Video: Answering Your Top AI Adoption Questions With Prosci Experts | Duration: 3560s | Summary: Answering Your Top AI Adoption Questions With Prosci Experts

Transcript for "Answering Your Top AI Adoption Questions With Prosci Experts": Yeah. It works. Good morning. Good afternoon. Good evening. Thank you everyone for joining us wherever and whenever you might be. My name is Diana Veach, and I will be your webinar producer for today. And we are so excited that you are here to join us for the next hour to talk about a super interesting and hot topic in our industry, AI adoption. So I'm sure all of you have been experiencing this, living it in real life, today, continually. So we have a panel here of a few of our Prosci experts to talk about all those AI adoption questions that we've been hearing from our clients. Now before we begin, I have a few help keeping items for everyone. Now I can't hear you. Stage. Oh, Caroline, I can hear you. So before we before we get started, we'll dump it we'll dump it to the contact content in just a couple of minutes. Go ahead and get started chatting where you're joining from. So if you use the chat on, I think it's the right hand side of your screen, you can chat in where are you? I'm in Spokane, Washington right now. We have folks, on the backstage joining us from Spain, from The Netherlands, from Europe. So we have a huge global audience here today. Awesome. Lots of people joining. So I'll give you my my, top three housekeeping items here. One, the session is being recorded. You will receive a link in about a day or two. We're going to get it up on our website. It'll take a little bit longer than normal because we're going to be translating the subtitles, so that way everyone has the opportunity to see the subtitles in their preferred languages. To that end, if you are joining us and English is not your main language, you can click on the CC button in the bottom right of your screen. Or right next to it, there should be a drop down menu where you can choose between other languages. So if you're not if English is not your preferred, you can, swap that out here, and our our platform will automate automatically translate those into the language of your choice. Now the final housekeeping item for today is if you have questions, there are a lot of you on the call today. We have over 7,000 people who registered. This is a very large global webinar. So we're not going to be able to answer all of your questions. But if you wanted to help us keep track and and submit your questions for q and a at the end with our speakers, use the q and a rather than the chat. I'm sure you're already seeing it with everyone sharing where they're from. There's a lot of people, and it will be difficult to keep track of what's happening there. But if you use that q and a option, that will help us triage and, collect your questions and put them into our our system so that way we can, again, keep track and make sure we're addressing those. Alright. So as people are logging in and getting settled, I do want to share a little bit about our our about Prosci for those of you who are joining brand new with us, who have not been with us on a previous webinar. Like I said, there might be maybe over 7,000 registered, but there might be 3,000 or so on the call. I just want to provide a little bit of background on who we are as a company and how we got here today. So Pueblo was founded in 1994, on the the foundation of change management. We've combined our deep understanding of people and how people go through change individually to create the AdCare model as well as the Prosci methodology for organizational change. We're used by companies worldwide. We're trusted by Fortune 100 companies as well as institutions in higher ed and in health care, governments, and and many other small, medium, and large sized businesses to help them manage and navigate change, to help their people thrive through change. We offer programs we offer training programs on change management for yourself as change practitioners, for organizations who wanna enable their teams and their people to change more effectively. And we also offer consulting services for organizations that have larger, more complex changes like AI adoptions, and we offer licensing of digital tools, elearning, and and other materials. We do have a global presence, as I mentioned, on on this webinar. Or as you can see in the chat, we have people joining from all over the world. Prosci services the countries in purple here directly. So we have a direct presence in, North America, Latin America, Europe, Singapore, Australia, Algeria. And in those purple in those light shaded purple areas, that's where the Prosci global affiliate network can support you as well. So if we don't have a direct presence, we likely work with someone in those areas who can then support you and your organization in meeting your change needs. Organizations worldwide partner with Prosci because of our research backed enterprise solutions, which are focused specifically on change success. So we're contracted with these organizations to help them upscale their people, drive strategic change, scale that change across their enterprises, and and achieve that change success. We were founded in research. We were originally a research organization. And our most recent research study included participants, from all over the globe, over 206 2,600 participants to provide, their insights in our were fed then into our the largest body of change management knowledge in the industry, our change management best practices research report. So if you have any questions about Prosci or how Prosci can help you and your organization, please reach out to us. We do have several folks on the back end of this webinar to support you today. So they'll be answering your questions. They'll be covering tech support questions. If you have audio issues, or if you have other general questions about how can you work with Prosci in your area, please chat into us, and we'll be sure to take care of you from there. And, otherwise, it is my pleasure to hand this webinar over to our panelists starting with our moderator for today, Caroline. Caroline, I'm gonna pass it over to you. Thank you very much, Diana. And, I think we should start today with just understanding a little bit more about our two systems with all the knowledge around how to navigate, with the AI and change management. And, could you tell us a little bit about how AI has been part of your work? Tim. Yeah. Thank you, Caroline. Tim Creasey, chief innovation officer here at Prosci. When I think about how AI has been part of my work at Prosci, I think about it individually as a team and then as an organization. So, individually, I lean on my AI intern daily. I run into challenges and think, how might I brainstorm? How might I track down information I get I need to get my hands on? How might I generate a first draft in a flash that I can put my SME polish on to, to bring to the right place? Leading research at Prosci, we have fully embedded and integrated AI into the research process to support the quality, speed, and the outputs that we are able to generate from a research perspective. And then organizationally, Prosci has leaned in to research AI to understand what works and what conditions need to be in place for AI adoption to be effective. We've researched how change practitioners are using AI to augment their work and have greater impact and outcome. We rolled out and Paul will talk about Kaya, but rolled out an AI change expert to support the change practitioners who are out there working hard to prepare, equip, and support people through the changes that they're working on. So AI has played a huge role, since 12/01/2022 when Paul sent me a video introducing me to ChatGPT. So, Paul, you wanna introduce yourself and talk about how AI has been part of your work? Sure. Thanks, Tim. I'm really excited to be here this morning. So I'm Paul Gonzalez. I lead the product team here at Prosci. Our team is responsible for all of the the training products, IP that exist in the on the the portal and whatnot, as well as the digital products, like Proxima, or Kaya as as Tim mentioned. Similar to Tim, AI is, part of pretty much every every part of my day at this point. A couple of areas specifically that we've been focused on, in terms of how I look to to work it in is is first off, how can we use AI to solve customer problems, the biggest pain points that our customers have in new and novel ways? This showed up right away with once we started experimenting with what you could do with LLMs and what was possible, with AI, we immediately started thinking about and building Kaya, which allows us to take our entire corpus of of knowledge and research and get it to our customers, in a completely customized and personalized way. And then also we look at how do we use AI to, accelerate or expand the capabilities we have within the product team. So one of the big things we spent a lot of time on last year was finding ways to deliver more video to to customers within the portal. One of the things that we looked for is that, with customers who are wanting more engaging content, more micro learnings and ways to learn post program. Video is always really a costly endeavor for us. The eye and the ROI was always really high. AI has enabled us to do that in a much more cost effective way globally. So we are looking for ways all the time to use AI to not only be more productive, and address challenges that we couldn't have before, but also solve customer problems in a in a new and novel way. Fantastic. And I know we did have a poll pop up. Yeah. So our audience is kinda sharing where they were. Oh, this is our next one. But it did look like a lot of folks on that previous poll were still sort of getting started, exploring, planning, trying to understand, you know, what is the surface area for for potential impact. We got another one here for folks to kind of Paul and Caroline and I are gonna talk a lot about us and the work we do, but I wanna hear from you all in terms of primary outcome organizations are pursuing here, related to AI. Not overly surprising that operational efficiency well, I guess two thirds surprises me a little bit. Yeah. That is really high. We'll dig into that a little bit as well and what we're seeing in the research and whatnot. Yeah. I think part of that's in Frontiers. Right? We can kinda move on to that next poll. But, Nathaniel Whitmore is one of the folks I follow. He talks a lot about efficiency AI and opportunity AI. And then efficiency AI tends to be sort of that first wave, and opportunity AI is where we're gonna start to see sort of that next level. Yeah. That's right. I think we had one more team where we're gonna hear from folks about where they were in their journey. Tim, where would you say you are at this point in your your personal AI journey? I have some colleagues. I think you and Linda that are more AI first than I are than I am in terms of having fully integrated it. I was blessed to be exposed on that very first video from you on Linda to AI helping build an ADKAR blueprint for an electronic health records deployment. And that contextuality, I think, sucked me in really, really quickly. So it certainly is a go to for access to information. Deep research lately, I've leaned really, really hard into. So and what about you, Paul? I know you're pretty far out there. Yeah. I think for the most part, I try to approach most of my job in an AI first way. Again, that's not to say that I mean, humans are heavily involved in this still, but there are very few things that I do on a day to day basis that I can't find efficiencies or, quality gains with that, you know, in terms of using AI effectively. So whether that's, you know, communications, planning, strategy, data analysis, working with my team, just having fun, doing silly things with the team as well. All of that is, is on the table, and just an exciting frontier to to explore, and find ways to integrate it into the work. So Great. Well, I think from my part looking at this, you know, being in the room with our two experts, just being a operational person from one of the markets, I think this pretty much mirrors where we are ourselves. That, you know, my own personal journey and my team's journey is like it's it's exploring and planning and maybe a little bit of early, implementation. And, also, when I see to where do we actually try to implement it organizationally, the first thing that comes to mind for ourselves is also where can we gain some efficiency fast? Where are there some some tools even in simple tools like, Copilot that we can use to do things efficiently? So I think it pretty good pretty good mirrors where where we are, on many of those places. But I would like to now turn to some of the more heavy stuff and a little bit about, you know, some of the research that we've done, in this area, where it shows actually that we see a vertical link between leadership engagement and AI success with high performing organizations showing strong sponsorship, are the ones who are doing the best while struggling implementations, with a negative engagement. So what does the what have you learned? How can leaders actively influence AI adoption and foster the right cultural conditions for success, and why am I also asking about cultural, conditions? Yeah. Caroline, great question in terms of that role that leaders play. You know, we Prosci has been doing research for twenty five, twenty eight years now, and, number one contributor to success is active and visible involvement by our senior leaders. And one of the conversations we have in the halls regularly is what's different about AI change and what's the same about AI change. And the notion that our senior leaders will need to play this active and visible role, they will need to build coalitions, they'll need to communicate directly, That came out abundantly clearly in the research. And in fact, there's a diagnostic and a AI adoption diagnostic we've built out. And the first four statements are all about the leadership role. We have senior leadership commitment. Our senior leaders clearly articulate the value AI brings to transforming organizations. We have a strategic communication. Leadership regularly communicates the strategic importance of AI initiatives and their expected impact. We have bold and balanced road map. Our AI road map clearly balances immediate outcomes with long term transformation goals. And then leadership participation. Leaders throughout the organization actively engage in AI discussions, pilots, or projects beyond just delegating it. So I think those leaders are extraordinarily critical in terms of being active and visible and we know that they need to be there articulating what success means both for the initiatives, and for the organizations. They need to find the project objectives, the organizational benefits, and where where we are going. I think it leaders need to go beyond saying don't be afraid of AI. They need to fill in the because we are going here with it. So, Paul, I know you're part of that AI workshop that we did, and there are some really good discussions in there around this. Yeah. It was really interesting. We ran a few AI workshops back in, April, May time frame, where we brought together about a 170 practitioners or so, I think, Tim, across the, across our global community to talk about what they're seeing, with AI adoption, what's working well, what's struggling, what are some of the restraining forces and whatnot. And to your point, one of the things that's consistent or the same about AI adoption as it relates to other changes is that, the role of the sponsors is critical. But there were two sort of, what I call, failure modes that that were highlighted during that discussion. One was the CEO sort of announcing this AI strategy, but the undertone of it was a bit of a, you know, acting from a place of fear. So maybe the the reason was, you know, competitor x is doing y, or I saw this article, and now we have to run this direction, or there's this board pressure and all and all that kind of stuff. I think, fundamentally, what you're getting to is this idea that there's this AI strategy that's being created that is disconnected from the business strategy that already exists. Right? That you're not focused on the customer problems or the employee experience things that you can create, and rather you are taking a a strategy that is for technology's sake. So I think this idea that, if the the leaders are showing up from a place of fear, they're on their heels about it, that shows through in the messaging. Right? It doesn't create that compelling vision. It doesn't create that desire for for the employees and people to participate, in that change. So that's, like, one thing to be really cautious of that might be a leading indicator of a of a failure mode, which is that are are the words and the vision that the leaders are setting, compelling? Is it a proactive one? Is it is it one that's really sort of painting that vision of what, what work might look like? The the other one that I thought was interesting that came through was this idea that, like, c CEOs announce this big initiative and then, like, offload it to IT immediately. Mhmm. That's interesting sorta another sorta leading indicator failure mode thing, which is, again, is this just another technology or tool to be implemented that goes through similar sorta procurement patterns, installment patterns, enable patterns, or is it something different? Is this about transforming how we work? And do we have to approach that differently? I was reading an article actually just this weekend. The CEO of Clorox, they make Clorox, obviously, bleach, and they make also ranch dressing of all things. I actually didn't know that. They had some great in insights about how their CEO was approaching, the transformation that AI can bring, and how they were sort of enabling it centrally, but they're actually pushing investments out to the edges. They were allowing teams to decide what tools they wanna use, how those teams wanna experiment with it, and then sort of pulling those things back in in terms of what's working. I think it's a great example of solid, like CEO and leadership and sponsorship there, which is to say, we know there's an opportunity here. We know this can accelerate our creativity, our positioning, how we can serve customers better, but we can't really see that centralized. We need to sort of push some of that innovation and investment out to the edges. So, again, if it's CEO announces this AI strategy and then immediately offloads it to IT to implement, that's probably a a smell there that something might be off and that perhaps the CEO is sort of, you know, talking the talk a little bit and maybe not walking the walk yet. So, again, those are two things to really consider about how you see your leader showing up, in the sense of are they acting from a place of defensiveness or fear or on their heels, and that they're immediately looking at as as yet another tool that's another sort of, like, me too thing to execute on, that you might wanna get in front of, especially as you sort of support, your organizations through through their changes. So, Tim, I don't know if there's anything you have to add to that or if that resonates from what you took away from those workshops. And, certainly, that's something that I heard, loud and clear in those discussions. Yeah. I think that notion that we need steering energy, not accommodating your catch up energy from our leaders is critical. And one of my favorite slides in the entire Prosci universe is that unified value proposition where we have the reason for change, success, the technical side, and the people side. And I think leaders are own explaining those outside ends. And I think you give us great examples of how treating AI as a transformation, not a tool, means we're gonna have to manage that and engage in that middle a little bit. Anybody who wants yeah. Google unified value proposition, you'll find a great article that explains, that model from Prosci Europe. Great. K. Moving on to the next, which is, which I think also is one of the interesting research findings that actually 56 of AR adoption challenges are tied to human factors. And I'm just having a hypothesis. I can also see that one of the questions that pops up in the chat a lot is, you know, what how does AI adoption differ from conventional tech adoption? And there might be something here. So how can adoption help people want to adopt AI and not just comply with it? And what are the key motivators and bloggers when it comes to integrating AI into to daily work? And then also if you could touch a little bit upon, you know, why do we is is it a is is it something specific that it's, it's it's, that AI adoption is is but very much on human factor compared to other tech implementations. Yeah. Great question. You know, that that adoption, the barriers, helping each individual through their own personal journey, that's kinda what Prosci Europe brought to change management twenty five years ago was that organizational change happens a person at a time. This AI change is gonna be very personal in terms of how we each get comfortable with working with a digital collaborator and then bringing that digital collaborator into the work that we do. I can't help but put on, like, an ADKAR lens here. Right? Awareness. Why? Why now? What if we don't? And I think that is pretty compelling, and people are kinda seeing that. Desire. The what's in it for me? What's in it for us? Organizational motivators, individual motivators. I think there's a lot of challenges there. Right? Conversations around job disruption, the future of technology, environmental impact. There's a lot of kind of bigger global desire barriers that we need to continue to address and navigate. And then also helping people understand that personal how do they get to be feeling more human as an employee because they brought, kinda AI to the table with them. I think one of the wild big differences and, Paul, maybe we can dive into this in terms of the difference between AI change and regular change. I make the joke from the stage pretty regularly that nobody smuggled a CRM to work in their pocket. Yeah. No one ever smuggled an ERP to work in their pocket. But right here, I have access to the most powerful models that my company may not be giving me access to yet. And so I I we we talk a lot about sanctioned AI usage versus unsanctioned AI usage and sort of the different adoption challenges in those two environments. So, Paul, I know that's something you've played with a bit. Yeah. I I think, in my mind, it's probably the most interesting what's different about AI adoption topic, right, which is this idea that in pockets of the organization, you might have adoption that is outpacing enablement in a way. Right? Like, you have some people that are really leaning in heavily for personal use, and getting access to the best of the best, the frontier models, the new tools, and seeing what's capable. And then they go onto their work computer, and they're executing and maybe using some of the internal tools, and they're like, what what's going on here? The the issue with that, right, is that, like, from a desire perspective, from ability perspective, then you won't be able to sort of engage in it, in a way that you're as excited about, right, or get the most out of it. So there are some things we can do here to to address this. So first off, I think it's a bit, like, of a of a disconnect to say like, to sort of not address the fact that some of this might be happening. Right? The best AI tools are just a browser tab away in a way, and and that's challenging, right, in terms of how we think about the security risk, the privacy risk, the governance risks associated with that. And yet those people who are leaning into that, they're not waiting. Right? We've seen this. Right? That, the employees are not waiting for this policy to evolve. I think it's I think the latest stats, someone can look it up. It's, like, over 50% of, like, ChatGPT use for work is on personal accounts or paid for personally. Like, that's a crazy number. Right? That's kind of the reality of that we're in. So I think there's some important things that we can we can do as teams. Like, one, create space for those individuals to bring some of those, like, unsanctioned uses forward. Right? I think those might be some of your most interesting use cases. Try to find a way to, like, maybe remove the fear that they'll be punished. Honestly, the reality is a lot of times the policies are so vague that that companies have laid out. People don't even know if what they're doing is allowed or not allowed or what the latest on the policy is. I think it's a really important step in making sure that you, sort of address that desire and even that ability that, that there as well. And the last thing is to is to, again, be clear about the path. Right? So if you if your internal tools are behind, some of those frontier models and those best options that exist, You need to be clear with your with your team about what is your plan to address that. Right? It's a bit disconnected or out of touch to say, like, here's the tool. We need you to use this tool. And then, again, the browser tab or phone away, there's just so many new capabilities that exist. And employees want that. They wanna feel that, you know, more creativity, better outcomes, better productivity, all that kind of stuff. So I think creating space to, like, lay out what that path is for how you're gonna close that gap. The reality that the consumer AI technologies might be outpacing the enterprise AI technologies in some capacity, and sort of managing that that that delta. And, again, this is new. Right? Like, maybe the bring your own device to work or cloud storage kind of stuff. These were sort of the other times where, like, individuals were, like, doing things, and using tools that the enterprise itself wasn't ready to to enable. But this is at a pace we have never really experienced before. So I think we just have to be thoughtful and and creative about how we bring some of that stuff forward, to not put people on the sidelines, right, in in a way because that's maybe where some innovation is happening. I don't know, Tim, if you have anything other thoughts to add to that. But, again, this is really about that ad card journey. Right? Making sure that we can move individuals through that, in in all ways possible. Absolutely. If if you're looking for thought leaders to follow, Ethan Mollick is one. And what does he call them, Paul? Like, the secret cyborg? Secret cyborg. Yeah. Yeah. When he describes that notion that that unsanctioned AI used the person smuggling AI to work with them on in their pocket. And he's got some great writing there, and Paul, you know, lays out some of those tactics for bringing innovation in from the crowd. The other side of the equation, I think, is we rolled out fill in the blank AI tool to everybody and nobody's using it. And there's a whole another set of adoption challenges that we face on how do we actually build sufficient adoption and proficient usage of our sanction tools such that the organization achieves the value it was hoping to when it started to invest in them. So there's a really interesting two by two of sanction, unsanctioned, done well, done poorly, and sort of the risks and steps that we can start to take in that space. Great. So, actually, I got to Paul. You're talking about, you know, there's something around that thing of pace that I think we should go a little bit more into. And then I think it's also gonna be brief you know, can be be a bit scary for for a lot of organizations is that you don't even dare working on something because you just feel like, well, it's gonna be outpaced by AI in no time. So do I even do I even bother? Do I wait for the next thing, or what do I do? So given the rapid evolution of what you call the half life of knowledge, what does this mean for how organizations build change capability and prepare people for continuous adaptation? Because I'm guessing continuous adaptation, we always talked about that, but it just seems like it's, it's accelerating. Yeah. Again, where where is it different versus relative to other change? To me, the the two most interesting topics are this one and that secret cyborg one Tim just mentioned. But, you know, this one's really interesting because the research on this that we've done Prosci is pretty clear. It's like 38% or almost 40% of of AI adoption challenges stem from some feeling of insufficient training. Like, that's our reality. Right? We need to equip, enable our people, to know how to use these tools in a meaningful way to to get the outcomes we're looking for. But there's, like, a paradox in this, which is that the other part of that coin is that the market is moving so fast that the skills we learn in terms of how to use these tools just decay very quickly. Right? So there's something like again, there's not an exact science around this, but if you look at sort of the evolution of of the products and the models and the capabilities that exist, the half life of these skills, so, like, how long does it take to make 50% of the skills irrelevant in some in some way, is, like, only three to four months. So the tools and capabilities are changing so fast, and people are saying I need to be trained. And then yet in, you know, four months, five months, you can debate the exact number, but it's somewhere in that ballpark. Maybe half of what you learned is now completely different. So how do we invest in and enable our people to get the most out of these tools when that investment equation seems kind of off. You're like, okay. I'm gonna do a big investment in training, and then it's not really gonna be useful, in a short period of time. Or I wait, and then I wait, and then I continue to wait, and now my my people and my team are are left behind. So a couple of examples of this. Right? Like, 2024, some of the big topics were around prompt engineering and how do you get better at prompting. A lot of the newer models have really helped close that gap. Right? So, yes, people need to learn that as a skill, but behind the scenes, the models are doing a lot of that for you. You know, maybe I would have talked about a year ago the the shortfalls of of image generation in these models. And then, you know, there's a week where you can't see anything but, you know, action figure avatars all over LinkedIn because now we can all of a sudden generate these these images really well. So I think there are ways to address this. Like, there there are ways to invest in our people, to enable them, to build the right mindsets around how to use this and less about the tool specific training. That's really what this comes down to. Unlike other changes where you might be, you know, learning a new process to, generate an invoice in an ERP or score a lead and rattle lead in the CRM or something like that, this is really about building, sort of a mindset about how and when you might want to engage with these these sorts of tools. So there's two things specifically to really, like, focus on here. Is first, how do we invest in our in our people, in our leaders, in our sponsors to get comfortable with the reality of this continuous change? Right? How do we build that ongoing change muscle, that needs to be is required for this this type of agility, right, so that we can take advantage of the opportunities as they arise. And that needs to happen at the the people level, at the manager level, and and all the way up to to the leaders as well. And the next is less about using the tool specifically, but building a a framework, so to speak, of spotting how and when I might be able to integrate AI into my work and whether that's to automate my work, to augment my work, to be a partner in thinking through something. So this is sort of you might hear this referred to as, like, foundational AI literacy, which I'm fully bought into about in terms of that being a better investment than, you know, how to use xTool, because, again, those how to use x just decays very quickly. So these two big topics, the investment in building that ongoing change muscle, and getting comfortable with that continuous change as an organization, I think, is a critical one. And then this other idea of how do you how do you spot the opportunities to integrate AI into your work? That is another sort of important muscle, and behavior to build. And I know, Tim, you have really good thoughts on both of these topics, right, about this org agility in this this current age, as well as a framework for for integration. Yeah. I think that notion and I love playing with words if anybody's ever seen any of my posts. You know, I I always get asked, can it do this? How do I get it do this? How do I get it to do this? And I think that's the wrong way to be asking the question. We need to understand when and where can this capability help me solve the problem that I'm working on. And so we've engaged in really building out this kind of AI integration framework to help people understand that when to bring AI to the table. It's all focused on tasks because that task is kind of that unit of impact. And, essentially, there's kind of three buckets that we talk about. First bucket, we call my work. That's human exclusive work that I will always be doing. It's Tim work. It'll always be Tim work. There's a bucket called for me work. That's the work that I could eventually have the robots do completely for me because it's routine and repetitive and it's the same over and over. And then the magic is in the middle, I believe, and that's the with me work. When can I collaborate with this digital partner to achieve things I could never have imagined achieving before? And what's interesting is you can talk about change, you know, change practitioners can think about their work and my work with me work for me work. I presented a bunch of higher ed IT leaders not that long ago. PMOs. My work with me work for me work, they can kinda sort that work out. The other thing that's interesting, Paul, as you mentioned is the capabilities continue to evolve. The kind of tasks that I put into those buckets might change as well. But it gives me an empowered framework to understand how to spot when I can bring AI into my work. And so we have a pilot e learning on this. We're bringing it into a a partnership with EDUCAUSE, which is a higher ed IT leadership group. So we're really starting to bring this frame. But to me, it's I've seen it be an unlock to shift people from what can AI do for you to what are you gonna do with AI. And once I know how to spot opportunity as the tool sets and capabilities change, I've been equipped to continue to to keep up with that pace of continuous change. Yeah. I think that's a great point, Tim. And I think one of the key things about that model or framework is that it's not static. Right? And I want to just underscore that point is that the way you would have have approached that exercise and how you would have thought about bucketing the tasks that you do on a daily basis, the the buckets those would fall into could have been very different, you know, certainly in 2022 when 3.5 was launched to what we can see today with some of the more advanced models. So being flexible, sort of unlearning maybe what you thought was true in terms of what it could or could not do to to take advantage of the new opportunities is critical. Right? So I think that's why these frameworks or these mindsets might be a better approach to enabling our people so that they can show up with better questions and not just, like, looking for an answer all the time. Because those questions create space for answers to evolve as the technology evolves for us to capitalize on those opportunities. I think that's really the path forward to balancing this need for training and enablement of our people, but also the practicality that this is a continuous emergent change with a pace we have never really experienced before. So I just think that's a really important, like, balancing act and to your point, investing in that that change muscle, getting comfortable with that continuous change, and having that mindset to spot opportunities is, we believe, a a critical path forward for for getting the most out of this this technology. Great. Let's move on to some more questions. Another thing, also, our data shows, a critical distinction between AI adoption and AI utilization. Many organizations turn AI tools for people, but struggle to achieve the inspiration required that drives real business outcomes. I think we also learned to that in the beginning. What are the conditions that move people from simple adoption but full integration of AI into their work? Tim. Yeah. The so how do we help move our people? There's lots of dimensions, I think, here. Organizationally, we need to think about creating the conditions for success, the conditions that will help our people be successful. And that's where the AI adoption diagnostic, is coming to the market. Again, research based factors that exist in those successful organizations. Organizational agility in the age of AI is certainly something that we need to consider. What are the moving parts that make up the agile organization? And I don't mean big a agile sprints and retros, but organizational muscle to sense what's coming at us, effectively adapt, and achieve the outcomes we wanted to regardless of what is coming coming at us. So building that change muscle organizationally, and we've got some great articles about that at Prosci. That so what Paul alluded to already, bridging the gap from enterprise I IT capability to consumer IT capability. I I used to make the joke. Like, if you wanted really strong carpet, you got industrial string carpet. If you want really strong paint, you get industrial strength paint. If you want crap IT, you get industrial IT. Because consumer IT, they're fixing things before I ever know they're broken. And corporate enterprise IT struggles to keep the pace, that this this is changing now. And so, yeah, those 20 conditions really start to set the stage. In terms of helping people, I think we started to allude to this already. It's really that mindset and equipping and enabling people to achieve what's actually possible to them with these new capabilities. One of the things that came out of that diagnostic policy around experimentation and creating a culture where people would actually, lean into and experiment, to win and to lose and to I know experimentation is something that that you think about in terms of how organizations are gonna really foster. Like, you don't just get to put a sign on the wall that says we experiment here. Right? It just doesn't work that way. Yeah. I think, you know, it's an interesting topic, and, you know, maybe I'll dig into that in a in a bit. You know, as it relates to how we move people from simple adoption through this you know, I I think to your point, Tim, there's all these conditions and factors that we have that are good indicators of that. And, by the way, we also have what Prosci has called for a long time, the human factors of ROI, right, which is the speed of adoption, ultimate utilization, proficiency, and how we measure that. And and I do think it's an interesting topic because I don't know if this is a a spicy take or whatnot, and it's a bit ironic that the the title of this webinar has, adoption in it. But I think sometimes we overstate or over index on the word adoption in this regard, and certainly, as we think about speed of adoption. And what I mean by that is you could measure adoption as logins or first prompt or, you know, how often is someone putting in a prompt into a certain system. That would be a measure of adoption, and yet it could be a bit vanity. Right? Because the way I put a prompt in, the way you put a prompt in, the way Caroline puts a prompt in, could all be completely different. Right? And and the way in which we found ways to integrate this into our work or the outcomes we're getting from it would also be very different. So I think that, you know, sup framework that we talk about and by the way, we have great IP on this through the new change performance framework that really helps us think about a structured way to approach this and to measure this and to apply this, is really important, which is that that that first measure of adoption, right, that first moment is just the start. We really need to think about ultimate utilization. One greater way one great way to think about that is through, like, retention curves. So looking at your team, looking at how frequently they're going back to using the tools to see if it's sticking, to see if they understand the use cases, to see if they understand how and when and where to integrate it with their with their work. That's a critical thing that we can do as as change practitioners and as a team, to to make sure we're driving the ultimate, objectives we want. And the next thing is proficiency. Right? We would see proficiency gains in the ultimate outcomes we're getting. Right? Are we able to address customer use cases and needs faster than we've ever been been able to before? Are we able to get more leverage in a way that we weren't able to before? That only shows up in those, like, sort of overall outcomes and benefits that we're looking for. So I think that that SUP framework, as we call it, you know, speed of adoption, ultimate utilization, and proficiency, that creates a critical, like, constellation of metric measurement required to really get that overall enterprise adoption that we're looking for. And I think one of the important shifts here that we have to be cautious of, is that, you know, a lot of times you might approach change management. There's very, like, a a process orientation to it, and data is sort of the output at the end of it. Really have to think about data as the input here. Right? People are already using some of these tools in some capacity. We already have good heuristics on measurements about how these things might play out. So how do we start with the data, see what's working well, see what pockets are adopting it well, and use that to inform the the tactics that we're gonna address to really drive, you know, high adoption of these tools? Because this is not a homogeneous change. Right? This is not let's roll roll roll copilot out to everyone, and they're all just gonna use it. No. HR might use it very differently than finance. It might use it very differently than customer support. So we really have to look for these pockets of adoption across that whole sort of framework of of of data and metrics to inform and drive that that ultimate, benefit we're looking for as an organization. So I just I wanna be cautious of over indexing on that first interaction and being like, yes. Everyone's adopted it, And then that retention really falls off. Right? That's not what we need here. That might be a nice win to put points on the board for, but the rubber hits the road with how frequently are people integrating this into their daily work and how proficient are are they with this new this new opportunity and tool and capability. And that's where we actually get those benefits. So I just I just wanna, like, highlight that. It's important to make sure we have the right conditions from a diagnostic perspective set up for success overall within the organization. And then we're rolling out parts of this this overall transformation, making sure that we're not over indexing just on that first speed of adoption metric, but also on the the the overall utilization and proficiency as well. I think that's where you really get the the the benefits. I don't know if it resonates at all, Tim, or or how you think about it. No. It may it reminds me directly of a client story that I had just recently heard, right, where and I'll connect it into one of the diagnostic factors. We regularly evaluate and meet the distinct AI needs of different roles and departments avoiding a generic approach. And so that notion of helping people see what it means to them, specifically to them. So the story I was told by a gentleman who's leading AI adoption in the organization, they had one particular group. They went in and did training about a particular use case. Just one use case. And then they left, and they come back a month later, and they're like, everyone's cheering and patting themselves on the back because they're all doing that one use case. And none of them have expanded at all to think about integrating the capability into other aspects of their work. So it was a interesting example of adoption without proficient usage, which is they kept just doing the use case as opposed to really integrating this capability. And maybe that's where that ERP CRM, kinda that old technology training, leads a little bit into this new capability. Well, that goes back to that analogy that you hear sometimes, like, you know, what are the use cases for electricity? It's like, I don't know. Right? Like, they're everywhere. It'd be hard to identify those things. Right? But as you sort of interact with it and experiment with it and play with it, you'll find those those natural integration moments. And, yeah, I think that speaks to that directly, Tim. So that's spot on. Great. Maybe, as we're moving on to the next question, it would be interesting, if people could chat in what could be a good way to actually demonstrate profession usage of AI. What would that look like to maybe just give each of us some ideas about what to look for so it's not just that one we get started that one use case. On that one, for you, Paul, with, with AI fundamentally reshaping how we work, what should organizations be doing now to prepare the people for ongoing fast paced transformation? So what do we knew do you need to do already now before we even define what we wanna do? Yeah. It's it's a good question. I think, hopefully, throughout the discussion today, we've already hit on some some good ideas. Right? I think we've talked about making sure that leaders, and sponsors are ready to sort of paint that compelling vision for the future. The diagnostic has some great ways to understand what are the conditions of of successful AI AI change. We talked about ADKAR and then we talked about a lot of stuff, but I think it's all sort of in this vein. You know, one thing that we didn't really dive into and, Tim, you just mentioned this, and it shows up ex like, perfectly within it's, actually one of the critical factors and conditions for success in, in the research. And it relates to this culture of of experimentation and how do organizations, build that and execute on that, to sort of get the most out of this tool. And I think there's, like, a couple things to, like, address here, which is that it's very easy to say. Like, we just need a culture of experimentation. But what does that mean? What does that look like? How do you get there? I think one of the great great stories around this is, there was a article, I don't know, a few months ago maybe, three or four months ago around Johnson and Johnson, like, canceled 80% of their AI initiatives or whatever the number was. And I'm sure there might be people from Johnson and Johnson on the call today that could fill in the gaps here. But, you know, I think some of the, the naysayers around AI would say, like, wow. What a failure. That's 80% of the projects just cut because they weren't working. From the flip side of that, that is 80% or 80 sort of like or however big the number was, like, just an immense amount of learning about was what was working and what wasn't working. Right? The the the nature of innovation is one that requires comfort with failure. Right? You need to be comfortable experimenting, learning, and then doubling down on the things that are actually working. Right? So that cut of 80% of those projects is not that AI failed in some way. It's that we've just identified the best opportunities for us to double, triple down on. And I think that's, like, sort of just an interesting, like, frame here. And that's not an easy thing to achieve. You can't that's not a switch you just flip on. Right? I think it's it's not this clean, but a lot of cultures can either reward impact and outcomes. Some cultures reward effort and output. That, like, if you're sort of rewarded on effort, it's very hard to sort of move to this experimentation mindset, frankly, because you're sort of trying to create some productive waste in a way. Right? You're trying to find ways to try things that don't work and throw them out and view these as lessons, not losses, and that's just, like, a hard thing to get to. So I think there's a few things that we hear about that work, and maybe there's, like, a couple popular ones that I'll throw in a couple more novel ones that I think are interesting. The first is people talk about creating deliberate space for this failure, right, or for this experimentation. Some people refer them as hackathons or promptathons or, you know, office hours with some experts. Just places and spaces where people can come together to try things, and build things together to see what works and what doesn't. One of the really, though, important things about that that I think is sometimes a mess that you have to sort of, like, work your way in is I really do believe this idea that novelty precedes utility in a lot of this new technology, and you just have to lower the stakes a little bit. Right? It doesn't always have to be, here's this massive problem that we need to solve, or we're gonna solve with AI, and and we're all gonna come together to, like, build it in this hackathon. It could be just a space to build something completely random, completely silly, not at all related to sort of the the the org outcomes that you're looking for, because really the outcome here is learning. The outcome is a lesson about starting to build that that pattern in your mind about where it can be applied, what might work, find ways to break it, see where it breaks down. I think that's, like, sort of an interesting, concept here. It's this idea that novelty precedes utility is a really important one when you're trying to build out this this culture of experimentation. And like I said, there's there's different articles that that have existed. I think that Johnson and Johnson wants a really good story here around how you just sort of let a thousand flowers bloom, and then you pick the ones that are working really well to to go down that path. And that's not something you just flip the switch on. The other thing too around this experimentation idea, I heard this phrase recently. I think, Tim, you and I are talking about this, this idea of exposure hours. And then this relates to hackathons and and promptathons, but you just have to try stuff. Right? Just getting exposed to how people are using it, what's possible. Maybe I actually, not maybe. I know I'm weird in this regard. Like, I I'm just so interested in this kind of stuff, and and I'm a tinkerer. I'll watch, like, YouTube videos of people just building AI automations, like, random things that I would never do. Because I'm just trying to, like, build a a a model in my mind of, like, where it might apply. Could I pick up an interesting lesson? And that's to be another form of experimentation. Right? It's it's the outcome and the the the goal of that is to learn and to maybe pick up on a an insight about where it could be applied or where it might not be applied. So, again, to me, this is not this is a critical factor for Successful AI adoption, and it's not just something it's not a switch you can flip. Right? You need to be deliberate about how you create the space for experimentation, and make time for it in a way. Right? And and think about it as an investment in learning. To me, this is another form of learning. Right? So I think you just have to be deliberate about that. So that's just a one big topic that I want to make sure that we got to today, Tim. Tim, I don't know if there's anything else you wanna add around, you know, what are some things we could be doing today, to to really address it and make sure that we're set up for for this transformation? You know, that's that's that's upon us right now. I think, you teed it up really nicely, Paul. To me, it's that finding the Venn diagram of the tasks we do and what AI is really good at and where that Venn intersects is our opportunity. Ethan Moloch suggests the notion of always having a set of tests that are failing in today's current models so that we can run them when the next model drops. And Ali k Miller had an amazing post just yesterday around setting up, like, a half day hackathon. Oh, I love that. Yeah. Having the right people in the room, the iterations. One of the other takeaways is that I think this capability has enabled for us is the ability to rapidly prototype. Oh, yeah. So we should be able to put something in front of somebody that actually lets them see our vision above and beyond hear us talk about the vision so rapidly. And I think that unlocks that learning cycle. So I know we're getting closer to the end. The what I the the thing I'd like to leave here is I end all of my keynote presentations with the phrase, ancora amparo. Italian and allegedly Michelangelo at about 87 years old was working on the Saint Basilica, and he says, encora amparo, which translates into yet I am still learning. And so here is Michelangelo, one of the smartest beings on the planet at the very end saying, every day I'm still learning. And to me, that is what AI gives us the opportunity to do, but we also have to approach it with that mindset of continuously being to to be learning about it. So Yeah. Yeah. Absolutely. Caroline, was there anything else on the list or any other questions I can No. I think think since we are we we are Prosci, and I can actually also see from the chat that we have a lot of, you know, change practitioners attending, attending the call. And we spend most of the time talking about how do we enable AI adoption at an organizational level. But we actually also did some studies around how are change practitioners actually, using, AI, and how can change practitioner increase their impact using it. Great. And so I know we had a pull up where some of those different use cases I actually chatted out a link also to our catalyst report. We've done a bunch of research on this. There's a 190 specific use cases, in that catalyst report. Content creation, content generation shows up at the top of the list, which makes sense because of what generative AI does. I think real time sentiment analysis and truly understanding the pulse of the organization based on the words we're using to talk about what's going on is a is a huge potential opportunity, for for those practitioners. Paul, any really interesting applications you've seen? Yeah. I mean, I think on on the long tail of this, right, like, how this plays out over time, I think a lot of what we see today is leaning into that sort of AI assistant type use case. Right? Helping me draft x, helping me improve communications y. And I think as this plays out over time I mean, ultimately, what this comes down to is a much more viable opportunity for personalized change management at scale. Right? That's really what this comes down to. Right? It's being able to equip every individual, every manager with, like, the the best tactics to to be running at that time based on where they're at in their journeys, based on how they've engaged with certain types of communications or training. And that happens through the use of, you know, AI assistance to help with this kind of stuff, but also through, automations and and agents and all that kind of stuff, which is where this will play out over time, I think. But, again, I think to me, this is all about finding ways to enhance that that human connection that we have and really focus on that people side, not replace the the engagement layer that's required, right, to really in inactivate and engage our people. But as all those bits of the process in doing that, finding ways to do do it more effectively and efficiently, I think, is kinda how this plays out. So, that's what I'm excited for. I think that's, like, a real opportunity ahead of us, because, ultimately, change happens at the the individual level. And the more that we can, you know, create that personalized approach, the more likely it is that we get, you know, employees have a better experience with that change and and we get the benefits we're looking for. Alright. Oh, Tim, we lost you there. Yeah. I get up up on my soapbox. If we can effectively integrate this technology into the way that we do our work, we can do our work at higher quality in less time with less mental strain and with more enjoyment. And helping people bring it to life is the key. You have mentioned heard me mention the AI adoption diagnostic a couple of times. The link will be chatted in if you wanna go download, some information about that and learn more about how to bring that research, bent to the work that it is you're doing. Caroline, thank you so much for your moderation. Paul, thank you so much for joining us. This has been a fantastic discussion around the future that every single organization, kinda has in front of them. So Yeah. This is great. And and, like, just behind the curtain a little bit, Tim, you and I just chat about this stuff anyway on, like, Tuesday morning, right, or Tuesday afternoon. So it's great to get to do it in front of, you know, 2,000, 3,000 people listening in or whatever the number is. So, really appreciated the opportunity. And and, again, thanks thanks, Caroline, for for facilitating and and moderating this. Thank you. One more behind the curtain behind the curtain shout out to Charlotte and Diana and Paula and Rachel and Maddie Yeah. And the whole marketing team that has helped pull together this session. Wishing you all happy prom Take care, everyone. Thank you.