Name: Applying ADKAR Through Continuous Waves of AI Change
Uploaded: 2026-10-14T15:16:19.850Z
Duration: 58 min 20 s
Description: Applying ADKAR Through Continuous Waves of AI Change

Transcript for "Applying ADKAR Through Continuous Waves of AI Change": Hello, everybody, and welcome to the webinar today. We're excited to be talking with you about applying ADKAR through continuous waves of AI change. We had such a tremendous response to this webinar today, so we're quite excited. I'm here with my colleague, Andrew Horlick, and together, we design Prosci solutions programs and member resources for change practitioners and leaders. And Andy's gonna share a little bit more about what to expect in our session today. Thanks, Lisa. I just, first of all, wanted to thank everyone for taking time out of your busy schedules to join us today, and participate in this webinar. Lisa and I want this to be a valuable and meaningful experience for you. And to that end, we're going to ask you questions along the way. We're gonna invite you to participate in a number of polls about AI integration. And we also want to encourage you to use the chat pane to share your reflections, your ideas, and your experiences with AI integration. Also wanna let you know that you're welcome to ask questions at any time using the question button in the upper, right corner of your screen in your control panel. We have a really capable team behind the scenes who are going to We have a really capable team behind the scenes who are going to respond to your questions as they come up, and we'll also reserve some time at the end of the webinar to address, some remaining questions at that point in time. Thank you, Andy. A few housekeeping items before we begin. Yes. This session is being recorded, and as soon as that recording is available, it will be sent to you. We are going to be referring to two handouts today in the webinar, and you're gonna find them in your document panel in the top right of your webinar panel. It's labeled docs. First two handouts we're going to be referring to. As Andy mentioned, you can ask questions at any time. I'm also very grateful for the ProSci team that's supporting us here today. And if you stay to the end as well, we're gonna share some ways you can learn and apply the ADKAR model. So AI driven change is absolutely accelerating. There are new capabilities, use cases, and impacts, and they're arriving in wave after wave. So we wanted to begin our webinar today, by checking in with you about how you're experiencing these waves of change, and we're gonna do that through two polls. And Diana's gonna open a poll here. You'll find that in the top right. It'll be highlighted for you in your webinar panel. And the question that I have for you is what kind of AI waves is your organization riding right now? There are four choices, gentle like Waikiki Beach, choppy like Bondi Beach, thrashing like Fistrel Beach in The UK, or maybe you're still sitting on the shoreline. I know we have a big global audience here today. I hope you found a beach that you can relate to. What are we seeing here, Annie? Well, the top vote getter right now is choppy like Bondi Beach. Yeah. Unpredictable. And by us... Hard to find a river. Yeah. Yeah. And that's by a substantial margin. 54%. at the moment of the voters have chosen Bondi Beach. Yeah. Certainly challenging circumstances. You can't really get a bit of a flow going yet in sort of, your your change work and initiative when you're kind of having this experience. Let's share another poll, and this one is, really about what you're noticing about people. So the question that I have for you is which way is the tide turning on people's willingness to use AI? Again, Diana's gonna open that poll in your top right. There are four choices. They go from rising, maybe people are more willing. I have steady and choppy, and the last one receding. Maybe people are becoming more hesitant, as time goes on. Thank you for pulling those up on the screen. Alrighty. I'm still seeing, Choppy as well. Interest is there, but confidence and or willingness vary, and I think that's pretty normal. I think as this goes on, we have pockets of early adopters. I don't think we're getting adoption in the same kind of, steadiness all across the organization. Certain pockets are leading. Mhmm. We're seeing rising. People are more open and willing. That's encouraging. Yeah. I... I'm a bit surprised by the low number of votes associated with receding, but it will be interesting to hear more reactions as we go through the webinar content. Well, actually, I invite you to share a little bit more about your experience, with AI change in your organization. You can go to your chat, pane, and just tell us your thoughts on this question. What makes it challenging to engage people in AI change? What makes it challenging? Already it surfaced quite quickly. Fear, right, that it's. going to replace their role. I see trust. Trust is the biggest hindrance. Trust, fear, ambiguity. Trust is still an issue. Fear of losing jobs. Yeah. I expected to see there's, you know, some common themes that will emerge, but maybe there's also differences, particularly with a sector you work in. I see lack of regulation, concerns about, equity issues. Those types of things also can be quite different depending on what sector you're working in. Trust and fear, though, is a big one. Yes. So let's. look at this a little bit because Prosci is a research based organization, and we keep a pulse on reading the research. And we are noticing a really big shift between 2024 and 2026. And what we think the research is signaling is that AI is accelerating a trust crisis. And these are three research findings that really paint that picture, and I'm gonna be moving through these left to right. So in the first one, in The US, UK, and Germany, about two thirds of workers believe business leaders are not being honest with people about which jobs AI will eliminate. So people aren't necessarily trusting the messages from their senior leaders. In the next one, adoption is accelerating, and we saw that in the polls, in terms of which way the tide was turning, but confidence, isn't keeping pace. So this is a research finding from ManpowerGroup, and they said that regular AI use is up 13 points to 45% of workers. But during that same time, confidence in AI fell by 18 points. And that's the first decline that they've seen in three years. The last one is really about trust in businesses using AI responsibly, and 73% of Americans say they have little to no trust, and that's particularly pronounced, with younger adults. So that's what we're sort of seeing the signal, about a trust crisis. And just be... Before we move off this slide, Lisa, I wanted to comment that, we believe that, you know, the implication of what you see here is that to effectively integrate AI in your organizations, your change leaders need to acknowledge and address this accelerating trust crisis. What our research says is that people are more likely to engage with AI when they trust the technology. The intent behind how their organization intends to use it, and the people who are leading the change. And when I talk about the people leading the change, we're typically referring to, let's say, a primary sponsor of a change initiative, sponsor coalition members, and the people managers of the teams that are being impacted by the change. And your greatest opportunity to influence AI integration success is to prevent resistance by clearly defining how people will be valued as their work changes and doing this before AI reshapes their roles. Proactive commitment is more effective than reactive damage control. Organizations that define and commit to what we call a set of people centered principles early can build trust and create the psychological safety that's required to promote honest dialogue and the willingness for people to experiment with AI. To that end, committing to a people centered approach is is, we believe, a prerequisite for ensuring people can experiment with, adopt, and integrate AI in their roles. This particular slide present... Presents five principles that provide a foundation for building a people centered approach to AI integration. We start with our people's values, needs, and work determine how we integrate AI. We protect what makes us human. We are transparent about choice. Our people own the outcomes, and we treat reskilling as a commitment, not a courtesy. I wanna reinforce that these principles are illustrative, and they're not intended to be prescriptive or exhaustive. Each organization should develop its own principles that are aligned with their values and culture regarding people. I also wanna emphasize that a people centered approach does not mean making unrealistic promises that there will be no job impacts associated with AI integration. It does mean being clear, honest, and responsible about what is known, what is not yet known, and being specific about how people will be treated if impacts occur. And that includes communicating openly about impacts, following collective agreements and or HR policies, providing new work opportunities to people who are impacted, and reskilling where possible. Proactively addressing the questions people are afraid to ask creates safety for them, And people centered principles affirm that AI will be implemented in service of people and never at their expense. So next, we want you to participate in a poll, and we're interested in learning, has your organization established people centered principles for AI integration? And your choices are not yet, you're in the early stages, they're in development, they're established, and they're embedded. And if you choose four or five, established or embedded, feel free to, in the chat pane, share an example or examples of people centered principles. What are we seeing here, Lisa? Wow. I was so curious about this poll. So of of course, we have people not yet in early stages. That's overwhelming. I'm quite encouraged, to see some established and embedded, and, again, we'd love to see what that is. But it sounds like this is a really, important opportunity area for organizations because I think this is a really key point, Andy. I think this is a prerequisite, I for engagement around AI. think it's... I agree. I think it's a prerequisite for engagement. I think this is a critical step in building trust. Right? Dealing with that accelerating trust crisis that the research suggests is going on. Thank you for. sharing that. Yeah. So trust in AI technology and the intent behind its use and the people leading the change creates the right organizational conditions for making change happen. With that foundation in place, applying Prosci's ADKAR model helps us understand what each person impacted by AI needs in order to change successfully. So Jeff Hyatt, Prosise founder, created the ADKAR model, and it was first published in 1998. And the model really came from just research into why some changes succeed and others fail. And the secret to successful change is actually something quite simple. It's really that organizations don't change, People do. And successful change happens one person at a time. So I invite you to tell us how well you know the ADKAR model. This is going to be another poll for you. Any answer is perfectly good. You can write yourself on a scale of one to five. Number one would be Adcar what? If you're learning about Adcar for the first time in this webinar all the way up to number five, you think you could teach a class on it. Diana will pull up those, share those poll results as they come in. Oh, we've got a lot of, AdCar pros here. Got it down solid, could teach a class on it. I'm actually really excited. We might be sharing Adkar for the first time with some new people. Yeah. Right. We expected a lot of diversity in the group today, so thank you for sharing that. Because we have a diverse group, I am gonna do, a quick intro to ADKAR. The ADKAR model represents five elements of successful change that must be achieved for a change to be successfully adopted and used. The acronym stands for awareness, desire, knowledge, ability, and reinforcement, and it was really created after studying the change patterns from more than 700 organizations. It can be used for... As a model to enable individual change, but it can also guide activities at the organizational level. This is a visual that we like to call the ADKAR model on a page. And for each ADKAR element, you're gonna see a definition of the outcome, what you'll hear, and triggers for building strength in each of these ad car elements. So, for example, for the first element awareness, the outcome is awareness of the need for change. It's not what's changing, awareness of the need for change, which we really build by sharing a compelling case for why, why now, what if we don't around the change. It continues on desire to participate and support the change, knowledge on how to change, ability to implement the required skills and behaviors, and reinforcement to sustain the change. That third column, what you'll hear is quite important. This is how you can get clues into where somebody is at in their individual ad card journey. So, for example, in an AI based change, you might hear somebody say, I understand why my organization is integrating AI. I understand how it'll change my work and my role, and I understand why it's important to do now. But you might also hear the opposite. You might hear somebody say, I don't understand why we're doing this, and I haven't decided to use AI in my work. That can give you clues as well as to where somebody might be stuck in their change. We call that a change barrier, and that just signals an unmet need or some greater support needs that people have. In terms of this as well, it's important to understand that people progress through Adcar at their own pace. No two people kind of go through it in the same way, and it is normal for people to move forwards as well as backwards. So, for example, you might hear somebody say, I understand why AI integration is important to my organization. I've decided to support this change. I'm beginning to build, my knowledge and skills. But based on that, they might have a poor experience with the technology. They might, feel like the output AI is producing aren't high enough quality, or they maybe have concerns about their reliability, or maybe their use of AI surfaces new concerns about their role, and then their desire dips. So from that perspective, we always like to say that the ADKAR model is sequential, not linear. The ADKAR outcomes need to be, achieved in sequence, awareness, desire, knowledge, ability, and reinforcement, but people can move forward and backwards, and that's important to remember. Many of you might have come to this webinar today with this question. Is the ADKAR model still relevant in a fast moving AI world? And we believe absolutely yes. While the Adcar model has not changed, what's changed is the level of sophistication of our guidance in terms of applying Adcar. We've invested a lot of time and energy in the last three years to really deepen the application of AdCar through research, through the experience of our global community, and we've developed two advanced AdCar model mastery programs, which we'll share more about at the end of the webinar today. So today, we can provide much more specific and robust guidance, proven practices, and activities for applying ADKAR to AI integration. We couldn't have done that three years ago. Also, I wanna expand on this topic because I feel quite passionate about this as I read articles in social media. I think people are confusing the pace of technology with the pace of human change, and those are not at all the same thing. Yes. AI change is different, but people are not. The human experience of change is universal and enduring. And while AI is moving very quickly and has the potential to be quite a disruptive technology, people still need time, clarity, and trust to support how they, how they change the way that they work. As change professionals, AI doesn't change those fundamental questions about change. What's changing, Who's impacted and how? And what do people need to really, succeed in the change? What continuous AI change does demand of us is increased adaptive capability, and that's why I think the ADKAR model is so relevant. As AI accelerates change, simplicity is going to manage more, matter more, not less. And that's what ADKAR does. It makes change understandable, actionable, and practical. Adcar will meet this moment. From my perspective, every path to AI value starts with people. Organizations that are trying to achieve value by focus on technology alone are not going to be as successful, and things like the Adcar model really keep change focused on the people who make it successful. So, yes, in a fast moving AI world, the AdCar model is the most scalable way to achieve people centered AI integration success. And, as AI keeps evolving with new capabilities, skills, workflow, even changing customer expectations, Adcar helps, keep that continuous adaptation in place because it's repeatable, human centered, and absolutely scalable, and that's why it's endured for more than thirty years. When you hear us talk about AI integration at Prosci, you're gonna see us represent this ADKAR model as a star. This might be new to some people who are are pros that have been using ADKAR for quite some time. It's starting awareness at the top and moving clockwise, and that's going to reinforce the adaptive and continuous nature of ADKAR. And in this environment, it's probably less helpful to think about there being a single defined future state or finish line that we're crossing and change, but I'd encourage you to reframe future state to next state, and there will be a quick follow on the next state. As we mentioned, this is the first handout in your document panel. It's a job aid called applying the ADKAR model for AI integration, And it's gonna go through important differences and key considerations, in each one of the ADKAR elements, and Andy is gonna walk us through awareness and desire. Thanks, Lisa. We think it's really important that if you want to, effectively integrate AI in your organization, you need to start by understanding how AI change is different from other types of changes and be able to understand what the key considerations are associated with those differences in terms of how your change leaders carry out those roles. So we're gonna look at a couple of the elements. We're gonna start with building awareness for AI integration. And the reality is because AI is in the news so much, people already have opinions about AI, and they're also using it themselves in their personal lives. In fact, some people are more experienced with AI than the leaders who are talking to them and attempting to build their awareness of the value of AI integration. The other thing that you also mentioned in the chat pane is many people, and I emphasize that, many people have concerns about their job security. So some of the key considerations for change leaders include don't introduce AI as something that is brand new. It's important to engage with people, you know, what people already believe, understand their current perceptions, and correct any misconceptions that they might have. Underneath a lot of that preformed opinion is real anxiety about job security. And if you walk in assuming people are starting from a zero level of understanding, you're going to completely miss out on what's driving their reactions. People also have significant concerns about data privacy, output accuracy, and responsible use of AI. It's important for change leaders to proactively surface and address those concerns. If they're left unaddressed, the concerns alone can block even a well crafted message from landing, so don't wait for them to surface as resistance. And lastly, excuse me, building awareness isn't a one time activity. As Lisa's mentioned, AI capabilities keep evolving and new groups will be affected. So awareness has to be sustained and refreshed continuously, not treated as a campaign you finish and then move past. Next step, let's have a look at influencing desire for AI integration, what's different, and what are the key considerations. AI touches something personal. For many people, their expertise and the way they do their work are part of their identity. AI can raise questions like, is this still my work, and do I still get to decide how I do my job? Some of the key considerations for change leaders include addressing identity, autonomy, and authenticity concerns by using small group and one on one conversations with people. Some people are already using AI, but they keep it quiet or they downplay the time it saves them. They're worried that showing what AI can do will raise expectations or put their role at risk. Change leaders need to create conditions where AI use is safe to share and to celebrate, not conceal. You need to make sure that people aren't penalized for their efficiency gains, for example, by simply being handled more work, which is the definition of performance punishment. And people compare the approved AI tools at work with the consumer tools they may use in their personal lives. And when the approved tool looks less capable, that comparison can undermine their desire to use the approved tools at work. Change leaders need to invest in closing the capability gap wherever possible and be transparent about why certain tools are approved despite the comparison because of data privacy, security, and compliance requirements. And I would encourage you to have a look at the other three elements, so knowledge, ability, and reinforcement, and and make sure that you have a good understanding of what is different and what are the associated key considerations for change leaders. So what we wanna do now is another pool. And the question here is when you think about a current or planned initiative, AI integration initiative in your organization, which ADKAR element do you anticipate will be the most challenging to achieve and why? So you're welcome to put the why in the chat pane, but we want to get a sense of which of these five elements do you think is the most challenging. Any surprises. here, Lisa? No. Desire. Yes. Desire always gets a lot of love. Yes. And I wouldn't say that the fact that desire is, the ADKAR element that people see as the most challenge achieved, I don't think that that is unique to AI integration. Right? Like, we see this pretty much across all types of change initiatives that being able to influence desire, helping people see what's in it for them can be, a significant challenge depending on the nature of the change and how people are going to be impacted. K. We can return to the slides. What we've done is, Lisa mentioned that we've done a lot of research over the last three years to further develop our understanding of the ADKAR model and how it can be used effectively on different types of changes and particularly AI integration. So we have built a set of what we call ADCAR element job aids, and they're specifically focused on AI integration. You will find an example of one of these in your docs, tab, and the one that we have in there is building awareness for AI integration. So in total, there are five guides that we've developed, and each guide provides a practical guidance for the associated ADKAR element. We talk about what's different and the key considerations for change leaders. So that same information that we just reviewed on the previous slides, you'll find that in these job aids. We then include proven practices for developing strength in the element as well as common root causes of barrier points. And, across the five, there are over 30 proven practices with more than 200 activities associated with them. And I looked this up, Lisa. We also have, across those five job aids, 53 or so common root causes and 215 activities. So this is. a very comprehensive set of resources with the intent to help you support if you're a change practitioner people, support change leaders to be able to apply the ADKAR model effectively. These job aids are available to Prosci essentials and premier members, and so you will find a link in the webinar document panel, to the ADKAR model body of knowledge AI edition. And so that link will work for people who have, a membership. Let's next take a quick look at some of the content that... In that job aid. So it really starts with, talking about what do we mean by building awareness of the need for AI integration, and Lisa touched on this earlier. Right? If you wanna build awareness, you have to help people understand why that change is happening. We identify who are the key players that are involved in building awareness of the need for AI integration. We touch on when you would measure the strength of that ADKAR element as part of your initiative. And we also identify what's the target generic outcome, if you will, for an air... Awareness, which is I understand why our organization is integrating AI, what it means for how I work, and why this change is needed. Then we go into the proven practices. So when I use that term proven practice, it's a research or community informed approach or method that's been shown to help build strength in a specific ADKAR element. For awareness, we have seven proven practices starting with building a compelling case for AI integration all the way to measuring the effectiveness of the awareness building efforts and adapting actions. So what I'd like you to do is just take a moment, have a look at those seven proven practices, and reflect on which proven practices are you already doing well in your organization. K. Having thought about that, let's go on and give you an opportunity to look at this a different way. We're gonna do another poll. And in this poll, I'd like you to identify one of these seven proven practices that you believe you need to strengthen. So if you could only pick one, which would be your priority at this time? Let's have a look. We've got. quite a spread, don't we here? Yes. We do have quite a spread. And, you know, I also saw a comment earlier from Shay around, like, really emphasizing the role that communication has in change, and that's certainly a high degree of influence around the awareness messages. Mhmm. So, yes, we're seeing some strong votes here around number four, proactively addressing role impacts ethical use of data privacy. And keep in mind the outcome for awareness is awareness the need for change. So that why message, really comes through in that building a compelling case for AI integration first before people understand those job impacts. Excellent. Thank you all for sharing. K. We're gonna go to a a deeper level now. So let's say you identified the proven practice that you wanted to focus on is to create the conditions for open, honest conversation about AI. One of the things that our community has told us is that the proven practices by themselves are not enough to help people understand what they need to be doing. And so in these job aids, we've gone to a deeper level of explanation. So for every proven practice, you will find a series of activities to make that proven practice a reality, if you will. So if we take that third proven practice and we explore it in more detail, what you'll find in the job aid is some additional explanation. So psychological safety is a critical enabling condition for building awareness of the need for AI integration. People who feel unsafe asking questions or expressing concerns will disengage from awareness efforts. And so we have a number of activities here. These are typically done by your primary sponsor and sponsor coalition members, which include explicitly giving people permission to ask questions, express concerns, and share doubts about AI without fear of judgment. I like the second one. Having your leaders model openness, including acknowledging what they themselves do not yet know about AI. That's where they can be a little bit vulnerable, and that can go a long way to helping build trust with people, promote two way dialogue, and acknowledge that uncertainty about AI's evolution is normal and expected. So take a moment. Think about, you know, what is one of action you could take to strengthen awareness of the need for your change. Thanks very much, Andy. And I think it's worth emphasizing again. These are not prescriptive. You need to think into these. But, based on our research and experiences of our global community, these are really the key differences and considerations for you and recommended activities. Thanks, Andy. It was great to go through that detail. So if Adcar helps us understand what each person needs to change successfully, but we've already established AI change doesn't just happen once. As AI capabilities evolve, they create these continuous waves of change. And so this is really the challenge that we need to address. And I'd like to start by introducing a term that we've started using here at Prosci, and that's an AI capability event. We define this as the introduction, enhancement, or expansion of an AI capability that changes how work can or should be performed. Now there's a number of examples here on the right side. You might be able to think of different or better ones than these, but they'll give you a bit of a sense. Maybe the release of a new model with improved performance in your AI tool, introducing an AI powered workflow, automation, perhaps deploying an AI agent. Any of these capability events, they create a need to prepare, equip, and support people as they adapt to new ways of working. And it's this context that we really need to think about applying ADKAR. And we really want to apply ADKAR at two levels, the organizational level and the individual level. We are gonna call that organizational level the tide and the individual level the waves. At the tide level, leaders create the ongoing conditions for AI integration at the organizational level by focusing on awareness, desire, and reinforcement. I'll say that again. Awareness, desire, and reinforcement. And they do this by communicating the why and the vision for success, building trust and engagement through leadership commitments, like the human centered principles that Andy talked about earlier, or reinforcing and celebrating progress and the impact of AI adoption. At the wave or individual level, each new AI capability event creates a new ad car transition at the individual level. I've given an example here of three potential waves, a new model or feature release, AI being embedded into a workflow, or maybe a new AI governance requirement is a bit of those examples. So why does this matter, the tides and waves? And I want you to understand how the tide at the organizational level influences individual change. So I'd like you to think this through for yourself. You don't need to put anything, in the chat pane or do a poll here. But I just want you to think, which ADKAR elements are strengthened when you communicate a compelling case for AI integration? Is it awareness, desire, knowledge, ability, or reinforcement? You can choose one or more. Right? Just think about that for yourself. Oh, I'm already getting some people sharing their answers. Thank you. That chat pane is compelling. Yes. In here, you'll notice that I've shaded in awareness and desire. They're two of the most primary impacted, elements when we communicate a clear and compelling case for change. Awareness gets strengthened because people understand why AI integration is needed, why it matters now, and what the organization's trying to achieve. It can also strengthen desire, particularly if those communication messages address job impacts, ethical use, data privacy, those types of things people are concerned about. That can increase trust, making people more willing to engage. So the bottom line here is the tide creates the organizational conditions that shape individual change. So I wanna contrast for you, a strong tide and a weak tide. So let's start over here with the strong tide. When we communicate that clear compelling case for AI change, it creates a very strong baseline of awareness, and you can see that I've shaded that first awareness element all the way up to almost two thirds. That means every time we have a wave of change that impacts individuals, we get to just build on that awareness that is already well established in the organization. The opposite happens for the weak tide. If we have an unclear or inconsistent case for AI change, it's gonna give us a low baseline of awareness. And you can see I just shaded in the feet of that a. That was probably a bit generous. And in this situation, when there's waves of change, we probably are starting from scratch needing to rebuild awareness, which means more work. K? So, really, the key message here is that your change management support doesn't need to start from zero at every time. If you create that strong tide at the organizational level, it is going to accelerate and make your change much more effective and just create a better experience of change for people. So if you'll see here with the tides and waves, I've added on what it might look like for an individual. So for Roberto, if he is experiencing a new model or feature release, he needs to go through an ad card journey as well. The thinking that we need to do is really what level of awareness, desire, support do we need given what's happening with the tide at the organizational level. If it's a strong tide, we may just be reinforcing those messages and spending most of our time really supporting Roberto to build his knowledge and ability. Right? So this is how this works in terms of the tide and the waves. We always need to think into what's required to support for people. There's another place where we really need to think into the change, and I'd like to highlight here that not every AI change will affect every role in the same way. We need to consider impact in addition to change support in in applying the ADKAR model. So in this situation, I've given Roberto some colleagues, Mindy, who's a data analyst, and Carlos, who's in IT operations. Now if the new model or feature release involve new content creation capabilities, those might really matter for Roberto. Roberto is a graphic designer. He would use those in his day to day work. Mindy and Carlos, not so much. That change doesn't impact them in the same way. You may have other waves of AI change that impact all roles. For example, a change in AI governance with an AI policy or use requirements may impact all people. So this is something you need to think about impact as well when you're applying the ADKAR model. So you've probably heard the expression, a rising tide lifts all boats. And so I just want you to think about, are you building the conditions that rise the tide or only reacting to each wave? So there's a poll here as well we'd love you to answer. Are you focused... Is your organization focused on the tide, the waves, or both? You'll see some results here. Oh, we already have some people. Good. We've got lots of waves and tides. We've got a pretty, I don't know, even split. Hey, Andy? Yeah. Although it is nice to see that we've got the largest number of people are focusing on both. Right? And. that is certainly important. Right? That you'd. be able to build, you know, a strong foundation and then support people to ride the waves effectively. Lovely. That's great to see. Thank you very much. So we're gonna begin bringing the webinar to a close, and we want you to, rather than thinking of your goal as calming the water, if you will, think of it as strengthening the capability of your organization and its people to ride the waves as AI continues to evolve. And we know that that's gonna happen, and it's gonna happen more quickly. I think you probably get a sense that Lisa and I have a strong passion for the ADKAR model. We've spent a lot of time working with it. ADKAR simplicity is its strength, but applying it effectively takes more than knowing the five building blocks. Many people understand the model at a surface level, but real impact and results come from engaging with the nuance beneath it. So when you move beyond the basics and apply the ADKAR model with depth and intention, you're able to unlock its full potential to guide individual change. So to summarize the content we've presented today, here are what we call five essentials for applying the ADKAR model to AI integration. First, start by deepening your understanding of how AI change differs from other types of change and the associated key considerations for change leaders and practitioners. That's a really important foundation to have. Secondly, the power of the ADKAR model is a result of its focus on outcomes, not activities. The activities are what has... Get us to the outcomes. It's the outcome we want. Third, applying the ADKAR model enables you to build the organizational and individual readiness to address continuous waves of AI change. ADKAR is sequential, not linear. You need to be prepared for regression at people moving backwards, if you will, having awareness and desire, gaining new knowledge, which then causes their desire to decrease. And so you need to be able to recognize a barrier point and take adaptive actions to address those barriers. And finally, people move through the elements of ADKAR at different paces, so support needs to be individualized, not one size fits all. And we have a final chat pane for you. So in your chat pane, what we'd like you to do is share one key insight you've gained from the information we've shared with you today for helping people adapt to continuous AI change. So let's flood the chat pane. What are some of those key insights that you've gained? Oh, several people are saying ride the waves or tide the waves. I'm hoping that's a memorable analogy for you. I think that's really helpful as well. So that's applying awareness, desire, and reinforcement at the tide level and the full ad car at the wave level. Mhmm. Oh, it's okay that. to be vulnerable, Chip. That's a great takeaway. Yeah. Yeah. Lots of tides and waves. That that analogy is really seeming to have an impact on people. Lisa, the really important what's in it for me as well. Oh, Mandeep says it's not linear. Yes. Thank you. That's a key learning point as well. We really wanted to emphasize. That's a misconception about Adcar that is often, shared. So thank you very much for that. Great. Well, thank you for sharing that, with us as well as others about what you take away from the webinar today. Prosci has many programs that can help you take the Prosci ADKAR model further. If you're looking to build your foundational skills, we know there were lots of people joining us today that are learning about ADKAR for the first time, you can do that in the change management certification program. If you are already Prosci certified and wanna deepen your practice, we have two advanced ADKAR model mastery programs. Level one is called prevent change resistance. Level two is called resolve change barriers. They're all focused around the tough stuff around resistance, and that resolve change barriers is our top rated, program at Prosci because it really equips you for the tough stuff and change. We also have a brand new program, a one day leading AI integration success. Andy and I, developed this for enterprise organizations. It equips leaders at all levels for continuous AI integration. It focuses on applying the ADKAR model as well as Prosci's AI integration framework to redesign roles and work differently. Again, that's a really important part of the change concept is understanding how a role needs to look differently, with, AI use. You can also partner with Prosci. We also have a consulting consulting practice being led by Paul Gonzales as well. So I know Diana will open a poll. If you'd like a reach out from Prosci to talk to you about any of these opportunities, please let us know. We'd be happy to, talk with you. You can also stay supported with Prosci membership. Prosci launched a membership, offering earlier this year. All of our training programs come with a trial period of our membership. As Andy mentioned, he previewed a couple of the, job aids that we have available for essentials and premier members, but you can start a free seven day trial at any time. Our starter membership is really great for learning the basics of the Ad Car model, and we have an essential premier membership that has, the majority of our content around AI integration. So, every path to AI value starts with people, and Andy and I would love to answer some questions, as well that you had. Lisa, just before we dive into the questions, our team behind the scenes asked me just to share a brief announcement with the participants. who are still with us. And that, announcement is that, you will receive a, copy or a recording of this session that'll be sent to you via email to the address that you use to register for the session, but we don't make the slides specifically available. And the other announcement is that, the documents or resources that we've talked about today, particularly the five job aids, they are available to essentials and premier members. So we gave two samples out. But if you wanna go beyond that, we would strongly encourage you to consider membership because it's a wonderful way to get access of all of our Prosci resources. So in terms of questions, Lisa, we have one here that you and I, talked about before the session and wondered, is anybody going to ask us this question? And someone did, Colleen. And the question is, is the environmental impact of AI via data centers intentionally being left out of the conversation here? And the way I would answer this is to say, we are very, very aware that, there is a lot of controversy and unhappiness, if you will, about the prevalence of data centers and the impact that they have on the environment. So we are aware of that. I would think that, you know, one of the opportunities for an organization that's developing people centered principles might be, if they can, to think carefully about who they're gonna select as their provider and what that provider of AI services does to mitigate the environmental impact of their data centers. So do they use sustainable practices? What do they do to to address the impact of that? That is obviously a huge issue for many people, particularly in The United States. And so, yes, we are very much aware of that. Let me see what else we might have here. Here's one, Lisa. My organization is also changing leadership in some of the areas heavily affected by AI. Could we use ADKAR as part of a leadership change? Yes. You absolutely can. You can use ADKAR to support any type of change, but you can also use ADKAR to help build skills around change management. We often apply ADKAR in the context of helping leaders learn to fulfill their role, whether it's a sponsor of AI change or a sponsor coalition member or people manager. You can build their awareness for their role, their desire, knowledge, and ability. So, certainly, that's that's a very great use of Adcar, and that's why it's so nimble and useful as well. K. Here's another one. Okay. Would would you say that this circular model for ADKAR for AI applies also to any continuous improvement, for example, lean Kaizen? Yes. Absolutely. It does. Yeah. You can think about this, anytime. You know, it doesn't matter how frequently the change is sort of occurring. ADKAR, even if you have a material shift, we used to apply it in agile based change. You can align, ADCARR work and milestones to each release. You can think about, the material impact or change, with each release if you're working iteratively. But, yes, you can think about it in the same context. Andy, there's another one that I thought was quite interesting. How do we manage AI integration in an organization that's already had several attempts that failed. How do we reset and manage the org past memory on this? And I think this is quite, an interesting challenge, when you've made an attempt and failed and you have to reattempt in a change. And I think what's really critical in this is to, first of all, really acknowledge past failures very clearly. And I think it's important to engage your organization in understanding some of the root causes and directly addressing how you will approach it differently this time. I think that's really key. Yeah. I would absolutely agree with that, Lisa. I think that, you know, if you have repeatedly failed to successfully integrate AI, you need to be honest about that. You need to be somewhat humble and say, you know, we need to learn from those experiences so we don't repeat the same mistakes over and over and over again. And I think people really value that, that openness, that honesty, that willingness to be a little bit vulnerable and, say, you know, we messed up. We didn't do things. And to engage people to help understand what needs to be done differently to ensure a better result on any future efforts attached to AI. Great. Some good wisdom. Here's one that I think many of us think about, and it's it's a real challenge. The question is regarding trust. Any tips on how to deal with all the news about the possibility that AI can or will be the end of our civilization within the next ten years? So it's it's it's obviously something that's very concerning. Right? That that we know that there is tremendous promise associated with AI. For example, you know, the benefits that we could get in terms of approving health care outcomes, speeding up the development of, you know, new cures to diseases, but then there are all kinds of perils attached to the technology as well, and I think that's something that all of us are wrestling with. And I think that's when we talk about the tide level, how important it is for leaders to reinforce progress and impact. And I think we really need to be intentional about sharing stories of AI for good, for value, for benefits for people. I think that's a really important part of what leaders need to be thinking about communicating and celebrating, are those kinds of examples. Mhmm. I'm just looking other questions? to see. Yeah. I'm just looking through to see if there's anything else. There was a comment earlier that I missed it. It was in the chat pane, not in the q and a, and the comment was really around it needs to shift from applying things like ADKAR or the Kotter model to, really addressing how AI will change people's work. And just to be clear, ADKAR is always applied in the context of understanding impact or change specifically on an individual. That's what makes ADKAR most effective. And Prosci has, Tim Creasy developed Prosci's, AI integration framework that really looks at defining how roles will shift and change based on AI, and that's kind of something that we would do as a bit of a first step, before, instead of applying ADKAR. So we absolutely agree it's an and. You need to do both because once you understand the impact, it's really important to then figure out, how to support individuals. K. I think we have handled most of the questions. So, Monthly. again, thank you everyone for taking time out of your day to join us. We hope that this has been a valuable learning experience for you. Yeah. Thanks very much. Have a great day, everyone.