Psychological Safety Is the Missing Piece of Your AI Strategy with Jamie Bykov-Brett

Speaker A: Most organizations do not have an AI tool problem. They have an AI behavioral problem.

Speaker B: It reflects the organizations that it is part of. Right? Is this going to take my job? Am I training my replacement?

Speaker A: Actually, am I literally training the model that's going to replace what I do?

Speaker B: If the AI revolution doesn't allow people to be more human, then what was the whole point?

Speaker A: Okay, we get it. HR admin can be a headache. Most teams are managing contracts, onboarding, compliance and payroll across different tools, different vendors and different rules. But companies like Elevenlabs and Shopify are bringing it all together with deal. ElevenLabs quadrupled headcount in 2025 across 54 countries. So build your global team with deal visit D eel.comnickdayhr Today I'm speaking with Jamie Bykoff Brett, who is an absolute expert at understanding and helping teams to get the most out of their AI training. We're going to discuss today why AI training often fails, but more importantly, how to design interventions that turn AI from a novelty into a daily working habit. So if you're keen to learn about one off AI workshops where any create lasting behavioral change, you're in the right place. If you want to know how to design AI learning interventions that embed into real workflows, you're in the right place. So without further ado, I want to welcome Jamie to the HR LND podcast. Jamie has fantastic experience in this realm and a really good website I'm going to link you to as well, which gives you so many resources from blogs to free assessments and more. We'll get into all the data, uh, without further ado, Jamie, welcome. How you doing?

Speaker B: Thank you so much. I am good, thank you.

Speaker A: We're both focused on problem solving, right? So we can handle a little bit of sleeplessness, I think. I think in particular for your side, you help organizations really, who invested in AI tools but haven't yet changed employer behavior. You really work with those organizations. And I think it's a conversation that matters now. Right, because the gap between AI investment and AI maturity is significant. I saw McKinsey, I think it was 2025 report on, um, workplace AI and it found that we, while almost all companies are investing in AI, only 1% of leaders describe their organizations as mature in deployment. So we're going to really dive into all of that data in just a moment. But before we do, my first question, ask all of my guests just to get things moving. What do the words human resources mean to you?

Speaker B: When it comes to human resources, I think it's important you're thinking about the wider ecosystem of the organization. Right. You're talking about the attributes that people bring to the table and how you can essentially make the most of those assets that come to the table. People are assets, assets within your organization. Their thinking, their productivity, their creativity, they're all part of what makes your organization flow. So when I think about human resources, I don't always think it's necessarily the best name, but I think that that's what I always attributed it to.

Speaker A: Yeah, I think that's fair. And I think there'll be people wondering, why have I invited Jamie onto the show in particular? There are a lot of experts out there talking about AI and they don't always get it from an HR standpoint. So I didn't mention introduction, but you are an AI transformation strategist, you're a TEDx speaker, but you're also CIPD certified and co founder of the Executive AI Institute. So before we dive into the nuts and bolts, I wonder if you can just give a bit of an overview of your experience today and why it's so relevant and so relevant for this particular show.

Speaker B: Well, uh, I actually started off in youth work. So initially I worked for a charity that supports young people face some of the greatest adversities into employment, education and training. I actually started off as a client, then went through the outreach, then started delivering programs. But when the AI aspect started to come into it is this was 2014, I was working on how to develop the STEM strategy for that charity across the uk. And essentially how do you get young people from marginalized backgrounds into science, technology, engineering and maths? Often the subjects that they've had the biggest barriers to participation with. And the young people, you would have youth workers as well. And I can guarantee you a lot of people didn't go into youth work because they wanted to be able to teach them.

Speaker A: They.

Speaker B: So we used to have lots of STEM by stealth. How can we get people learning STEM without having to be on the nose? This is what's happening. But when I was doing that, I was looking at, uh, AI and automation and basically trying to forecast what the next 10 years will look like. And, um, this is where, when I was looking at the changes from a socioeconomic point of view, I'm saying the middle section, our middle criteria are white collar jobs. Those that's going to be the section that shrinks. And we could see this 10 years ago, like everything that's happened to date is something that was very predictable in the way that it was looking at. We knew the Technology would come to this pace. And I say no. Now it's very easy to say 10 years later on when you go, I knew it back then it was all going to happen, but I felt there was enough trending that's going on. So essentially that's where I started off my career. But then I've since then supported on digital transformation projects, worked with the second largest philanthropy in the world, working within in hr, working in talent development. And that was before I went self employed just before the pandemic. And essentially one of the reasons I'm passionate about it and why HR has such a big factor into this is because ultimately AI changes the way that your organization works in a way that's really hard to describe in lots of ways. But I would think about it from we went from the agricultural revolution to the industrial revolution. If you're thinking about HR from agricultural to industrial, I know it probably wouldn't be quite a direct um, translation but you're thinking about the way you reskill people, but the way that those people participate in the economy, the way that those organizations work, the way that everything essentially connects together. And you're essentially looking at, at the moment the transition from the industrial economy to the digital economy. And that is going to be as monumentous as agricultural to industrial. So when we're thinking about the way that our organizations, the shape of our organizations is going to change, that is the level of change that we're talking about. And I can guarantee you that your failed change project from five years ago is going to look very small in comparison to the amount of change that you still have coming up.

Speaker A: I mean you've articulated that really well. I saw a video by Elon Musk a couple of weeks ago and he was, everyone's talking about this revolution we've experienced. He said, let me be clear, it's a digital revolution we're experiencing and those that uh, have manual jobs won't be impacted certainly not as quickly as those are, ah, working in roles that are impacted by digital transformation. I have to ask you the question though. So you were started to make these predictions back in 2014-2024. We're now 2026. I think the last two years though have almost accelerated as quickly as those 10 years we had before that point. Are ah, we roughly where you predicted we would be at that point?

Speaker B: Yeah, it's not far off for sure preparing a generation to take the place in the digital revolution. And a lot of the forecasting that I had then is it has come to pass now. You know, the way we're looking at what jobs are at risk, how much automation is going to affect the shape of the economy. It was easy enough to be able to go, we are going to be impacted by automation. And uh, technology has always been the biggest driver for change within the workplace. Right. It drives organizational changes and has done for such a long time. But this isn't the first time we've gone through a automation revolution. If you look at car manufacturing in the 70s and 80s, the increase in our technological capabilities impacted those jobs and those jobs then change and they evolve. So what I would say is something that's happening quite rapidly is just the technology was always going to be once it started going, once the boulder started going down that hill and picked up a bit of momentum, the extent of which we see change is going to happen much greater. And that's really hard for organizations to be able to manage because you're essentially trying to forecast the future. And most organizations at the moment are just doing things like their co pilot licenses, getting people to be able to utilize that within the system. And I'm working with, uh, my self employed agency has 28 agents that work around the clock for me, that send me emails, that have a hierarchical structure that allows me to be able to have delegate off and also have information dedicated to, they work autonomously on a server that sits in my cupboard and that is where I'm at with it. And you're always trying to teach people where things are going. And I can guarantee if you've just come on board on the AI journey, you are scratching the surface of it because you are really seeing, you're just using a chatbot interface that's, that's already out of date, that's already very 2025. Whereas if you're looking at agents and loop systems and everything like that, we're talking about a much more impactful technology on the workforce.

Speaker A: You raised a couple of points and you're absolutely right. I mean that's a really good example of how quickly this space has started to evolve. I think a lot of the headlines though, uh, the headline grabbing news we're seeing is the impact it's going to have on jobs. Right? We're seeing report after report. Maybe I see them all because I work in recruitment, so maybe I'm looking for these things while I'm seeing them. But I'm also seeing an HR industry here that is working harder than it's ever worked before. A lot of people actually leaving the profession because they're struggling with burnout they're spinning plates now. We've not just we had AI coming into the space, we've had globalization, we've had employee engagement, we've now got paid transparency, all these things. It's really hard to keep all the plates spinning in the air. And I would argue that we need AI now more than ever before or it won't be AI replacing jobs. We will need HR professionals because they will be leaving in their droves because they're overworked and they're burnt out. And I think actually sometimes the media's got this wrong. It's not just about whether AI will replace like for like. It's like if you have a spoiled child and you keep giving them stuff, they want more, they want more, they want more. And I think we want more. We want all this AI adoption, we want all these wonderful tools. But alongside that, the demands of our employee population that we're managing are uh, also going up in line. And they want more of us from hr, they want more from our organizations, they want our businesses to give us more and more. And therefore though it's helping us achieve more and that's great. Actually, I'm not seeing any reduction from my perspective in the amount of work that the HR leaders that listen to this show, in particular the L and D professionals, have to deliver. So how are you seeing it? Because I don't know if it's just media headlines, I'm a huge advocate for AI for those reasons. We need it more than we've ever needed it before. But that doesn't mean it's necessarily the place one for the other.

Speaker B: Mhm. Yeah. AI is a mirror. And as a tool, if you think of it as a mirror instead of as a digital application in itself, it reflects the organizations that it is part of. Right. So if your organization is constantly aiming to get more from people all of the time, depending on how they're doing it, AI is going to enable that to happen. If you go into an organization and you go, we want AI to be able to create a better work life balance and we want it to be able to support with that. And then you will be able to go and do that, you'll probably see a reduction in human workload. But that's not uh, what happens with most organizations. What they'd normally go is we want to get more out of the people that we got there or we're going to cut the amount of people that are there. And that's where once again, it comes to organizational design. And you're talking about burnout. Uh, you're talking about people being able to participate in the workforce and, and how those HR departments are looking at the moment. Most organizations are still at this industrial mindset. They were designed for a different era, and what they haven't done is gone. Wait a second. If we were going to restart this organization again with our current technical understanding, with our capabilities that are there, would we just go and pick up an industrial organization where essentially people fill the gaps in machine capability? We treat people like machines where they recite information, regurgitate information, repeat information. They've gone through an education system that's rewarded them for basically acting like an AI, being able to retain all of that information. Or would we go, wait a second, machines now, machine better than people do. So we want, uh, our people to people better than machines. And that's going to mean changing the way that they participate within that. And that is why it is beyond the technical transformation. There is a huge part of change management here because you are getting people to let go of one psychological process of dealing with things and engaging with a new psychological process of dealing with things. Yeah, so you've got people dealing with an old transformation, the psychological process of change and transition, and m moving into these new patterns. And that doesn't happen because it's just driven by technology. That happens because you help people on that journey to be able to do that. Change does not take place because you bring in copilot. Change takes place because you support people through that. And the demands that people are being asked for is also because they're participating in a society that was set up for that industrial workforce. So they're asking more from their organizations because they're getting less from elsewhere. They're getting less support in the wider systems that they all participated in. Community, government, all of this kind of stuff, which are key pillars of our, uh, transition. And we go, wait a second now we need this from our organization because if it doesn't come from there, where else is it going to come from? And that's causing a lot of the burnout and the challenges that you're seeing within the workplace. So just look at this from an organizational perspective. For us to go much wider, this is a countrywide organization, worldwide perspective of going, how do we change based on what our, uh, technological capabilities are, uh, and how do we support people with it?

Speaker A: So ultimately, just to condense some of that down, in summary, actually, one of the reasons AI adoption is either slow or it fails or is underestimated is because we try and adopt it but we adopt it with the wrong question. From the outset in how we wanted to use it. We've got to ask a slightly different question. You start with two sort of binary questions of what organizations often do. But actually that's not necessarily the question we need to be asking if you want to get the best out of our AI adoption. Is that what you're seeing when you go into organizations?

Speaker B: Most of the training that I do within AI, the first part of it is all around human centric skill development. Okay. In some of my bigger case studies, we haven't touched AI for the first month and a half when we're doing AI training. And that's because AI is an intent driven interface. Right. It reflects what you want to put into it. So a lot of the time you have to think about what's the human skills that you bring to the table. If you've got an AI that can generate a document very quickly, well, what's the human component of that? AI can have autonomous action, but actually it often needs an operator there to be able to do it. The human component is really essential to getting good AI outcomes. That's why you're seeing a lot of organizations that fired a bunch of people last year because of AI and putting on that, rehiring them back. Exactly. Because the human component is essential to that. And this is where I always put it down to. Well, what are things that are really hard for machines to be able to replicate, which human beings bring to the table? Curiosity, creativity, critical thinking, communication, collaboration. These are all really fantastic aspects of what we have as human beings that you're not going to see. AI is being able to replicate as quickly as capable. They might augment it, but they're not the source of it. And that's why a lot of the time we're building up confidence with people, we're building up clarity with people. We're actually getting them to act like people within the workplace because once they have that clarity and intention, they'll be able to utilize not just whatever AI system you've implemented today, but any AI moving forward. Because the interface is always the same, tell me what you want. And as soon as people are actually better at being able to do that, then they get the best out of AI.

Speaker A: Uh, it's a bit like the world of coaching. We've seen HR evolve from command and control into a coaching mindset which is all about asking questions and asking questions and prompting is really important. I'm interested though, because I looked at your website, which I will direct people to because it is fantastic and you give so much free resources there that I want to make sure people take a look. You talk about yourself now as someone who sits between people, technology education and practical implementation. Was that a learning curve for yourself when you went, you took that self employed route? Did you initially start by just having people adopt the tools and then you kind of realized along your own journey that that's only part of the algorithm, for want of a better word, that you need to be able to be more holistic, I guess, in the way that we get the best out of AI.

Speaker B: Definitely a journey, because I went self employed the week before we went into the pandemic. And you can imagine that nobody was interested at the time in training and

Speaker A: facilitation for navigating it. I know it was not easy. It certainly wasn't for us either here. In recruitment, everyone was on a recruitment fee.

Speaker B: Surprisingly, there was very little resources about how to set your business during a global pandemic at the time. And trying to figure that out was interesting. And actually, one of the things that I did before going into more of the AI space is you didn't really have the commercialization of AI. So we were designing virtual retreats and training sessions in virtual reality and shipping off VR headsets to people to be able to kind of do that bit. So I've always been like, okay, well, you know, you have to adapt to the situation that you're in. And at the time, you know, being able to make sure people could do their whole work conferences, but we created a VR environment was a great way to be able to do it. But when it came to teaching around AI, I think I actually go back to youth work. And I go, okay. Getting people to do STEM subject was not about shoving STEM down the throat and being like, this is a great career opportunity. This is the best thing for you. God, if you've got STEM skills, you're going to be set for the future. It's about getting people genuinely curious about it. It was about having fun. It was about, you know, doing something that you hadn't done before. It was about like, I used to design sessions for young people to be able to go and use drones, and they didn't realize all of the different STEM stuff they were going through. But then at the end of the session, kind of be like, you realize that you've done engineering, you've done maths, you've done your technical bit. All of that stuff was kind of embedded into what was going on. And I think it also comes down to as I said, Change Project is understanding the human component of that. If you don't, training is just one part of that change curve. If we're going into our kind of Kubler Ross change curve, you don't just start with training. Once you've done the announcement, there is a whole journey that you have to take people on. And training fits in nice when you're getting people to experiment with a new future. But before then it's having good comms. Before then it's being able to give people awareness and set a vision for the future. Those kind of things are really important on the journey, but they're often left out. So my, the whole point of what I deliver is going depending where your organization's at depends on the intervention that we're going to work with. And that's how we kind of help people come all the way through rather than we'll do training, we've got co pilot licenses, things are good.

Speaker A: I know we've got some exasperated HR leaders out there who have delivered AI training program to organizations, but actually it's failed to change how people actually work day to day. So from the L and D leaders listening to this that want to be able to embed it more successfully, want to make sure that actually we're not just ticking boxes here, but living the training, we're actually making sure the AI has a measurable impact on organizations to improve the way we deliver our service. What would be some of the tips and guidance you would give from your experience?

Speaker B: So you say measurable impact, but a lot of these people don't start uh, off by trying to measure impact at all. Yeah, most of the time when I go into an organization, they go, uh, give me a list of AI tools that we need. I'm like, why? I don't even know what you need yet. I don't even know what you're doing yet. Okay, you want a list of tools but you haven't gone, what's the right workflows? What data have we got? How do we design those systems and

Speaker A: actually have at least they're not resistant to change? I mean sometimes you'd come in and go, I don't know what. At least they're giving you a list. That's a starting point. Right. They're open minded enough to who want to bring something in. So that's a positive. I think. Go back just a couple of years ago and people were very resistant to bringing any AI tools in and any kind of language learning models, whatever it might be. So I would Say, actually, I know it's a roundabout way, but there's progress in there somewhere.

Speaker B: Yeah. But once again it comes down to if we look at it from a change project, when I was starting off supporting like larger businesses with AI two years ago, they didn't want to know anything about it. It was all working with small enterprises and the occasional medium enterprise. Now because apart that change curve, because they've gone, okay, no, I accept that this is going to be a reality now. First of all, it was shock and denial. You look back and you can go, okay. People were like, no, it's never going to impact jobs. No, we'll never be able to use it in our industry. No, we're not the right use case. And now as people have come through that change curve and they've gone, oh, wait a second, no, we are going to need to do this. This is going to be an important part of our organization. And now they're panic stations because they're like, we need to react to this. So. But when you're supporting these organizations on that measurable impact and what they want to be able to do, for me, it's all about, wait a second, let's look at what you're doing already. And then splitting into three categories. Automate, eliminate delegate. Okay. Or I should say eliminate automate, delegate.

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Speaker B: So the first thing we do is we'll go, let's look at every process and go, do you need to be doing this? Okay, Automating a bad process doesn't make it a good process, it just makes it a bad process done a lot quicker and often with much more impact, negative impact attached to it. So first of all, you know, we start with that bit, then we're going into, okay, based on this workflow, what do we need to automate? Where does AI fit into what we want to be able to do? Is it repetitive? Does it involve a lot of text? Could you teach an intern to do it? Great, that's going to be primed for AI to be able to help with it. And then most importantly, you have your delegation bit. Where's the human in the loop of this process? An AI can never be held accountable, so it shouldn't ever be making a decision. If you got an AI autonomously posting to your socials and it posts something that you don't like, you're responsible for that. And AI isn't responsible for that. They're not going to go, yeah, that was just an AI's fault. So you have to look at that human accountability as well. And once you start to kind of take those two steps back to come forward, then you can start to get meaningful results from the tools and AI and stuff like that. But actually more meaningfully, it starts off with, how are we going to help people within this organization to understand how this organization works so we can get AI to be beneficial to them?

Speaker A: So with all that in mind then, because that will make sense. I quite like this. Eliminate automate, delegate. That works for me and it makes sense. But what does a genuinely effective AI intervention look like then? How is it different from the one off workshops of the past? Like, are there some key ingredients that help lean people from I understand AI to now? I confidently use AI in the flow of my work.

Speaker B: So once again, every intervention depends on the organization and what you're delivering. And not everything needs to have AI as the solution and the answer to it. I think that's the thing that often surprises people when we work with organizations is you need to position yourself with what works for your organization. I always use the example of you can get a chair from Ikea, uh, you can get a chair from Carpenter. You end up with a chair at the end of the day. But the story behind that changes your complete perception of the value with it you could go, all of our customer service team are human beings because we want to connect you with people and that connects you to our organization. You're great. Okay. That's part of the sales point of being able to go with you as a brand. Whereas you might go, we have support on demand, anytime, any device, anywhere, whenever you need it. And then you go, cool, though. That's your brand positioning for the support that you give. One of those might involve AI, one of them might not. And what you're essentially trying to do is work out for your organization. What parts do you want that AI story? What parts do you benefit from having AI in it? Uh, maybe you don't need AI. Maybe AI is not the right solution for you. Maybe there's something else that's there. Let's not just try and give quick answers, but actually break it down by your organization and go, what works for you for the story you're trying to tell, for the people you're trying to support, the services that you offer? And then let's marry up those right tools with the right things.

Speaker A: So once you've got that marriage, then you've got those right tools for the right things. How do you design for the behavior change that's required so that you're not just dealing with knowledge transfer? Uh, when it comes to AI adoption?

Speaker B: Yeah, I would say the first thing is we're looking at this as a change project. We're not looking about this as a technology project per se. So the first thing we want to be able to do is honor the past. Wherever your organization has come from, the first step is always being able to go. However it was done previously worked for that moment in time. And if we're going to help people go through that process of psychological train change and transition, we first need to go, wait a second. It's not that everything in the past was bad. It's not that everything that we did before here was not the right call. And this is where we're doing moving forward. What we're starting off by going, okay, we need to support people to change their patterns, and therefore we have to look at what's going to change, but also what's going to stay the same. Because that's what helps people to be able to engage with those new futures. When most people introduce a new aspect of AI training, all of that kind of stuff, the first thing they do is they go, this is the way to be able to go and do it. This is the future. And they're very vague about what that is so people go, oh my God. It could impact everything. Or actually it could impact these three tasks that I do on a day to day basis. And that's all down to comms. That's not even having to deliver with a knowledge transfer or anything so far. That's not training them on AI, that's helping them on the transition to be able to go through. Then you can start to go for a little bit of more of the okay, this is what AI is, this is how it works, this is how you get results from it. This is what it can do, this is what it can't do. And that kind of helps people to kind of engage with that bit. And then generally what I would say is we go on to real use cases. So this is where you start to pilot things. But small pilots, you know, things that you can test as an organization and you can start to basically go, we've tested it on this, this is the things that we've learned, this is what worked, this is what didn't. This is what we'll have going forward. So you have that room for error, you have that room for mistakes and you're also sharing that learning with the organization in a container that's safe to be able to go and do. So then you're in a space that you go, okay, we've got our uh, people knowledgeable, they get what they're doing. We have the piloting now we can kind of go into some of the, maybe that more testing use cases for everything else that we do. But that's a journey. That's not a half day training session.

Speaker A: No, fair enough. There must always be that. And I'd love to ask some case studies in a moment if you've got one you want to share before I do. I'm aware that even in my own business, like we organize training, there'll nearly always be someone that hasn't quite adopted it. They haven't just don't want to use. Whether it's AI, whatever the initiative is afterwards. I'm interested to know if there's any sort of commonality around the kind of training you deliver when there is a reason these people don't follow. Is it down to the content, the context, the manager, the workflow, the culture? Is it something else? One of all of those things? Because we know that not everyone is always going to be fully on the train, no matter how good the training might be.

Speaker B: Yeah, I mean I always go to any organization, send me to the people who are going to be the most resistant and that's not because, you know, I want to be able to kind of tick anyone off or whatever. What I want to do is they're normally you canaries in the coal mine. There's reasons they have lived experience of why this thing might work, why they might not. I want to turn those people into my change agents. I want to make sure that those are the ones that I talk to before I go into a training session, because they could derail everything. And what I want to be able to do is go, let's address the key concerns you have in the room. Let's give you the space to be able to talk about it, because if you haven't had that space to express it, we're going to go into a training session. You're going to go, this barrier is up. And we have to understand as well, AI as a technology does threaten human fundamental needs. Okay? It threatens our sense of trust. Do I know this was written by a human being? Do I not know if it was written by a human being? Is this video real? Is this not real our sense of safety? Is this going to take my job? Am I training my replacement? It changes the way that we fundamentally act within our communities, within our workforce. So there's some real genuine concerns that are there. And what we want to do is address those concerns. So we're going to make sure that we're looking after those people as well. And then when it comes to training, like I've delivered training before for digital, uh, transformation first, like a CRM system, and it was with mentors. And one of the people that I was working on was still sending their mentor reports in by typewriter. I was like, I'm trying to train you on a new CRM system and you're literally still typewriting. This is eight years ago, nine, uh, years ago now. So, okay, there's always different spectrums of people being able to do it. But actually, anecdotally what I found is some of your people that are the most technical are some of the people that struggle to adopt AI the most. And, um, one of the reasons behind that is because they are used to being able to kind of go, I know what I'm doing, I know exactly what kind of outcome. It's very deterministic. And some of the people that benefit most from AI are those who know what they want, but in the past haven't been able to get to be able to do that. Now they can use AI to kind of basically fill in the gaps of what they didn't know previously in order to get an outcome. And that's why I see some of the biggest growth. Not necessarily most technically minded people, but some of the people that maybe struggled to be able to kind of get to the outcome previously because they didn't have that technical know how.

Speaker A: I mean, I'm glad I asked the question. I think you're one of the first people putting you on the channel. I think you are the only person that I've seen combine the conversation around psychological safety and AI training. And um, I haven't seen those two things merged in the way that people are delivering AI adoption training at the moment. But actually you talk a lot about in your work and on your website as well, around the role that psychological safety plays in helping employees embed AI in their daily routines. You kind of lent yourself into that a moment ago by making sure people feel safe to be able to use it. People understanding where that resistance might exist because they've done great things in the past. I don't know what my question is here, but I just think it's a really important observation to make because I just haven't seen that connection made. And obviously it's really important to you that people do feel safe in their training. I guess the question would be, I'm assuming you've seen much better adoption. Ah. And embedded kind of results when people do feel safe with the platform and the tools and the software implementing rather than just this is how you do it. Process.

Speaker B: Mhm. Yeah. As I said, my background comes from youth work and that's dealing with sometimes some of the most brilliant but also volatile situations that people are in. I've done outreach in prison interventions and gang interventions and all kind of sorts of different stuff there. So you go, well, when you start to look at it from that point of view, if I want somebody to be able to learn something, the first thing I have to do is get through the emotional barriers to them being able to learn the thing. The content could be grade A110%, but it will mean nothing if somebody goes into that room and they're petrified. Had young people that were coming on a training program that was delivering that hadn't left the house in four months. Okay, they hadn't seen another human being outside their household within that time. And therefore the barrier to learning is not the training content. It was can I get them from out of their house onto a training course? And that for me becomes the grounding of. Everything that I want to do from an AI perspective is people have got really valid concerns. The resistance that people feel is not out of nowhere. It might be because you've got an organization with legacy of failed change projects. I worked in one organization where someone had come in on their first day, and by lunchtime on their first day, they were talking about a failed change project that happened five years before they'd started in the organization. A failed change has a legacy and it already impacts things before you even got through the door. If they can get through that on halfway through day one, then you know, we've got a lot more challenges to be able to go and face there. So, yeah, I think when it's done well, it starts to go well, actually. People see that this is a tool for their personal development. We see it can augment their capabilities of what they want to do, get better results fast, and then we can start to go, okay, now you get in a good place. Now you get in that measurable impact. But also it depends on what you're measuring. Right? If you're just measuring output, uh, or token usage, which is what a lot of the AI companies do, then you're going to get a real false compare instead of going, are we actually getting value? And what is that value and what does that value mean to us?

Speaker A: I think outside of podcasting and recruitment, I work as a specialist fear coach. And that's actually something that's come to mind to me. Giving that response on psychological safety as well is I think organizations do probably underestimate the emotional side of AI adoption. I don't think it's a subject we're discussing enough. It's great we're able to bring it to our listeners ears today. But I think there's also a lot of fear attached here. Right. It's not that they're resistant to AI. I, uh, think that's probably going to be a fear of looking incompetent because they haven't dealt with it before. But actually we start this conversation where there's a fear of being replaced, that if we get so good at this, actually am I literally training the model that's going to replace what I do? And I think that the headlines don't help with that. So there's a fear of even bringing it in because you want to be great, you want to be the AI leader, you want to adopt it. But actually what does this mean for me long term? There's a fear of making mistakes because AI, as you said earlier, if we get it wrong and we do this at scale and we go too fast, actually amplifies the mistake much quicker than A human could. And it rows and rows of data. And I think also there's a fear of being judged for using the tool. You know, we know that AI can help improve our writing, can help improve the way we use a spreadsheet. And actually there's a fear of, you know, if I don't use it correctly, or am I going to get judged that I've used AI rather than use my own voice? I think there's a lot of fear attached. I hadn't realized it when I asked the question before, but maybe my fear coaching is kicking in. But it must be a huge amount of fear. Not just around wanting, not knowing where to get started, but actually the fear is later on in the process. What if I get started, I replace myself, you know, um, have you experienced some of that as well in your work?

Speaker B: Totally. And people's attitudes towards things change as well. You know, if I was thinking about walking people through AI two years ago, I would go, okay, there's a huge barrier here when it comes to AI writing. You know, there is a huge prejudice there to going, this was AI written. I don't like it because it was AI written. And nowadays everybody uses it as a shortcut key on their emails to be able to go and send an email or whatever that might be. And, you know, that is an attitudinal thing. But once again, takes time. It's the psychological process of going, well, it's new. I don't like it. Shock denial, those are always our first two stages. But you also have to remember, if you're seeing shock denial, you're also seeing someone who started the change journey. Okay. It's a really good indicator to look for the people that bring up those resistance, because at least they've started. They have to engage with the process. The people who are ignoring it, that can sometimes become a bit more of a challenge. But those who are actively pushing against it, I'm like, we've already started, the lesson has begun and we can start to bring people through in that bit. So, yeah, when we're looking at the fears around it, there's some really valid fears. Like, I want to make sure that we can go into an organization. We can go. We need to design our, uh, AI strategy that mitigates against those fears. We need to tell people what it is going to change and what it isn't going to change. Change who it's going to impact, how it's going to impact them, where we see the direction, where we see AI as part of what we're doing. And that's why once again we don't always start off with a training session. We start off with a strategy. What's the story you want to tell? This once again comes down to human. Storytelling is a human superpower. Right. We need to give your organization the story of what's going to drive their behavioral change. And that's going to be more than this is what AI can do and this is the tool that we're using.

Speaker A: Fantastic. We spent a lot of time today talking around the emotional side to adopting AI. Tell us a little bit more about your business. If you've got a great use case example where you've worked with an organization that maybe went from zero to one would be fantastic. People want to find out more. You can go to biker.net there will be a link in the show notes. But be great to understand people are listening to this. They go, you know what, I need some support with my AI journey. I need some support with designing an AI capability program or whatever it might be. Tell us a little bit about some of the work you do and if you've got a use case to uh, reference, that'd be fantastic.

Speaker B: Sure, not a problem at all. So one of the clients that we had last year was insights. So most organizations have probably utilized Insights. It's a psychometric often used within kind of training facilitation. So I was working within their training and learning teams and essentially building a AI training program with them to be able to support their organization through being able to adopt these new technologies. And they were very forward thinking and very practical in the way that we did it. But essentially we started off with couple workshops a week. So one workshop was focused more around the human centric AI development. So this is where I would always go. It's teaching people the information, but it's not, there's no pressure, it's not necessarily applying it all to your work and to the job that you're doing. It's giving you the awareness, it's taken away some of that fear by giving you information that you need in order to be able to kind of operate it in a way that you feel confident and safe with. And then we would have a second workshop in the week which is more aimed at going, what's your use case? What's your problem statement for today? Okay, this person has this problem. What AI tools can we use and how can we go and resolve the challenges that they're facing? And that was much more about applying it to the role they're having. And um, from the end of that program results on it was fantastic, I think. So we had 100% of people agreed that it was seen as a real proactive step in their personal development, which is a really positive aspect from my point of view. 88% strongly agreed with that statement. We had from a Net Promoter Score, 9.7 out of 10 Daily Adoption Usage went up. Uh, understanding the relatability to the role and their role went up. Uh, so we can get these really good results. But that was also over a six month spell of kind of working through the organization to be able to go and do that. So I think that's probably one of the most impactful ones where I could say, okay, once again, from a transparency point of view, I'd say organizations have only really been willing to engage with it at this kind of level for the last 18 months. So you're not going to find a wealth of going, yeah, you know, there's a hundred organizations in case study. I would go. We've had some good longitudinal ones in a short space of time and we're starting to see the positive results from that. So it is early days still, but yeah, we're beginning to see the organizations that implement this well really do get results.

Speaker A: What a good time to be in the space. Right, Your details, your website, you've got gap analysis on their school cards. People can download a number of free resources. If you want to find out where you sit in your own AI journey, I'd say your website's a great place to start. Right. And they can find out where they are. They can use those free resources to give them a foundation to know what they might need to implement it to take them to the next stage. My last question before we open the HR LND vault is this. How should L and d evolve in 20, 26 and beyond if AI adoption is now becoming part of organizational performance rather than just digital skills?

Speaker B: So when it comes to organizational performance, I think one of the first things you have to do if you're trying to take people in a change project is realize that the change is going to disrupt some of that organizational performance. And sometimes there's uh, a desire to go, we're going to implement the tool and then we expect within the same year people to be doing better. We expect them to have sent more sales calls, you know, sent more emails, develop more proposals, all of that kind of stuff. One of the first things you need to do is realize that this is a disruption. You're not going to get the results as soon as you come into it. So if you're LND at the moment, you go, wait a second, we need to talk to the organization, maybe not about upping performance targets this year, but being able to make sure that we understand that because of that disruption, you don't want people to feel the pressure to perform the same way they were before while introducing something new, because otherwise they'll just do what they know. They'll just do the thing that they're used to doing. And that's why you don't get adoption, because you go, I can definitely hit that if I know this. So I think from an L and D and a performance perspective, that's where I would go, okay, you might have to have a drop of performance this year, but with the idea about how you're going to build up and how you're going to measure it the year after. And that gives people the time to experiment, play. This is all as important as, uh, any kind of training intervention is. Do people have time in their workday to just give it a try, to try something new? Can you do something playful that gets people experimenting with it? You know, there would be lots of times when I'd work with previous organizations where I just set challenges before we come into the next one. I want you to have been able to generate X, Y and Z, or I want you to see what tools that you think are best at being able to achieve this kind of outcome. And therefore you have this, this way of people being able to learn, not just through information gathering, but from something that's actually more like how human beings learn. That's going to be a shift for L and D, particularly when we're always going, this is our training program. This is what we want to get. This is how we expect everyone to see afterwards. Let's measure the end of the program, and then let's measure three months later. Uh, the best interventions are probably quite hard to measure, but they are more important than sometimes just the learning interventions as they are.

Speaker A: Yeah, I think you've given us some fantastic insight and more than I've anticipated, if I'm honest. I think we've really discovered that AI adoption sometime is too focused on the technical, and the real barriers to it actually are more emotional, cultural, behavioral. Even given so many examples of why that's true, I think we need to think about from an L and D perspective, we need to think about how we redesign our training to accommodate those three elements. In particular, the thing you just mentioned there, I've probably been guilty of this a little bit we invest heavily and therefore you want an instant return. So in recruitment, AI is having a massive impact. We've invested very hot, you know, very heavily in various tools and things from within our own business. And instantly I'm asking for high performance and actually it's maybe reflect and go, maybe I'll ask for high performance next year. Let's get it embedded first. Because you're absolutely right. I've seen what you said. People go back to the root of water. They go back to what they know will deliver a result, which means they don't use the tools we spent heavily on. And I will make the assumption that the AI tools haven't worked. It's not necessarily the case because they haven't been embedded or haven't been utilized. So really, really good learnings for me as a business owner to take away. And I hope it's been the same for our listeners. Before we open the bot, are there any questions I haven't asked? So you're an expert here. You see this from a, ah, different viewpoint. Is there anything here that you want to just have taken opportunity to give our listeners some additional information that might help them on their, on their AI adoption journey?

Speaker B: I suppose if you're looking at it from an L and D perspective and kind of on the point that we were just on, is now's your time to actually train based on how human beings learn and stop trying to treat them like machines. Okay? They are human beings. They learn differently. They're not just information resources. You can't give them a, uh, download and go, this is our organization, this is the way we operate everything else, you know, now you have to go, okay, how is the best way of being able to get things out of human beings? And that is not going to be information downloads. Let's bring things back. Let's make sure that if the AI revolution doesn't allow people to be more human, then what was the whole point? Okay, yeah, right. Let's start by going into answering that question and then we'll start to see

Speaker A: the journey to take people interestingly, even from a, uh, recruitment lens, I've seen the recruitment process become more human as a result of AI, but not in the way that I anticipated. I think we've seen such homogenization around CVs, for example, where everyone looks the same. But we have to now have more conversations to really get under the hood as to whether people have the experience they say they have. Back in the day before AI people would have individual CVs, they'd write them In a way that they felt looked right, best represented them. But that doesn't happen anymore because it's quicker, it's easier, it's faster. And as a recruiter we've got to really understand what the personality is, what their the right culture might be, what's going to evolve an organization. And we can't do that with having more conversations. And I think there was a curve in our own business where a lot of recruiters became email warriors, automation machines using AI to pick a little bit of a LinkedIn profile to make it look bespoke. But in reality you actually are stripping out that personal connection. You're stripping out the ability to really ascertain whether someone can do the job in the way that organization needs it done. And it's taken us back. And I think it's a good thing. It does actually, without knowing it and not the way that I would have predicted it. We're having more conversations now because we have to even more manual assessments to make sure people can do what they say they can do. And it's been an interesting journey to be part of and to watch certainly from my recruitment lens anyway, so it's been interesting. Well, let's open the HR LND vault. So four short sharp questions for you. The first is this. And it'll be interesting because you come from a slightly different background with your youth work. But what's one piece of advice you would give or you wish you had rather at the start of your career?

Speaker B: I think once again very fortunate having gone through youth organization that supported me as a teenager to kind of go through that journey. I think T shaped learning is probably the best one that I always take away and I always pass on to others is if I was at the beginning of the journey, develop a breadth of skills, you know, try out a range of jobs. You don't have to specialize too quickly. Once again that, huh, that comes into a bit of machine learning. We kind of take young people through this pipeline of education and then they have to specialize and choose what they're going to do by the age of 17 and all that kind of stuff and then they start to develop that depth of knowledge. And I would say particularly in the era of moving into, particularly in the digital economy and the digital workforce, develop a breadth of skills. The generalist is back. Take on those knowledges and then when you have found the things that really resonate with you, that help you, that's when you start to go into a deeper specialism. And I've always found that that's been really meaningful for supporting people on their journey to learn and it will serve them well in the future that we're moving into.

Speaker A: Fantastic. And what's one piece of advice you would give HR leaders in 2026?

Speaker B: Now is the time to really test your mental when it comes to leadership, because that is the most human centric thing you're going to bring to the table. You are leading people. Okay? And actually you will be leading people and probably some AI agents upcoming year. You'll probably have a blend of team that combines the both. So what you want to be able to do is make sure that you are combining the strength of both. And that's going to be, as you said, looking a lot more deeper than you previously had done before. It's going to be really understanding the people you bring into the organization and remembering that AI essentially is a statistical aggregator. It gives you the middle of the bell curve. And for the whole of our industrial, uh, world, we've trained for the middle of the bell curve. We want people to kind of, of be able to fall within this space. The real value you're going to see is from people who can think outside of that. So instead of always looking at culture fit, because AI is going to be a fantastic culture fit for you, look at culture ad you know, who brings you the perspectives that are new, who gives you the perspectives that don't come from the middle of the bell curve, because that's where you're going to see a lot of value.

Speaker A: Yeah, well, I couldn't agree more. I said every client I work with, you don't want a culture fit. You want somebody who's going to evolve your culture. You know, what's the thing you're missing? You've got an opportunity now to take a fresh perspective on the person you're going to replace. What were the shortcomings? What are the things you want to find in your next person going to take you forwards? I'm really glad you mentioned the culture. I think it's super important and it leads in nicely actually to the third part or third question I've got for you, which is what's one belief about leadership? Do you think needs to change command and control?

Speaker B: I think that leadership is a lot more about getting people on board. It's not always about having all of the answers and particularly if you're going into an uncertain future. If you're a leader, the main thing that you provide people is a sense of stability in unstable times. And that is a, uh, really, really difficult challenge. To be able to. In this moment, it's about telling people what everything needs to be. Now it's going to be about how do you provide a bit of certainty. And you don't necessarily know exactly what's going to come next, but you get to decide that as well. Part of being a leader is being able to go, we don't know everything that's coming along, but this is the direction we want to take. This is what's going to reflect us as an organization and getting people to buy into that journey. So I think if you start to look at it as leading through uncertain times and being that stability for people, it'll be a lot more meaningful than looking at, ah, these are performance targets. This is our bottom line. This is what we need to be able to get to. Because the chances are, ah, that won't stand up to the rigidity of change and the accelerated pace of change that organizations are going to go through.

Speaker A: Yeah, love that. Couldn't agree more. And last but not least, a little bit of fun. If HR could be summarized up as a song, a book, a movie, or even a quote, what would it be and why?

Speaker B: So if I was to take it as a quote, and I'm paraphrasing here, where I see HR and at least the optimistic version of hr, uh, I would utilize a quote from the late Ken Robinson, which is, human beings depend on a diversity of talent, not a single conception of ability. The heart of our challenge is to reconstitute our sense of ability and intelligence. And that's where I see HR coming in, is it's now a case of going, wait a second. We need to value the broad range of human skills. We don't need. We've got robots doing the middle of the bell curve. We now need to value the fact that it's another old quote. I know it was misattributed, but if you judge a fish by ability to climb a tree, it'll spend the whole of its life thinking it's stupid. And now you're going, wait a second. We need that diversity of talent. We need that diversity of thought now. We need to create organizations that can actually accommodate it.

Speaker A: Love that. Uh, what a great way to round off the show. And for those that have enjoyed what you've listened to today, please do check out the website. There is a link in the show notes. You can go to bykofbret.net that's B Y K O V-B-R E W T.net I will put a LinkedIn profile as well, if people want to connect in with Jamie to continue the conversation. But look, this has been a, I think a really important conversation. It's not one I've had loads of conversations about on this show about AI, but not one around psychological safety, not one around behavioral change, and not one around really challenging the assumptions that people have around AI adoption. So thank you so much for joining me today on the HRV podcast. Jamie, it's been an absolute pleasure and I think our listeners are going to have an awful lot to think about and to take away from this episode. And of course, please, please, please, I'm not just saying this, go and check out his website. There are so many free resources that can help you on your own AI journey. Go and check it out. There's a link, as I say in the show notes, but there's gap analysis and cards and a whole lot more blogs and see the TED Talk as well. Jamie, thank you so much for joining me today. It's been a pleasure and uh, I think our listeners have got some real insights, some real value from today's episode. So thank you. Pleasure.

Speaker B: Thank you.

Speaker A: Deel is the platform to hire, manage and pay your global team. Onboarding, offboarding, Payroll, compliance and IT access across 150plus countries all on infrastructure owned end to end. Companies like Elevenlabs and Shopify already scale effortlessly with deal. ElevenLabs quadrupled headcount in 2025 without adding tools. So build your global team with deal. Book your demo@d e l.com NickDayHR if you enjoyed that conversation, you will love this one. Click here to watch our previous episode of the HR LND podcast where we continue exploring the ideas, the people, the insights and the leaders who are shaping the future of work. And don't forget to subscribe so you never miss a future episode.