The Biggest Mistake Scaling Companies Make With Talent

AI can make work faster, but faster is not the same as better. We sit down to look at the talent landscape 2030 through a practical lens: what work will still need doing, what skills will matter most, and why so many organisations are mistaking tool rollouts for real transformation. If 2030 feels far away, it is not, and the choices we make now will shape whether we build capability or spend the next few years firefighting.

TL;DR / At A Glance

• shifting workforce planning from roles and headcount to work, skills, and capability
• why layering AI on top of old workflows creates faster output but not better outcomes
• the middle manager squeeze: quality control, bias checking, and coaching under pressure
• preserving entry-level learning by designing deliberate practice and critical thinking
• training as part of the operating model rather than a once-a-year development event
• building internal talent pools and smarter hiring for hybrid AI plus domain roles
• psychological safety, fear of job loss, and the burnout risks of removing “breathing space”
• using AI to improve decision quality by 1% every day across the organisation

We dig into the hard truth we see across sectors: AI often gets layered on top of the usual way of working, creating a “fast car in traffic” problem. The result is pressure in the middle, with managers acting as the buffer between executive promises of efficiency and the reality of nervous teams, messy processes, and quality risks. 

We talk about “AI slop”, why managers end up checking accuracy, relevance, and bias, and how juniors can lose the learning loops that build judgement, resilience, and professional confidence.

From there, we move into what actually helps: redesigning workflows, planning for skills not job titles, and treating learning and development as part of the operating model. 

We explore internal talent pools, smarter hiring for hybrid AI plus domain expertise, and the role of psychological safety when staff fear that “efficiency” really means job cuts. 

The big takeaway is simple: use AI to augment thinking, create time for deep practice, and improve decision quality by 1% every day across the business.

If you want a clearer, more human approach to workforce planning, people leadership, and AI strategy for 2030, listen now. 

Want to learn more about human centred leadership? Then go to my new 8 part series on the Human Operating Model Human AI Operating System a guide to how modern businesses need to be shaped to win in the era of AI.

Subscribe, share with a manager who is feeling the squeeze, and leave us a review with the one work process you would redesign first.

Transcript

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Welcome And Why 2030 Is Close

SPEAKER_00 0:03

Hello everyone and welcome back to our podcast. Delighted to be back with you today. For those of you who are new to the show, my name is Laura Lawless. I am CEO of Leisure Way HR Consultancy and I work predominantly with mid to medium-sized businesses in the space of people leadership and development. Kieran, I'm really looking forward to this conversation because today, when we talk about the talent landscape 2030, it can sound like we're talking about something far off in the future. But 2030, as we know, is actually really close. And for a lot of organizations, it really is only one strategy cycle, maybe two workforce plans. But the decisions that we're making now will shape whether they're ready or whether they're scrambling. So today for me, the conversation isn't about predicting the job titles that might exist in 2030. And I think that's where a lot of organizations have been getting stuck. I think from today, I'd like to look at what the work is that will need to be done and the skills that will be required. So it's moving again from that competency to the capability, but with a focus on the human value. And of course, AI, as we know, is a huge part of that. But I don't think that the real challenge is simply whether organizations adopt AI or whether in the main have already or created sand boxes to adopt that AI model, but it's whether they're designing the work properly around it. And you'll like this one, Kieran, because it was an analogy I came across when we were talking about AI and systems, because I know it's something that's close to your heart, is the idea of giving somebody a really fast car, but leaving them stuck in the same traffic that they're all stuck in. And I thought that was a nice way to consider when we're looking at AI and how it could have the potential to be an enabler. But if when we're not looking at that redesign of work around it, you know, we'll see the same pattern we've seen with so much transformation, big ambition, kind of that pressure in the middle, and the people on the ground left trying to kind of figure it out.

Plan For Work Not Headcount

SPEAKER_00 2:11

So I think there's three big areas worth getting into today. So first, looking at organizations, how they move from planning around roles and headcount to planning around the work and skills piece. Second, and one of our faves, uh, Kieran, as well, is why managers are absolutely critical in this shift and why we can't keep overloading them. And thank you for sharing that article with me as well. Always interesting to get a different perspective. Um, and third, we'll reference that article again and we might share that in the comments actually for today's podcast for those of you who like an academic read. And third, how learning has to become part of that kind of operating model, not just something that we kind of send people on when we're like, oh, it's the performance cycle, it's the personal development plan, and we better send them on that because the gap has appeared. So maybe a good place to start is you know, when we look at the pace of AI and workforce change, and I'll pose this to you, Kieran, to just kick us off. Is do you think organizations are redesigning work or are they just layering on that technology on top of the usual way of doing things?

Why Most AI Rollouts Fail

Kieran Gilmurray 3:17

Yeah, I'd love to say the first redesigning work, but they're simply not. The vast majority of organizations, it's what I see in a daily basis, big, small, global, international, what McKinsey described, what Bain and everybody else are talking to. It's just faster stuff. Uh, give people a little bit of AI, whack a co-pilot, Gemini, perplexity, clawed license on the desktop, and expect them to transform it. You know, giving people the tool is not transformation. Uh, putting a bit of AI paint on top of what you're doing gives it a new shine, but it's materially the same stuff. And to go to what you were saying, it's that squeeze middle again that not only have to try and make sense of this, but they're trying to have to make sense of this as their teams produce faster work, very often more AI slop than actual quality work. They're attempting and and need to be the buffer between the strategy. We now have a group of executives and senior people who love the idea of AI because it's been promised as yet more operational savings, aka head cuts. And I think there was a recent study in Harvard that said 73 plus percent of executives are excited. And then the reality on the ground is only 30% of staff are actually excited because they're panicked about the psychosocial, the uh workplace ability to have a job and everywhere else in between. That's the

Managers Stuck Checking AI Output

Kieran Gilmurray 4:43

front line. And managers are getting squeezed in the middle to interpret the strategy, do something on a day job, cope with very nervous staff, make work go faster. And very often that group in the middle have suddenly had resources removed from them, people, money, and everything else. And this is called transformation.

SPEAKER_00 5:01

And that and it's that that that that movement of work, it's it's not it's not removing work, it's just moving the work around. And suddenly you've got that middle manager group, right? Checking out. I worked actually with the a group recently where you know a middle manager was, you know, there was a middle management uh people leadership program, and one of the middle managers gave an example of where one of her team members used AI to produce this you know amazing looking report. 20 minutes, boom, done, Bob's your uncle. And sounds sounds productive, right? But then the manager had the challenge that she knew it had been produced using AI. She had to then go check and say, is it accurate? Is it relevant? Is it biased? Does it actually reflect the client context? And more importantly, I think, and is has the junior actually learned anything? And I think that's one of the big challenges our middle managers are seeing, where you know, typically when as a grad or as a junior coming in, you would have learned you know the sit by nelly approach, whereby now we're we're pushing towards automating those those tasks that enabled my learning that enabled me to build judgment.

Kieran Gilmurray 6:16

Yeah, I think that's it. You know, learning has to be hard. And I and I don't mean difficult in that, you know, oh my goodness, I hate coming into work. But to retain knowledge, you you have to struggle, you have to apply critical thinking, you have to understand what you're doing. And that is the bit that I feel sorry for managers, and let me rephrase it, who haven't guided their team to produce quality output. So at the moment, a lot of managers are doing exactly that. They're giving people licenses, the companies have provided a limited bit of training, someone's chucking something together pretty quickly, handing it over to the manager, and managers used to be able to take their time and quite right too, to actually review, analyze and assess the work are now just basically producing and seeing slop. They're fixing it, handing it back, the employees having another go at it and handing it back again. Now, look, there's a better way, which is of course train people how to use AI, of course, encourage critical thinking and bring talent on board the journey with you. And then also, you know, I nobody hands me rubbish. I teach them how to do critical thinking using AI oddly enough to test the assumptions. My coaching has altered, my onboarding of my team is altered, because yes, you're right, it'd sit with Nellie and rinse your peat. And I worked with a lot of law firms who are still getting graduates in who could do the work in half the time and critically analyze it and think about it in about 20 hours, but they're making them do 80 hours a week with AI. And the reason is, well, we did it, you know, or I know no better. So the managers are getting squeezed, but I but I and I do feel sorry for them, and they do accept work that they shouldn't. But unless we train our talent, unless we reinvent how we deliver work, then those managers are just going to keep getting more of that slot, as I call it, Laura, not the good quality

Keeping Judgement When Tasks Automate

Kieran Gilmurray 8:02

work.

SPEAKER_00 8:02

So But if we look at some of those kind of core skills or those entry-level tasks, just by way of a say working example, and we are moving towards again keeping our focus on that 2030. If AI takes away some of those kind of traditional entry-level tasks, how are we going to make sure people are still building that judgment, resilience, and professional confidence?

Kieran Gilmurray 8:26

Well, well, that's that's the piece. And it doesn't mean that AI has to. So let's take an example. You and I don't calculate with an abacus anymore because we've got a scientific calculator that can do the job. Because we trust that we understand the mathematics and everything else, and now we're using a tool to augment the process. Chucking AI in someone's desk and calling them a marketing professional or a data scientist without actually explaining how to do it does not make a marketing professional. So there is no shortcuts to providing, you know, good quality, structured training and in-depth training for the particular art, science, or tool that you're doing. And I think that's the mistake that people are believing with the hype. Roles are blurring, of course, because I do marketing, I do copywriting, I do finance, I do analysis, but I know what I'm doing because it's taken years to learn those basic skills. Now, I'm not going to fit someone into my way of having onboarded in firms, which is just sit and read through the documents and watch it. I might get AI in to critically analyze the document, I might get AI in to assess differences between documents. I might get AI in to help me coach because I can build a virtual coach, build a virtual board, I can test for assumptions in the document. And how I coach and how I reflect with my team, that needs to vary. So, for example, I might get you to do something by yourself. I might get, and that'll give me an assessment of a manager of your personal capability. I might get you to do it solely with AI. And again, I might get a good answer, I might not, but it provides a critical opportunity or moment for me to coach you, or I might get you to do it with AI or do all three at once to teach you that it isn't about a shortcut. You you have to learn. But equally speaking, I'm not going to make you use an abacus account and AI, if there is an easier, more efficient way to do it and you understand what you're doing, I'm absolutely going to get you to do that. Now, the only other bit I would say is this all sounds very great, and lots of companies are focusing on getting people to do more. Managers get more done the day, employees get more done the day. But we do have to be careful with that because it isn't just about doing more, it's about using a tool to augment thinking, which is what you're describing there. Using uh AI to research so you produce better quality work, using AI to help you invent or innovate where you have access to some of the best models in the world that have been trained on the best things, and therefore the research and depth of research you can do in a small amount of time, all that can be automated to provide you now with the data you need to be even more critical thinking in a good way, to have learned more, to have innovative more. And when you've saved that time in terms of all that research and automation, now reinvested in the depth of learning and the quality of learning so that AI do the work, but it doesn't remove the need to deeply practice and build uh build capability in your skill set, in your sector or whatever else. So data training to approach things differently.

SPEAKER_00 11:25

And then I think where where the benefit of that will come in terms is reducing, say, yes, that that lower value, maybe transactional demand. You're strengthening capability, you're training that that AI enabled, maybe it's case prep or data interpretation, whatever it might be. So you're moving the, I suppose that's from a HR perspective, you're moving that talent plan away from just hiring another headcount to planning around again that work, skills, and human value. And that's where we're seeing a lot of organizations moving towards building internal talent pools opposed to we need to recruit and that that kind of traditional approach of we need another headcount and we need to go externally for that. Instead, we'd need to be looking at that in-depth training element and building capability internally.

Build Capability Instead Of Hiring

Kieran Gilmurray 12:19

Well, I think you've arranged the things there, Laura. So so let's revisit just that in order. Businesses can't afford to keep hiring, you know, that that that's just an economic fact. Uh but it's not only that. If every time you have an interesting job to bring into the business, you go out to the market, it sends a message to your staff saying, well, actually, you're not capable, which sends a signal to me that the business hasn't been career pathing their own talent. And that triangle between the business, the people strategy team, and the tech hasn't actually worked. It's broken down. Because all teams need to be looking, and my recommendation now is 18 months ahead and redesigning roles and redesigning training, career paths, or whatever you want to call it. Not every year, you mention two or three cycles before 2030. It's only three and a half years away. I'd be revisiting that every six months, where you don't have the talent inside of your business, like a frontier engineer, which is somebody really understands AI and agentic AI and whatever else. And you know, a frontier design engineer or a frontier software engineer or marketing engineer, it's someone who has AI, agentic skills plus domain knowledge. Go and hire them, of course, and bring them in and transfer the knowledge. But if you're simply repeating what you're currently doing, and let me give you an example. I I worked with a company recently, I was helping them build their leadership team and their contact center again. Uh, people left, they replaced the same people. People left, they replaced the same people. And then they wonder why they were getting the same answer. I stopped them doing it. It says, look, next time four people leave, hire two engineers who understand your business domain and understand AI and automation and data analytics and decision science. Use those four to automate the work of the other 196. And within months, they'd freed up capacity of 36 people. Now, they didn't get rid of the 36 people. People were going, they didn't need to, but then they're able to redeploy people to make the phone calls that they couldn't. And this is the thing inside the business. None of us are shy technology. Technology is all the same. Technology doesn't cost a lot. The bit we're shy with is hours per day, and therefore our decision quality and work quality isn't where it needs to be. Nor at times do businesses actually have the time to develop themselves or their talent uh during the working day. And that's left to you and I. And that's the bit that I mean is you need to think differently, not just the workflow, because I need time to rethink the workflow. I want to use AI to create more opportunity to spend more time doing better quality work and better quality learning. I don't just want to free the time up to fill in more work because that's signing me up for more heart attacks. And if you remove some of the mundane, remember the only thing I'm left with is heavy thinking emotional work, which will lead to psychological or cognitive overload, which means I'll burn out myself and my staff as well. So there's a real balance here to applying the technology, redesigning the roles to free up the capacity to teach people about the impacts and the positive and the negative about those, to create better and more valuable work once looking after people's psychological well-being and their psychosocial health to allow you to have the time and the enthusiasm of your colleagues to reinvent your business because ultimately you're going to have to reinvent your business, not just go faster and better at deliver at delivering what you currently do.

Psychological Safety And AI Fear

SPEAKER_00 15:35

Have you seen this? And my ears are pricking up a little bit more here when you're talking about the psychological safety element, because there's a lot of talk about that at the minute. There's a lot of interest in, you know, what's that all about? How is that going to play a role? Is that something that I need to be aware of? And when we look at that talent landscape 2030, you know, the role of AI, the internal talent marketplaces, where does psychological safety come into play in all this?

Kieran Gilmurray 16:02

Sadly, at the moment, not very many places at all, unless you've got someone like myself in. Because I started an AI 20 years ago. And you could see the absolute positives then. Today it's just cheaper, quicker, better, faster. But you could also see the impact on staff. And let's try and explain this a little bit. You and I, like many leaders, are excited by AI because we know how to use it. We know its positives. And everybody gets so excited by that that they forget to think of the negative. So let's let's talk about that for a moment. So in the US, uh I think it was Stanford University, 10,000 graduates walked out when the person on stage talked about AI. Eric Schmidt at a conference recently or one of the again graduation ceremonies, the students booed him when he talked about AI. But executives today, if you were to read and listen to what they're saying and read the shareholder reports, are so excited by it. And all other staff are so excited, they're not. They're terrified because the press says you will lose your job. That's what's happening. 100,000 engineers. So they're even an immediate disconnect between what an executive team wants and what people are feeling on the ground, whether you graduates or there.

SPEAKER_00 17:08

I'm laughing, and I'm interrupting you briefly because I literally only worked with the client there, I think it was like last week. And you know, we worked, we designed the program, everything was fantastic. And as we were signing off on the content, the the ask was said, don't forget to scare the life out of them about the use of AI, because I can't handle a GDPR complaint, or I can't handle you know some issue with data breach. Don't forget to scare them.

Kieran Gilmurray 17:35

Yeah, no, I I I get that. I don't think we scare anyone. We educate and we provide tech that actually helps. And AI can oddly enough do that as well. But but people are already terrified of AI because it let's be transparent here. Most businesses wrap up the word AI efficiency to mean restructure. There's a small handful of firms, and it's a small handful, which is really awful, who want to grow the apple pie rather than shrink it using AI. I could count it in less than one hand. And when we get this type of technology, everybody immediately defaults to automate what you hate. Well, automate what you hate means actually, I like that part of my job. Yes, you for you it's not efficient, Mr. Mrs. or they employer. And if we do make my role and again listen to the vendors, 20, 30, 40, 50% more efficient. Well, hold on, if there's a hundred of us in this business and we make it 50% more efficient, what are we doing with the other 50% people and businesses are neglecting to tell people we're not gonna hire? Or in one instance, that frontier engineer that came in and redesigned the business, they got laid off. And I'm just thinking, oh my goodness, what message are you sending to people? And that message is when a great technology comes in that could have tremendous benefit for you, I, and everyone else, we're gonna make you unemployed. So next time we want your colleagues who are left behind, who saw this happen last time, to be really excited about the technology because we're about to kill your jobs, but we're not willing to tell you. Mine is you should absolutely automate what you hate, but be conscious that if you remove some of what uh leadership teams call the mundane, that's actually called the breathing space, where all of the work isn't complex for every moment, it's not emotional for every single second. I've actually got a moment to think and to breathe and to stop. And today I'm working in my home where most of us are. I don't have the person beside me to say, Lord, do you hear what that iJ just said to let off a little bit of steam? Instead, I'm in my most private place, I've been abused on the phone, I'm cognitively overloaded and I'm burning out in front of me. And then I'm expected to produce higher quality work and reinvent the business. The math doesn't work. Chucking a load of licenses in, cutting your staff by half, and expecting double the work. It's marketing fixation. It's selling at Christmas twice a year and it doesn't exist twice a year.

SPEAKER_00 19:51

But when we look at that that concept of psychological safety, in reality, that that message is something that organizations are not realizing. There's no safety. Forget your internal talent marketplaces because people don't want anything to do

Talent Pipelines Dry Up Fast

SPEAKER_00 20:18

with that.

Kieran Gilmurray 20:18

I don't want anything to do with it. Or the companies that are not hiring their next generation of talent and are wondering why 18 months. And I'm starting to see this. I advise companies of this months and months and months ago. Well, not hire graduates. No, change how you hire, change who you hire, change what you're hiring for, change the onboarding program. One classic, I can't name them, unfortunately, I'm ND8. VP two years ago, saved a fortune, got uh absolutely praised, promoted for it inside of the enterprise. And lo and behold, 18 months later, the talent pipeline is drying up at a rate of knots, and they can't hire the people that they need because they don't know the business, and their leadership team is starting to exit because they're reaching that age and they're starting to panic. The people who are left behind used to do eight to six, welcome to leadership folks, make no excuses for that. They're now doing in some instances seven to eleven just to try and keep up. And guess what? The good news is the talent isn't there, they're not thinking about it. Now they are now because we went back in and said, right, it's not a case I told you so. But look, the these are the bits that people aren't seeing in AI because they probably haven't been around the block a couple of times, like you or I, to understand that look, you need to think end to end. But if you haven't got your talent pipeline, you're in place, you overload your middle managers, all of a sudden they can't redesign anything. And if your senior executive team are blind to what's happening on the ground and your HR leadership team is is having to build the Kool-Aid into what you're doing, then you've got a recipe for disaster. Now, there are fixes to all this, folks. So Laura and I are not sitting here telling you about a misery story and welcome to a pity party, dive deep in, change how you hire, recognize the psychological and the psychosocial impacts on your middleman. Management layer, give people the training, give people the vision and the excitement that it isn't just about cutting costs. And of course, we should all be cutting cost, but this is about using a once-in-a-generation technology to allow you to do more of what you love, to grow a bigger pie. But you have to be conscious end. The big thing is ever, Laura, and this is the bit we miss, talk to your staff. Ask them work. Imagine that.

SPEAKER_00 22:24

Imagine actually engaging and recognizing where those pinch points are and doing something about it.

Kieran Gilmurray 22:31

Oh well, very revolutionary. You don't need someone, I guarantee someone will say, Is there AI for that? Tell you what, we'll have a conversation with a hu another fellow human. My my personal one is we need to be more human than we've ever been. We need to recognize that this is an extraordinary moment in time. We can't wait forever to plan this, you know. But what's your north star as a business? What's your honest to goodness reason for introducing AI? And if you don't understand it, bring someone else in at the moment who does. But don't just put a little bit of extra paint on top and expect more and better and redesign businesses because that's what you're ultimately going to have to do. Because people outside of your business, outside of your sector, are just re-using the technology to redesign value as it's given. Start somewhere. Start by talking to your team, start by listening to their fears, start by giving them the training, give them the guidance that they need. Check in on a regular basis, adjust your career pathing and trust your team as adults and work together to redesign the business because I'd be using the time, not to dump my staff, I'd be using the time to free the capacity to allow us to think, to allow us to better decision quality. Because ultimately, when you think about it, Laura, the success of a business is the success is a function of every decision you make. So if I can use AI to do better work and make better decisions by 1% every day by every employee, imagine the type of business that we would actually create. And if you invest in me, you growing me were a successful business. I'm going to go through walls to help you.

SPEAKER_00 24:05

And I love that example of the 1% improvements. It's it's one I would um re refer to regularly because it's one that resonates, it's simple, it's not complex, and it's one that we we we don't engage with actively when we're, as you said, being human to human, when we're looking at our talent landscape for 2030 and looking at that, that's that's what it is. 1% improvements across a number of roles, people, skills, whatever it might be, that's your transformation.

The 1 Percent Decision Advantage

Kieran Gilmurray 24:36

That's it. And it doesn't as you know, it doesn't have to be more complicated. So if we're advising people today what to do, and please don't walk away frustrated. Laura and I have been in business for maybe 10, 20, some of us 30 years here. The basics and the fundamentals haven't changed. You know, good people plus good technology equals a really good business. It it doesn't need to be either or. AI is not the answer to everything, it's not just automate what you hate, it's invest in your people, invest in the technology, create the space to allow you to innovate. Because AI is the hottest thing now, and it will be for quite some years. And this should be a call to action. If you're listening to any of the series that Laura and I have done, it's there is a burning platform. There's a need for urgency. There always is. Not burn out your staff urgency. But in 2029, we're going to have quantum computing. In 2035, we will have additional technology benefits to bring into businesses. You should be innovating all of the time. But behind innovation, it's not just the technology that's going to transform your business. It's good people. Good people need career path, good people need coached, good people need time to think, good people need invested in. And the best place for that is an organization that's successful, an organization that promotes psychological safety, and an organization that puts its people, not necessarily its technology front and center. And I just wonder, worry and pray and hope, Laura. Imagine if we spent as much time growing our middle managers, spending as much money uh rewarding, celebrating, promoting them as we do technology at times. Imagine the type of enterprises we might actually create. We might have people who want to go to work excited and want to stay there for the next decade or two, as opposed to work being uh just a particular moment that we endure during the week and hope to exit on a Friday. That's never going to work. It never has and it never will.

Practical Takeaways And Next Steps

SPEAKER_00 26:36

We're on the home stretch ourselves today, aren't we, Kieran? But yeah, no, I I echo uh everything that you've said in terms of the takeaway as we we we're keen always with these um conversations that that people are able to take something practical away. And I think for me, certainly, it's that investing properly in managers, you know, they're the bridge between strategy, technology, and that actual performance, you know. And I think when we look at future-focused organizations and the future of talent, it's it's those of us who will be able to redesign the work, build the capability internally, and have that learning agility that we keep learning faster than the world around us changes. And that's no easy feat, but hopefully you'll have taken some tips, ideas, thoughts um away from today's conversation that will drive that curiosity and impact within your own organizations.

Kieran Gilmurray 27:38

And if you don't have that knowledge, come and talk to Laura and I, and we'll happily come in and work with you.