From AI-Assisted to AI-Led Change with Elliott Martin

Speaker A: You're listening to the Change Management Review podcast where we bring you the best tactics, strategies and actionable insights for organization change. We talk with impactful organizational change practitioners and leaders. And now your host, Teresa Moulton.

Speaker B: Welcome to the Change Management Review podcast. My name is Teresa Moulton and I am the CEO and Chief Executive Editor in Chief of Change Management Review. And I am happy to have Elliot Martin here, uh, to talk about AI and change management today. Elliot Martin is the founder of Future of Change, a company focused, uh, on helping change practitioners and organizations understand how artificial intelligence is reshaping the way change is delivered. Elliot has more than 20 years experience in change management and transformation, working across complex organizations to help leaders, teams and stakeholders navigate change in a practical and human centered way. He recently published the AI LED Change Management Playbook, which he has made available as a free download for the change management community. The Playbook is designed to help practitioners understand how AI can elevate the craft of change, including impact assessment, stakeholder engagement, communications, readiness, learning and measurement. Elliot is currently helping clients think through what AI led change looks like in practice, including where AI, uh, can generally genuinely improve change delivery, where the risks are, and what this means for the future role of the change practitioners. So, without further ado, welcome Elliot.

Speaker C: Thank you very much, Teresa. Thanks for having me. Great to be here.

Speaker B: Yes, I was very much looking forward to having you, uh, on the show today. And one of the questions um, that I have for you just right off the top is tell us how you came up with the idea for the Future of Change as your company and um, the book that you're putting out.

Speaker C: Yeah, absolutely, absolutely. Um, so, yes, it was ah, definitely a good time I think, to start focusing on AI in change management. And the reason that I started really trying to double click and focus down on this space is because after, as you said, nearly 20 years in change management, I've long felt that there was an opportunity to kind of refresh and change the way that we do things. Not that I don't love PowerPoints and Excel documents as much as sort of the next person, um, but I think that there is, there's definitely the need to try and kind of refresh what we do in this space. And so as AI came out, I started to try and experiment with it a lot on my current program. And the truth is I'd been a bit of a fanboy of AI since probably 2023, uh, using it to, uh, to design and create and sell mugs online. So that's kind of how I got into the AI space. Okay. It was um, just sort of a bit of a niche hobby at the beginning. You know, I was using it for image generation and, and sort of doing, creating, art, printing it on mugs. And then how was that? Um, I think I sold like 40 odd mugs. Um.

Speaker B: Wow.

Speaker C: But it wasn't really a uh, a great business strategy because I was only selling Christmas themed mugs and Christmas comes but once a year. So it was uh, it wasn't really a, uh, a money making scheme. But what it was was a great introduction to AI which.

Speaker B: Yeah, uh, fantastic.

Speaker C: It was a lot of fun. Yeah, no it was, it was, it was a lot of fun. And um, but then as the technology started to improve, I thought, you know, there's got to be ways that I can use this on my own program.

Speaker B: Right, right.

Speaker C: And that's where. That's kind of where I started to go down a rabbit hole. And the more, the more I started what the capabilities were, the different frontier tools, how they could be used, I started to realize that actually there was applications across each of the domains within change management. Anything from, you know, how we can improve impact assessments to comms engagement, pretty much all our main disciplines. And so, uh, through the, through the, I guess experimentation I realized that there just wasn't a lot of literature around, around this, around how change management can be uplifted through, through AI, the, the uh, documented examples of improvements. And so that was kind of what led me to think, you know, what I should start, uh, sort of putting some of this, this on paper. And it was originally my thinking was it was going to be sort of a white paper, just like a very short kind of m. More of a white paper than a, ah, than, than a book. But as the use cases sort of compounded and I was writing in parallel with delivery and then so all these new use cases I started to write and I. Getting a bit, a bit uh, bigger than I would like. So it ended up turning into, into a playbook, which was designed to be so practical. Which is why there's, there's. It's, it's freely available, it's not a novel. It's designed to be a, a practitioner's guide if you like. So yeah, quite the journey.

Speaker B: Sounds amazing. I really love the way you uh, were so self determined. Um, you know, you saw a need, you cared enough to fill the need and then you fill the need. I mean that's, you know, a lot of people would maybe not be even curious enough to see the need or then think I'M going to do something about it. Not and then make it available to everybody. I mean that's quite uh, uh, altruistic of you.

Speaker C: Thanks. I think one of the good things about the change community is people are always willing to kind of share. And so I've learned so much from people doing similar things in the past, you know, from uh, publications including, including from the Change Management Review. Right. And so the fact that practitioners are willing to share things I think is it's good to give back.

Speaker B: Fantastic.

Speaker C: Hopefully this is an opportunity for that. Yeah, yeah.

Speaker B: And hopefully we can amplify your um, work so that there's more uh, people getting to use your resource that you created.

Speaker C: Uh, yeah, I mean, I hope so. I think hopefully there'll be use cases in there and key, key thought ideas and takeaways that people may not have considered, uh, with AI especially being so new and it's all, it's improving and changing all the time. So I think a lot of people are just struggling in general just to understand how they keep up with this technology. Uh, and, and so hopefully there'll be, there'll be some nuggets in there that people can take away and, and start implementing, you know, on their programs right straight away.

Speaker B: What are some of the, what are some of the um, best use cases that you found that are in the book?

Speaker C: Yeah, So I think that the good, the great thing about, about this technology is it can be used in I think pretty much all, all disciplines through, through change. Where I have found the most exciting ones fall in, in how we engage with our, our stakeholders. I think that's, that's a big area and I can talk through some of the, the examples there, how we reach people in a more engaging way. Um, and then the second bucket, I think that's been really sort of, um, really exciting for me is in the measurement space and how we can start to gain analytics, analytical data from our stakeholders that enable us to transform the way in which we do change. And uh, that's kind of the big takeaway for me, which is by using this technology properly it ables us to do not just things better, although it also enables that, but it enables us to do things differently. And so, um, but so, so that's kind of the big ones for me to give, to talk through some examples to kind of bring, bring that to life. So I mentioned like engaging with, with stakeholders and what does that look like? One of the things which I've always said, Teresa, is that if programs, businesses want an engaged audience, then they need to have any engaging way, uh, engaging artifacts that they send and that they use to reach their audience. And so many people think okay, if I can, if I can send a communication here or there, I might do a Viva post here. I'll do a Slack post here. I've got multiple channels. Why aren't people listening? Well maybe people aren't listening because those, those channels are not innately engaging for everyone. Right? I mean most people, an email here and a Slack message here is mostly just a distraction. And so how do we make that more exciting with AI? So one of the ways in which uh, I've had some success with a program I'm working on at the moment is by uh, using and creating podcasts very similar to this but using um, using a. And so for example, uh, on the large scale transformation that I'm supporting at the moment, there's a series that we are running which is purely AI generated. We use project documentation which we specifically curate with the details about what we want to get across to the audience. We put it through an AI tool and it creates a podcast based off the instructions that we give it. It covers off the points that we want it to cover off. But it does it an interactive way where you have two people speaking to each other and you would not know that these people are not human. Um, in fact, precisely because you wouldn't know. It's why we tell people for honesty and transparency. Uh, but what that does is it gets the information that we want across in a format which has traditionally been very labor intensive to produce. The idea of having a podcast, you might sit down with the CEO and you go through back and forth. That has long been a change management tool. But what that has always taken is significant labor, significant time, access to stakeholders. You need to get the, you need to get the lighting right, you need to get the sound right. All that stuff goes away, uh, with creating these, these via AI. And so what we've found is that the, the low end barrier to entry has meant that we are able to produce uh, these very quickly at, at extremely minimal effort.

Speaker B: Is that Notebook LM.

Speaker C: Um, we use. Yes, exactly. We use NotebookLM. I think they just re, um, they retitled it to Gemini Notebook now. Yes, which they conveniently did straight after I published my book just to make sure that. Um, but so, yes, yes, so that we've found that that's been uh, very well received. Um, and also because I think people are uh, uh, becoming more used to longer form content outside of work. And so I think at work they're able to access it, uh, and listen to it in different ways on the way to work, on the way home from work, between meetings if they need to. Um, or people that can multitask much better than me, you know, maybe while they're writing emails, I cannot do that. But I've seen, I've seen people, you know, do that. So, so I think that that's one way, um, the other way just from, from um, there are many. But the other thing that we've been pretty successful with and I think is a great opportunity is the use of uh, digital avatars. There are multiple tools that uh, that do this in the market today. Um, but, but these are great because they, they offer multiple, multiple benefits actually. So one is that you have the ability to create digital avatars of actual people. So the stakeholders, the CEO, the program director, which means that you can deliver communications through them without their need in the future. Uh, they just go through an appro. So one of the programs I'm supporting at the moment, they had a need to communicate frequently from the leadership down through to the people on the ground because they had some feedback that there was a gap in communication. This was internally on the program and so uh, this was something that I suggested. Okay, well look, if you guys are having meetings every week that you just don't have the time to pass down and you still want, uh, sort of a personal approach, we, let's, let's create a digital avatar. We sat down with the leadership team, we created the avatars. At the end of their meetings they send through the keynotes, we put those keynotes into the tool and all of a sudden people are getting the message from the avatar of the program director. And so um, that's kind of one use case. Yeah. And in some ways it's pretty scary. Um, but it's also been well received. And I think my preferred use case is actually not the Digital Twins. It's, it's actually the Pixar style characters because what they are able to do is, I'm sure you've, you've done many a, uh, a Persona profile. Right. You know, typical in change management.

Speaker B: Yes.

Speaker C: We use, we've used Personas pretty, pretty heavily over the last sort of decade. And you'll have Sally up the top left of your PowerPoint slide that says yeah, here's what Sally does in her day job. And.

Speaker B: Right.

Speaker C: The reason those Persona cards are great is because they make things relatable for the audience. Well now Sally can cannot be a 2D picture in the top left Hand side of uh, a PowerPoint slide, Sally can become a motion, a uh, live motion graphic that speaks to you.

Speaker B: Yes.

Speaker C: And all this is just so you know this stuff is almost under the click of a button these days. And so by having all these different, these different uh, like kind of Pixar style characters that, that talk to their stakeholders and say hey, you know, I'm Sally, I represent the billing team. You know, here's what things are going like it in the billing team in my space. I'm going to look through these changes and I'm going to have to go through X, Y and Z. You know that is also a very exciting use case. And this is, this would have been total fiction, Teresa.

Speaker B: Like right.

Speaker C: Three years ago. Right.

Speaker B: So um, what tools are you using for that?

Speaker C: There's a tool called hey Gen and there, that's the one that I think is sort of leading the market at the moment in terms of the digital avatar space.

Speaker B: H A G E N A H

Speaker C: E Y G E N. Yeah.

Speaker B: Okay. Okay.

Speaker C: And so uh, it's, it's definitely worth checking out. The other benefit, uh, which I think is one of, one of the great things about these tools is most of them have a, like a free plan. So the things that I'm talking about here, it's not like I'll pick, you know, teams need to lay down a big investment. The truth is like hey Jen, as an example Gemini Notebook which you referenced earlier, they all have free plans.

Speaker B: Right.

Speaker C: And so creating a once off AI podcast or creating a digital avatar just to test it out is. Right, uh, is free, you know, so definitely no reason not to jump in and give it a go.

Speaker B: Right, Yes, I agree. And uh, it allows for personalization uh, as well.

Speaker C: Exactly. Hyper personalization as I refer to it. Absolutely. Which is a big thing for us.

Speaker B: Yeah. How have you seen hyper personalization be implemented?

Speaker C: Um, yeah, so it's a good question and it's one of the things that I'm preaching about a lot this hyper personalization because I think AI enables that in a way particularly within comms that uh, before has just not been practical. And so one of the things that I speak, there's actually a prompt in the prompt library that focuses on how to hyper personalize communications. Um, but if you think about, we talked about uh, Persona profiles for example. So a lot of change teams, they have data on the different stakeholders that they need to engage with. And so what AI now enables us to do is feed that data in and then ask it through the appropriate prompts. To tailor a standard narrative for said audience. So what does that look like? If you feed in, let's say there's a program narrative or a couple of key messages. Now the key messages might be generally the same for a large number of stakeholders, but as always, the devil is in the detail, the nuance. What makes communication successful is that someone thinks it was written specifically for them and not as a broad brush. And so if you have a pset of messages and you feed that into AI and say, here's the core narrative that I would like to communicate, I want to make sure that it's these three points as an example and here are all my Persona profiles. Here's how the billing team likes to be communicated to. You know, here is, here is how uh, the uh, people and culture team likes to be computer communicated to. Here's the tone, here's the things that they have going on which might impact the way in which we should deliver the messaging. Here are the sensitivities that apply to each one. You feed that in. And now we'll have everything it needs to take the key message. It won't defer from the key message, but it will tailor the key message for every number of stakeholders that you've provided. Provided. So all of a sudden if you have 15 different, um, 15 different, let's say major user groups on your program, stakeholders, the same key message within a click of a button and some prompting, you now have 15 different sets of the same message hyper personalized for that particular group. Um, and so what tool is that, Elliot? So just using that process that I talked through, that can be done with any of the frontier models. So if people are using Copilot.

Speaker B: Yeah.

Speaker C: Um, they can literally take the prompt, uh, that, that's, it's at the back of the playbook. But, but you know, even if you were writing it from scratch and just prompted into copilot, if you're using ChatGPT, if you're using Gemini Claude, all of the frontier models will, will, will be able to do this seamlessly. Um, the art is in making sure that you, you get the inputs right. Um, the beauty is as, as, as change practitioners typically we have this data available for our stakeholders because it's what we do. It's just most people aren't um, thinking or most people don't know necessarily how to leverage what they already have available and let AI, ah, do the hard work.

Speaker B: Yes, exactly, exactly. And so what are some of the challenges that you have experienced, um, within change professionals embracing AI?

Speaker C: Yeah, there are a Few challenges. I think the challenges come down to people sort of of, uh, getting it wrong in a few different areas. Um, the first one, I think is governance.

Speaker B: Yes.

Speaker C: And the second one is what I would call accountable, um, human oversight.

Speaker B: Uh-huh.

Speaker C: And to break these two down, um, there's a framework that, um, I pulled together and it's sort of the spine of a lot of the work that we do at Future of Change. It's called the Jeroa Framework for AI LED Change. And it starts with the G, which is governance, and it ends with A, which is accountable human oversight.

Speaker B: Okay.

Speaker C: And. And the reason that I bookended the framework, um, with. With. With these two is because this is where I think the challenges are that, that I've seen. So the first one is governance, which is that most people that even are diving in, and all credit to people that are diving in trying to get into this space. That's, that's what I would advocate for. But, but I think the governance is important. And when I say governance, I'm saying, well, what is the programs, the company's, uh, AI policies, what tools do I need to get approval for? Who within the change team is going to govern the use? What are we going to use AI for and what are we not going to use AI for? Because it is exceptional at some things and it should probably avoided for others. And so simple governance up front, I think can be. Is a hurdle or a challenge that I think people don't necessarily think about because they're probably doing the right thing, which is trying to unpack, trying to die in.

Speaker B: Right.

Speaker C: But what that can end up with is, you know, almost the way you used to have shadow it back in the day.

Speaker B: Yes.

Speaker C: The shadow AI. And all of a sudden I've used, uh. And I've used an AI tool that wasn't approved and. And I, uh, get in trouble because the. My company's, uh, data policy was. Was not. Not, you know, this wasn't compliant with the data policy as an example. So all of that can be totally eliminated by going through and addressing properties, governance upfront. Yes. Uh, and all those hurdles and challenges sort of go away. And the second one, what I call the Accountable Human Oversight, this one sounds like it should be obvious, but unfortunately it doesn't seem to be so obvious, which is that the better that AI gets. Theresa, I feel like going back 12 months or even 18 months, people were like, you know, I don't really trust this stuff. You know, I don't really trust it because it's getting things A bit wrong. Now as the models have improved, people have not only sort of overcome the trust gap, but they're also becoming reliant I think, and um, a little bit complacent. And so I'm seeing examples of where people say, okay, I asked AI to do this, uh, and therefore it gave me an output that I then distributed, but I didn't check it. And this is where I think that is both a hurdle and a trap. And so accountable human oversight needs to always be front of mind. Like that example I gave about the hyper personalization of comms. Those, those hyper personalized examples need to be, need to be reviewed by, by someone at a minimum. And the more sensitive the information, the more thorough the review needs to be. And so this is a hurdle, this is I think a challenge for people to understand because they look at it and they go, it's perfect. That's so good. It's, it's, it's smarter than I am, you know, who am I to review this sort of thing? And, and the truth is, well, it definitely requires human oversight still. Everything, even if the result of that is, I now confirm that I do not need to edit.

Speaker B: That's right.

Speaker C: But that needs to be there. So those, the, both the governance piece and the accountability from the, from, from, from the human, I think is the challenges and the hurdles that people need to sort of focus on to get this right in implementing.

Speaker B: Yeah, I totally agree with you. And on the governance piece, one of the aspects of it that um, I find interesting is the definition of governance and that it actually, you know, if you look at it from a PMO perspective, you're looking at it from the governance board all the way, you know, structurally all the way down to the task level where these decisions are made on HDL and HODL and you know, in the loop, out of the loop and decision rights and all this other stuff. Um, but at the core element of what I hear you talking about, it sounds like um, change professionals need to be involved in making the decisions around what is okay to uh, approve and not approve, uh, for AI's involvement in some of the work that's getting done. And so I agree with that. And then just this week I uh, had a conversation with a couple of colleagues and um, went through some of the transcripts of some of the courses that I've been teaching on in the AI Change Impact Lab. And I came up with this realization that no one in the organization is really, uh, putting the foot down on how governance should be executed at the detailed level. So like who in the organization is actually going to say this is okay and this isn't okay? And is change really positioned to be that person? Is that part of their role?

Speaker C: Yeah, I think that's a good question. And I see that there's different levels and depending on the size and the nature of the organization, there'll be different levels of maturity around their HM draw AI governance. Certainly I think that the broader AI governance should in my opinion, live at an enterprise level. And so that's kind of why I mentioned that ideally the change teams need to be on board about the governance within CHANGE as to whether they are compliant with what should be an enterprise data security policy. So even the organizations that are not yet caught up with a particular AI policy which m might, um, they will likely have a data privacy and security policy. Even understanding whether or not to what extent AI is compliant with that policy is kind of important. But even saying that whilst I think that the general governance for how to use AI at an enterprise level lives not necessarily with the Change team, but at the higher levels at the C suite, so that it is enterprise wide, what I advocate for is governance, a governance framework within the Change team along irrespective of the maturity of uh, a wider governance. And so the reason for that is that as change teams we alone need to understand several things. So the first thing we need to understand is if there is a overarching AI policy, to what extent are we compliant? That's kind of the first part. Um, but then we need to understand, well, okay, so which tools uh, should are we advocating for use? Because you don't want John in this space signing up to this AI tool and then Jane in this space signing up to this AI tool and then all of a sudden you have duplicates that weren't necessarily needed. Uh, um, and where is those tools storing their data? And are we all okay with this? And so even within the Change team I think we need to establish, okay, so this is how we within CHANGE are going to govern our use of, of, of um, of AI. This is what we think is approved. Here's what we're going to use it for within change and here's what we're not going to use it for. So things that are of extreme sensitivity, things that may result in uh, like personal, like detailed information, um, you know, anything that might be sensitive in terms of job losses we're not even going to put through AI like anything of that is going to be excluded. Okay, that's going to be in our government's policy, um, put as accountable within the team for reviewing the outputs for each one of these tools. Um, like these are the sorts of things that are governance at a, at a change team level.

Speaker B: I see what you're saying.

Speaker C: Yeah. And I think they just uh, it's too easily kind of missed but, but so easy to get right up front that will, that will overcome um, possible pitfalls down the, down the track. I um, do think that a wise organization would seek the advice from their change team and their experts in this space around what is applicable use more broadly. Um, but I think that would be the smart call. But almost two levels of governance. I think.

Speaker B: Yes. The reason the question comes up for me is uh, the narrative that's out there and all the research as you know is around AI adoption and what's causing the friction is workflow integration. And then it's a question of well who's in charge of workflow integration and, and change management isn't as involved in workflow integration as I think they should be. We're getting four deployed engineering coming from the labs and uh, they're putting people on the ground to actually configure the um, technology into the workflows without having change in the room to talk about governance at ah on uh, governance issues, on people issues, um, for workflow integration.

Speaker C: So and I think, I mean a change being involved in that space would have, would be I think beneficial regardless. Right. Because what does it mean from a change perspective when you're integrating these workflows? Right. There's an assessment on that that would be valuable in and of itself. Um, I would argue, I do think though I have this view that there's a lot of uh, there's a lot that you'll read a lot, a lot out there, especially the consulting firms, they love to talk about how you know, what's hindering successful AI usage. Like you mentioned is like the data isn't established, we don't have the inputs, you know, how are we supposed to integrate end to end? And I think that there's obviously truth and an element to that. But what I think people sometimes miss is that, that yes, uh, you know having, having the data and the appropriate inputs are obviously important when you start orchestrating complex end to end workflows.

Speaker B: Right.

Speaker C: But what I also think is true is that uh, there are so many use cases and so many benefits and opportunities for efficiency for companies to use AI that don't require lengthy detailed integration, that don't require uh, you know, uh, hundreds of thousands of dollars to be spent on fixing data just by getting people to understand how they can get the most out of, out of the tools that are available already right in front of them. So I think there's, there's people look at it and they think oh you know, AI transformation means we need to be uh, implementing AI into all of our complex workflows. Now that's, that's of course that is an element of AI transformation and that will be complex. But, but I think there's also just an element of well, people uh, don't know how to, how to. My experience at least is that people simply don't know how to use the tools that are already available through to them.

Speaker B: Yes.

Speaker C: To improve their, their uh, their efficiencies in their day to day work. Because um, because it just hasn't been explained to them the same way. If you rolled out you know, an S4 HANA program or a Salesforce program and you didn't tell someone how to use it, um, they would not be getting the maximum efficiencies out of that tool. And so I think a lot of companies these days it's hey guess, right? We've, we've installed, we now have all have access to Claude. Great. We don't have, we've all got now Microsoft 365 copilot licenses. You know, um, go forth and conquer. But people are like okay, you know, so what am I using it? Am I just using this as a chatbot? Like a chatbot that they don't even, they don't haven't been taught all the nuances that of connected and all that kind of stuff.

Speaker B: Right.

Speaker C: So I think um, that's in my opinion also one of the, the hurdles that uh, that is, is a barrier to AI transformation. It's a lot simpler I think in some cases than, than uh, the complex use case reasons that are often spoken about. Of course it's both but I agree.

Speaker B: Um, you know there, there's also data out there that shows there's a quite a lot of value to be captured by automation of the tools, not necessarily the innovative uses of the transformation applications of the tools. So you know there's arguments on, on both ways. Um, and, and I, I don't think all the automation opportunities have been taken advantage of yet either.

Speaker C: Yes, yes, agree, agree. As always it's multifactored these things. But exciting time for us, Teresa.

Speaker B: It is. So um, your company is the future of change, if I remember correctly.

Speaker C: Yes, that's right. Future of change. Yep.

Speaker B: So tell me what's coming down the pike for uh, the change management profession. What's uh, what are change management professionals going to be doing in six to 12 months that they're not doing today?

Speaker C: So I think um, if my sort of body of work and my thesis, if you like, on how this technology can be implemented is successful and sort of accurate in the way things are heading, I think the big shift, and this is kind of the real, the premise behind what I sort of put forward, what I argue, what I advocate for, is a shift from what, what I call AI, uh, assisted change to what I call AI LED change. And I'll explain kind of what I mean by this because it's about moving from change as a practice being reactive, uh, to being more proactive. And so why do I say that? So at the moment, if you think a lot of, of what we, what we do as change practitioners is dependent upon uh, is reactive to data that we, we have to wait and collect for. So typically, you know, the standard process might be we have to invest heavy amounts of time and labor to, to, to draw out impacts from speaking with our stakeholders about a particular change. Um, then we, then we might have to send out a, a survey to ask people, our stakeholders, how they are feeling. We wait for that survey to come back and then we wait for this data to come to us before then saying okay, the data of the impact said this, the results of the measurement survey said this and therefore we need to now do X or Y. Right. This is kind of the basis of the way the profession has been up until this point 40 years. But I think with. Exactly, exactly. And so I think that the shift that I think we should be moving to, and I think we'll move to is less of that waiting and more of the real time interpretation of this data leveraged by the capabilities that are brought to us by AI and then being um, proactive or immediately reactive if you like, as opposed to reactive in chunks. Let me give you an example about what that might look like. One of the things um, I helped, ah, a program roll out recently was uh, what I refer to as a change companion. And a change companion is simply my uh, terminology for uh, a chatbot that is designed for stakeholders to ask questions about a program. We created this, it's effectively just an agent. We created this agent. We gave it the data as part of its knowledge base that we wanted people to be able to have access to. So yeah, so the, that forms its knowledge base. We marketed it and I think it was very well received because people were just like you know, pinging it, pinging it, pinging it. We got really good sort of interaction. And so what does that mean? It means that at the end of every day I run a report that says, what are the questions that people are asking about this program or just in general? And what that tells me is a bunch of different things. It tells me, uh, here are the main things that people are worried about. Here are the main questions that were asked that were able to be answered from the data in its knowledge base. Here are the questions that were. Here are the main questions that were asked that was not able to be responded to within its knowledge base. And so then we take this information and to be honest, I mean this is not an end of everyday activity. You know, it's in every sort of week or every fortnight.

Speaker B: Sure.

Speaker C: But the point is, even at the end of every week you can look at it and be like, oh, do you know What? I had 50 people ask a question about how the, the warehouse staff were going to be impacted. That tells me that maybe next week I should do a comms that includes how information about how the warehouse staff are going to be impacted.

Speaker B: Right.

Speaker C: And then I am, I am meeting that need before I get told through my conversations down the track. Uh, this group of people are worried because they don't know how it impacts them.

Speaker B: Right.

Speaker C: Um, or another use case which I think is very beneficial is we're also finding that the questions that the tool was not able to, that didn't have the answers for. And that might be because, you know what, the people always want information before, maybe even the program or the transformation knows what the answer is. Um, but what we can do is we can then simply say to people, by the way, we understand that there's an interest in knowing when the next release state is. Um, but we'll have this information to you as soon as we know it. So at least expectation, manage the expectations. And so all of a sudden this is just one use case, right. Within AI that highlights that all of a sudden we, the team now is being significantly more proactive than has ever before been possible. Which down the line will translate into ah, um, better stakeholder results, people being more informed. The typical, uh, stories that come towards the latter end about I wasn't informed in this, I'm not getting enough comms. Regardless of m. How much effort the team has put in will start to be diminished, uh, because you're putting out the fires before they actually start. And so I think that um, this is my prediction for how change management is going to be vastly improved by this technology. It's uh, enabling us the ability, unlike ever before, to respond at a pace that was just not practical or feasible.

Speaker B: Yes, I think it's uh, definitely going to give us the anticipatory capability and the predictive capability to address, uh, things real time or in advance. And uh, that's going to save a lot of angst. Um, and I think the result can probably be less change fatigue in the organization over time. Um, with multiple initiatives, if people are starting to get those red flags sooner and the change team can actually react to it, it more quickly. So, um, there's a lot of opportunity here for change professionals that I'm really excited about because, you know, we haven't had a new exciting tool or something to leverage our work in 40 years. I mean this is, this is a huge opportunity and uh, for us to wrestle this rabbit to the ground and really, really, you know, get it and jump in and optimize it is something that we really need to take advantage of as a profession.

Speaker C: No. Yes. I couldn't agree more. I think the opportunities that lay ahead of us are quite exciting. That's kind of why I decided to sort of double down in this space because I think it's improving all the time as well. I think the truth of the matter is this technology is now uh, the worst it will ever be and it's already so good, which is of course hugely exciting for what lays ahead.

Speaker B: Right. That's fantastic. So we're about out of time. Elliot, um, do you have ah, a thought or a key point you'd like to leave with our audience? Um, about our conversation today?

Speaker C: Well, Teresa, the conversation has been great, so thanks again for having me on. I think, think, you know, as a parting thought, I mentioned before, like my, my view of the difference between sort of AI assisted versus AI led. I didn't sort of really get too much to pack into that. But, but what I would say, I guess in closing is that for me, you know, AI assisted is, is giving change practitioners the ability to do things faster, which anyone that works in this space is going to appreciate. Like who doesn't want to do the same work faster, like everyone.

Speaker B: Right.

Speaker C: Um, but, so that's what I would call AI assisted, um, hugely beneficial. But the bigger case for us and what I sort of put forward, um, as part of the work that I'm doing is what I call AI led. And that is more than the same work faster. It's how do we transform the way in which we do change because of the technology. And that's kind of the direction, I think, think that. That, that we can move in. I think if people can, um, sort of get behind that concept, which is maybe step one, how can I leverage this technology to do what I'm currently doing and save a bunch of time and maybe be. Be a bit more exciting? That's already a great first step, but then I think the next step is how do we leverage it to transform, uh, the way in which we do change? I think that. And there's definitely, uh, the opportunity is there. And so I think that's the direction we should be aiming for more than just the same work faster.

Speaker B: I agree. Well said, Elliot. Well said. Thank you so much for, uh, being on the show. How can people continue the conversation with you? Where can they get in touch with you?

Speaker C: Yeah. So always happy to engage with fellow change enthusiasts that can be reached on LinkedIn or just, uh, anyone can head to futureofchange.com and, uh, all. All the information about the company is there, the playbook is there. Uh, the tools that we are creating to help, uh, change practitioners are all accessible from futureofchange.com Fantastic.

Speaker B: And I just want to thank you for doing that work and making it available to everybody. That's a very generous, um, piece of work that you've given to the community.

Speaker C: It's an absolute pleasure. Um, so, yeah, thanks, uh, for saying that.

Speaker B: Yeah, definitely.

Speaker C: Well, it's been great. Teresa, thank you so much.

Speaker B: Yes, you're welcome. Thank you.

Speaker A: We hope you enjoyed this episode of the Change Management Review podcast. To get alerts for new episodes, be sure to follow us on LinkedIn, subscribe to our weekly newsletter, or subscribe to follow follow on Apple, Spotify, and wherever you find great podcasts.