on engineering and the strategy of returning higher value to the business. So they stopped doing the mind-numbing churn, the These sort of repetitive tasks and focus more on what was going to return money to the business and be more strategic. And that to me is what's really crucial here. And this brings me to the the role, vital role of a line manager and or an organizational leader or a high level manager. What I want you to do is think about your job is not about looking at processes or tasks or looking at the administrative burden. AI is all over that. Your role now is to focus strategically on talent and remapping of roles, looking at the work and the impact on the wider organization and beyond. Looking at the impact to our customers, for example, just like IKEA did. Then you need to look at what capacity you're freeing up by doing all of these tasks and ask what higher value work can my people do now? But to get there, employees have to redevelop their skill sets. And we need to address a common misconception. And I keep hearing about this at hate with friends and relatives and people who work alongside. And that is, I'm too old for AI, or it's a bit beyond me, or it's a bit too much. And I want to bust that myth right now. You are absolutely never too old to adapt. And in fact, Anthropics Index, that that report I was telling you about before, found that workers in the most affected roles were the ones that were addressing this. And these were highly experienced managers and professionals who were affecting this change. So, where to start? And this can be the overwhelming point. You go, what do we do? Where do we start? Well, we're going to break this down into about five or six key areas for you to focus on. So these are the key skills that you need to learn to remain future-proof. And this is a mix of technical fluency and uniquely human strengths. So the first one is prompt engineering and AI collaboration. And what this is about is you understanding and really learning about how to communicate with your chosen AI platforms, Copilot, Claude, Chat GPT, or whatever else. And the key thing is to really understand how you get the best out of the system. So it learns from you, but also you learn how to really curate and iterate and refine the data that it's analyzing and producing for you. And once you've got that, you're on the first step. The second step, therefore, is around data literacy and interpretation. So this is moving from just collecting data or getting information back from your AI. And just look at it and going, that looks great. You really need to really go into the depths of the data that's been produced. And can you turn that from just being data and information and turn it into business strategy? If you can do that, that's step two, and you're onto the winner. Now the next one really is around a uniquely human skill, and that is critical thinking and verification. Now we all know that there's a lot of hallucin hallucinations in AI. There's lots of bias. The internet's biased where it's pulling a lot of its data from. So you need to be looking at what information it's providing back, validating it, looking at the the information. Is it correct? Aud auditing it for bias and applying that human element, that human oversight to those machine areas that will be there. Don't trust it on face value. That's step three. Step four is looking at creative direction and curation. So again, this is moving from just basic text or layouts or simple text and information, and really looking at how it can help you define your vision, the strategy behind your work. It's almost like an additional person on your team, and it can help you do a lot of that work. So, really looking at creative direction and curation. Next up, I think this is one of the Biggest steps. It's one of the most important steps, and that is relationship management and empathy. It's the human element, it's what makes us who we are and so unique. So, what I'm looking at here is really focusing and doubling down on those high-touch, high complex problem issues, the bits where you know you can offer a real value by offering the personal touch and and really demonstrating and and demonstrating. Why you are so unique compared to a machine that could just do this if it was a process. Now, I hope that's helped at least pro, you know, start some theory, sort of start some thinking. So, as a line manager, right now, your primary focus should be about helping your team build these exact competences. So get them on board, involve them in the discussions, involve them in the transition, get them on there as allies. And look for adjacent growth. So if you're saving time here by trimming a particular process because AI would take over, what do you do? What is going to be most mission critical with that spare capacity? Now, I don't think that AI is coming for your job particularly, and that's borne out through this research and this data. But what might be coming for your job or b or for your organization is someone who's got those skills who can bring AI to life and bring the human element and that is what you need to be looking at as a line manager and really protecting yourself, your business and your wider team. And you really need to be looking at making these changes today before you get left behind. This is an exciting process to be part of because it can AI can save you so much time and effort. And that's particularly the bit that I love about it. I can use my personal skills, I can use my human skills to really bring all of that to life. And I'm going to get AI to take away all that churn and all that hard work. And that is what I'm focusing on. Now, I hope you've enjoyed the first Managing Potential insights. And let me know how your organization is reshaping roles in the comments. And if you have enjoyed today's conversation, hit the subscribe button, go to our community, which is on managing potential podcast.com, sign up to the newsletter and get regular leadership insights delivered straight to your inbox. But until next time, have a great day. Take care. Bye bye.