LH130 Learning Technologies London 2026 Show Special

Nowadays I have a completely different workforce.

Yeah.

It is an augmented workforce because it's a combination of humans and AI or AI agents.

Yeah.

And this is the shift that our companies, even our humanity is facing.

This podcast is supported by Synthesia. Training videos shouldn't take weeks to make. Synthesia lets you turn text into professional multilingual training videos in minutes. It's fast, scalable, and so simple anyone can use it. You can write an email, you can make a video.

Hey there, my name is Alex and I'll be guiding you through this training session.

70% of Fortune 100 companies use Synthesia to modernize onboarding, Simplify compliance and scale global training. Podcast listeners get an exclusive 30-day free trial. Go to synthesia.io/greatminds. Don't miss it. This offer won't last.

Welcome to The Learning Hack, a podcast about the people and technologies that are creating the future of learning. I'm John Helmer.

Now goes what?

Learning is cool.

Cool.

Learning is cool.

Learning is cool. Learning is fun.

Knowledge is power.

Knowledge.

I may have said this before, but cynical people will tell you it isn't necessary to sign up for big learning conferences like Learning Technologies London every year. Come along in 12 months' time, they say, you'll find people saying much the same thing. And to be fair, for a long stretch they had a point. From one year to the next, there really wasn't that much new to hear about. This year was different. This year there's a real movement in the space. New energy in the vendor market, a starker agenda on the buyer side. AI is shaking things up fast and in ways that aren't politely sitting still while we get our heads around them. The conversation has shifted from "we're piloting this" to "we're doing this," and the very first guest I spoke to, just after he came off the keynote stage, told me something I found genuinely jaw-dropping. He's using AI in his own work, but it's not a chatbot. It's not a course generator. It's a cluster of agents running on his mini Mac, talking to him through Telegram and OpenClaw, doing the work that used to take a team. He calls it the augmented workforce. He's also been a CEO and general manager of multimillion-dollar businesses across Europe and North America for 30 years. So when he tells you the workforce now includes AI agents, it's not theoretical, it's real. So that's where we'll start with him. Mariana, who is he? Hack Facts.

Giovanni Giamminola is an AI advisor for business transformation and the author of the 2025 book, The Augmented Manager: How AI Makes Management More Effective, Creative, and Strategic. Italian by background, Catholic University of Milan and Harvard Business School trained, he spent 30 years running European and North American businesses with revenues above €1 billion as CEO and General Manager. He now advises corporates on the cognitive shift AI demands of management itself.

He delivered the opening keynote at Learning Technologies 2026, So, Giovanni, thanks for taking the time to do this. Well, it's a pleasure, Henning Haack. Your book, The Augmented Manager, is in a bit of a crowded genre. A lot of books about AI and management at the moment. But your argument's a bit different from the others in that you say that the shift that we need is not technical, primarily it's cognitive. So can you give us a brief summary of your thinking on that and the book? And what is it the people who are in your keynote this morning— I was in the keynote, I really enjoyed it, I thought it was great— what will they understand now? What will we understand now that we didn't before we walked into that room?

Yeah.

Well, you know, I have been working as a manager for 30 years and I've been leading organization across Europe and North America. And nowadays I have a completely different workforce. Yeah, it is an augmented workforce because it's a combination of humans and AI or AI agents.

Yeah.

And this is the shift that our companies, even our humanity is facing. It's the fact that nowadays we are working, discussing, planning. We are leading conversation in a hybrid way with together with humans and with AI. And this is unprecedented. So, uh, my book start from this point of view, the fact that We are not in front of just a new technology. We are not have to deal with the introduction of a tools on our phone or our computer. Here we are really facing a revolution and it's time that we stop thinking shorter., and therefore we start thinking long term because this revolution is impacting the way we live, the way we work, and most of all, the way we think.

You showed us some really interesting stuff there. You're, you're, you're running OpenClaw on a, on a Mac mini. Interesting one. Uh, a very new development came about this year and, um, through Telegram from your phone., and you have a variety of agents running. How do you, when you talk to other people at your level in other companies, how would you kind of position yourself against them? Do you find that generally your peers are further up the track than you, further behind the track than you? I mean, are you unusual?

Are you a— Absolutely unusual, I would say. Yeah. I mean, I've been, consulting corporates for the last 1 year and a half, and even the most advanced ones, they are really trying hard to catch up with the technology. But the point again is the mindset. So we are facing this revolution in the old way. That is that even corporate, they see a technology, then they plan for it. They start doing so-called POC. They start to do small projects. Then they go through a vendor comparison process. And this takes ages for companies. And by the time they have understood what it's all about, got all the vendors, set up the framework and everything. And then this technology that they haven't even started to work with, it's already outdated.

Yeah. Yeah.

Okay. So here it tends to be for jazz, even in companies. They have to— companies need to change and adapt in an unprecedented way. It's time to experiment, to create laboratories, to start thinking not anymore with this structured approach, but really to find, to create the right space for this technology to— so for people to experiment the technology and to work with it in a secure and safe way, but without all the frameworking and the boundaries and the timings that requires regular technologies like we did for the past, I don't know, 50 years.

Yeah. So it's a big change and it's happening against the background of some really tumultuous change in the external environment. I know people tend to say this at conferences all the time, but this year really has been quite extraordinary. We've had tariffs and we've got soaring energy costs and all the rest of it. So you're asking people to make a very big transformation in the middle of all that. Isn't this a time when it'll be battening down the hatches rather than actually being able to change?

Yeah.

You know what the most provoking sentence I pronounce lately in my workshops in corporate, it's that everybody right now should feel as if they had all promoted by this technology. Everybody has to understand that their job has changed. So instead of saying your job is gone, and this is the way media are discussing the AI topic, seeing it as the threat that will destroy our job and so on. And I see it on the other way around. I see it as a promotion, as a promotion to move from our current job to a new one, which requires much more human skills, much more, let's say, cognitive skills, and much less technological skill because technology will take— AI will take care of that part. So we are only— we are the one that are going to press some buttons. Yes. Like you did to start this podcast. But at the end, everything will be done by the machine. So that's the great opportunity to be promoted as a new type of human that works on more on the relationships on the shop floor, by customer, by suppliers. And that's the great opportunity if we take it positively in this way.

So we should look at it as I just got another 5 employees in my department free. Yeah, almost for free. But the thing is that they're AIs, they're not necessarily humans, which in some ways causes problems. They don't come with a load of tacit knowledge. On the other hand, Saves you an awful lot of problems as well. What's that look like on a day-to-day basis though? What is it like being that kind of promoted manager with these augmented abilities? Give us a sort of rundown of your Monday morning.

Well, in one way it's fantastic because I had so many times had the feeling of a burnout because of overwhelmed by all mails, phone calls, meetings, online meetings, meetings and then being in CC in so many mails. I've, I've managed very large organization, multi-billion dollars organization. I was the general manager of those organization, and you can imagine how much information I had to deal with, how many phone calls, messages, WhatsApp, Teams, and so on. And sometimes you really feel overwhelmed, but this is the time where we can get— we can delegate this part, but we can automate all this part. But this does not mean that I will be fired in, in, in the sense that my job, my old job for sure will disappear. But the new job is to move in this new era where we drive value from the way we act, we interact with people, we run conversation, we have more valuable discussion, and therefore all the paperwork is done by agent for us. And so that's the point of my book.

Is there a danger there that you become more remote as an augmented manager?

No, this is the opposite. After COVID, we all became very much remote people, right?

Yeah, I don't necessarily mean remote as in on a different site. I mean more if an AI is answering all your emails and a lot of your kind of routine communication is done through AI, is it harder for people to feel that they have the kind of human touch in terms of communication?

I perfectly got your question, but still, I remain on my point. That is, COVID gave this extreme acceleration on remote work and most of the conversation moved from presence, from being present to online. Now AI is accelerating the opposite because the, let's say, bureaucratic conversation we have, the organizational conversation can be delegated to AI. So it's then we have time like today to be in conferences in real presence, to meet with people, not anymore online, but live. And here when I'm live, my conversation with you, it's different than the one I can have on remote because here we are exchanging also a different type of information through our, our communication, our capability to, to read each other between the lines with our body language, our conversation, and our, the exchange of the idea. It's more fruitful, it's deeper, it's more valuable in a way.

You made a really interesting point in your talk that kind of stopped me in my tracks. You said you don't have to sit behind a screen anymore.

Yeah.

Because everything, you know, the analysis and all that spreadsheet work is being done by an agent. You've got to get back to work. You've got to get, you know, not touch grass exactly, but you've got to touch them, you've got to be physically face to face with employees more. That was really interesting, I thought.

Yeah, I think I said this line even before in the past, because I remember when I had large groups of financial controllers and they were joining me in meetings and delivering lots of printed the spreadsheets with all the data analyzed and well-formatted, and presentations were perfectly done. And then during the meeting, when I was asking questions about the real business underneath those numbers, the real problem in production or the real issue with the customers, they didn't, they didn't have the real answer for me because they were spending so much time in producing the information. Right? So I said sometimes, I told them many times, you are not a newsstand that it's here to distribute a magazine. You are the journalist that should write the news yourself. And to do that, you have to go on the shop floor. You have to be by the customer. You have to really understand what's the business all about. This is your job. Your job is not to create spreadsheet, you are not doing your job. And people were not happy about my comment, but in reality, we are all, as you mentioned, we are all in a way pushed now by AI to change our job and to see the value of going back to what I say we were hired for. We were hired not to create a spreadsheet, but to manage business and understanding the root causes, finding solution to our scrap rate, to our margins, to our purchasing condition.

Right.

So what's the first capability an L&D function needs to build if they want to do this? Well, do you think? And what should they stop doing to make room for it? Sitting behind a spreadsheet, I suppose.

Yeah, exactly. So we, as I said, we need to start laboratories where we experiment. So we have to face, in fact, the unknown. We have to face the fact that we are in front of a a revolution. Okay. So the first thing is to understand that the learning path, because nowadays if I want to learn something, to be honest, if I go to AI and I ask AI to develop for me a content tailored to my knowledge, to my level of expectation, to my maturity in that specific matter. I can get nowadays fantastic learning products right out of the prompt. So even very well formatted with exercise examples. So, so, so what is going to happen now is that L&Ds, they have to understand that the real value they can bring in, it's not in the design itself, but in the content that you bring in the learning experience. And so to bring content, that means that you have to know and to master the specific matter that you want people to learn. And therefore the challenge is to start, you know, really learning more and mastering more the, the subject, the topic and the people and learn more the people that this specific content need to, to address. So it's a completely different job if you see it from this perspective.

So talking of jobs and coming back to your point about AI not being replacement and taking people's jobs away, but being augmentation and getting a promotion. People are talking, I know it's too easy, you know, there's a kind of rhetorical thing here which AI companies are caught in is that they need to say, they need to sell it by saying it's going to give you greater productivity. And in a current climate, that means kind of reduction of headcount. For people. And we see that's happening as a kind of knee-jerk in some places. But even if you believe that there's a kind of generative capacity given by AI, that everything increases and grows through that, it does put the middle managers in an organization under a degree of pressure. As you say, they've got a promotion. Can they handle it? Do they want to handle it? Does AI give a bit of a harsh message in terms of step up or step out?

Absolutely. I think you're definitely right because like every transformation, like every time we had a disruptive new technology becoming available to everybody, of course there will be people that they would not be happy to leave their comfort zone, to challenge themselves. And they, you know, there have been so many leaders I've met lately that when I talk about AI, that they answer to me, okay, I'm lucky that I'm only 5 years away from retirement, so I don't have to deal with that. So because this change required efforts, and we by nature, we don't like that. And so there will be lots of people, they will be left behind, unfortunately, not because AI is just cutting jobs, because this always happen with every kind of technology. Okay? So when we move from agriculture to industrialization, How many people that were capable of working in a farm were willing to move and work in a factory? How many? But of course, then it's up to us. And we will see that this transformation will be very difficult for sure. There will be probably some social issues. I can see that happening because people, they, they are, are difficult, um, to change. That's the, the point.

Difficult for all of us. Uh, talking of change, you come from a career running large businesses across Europe and North America, um, not from HR, not from edtech. So it's not like you've kind of grown up within that particular organizational silo, um, across many companies, but always in that you come from a more general kind of business management background. So with that perspective, what do you see in the L&D conversation that it tends to miss?

Well, you know what, when you are a successful general manager, you really need to be able to manage not just resources in general, but people specifically. And people, that's my view also for myself in my career. I always wanted to learn things. And for me, successful people are great learners. And to be a great learner, you need to have help. So I highly value L&D people. To the extent that they are really capable to let the learners learn, to let the organization further develop. So we have nowadays the highest need, in my view, of L&D people in our organization, the one that understand that everything has changed and they are willing also themselves to put them in a new role, to be promoted from the former LDs in the new LDs that is working in a little bit different way. That is just, we discussed earlier, right?

Okay.

We're limited for time here. Lastly, who are you reading and listening to right now? Who, and who do you follow? Not only on A&M AI and business, but perhaps more generally as well?

Well, there are many, again, there are many sources of information that I continuously listen to. And to be honest, most of them are content in podcasts. Either in YouTube or in Spotify and other, but even in TikTok, I can tell you that some, some kind, in some cases I get very small content. I like to get small summarized content in, in, in couple of minutes. So rather than when I also have longer have more time to listen to more structured content. And nowadays there are many, let's say, content creators that are, most of them Americans, and there are leading voices in AI, starting with, for example, Andrew Karpathy, which is One former AI employee is now one of the most interesting thinkers, I think, in terms of AI. But on the other hand, I also took the time to read some books. And there is one specific book that I enjoyed lately very much. And it's titled The Long Path. And this book, it's really about the fact, as I told before, that it's time for us to think of, to think of the future of our, of humanity, of the next generation. We are in a moment where we need to be the founding fathers of the future that we deserve. Okay. And so it's now that we decide to take— we need to take some decision about what is good or what is bad with AI and start creating that foundation. So now it's time. It's our privilege. It's our duty, this specific moment in history that we manage this revolution with the right approach.

Yeah, it's hard, that, isn't it? Because you think, when this settles down a bit in 3 months or so, then I'll kind of make a decision about how good it is, how bad, how we ought to kind of map a path forward and what advice I should give to my kids and grandkids. But it doesn't slow down.

Yeah, absolutely.

Yeah. And helping us navigate this fast-changing world. We're very grateful to people like you, and thanks very much for taking the time.

Thank you very much.

Thank you, Bonnie.

Okay, John, it was a great pleasure having me here. Thank you so much.

If you're looking to connect with the top minds in learning and talent, there's no better place than an iVentiv event. Since 2009, iVentiv has hosted over 300 events for global heads of L&D and talent. I've attended multiple iVentiv events myself and found them to be invaluable. Less about sitting and listening and more about real, impactful conversations. It's an unmatched opportunity to collaborate with leading chief learning officers on the challenges they're tackling today. Join the conversation. Visit iVentiv.com/learninghack. That's i-v-e-n-t-i-v.com/learninghack. Www.axonify.com/learninghack.

Hack facts.

JD Dillon is one of the most prolific voices in workplace learning. 25 years across operations and talent development at Disney, AMC, and Kaplan, followed by 10 years as Chief Learning Officer at Axonify, which he left in April 2026 to go independent. He's the founder of LearnGeek, author of the 2022 book, The Modern Learning Ecosystem, and his second book, The Frontline Enablement Playbook, comes out as this episode airs. He writes the Enabled newsletter.

So JD, it's great to have you on The Learning Hack. Thanks very much. Appreciate having a real pro who knows how to lapel mic on.

It only took 4 years of college to learn how to put on a lapel mic.

All right.

Went to school for radio production, so.

All right.

Yeah.

I feel intimidated now.

That's why I appreciate the level of production on your podcast, because I can tell.

Thank you very much.

Yeah.

Good.

Hear that, folks.

So we just finished a very good panel session that you hosted that was brilliant. Session was called Enablement on the Job: What the Frontline Teaches Us About Learning. And your new book, The Frontline Enablement Playboi— Playbook.

It's a very different book.

It's very different.

Yeah.

Comes out next Tuesday.

Yep.

Tuesday, May 5th.

When this airs, it will have been out for about half a week. So I won't ask you about presales because I don't want to get too nerdy about that. But can you briefly describe your thesis in the book and what the audience will have learned, will know more when they leave that session, when they left that session, well, I left that session, when we left that session than they knew when they walked in the door.

I think a lot of times we think about themes like employee experience based on the idea of doing what's right or what's best for people. And I think the unfortunate reality is a lot of organizations don't make decisions based on the experience of their employees or what's best for people. I think they'd like to think they might, but in most cases, it's what's right for shareholders and stakeholders. So if we're going to try to do right by the frontline workforce, the largest part of the workforce, 80% of the working world, and at the same time, help them do their jobs most effectively so they can feel proud and confident in what they do, I think we have to start from the perspective of, well, how do we do what's right for the business to earn the investment necessary to do what's right by the frontline? So in the stories that I'm telling about deskless workers and in the book, The Frontline Enablement Playbook, I come at the story from a very very practical perspective to say this is a story that's equally for people in learning development, HR, as it is for people in operations. Anyone who's responsible for driving business outcomes through the everyday performance of frontline teams, that's the story we're telling. And we're doing it from a very practical perspective to say it's about driving results, but we can do that by doing what's best for people who we're relying on to do their jobs every day.

Make a headline of that statistic you just mentioned. 80% of the workforce is deskless, is frontline. I'd heard that a few months ago and from my podcast co-host, Donald Clark. Got a feeling he might have been listening to you about that. But it's an extraordinary statistic because, you know, I've been coming to this show since 1999. I've been in this industry for a while. And there is this assumption that it is about people at desks, it's about knowledge workers. And actually, that's only 20%, but the whole debate seems to be focused on that 20% and ignoring the 80%. Really notice it when you're judging awards because you'll get kind of submissions from food outlets and they're just so different in character and type and what they do than the rest of the knowledge workery stuff. But What are we missing by L&D being so focused on the 20% in terms of the rhetoric and what breaks if we don't put that right?

Well, the good and the bad news is it's not just L&D. So if you look at pretty much any conversation about the future of work, for the past 6 years, we've been having this debate about remote and hybrid work. Does not include most of the frontline workforce. And if you look at conversations today around AI and the impact on the workplace, for the most part, it's not including people who are performing deskless jobs and using technology in different ways or have a different relationship with technology and the potential that AI has, as well as the challenges that it represents. So this part of the workforce is just largely absent from these types of conversations, including when it comes to the employee experience, learning and development side of things. It's an unfortunate reality, and I think a legacy perspective that a lot of people have who are decision makers who think about the frontline workforce as let's say, quote unquote, low-skilled work or introductory jobs or, oh, we have a high turnover environment, so people are just going to leave anyway. So why would we invest in them? Yeah, but then if you look at the research, if you look at the numbers and if you look at the brands around the world that people respect that are some of the most profitable businesses in the world, you see a difference in terms of how they treat people and how they lean into and prioritize their frontline teams. And that's why they're able to have such profitable locations and be able to to elevate a customer experience that makes them top of the list in those categories. And they can draw a straight line from that experience driven by the frontline to profitability and revenue for the business. So the proof points are there. It's just overcoming legacy systems, legacy practices, and legacy perspectives to be able to unlock that potential for the business. And I like to reference stories from my own experience because I came from frontline operations, and I was a frontline manager before I got into L&D. And in, in organizations like Disney, we spent a lot of time sharing letters from guests and talking about the experience people had when they were visiting parks and resorts. And almost every letter, it didn't focus on the quality of the food, it didn't focus on the attractions people experienced. Those were key to bringing people into, into the environment. But what, what they wrote letters about, what brings them back, is the interactions they had with the people doing the jobs every day. It was the frontline team. So the results are there and the proof points are there, but it's difficult to get people to shift that perspective of what they think about the workforce and what actually drives results for their businesses every day.

Yeah.

So interesting. And of course that split was really intensified in the pandemic when you were either among the delivered to or among the deliveroo. Mm-hmm. And suddenly all this frontline roles were so visible. And also it was like, well, we've got to stay at home, but they don't. They're delivering us food and surely they're being exposed to all this, you know, all these germs and this terrible virus and all the rest of it. It kind of brings it home, I think. But it's a really interesting dynamic. And we had lots of interesting stories in your panel from Simon Gibson who referenced working at M&S. I had an auntie who worked at M&S all her life. She loved it. They were a great employer. And you see that in the staff at the tills. They're very kind to the elderly, I can say that. So your last book, The Modern Learning Ecosystem, was out in 2022. Four layers in that: shared knowledge, performance support, reinforcement, coaching., and then generative AI comes along. Does the framework still hold up intact? 4 years on, what's the biggest revision you'd make to it? Is there, is there much in, you feel, in hindsight?

It's a great and timely question, and I don't even think you know why, because my first book came out the day before ChatGPT went public. So I had that moment as the author going, oh no.

Yeah.

So the world just changed and our relationship to technology just changed and I just released a book.

Ah.

So I actually wrote in the months following the release of the book, a 12,000-word addendum.

Right.

It's available on the book website, jdwroteabook.com. I use that as a handout for sessions that I do around the idea of a learning ecosystem. And it basically attaches the AI story to the framework in the book because the good news is that the framework that I share in the book and is actually carried over into my new book, Yeah. It's grounded in just the reality of what it takes to help people do their job. It doesn't get so specific as to say, well, how do you bring those ideas to life? So if you take the foundational layer of shared knowledge, the concept that access to reliable, timely information changes what people need in terms of learning and support on the job. Because if I can self-serve my way through a problem, I don't necessarily need training. Or if it's something I don't need to retain, it's something I can look up. On the job to solve a problem or complete a process? Let's do that instead of forcing information at people that they're not going to remember, don't need to remember. So I didn't get so specific as to say, well, how do you enable that access to information? I provide a bunch of examples, including from my own work. I talk a lot about wikis and search and knowledge repositories. And now you layer the AI conversation onto that. And if I wrote it today, I'd be talking about digital assistants and how— that fundamentally changes our ability to interact with the information. Because as I share in the panel that we just spoke to with deskless workers, there's a reality in that brand of work that people, again, consume and share information differently. Some people in deskless jobs might not be great readers for various reasons. So they might be struggling today just to be able to understand information, yet alone find it or apply it on the job. But now we're in a world Or maybe the SOP was written by the legal team for the legal team. Really difficult for anyone to understand, let alone someone who maybe not having the greatest reading skills.

Right.

Well, now I can talk to the SOP, and if I don't understand what I'm seeing, I can ask a question and it could talk back to me. So instead of me having to struggle through reading this information, I could have a conversation. And if you take it to the next layer, I might be able to have that conversation in my preferred language. So the SOP may be written in English, I may prefer to speak a different language and now I can engage with the information in a way that makes sense for me, but it still activates that foundational layer to say, well, if I have the information necessary to make the decision, that's where we start instead of saying, well, we need to train people on this information.

Yeah, I think it's interesting. The interesting thing about generative AI is the way that it talks back to you at your kind of level. And I've been developing what I do using kind of move from ChatGPT to Claude. And it sort of gauges my level of technological sophistication, which isn't huge. But then as I get more comfortable with it, I think I'm going to move on and do more difficult stuff. And it shifts, shifts in its register. The trouble is it starts talking coder and I kind of get a bit lost. But I mean, it is interesting the way that it can that it can make those chips. But on the AI question, you also know a question to the panel, to the people, so that it made no difference at all to frontline workers.

I think again, the relationship between technology and frontline work is meaningfully different than what we see in a corporate space. Let's take a restaurant server. The restaurant server may have one screen that they use throughout the course of the day. It may just be the point of sale system. So they might be carrying a handheld device or they may be going to a back room to ring in an order. But they're expected to retain a lot of information and they're not maybe permitted to look up certain information, 'cause that's not the level of service that's expected. Yeah, yeah. To look up every particular ingredient or detail. Or in an environment where there's a lot of consideration around things like allergens, they may be required to go get a chef to come out and have a more detailed conversation. So they're just not living and working with technology every day.

Yeah.

So AI might not be part of their day-to-day workflow, However, there might be back-of-house considerations or support considerations around things like scheduling, around payroll, around different parts of the employee experience where this technology can help reduce points of friction. So scheduling as an example, helping people get their schedule, using demand data and forecast data to help people get their schedules earlier. So I'm not waiting until the week prior to my work to know what my shifts look like. Because if I'm a server, I may have a second serving job. So I want to know where I can pick up different hours to maximize my earning potential. And if you have better scheduling technology that can feed me my schedule weeks in advance, because we understand what demand on the restaurant is likely to be, I can manage my life more effectively. And that's a way that AI could be impacting me on the job, but not in the way we think about when we talk about ChatGPT and Claude and Gemini and these types of tools that some people are using more hands-on.

Yeah.

But one of the great things about that panel session was there is so much kind of specificity about what it is like to be a frontline worker, what it is like to be in charge of training and learning for frontline workers. So many little work situations that people talked through, problem solutions. You know, we get some great conference sessions from learning technologies, but sometimes it It all feels a bit abstract. It's great to be grounded in actual work situations in a way that isn't around computers and desks.

And that's a good point around representation of the frontline and deskless workforce. Because yes, I would like to see more sessions like the panel we just had dedicated to the conversation around this brand of work.

Yeah.

But it can also be represented in broader conversations. Let's say you're having a general conversation or a general presentation about instructional design. Are you making sure that the ideas that you're sharing are equally relevant to people who are doing different types of work? Because a lot of times I'll attend a session on a generalized topic, and the person who raises their hand and asks the question presents a different context and says, well, I work in an environment like dot, dot, dot. And often it's a highly regulated frontline environment where the time availability is different, the tools are different, and they're trying to make this idea work, because they have a similar problem. They just need a solution that matches the context of the people they support. So I think everyone in the space can do a better job reflecting the reality of frontline work if we just become more familiar with it. And we just use that as a lens whenever we're writing or speaking to say, does this work in a deskless environment just like it would in a desk environment? Or am I trying to be specific to a certain type of work? And then I can do that.

Yeah, I remember way back when mobile learning came in, when Steve Jobs invented the iPhone and suddenly it kind of took off. Talking to some very smart instructional designers in the LearnTech company that I was working with as a consultant at the time, they were saying, you know, we've changed this, we brought in something new, which is the context of use, because that person might be on a mobile when they're doing this e-learning. So we have to start thinking about that, you know. But the example she gave, I remember, was, you know, on the way to work, there'll be strap hanging in the tube and they might want to look up their schedule. It was still about knowledge worker, so to say, that was still the frame. So that's very interesting. And then people began to talk about things like, well, how do people on building sites get their e-learning? Oh, we've got these things called tablets. Maybe they could use those.

And do they have internet connection on the job site? Right. Like there's so many different factors when we talk about learning technology. I can almost guarantee there are no booths at this event that talk about hardware. Yeah, we just make an assumption that there's a screen available when if you go into a logistics facility as an example, the screen that someone uses to do their job might be mounted to their wrist.

Yeah.

And if that's the access point to information that they have, that's a very specific consideration, not just in terms of design of content, but you also can't hold your wrist up for an extended period of time to review a lengthy video, even if you can watch the video on that screen.

Yeah.

So it, it requires us to understand the workflow and the experience and all of the access points so we can use every access point that's available to help people do the job. And it's also why I can't walk into a frontline environment and not start hunting for screens. If I go to a restaurant, I'm very interested what the handheld device the server has is because I've spent so many years looking for access points inside of environments and finding creative ways to get people to information, even if it's not the traditional method or time or device that you might think of.

Yeah, a lot of the early adopters of e-learning back in those days were like retail banks, you know, Despenser stuff. And then it started to move into retail and people would bring up these things like Well, actually, we've been talking to the L&D manager there. There is only one computer screen in the whole of that huge mall.

It's probably in the manager's office, which is probably locked a lot of the time, or the manager's on it doing their stuff. I often talk about grocery stores. There tends to be a second floor that most people never see because that's where the HR office is and the store manager's office is. And that's often where the computers are.. But if my job is to work in the deli, yeah. And I clock in and out near the deli, I never leave the deli. So I'm never making it to that second floor.

No.

But most people don't know that, um, deli scales are actually internet-connected devices with touchscreens.

Yeah.

So if you're gonna help people who work in the deli access information, the access point's sitting right there. It's just not commonly used. And it's not that you can't use it for a click-through e-learning module cuz they're customer-facing, but if you want to provide information on recipe information or product details, the point of access is right there. And I did a lot of time studies in these frontline environments about, well, where is the access point information? How long does it take you to get there? How long does it take you to print it? You're probably printing a bad black and white copy that's barely legible and then bringing it back. It would take 17, 20 minutes for people to find information when there are devices now in the workflow. We just have to think about using them differently.

Yeah, but there was a phrase I heard today that I haven't heard for about 15 years: time and motion studies.

Yeah, yeah, totally.

A lot of the heads of L&D listening to this are in knowledge worker organizations, not running a fleet of convenience stores or portfolio of care homes. Um, for them, the Frontline Playbook might feel like someone else's problem. What's the one capability from the book that, that would change their work even if they never deploy a headset in a warehouse or up on the ground floor.

Time. So when you talk to people who support frontline and deskless teams and you ask what's the biggest obstacle they need to overcome, the answer is time. Because in a frontline space, labor hours are managed in such a way where they only schedule the number of people necessary to do the job.

Yeah.

Usually less than the number of people necessary to actually complete all the tasks. And as a result, there's no time for anything extra. And most frontline workers can't do things away from work or off the clock. So how do I find time in an environment where there is no time? And I previously supported contact centers. Rule number 1 is don't take people off the phone because that's where money's made for the organization. So now I'm thinking about learning and performance in terms of minutes as opposed to in terms of content and courses. If we can figure out how to manage people's time and fit support opportunities and development opportunities into time that almost doesn't exist, you take those learnings and you apply them to a corporate space where people also don't have time, but they have significantly more autonomy.

Yeah.

So someone who works in a corporate job might have a packed calendar and have back-to-backs, but they can decide to not go to that meeting, or they can decide, I'm going to do something this weekend to really catch up in my development. So if we can figure out how to fit development into a time-strapped environment like the frontline, we can take what we learn and the practices and the tools that we apply and apply them in a corporate space to help people learn how to do different jobs. Because time is an issue for everyone in the workplace, especially in a do-more-with-less type of environment. But if you can figure it out for people who work in deskless jobs, you can definitely port those practices over to the corporate space.

Yeah. I remember thinking when I actually had a salaried job, I could get so much more done if I didn't have to make tea for half a dozen people every time I went to have a break. You've just closed a 10-year chapter at Zonify, one of the platforms that's been pushing hardest on AI for the frontline. As of this month, you're on the other side of that conversation, independent, a foot outside the vendor world with the full insider view still fresh. Use that vantage point. What's actually working for frontline workers right now that wasn't working 18 months ago? And equally important, what's still vaporware?

The conversation about how to enable frontline teams more and more lives with the operations team and not learning and development.

Right.

And it's something that I learned very clearly. It's something I knew walking into learning and development because coming as an operator, I remember points of friction introduced by the L&D team. I remember being frustrated by the training team because they were getting in my way as a manager, or they weren't providing the support I thought I needed for both me as well as the people doing my job. And I carried that perspective into my work as a learning and development professional. And then when you're on the solution provider side and you're talking to people who have a vested interest in helping the frontline teams perform effectively, you realize, one, the partnership is essential between L&D and the frontline operations team because the operations folks own the day. They own access to the employee. They control how people spend their time. If they're not bought in from senior operations management to the people who are running those frontline locations, L&D has no opportunity to do anything. But operations owns the problem. They don't just own the access to the employee and the time. And they're the ones who, if they understand the value of providing a solution that's going to help their frontline teams execute and help them hit their goals, they are willing to invest. They're willing to not just make the time, but also implement new tools and systems to do it. So I think more and more, the key to being successful in enabling a frontline team— and it's something I learned as an operator, as an L&D professional, and as a solution provider— is being on the operation side of the conversation and not the HR side of the conversation. And I think that can be a hierarchical challenge and an influencing challenge for L&D. And I think when it comes to the vaporware question, Over the course of the last 5 or 6 years, because there has been a bigger conversation around deskless workers, in a lot of cases for unfortunate reasons because of the challenges they faced, I think you've seen a lot of solution providers look in that direction. Giant multinational companies, billion-dollar software companies, in addition to newer startups and established learning technology companies. So you see, if you go to people's websites, you'll see a category for for frontline workers, or if you go into the expo hall and you ask people about deskless workers, you'll see people talking about, "Yes, we can support the frontline teams." But I think the vaporware challenge is, well, how often is that a shift in messaging as compared to a shift in the solution in a meaningful way? Do these folks understand the experience of the job and the connection between everyday learning and performance for frontline team members and results for the organization? I think there's a big difference, and I saw it from, as we were developing our solutions as well as competing with other solutions. There's a big difference between teams that understand and build for the frontline, and commonly they're building for specific use cases. So it's not a team that can say, "Oh, we can support any frontline workforce." No, they can't, because there's a huge difference between supporting a remote construction team versus supporting people in a retail store versus supporting a contact center operation. They're all frontline workers, but the context is different. So the most successful frontline solutions I've ever seen, they're supporting specific environments. They're very good at manufacturing, they're very good at food service, and it's usually because someone from that business, often the founders, are from those environments. Yeah, they intimately understand the challenge, and that's what motivated them to start building a solution because they didn't see it in the broader marketplace. So if you're, if you're looking for solutions that enable the deskless workforce, I'd say do your homework in understanding where these solutions came from, who's on that team, and what background do they have inside of this environment. How's that informing the roadmap and the product decisions? Because I think you'll see a meaningful difference between legacy technology that was built for corporate and that environment as being pushed in the direction of frontline versus solutions that were built for this brand of work.

That's so interesting. If you look at the history of training companies. You know, there were no great big global training brands before the internet because it was very organized around sectors. You know, you would have kind of sales and leadership specialists, obviously, but you'd have, um, specialists in, in defense, in the construction industry, in this industry, that industry. Um, it, it's only where the internet came along— well, e-learning in general— that you've got this kind of generalist approach and And that's when that kind of 80/20 split starts to happen. So you're saying really, as a buyer, you need to be a bit— well, it almost feels like you're saying we're heading back that way. Really what we need are sector specialists in this industry. We don't need huge conglomerates that have grown by M&A, not mentioning any names.

Yeah. But a lot of the conversations I'm having, especially with the younger technology companies, are people who are building point solutions to solve very specific problems. Yeah, they're not trying to be a platform. They're not trying to do everything for everyone because no one can. I think the larger technology companies, you see that there, they might be able to support a lot of needs for a certain part of the workforce, but it gets really hard to do everything for everyone, which is why a lot of larger solutions don't reach the frontline workforce because hand-me-down solutions don't match how the job is done every day.

Yeah.

So you're seeing a lot of people take advantage of what technology can do today in the AI conversation. To do very interesting but very specific things to support parts of the workflow. So they're not trying to catch all or be the solution for everybody. And I do think the point solution and the kind of industry or context-specific solution is the way forward. 'Cause you will have these big catch-all AI-enabled tools that do a lot of different types of work, but can they handle this particular challenge that this particular type of manufacturing facility faces? They're unlikely to be able to do that. And the other thing about frontline work is it's high stakes, high risk. When you think about AI as an example, let's talk about a digital assistant as a generalized tool.

Yeah.

If you're a corporate worker and you have a digital assistant and you ask it a question about your expense report and it gets it wrong, I did my expense report wrong, accounting's going to yell at me.

Uh-oh.

If you're a frontline worker and you work in a high-risk, highly regulated environment and you ask a digital assistant a question, and it gets it wrong and you take action based on that inaccurate information, not only will you get something wrong, not only might you upset your boss or customer, you might get hurt. If you ask a question about how to dispose of a particular chemical and it gives you the wrong information, you do it wrong, that's a high-risk value proposition. So I, that's where nuance is required and specificity and a tool that matches this environment because the stakes are higher for the people doing the job as well as for the business to be successful because it takes, it takes one mistake to get someone hurt or one mistake to turn a customer off forever. And those are the stakes that frontline workers live and breathe with every day, which corporate team members don't often have that one decision changes everything type of moment because they just do work differently.

I talked to somebody who'd worked in the civil service once in the UK and she said, I made a terrible mistake and I buried it in my in-tray. And I knew it was there every day for a year. And in the end, I left because I knew that eventually someone's going to find out. Well, that's not what it's like if you're, as you say, in a frontline position where, you know, you get it wrong, somebody gets injured, worst case someone dies, you get sued, possibly go to prison, whatever. The training has to work or people get hurt. It's a really strong driver.

Such an interesting discussion.

We've got to wind it up. Who do you follow nowadays? Who do you read? Who do you pay attention to about kind of learning and AI, but even more generally?

So I think today, it's a great question because I think today it's even more important to focus on sources and who you follow versus focusing on topics because there's so much bad information and so much AI-generated garbage. And I don't know if anyone is having a better experience reading information on the internet. I know it's not me. So I lean into particular individuals on different types of topics. So Dr. Philippa Hardman is, I think, a key source when it comes to the AI conversation, as an example applied to this side of the fence. Given that I focus on frontline and deskless workers, I do a lot of research and a lot of listening outside of the learning and development and HR space. So a great example is a woman named Monica Rothgery, who was previously the chief operating officer for KFC.

All right.

And she wrote a book called Lessons from the Drive-Thru, where she talks about her experience as a restaurant manager coming out of the military. Now she's running restaurants and the experience of being a manager and trying to figure out how to do this job and how she went from that to being the chief operating officer of a global brand.

Yeah.

So she's a great individual to follow and it's a great book to read. And then the last book I read, because I have a hard time reading a lot while I'm writing, 'cause I'm staring at text so much, I kind of just need to go away for a little while. But I read a lot in the lead-up to writing my latest book. And the last book I read before I started writing kind of set the tone for the story that I was gonna tell. And it's a book called Nickel and Dimed. I cannot pronounce the author's last name, but if you look up Nickel and Dimed, a lot of people I found have heard of this book. And it apparently required reading in some schools. And essentially what it is, it's the story of a woman She was a journalist and a writer, and she went and lived inside of different frontline jobs for a period of time. So she didn't take anything with her, took a little bit of money, rented a new car, rented a new apartment, and lived the life of frontline workers to understand the experience and what it's like to live on minimum wage and to live on tips as a server. She worked in retail, she worked in housekeeping, and it's even less her story, it's more the story of the people she meets along the way, the person who lives in their car outside the workplace because that's all they can afford. Afford right now. And she helped set the tone for the story that I was gonna try to tell in terms of a little bit more solution-oriented, a little more practical, but also empathetic and human in its nature of understanding the experience of frontline work and then how we can support it. And then what I realized after I read the book, 'cause it didn't click right away, she was doing this work at the exact same time I became a frontline worker. I got my first job while she was writing her book.

Wow.

So while I was standing outside of the mall on a curb waiting to go into my first job at a movie theater, wondering, can I do this? Because I was a scared high school kid. She was in the Florida Keys learning what it's like to be a housekeeper and a server in these types of environments. So we overlapped in our frontline journey. But while she was, I think, learning the reality of these jobs, I was the stereotypical frontline worker. Because if you ask people, you know, when you picture a frontline or deskless worker, who do you see? I think a lot of people describe me on my first day because I was a 17-year-old at my first job learning how to work in addition to learning how to do this job. Barbara was learning what it's like to try to live in these environments and on this pay and with this level of support and frustration around not having a great manager or dealing with challenging customers. So if anyone's looking to really kind of get an understanding of the story of the frontline workforce so you can better support them, Nickel and Dimed is the best book out there.

Interesting. The times I've most often heard that phrase in the last 10, 20 years has been buyers complaining about learn tech vendors.

Yeah.

Oh God, they're nickel and diming me.

That too. It's never a good story in the end when it comes to that phrase. But it's a powerful human story when it comes to just getting into the minds and the experience of the people you're trying to help. Because I think a lot of people who, and I'm an example, a lot of people who've helped frontline workers come from frontline operations. And I find a lot of people don't think of themselves as L&D professionals, even though they oversee training for their frontline workforce. If they're in retail, they're a retail person who's now in charge of retail training. And I think that's on us as a professional community to find ways to reflect their reality more so they can see themselves in the profession and understand there are stories out there that are relevant to them. There are practices being shared in webinars and at conferences and in books and in articles that are worth reading because it comes with their context. So they don't have to look at the story and figure out, well, how does this work for me? 'Cause my workplace is so different. No, we can present their context and include them in the story because like we started the conversation by saying, this is 2.7 billion people.

Yeah.

And this is 80% of the people that we collectively support as a profession. I think we can do a better job representing them. So people who do this work and support people who do the work see themselves in our storytelling and that can take advantage of the larger community to improve the work they're doing every day.

Gotta see it to be it. Mm-hmm. And you being seen so much with your books and appearances, I think is helping people to be more like you. Is that a cheesy enough wrap-up?

I'm trying my best.

Thank you very much, JD.

My pleasure. Thank you so much.

It's great to talk to you. Hack Facts.

Alicia Sanchez is Chief AI Officer at MPF Federal LLC, a federal contracting firm headquartered in Maryland. A doctorate-holding research psychologist by training, she spent 18 years with the US Department of Defense as an early innovator in games and simulations before moving into healthcare AI and federal consulting. eLearn Magazine profiled her in their Rock Stars of eLearning series. At LT26, she sat on a day 2 panel on future-proofing L&D alongside David Kelly and Geoff Stead.

So, Alicia, welcome to The Learning Hack. It's great to have you on.

Thank you so much for having me, John. I've been really excited about this all week.

Yeah, well, I'm especially honoured. I think eLearn Magazine profiled you in their Rockstars of E-Learning series. So it's an e-learning rockstar we're talking to here.

Yes, well, zero musical ability, so don't get your hopes up. Oh.

Anyway, Chief AI Officer is your current job title, and it's still a rare enough one that when people see it on a panel lineup, they pay attention. But what does your job actually involve day to day? And what's the thesis you brought to Learning Technologies 26? What did you want the audience in that room to understand when they left that they perhaps hadn't understood when they walked in?

So, I think that— I don't think there's anyone in my life that would argue that I am not your traditional CAIO. I'm spending a whole lot less time with the tech stack and a lot more time really thinking about AI-based solutions to problems that we see either in organizations or agencies or across our US military or any military for that matter. And I think what's interesting or how I've arrived, if you will, at this point is because I was an L&D person for a million years. And I pivoted about 2 years ago, almost 3 now, from a games and simulation expert. I'd spent 18 years with the Department of Defense here in the US doing games and simulations over to a health insurance enterprise architecture operational AI job. And I did that specifically because I felt like AI was going to be very important in our lives, but I wasn't going to get the practical hands-on experience that I felt like I really needed to be able to command this technology. So I made a jump. And so L&D will always be near and dear to my heart. I had to unjump. The reason I'm back is because I got laid off. Needless to say, I would say I AI'd my way right out of a job. But really, the moral of this story is that the AI people get laid off too, right? And so, so that was the situation. And I landed back in what we call GovCon, which is a group that specifically works as a contractor to the federal government instead of going full back into Fed, because I still believe in the mission of our government. I still want to protect our warfighters. And so my group is called MPF Federal or MPF LLC, and it is Mission, People, and Future. And so that really spoke to me and it allows me to combine these two worlds. And that's what I wanted our friends at LearnTech to really see, to understand that there is this, this fear, this perception of AI as this, this ominous body of knowledge. And as L&D professionals, I want us all to understand that AI was created in the only way that humans could create something in homage to ourselves, right? In homage to the human brain. And that the only thing that stands between L&D professionals and really being commanding in the rollout and the future of it is a very thin veil of technology and vocabulary. But we've got the inside track. In the time, it is now. There's never been a more interesting time in what will likely be our entire careers where work is changing so much. People need to understand how to perform their jobs, how to do their jobs. They have to relearn a whole lot of things. And that this is a time for us to emerge as champions, not to be concerned about what the future means for us.

Yeah.

Yeah.

And you're so right there to point out that AI is kind of made out of all of us. It's the sum of of, well, not the whole sum of human knowledge, but it's the sum of everything that it can crawl that's been kind of written and put down and thought explicitly in noughts and ones for a very long time over a wide area. And I suppose in a way it's, but it's a surprise to meet ourselves coming this way. And we look at it and we think, well, I don't like that, or I'm worried about that. You know, really, we're worrying about ourselves, I think. Are we ready for AI? I don't know. Your AI Readiness Framework splits the question into two parallel tracks: organizational readiness— are organizations ready for AI?— and workforce readiness— those of us who have to work in those organizations ready, or as freelancers like you and I. And then three dimensions within each of those categories: preparedness, ability, and willingness. It's a useful bit of scaffolding that gives the concrete picture. What does each dimension actually look like when it's present? And which of the 3 is the hardest to move in practice?

Yeah, so when we created that scale initially, we called it Ready, Willing, Enable. It was our RWA scale. And we quickly found out that readiness was a composite, right, of these 3 scores and that preparedness was really focused on on do we have the governance, do we have the ethical guidelines, do we have the strategy in place. Ability was really about our self-efficacy and our perception of others' ability. And then willingness was the desire to actually participate and not, you know, try to bomb a— humans can bomb a pretty good AI initiative, but our real internal drive to be part of this. And so, we created it dual-scaled, which is so interesting because we are collecting perceptions, we are collecting self-report, and it's really important that we ensure that we have eyes wide open from both sides. If we see a very high preparedness scale from the organization, like, "Yeah, we've got everything in place. Everything's totally ready. We're great." But the workforce is like, "Mm, I don't think they're that ready," then we're going to see some issues in implementation, right? If we see the same going on with ability, like everyone's reporting, "Hey, we're not really ready. We need more training," but the organization feels like they have provided all the training that could be necessary or vice versa, then we're going to see some misses. So we think it's really important not to just think about it from the perspective of one or the other, but how that impacts. One of the things that we do that make, I think, willingness so interesting is we will ask an organization, "Hey, if you have all this newfound time for your employees, what are you going to do with them, right? Which is sort of that trigger, are you going to lay them off question. A lot of people don't think about that. We just say, oh, they're going to be freed up to do more important things. What more important things specifically? Is there something you have in mind? And then we ask somewhat, you know, provocatively the employees, is there something that you think would help you if you had more of in your portfolio? Like, maybe do you believe that having AI skills would be beneficial if you have to find another job to try and compel them to increase their willingness to participate? And so we're really happy with the way that is performing right now, and we're hoping to see a lot more organizations take this very dual-sided approach to ensuring that the two sides have a good understanding.

Yeah, I totally understand what you mean there by saying that the organization gives you all this training and then assumes that you're ready. I must have read, I must have watched a million videos on YouTube about agentic AI and kind of moving up in your use of AI. I think, well, I know it all. And then I actually, I mean, this is talking on a very individual personal level, I'm a freelancer, so that tends to be the way I think. And then I actually put my hands on the thing and say, let's do this. And suddenly I realize I'm completely woefully unprepared for what it's going to ask me back. But you say an interesting thing in your Learning Guild piece, that organizations are treating AI like something that can be scheduled and rolled out once governance is perfect. Data is pristine, and everyone has completed a course. That's a pretty direct swipe at how a lot of big employers are actually behaving right now, maybe slowing things down. Where are you seeing this happen, and what do organizations that are genuinely operating with AI as current behavior rather than future capability do differently?

So I think that when we do see that, because we do see that with some larger organizations, I'm not naming any names, but what we see that is, It's almost a fear response. And a lot of times for us, it gets to what is the motivation of that organization to start using AI? Are they, are they being shamed into it? Is it keeping up with the Joneses? Is there a mandate that indicates that they must? Or have they really come to this because they believe that AI could be beneficial for them and their organization? And so I think that what we what we really should be doing is offering, you know, measuring this. This doesn't have to be a gut reaction. We have the motivation to adopt. We have to wait for perfect if today was the '90s. We don't have to do that anymore. Now is a time for speed. Now is a time for trial and error. Can you do too much? Can you change too much all at once? 100%. What do you need to get started is really the question. You need like guardrails, right? You need to make sure that your data isn't running out the door and you need the desire of your organization and your workforce to create a strategy that ensures that everyone understands how this fits into the future of your company. So, when we see orgs that are really doing it well, they're taking those small risks. They're involving their workforce. They're rewarding failure and innovation simultaneously, right? They're really taking a look at how this gets them to the next level. And that's what we're seeing be successful. And oh, most importantly, they are not bolting things on, right? I think it's really important. Bolt-on has never been a positive term. I don't think I've ever heard that used positively, but I think it's really important to think through Efficiency is an example. When we say, oh, we want to be more efficient and we're just going to take these SOPs and we are going to— or this existing workflow and we're going to add AI and it's going to make it more efficient. What we're missing is the ability to really rethink an entire perspective of how our organization gets work done with this important technology now in existence, not in spite of it, not using little parts and pieces of it. I don't think we'll ever see the big picture. I mean, the SOPs likely wrong anyway, right? Because if you don't involve your humans, then they've probably created little efficiencies around it the entire time anyway. But we see those as the opportunities that are missed that are often the result of, we've got to get it perfect right now, this one time.

Yeah.

My own experience of adopting more AI use is that it does have big impacts on your workflow. It has to change the way you think about it radically, but it ends up being a bit like fixing a bicycle while you're riding the bicycle. And I think a lot of people are in that, you know, as you say, you can't just kind of wait around till everything's perfect. So that's kind of where we are. If I wanted to pick a hole in your argument, it might be the following: readiness is a useful frame, but it can also become a permanent waiting room, can't it? If the failure mode is organizations waiting for governance to be perfect before they move, Failure mode of your alternative could be organizations who use, we're getting ready as a permanent excuse for not deciding. So when is ready enough? What's the indicator that tells an L&D leader it's time to stop preparing and start operating?

I mean, the wonderful thing about deciding if you're ready is that it's probably going to be very specific to every organization. But it is measurable and it doesn't require a big, huge two-sided assessment, right? Like some of the most wonderful things I've seen done are, you know, asking people if they're already using it because most likely they are. And I think that we're far enough along to realize that when we have a lot of AI use at our organization and we haven't brought AI to the table, when people are using unsanctioned tools and they've created their own pathways to it, then, then we're really opening ourselves up to risk, right? And so, a lot of times organizations have to take the jump because they realize that most of their organization already has, that they're the ones that are holding everybody back. But another strategy that I've seen work really well is offer some training, right? Just offer some training and then ask people, why did you sign up for this training? Is it because you want to learn more? Is it because you're fearful of what we've got going on? Or why did you not sign up for this training? Is it because you already are using it and you already understand AI pretty well and you feel like, you know, we're the ones holding you back? You've got to talk about it with your organization is the bottom line. But what we do see the real superstars doing is embedding outcome-driven learning in the flow of the work. So they're using AI tools that they normally would. They're reaching outcomes because those AI tools that are in their everyday practice are actually starting to create measurable outcomes. And it's not this big snap the chalk line, hey, now we're all changing the way we've done things. It's a beautiful evolution that reflects the, the moment in time where we have reimagined the work and we've communicated it appropriately and everybody believes that this is where where we should be going for the greater good of our organization and at the individual level, and that we've prioritized just being ready enough to get started. And so could people stand behind it as a little shield as to why they haven't started? Absolutely. But I think the readier an organization is, the less likely that becomes possible because the organization will ultimately move on without its leadership.

The line in your piece that I think will be very much appreciated and understood by my audience in the UK, and learning technologies especially, is that L&D's unique value isn't in owning content anymore. They'll have heard this quite a bit. It's in shaping conditions, helping define when human judgment is required, and enabling people to operate responsibly. That's a pretty significant reframing of the function, but one that I think people are getting used to the idea of. You were at Learning Technologies, maybe what you encountered there was differently, but for the head of L&D listening to this, what's the first capability they need to build to do that well? And what's the first thing they need to stop doing to make room for it?

I love this question. So I think when we think about the grand scheme of it, the first thing that we need build his capability is, is that pivot away from content creators into performance architects. And this is really sort of a nebulous term, right? What does it mean to be a performance architect? Are we just really rethinking the way that we're doing things? But if we know anything about AI and if we know anything, if we've tinkered with it enough, what we know is that there is the real ability for AI to create outputs that can disguise as high performance, right? That there is the potential to have an illusion of being a high performer because you're using AI to do a lot of your work. And that's most primarily in knowledge work. But if we think about how we combat the ability to have AI masquerading as the performance by putting people in charge of it, what we do is we realize that we've really got to unpack expertise in a new and a different way so that we we can actually architect performance with this technology at a different scale, and that we can really think about how we can be accountable for the output, how we can employ cognitive discernment, how we can ensure that we are resilient enough in our skills that if for some reason AI fails, we can detect it. But if it completely removes itself, that we can still perform this task, right? Which sometimes means building a little bit of friction into our learning assets. But also that we can manage these agents, that we can create and manage agents as part of our readiness. And so thinking about it from the, what do we really need to know about what the outputs are going to be and how do we ensure that the human remains very connected to that, we have a capability that brings us to the next level. Now, of course, how do we make room for that? We've got to stop doing something, right? Because we can't do it all. And for me, it's pretty simple. It's, we've got to stop being the librarians of content, right, of curriculum. We've got to— actually, Philippa Hardman had a really cool LinkedIn post a couple of weeks ago, and it was about the course as a dying unit of learning, right? We know that as AI makes performance even so available that our learners are going to suffer even less motivation to be part of our formal and episodic learning courses, and that they really want something that is more of a performance support scaffold. Scaffold, and that, you know, completed courses have never really— or course completions have never really been the indicator of, "I'm ready to do this job," but that's going to continue to fall away more and more. And so how do we really reshape our entire perspective so that we can be the ones who are leading the charge and not a content or a formal learning bottleneck, which I'm so terrified that L&D could someday become if they don't really take charge now.

Yeah. And to back that point of yours up in the wider world, I suppose we've seen people run into a lot of trouble trying to do AI transformation based on consumption metrics. You know, people have had leaderboards, token leaderboards. Are you using enough tokens? And people maxing out their tokens so they can get to the top of these with the result that one company, at least I think it was Uber, burned through its yearly budget for AI in 3 months. I mean, we're realizing this stuff is really expensive. So, you know, the kind of metrics based on how many courses you take is analogous to that really, isn't it? Consumption metrics rather than performance metrics.

100%.

Yeah.

So you come to all this from a federal contractor context, as you mentioned earlier, rather than a big tech or a SaaS one. The regulated environment is usually seen as a constraint, slower, more paperwork, more caution. I've heard this most of my working life, I think, people running down the public sector. But I suspect you're going to tell me it's also a source of insight the private sector might be missing. What does working in the federal space teach you about AI that leaders in less regulated environments tend to underestimate?

So, you know, a lot of people think that working for the federal government is sort of the definition of hearing no. No every time, right? And those people are correct, right? Like, we hear no a lot. And so, that's certainly something that we have all worked our way through. But there is— there's such high risk, there's such high ambiguity that it really does force us to ensure that AI in a federal system has all of the ethical guardrails, all of the governance, the provenance, the security, the logic, and the trust built into it. And that's great, but I think that this perception of the federal agencies or the government or defense moving slowly truly is a thing of the past. And I think that AI is paving the way for the future. So one of the things I'll be speaking about later this year is a construct that speed is the strategy, right? And it's such a departure from what we have been taught as humans, right? Like when your kids are learning to drive, it's slow down. When you're falling in love, it's take it slow. We've been sort of taught that slow is safe for some reason. Have you ever done this where you're pulling out of a parking space and there's a chance you're going to hit the car next to you, so you go really, really slow as if that's going to provide any actual barrier between you and the car, right? But it makes you feel like I'm more in control of things. And And what the federal government is starting to realize is that with a technology that moves this quickly, that the true risk of falling behind when other countries and other entities are able to move so much more quickly than us, that it's no longer a luxury that we can afford. So speed is the strategy. And we're going to see, I think, a lot of that. The Fed has the big budget. That does not hurt, right? That certainly gives us the ability to take risks. In L&D, there's always— you know, I worked games and simulations for a million and a half years, and it wasn't like the commercial industry where you had the luxury of building 3 games because someone might like this perspective better, or someone that there was player preference. No, you could build one. One. The government, you know, where I did it, we could build one. But as budgets do increase, we have the ability to seed different projects, to do more experimentation, to take small risks and to see what's working, so long as we don't sacrifice quality because of speed. Quality still has to be there. We've just got to do everything faster. And hopefully that'll be perceived soon. Hopefully that will become something that people say someday, oh, the government, they're leading the edge. They get things done quickly. They don't have as much red tape as they to dare to dream.

Quickly and with quality and hopefully with safety as well, because, you know, to kind of push back slightly on that move fast and break things, we've seen that be a rather hair-raising strategy occasionally, but I won't get into that. Lastly, who are you reading and listening to on this sort of stuff right now? Inside AI, inside L&D, or outside both?

So strangely, I'm a huge science fiction junkie, like big time, big nerd.

Are you saying this particularly because, you know, I'm launching a science fiction podcast?

I did not, but we're going to have to talk about that.

Stay tuned.

Right? Stay tuned. Almost all of my innovations in AI have a basis in science fiction, right? And so I feel a little bit like a bit of a copycat, right? Like, so Neal Stephenson's The Diamond Age is the influence for the Primer. That we're creating. I have an AI-based wargaming system called Atreides that has, you know, the entire House of Dune, right, involved in it in one way or another. And I'm obsessed with Isaac Asimov's— he had a short about everyone's forgotten to do math. And there's this one guy who all of a sudden works it out. He backwards-compatibles this math in this manufacturing process. And he's, you know, heralded. Someone steals the idea. He winds up— it doesn't wind up to have a happy ending. Most of Asimov's work doesn't. But it's really interesting how I think science fiction has predicted so much from learning and development as well as from technological advancement. So fascinating that you're also into this. So I read a lot of that type of stuff. Within AI L&D, I'm a huge fan of Marcus Burghardt, who I saw at Blendtec for the first time.

I saw him.

I looked, I must have, he must think, I don't know what he thinks, but I was so excited to see him. I had posted about like in his session and he came over and he gave me a hug and I just completely was like, and I couldn't, I couldn't use words, and I'm not usually a fangirl. I love Mark Ramos's perspective on AI and how L&D, we should be moving the strategy forward. I love Philippa Hardman, Dr. Philippa Hardman, big fan of her work. Gary Klein in the straight L&D world is very big in cognitive task analysis, and I'm really interested in how we can use AI to help people deconstruct both requirements, expertise, and institutional knowledge. We get a little mind veil to keep nodding at AI, product that centers on that. So those are the things I'm completely obsessed with right now. And I'd love to hear your list and I'll be paying attention to your podcast for sure.

Well, because I'm launching The Tech Imaginarium: How Science Fiction Made the Modern World, the new podcast, plug, plug, on the 22nd of May, I'm mostly reading science fiction. We have 2 episodes about Asimov, off. So I'm reading I, Robot. I'm watching I, Robot with Will Smith, which seems to have very little to do with the book. And a number of other people as well. And J.G. Ballard, just because I like him. He's not in this series, but in the next one. So watch out for that. And apologies for hijacking your—

No, I had no idea.

—your interview with a plug. Okay.

Gratuitous.

Right. Anyway, thank you very much for taking the time. I've really enjoyed that. Thanks a lot, Alicia.

No, this has been great. Thank you so much.

Cheryl Clemens is CEO and co-founder of StoryTagger, the user-generated video platform for workplace learning, which grew out of LearnerLab, the consultancy she co-founded in 2013 with Carl Hodges. She has spent more than 20 years at the intersection of edtech, corporate learning, and digital communications. Storytager turns employees into credible on-camera storytellers using guided prompts and smart recording technology. She was John's second ever guest on The Learning Hack almost 8 years ago. Storytager was a media partner at LT26.

So hi, Cheryl. Welcome back to The Learning Hack. You were on our second ever episode in 2018, and it's been too long.

Ah, thanks very much, John. It's great to be back.

And you've been coming to Learning Technologies for many years now in various guises. That sounds a bit wrong, doesn't it? Guises, as if you're a a Marvel superhero in a spandex costume, which perhaps you are. So tell us what you've been up to at Learning Technologies this year and how you found the show.

Fantastic. No, thank you. So yeah, that's right. We've been a media partner for a few years now since we first exhibited at Learning Technologies with the event using Storytager to capture conference session previews and reflections on kind of the hot topics in the run-up to the show. So this year it was is kind of what's in store for the next chapter of L&D, which I'm sure you're aware of is the big kind of conference theme. And I think Learning Technologies, John, is where we first met probably about 15, 16 years ago when it took part, took place at Olympia. Yes. I remember the hand and flower. Yes. And yeah, and obviously one of the best things about Attending LT is catching up with everybody. And yeah, it's a brilliant barometer of what's happening and where the industry's at.

Yes. I've forgotten all that. Yeah, I probably did. And it was probably when you were the marketing person at Brightwave and I was the marketing person at Epic. That's right. Yeah. Or wherever. And I used to joke that I thought you were very bright and I thought, and I used to introduce you to people as the second brightest person in marketing.

He did, I'd forgotten about that. Otherwise I'd have had a witty retort. Yes.

And the hand on flour, I remember our joke then was the e-learning industry was actually quite small. And the joke was that if just a well-placed bomb in the hand of flour would completely wipe it out if you did it after the first day, because just everybody was there. And I'm still seeing those people, which is interesting. Must be the same for you as well.

As well. I am. But interestingly, you say that because I guess in those days everything was concentrated in Hand Flower pretty much. Yeah. There's obviously an increasing amount of great stuff happening on the fringes now with kind of BarCamp, CLA100 Breakfast, Learning Network drinks, and obviously all of the kind of provider events. So, it feels that it's got considerably kind of bigger with more of those events happening around the side as well. Yeah. But yeah, and I guess sort of back in those days I probably did a mix of kind of expo and conference. And previously we've had a stand with StoryTagr. This year I was kind of keen to go to the conference, which I haven't done for a few years, to immerse myself in what's happening in a kind of deeper way and catch up with customers and partners there as well as the expo. So yeah, it was really interesting to go to that and as I say, kind of immerse. And there's obviously some kind of key themes that stood out for me pretty instantly. So really happy to kind of touch on those.

Yes, please. Yeah, tell us about it.

Yeah, no worries. So I think, to be honest, this was kind of immediate for me, even after kind of day one, and they've stayed with me as well. So there's obviously kind of still kind of more to digest, but there was very much that kind of, that shift from content to context and capability was very much alive. I know we've been talking about that for ages, but there was a real kind of focus on reflection, real-world practice, experiments, and peer learning as areas where L&D need to concentrate their efforts. I think kind of having all of that together was the first time that I'd kind of heard that repeated so much, you know, through different conversations. There was also lots on increasing and evidencing impact with a renewed, I thought, attention on kind of evaluating the right things. Linking learning to business KPIs and starting out with that in mind. And you may be familiar with this quote. I hadn't heard it before, but it stayed in my mind from Megan Torrance's session, and that is that MPS is the root of all evil, which I think comes from Will Tellmire as a quote in terms of what people are actually kind of measuring from their various programmes.

MPS? Could you explain the acronym?

Ah, sorry. Net Promoter Score. So, it's basically— Net Promoter Score. Okay. Yeah. So, it's basically all of the learning programmes that kind of progressed from completions towards satisfaction, but left it there and didn't actually go that kind of stage further. So, hence it being kind of used as a kind of root of all evil. But there was a lot, I think, at the show, at the conference that was going beyond that. And I'm sure we'll kind of pick up on some of that as well. As well.

Yeah, there's a two-letter acronym that has dominated most of the conference that you haven't mentioned yet, but we'll get on to that. AI. AI, yeah, not yet.

And of course, it's— yeah, absolutely, and enabling what we've just talked about. So, very much enabling the whole piece around evaluating the right stuff and doing experiments and kind of measuring that. So that's, I think, the conversation around it had sort of inevitably matured. And I'd be interested, yeah, to hear your view as well, John. But for me, it felt like a lot of the sessions that they still kind of prioritised a kind of a human or an AI lens, which is, you know, which you'd expect. But overall, that there was more kind of harmony, nuance, and much more around kind of augmentation rather than maybe the doom or sort of boom of previous years. Yeah.

I suppose I take a more cynical take on it that what AI is doing is making possible, seems to be making possible a lot of things that people like you and I and a lot of other consultants and voices in the space have been talking about for a long time. Like you said, better evaluation, learning in the flow of work, moving from context capability, all those themes. And a lot of our type of people are saying, now is the moment, it's all going to happen. And we're going to be able to say, I told you so. I just slightly wonder whether a lot of practitioners who are still harnessed to the kind of content and LMS model, because they're very compliance driven, because they have to, because that's what their organization understands by learning, are just going to stick their fingers in their ears and go, la la la. It's the same old people saying the same old stuff, and now they've got a bit of more ammunition to throw at us, but they're not winning the war. But I think that's a bigger question. Maybe we should pick that up later, perhaps. But I want to talk a bit more about StoryTagr. StoryTagr grew out of the idea, I think I'm right in saying, that the best people to explain how work actually gets done inside an organization is, or are, the people doing it and using first-person video as the medium for that. But StoryTagr has evolved since then. Can you bring us up to date on it now and the value StoryTagr delivers?

Thanks, John. Yeah, that's right. So, yeah, StoryTagr grew out of that original belief that the best people to explain how work gets done and spread the best or kind of new ways of working are the people that are actually doing it, living it, feeling feeling it. And that was very much around video storytelling, so giving people a guided way to reflect on and share their tacit knowledge, lived experiences, and work stories around a specific purpose. Because basically, when kind of someone relatable talks about a challenge they have actually worked through, it lands with extra impact. They are peers, they've tried it, done it, got the t-shirt, and And in behavioural science terms, this makes them our kind of credible messengers, building trust, because they can also add that kind of really important role-level and task-level context that top-down comms generally can't. In terms of format, obviously audio is a great medium to capture natural conversation, warmth, nuance, especially in long form. We chose video at that point because we found it created the most connection and influence. For short stories, say, that are kind of 90 seconds to kind of 2 minutes. So reflection and storytelling has always been at the heart of StoryTagger. It's never kind of just been about capturing soundbites. It's about helping people share something meaningful. So the emotion, the challenge, the context, the learning, and what's changed as a result behind the experience. So kind of helping people to become changemakers within their own organization. And what has evolved is how customers are using it and where the value is showing up. So, a number of customers embedded StoryTagger into leadership and cohort programs from the beginning. And we're starting to, you know, we started to see really amazing results there. So, over the last kind of 18 months to 2 years, we've focused on the typical and quite deep-rooted pain points that teams experience when running cohort learning and leadership programmes. So, those are, you know, they'll probably sound familiar, but busy people are often slow to connect and take accountability for their learning when they drop into one of those kind of programmatic pieces. When they do, learning does not always transfer into their role. And even when behaviour does change, it can be hard to see, capture, or measure. And this of course makes it harder to prove ROI, especially when stakeholders are increasingly demanding evidence earlier. So StoryTagger has evolved into a kind of reflective storytelling and impact tool that basically plugs into programs to build that trust, connection, and intention from day one, increase learning transfer, and make behavior change visible throughout, not just at the end. So yeah, participants reflect, share insights and specific examples of application with each other as short-form video stories at key milestones using those prompts set against kind of business and learning goals. We've also introduced an AI analysis layer which basically takes all of the rich story data to produce insights and impact reporting. At an individual and also a cohort level, mapped against those programme goals again, sort of business priorities, and also an evaluation rubric. So it kind of takes all of that great stuff that was already there, but aggregated, and, you know, it feeds back into a loop with the, you know, the programme. So the value now is in how StoryTagger basically works in that loop to create the conditions for impact, increase impact, and prove impact as the learning is happening.

So really interesting to hear that somebody's using AI for evaluation there in a very concrete and practical way. So you brought it up, I didn't. AI, it's very much with us now in all areas of learning, both as helper, opportunity, and competitive threat in some cases. And some people might think it's a competitive threat to First Person Video. But what effect has the rapid recent growth in AI's capability and print had on your business?

Good question. So yeah, absolutely. And just briefly going back to, I guess, learning technologies, it was interesting to see Danny Johnson's research on how this is playing out in the market.

Always interesting.

Always interesting. Great session. And there was a lot around AI supporting the before and after stages of learning with diagnostics, assessments, and measurements. So that was all kind of creating a bit of a surge kind of beyond content at the beginning and the end. And that basically relates to, you know, to some of the work that we're doing. So for us, the kind of rapid growth in AI capability has enabled us to effectively evaluate behavior change by adding that analysis layer on top of the existing storytelling layer, which then analyzes the, you know, that proprietary story data. And, you know, as I just mentioned, it can now perform a really powerful feedback loop to increase and improve— sorry, prove impact. So, yeah, the storytelling layer, I guess, kind of, you know, allows humans, the participants, to generate that new meaning from what they're experiencing, what they're learning, what they're, you know, trying and finding out as a result. And then AI can analyse and evaluate that impact and sustain the learning by pulling those insights back in and measuring it against a rubric. Which would've been very difficult to do before.

I can feel my callous cynicism beginning to abate as you talk. You mentioned storytelling there, and I wanted to get to this because in a way it's kind of the heart of your proposition. And storytelling's a core activity in learning, always has been since earliest times, and it's heavily entrenched in the culture, even though people like yourself might have to remind the industry of that on occasion. But it's occasionally come under fire as a learning modality. Most recently, there was a book that I think we talked about briefly, The Story Paradox by Jonathan Gottschall, came out in 2021. Subtitle, How Our Love of Storytelling Builds Societies and Tears Them Down, rather apocalyptically. My podcast co-host Donald Clark picked this Yes. I recently saw a very sniffy review of this book in, I think it was in New York Times that more or less demolished it actually as a serious book, which is interesting. But I think when I read it, I thought there was something in it. And we tend to think now that, you know, somebody's story, that's what it's all about. We hear so much of it on kind of mainstream TV. I mean, the BBC, especially people's first-person stories, it's really taken that on. To heart since the arrival of the internet, maybe. But stories have always had a kind of ambiguous type of valence in our culture. You know, when I was a little kid and, you know, if people think you're telling them porkies, they would say, look, are you telling stories, John? A story story can just be a kind of tissue of lies. In psychoanalysis, for instance, I'm sorry to kind of head off into kind of uncharted areas like this and perhaps slightly dangerous ones, but a psychoanalyst will view somebody's story as something to be unpicked and perhaps eventually overcome and contradicted because people are telling stories about themselves all time that are just that, not necessarily true. It's a fascinating subject for me, as you realize, and something to do with my background as a fiction writer, I suppose. But how do you respond to those criticisms with regard to StoryTagger and its core proposition?

So yeah, a lot to unpick there. Yeah, too much. I'm sorry, pretend I didn't say that. No, no, no, and to be honest, next time I see you, let's talk even more about that. But I think firstly, I think it's worth saying that inside and outside of learning, storytelling is kind of even more important than ever. To be honest, for everything that you've just described, whether it's kind of good or bad, lots of uncertainty and change happening. It helps us make sense of that. It can cut through the noise, obviously not always with the truth. It's core to kind of shared understanding cultures and kind of ideas spreading. So obviously in kind of wider society, but also within organizations. And it's interesting you mentioned the Story Paradox book. I've got to confess I hadn't read it, but I've So, well, very much, it's on my kind of hit list now. But everyone I meet, and I imagine that you meet, perhaps apart from Donald, always say they love storytelling, which makes complete sense because it is core to what makes us human and how we relate to each other.

I think he's got time for it, but it's just he was interested in that book just because anything that contradicts a well-held opinion is gonna get a bit botched.

I dunno how he feels about it now. It now.

Not sure. No. And it's really good to get that other view, isn't it? But I guess kind of because of all of that, it can also be a bit of a catch-all and can mean really different things depending on how, you know, it's used. You obviously gave the example of, you know, kids telling porkies. And we obviously know from kind of social media and conspiracy theories that fake stories can be used for nefarious means. And I think also as a kind of learning modality, that's— some of that criticism is valid. So storytelling definitely in itself is not a panacea. And as always, context matters. And I guess a few things kind of connected to that. A compelling story can make something feel universally true, even if, you know, it's just one example So, you know, the context of when it is true and under what conditions is really important, and that goes for any type of storytelling, including first person. I think connected to that is also the ability of stories to kind of include and exclude people. So that needs to be factored in, particularly in kind of, you know, organisational contexts in organizational culture and learning environments. To just give a really quick example, I guess, of that, if you only heard from leaders who, for example, naturally kind of influence through their presence, they're big on visibility and it's just something that comes really naturally. If they told their story about how they just sort of, you know, do it, it's amazing, that is likely to put people off who have to work harder and basically kind of deconstruct what goes on behind that and understand how to get better at it. And then thirdly is storytelling can be a really kind of superficial wrap around learning if it's kind of, you know, basically edutainment. So if it's something that we simply consume rather than a learning experience, which is, you know, encouraging us to think critically, reflect, etc., then you know, it's going to have— it's not going to have as much impact, basically. If it's just something, you know, "Oh, that's, you know, enjoyed watching that." But I guess so, yeah, in terms of StoryTagger, we've built in a lot to mitigate this. Firstly, you know, it's peer learning, so it's clear it's one person's view of what they are learning and what they are trying in their context and the results of this. It's aligned to programme goals. We guide them on a kind of narrative arc with prompts and specific questions. That then unpick the challenge and draw out structured responses which can be analysed and others can learn from. So, it's not, you know, you don't get just that sort of vanilla soundbite or something that doesn't have the kind of the meat behind it as to why, you know, something's happening. So, it's very much about active learning. It's about setting intention, reflection, and practice, and then codifying, you know, what you've learned or achieved as a result and why. And I think in terms of learning, that social point, John, is really important for lots of reasons. Because if you were doing like an individual text-based reflection, there's more chance that people lie to themselves. They use AI to complete it. I've had a few conversations with people who have basically used AI to complete months' worth of learning logs. Which— and they also have to hold themselves to account. So, when you basically— when you share your reflection with peers, you get the audience effect, which basically means that we know, you know, we know our insights are being shared, so we think more critically and we put more effort into codifying it. And that kind of accountability, that peer accountability raises the bar for how we embed learning and also the chance of success because we've announced it and we don't want to feel foolish foolish, it's more likely to happen.

That's a new one on me. I've heard that people, you know, students will cheat on their essays with AI and loads of stories about that. I didn't realize people were cheating on their training with AI. I shouldn't laugh so much because it's tragic.

It is tragic, but that's why, yeah, but we need to be aware of this stuff, don't we? So, you know, so actually the video and peer piece really mitigates against that.

Yeah. You are, you are, and you've always been a very cogent and passionate and impressive advocate for what Storyteller, StoryTagger does. But so that we don't make this too much like a product push, when is StoryTagger the right answer and when is it perhaps the wrong one?

Thank you. Obviously, great question, John. I think Hagger is the right answer for heads of L&D, leadership development, and talent who basically need to shift mindset and behaviours for a group of people or a whole organisation and prove that they've shifted. I think you can probably tell from our kind of conversation, it's a very human and cultural technology. So, it's very much for people who care about the benefits of people learning from each other, holding each other to account and showing what's changed, applying their judgment, all that stuff to produce momentum. If a programme is a kind of tick box exercise or purely, you know, or purely a kind of technical skill which needs a formal assessment less so.

Lovely. So there's a lot of volatility in learn tech just now, and it's very difficult to make predictions and forecasts against that background. But business owners have to take a view just in order to operate, really. So what's the next 3 years look like for StoryTagger as a company? How do you see it?

So yeah, so 3 years is a long time in politics and learning technology.

Well, we always used to say 5 years for a question like this, but you know.

I think it probably needs to go to 1.

Just seems to be, maybe 6 weeks.

Yeah, exactly. And actually, if you think 3 years' time, we'll be knocking on the door of 2030 by then. Oh dear. So I feel I should be careful about making predictions unless I'm directly referring to the World Economic Forum Economic Forum. But, um, but for us, uh, the direction I think is clear for now. Um, so the need for connection, uh, critical thinking, judgment, reflection, human skills— that is not going away. Um, if anything, those things are kind of, you know, becoming more important as AI increasingly becomes embedded in work and learning. We're only going to hear more about them. So for the next 3 years, or certainly the next year, It's about continuing to help organisations create, you know, those conditions for increase in evidence, the impact of learning and change. So we'll keep, you know, strengthening the experience and the impact data. And I guess from a kind of commercial point of view, we're, you know, we're taking more of an intentional approach to kind of partnerships and integrations as well where it makes sense. So the focus really has to be, you know, us staying close to the problem that we, we solve best, really.

Lastly, who are you reading and listening to? You to on everything at the moment, inside L&D, outside, and who is making you think the hardest?

Thank you. So definitely I've got a reading recommendation from today, but I've got to be honest, I'm quite eclectic in what I read and listen to and tend to follow ideas, I think, rather than specific individuals. So kind of follow what's happening in leadership, AI, behavioural science, learning transfer, innovation, neuroscience, and the kind of wider conversation around how people change. So that will be, you know, reports, blogs, all sorts, podcasts, obviously. And particularly drawn to thinking that combines that human behavior piece with AI, which won't be a surprise, rather than the kind of zeal of pure compute. 'Cause I think I followed a few people over the last 18 months, and it's kind of gone down a little bit of a rabbit hole just in terms of, you know, kind of how compute is scaling as opposed to anything actually as a result of it. So, I think, you know, following kind of ideas and shifting as things change and, you know, people have their high moments and low moments is, I think, thinking then. That's always good. I guess within the industry, you know, people like Emma Waber, Markus Bernhardt, Doreen Iles-Hoffmann, among others, give an interesting kind of spread of thinking. But the people people I think that make me think hardest at the moment are our customers, which, you know, probably doesn't come as, you know, a surprise really. I guess, you know, they're the people at the sharp end who are solving these problems right now, identifying the challenges, creating KPI dashboards that programmes need to align to, and helping their people, you know, navigate all that change. So, yeah, staying close to them, understanding how pain points are evolving, with a squinting eye on the horizon is the best way forward for us right now.

Yeah, I'm feeling a hard relate about that. Not that, you know, I have customers, but I think I learn most from my guests, and I've certainly learned a lot from this guest. There's a cheesy wrap-up for you. Thank you. Thank you very much, Cheryl, for sharing that and taking the time for this today.

Thank you very much for inviting me back.

Hack Facts.

Matt Caldwell works in in people experience and DEI at Axon, the public safety technology company. He completed his master's degree in Florida in 2020 and has come up through HR benefits and DI roles, moving to London in 2024. At LT26, he was one of the conference's 30 Under 30, the cohort program run by Nigel Payne that profiles emerging voices in learning.

Folks, I was walking around the concourse today. Guess who I ran into? Timothy Chalamet. You must get a lot, a lot, Matt. Apologies for that, for that opening. Matt Caldwell here is one of the 30 Under 30 in the 30 Under 30 program run by Nigel Payne so, so well at a conference. I always talk to someone from 30 Under 30 in this and also in OEB because I think it's It's so interesting to kind of break down those barriers of assumptions of what young people know, what young people don't know, what old people know, what old people don't know. And you work in the field of DEI, so you're quite involved with ageism as well, wouldn't you?

Yes, and I think it's, you know, it's so important. We always try to remember not to make assumptions about some of those typical DEI groups that we might think of in our heads. But age is a huge piece of it, especially in this new era of AI and faster technology. Yeah.

So fill us in about your background. I could tell from your accent that you're from the States. Tell us a bit about that and how you came to be in London.

Yeah, so I finished my master's degree in 2020 in Florida in the United States and took a role shortly afterwards with Axon, and was in Arizona at their headquarters for around 4 years. As an organization, they have grown from 900 colleagues when I started to more than 5,000 colleagues now in 2026. And the opportunity presented itself in August of 2024 to relocate to London as we continue expanding globally. So it was an opportunity from the people experience side and from the DEI side that I was really excited to take on.

So taking it back a bit further, how did you get into learning as a professional field?

Yeah, so I joke with my family all the time that I come from a long line of educators and teachers and school staff and have really had a passion for seeing the transformation in the classroom and rotated through a few different roles within the HR space, benefits, DEI specifically now, and really just loved seeing that impact that can be had from a training experience. Right.

And what was behind the move to London? Was it solely that the opportunity opened up within the company, or do you have family links here, or did you just want to experience a bit of European culture?

Yeah, I think, you know, the culture piece was really important as I thought about my long-term career, wanting to to have experience working with colleagues in Europe, you know, not just for those colleagues, and just the, you know, trajectory of getting to expand with the organization.

Oh, and how did you hear about the Search Youth Association program?

Yeah, so I was, uh, this was actually referred to me by a colleague that knew about this experience, and I, you know, have heard of Learning Technologies. I followed Learning Technologies for a while, and it just seemed like a great opportunity opportunity to connect with others that were in the space and, you know, meet a lot of really great people.

So what, what are your hopes for it?

What do you hope to get out of it? Yeah, I think it's, it's a few different pieces. I think the first is being with other people that are in this industry, kind of learning about their challenges, their opportunities. Even already in day one, I've connected about some events that we're hosting, some training just to bounce ideas off of. And then additionally just being in the room and listening to the cutting-edge technology and the leaders in the industry and being able to bring those insights back home.

All right. Um, so what are your hopes about how your career might progress from this? I mean, do you, do you see yourself back here in a year's time, or—

Yeah, you know, I, I ultimately would love to be leading the training function for an organization And you, from the very first day of new hire onboarding throughout an employee's lifecycle, really being that source for them to succeed within an organization and absolutely would love to be back here next year.

Yeah. So we talked a bit about ages and, and different attitudes. So one of the things that people of my age group get wrong about people of your age group a lot is, well, you know, they've grown up with technology, they know absolutely everything about it. So you don't have to tell them anything. What, how do you feel about using technologies as a younger person and how helpful it has been, how difficult in some senses?

Yeah, I think it's both. I think when it's used correctly and knowing what that ratio is of, you know, when I'm gonna use AI-generated thoughts or when I'm gonna really critically think about this on my own, my own, I think it has the ability for us to grow and do more so much faster. But, you know, working in the DEI space, I think it's also really important for us to remember if we're giving bias information into AI, it's really important for us to be cognizant of what we're taking out. And we've also, you know, regardless of age or background or generation, we have the ability now to have some of the best bias fact-checkers ever, because we can use our AI tools to hold those safe conversations and ask, am I being biased in my response? Here's what I plan on discussing. We've never had that ability before. And so I think when used correctly, the technology can really be a game changer.

But how would you feel about the AI's ability to define bias? Because there is a skew, isn't there, in every, yeah, every training set, as it were. For instance, I tried to get ChatGPT at one point to help me compile my shopping list. It had never heard of coriander, although I've heard coriander, and didn't sort of say anything about it. What does that mean? Just blanked over it, just completely jumped over that sentence until I realized, oh yeah, you have to call it Cilantro. There's that specificity to AI that we very often forget about. And if you want to do something that's unbiased, how do you, what's your benchmark for lack of bias?

Yeah, I think the biggest tip that I use personally and that I always advise others is ask to be disagreed with, ask to be challenged on your thoughts, ask to be given examples exact opposite opinion. And you can prompt AI, you know, say that you have the exact disagreeing opinion of me, find everything that's concerning about XYZ. Is it going to be perfect? No. But I think it will still allow you the opportunity to just challenge your own views. I think a lot of bias comes from assumptions, even if, you know, not with malicious intent. And now you have this ability to really quickly sort of pause and challenge maybe your own way of thinking.

Yeah, I moved recently from ChatGPT over to Claude, and I found it, it's more likely to do that. But after a while, it, it became, it felt like a, a trope that, um, I, I would get this phrase coming back, well, my honest opinion, John, is, and you know, I really don't think, and, but at first I thought, this is amazing, this is really refreshing after ChatGPT's sort of obsequiousness for months and years, even gaslighting. Sure. Then after a while I thought, well, am I being gaslit now by just a kind of an amended rhetorical kind of framework, these answers that makes it feel more unbiased? I don't know.

About that? I think it depends on, again, how you're using it and just recognizing, again, it has been given a source of some kind that was even referenced in the keynote earlier today, that it's a first check. And I think particularly, you know, giving it the feedback of, nope, I need you to really disagree. It's okay to hurt my feelings. I've given it several examples examples of difficult conversations we've had to have with managers. And, you know, pushing back on the AI and going, nope, it's going to be more difficult than that. Here's the added context. While that may not be as fast, and I think sometimes people associate the success of AI with being a very quick return, what it will still give you is the ability to really challenge your thoughts.

How do you feel about the, um, how have you found be, uh, the use of AI within your organization? Because freelancers, me, uh, like me, we have no constraints on, on what we use other than kind of time and money. Um, for people working within organizations, it's, it, it's often very diff— different. You have got directives about what you can use, what you can't use. Um, uh, the keynoter today said that it's a top-down initiative because if the CEO doesn't stand it, yada yada yada. And what's your specific experience about that?

Yeah, so I, I think what organizations need to remember is when you're not using an enterprise or a company-given license, that every piece of information you are putting into these tools, into these platforms, is no longer yours. And particularly in countries like the United Kingdom or in certain states like California, you— there are very, very strict privacy laws, even with things I know in the talent acquisition space, this is becoming a hot topic of parsing resumes and, you know, uploading information. And so I, my advice from the, the corporate lens, especially those folks that are in corporate, you know, check with your information security team first, always check and aim on the side of caution. I think especially as governance around AI continues to evolve, evolve that, you know, we may see where there are use cases of, you know, people's names being dropped in, for example, which might not seem like a big deal at the time, but these systems are all so new. We don't know the long-term effect of this. So I, I always caution, follow the guidance of your internal teams, you know, trust our guidance. And if you have an idea of a tool that can be really helpful, I always encourage whether it's managers or individual contributors, bring that to your teams and say, you know, this could save us time, we recommend this. But I do think it's important to always remember the potential drawbacks.

Yeah, but my perception with that is that the more information, the more the AI you're using knows about you, more you can give it context, especially when you're moving into territories like co-work and so on, the better it is, the better it performs. But the more information I've I'm giving it, the more I start to get slightly nervous about that. You know, what if the ownership of this company changes? What if relations between the country where it comes from and the country that I'm operating in change even more dramatically than they have? Is this a hostage to fortune? Is this kind of ease and utility a bit of a devil's bargain?

Broken? I think, you know, there's no ultimate answer for this. And I think that it, you know, even in the conversation about, you know, how much of your brain is no longer being used because you're just defaulting to AI. And there have been lots of skits and videos about people that are using, you know, ChatGPT. How do I order at a restaurant? How do I, you know, pick up my fork? And, you know, I really think if it's making you more efficient and it's truly adding value, then it's time to use it. But I also, you know, I think we don't, we don't know the impact of what this will be in terms of some of those government relations. This is such an unknown area. Yeah, that's it.

In a way, that's slightly different answer to a slightly different question, which is, you know, will our brains drop if we use it too much? I'm, I'm worried about the data privacy thing. I mean, you know, the, the old cliché is, um, Americans Americans are really obsessed with freedom. Europeans are really obsessed with privacy. And I think to a degree it is true. It's the privacy worry, isn't it? That is part of the devil's bargain.

Jaswin. And I think it's important for organizations to remember, especially organizations that are working globally, you know, to your point, if you're a global organization, what might seem okay, whether it be in the United States or whether it be in the European Union or wherever you may be operating, if you were handling employee data of any kind, it's really important to make sure that you're, you're vetting the local laws for where that employee is based, because that is ultimately what, you know, could impact whether it be a legal situation or a privacy situation. And, you know, I think every time that we add social media would be that other example of this. We, we pretty much give up seemingly everything with our social media account. Yeah. And kind of have to ask yourself the same question, you know, is, are the benefits that I'm receiving, whether it be on LinkedIn or Facebook or whatever, do I feel as though I'm having that same peace? And I, I think that's probably the closest analogy recently that we have is, and of course, you know, I've seen a lot of guidance that you shouldn't post on social media when you're leaving for vacation because it might attract other people because of the privacy. So I think it, you just have to really kind of make those individual decisions on what you're willing to go out. And as a company, you have to be really cautious about individuals.

Yeah, I used to, um, as a little joke, when I was going around on holiday, I'd put on Facebook, we're going to— we're on holiday, burglars please ignore. And my wife said, you know, stop putting that in there. You don't know what the algorithm's going to pick up for burglars. And, you know, burglars have Facebook accounts and blah, blah, blah. I sort of feel that The interaction with, I mean, we're kind of a bit wise about that data trade-off with social media now. We're used to that, that kind of Zuckerberg selling it on to organizations so they can sell you stuff. It goes a bit deeper, I think, with AI. I mean, my AI knows I had a stroke 2 years ago. That's a vulnerability. Yeah. Uh, you know, all sorts of worries kind of accrue about that relationship. And yet the more it knows about me, though, the fact it knows I have those health issues means when it writes me an itinerary, it says, um, here's a little gap in your schedule for a, for a nap. Yeah. That kind of stuff. So, but I still think it's a bit, it's a bit difficult. Anyway, who do you follow? Um, not only just on AI, AI and business, but more generally as well? What, what, what do you read? Um, who do you follow? Who do you recommend?

Yeah, so I follow Harvard Business Review pretty regularly. I think they're posting out Harvard Business Review pretty regularly just on their AI governance and their guidance for the HR space. SHRM has posted a lot of guidance on AI that we find really helpful in terms of, you know, who's here and who's not here and sort of what, sort of what those local guidelines are. Of course, learning technologies is always, you know, promoting great vendors and great organizations as well. Nigel Payne is a fantastic person of Yellow. And, you know, from a non-AI perspective, more of the human side, Brené Brown is someone that I listen to a lot. It's just from the human element, especially as this is changing with technology. So those are the ones that that come to mind.

Thank you very much for taking the time today. It's been really interesting.

Of course. Yeah, this has been great.

Thank you so much. Best of luck. I'm still around in 3 or 4 years' time. I'm sure I'll see you here as a CLO of a major company.

Hope so. That's all on the Learning Hack Podcast for this time. And that's all on this season of the show. Many thanks to all 5 of our guests and to our sponsors. The Learning Hack is completely independent and transparently funded by sponsorship. If you want to help others find us, please like, follow, rate, review, and subscribe on your podcast platform of choice or on YouTube. The Learning Hack will return for a new season in September, but this feed is not going quiet over the summer because Jon is launching a brand new podcast.

It's called The Tech Imaginary. Imaginarium: How Science Fiction Made the Modern World. 6 episodes dropping weekly into this same feed starting Tuesday, 26th of May, 2026. My co-host is the writer and consultant Ezri Karlebach, former fellow of the Royal Anthropological Institute, founder of Space City Breakout, and a lifelong science fiction obsessive. We're tracing the lines from the speculative fiction of the past 100 years to the technology we're now living inside and using extensively for learning. Asimov and the Robot Laws, Frankenstein, the Foundation series, the predictions science fiction got right and the ones it didn't. Subscribe to this feed and you'll get the Tech Imaginarium automatically.

Meanwhile, stay curious, learning people.

Wow! Now I finally get it.

The Learning Hack Podcast is supported by Learning News, the learning sector's newswire.

Rob and his team are good friends of the podcast and we really value for the help and advice we've had from them, and they do a great job. For the very latest news from around the learning sector, for interviews with learning leaders, the latest from learning sector vendors, and features on workplace learning, go to learningnews.com.