Decoding AI(Part 1): Signal From Noise - Real AI Strategies for DAPs, People & L&D

Speaker A: Foreign.

Speaker B: Hello everyone. Welcome to the Digital Option Show. We are excited to launch a two part miniseries designed to help you navigate the most transformative technology of our time. AI. This is decoding AI top leader insights on this transformation wave. Over the next two episodes, we are cutting through the hype and bringing you practical, actionable insights from nine incredible leaders who are at the forefront of AI's impact on business, learning and technology. In part one, today, we are laying the groundwork. We'll explore AI's foundational effects, how it's reshaping core technologies like digital adoption platforms, the crucial human elements of AI adoption, how organizational strategy needs to adapt, and the evolving landscape of AI and learning. We have four exceptional guests lined up for you. Sharath Hari from Everest Group, business transformation leader Christopher Lind, David Kelly from the Learning Guild, and the learning design consultant Christy Tucker. So let's dive right into it. We begin with Sharad Hari, Vice president at Everest Group, who brings a sharp focus on enterprise technology and digital adoption platforms. We asked Sharath to break down the dual impact of AI. Firstly, how AI is set to disrupt or uh, transform the DAPP market itself. And secondly, how digital adoption platforms can assist organizations in their broader AI transformation efforts. Here's his take.

Speaker C: Yeah, so a couple of things, right? So one is how will AI really disrupt or transform the DAP market? That is number one. Number two is how, how is DAP going to help organizations in their AI transformation?

Speaker A: Correct.

Speaker C: Now we have been in the digital transformation era for so long. Going forward, it's all going to be AI transformation. So I'll talk about that first. So one of the things that we have seen, um, organizations, um, facing difficulty is really around AI adoption and managing all the compliance and uh, risks and all those things. So that's why a lot of organizations are still stuck at the pilot stage, right? And add to this the problem around people and change and all those things. Um, so one of the things that digital adoption platforms are doing here in this particular problem statement is guiding users on how to use specific AI tools. Of course, everybody is new to this. It's not like we are master or expert at using AI tools. And there are certain rules that we have to adhere to, there are certain company policies, um, there could be certain things that you're not supposed to input in certain tools and so on, right? So distribution platforms can actually act as a guardrail saying that, hey, have an announcement or have a guidance just telling the employees that hey, don't do this or have a take, take a look at this document. Go through this and see what you can do with this application and what you can't. So that's one of the use cases, uh, specifically on AI transformation and AI adoption. The other angle is, hey, how is AI going to impact the DAP market? That's a discussion that we can talk for an entire hour or so. But fundamentally there are four different categories where you can put the impact in. One is summarization, one is suggestions, one is authoring, and one is insights and planning. So if you look at summarization with the help of generative AI, most of the DAP providers, at least some of the leading ones, are able to actually bring in content from many places and actually summarize and give to the employees in a succinct and clear and concise manner. We have seen that use case playing out really well for a lot of customers that we've interacted with. The second one is around suggestions. So if you're actually an employee who is really struggling to use a particular application, the actual AI can actually nudge you and tell you that, hey, why don't you try doing this? Yeah, why don't you take this path instead? Or it could also say that, hey, your deadline is X date and you don't have a lot of time to finish certain things. Why don't you do it right? From an authoring perspective, it has actually helped a lot of content authors build those workflows and guides and content using generative AI that has saved them a lot of time. Lastly, on the insights parts, Insight part that is really interesting because there are a lot of dashboards and analytics that the digital platform provides. And having that AI layer on top of it can actually help the leaders or help the admin or analytics data analysts actually identify what are those insights that are relevant for them and how they can take action on it. It's not just about showing what the insights are, but it's also about what steps need to be taken. So I would say the impact is massive and it's only for the better.

Speaker B: So in this following segment, Sharad recommends three practical solutions looking forward in 2025, which anybody can take up when they are thinking of technology transformation or anything related to AI. Here's it for you.

Speaker C: Yeah, so one of the things that we are hearing in the market is around how do I drive this AI adoption and AI transformation. I'm expecting a lot of new AI tools to come into the enterprise ecosystem. If I look at the number of applications that I was using two years back versus now, it would have Doubled. One of the suggestions for organizations is see whether you need all those tools, um, or whether you can leverage your existing tools and ask them if they can bring in the AI capabilities. Because one of the things that I've seen is you need to have connected experiences across the ecosystem for employees. Otherwise the adoption is going to go down and the productivity will go down and so on. So one of the things that I typically ask them is do you know the AI and tech landscape? Right. So currently if you look at the AI landscape you can directly work with the LLMs. That's one. Or you can work with native AI tools that are generic in nature or you can work with native AI tools that are specialized for certain processes or you can work with embedded applications. Those would be your erp, hcms or other applications that are embedding AI into there uh, product. First is understand the landscape and choose what works best for you. Just because everybody is talking about AI doesn't mean you have to go out in the market and buy the first thing that you see, have a strategy around it and try to see what works well for your organization. Ecosystem that is number one. Number two is around people. I think we discussed this before as well.

Speaker B: Mhm.

Speaker C: But fundamentally people, the people aspect of change or people aspect of technology is sometimes forgotten I would say. So identify how you can get buy in from your employees. Communicate what's in it for them across any of the initiatives that you're doing. So that it's, it empowers them to perform to their full potential.

Speaker A: Mhm.

Speaker C: So that's another one. The last one I would say is specifically on digital adoption platforms. Again when you're looking at digital option platform vendor selection, when you're trying to buy a digital option platform, think about what really works for you in your organizational context. Think about the use cases that you have right now as well as what you may have in the future. Um, and lastly I would say again I talked about a little bit before as well, but it's about um, moving from the change management philosophy into a change mindset. So try to build that into your organizational culture, your processes and try to build that culture of continuous learning and adoption. We also talked about skills based organization. Let's not forget that.

Speaker A: Right.

Speaker C: Uh, and again that's going to be key going uh, into 2025. And one thing that I would ask organizations to consider is Skills is not an HR or L and D initiative, it's a business initiative.

Speaker B: That was Sharath Hari giving us a comprehensive overview of AI's transformative role for digital adoption platforms and offering some practical advices for organization embarking on their AI journey in 2025, particularly emphasizing on strategy people and fostering a change mindset. A, uh, key element Sharad highlighted was the people aspect of technology adoption. Our next guest, Christopher Lind, executive advisor of Business transformation at Christopher Lind company addresses this heads on. We explored with him the common fears and resistance to AI, particularly how pre existing mindsets, what he calls being in a box, can make teams vulnerable and how leaders can navigate these human hurdles.

Speaker D: So what's funny is this whole AI challenge is very much tied to what we just talked about in that again, some of the fear and resistance we're seeing to AI is because people are in this box and they've never seen outside the box and they've never had the box rearranged differently. And suddenly AI's coming, going, I'm rearranging this box for you whether you want to or not. And everybody's going, I am not okay with this type of a thing. And so some of the things that I look for in terms of readiness is how well do you understand the box you're in right now? Because if you don't understand the box you're in right now, you are extremely vulnerable to AI rearranging your box for you. And in a way you actually don't like or actually is not good for you. Because while we'd like to think of AI as just this alternative, to us, it's an alien intelligence. It does not think and experience and behave the way we do. So the way it makes decisions is very different than us. And so what I see a lot of people struggling with and part of the reason I get concerned when I hear people going, oh, we're going to do this with AI, is I'm like, but have you taken the time to understand your box and have you taken the pieces out and examined the pieces and decided certain pieces, they need to be in this box and they need to be in this box for a specific reason. And these over here, maybe we do need something that can help us think differently about these pieces, or maybe it can rearrange these pieces in a different way. And if you don't do that's where not only do you run into resistance because people start just duct taping their box shut, but also you run the risk of building a box that is not going to help you get where you want to go. And I think that's where I see the resistance. And a lot of times that's where I end up leading people is instead of just going, where are we going to use A.I. i'm like, where are we right now? And I think a lot of people, they don't know where they are right now. They're where they are because that's where I happen to be. But if you actually probe deeper and go, but why do you happen to be here right now? A lot of times it's, I don't know, because I am. And that is not. You're not ready for AI if that's your answer to that question. So even on this whole theme of deconstructing these complicated problems, I get really nervous when people say they're going to have AI make decisions for two reasons. One, that's dangerous territory. And two, even just from a legal compliance standpoint, AI cannot be held accountable. So someone's going to have this blowback on them at some point. So even when people say, oh, we're going to let AI make that decision, I'm like, if it goes wrong, just remember that's not a legally defensible excuse. So let's just make sure we're not. We understand when we're saying we're letting AI making the decision, we actually mean we're deciding and will be accountable for whatever decisions it makes. And suddenly when you frame it that way, a lot of times we were like, hang on, I don't want to be responsible for that. And you're like, you are, whether you want to be or not. So I think that is a big part of it. But where I see it being really helpful for this kind of stuff is I just even think about myself where sometimes I'm faced with challenges, where it's like there are quadrillions of, uh, possible options and things I can't possibly take into account. That's where AI is a fantastic thought partner to pressure test, to bring into the mix to go, here's what we're thinking, here's the data that we have patterns might we be missing, what considerations might not be in here. So that it's framing up to you and analyzing some of this stuff that you just wouldn't even have the human capacity in your lifetime to work through ultimately so that it can feed to you what you need to make better decisions. So I actually think, even on the creative side, here's a good example of it. I have insane creative ideas in my head. I never was formally trained as a graphic designer, so I am physically incapable of creating some of the things that I would like to visually. So when I create my YouTube thumbnails. I could go into Illustrator or Photoshop and try and create something and it would look like my 7 year old daughter made it and they'd go, what is that? But I also don't have the money to go hire a professional graphic designer to create custom thumbnails for everything. However, AI can help me create what's in my head and bring it to life in a way that would otherwise be formerly impossible. And I think those are examples of where you go, so do we make AI make all our images? I'm like, no. But might it in some cases or might it bring out the best of who you are in certain situations? Yes. And I think that is how we should be looking at it rather than do we pick a human or do we pick an AI for this situation? That's an arbitrary, that's not the right question even to be asking.

Speaker B: Christopher Linned with that insightful box analogy stressing the importance of understanding our uh, current operational realities before implementing AI. He also provided a clear perspective on AI's strength as a powerful thought partner rather than an infallible decision maker. This theme of understanding context and adapting our strategies is crucial. Next we hear from David Kelly, ex chairman of the Learning Guild, a leading voice in the learning and development community. David discusses why a uh, one size fits all approach to AI simply doesn't work and how organizations, particularly L and D function, must tailor their AI strategies and support methodologies based on their unique context and the ways AI is reshaping work.

Speaker A: Obviously the big one that everyone's talking about right now is AI. And it's an easy example to talk around because it echoes what I've been saying. There's um, the every, lots of organizations are going to look at AI differently. So how should I be using AI? What does it mean for my L and D group is really contextual to how your organization views AI. If you have an organization that is extremely cautious about how their use of AI and their protector and they, they haven't figured out their data strategy around it and things of that, so that is going to shape how you support AI, uh, in your organization. Flip side is also true. If you have an organization that is extremely innovative and is giving and is allowing people to explore the use of these tools to create new solutions, you should be doing that yourself. The big thing that I think is sometimes missed and again I'll uh, use AI as the example but this applies to all the technologies that are there is how are people learning through these new technologies that are disrupting Their work. Because AI is a big one. AI is one that is very much disrupting how work gets done in organizations. And there's lots of different ways to view our work. One of the ways that I often look at it is our job is to support people's work. Simple. We talk about it through learning. But our job is very often to support people's work. And if the way that they do their work is changing, then uh, the need, the way that we need to support them should be changing accordingly. And we don't often do that. We often just change the content. But uh, we don't change the methodology. And we should. This is going to be fundamentally changing the way that people work. Therefore, there should be some significant changes to the way that we're supporting people for the work. We should. We need to be looking not only internally at what we do, but externally to what the people we support are doing so that we can adjust what we're doing to continue adding value to that. And I think that's huge because we are in this environment right now where a lot of the stuff that has traditionally been brought to a lot of the value that has traditionally been brought by the humans who work in L and D is increasingly being provided by technology. That's how technology works. We're just in an advanced state of this. As technology starts to be able to fill some of those spaces that were previously faced by human, that were previously filled by humans, what are we replacing that with? People tend to look at that through the, through the lens of risk. I tend to look at it through the lens of opportunity. This stuff that I used to do that a machine can do better than me today, let the machine do that. What can I do with that time that I filled? And that's exciting to me. So I think the human, I think long winded answer to your question. I think that we need to be looking at how our workplaces are changing, how the AI is changing the way work is done, how AI is automating some of our processes and what we and the workers that we support are doing differently with that bandwidth that's becoming available.

Speaker B: David Kelly there emphasizing the need for L and D to look both internally and externally, to adjust how we support our workforce in an AI driven world, and to see the opportunities AI creates for higher value human work. So as AI continues to evolve, how is it specifically impacting training and learning development? For our final insight in part one, we turn to Kristy Tucker, an experienced learning design consultant at Syned Learning. Kristy shares her views on the realistic short term and long term effects of AI on training, particularly in areas like scenario based learning and the future of software training.

Speaker E: So I think there are probably short term and long term effects for AI and we should always keep in mind when we're looking at AI trends and people are talking about things, the tendency is for people to overestimate the impact in the short term and underestimate the impact in the long term. So you should always keep that lens in mind when you're looking at the hype about AI. I think AI is great and I think it is useful in many ways. I think in terms of scenarios, I think we are starting to get some of that. I have certainly experimented. I do see some people doing chat based scenarios and doing practice with guardrails. I think those are still mostly a novelty. I think they're not quite there for most organizations, for most skills. I think that large language models, the LLMs like ChatGPT and um, Gemini and Copilot do inherently hallucinate. So it is part of how they're built and it's not possible to really eliminate that completely. So you end up with things like the Air Canada case where they used a chatbot for support and someone had asked a question, got a plausible but wrong answer, uh, followed what the chatbot did and it was wrong. And then the airline, uh, I do think it was Air Canada and hopefully I'm not misspeaking, had ended up losing a lawsuit later because they said, oh, that was our chatbot, uh, that wasn't us providing that support. You're responsible for that. We don't owe you a refund for having given you bad advice. I think that the organizational change for accepting responsibility is not there for AI things. Not that humans don't make mistakes. Right. A human can give bad advice in a support chat. We are, I think the cultural change is not quite there yet for a

Speaker C: lot of these things.

Speaker E: I think we will get there more, but I think it's going to be a lot slower than what a lot of the hype implies. I do see some promising things. I'm not sure quite as much for software training just yet, but I do see some things where using voice for scenarios is definitely much closer to the real skill that you're doing. It is it, uh, you know, if you're trying to practice customer service skills, it is much more realistic to use your voice as the response. And if you can have an AI listen to the voice, parse the response and then say okay, you mostly said the answer, that goes With a. So I'm going to put you on this branch with some more guardrails on it. I think we're going to see some of that initially and that will feel less risky to organizations initially. Eventually I do think we will get more of the more open ended chats. We certainly already have some of these in in language training and in other things I've even seen claims that saw uh, somebody had the claim that really the software as a service whole industry is going to go down because AI will be able to develop software training. You'll just ramp it up and build your own customized software. If I have real skepticism about the timeline that was expressed in the post that I read, I think where they were like oh this is going to be in the next year or two and I'm like nah, uh, people aren't going to be that we're not going to have that culture shift is not happening in the next two years. Would I think longer term will there be people who are interested in building customized software for themselves with AI? Uh, yes. Which means then part of the training is in training people. How do you do that? How do you prompt for that? We may, I would bet that there will be some interim step of you have most of a software program built as the base but you have some um, ability with AI to customize it. And again then the training is going to be in uh, how do you do that customization, how do you troubleshoot it? That's going to be a completely different world for technical support when you don't have a stable code base and you have AI generating your code base on the fly. I don't know what technical support then ends up looking. AI is going to have to help with the technical support for part of it too. But that's going to be a very different kind of world if that's where we're headed with AI and software. And I think that will happen in some cases. I think that's going to be a much longer change because there's a lot of cultural shifts and logistical shifts that have to happen in order for that to happen. It's again the, in the short term I don't think that major like software as a service, the industry is not going to be gone two years from now. That's too big of a change to happen in that short of a time. 20 years from now. I think it will look different. I think if we look 30 years on, AI is going to have the kind of impact on our lives and work that having the Internet and having the personal computer have had, uh, and it'll AI will be integrated in so many things in at least little ways, in the way that the Internet is integrated in so much of our lives now.

Speaker B: And that was Christy Tucker offering a valuable dose of pragmatism, urging us to look beyond the immediate AI hype in the learning space and prepare for its more profound long term integration. Much like the Internet and personal computers have fundamentally changed our lives. What a wealth of perspectives to kick off our, uh, Decoding AI miniseries. We hope Part one has armed you with critical insights as you navigate your own AI journey. Be sure to join us next week for Part two of Decoding AI Top Leader Insights. On this transformation wave, we will welcome five more incredible leaders who will delve deeper into other critical facets of AI's impact. You won't want to miss their expertise. To ensure you catch Part two, please subscribe to the Digital Option show wherever you get your podcast from. Thanks once again for tuning in. We'll see you next week.