Jessie Tung (00:46.522) Hello and welcome to this episode of Digital Humans Podcast. My name is Jessie Tong and I'm joined with my co-host, George Seiler.
Jessie Tung (00:58.626) This podcast is about learning from leading experts on how.
Georg Seiler (01:32.397) Hello.
Jessie Tung (01:34.178) This podcast is about learning from leading experts on how we can harness the best of digital to evolve our organizations and lives. It is about putting people at the center of transformation initiatives and harnessing the incredible opportunities that technology presents.
Georg Seiler (01:53.184) And today's episode is all about learning. And we're delighted to be joined by our guest today, Miles Runham, Senior Analyst in Digital Learning at Fosway. Miles, welcome.
Myles (02:05.624) Hi there, thanks for having me. I'm really looking forward to the conversation.
Georg Seiler (02:09.612) It's a great pleasure to have you. Allow me to give a quick bio about Miles. So Miles is a veteran in the digital and learning space, boasting over 20 years in diverse experience in research, marketing, and business leadership. Notably, Miles led ASK.com's European Business and spearheaded digital initiatives at the BBC Academy. We're delighted to have you with us today, Miles. Let's set the scene and perhaps could you tell us a bit more about...
Fosway and your role as a senior analyst in the digital learning space.
Georg Seiler (03:33.1) Tell us more about Fosway and your role as senior analyst in the digital learning space.
Myles (03:38.644) Sure, so Fastway Group is a specialist analyst organization, so we focus on the HR and learning markets in Europe. We're certainly, I think, we like to think we're the premier service for Europe for those. So we look at the HR talent and learning markets principally focused on sort of technology and technology related themes. So that's platform provision in the digital learning space. It's a lot around content services and digital learning solutions.
So Fozway's primary goal, I think, is to help customers navigate the market and to make the most effective and useful valuable buying decisions. So that involves us understanding market trends, technology trends, and reviewing and understanding the vendor space so that we can sort of decode that vendor space on behalf of buyers and potential buyers so they can approach the market with great sort of confidence and clarity.
Myles (04:37.776) report that's offered is a nine grid report, which is what market highlights and analysis and vendor analysis for each of the segments in our space. And I'm responsible for that digital learning segment, which sort of alongside the learning systems segment.
Georg Seiler (04:52.288) And you're in the midst of the current evaluation, I understand as well.
Myles (04:58.6) That's right. We're in the thick of it for the digital learning analysis. Yes. So we're sort of, and the analysis includes of data capture from, from a number of vendors. So about their features and functionality about their sort of corporate and business performance. We run briefing sessions with each of the vendors to understand, you know, sort of talk through trends and get underneath the, get underneath the skin a little. And then we also, we supplement that with customer references. So we're in the thick of that now.
over the next, well, pretty much January and February sort of is my life is briefing meetings at the moment, which is it's fascinating. Actually, it's really, really interesting part of the job.
Georg Seiler (05:34.024) Looking forward to getting into that during this podcast. I'd say maybe as a as an overview question just because it's kind of burning in terms of top of mind is how are you seeing the customer expectations shifting and the market shifting it's been such a well a Rapidly evolving space in the last 12 months certainly. So what are you seeing?
Myles (06:03.132) Yeah, it has. I mean, I guess, you know, it's almost impossible to have any conversation about the digital learning market without using the words artificial intelligence. I think that's a really interesting driver. I think what's perhaps more interesting and perhaps more encouraging is I think there's a lot more meaningful conversation about understanding and demonstrating value in digital learning services now. So that's, you know, on the corporate side, I guess that's...
the broader value of an L and D function. So what's this team here for? What value do we offer to the business and how to be much more focused and targeted about that and then how to work with vendors to ensure that you're demonstrating that. I think it's more, I think what's really interesting, I think it's becoming more than just a sort of simple.
Georg Seiler (06:35.693) Hmm.
Myles (06:52.732) issue of trying to demonstrate the ROI of a program. It's more about understanding what value is brought here. Why is this interesting? Why is it useful? And why, perhaps, relevance is the key. Why is this relevant to the business and why is this relevant then to the learners that we're trying to support? So I think, and I'm sort of hoping that will direct some of the exploration of artificial intelligence to more in the most valuable direction rather than just sort of keep up with the trends and adopt.
adopt because it's fashionable. We'll see. I think we'll see. Maybe I'll come back to you on that in a few months time when the analysis is complete to see, you know, which side of the, which side of that, of that tension we end up on.
Georg Seiler (07:26.656) Mm.
Georg Seiler (07:38.08) Oh, fascinating to hear more about that. And I suppose it does connect as well with the topic of in order to thrive as a, what we refer to on this podcast as a digital human, it really is important to have a mindset of, of constantly evolving your skillset and, and always learning. So do you think that drives, uh, the LND space and also learning outside of just. Should we say the traditional LND space, but just that.
mindset of always learning. Do you think that's a real driver of this acceleration on your side?
Myles (08:14.084) I think sometimes it is and I think it should be. I think there has been, I guess it's a bit of a tendency and a risk for L&D to sort of fall back to the tried and tested. So, you know, the creation of programs, design and development of programs and content. I think what's interesting now is the ability to and the enthusiasm to experiment. And I think perhaps that's where that kind of curiosity comes to life. And I think that's true for us as individuals where
And it's not so much to keep up with the trends. I think it's to find out what's valuable and interesting and useful for us. So that might be, you know, using some of the latest tools and understanding, you know, how that changes the way we work or how we learn ourselves. And I think in the corporate learning context, I think that experimentation is really important to understand what's most valuable in your context. And that might be to inform the skills you need, you know, in your, in your team. It might be to inform.
you know, investment decisions, or it might be just to inform, you know, how you think the trends are going to develop your service. So I think that sense of experimentation, trial and test is really important.
Georg Seiler (09:22.956) fascinating. Are you able to share any exciting experiments that you've seen or come across through your research recently? I'm curious as what's exciting you at the moment.
Myles (09:37.064) So I think one of the things that's really interesting about, there's been a lot of excitement, a lot of buzz about digital assistants and co-pilots and whatever that might mean. I think one of the things that some early conversations around this idea of kind of stacking these assistants so they can manage a whole process automatically themselves. So that might be, you sort of have a hierarchy of these.
co-pilot tools that will manage, you know, perhaps, you know, a whole management of a process to become quite sophisticated and quite powerful. That feels like it's sort of some of the early conversations around how that might change, how systems are applied and managed in an organization, you know, how that really, you know, sort of super powered automation could become quite interesting. It sort of feels like a glimpse beyond.
some of the automation that we've seen with chat GPT and generative AI tools, it's almost what happens when you combine these tools across, you know, in a cross an organization as they, as they kind of manage themselves or connect and coordinate with each other. I think that feels like a really interesting sort of a perhaps, you know, an early signal of, of value that we haven't seen until more recently.
Georg Seiler (10:36.814) Mm-hmm.
Georg Seiler (10:54.816) And a follow on to that, what do you think is the time horizon that we're looking towards maturity of those things? Because obviously there's data concerns, there's implementation considerations, there's adoption of ways of working. There's the whole AI accuracy at certain times. How how far off do you think we are from realizing this type of vision that you're painting here?
Myles (11:10.601) Yeah.
Myles (11:24.552) Yeah, I think it's still a good number of years off. I don't want to be too precise as a hostage to fortune and then this podcast will haunt me at some point in the future. But I think you're right, no, understood. I think one of the interesting things is I think there is a sense that the hype and the expectations are perhaps too high too soon. So I think there's an expectation. Perhaps this is...
Georg Seiler (11:34.152) That's certainly not the intention.
Myles (11:48.72) maybe fueled by all of our own individual uses of tools like chat GPT. We thought, you know, you had that sense of when you first use it yourself, think, wow, okay, this is powerful. This is going to change things. And then that translates into an immediate sense of impact in, you know, in your, in your job and in industry, et cetera. Whereas you've already referenced some of those challenges to, you know, there's, there's some more mundane challenges of just accessing budget to apply these tools.
managing new technologies, potentially startups through corporate procurement processes, etc. There's some sort of mundane and dull barriers and then there's some of the bigger ones. I think that sort of data readiness feels like a really significant challenge for lots of organizations, accuracy, reliability, interoperability, and sort of I guess the availability of data becomes a really, really important implementation challenge. I think
So I think whilst the promise is great and perhaps the promise is right, I think it's going to take some time for the real value to unfold in our working lives. I think perhaps maybe the one area where that's less true of is probably in the content design and creation where generative AI tools are so ready for that and are already maturing rapidly there. I think perhaps we'll start to see some of that impact closer to hand throughout the course of the year.
of this year maybe, of this year on internet.
Georg Seiler (13:11.876) Thank you. That's a really considered answer. And it makes me just to sort of, I guess, try and synthesize it. It sounds like certain tools for specific jobs are at a point of maturity where they can already add value and, and be used. The, the mega opportunity is forming and there's glimpses of this, but it's, there are numerous considerations to operationalize that scale. That's, that's.
kind of what I took away from that. Would you say that's fair?
Myles (13:44.744) Yeah, I think that's right. I think one of the things I think probably that when we think about AI, we tend to think about generative AI now. And I think there's a little bit of caution there. There's other areas of sort of machine learning, personalization, recommendation, et cetera, that have always been, what feels like they've always been around, those will continue. I think one of the things that we often slip into when we talk about generative AI, we tend to be talking about automation, process automation.
And I think that it feels like that's an area where we will start to see some of the, whether you call it benefit or risk, but some of the impacts perhaps more quickly of some areas where you know what the process is, you know what the desired outcome is, and there's an opportunity now to kind of reduce the cost and increase the speed of those processes. So I think that's perhaps, just to sort of try and refine the point a little bit in that way.
Jessie Tung (19:36.497) Miles, we would love to hear some great stories from you. What are the great examples of digital learning experience that you have seen recently working as a senior analyst?
Myles (19:51.336) So I think, yeah, it's interesting. I think one of the, I was thinking about this the other day, and I've just had this sort of personal experience, but I think there's something interesting here that, so you might be able to see in the background there, I'm a terrible amateur guitar player who tends to buy gadgets to try and improve my playing, which never works. So there's this sort of insight there. And I bought recently this small, it's a really small
kind of digital amplifier, the idea being that it's kind of, you know, hand sized, mobile, you can take it away. And I was trying to sort of set that up and figure out how best to use it. And one of the one of the most useful things I found there was a sort of community. And there are these communities now all over, you know, YouTube and TikTok, for any whatever your interest is, there's a community around it. And I was thinking that something really interesting about the power of that as now a resource that I entirely rely on.
to learn anything now, is that someone's got some insights. I think one of the interesting things about those communities as well, and this might be particularly true of something that's more in the creative field, like, well, my guitar playing's not necessarily that creative, but is that there's no right answer. This is kind of, people are sharing their stories, and I find it useful that I use it this way, and share a little recording of that, and it helps me do these things. I think that experience over the last couple of weeks
highlighted the value of what community means for us as we learn and the fact that it's kind of global, it's entirely made up of people I would never have met. But there's all sorts of interesting signals of authority and trust in there and real, really valuable problem solving. And it was a personal experience that made me reflect on that value of community and the way that digital has kind of, has enhanced that experience.
Jessie Tung (21:43.857) That sounds brilliant. So much easier access to knowledge and also you can learn with the others as well, not necessarily having to go and find a teacher to learn from that particular person as well. So yeah, that's a great example. So, another question. Yeah, around the hybrid working so after COVID. Most people don't work full time at the workplace or at office anymore.
Myles (21:57.472) That's right.
Jessie Tung (22:12.369) Have you seen any changes in terms of the learning? So in particular, how is learning adapting to the?
Myles (23:30.972) And I think then as that experience has sort of evolved into some form of hybrid, I think there's a little bit more of a sophistication there. And I think what I'm optimistic about is that there's a better sense, a sort of better design sensibility around that solution now of what's the context, understanding the context for each individual and then designing an experience around that, not just saying, you know, that this is a question of distributing to hybrid audiences. This is about...
understanding the value in a hybrid context, using different tools. It may be, as we were just discussing in our community content, it might be more on demand, not trying to wrap everything into content in a classroom and being a bit more subtle. And I think there's, you know, the, and the market has sort of followed that need. I think, I think that the challenge then, uh, alongside that is to sort of the skills of managing that tool set and managing an ecosystem around the hybrid organization. Um, because
Myles (24:31.097) As far as I see the data on what hybrid means, organizations are still experimenting and discovering what works for them, either mandating back to the office, the Tuesday to Thursday in the office, or I think everybody's trying to find what works best. So I think having a finger on the pulse and being as responsive as possible to organizations as they emerge, as their understanding emerges is really important there.
And I see that as a kind of design challenge, I think, and the best L&D services are taking a design approach to that.
Jessie Tung (25:04.645) That's brilliant. I mean, as an individual, I feel I'm much more empowered as well because I'm empowered to, uh, to learn from anywhere and then through so many different platforms, tools, and then also channels as well. So let's talk about your BBC story. Shall we? How
I was fascinated when I look at your profile and then understand that you spent many years working at the BBC. How different are the needs between a corporate and educational setting? Would you mind giving us a bit of the point of view on that as well and using the BBC as the story?
Myles (25:45.236) Yeah, okay, yeah. I think it's interesting. So I arrived at the BBC in the BBC Learning Service, which is, and the BBC Learning is effectively the BBC's education service. And I arrived from the search engine industry, a very product led organization into a kind of product service and media education environment. And one of the things I found most interesting and really kind of exciting was that in the schools context.
the curriculum is a brilliant environment for product development. And that might sound slightly perverse because, you know, the curriculum is very structured and can be seen as a, I suppose, a restriction. But one of the things that working to a curriculum allows you to do is to be very, very clear on your audience, very clear on your audience needs and very clear on what relevant content looks like.
And the most clear example of that, I think, is the success of BBC's Bytesize products. And I was very, very lucky to manage the Bytesize service with this brilliant team, with an established service, you know, strong brand, et cetera. And it was just, but it was fascinating to see, you know, how that the design choices revolved around understanding those needs, you know, from the curriculum. And that sense of structure and clarity of expectation, it's not always present in an education environment, but that was really, really interesting. And it made.
I suppose it made our decisions about prioritization and investment much, much clearer because we knew, you know, you know what's coming, you know when the exams are, you know what the big subjects are, you know what the, you know, what the exam bodies are specifying. And it gives you a really good sense of, you know, how to design. I think that, that was a really interesting insight for me. And then seeing obviously how the team had, had evolved around that, that's, that's almost set of requirements. So that was probably the most important lesson I learned very quickly in joining the BBC.
Jessie Tung (27:36.621) Oh, that's brilliant. And by the way, BBC used to be my client as well. So I did have some, yeah, I did have a few years of experience working with them as well. Yeah, brilliant organization to work with.
Myles (27:41.212) Ah
Myles (27:50.416) Yeah, that was another interesting point, actually, I suppose, to the BBC was really, it's perhaps less so now, but what has been right at the heart of digital content commissioning and digital service commissioning for a long time. So it's always had that sort of sense of kind of leading edge development and has been an attractive customer for lots of agencies and suppliers. And that's a great place to be as well. You get, you have lots of interesting conversations. You're exposed to lots of interesting ideas because of that as well, which was great.
Georg Seiler (28:17.19) a willingness to innovate and to try new ideas.
Myles (28:22.212) Yeah, I think there's partly an expectation as well. I know, again, my time at the BBC is a little distant now, so I'm not sure to the extent to which it's still true. But I think there was an expectation that BBC would be trying to do new things. So, you know, when mobile content arrives or mobile technologies arrive or, you know, the internet itself, the web, the browser, et cetera, there was a sense that the BBC would be doing something to sort of evolve itself and also to help, you know, because of...
indicate to audiences what the value can be. And I think that was a great place to be.
Jessie Tung (28:55.157) Exactly. So, okay, I want to wrap up my section with the topic of impact. So two more questions that's a specific topic. You know, AI is the buzzword. Most of the software companies, they are talking about AI, and then also a lot of organizations, they are trying to understand AI. So in your view, what would be the impact of AI in the learning area?
Myles (29:25.832) So I think it's going to impact everything really. So let's try and break that down a little bit more helpfully. I think we've already referenced, I think automation will be an important impact. I think one of the interesting things just to build on that point is particularly thinking of generative AI tools. They work brilliantly because they're excellent at predicting. And I think in a lot of the e-learning world is, and this is going to sound damning, but it is kind of predictable. So the formats.
the modes, etc, are well established and very well known. So I think they're sort of ripe for, for automation. So I think we will we will start to see the impact of that. I think one of the most exciting things probably is this sense of personal intelligence that can be applied in the learning world, that will be able to understand individual context, genuinely personal individual context, there's great potential there, I think that will help us then direct, you know, relevant skills development, you know, and various
capabilities that would be able to give us genuinely personal sense of planning and direction and then monitor and manage and help our progress through those plans. So I think that sort of sense of personal intelligence is really powerful. Skills intelligence sort of alongside that, the ability to digest and ingest vasts, you know, sort of amounts of structured and unstructured data to help paint that picture. I think those are some of the most
the most interesting areas. So I think it's about intelligence. As you mentioned earlier, George, I think that good data in is going to be vital and understanding that. So I think our responsibilities to sort of be literate about what these tools are good for and what they aren't good for and how strong the data in is, is going to be really important as well. But I think those are probably some of the biggest areas.
Georg Seiler (31:12.252) Hmm. AI is very good at cleaning data though, as well, actually. And harmonizing disparate data sets. Uh, they talk a lot about data lakes, for example, and, uh, having lots and lots of information from different areas. And then if you have a use case or a particular mission for one of a better word to achieve, then the.
the AI tools can really help to cut through all the noise and give you those insights in a very pragmatic way and in a format and style and to a level of detail that you request. So it is really, it's very exciting when done right.
Myles (31:53.252) Yeah, it does. Yeah, I think that's right. I think it makes it makes the task of being a good data analyst much more achievable. Perhaps, you know, for those of us who might not have that background, I think it becomes much more readily available, I think, which is going to be really interesting.
Jessie Tung (32:05.485) Yeah, those days of using Excel only is gone, right? So you will not have to do so much of the heavy lifting anymore. And that's a great news for us all.
Yeah. So shall we talk about what good looks like? I mean, Miles, in your view, how to build a really effective digital learning program? What? Yeah. What are the must do's? I mean, for those people who are building their internal learning programs.
Myles (32:24.384) That's right.
Georg Seiler (32:24.641) Yeah, absolutely.
Myles (32:44.568) Yeah, I suppose I think that the key to it for me is relevance, I think. And I think this can be a challenge sometimes in the workplace context where, you know, an LND team may be given a particular objective to follow. But I think it's about understanding what does personal relevance look like for your audience and treating that audiences as, you know, as individuals, not just a, you know, a cohort or an audience population. And I think.
To understand that relevance, then I think a lot of it's about that sort of the design discipline of thinking through upfront understanding, you know, motivations and attitudes and what the real needs are. I'm not making assumptions there. So that's about sort of research and analysis if you have the time. And I think one of the things that I've, then this is probably from my search engine days that I think is really, really important is, is
trying to find time and space to prototype and test, to check your thinking and work out what does a relevant experience look like and understand that response. So I think a lot of this is really, it's not necessarily a technology challenge and a technology solution, it's a design challenge and a design solution. And it's not just about instructional design, it's about genuine sort of design thinking and that might include now neuroscience or it might include behavioral science, etc.
But bringing those, that understanding and discipline together, so to be as sharp and relevant as possible, I think that's the key.
Georg Seiler (34:54.744) If you look ahead into the next five years, what will it mean to be a digital human? And I think in the context of our conversation now, it'd be really interesting to gain your thoughts, particularly from a learning point of view and from for anyone out there that's looking at where should I be focused on my development or how can I augment my skill set with to prepare for the world that we're moving towards.
We'd love to hear your thoughts on that.
Myles (35:28.821) Yeah, it's
Myles (35:55.5) why are they doing that? To be a little bit smarter about those questions, I think, feels really important. I think being aware behind that, being sort of literate about our own data, feels like a really important survival technique, but also to take better decisions for ourselves. I'm saying these things because I'm trying to do that for myself now, thinking, okay, well, what is really going on here? And why is it important? Why is it important to me?
I think one of the things that I think that on the provider side, I think that's becoming really significant, particularly in the learning context, is how high expectations are now of how well understood we believe we should be. And so I think we're expecting really to be really well known. And it's an interesting balance with respect to be really well known by the tools and the providers, the tools that we use, but also balancing that with how, how do they know us that well?
And it's slightly of interesting tension between the data we give up or the data that's discovered about us, that powers how well we're known and how great those experiences are and how we feel about that. And I think that feels like something that we all need to be kind of, you know, as smart as possible about. Don't take anything for granted. If something, you know, if something's free, then, you know, then how and why is it free? Those are the kind of questions that are preoccupying me now, sort of approaching, you know,
the next chapter as we seem to accelerate even faster into this technology future.
Georg Seiler (37:27.136) Absolutely. Wise counsel there. Thank you so much. All right. Well, this concludes our episode. Miles, thank you so much for joining us today. It's been a pleasure to hear your story and your insights. And we want to thank everyone for listening to the Digital Humans podcast. Thank you so much.
Myles (37:31.939) Hopefully.
Jessie Tung (37:55.557) Thank you, everyone.
Myles (37:58.528) Thank you.
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