So welcome, everybody. Our topic today is reimagining work with AI. I'm Greg Worman. I'm the cofounder of ExperiencePoint, and I'm being assisted today by ExperiencePoint's amazing Kiara or Kiki for short. And you're in the absolute right place if you want to help advance your AI strategy through reimagining work. Folks, we're recording this webinar, and we'll be sure to make it available to you. But you're gonna wanna stick around for the full sixty minutes today so that you can get access to our Reimagination Lab. So this is a tool that we've built to help you and your colleagues rethink your own work in the era of AI. But before we jump in, just a few words on ExperiencePoint. At ExperiencePoint, we believe the best way to learn anything is through experience. So our mission is to help people gain that experience in ways that are faster, safer, and sharply focused on the things that matter the most. These are just some of the organizations we've had the good fortune of working with. And that's really enough about ExperiencePoint. We wanna get to the more interesting presenters on this Zoom. Also, these are people that ExperiencePoint has had the pleasure of working with. Today, we have the honor of being joined by Tara Dignam of Autodesk, Laken Masterson of Indeed, and Jamie Li of Manulife. And they're each gonna walk us through their own story of how they've been reimagining work with AI. Now if you have questions for any one of our speakers at any point, I ask that you please pop that question into the chat, and we will endeavor to get you an answer before the end of the webinar. Before we begin, we do want to take a poll. So we're going to ask you three questions, each with two possible answers. Choose the one answer that you feel best describes you or your organization's adoption of AI. Now, Kiara, Kiki is going to drop the link in the chat. I believe it is now there. Yes. So please go ahead and complete the survey. We'll give you about thirty seconds for this. I'm gonna leave the poll open if you're still trying to, wrap that up. But before I share the results, I first am gonna share with you a story, and this one comes from the world of professional sports. Okay. So imagine you're running Major League Baseball. And one thing, of course, that people hate is when the umpire gets the call wrong. And by people, I mean absolutely everybody. So not just the players and the coaches, but also the fans. And now with AI, you can determine balls and strikes more accurately than any umpire ever could in the history of the game. So let me ask you, what would you do? What's the obvious solution? Well, most people, and frankly, most organizations arrive at the same conclusion. You replace the human. This is one of those tasks where you will get better results if you outsource it to AI, and that's exactly what Major League Baseball tried. They experimented first in the minor leagues. AI made every call, and then the umpire had a little earpiece where the call was communicated to them. And the umpire's job was to, in turn, sort of publicly announce the call, so ball or strike. And, technically, it worked brilliantly because accuracy actually did improve. But then Major League Baseball realized something profound. They'd optimized the task without improving the overall experience. They'd focused on the wrong question. Everything changed when Major League Baseball stopped asking how AI could replace the umpire and started asking how AI could help create a better game experience. Today, as many of you probably know, the umpire still makes the call just as they have for over a century. But if the batter or the catcher disagrees, they can challenge the call. So the AI takes a look, and in mere seconds, the decision appears on the stadium's jumbo screen. The call is confirmed or overturned in front of everybody. AI didn't replace the human. It created a completely new experience for fans, players, managers, and, yeah, even for the umpires. It's a completely new level of engagement. So when calls are overturned on the stadium screen, fans get to jeer the umpire. They get to celebrate their team. Teams, have a limited number of challenges, so there's actually sort of a new strategy required. And corporate sponsors get a little more mindshare in the game. As you can see, this overturned call was brought to you by ExperiencePoint. So that's the lesson. If we simply ask, what can AI do better than people? Our answer is almost always going to lead to replacement. But if we ask, what becomes possible because AI exists? We get reinvention. Major League Baseball didn't use AI to build a better umpire. They used AI to build a better version of baseball, and that's the opportunity that exists today in every organization, not simply doing today's work faster, but designing work that wasn't possible before. So now I'm curious to see, based on the answers of our entire audience here to our poll, where most of our organizations are at. Wow. Okay. It's a little more evenly split than I thought, but definitely we're skewing towards replacement thinking versus reimagination thinking. So, on the sci fi analogy, most of us are definitely thinking AI is providing us with a bit of a booster rocket as opposed to a time machine. Most of us all, in the look around test, are seeing people do the same things with new technology, although quite a bit of us are seeing new things with new tech. And then finally, the Hollywood film, it's more like Speed than it is Inception. Okay. Fantastic. So thank you very much, everybody. It looked to me like a fairly even split even though there was a little more replacement than reimagination. We have a lot to learn from one another about the type of activities that are gonna help us make the leap. And no doubt, as we engage in our reimagination activities, we're bumping up against all kinds of opportunities and challenges where we could use some help. As I stated earlier, ExperiencePoint believes the best way to learn is through experience. And so today, we're gonna learn through the experience of others. And we're gonna start with our first guest, Tara Dignam. So she's a learning solutions designer at Autodesk, where she's helping lead the learning side of the company's AI transformation. She designs programs that build AI fluency across all levels of the organization and has a particular talent for complicated ideas and turning them into learning experiences that actually stick, which makes her answer to our opening question especially appropriate. We asked everybody what's one task you would gladly hand over to AI. Tara's answer, approving her husband's meal choices. Apparently, she has become the chief meal approver in the household, a position I'm sure that comes with considerable responsibility and, I assume, absolutely no compensation. Tara, it's great to have you here with us. Quickly, for those that don't know, can you tell us a little bit about Autodesk and what Autodesk does? Hi, everybody. Thanks, Greg. Yeah. Autodesk, I suppose, is best known for its flag product AutoCAD, which is used predominantly in the design of buildings. Architects would use it a lot, but it's now a whole suite of design software from film to music to computer software itself. So it's really everything that touches design is kinda touched by Autodesk at this stage. Which makes it sound to me like there would be lots of opportunity for AI in your work, maybe even in the product itself. Would you be able to tell us a little bit, about your AI journey, and where you're at, how you got there? Sure. So I guess I joined Autodesk only a short while ago, a year and a half ago, but Autodesk has been in the AI arena for a good ten years now. So before I'd even really heard mention of AI, Autodesk was already building a bank of information on that. But when I joined, we decided we needed to move our AI usage forward across the company. And we have a company full of people who are engineers, developers, etcetera, who are already getting stuck into AI usage. But we needed to bring everybody on this journey. And we initially, and this was back in November, which feels like a million years ago now. But initially we were kind of focusing on the tools, prompt engineering, responsible usage, things like that. And we quickly discovered that that wasn't going to help people actually use AI in the most productive manner. And quite like that example you use there with the baseball, we were focusing in the wrong area. So we had to move, I suppose, from knowing how to use AI to a point of where to use AI. And where we're at now, I guess, is we're trying to promote a reimagining of work and workflow within Autodesk, considering AI as a teammate as opposed to just a bolt on tool that you would use to, you know, formulate your emails or or things like that. You know, think bigger picture at this stage. I'm really curious to know you mentioned, helping figure people figure out where to use AI. Did you establish some sort of guidelines, ways to help people determine, as the prompt says on the screen, helping people solve the right problems? It's we actually came up with a a framework because what we wanted to do was move from this micro territory where we were with our trainings, kind of introducing people to these various tools that we have. Look at this buffet of AI tools we have for you and kind of get them to take a step back and go, okay, well, what isn't working here? Is named attention and see how AI can come into this? And the only way we could think of encouraging people to experiment safely, and it was in an atmosphere of safety, was to develop a framework within which they could work. So we developed our own framework that we labeled PRIME. And PRIME is P for pick your workflow, R for redesign or reimagine, I for implement guardrails, which is where the kind of the human value comes into it. Kind of you've ticked those boxes. Now it's time to set your milestones for your human judgment and what's safe to do, what's not safe to do, what's the downstream effects of this. Then M is oh, god. What was M here? Oh, yeah. Meaningfully experiment. And E is evaluate and iterate. So with that, we are building out sprints for teams across Autodesk using, the PRIME framework and our own agents as a coach to help people kind of consider how best they can use AI as part of their whole system. That's fantastic. And I'm curious to know, with that framework in hand, what has been most challenging for people along the journey? I would say and I think and I'm speaking from a personal point of view as well, not just the learners I work with. It's been taking what we know of tools that we have up until this point and I suppose divesting them of their authority. If you look at, let's say, Excel or Word or a calculator and the laws of physics, there's a right and a wrong, you know, and the computer tells you what's right and what's wrong. And now we're getting the same thing from AI in a very authoritative tone. This is the truth and these are facts. And it's leaning into our human judgment more so now and saying, actually, that's incorrect. What you've written there is entirely incorrect AI or what you've given me here is nonsense. And it's having the confidence in your own knowledge. Think claiming that back is the tricky part because for so long now we've been used to being told by computers, you know, two plus two equals four. And now we really need to realize that we are the value add. It's not the AI. We're the ones that are going to be saying this is correct, and this is appropriate, and this is what works. And I think that that has been a tricky part for me and for, I think, a lot of people as well to realize that our our value in this whole system. Yeah. I I totally agree with that, and I loved earlier when you said that people needed to see AI more as a a team member. Now it's a bigger shift too because it's a team member who's really earnest in there and providing you with results and suggesting that those results are correct. And then when you look under the surface, it's like the team member has made a lot of stuff up. So, I'm also curious to know what are some of the things that, you wish you had known back when you first started that you now know and that could help others who are about to go on this journey? I would say this. I think I wish I would have understood how in the weeds I was with AI tools. It took me a while to take a step back and kind of realize that using AI doesn't mean getting it to schedule a calendar reminder or this, that and the other, Sure, that's great. But it's so much bigger than that. And to be able to just stop what you're doing and say, okay, enough. We need to stop this now and reimagine what we're doing. Because I've seen that we did this ourselves at the very beginning when we launched our learning strategy. We went about it in a very traditional way, you know, like introduce the information and scaffold learning, etcetera, etcetera. But really, I came to a point where like, woah, this isn't going to work because this is very different to anything we've taught people. And by the way, we're now behind the curve. People are already implementing these tools far beyond what we had expected at this stage. So it was, I guess it was kind of like calling my own knowledge into question and constantly reevaluating and being as nimble as absolutely possible in this context of shifting sands and saying, it's not about the tools, It's about the way we think about the problem. Well, with that in mind, I have another question for you. But before I ask it, I'm going to encourage other folks if they would like to ask a question of Tara to go ahead and put that in the chat, and we'll go from there. But while we're waiting for folks to put those questions in, With the whole notion that this is different than any other learning that you've had the opportunity to design before, in your intro, I mentioned that you specialize in building learning that sticks. ExperiencePoint has a vested interest in making sure that our learning sticks. What are some of the techniques that you've found with AI that you need to ensure you use so that learners walk away really remembering and are prepared for application, the various lessons that you've taught? I guess in this instance, it's kind of it's a behavioral and it's mindset a shift. And that's quite tricky to assess. So we went about it. I suppose we wanted people to employ their their their new tools. We wanted them to, change their way of thinking about how they implement AI. And what we wanted to do was to be able to use that information and share it amongst others. We wanted people to know that what they're doing, their reimagining of work has value in the company and value for everyone. So we're creating a repository of people's experiments. And we want to spotlight the experiments that went awry, the ones that didn't work, because we find that there's the most learning in those, and they're the most valuable for people across the company. And we want people to know that you you created an agent, and it's doing this. People feel like, oh, it's only the developers. It's only the software engineers. They're doing great things. It's like our second most used agent at Autodesk is an agent that writes your SMART goals for you. You know, and it's just like, see, this is what people want and this is the thing we want to share this, but also how did you get there? What was your journey? Where did you find the blockers and share that across the board? So we're trying to highlight people's failures, I suppose, so that everyone can learn from us. And there can break down silos and people can help each other out and say, I worked around at doing this. Thank you for sharing your experience. Let's work together. That's fantastic and very instructive for us. I see I asked for questions, and now I have way more than we're gonna be able to get to. Maybe I'll just ask you one more. This one from Colin Hunter. What has the impact been on leaders in terms of creating conditions for their people or not? Sorry. I just I didn't hear that. Greg, could you say that again? Sorry. Yeah. What has been the impact on leaders in terms of them creating the conditions in the organization for success or or, you know, what are some of the mistakes leaders have made? I assume, Colin, in your question there about not doing not to create the conditions. That's that's actually a really interesting one because we have found that it's been it's been a varied response. And I think there are a lot of leaders find it hard with AI being used in various ways across their team and them not being the only person with the knowledge. Not the only person with the knowledge, but the person with the most knowledge, let's say. A great flattener, is AI, and it kind of breaks down the hierarchies within teams. And we found that leaders who are able to embrace it and be there as a support and a coach to their teams, those teams are flourishing with AI and they're really experimenting with psychological safety and they're very happy to do so. But there are leaders that have struggled with that and having team members far more junior than them being far more successful in their AI usage. And they're the teams that haven't succeeded as well. And I've been a bit, I suppose, stymied in in their progress. Tara, thank you so much for spending time with us today. Thank you for your responses. There are far many more questions in the chat. I don't know if you'll have an opportunity, but if you can reply to some of them, I'm sure our audience would appreciate it. Absolutely. Thanks, Greg. Yeah. Thank you. Alright. Next, I'm delighted to introduce Laken Masterson, VP of operations at Indeed. And Laken has spent more than sixteen years at Indeed building and leading high performing teams and figuring out how to make complex operations work better at scale. Now more recently, she's been applying that expertise to AI and automation, finding ways to dramatically improve efficiency without losing sight on the client experience. And as we're gonna hear today, that work is increasingly about more than simply making existing processes faster. It's about rethinking how the work gets done in the first place. So I asked Laken the same question we did of all of you. What's that one task you would happily hand over to AI and never do again? And she promised me an answer, and she has kept me in suspense. So, Laken, welcome. And before we get into the serious stuff, let me know. What are you handing over to AI? Mine thank you. Mine is taking out the garbage. And so at first, I thought to myself, okay. There's no way that AI can help take out the garbage. And then I decided to challenge that assumption, and I was reflecting on all the steps that it takes in order to do it. And I spent spent some time literally going through, like, how can AI help. First, I asked the question. How can I build a robot to help me take out the garbage? And I asked the question, can AI help me build a robot that takes out the garbage? And so, you know, I think in general, it's a silly it's a bit of a silly one because one might imagine little robots kind of in the house doing the thing, but, like, it's actually practical. Like, I had said that AI can write the first version of the model. I could design a remote control that works. It can do a whole bunch of quality assurance. So I think, in general, it's a it's a funny one because I hate taking out the garbage, but it is actually, like, a lot closer than I thought it would be. So it's a good it's a good reminder to just continue to pressure test. I love it. I love it. And I'm first in line to purchase either the fleet of mini robots that you have that are going to do that or whatever other solution you could come up with. Laken, I think many of us have seen or heard the ads, but could you tell us a little bit more about Indeed? Oh, sure. Yeah. So I'm lucky enough to have been part of this journey for the last sixteen years, so it's been a lot of fun. And I was reflecting on this because if you had asked me what Indeed was a couple years ago, I would have said it's the number one job site, and I probably would have focused a lot on the 360,000,000 unique visitors that we have every single month, which is still amazing and impressive and fantastic. But we actually have changed a lot of our messaging and narrative in the marketplace around how we are AI powered. So the new kind of way that we're thinking about our business is it's evolved from being evolved from being a comprehensive job search engine to an AI powered two-sided marketplace that connects job seekers and employers. So we're intentionally putting that at the forefront of our work, and the main goal is to ultimately help those connections happen simpler, faster, and more human. And so there's this balance in the work that we do around how do we make sure that we're using technology to the absolute best of our advantage for all of our job seekers and employers. But we inherently know that hiring is human, And so it's finding that balance of using our technology to help people connect faster, but allowing humans to make the decisions that are right for them, both from an employer perspective and a job seeker perspective. So we've technically used AI in our work for the last two decades, starting using, you know, matching models and LLMs and things like that very early on. And now our products are extremely AI focused and AI, like, led, and so are our teams. So that's the transformation that we've gone through. That's outstanding. I love the fact that you're looking across the offer and trying to figure out what are those things AI is better at doing versus what are those elements that still need to remain uniquely human. Are there any examples that you might be able to share with us of how AI has been integrated into the offer? From an external perspective, we literally have an AI sourcing assistant, an AI screening assistant that recruiters can use in their job to save time, money, effort, etcetera. So we intentionally talk about that in the marketplace around things that we offer. And internally, it's it's like AI is built into every single thing that we do now to try to make smarter decisions, but we are really focused on the ultimate hiring decision should be made by humans. And so we'll do everything in our power to understand and collect information around the employer's needs and the job seekers' needs and to find that right match and that right fit. But at the end of the day, the humans should be making the decisions. And so those are it's an interesting balance because sometimes we'll try to test something where we try to say, like, hey. This is absolutely the best candidate for you. Like, we know it. But we certainly don't necessarily position it that way. We say, here are the list of top candidates, and it's up to you to decide how to proceed with that. And we're trying to maintain that balance. So that's been an interesting example. I'm also curious to know on the operations side internally if there are any examples you might be able to share about how AI has been integrated into your into your efforts. Oh, 100%. So my job is to oversee our operations team. It encompasses client operations team, program management team, and all of our business process outsourcing for all of Indeed. So it's everywhere is a quick answer, but a specific example is, for me, it really comes down to what is the work to be done and what is the best way to do that work. And so we actively have categorized every single piece of work that our team is doing on the operations side, and we've identified the the highest volume of activities that cost us the most amount of money. And we've looked at it from a lens of not just remove the work or reduce the work, but what is the absolute best client experience that we could provide? So we start there and redesign. And so a recent example that we've done is we've we're launching our TalentScout AI first experience on our help center and soon to come in our employer accounts where, basically, clients can just ask a question, and the answer will be surfaced to them right away. So instead of saying, you know, how do we optimize this path that looks somewhat like client has a question, client asks sales, sales asks an internal team, the internal team figures it out, and then kind of goes back to the client, we're literally just putting the the knowledge and information into an AI system that could be produced. And and I know that a lot of folks are doing that, but on our side, the major takeaway is start from scratch. Like, don't just optimize what exists. And that was what we were starting to do, you know, two or three years ago. Look at this process. Where can we cut? How can it be better? Now it's like, does this process need to exist? Like, does this process serve us? Does it serve our clients? If our to get them the best experience, then let's change that entirely and spend all of our time understanding those inquiries that they have and how to get them the answer as quickly as possible. So that's a good example lately. Yeah. Definitely. It sounds like, rather than replacing the umpire, adding a whole new element to the game, which is phenomenal. Yeah. In your experience, what has been most challenging for people along the way? My personal experience is that I you know, I'm always, like, in a in an efficiencies mode. And so, like, I I'm one of those folks who, like, I hate standing in line. I'm always redesigning or reconfiguring the way that we should get on and off airplanes. Like, those are the types of things that I think about on a regular basis to try to Try to make everybody's life more efficient. And sometimes my my perspective is sometimes I'll go a little bit too far. So I'll say, like, imagine a world where we don't have to do this work at all. Imagine a world where our clients have this delightful experience. And it's no one's fault, but it's really, really tricky to lift your head up on something that you work on every single day and say, like, how can this exact thing be redone? I think we've all experienced that. I just even the garbage example I was thinking about. But, like, it's like, how do we get from here to quantum leaps ahead versus how do we just iterate on the smaller components? And that's the work I've really been doing within my team to to help bridge the gap. And I know you didn't ask for this, but we worked with ExperiencePoint on a couple engagements that just help our leaders think differently within operations. So we did a huge sprint with y'all where we taught the team about design thinking and human human centered designing and things like that. And I literally still see it showing up in the teams day to day. So the way we used to think about problems in terms of, like, oh, how do we, you know, add something to superpower us is now like, well, I wonder if we never had to do that. How would we wonder if the client needed this thing. How can we best deliver it? And it's been really, really neat to watch that. And so you've actually helped us bridge a little bit, but I think the hardest part is lifting your head up and finding the right space to do the work to actually help you achieve what you're looking for and go further than you would if you just kind of iterated slowly. Yeah. I I love the notion of the art of subtraction and being able to just remove those things and questioning them, which I think AI has presented us all with an opportunity to do for the first time in a long time, and then deciding what really does deliver value and keeping those things and those that don't, finding a way not to do them anymore. Sorry. I just Sorry, Link. I think I spoke over you there. It's okay. Go ahead. I was just agreeing with you. Oh, okay. Hey. What's something that you did to set people up for success, that you're most proud of? In general, the partnership with y'all was helpful. I think that that helps us kind of embed how do we want our leaders to be thinking about these activities within their own team and to question and subtract wherever possible. We also have this this program internally called our AI Explorers Program where we have asked folks if they're interested in being on the forefront of learning brand new technologies and looking at things differently and basically building their own solutions to them. And so we have six or so folks from the operations org involved in that, and they they're partnering with our internal AI for Indeed group that is really leading the charge on the transformation of our business in this way. And what I think the the key takeaway is is basically, like, giving them the time to work on it. And I'm still working on that. Like, even if you ask the folks that are that are in the in the program, they'd say, like, oh my gosh. I wish I could do even more, but there's only twenty four hours in the day. So in general, it's like setting an intentional program, working with external partners who can help us push the boundaries, and then putting people in charge of the actual work and empowering them and enabling them to be able to do that work. You could say to everybody like, hey. Go use AI in your day to day, and you you don't get anywhere. You have to kind of concentrate and make a at least in my experience, you have to concentrate and make a concerted effort to do it. The effort and the scaffolding. Laken, thank you so much. And now I would encourage folks again, any questions that you have for Laken to please put them in the chat. And Asif is gonna kick us off here. A question for you is how do we ensure I think how do we ensure data security while embracing AI in our work? So the only the easiest questions for you, Laken. Of course. Yeah. So so I am in a very fortunate position that Indeed has entire teams dedicated to this. One of the main things we care about is ensuring job seeker trust with our platform. So we have entire trust teams that think about the security components and and and responsible AI practices that we employ. So I'm lucky to use the scaffolding that has already been created in this space. But I would recommend if your company hasn't to create your version of what do you want your responsible AI practices to look like and then use them in each one of the areas in your business that makes sense. Like, we're lucky. We literally have, like, a constitution written out that says, you know, AI in Indeed will never ever ever discriminate against x y z thing. And so, like, we use that as the baseline for the humanity that we as Indeed want to produce to the world. And then every single thing that we build references the constitution so that it will never, go past it. So I would just encourage your team if you don't have someone in charge, put someone in charge as trust and security and, have them provide the guidance to the rest of the organization, which we're really grateful to take advantage of at Indeed. Thank you for that. And one last question for you, Laken. Pam is asking if you could say a little bit more about the AI Explorer Program. What is the framework, or what are some of the program elements? Oh, sure. So thank you for asking, Pam. This is a operation specific program, but we're taking advantage of another offering at Indeed. So the AI for Indeed team is helping every business within our org on our AI transformation processes, and they provide a structure to us that says, like, hey. We're gonna work with you for the next I think it's four months or five months. We'd like you to assign some architects, some builders, and some people who are really interested in actually doing the optimization work. It's what we've done is we've created like, we've got six people on the team. Couple of them are builders. Couple of them are captains. And then I I and a few others are some exec sponsors that kinda help guide the work. And we do check ins every month with the team on, hey. What are the new we have we basically have a process to say, gather ideas. So idea generation is at the forefront. Like, anybody can submit an an idea, and then we have a process for evaluating which one of these ideas is gonna get us the furthest, which one of these is gonna provide a better client experience, save us time, save us money, save us effort? So we prioritize at the leadership level there, and then we literally let the team go build it with help from the AI for Indeed team. And so we're we're ultimately building product managers in the AI world who are also operators. So our goal is that our whole team actually ends up in that position. Because if you look three years from now, that's going to be the role of an operations team member is to figure out how to automate the work that they're doing. So we're taking the learnings from this program, and we intend to expand it out to others. But it's basically a structured program where people can raise their hands, say they're interested, and get after real problems, which has been really useful for us, and I'm happy to share the the framework ham with you afterward. Laken, thank you so much, and thank you for offering to share the framework, and thank you for generously sharing your experiences with us today. Really appreciate it. And now I'm delighted to introduce to you all Jamie Li, AVP of learning and workforce capability at Manulife. Jamie is a recognized thought leader at the intersection of AI, workforce transformation, and human performance. Her work focuses on a question that's right at the heart of our conversation today. How do we help people and organizations actually thrive as AI changes the very nature of our work? She's helping Manulife answer that by building the capabilities, leadership practices, and new ways of working needed to move from simply adopting AI to genuinely reimagining what's possible with it. And, apparently, Jamie already has one very specific job in mind for her future AI agent. When I asked her what she would happily hand over to AI, she said buying concert tickets. Because as Jamie put it, buying tickets these days feels more like, or feels less like a transaction and more like a competitive sport. So her dream is an AI that knows what she likes, watches for tickets that go on sale, joins the queue, finds the best seats within her budget, and simply sends her a message saying, good news. Guess who's gonna go to b t BTS this weekend? So, frankly, Jamie, I think you really have identified the killer app for Agentic AI. And, again, I'm lining up to to buy as soon as it's ready. We're thrilled to have you. And as I've asked our other guests, if you wouldn't mind just saying a little bit more about Manulife so, those that are unfamiliar can, understand what your organization does. Sure. Thank you. And it's a pleasure to speak to all of you today. Manulife is a leading international financial service provider, and we are headquartered in Toronto, Canada. We're anchored in our ambition to be the number one choice for customers, and we operate as Manulife across Canada and Asia and primarily as John Hancock in the United States. And, this is where we provide financial advice, insurance, and health solutions for individuals, groups, and businesses. Thanks, Jamie. And I know, you have been on the AI journey for a while. I'm wondering if you could tell us where you're at and how you got there at this particular point. Sure. So our AI learning and capability building journey started in 2024, when we recognized that AI wasn't just another technology rollout. And so building on the digitization transformation, Manulife began back in 2017-2018. We saw the potential, for AI to fundamentally change, you know, how work gets done. So we knew we needed to focus on, on the capability of our people as well as the technology itself. And what's I think the most important thing as as as we talk about our journey is that we never thought of this as simply an L&D initiative, operating on its own. From the very beginning, our work was tie tightly connected to our broader AI transformation strategy. So if you think about work streams and cross functional teams, that technology working towards our transformation agenda, leadership commitment is another piece, governance, learning and capability building kinda rolled up into, how does that show up for our our colleagues in adoption and enablement? And this was all designed, in in concert together. And we also view learning not as an end goal, actually. We look at as an enabler, for business transformation. So if I take a look at, you know, where many organizations started, we too started in building awareness, foundational skills. We had learning pathways. So we we start off with personas. You know, we have our AI foundations, AI for leaders, and then we had a very overarching persona of producers and consumers, again, just to kinda get everybody into, learning more about, this area. And then slowly through the evolution, it became more role based based on needs. Leadership programming got had AI embedded into it. We started communities of practices, promptathons, like most of us start off with. And and through there, we also provided practical resources. But, well, I will say is probably one, interesting and probably differentiating piece is we established an AI champions network. Initially, it was to harness the energy that our early adopters had, those who love to learn, and it but it actually quickly evolved into something bigger. These champions actually became more like peer led change agents who had a, a partnership with their AI leads in their segment and their business. So it it it was organic at the beginning, but we were trying to find ways to be disciplined and harness that momentum in a way that we can focus and scale. I think the other piece as well is, as these champions, were working within, their their peer group, they were able to apply a lot of the AI knowledge, tips, and things like that, throughout. So a lot of use case sharing, a lot of, lessons learned kinda share more broadly. But then it start when people started to be more comfortable with AI, the conversation naturally evolved away from how can AI help me do this task faster to should this work be done differently altogether. And that, I will say, came hand in hand with the the business strategy around that. So I think the mature the maturing process happened at the same time at at this yeah. Happening at the same time and kinda converging. So, really, when we where we are today is our success isn't just technology alone or learning alone. It's actually treating it as more as an enterprise transformation effort and how everything, strategy, tech, leadership, learning, and chain, all reinforce one another. And that's really been the foundation that allowed us to move begin to move beyond adoption to more reimagining work. And that's a pretty rapid evolution, it sounds like, if, you're saying 2024 and now today, you're at this point where there is this fully integrative approach. I'm curious. What were some of the things that you think really kind of sped you along? You know, what what worked really well? I think a combination of that like I said, from the very beginning, our work stream was part of the larger AI transformation. So what that means is there is, buy in. There isn't we're not wasting time trying to convince people. I think we have the opposite problem. It's trying to manage the the the request coming in to prior prioritization. So, at the enterprise level, there were some very key themes that we focused on for for those for that work stream. But we also then allowed the business to decide where they want to play as well given they have a very specific business strategy. And where we converge was, you know, is it that they curate based on what we've, developed and designed and piloted so they know it's a sound and and and great program? Or do we come in and consult because there is a pool of people who have, experiences and also conversations, discussions across the organization. So it was a way to harness it. The rapid part of it was we actually part of my team, we have a dedicated team who's part of a a sprint squad, and our sprints are three weeks long. And we just go through so the the beginning of the sprint, we decide what's the priority. We all agree on it. There are intersections throughout those three weeks where we come together and share progress. Do we quickly course correct? Are we going to the right thing? And at the end, it's a demo. It's not a talk about. It's a show. So it really, puts a a fine point on what are we focused on. And if we feel that we need other capabilities that are sitting in other teams, we think about, short term assignments. And, also, at some point, we just we were thinking like, okay. The demand is growing. The need is growing. The scope is growing. Then we also took a look at resourcing. Not just budget, but, like, where are we spending our time. And very much on a monthly basis, we go back up to our steer code. Again, make making sure on alignment, but also they understand where we are from a progress perspective. So while large, we acted nimble. It was messy sometimes, but I think everybody who was on the squad, understood that, and, we just kept the lines of communications open and very transparent on where we are, where it's working, where it's not. Curious to know, in that sort of large scale organizational transformation, there's always challenges. What has been most challenging at Manulife as people have gone on this journey? Yeah. And I think you hit it right there is we're large. Right? So not just from numbers, but just from a geographical, where everyone's located. So it it really is about figuring out, how to coordinate. Right? But I think the the key thing is we we look at this as an ecosystem. So that's kind of our lens that we look at things. So it's not my project, your project, my initiative, your initiative. It's like how where are all those intersections come in. And where it's intersected, we understand that certain decisions will impact others fairly quickly because a lot of our initiatives at at the outset, we we try to at least high level identify where those intersections or independencies are. I think the the other piece is making that switch that, you know, this isn't about technology only. Actually, more importantly, this is about people and our organization. So at the individual level, most people are are comfortable using AI to kind of prove existing task. But when you look take a step back and look at the bigger challenge, it's like questioning long standing assumptions. So I I would say that's the other piece. You know, there's a lot of people with long standing assumptions, and people don't like change, in general. But, like, once we understand, like, the why behind it, the value, and the impact, and most importantly, we're all part of that conversation because people people are closer to the work to the work, are generally the ones who can give us the information, the data, the lessons learned, and and also a bit of, foresight into what could be possible because they've spent time doing the work and thinking about it. I think the other piece I would say is, we we have to have a certain level of proficiency in I don't have a word for it, I coined it, like, AI acumen, like, understanding it and not just understanding the technology side of things because decisions need to be informed, where to invest, where to redesign, and where to maintain human judgment, which we heard from Laken and Tara as well. So, that AI acumen is is really important. So I think that is probably some of the challenges we've had, along the way. Jamie, thank you so much. Folks, if you have questions for Jamie, now's the time to pop them into the chat. Richard is gonna start us off here. Jamie, do you have any sense of how much time people poured themselves into this flow? And I believe that's referencing what you mentioned earlier. I'm sure it differs between champions and their peers. It sounds like the best of peers pulling each other versus top down directive push. So I think the time, it really is dependent on the individual and the role that they're playing and where they are in their own personal journey. But what I can say is that Manulife of, from a learning culture perspective, it's something that we take very seriously. So, I'll give you an example. We have something called Fuel Up, which is dedicated time for learning. Generally, it it it falls on, like, on, like, a second Friday of the month. So there's four hours dedicated. So from that point of view, if you want to go into those to those learning paths online, you know, on demand, self directed, there's there's pop there's ability to do it there. For folks who are in roles that it's hard to walk away from, like, especially a lot of our client facing roles, we make sure a lot of our our learning opportunities are are bite sized. But also the other piece is we're making sure that, more and more of this is available in the flow of work, meaning technology bound or even in business processes. So things like if you wanna talk about, and bringing up the idea of, like, what are some of the process we want to, reimagine? We have some tool kits that you could a a everyday, leader can have in their team meetings. So we're looking for different ways to do that. But I think from a champion's perspective, we've started off pretty organic, but now we're at a point where there are the there is an expectation, but it's also not side of desk work. So it's recognized. It's part of your your role or it's an objective. So there's some way to to track. There's a way to recognize, and there's a way to also set expectations. Right? So I think that's the other piece I will say, the involve the evolvement of our champions network. That's fantastic. Really appreciate it, Jamie. It sounds like having that kind of combination of learning in the flow of work as well as dedicated time for somebody to be able to just focus their energy and efforts on something that interests them both seem to be critical. And thank you so much for your description of Manulife's journey. This has been very valuable for all of us on the call. Alright. As we come to the end of the hour, I imagine a number of you are out there wondering, is there a simple set of tools for doing this type of work? And what is this type of work? It's inspiring us to think differently about the things that we are doing on a day to day basis in the age of artificial intelligence. And the answer is a resounding yes. ExperiencePoint has put some of our best thinking into a series of moves that anyone can learn. It's very simple, just two questions and four actions. And we're excited to share it with you all. And only thing you need to do to get access to our Reimagination Lab, is now a link in the chat. So go ahead. Kiki has put that link in the chat. Click on that. Survey will take you less than a minute, and what it does is help us shape our future sessions around what folks like you actually care about and what you need. So thank you very much, Tara, Laken, Jamie. If there are any other questions folks have, just go ahead and pop them in the chat. We have a couple of minutes, and, we'd be more than happy to try and address some of the other questions that might still be outstanding for folks. Well, it looks like we're all out of questions for the time being. Again, Tara, Laken and Jamie, thank you so much for being with us today, sharing your stories of AI adoption. Your generosity is gonna help so many of us take our next steps both more confidently and competently as we go on this AI adoption journey. And finally, everybody, we appreciate you all being here with us today. Again, please complete the survey, and in exchange, we'll give you access to the Reimagination Lab, which will be a a wonderful approach to eliminating, simplifying, strengthening, and creating new ways of working in the era of AI. All of the registrants for our workshop are also going to receive the webinar recording in the coming days. So with that, let me thank you all again and wish everybody a great rest of your week. Thank you.