[SPEAKER_01]: If you want to grow the reach, revenue, and impact of your learning business, you're in the right place. [SPEAKER_01]: I'm Celisa Steele. [SPEAKER_00]: I'm Jeff Cobb, and this is the Leading Learning Podcast. [SPEAKER_01]: Dr. Philippa Hartman, aka Dr. Phil, has said of AI for learning that if early 2025 was the age of rapid adoption, late 2025 is looking like the age of constructive AI pessimism.
[SPEAKER_01]: And I feel like constructive pessimism does describe what we're seeing with learning businesses. [SPEAKER_00]: Yeah, we do seem to be past any artificial intelligence as silver bullet kind of thinking and arguably we never should have been there. [SPEAKER_00]: AI is a technology. [SPEAKER_00]: It's a tool that can be used for many things and it can be used well or less well. [SPEAKER_01]: Right. [SPEAKER_01]: And AI often magnifies what's already happening, what's already in place.
[SPEAKER_01]: So if your metadata is a mess or your catalog of courses is cluttered [SPEAKER_01]: Then an AI recommendation engine might help you give perspective customers recommendations faster, but those recommendations won't necessarily highlight the best path for the learners or result in any higher sales. [SPEAKER_00]: So today we want to talk about AI and its use in learning businesses by placing AI inside our learning business maturity model.
[SPEAKER_00]: And we believe this can help you pick the next move for where your learning business is. [SPEAKER_01]: AI is not going to fix an immature learning business, but AI used thoughtfully can help a learning business on its path to greater maturity. [SPEAKER_00]: So here's what we'd like to do in this episode. [SPEAKER_00]: First, we'll offer a short learning business maturity model refresher with stage by stage AI plays mixed in.
[SPEAKER_01]: And then we're going to look at base requirements that you'll need to address before making progress with AI. [SPEAKER_00]: And we'll talk about a few pitfalls to watch out for and we'll close with immediate steps you can take plus a rolling plan for the next 60 to 90 days.
[SPEAKER_01]: All right, so let's talk about the learning business maturity model as a quick refresher this encompasses four stages of maturity across five domains and the stages are static reactive proactive and then ultimately innovative. [SPEAKER_01]: And the domains covered our leadership, strategy, capacity, portfolio, and marketing. [SPEAKER_01]: And we believe that AI shows up in all five of those domains. [SPEAKER_01]: And the use of AI influences how mature a learning business is.
[SPEAKER_01]: Whether it's stuck in that static stage or whether it's able to advance to the innovative stage. [SPEAKER_00]: And so we'll offer some quick descriptions of the stages, just hearing these may help you self-place yourself while you listen. [SPEAKER_00]: But we also have an assessment that you can take to gauge your learning businesses maturity. [SPEAKER_01]: We do. [SPEAKER_01]: So be sure to check out the show notes at leadinglearning.com slash episode four, six, four.
[SPEAKER_01]: There you'll be able to get access to fuller descriptions of the stages and the domains. [SPEAKER_01]: And you'll also be able to find out how to [SPEAKER_01]: But for now, we'll do short descriptions. [SPEAKER_00]: So the first is static. [SPEAKER_00]: And the static learning business primarily maintains past practices with limited response to changing learner needs or market conditions.
[SPEAKER_00]: At this stage, processes may not be strategically aligned to the changes occurring in your market. [SPEAKER_00]: Growth efforts tend to be at-hoc and opportunities for innovation, operational improvement, and learner engagement may just be flat out overlooked. [SPEAKER_01]: So that's the first stage.
[SPEAKER_01]: The next stage is reactive and a reactive learning business is actively responding to market conditions, but it's often doing so in a short term tactical way rather than thinking long range strategy.
[SPEAKER_01]: And at this stage, organizations may meet [SPEAKER_01]: pretty immediate needs and do that fairly effectively, but they may struggle to proactively shape offerings to build long term relationships or to consistently align what they're offering in terms of learning with their broader strategic goals.
[SPEAKER_00]: And now at the next stage, a proactive learning business has shifted away from primarily reacting to market conditions and moved toward a more deliberate and strategic approach to managing and growing programs and services. [SPEAKER_00]: At this stage, an organization has already established fundamental processes and is optimizing its offerings with foresight, innovation, and efficiency.
[SPEAKER_01]: And then the fourth and final stage is the innovative stage and once you have an innovative learning business, this organization is really leading. [SPEAKER_01]: It's setting trends. [SPEAKER_01]: It's continuously innovating, really evolving to meet learner and market needs.
[SPEAKER_01]: So at this stage, the organization has really strong foundation of processes and it's actively shaping the field or profession or industry that it serves through its offerings, through strategic foresight, and through a collaborative ecosystem where it's really trying to position learning as a core driver of growth and impact. [SPEAKER_01]: So keep those stages in mind and let's now talk a little bit about how AI use might look at each of those stages. [SPEAKER_01]: So static.
[SPEAKER_01]: At the static stage AI tends to be used if it's used at all in pretty isolated instances and it's primarily used to solve immediate problems rather than being part of a broader longer term strategy. [SPEAKER_01]: So, you know, we've got [SPEAKER_01]: sporadic AI adoption, no real clear strategy or integration into the core functions of the learning business.
[SPEAKER_01]: Maybe AI is used to address surface level issues, but it's not really being used to drive more fundamental business operations or even transformation. [SPEAKER_01]: And even though AI might be providing some data or helping a learning business to assess the situation, decision making is still largely manual. [SPEAKER_01]: So that sort of AI data might just be kind of one input along with others. [SPEAKER_00]: Right.
[SPEAKER_00]: So all of this means that AI tools operate in silos, which is characteristic of static learning businesses in general. [SPEAKER_00]: Those AI tools are being added to the silos and that leads to inconsistent results. [SPEAKER_00]: There's a lack of AI expertise among staff and that results in suboptimal implementation and there just is no long-term AI adoption roadmap.
[SPEAKER_01]: So in terms of what AI might actually be doing in a static learning business, it might be doing things like automating transcription. [SPEAKER_01]: So if you have recorded educational sessions webinars or instructor lead online classes, you might be using a tool if you're at this static stage to capture what's being said create automatic AI driven transcripts. [SPEAKER_01]: Possibly, you might also be making use of something like a chatbot to help with customer service inquiries.
[SPEAKER_01]: But odds are, if you're doing that, it's probably more internally focused and used rather than being something you forefront for any of your customers or learners. [SPEAKER_01]: And you might have maybe some data analytics and data dashboards that are offering some AI generated insights, and then maybe you're also making use of AI to help with some aspects of content curation.
[SPEAKER_01]: So maybe it's helping suggest tags for learning materials, for example, and so you're getting some auto tagging happening. [SPEAKER_00]: And at least you were, you know, somewhat cautious and saying, might before you went through that list of potential uses. [SPEAKER_00]: And you can call me cynical if you want, but I think that most static learning businesses aren't doing much more than maybe that automated transcription.
[SPEAKER_00]: And maybe, maybe they're using an AI powered chat bot to help with customer service. [SPEAKER_00]: But in the case of learning businesses embedded in associations, for example, that bot is often owned and managed by another part of the association like membership. [SPEAKER_00]: And we just know in general from our research, the data we're getting back last year and so far this year.
[SPEAKER_00]: that a lot of organizations really just have, they're not even just either not discussing AI at this point or they're discussing it but have no clear plans for implementation. [SPEAKER_00]: So for that's about half of the people responding to our research and these static businesses are almost certainly going to fall in that 50%. [SPEAKER_01]: Right. [SPEAKER_01]: And so you're talking about research that we do on an annual basis to look at the learning business landscape.
[SPEAKER_01]: We've talked about that research in past episodes. [SPEAKER_01]: We'll definitely be talking about the data we're getting currently in a future episode. [SPEAKER_01]: So stay tuned for more about that. [SPEAKER_01]: And yeah, I think it's absolutely worth pointing out that a static learning business may not really be using AI at all. [SPEAKER_01]: So I think for a static learning business, AI use goes from zero up to what I mentioned.
[SPEAKER_01]: And I think most of those use cases that I was mentioning, even if they are in play, it's [SPEAKER_01]: Probably largely due because those are AI features baked into platforms that the organization is already using. [SPEAKER_01]: So you know, transcription of an online meeting might be because it's really easy to have, you know, an automated AI transcript created or if you have data dashboards that you look at the system behind that probably is making use of AI.
[SPEAKER_01]: So it's almost kind of like a little bit of a hidden use of AI. [SPEAKER_01]: You might not even as a static learning business be totally aware of the use of AI. [SPEAKER_00]: right and you know so you're not in that case probably fully benefiting from it. [SPEAKER_00]: I think part of becoming a more mature organization is becoming a more conscious organization of being intentional about having access to these tools and using them.
[SPEAKER_00]: So to continue with AI in that static learning business context, a static learning business, [SPEAKER_00]: might have as a goal, things like establishing guardrails and looking for maybe just one widespread efficiency when with AI. [SPEAKER_00]: If it is going to bring that AI in, so to do that, you might do something like create a short plane language, AI policy, certainly a good thing for an organization to have at this point.
[SPEAKER_00]: identify safe tools and make that list available to the relevant stakeholders, offer some internal training. [SPEAKER_00]: So maybe something on prompt hygiene or do do not session. [SPEAKER_00]: You might establish a dedicated AI working group or task force to explore AI applications. [SPEAKER_00]: You can begin cleaning up your metadata, you mentioned metadata earlier, Solista. [SPEAKER_00]: You need good salad data to use with AI and it doesn't have to be all of your data.
[SPEAKER_00]: Not all of your metadata, but some, you know, clean up your titles, clean up your summaries, your tags for your top products, maybe just pick a number of them or a percentage of them that you're going to tackle. [SPEAKER_00]: You can begin tracking AI-generated insights on a more consistent and intentional basis for decision-making validation. [SPEAKER_00]: Any might pilot AI-driven personalization and small-scale learning experiences.
[SPEAKER_00]: So all aspirational things that have static learning business might want to try to strive for with AI. [SPEAKER_01]: Yeah, and with all of these stages, from static all the way up to innovative, be thinking about how you're gonna measure and see what progress you're making.
[SPEAKER_01]: So given what you were talking about Jeff, at this point, at this stage, you might have key performance indicators that would be something like the percentage of staff trained on your AI policy, or you mentioned that sort of do-do-not session. [SPEAKER_01]: How many of your staff have attended a training like that?
[SPEAKER_01]: It might be that you're looking at how we reduced response time if you do deal with customer service inquiries about your learning products and services, or, you know, we were talking about metadata, maybe just kind of what percentage of your metadata has actually been verified and kind of cleaned up if needed.
[SPEAKER_00]: And I think a major message is even at that static level to just start engaging with AI at some level, even if you're not directly applying it to your learning programs or really actively into how you're running your learning business, just getting your staff, your volunteers experience with it. [SPEAKER_00]: We're a big fan of Ethan Mullick and he talks about just needing to spend that initial 10 hours with AI to just get a feel for what it can actually do.
[SPEAKER_00]: And that's going to be a big step, I think, for a lot of organizations [SPEAKER_00]: seeing the path beyond static as a learning business. [SPEAKER_00]: So that's the static level. [SPEAKER_00]: Let's talk about the reactive level that the next stage in the maturity model. [SPEAKER_00]: And if the reactive stage AI adoption expands, at least in theory, to improve efficiency and automate repetitive processes. [SPEAKER_00]: So AI becomes that sort of operational enhancer.
[SPEAKER_01]: And so at the reactive stage, AI might help with things like automating some administrative or operational tasks, so maybe there's some, you know, standard email responses that need to go out, that's the kind of thing that AI might be able to help with, maybe AI's taking a look at your learning analytics, and that's helping you make some decisions about what you offer.
[SPEAKER_01]: And again, that's probably the stage still just AI-playing a role and not yet being a real court driver of decisions, but making sure that the AI insights are included and what you're thinking about. [SPEAKER_01]: You're beginning to probably have AI tools integrated into some of your processes here at this reactive stage, but you're probably not yet really fully connecting those AI tools to all of your processes.
[SPEAKER_00]: Right, and so some common use is, and again, I think a lot in a lot of cases these are going to be things that are just sort of baked in to software and processes that organizations already have, but you're maybe becoming a little bit more intentional about it, so you might have AI driven recommendation engines that are suggesting content based on learner behavior, you might have AI assisted LMS automation, so doing things like grading or feedback generation.
[SPEAKER_00]: AI-powered email marketing personalization or basic AI-driven learning analytics to identify trends. [SPEAKER_00]: Again, you might find all of these things already existing in platforms that you're using and you're starting to take advantage of them. [SPEAKER_01]: So at this reactive phase to kind of make a little bit more progress, a learning business might be trying to standardize what's working, where they're beginning to see some benefits from use of AI and AI tools.
[SPEAKER_01]: You know, so, [SPEAKER_01]: What they might want to do is look at creating a roadmap that sort of shows how the different AI-powered systems might feed into one another and become more integrated so that you're better able to leverage the AI insights.
[SPEAKER_01]: You might begin to explore using AI for some adaptive learning, you know, is there a way that you can help personalize those learning experiences just a bit based on what you know about the learner and the content that you have on hand. [SPEAKER_01]: And then certainly I think a reactive learning business is going to be providing more training for staff.
[SPEAKER_01]: You know, how can you help them get up to speed more fully on the tools that you already have in place specifically, but also the general uses and possibilities of AI. [SPEAKER_00]: Right, you know, and some simple key performance indicators, KPI's here might be in addition to what we've already mentioned for for static.
[SPEAKER_00]: They might include things like time to create your learning experiences and that might be the total time that's required to actually create a particular learning experience a number of hours. [SPEAKER_00]: It also might be the length over time, you know, to take one week or to take, you know, eight weeks to get something done has AI helped to collapse that process. [SPEAKER_00]: Or things like reduction of routine task.
[SPEAKER_00]: If you know it took 10 steps to get something done before, now you're getting it done in six, that's a game. [SPEAKER_01]: So we've talked about what AI can look like in static learning businesses and then in reactive learning businesses. [SPEAKER_01]: So now we're going to talk about stage three proactive learning businesses. [SPEAKER_01]: And at this stage, organizations have begun to embed AI into their strategic decision-making processes.
[SPEAKER_01]: So AI's playing a role in things like program design in personalization. [SPEAKER_01]: and even in business strategy and you're making use of predictive analytics to drive learner retention, to even inform curriculum adjustments and you're using automation powered by AI to help with marketing, to help with operations, and to help with learner engagement. [SPEAKER_00]: Yeah, I think in this proactive stage, it's probably where AI really starts to sing so to speak.
[SPEAKER_00]: And when we hear about organizations that are at this point implementing AI in one way or another, and that's showing up in our research, this is the type of role that AI is playing. [SPEAKER_00]: And some common AI, [SPEAKER_00]: use cases or things like adaptive learning systems that's a tailor content dynamically to learner preferences. [SPEAKER_00]: We definitely ask people how they're going to use AI.
[SPEAKER_00]: It's being able to adapt to learners and provide that more personalized experience as always top of the list and they're starting to do it in this proactive stage. [SPEAKER_00]: Next, I want to be, you know, AI powered career path recommendations based on skill gaps in industry trends. [SPEAKER_00]: Another one would be predictive analytics that forecast learner retention, so are they going to be coming back, course demand, and pricing models, even.
[SPEAKER_00]: AI assisted instructional design. [SPEAKER_00]: So using it to do things like generator, your first drafts of learning materials and even going beyond that. [SPEAKER_00]: In many cases, obviously looping in your subject [SPEAKER_00]: And then finally, AI-driven customer segmentation for targeted marketing strategies, making sure you're actually reaching those right audiences, making them aware driving conversion with them.
[SPEAKER_01]: And because AI is starting to sing in this phase, I mean, there are challenges or things to be aware of. [SPEAKER_01]: I mean, I talked about constructive pessimism at the opening of the show. [SPEAKER_01]: And so just being aware of some of the limitations or biases and making sure you're dressing those. [SPEAKER_01]: So thinking about ethics and bias and compliance issues will be really important at this phase.
[SPEAKER_01]: because you are trying to rely on AI for some really core and important things. [SPEAKER_01]: So you got to make sure that you're doing that in an ethical way, that there's not untoward bias in there and that you are complying to whatever guidelines you need to comply to for your learning business.
[SPEAKER_01]: You want to make sure that the AI generated insights are being used effectively for decision-making, that they're not necessarily dictating the decision, but that they're really being used to inform the decisions. [SPEAKER_01]: And then, of course, there's going to be some challenges to look at around scaling the solutions that you're finding across your portfolio or throughout your learning business.
[SPEAKER_01]: So, you know, this is about taking what's been working and really trying to kind of institutionalize it and make sure that it is there and happening at scale.
[SPEAKER_00]: So, you know, to really advance in this stage with AI and remember this stage, you know, we're talking about organizations that are reaching the point of being conscious and intentional and how they're thinking about their maturity and the different domains and you're going to want to look at really integrating AI across all five domains and measuring the outcomes you're getting.
[SPEAKER_00]: So, [SPEAKER_00]: You know, you want to implement AI governance frameworks to ensure ethical AI use. [SPEAKER_00]: You're going to want to invest in deeper AI driven learner analytics and experience mapping. [SPEAKER_00]: You're going to want to develop AI power business models such as AI generated courses or AI enhanced credentialing programs.
[SPEAKER_00]: or using AI experiences themselves, so it may be that something more like a custom GPT becomes your course environment rather than just the traditional course or the traditional credential. [SPEAKER_01]: And so in terms of what you're measuring at this stage and those key performance indicators, you might be looking at things like, okay, is our use of AI actually changing our completion stats or we seeing more learners complete?
[SPEAKER_01]: Are we seeing the satisfaction of learners improve? [SPEAKER_01]: Are we converting more of our prospects into learners who sign up for and complete a learning experience? [SPEAKER_01]: Are we seeing changes in the time to certification or the time to competency, whatever you're measuring there, but really kind of looking at what is the larger scale impact of the use of AI? [SPEAKER_00]: Right.
[SPEAKER_00]: So that's that proactive level and then finally we get to the innovative stage of the maturity model into extend our metaphor. [SPEAKER_00]: You know, if AI is starting to sing in that proactive stage, this is where we start to hit four part harmony. [SPEAKER_00]: Things are really coming together here. [SPEAKER_00]: So in that innovative stage, AI contributes to differentiation and, of course, you guessed it. [SPEAKER_00]: It's going to contribute to innovation.
[SPEAKER_00]: AI is really central to business operations, to product innovation, and to competitive differentiation at this point. [SPEAKER_00]: So, you know, at this point, you can use AI for things like autonomously identifying market. [SPEAKER_00]: So, you've given it the power to go out and do the research and really find the new opportunities for you.
[SPEAKER_00]: AI is going to be enhancing learner experiences through really hyper personalization really getting down to that individualized learning path that so many organizations are striving for right now. [SPEAKER_00]: And then AI driven insights are going to be used to continuously refine your business models and to drive your revenue growth. [SPEAKER_01]: So this could look like in action things like a generated course content that is adapting in real time to learner progress.
[SPEAKER_01]: So back to your point about individualization. [SPEAKER_01]: Like your that course is responsive to what I Solisa I'm doing in that moment and it's adapting and giving me that just right content for me. [SPEAKER_01]: AI might be power in coaching or even mentoring systems. [SPEAKER_01]: I don't know how many folks out there are dual-lingo users, but there's the lily video call feature now in dual-lingo.
[SPEAKER_01]: And I think this is a long-goes idea where you have kind of un-call someone who can respond in real time to you and by someone, I mean, an AI agent there. [SPEAKER_01]: But it's someone to assist to coach, to mentor, in real time. [SPEAKER_01]: You might be using AI driven business intelligence to inform new product development. [SPEAKER_01]: So what products should you be developing and what should those products look like?
[SPEAKER_01]: And you're really leveraging AI to help with automating decision making, especially kind of lower level, very clear decision making. [SPEAKER_01]: But also to help with the predictive modeling so that you're not only making the right decision in the moment, but you're trying to think about in the future where do we need to be in what should we be doing now to make sure we're in the right spot down the road?
[SPEAKER_00]: Right, and I can easily take you through all sorts of scenario planning along those lines to play out. [SPEAKER_00]: what might happen with particular products and audiences. [SPEAKER_00]: But of course, this does come with challenges. [SPEAKER_00]: You're going to need to be able to do things like ensure that AI dependency doesn't lead to a loss of human oversight. [SPEAKER_00]: You need to keep the humans in the equation there, obviously.
[SPEAKER_00]: You need to manage the risk of AI driven automation errors. [SPEAKER_00]: As you start to give up some control to AI, start to use AI agents more. [SPEAKER_00]: And then, you know, maintaining a balance between AI driven efficiency and human centric learning experiences. [SPEAKER_00]: You just got, you got to keep the human in mind. [SPEAKER_00]: You can't create a learning experience. [SPEAKER_00]: It's so efficient that it just takes the inefficient human out of the equation.
[SPEAKER_01]: And so if you're at this stage, if you're an innovative learning business to help advance your use of AI, you're going to want to continue investing in AI research and AI development so that you can use that for ongoing innovation.
[SPEAKER_01]: You're going to make sure that you have really good policies in place that help you govern your use of AI and help ensure that you're using it in an ethical way that you're being [SPEAKER_01]: as transparent as you need to be with stakeholders about how you're using AI and then you also want to be thinking about ecosystem partnerships and how can AI help you in extending your market influence and you're reached through some of those partnerships.
[SPEAKER_01]: So you might be doing scenario simulations for practice, you might be [SPEAKER_01]: integrating some co-pilots with employers so that you are surfacing just in time resources, you might be leveraging those coaching experiences. [SPEAKER_01]: There's a lot of ways at this point that AI used could play out once you're at that innovative stage. [SPEAKER_00]: Right. [SPEAKER_00]: And of course you want to continue to develop KPIs that are matched for this stage.
[SPEAKER_00]: Obviously, you can build on everything that we've talked about for the earlier stages.
[SPEAKER_00]: But you're going to want to look to it, you know, the the percentage of revenue that's coming from AI enabled products as you start to introduce those products, you want to look at the renewal and the retention rates around those products and what AI is helping to drive and you want to be able to really measure demonstrable performance gains, you know, if you're starting to really individualize those learning experiences, those pathways, what kind of performance gains is that resulting in and of course you're going to want to use those in your forward looking marketing.
[SPEAKER_01]: So to summarize the story of AI use in learning businesses, moves from, well, from zero, if we want to start there, I guess we should start there, because we know a lot of organizations are zero, right? [SPEAKER_01]: It's also sort of no conscious use of AI up to ad hoc use of AI, where it's much more kind of hit or miss or gets used sometimes, but not by everyone.
[SPEAKER_01]: then it moves to inconsistent use without clear goals at the less mature end and then we're moving towards much more consistent, embedded, strategic use of AI as we move towards the higher ends of maturity and up to that innovative stage. [SPEAKER_00]: right and so the movement is from initially engagement to experimentation, to operational enhancement to strategic support, to playing a real role in differentiation and innovation for your learning business.
[SPEAKER_01]: And I think it's probably important for us to acknowledge here that we don't see doing nothing with AI as a viable option. [SPEAKER_01]: Even if your learning business decides to do nothing with AI, AI still arrives. [SPEAKER_01]: I mean, it's going to be that curious staff person or maybe a rogue staff person who's going to be engaging in the use of AI or AI is going to be baked into some tool that you are already using. [SPEAKER_01]: So again, it doesn't seem viable to us.
[SPEAKER_01]: So just sort of say, we don't use AI. [SPEAKER_00]: No matter where your maturity is, you're going to need to have some things in place if you're going to take advantage of AI. [SPEAKER_00]: There's some base requirements for any kind of AI use case that you might pursue. [SPEAKER_01]: And so you want to have these prerequisites in place. [SPEAKER_01]: So first, you need that AI policy.
[SPEAKER_01]: And we advocate for being short and clear so that it's the kind of thing that [SPEAKER_01]: You know, someone's eyes don't glaze over as they're trying to read through it. [SPEAKER_01]: You want it to be short and understandable. [SPEAKER_01]: You want to cover what's allowed, what's off limits. [SPEAKER_01]: You want to have some rules around sensitive data.
[SPEAKER_01]: You want to make sure that there's a path for a staff person who... [SPEAKER_01]: They want to do something that's not covered by the policy. [SPEAKER_01]: Who do they go to to either request a special use or to potentially say, Hey, I think we need to change this in the policy. [SPEAKER_01]: And I think you need to have a plan for revisiting that AI policy on a regular basis. [SPEAKER_01]: And it needs to probably a pretty short time window given all that is happening with AI.
[SPEAKER_01]: I will say that we have a forthcoming episode where I talk with Tori Miller, Lou, the CEO of AIM, and I know that they have a very short and sweet AI policy. [SPEAKER_01]: And I think that's a good model in terms of just having a handful of bullet points about how you can use AI. [SPEAKER_01]: So you're going to want that AI policy. [SPEAKER_01]: You're going to also want a list of safe tools and let your folks know how to access those. [SPEAKER_01]: So what are the approved apps?
[SPEAKER_01]: Some of these might be embedded in systems that you're already using if it's more of a free standing AI than you're going to make sure that folks know how to [SPEAKER_01]: access it and sign in and log in and specify any settings that they might need to use in the tool. [SPEAKER_01]: For example, checking a box, this is hey, don't don't share my data back with the company that's creating the AI tool.
[SPEAKER_01]: You're going to want to make sure that you [SPEAKER_01]: are clear on privacy and intellectual property and accessibility issues, so what type of content is someone in working in your learning business able to share with AI what sort of consent if any do they need, what are the copyright issues, those sorts of things.
[SPEAKER_01]: And then, you're going to, of course, have to have internal training, because if you have this policy, if you have the safe tool list, you need to make sure that folks are aware of all of that. [SPEAKER_01]: And then, in that internal training, you might also get into some things like prompt basics and covering the intellectual property and the personally identifying information, do and do knots that you have in there. [SPEAKER_01]: So, there's that.
[SPEAKER_01]: And then, there's also just the data hygiene issue, which we've mentioned before, like kind of cleaning up your [SPEAKER_01]: metadata making sure that the inputs that AI is going to be using are accurate so that the AI then can make good recommendations because you know that what you're feeding into it is accurate.
[SPEAKER_00]: And there are two realities that you always need to plan for and this kind of goes back to being [SPEAKER_00]: People are going to use unsanctioned tools unless you provide safe options. [SPEAKER_00]: So to go back to those policies and planning, make sure you're providing those options. [SPEAKER_00]: Second ambient AI, as we've already sort of alluded to, vendors are baking AI into the tools that you already own.
[SPEAKER_00]: So you're probably using AI without necessarily being conscious and intentional about it. [SPEAKER_00]: You want to raise that up to the conscious and intentional level and your policy and your safe list should cover both [SPEAKER_00]: the shadow AI scenario and the ambient AI situations. [SPEAKER_01]: So we'll touch on a handful of common pitfalls things to try to avoid as you're going further with your use of AI and your learning business.
[SPEAKER_01]: I mean, first, I'll just say that the doing nothing or thinking you can do nothing pitfall. [SPEAKER_01]: I think we've already covered this, but basically doing nothing just doesn't seem viable. [SPEAKER_01]: AI exists in your organization. [SPEAKER_01]: So don't think that you gain anything by not taking the time to create that AI policy and do the other things that we're talking about. [SPEAKER_00]: Yeah, definitely.
[SPEAKER_00]: And then as much as we encourage people to experiment and to pilot, you need to watch out for what we call pilot purgatory. [SPEAKER_00]: So lots of tiny tests, but nothing really gets scaled or institutionalized.
[SPEAKER_00]: And this is very easy to fall into, because once you start playing with AI, it's easy to leap from, you know, one thing to another and oh, I can do this and oh, I can do that, but you have to be strategic and figure out what is the thing that we really need it to do for us right now. [SPEAKER_01]: I think another pit fall to avoid is feature shopping when it comes to what you might be looking for from platform vendors.
[SPEAKER_01]: So if you are in the market for a new technology system, it can be exciting to see those shiny features around AI that a particular platform offers. [SPEAKER_01]: but what you really need to be thinking about is what would what are we trying to achieve? [SPEAKER_01]: What are the outcomes that we want and then how do we get to those outcomes which may or may not be through those sort of shiny exciting features there might be a non-AI way to do it or a better way to do it with AI.
[SPEAKER_00]: Yeah, I was the same thing that occurs repeatedly with technologies. [SPEAKER_00]: We've seen it with learning management systems again and again over the years. [SPEAKER_00]: You go after the shiny bells and whistles and you forget about what are those outcomes you were trying to really try and to achieve. [SPEAKER_00]: Another one is just the basically the garbage in garbage out data rule.
[SPEAKER_00]: I think anybody who's used any form of AI really, I'm thinking mainly of sort of the generative chatGPT, Cloud type models, but anybody who's been using AI knows that you have to be careful [SPEAKER_00]: what you feed into it, how you contextualize what you feed into it or what comes out the other end can be junk or worse.
[SPEAKER_01]: And then I think the last pitfall that I'll mention is just the you want to avoid kind of taking a set and forget approach or to trying to just totally offload to AI. [SPEAKER_01]: You really need to [SPEAKER_01]: Make sure that you or someone on your team is reviewing what comes out of these AI tools that you're making sure to review for bias or other issues and that you really do remain engaged and actively assessing what's working how it's working.
[SPEAKER_00]: And then it's probably worth saying at least a word about sequencing of getting AI into your learning business. [SPEAKER_00]: And we'll propose just a simple order of operations for that.
[SPEAKER_00]: First, starting with policy, making sure you've got those policies in place, second data, making sure you've got your data in order anything that's going to be fed into the AI that that you've got that cleaned up that it is good data that you know how you're going to contextualize it going into the AI.
[SPEAKER_00]: Then doing those pilots, we said, be careful with those, but you do need to do them in order to get your your footing and figure out what the next steps are going to be. [SPEAKER_00]: Next review, the results that you're getting, then and only then move on to trying to scale the positive that you have achieved out of leveraging AI.
[SPEAKER_00]: And then possibly, and this is not always the end game, but it may be an end game, and it's something you should definitely keep in mind, product ties. [SPEAKER_00]: If you come up with something as you're implementing AI, the does represent a product that can be put out to your marketplace you want to be able to go there. [SPEAKER_00]: Be clear about ownership and responsibility, just like anything else you would ever implement.
[SPEAKER_00]: You want clear roles, you want clear measures, you want clear KPIs at each step. [SPEAKER_01]: And with AI things really do move fast, which means plans can age fast. [SPEAKER_01]: And so that means you probably want a relatively short window for your AI planning. [SPEAKER_01]: You know, maybe a 60 or 90 day window, you know, we're talking months, not years. [SPEAKER_01]: And you're going to want to revisit any plans or policies that you put in place.
[SPEAKER_01]: And maybe that's even as frequently as on a monthly basis. [SPEAKER_01]: You know, just a quick look to see, okay, do we need to refresh the scope of what we're covering or any of the tools in our safe list or has anything changed about how how we feel about risk related to any of our use of AI?
[SPEAKER_00]: Yeah, that's really, we haven't used the term so much here, but it's being serious about your AI governance, basically, how are you managing AI through your organization over time? [SPEAKER_01]: All right, so we said we would talk about some next steps, both in the shorter term and then the slightly longer term. [SPEAKER_01]: So, [SPEAKER_01]: In terms of what you might do this week, first is identify your learning businesses maturity.
[SPEAKER_01]: You can either use the quick descriptions of the stages that we offered, or, and we would love for you to do this, take the online self assessment that will allow you to see your maturity both overall and in terms of the five domains covered by the maturity model. [SPEAKER_01]: And those are leadership, strategy, capacity, portfolio, and marketing.
[SPEAKER_01]: And once you know where your maturity is overall, and then in terms of those domains, you want to pick what's the domain where you might have the highest amount of leverage if you were to begin using AI or to use AI in a more conscious way in that domain. [SPEAKER_01]: So this might be where you're seeing some of your big is bottlenecks or it might be the area where you see a really promising opportunity.
[SPEAKER_01]: And then what you could also aspire to in the next week is to set up a meeting where you're going to run through potential use cases that will speak to that high leverage domain. [SPEAKER_01]: So you're going to want to get the right folks in the room.
[SPEAKER_01]: you're going to want to generate some ideas, use cases that AI might help you with and you're going to want to then narrow it down to just one and as part of that group meeting, you know, define a KPI of what it looks like to actually have achieved something meaningful with that use case. [SPEAKER_01]: So again, you can figure out your maturity and you could begin to put the mechanics in place of having a 60 minute use case workshop get that on the calendar with the right folks.
[SPEAKER_00]: And so once you've done that work, you can move into a rolling 60 to 90 day plan that actually run the pilot and that you've kind of sketched out. [SPEAKER_00]: And in the first month of that, you're going to want to really get your ducks in a row, make sure you've identified all of the elements of the situation that you're trying to improve upon. [SPEAKER_00]: So go beyond those use cases to really flesh out. [SPEAKER_00]: What is the situation here?
[SPEAKER_00]: How are we trying to change things? [SPEAKER_00]: Whether that's making something better, or whether it's filling a gap, or something needs to be created that wasn't there before.
[SPEAKER_00]: Make sure you've got all your data in order to clean up anything that's going to be used as part of this, whether it's a learner history or meta tagging, whatever the case is, get that data cleaned up and then really lock in on those KPIs that you're going to use to measure the success of the pilot.
[SPEAKER_00]: And your second month, or maybe beginning in that first month, you're going to be actually running the pilot going through the necessary motions, collecting the outcome data that you get from it to be able to compare against that baseline situation that you'd defined, and make sure all along you're documenting what you learn in the process. [SPEAKER_00]: And then by, you know, sometime in month two going into month three, if it's all working, then you start looking towards scaling.
[SPEAKER_00]: So that made me investing more in it, putting more resources against it, and made me ramping up marketing communication around it if you're actually going to take it out to a marketplace at that point. [SPEAKER_00]: or if things aren't working, making that, you know, rapid and disciplined decision to sunset. [SPEAKER_00]: But of course, be sure to recycle what you have learned in the process of that pilot. [SPEAKER_00]: That's one of the main reasons for running pilots.
[SPEAKER_00]: And then take that back to any backlog ideas you have and start the process over again with a new pilot. [SPEAKER_00]: Now, the guiding principle in all this, just to borrow a phrase that Mike Moss, who's the president of the Society for College and the University planners, [SPEAKER_00]: that you have permission to play. [SPEAKER_00]: This is something he gives to his staff in the organization, gives to staff and volunteers at Scup.
[SPEAKER_00]: You've got that permission to play, but make sure you're learning from that play and carrying those learnings forward. [SPEAKER_00]: We'll wrap up in just a moment with a recap of what we've discussed in our look at AI's place in the learning business maturity model.
[SPEAKER_01]: At leadinglearning.com, Sasha episode 464, you'll find show notes, a transcript, options for subscribing to the podcast and links to some other episodes and resources related to what we discussed like the maturity model and the assessment. [SPEAKER_00]: If you enjoy the leading learning podcast, please share this episode or another episode with a colleague or co-worker, you feel would appreciate and get value from it.
[SPEAKER_01]: AI won't magically fix an immature learning business, but AI when it's used thoughtfully can help a learning business on its path to greater maturity, on that move from static to reactive, to proactive, to innovative. [SPEAKER_00]: And AI can play a role in all five domains covered in the learning business maturity model, leadership, strategy, portfolio, marketing, and capacity.
[SPEAKER_01]: We hope that thinking about AI within the context of the learning business maturity model will help you think about where AI might responsibly help you progress in the near term. [SPEAKER_00]: Give that question some thought on your own and with your team, where can AI amplify your maturity in the next 30 to 90 days? [SPEAKER_01]: Thanks again and see you next time on the leading learning podcast.