Name: Adobe on AI: Navigating AI adoption across the enterprise
Uploaded: 2025-10-02T17:30:43.412Z
Duration: 1 h 4 s
Description: Adobe on AI: Navigating AI adoption across the enterprise

Transcript for "Adobe on AI: Navigating AI adoption across the enterprise": Hello. Welcome. Thank you everyone for joining us for today's session, Adobe on AI, navigating AI adoption across the enterprise. I'm your host, Lindsay Morris, from Adobe, and I am thrilled to be joined by Adobe leaders Emily McReynolds and Toni Van Winkle. Emily McReynolds leads global AI strategy for the digital strategy group at Adobe. Her role focuses on enterprise AI adoption. She has over fifteen years of experience in data governance, machine learning, and AI across technical research and industry, including at Microsoft and Meta. Bringing her experience deploying AI across multiple companies, she deeply understands the challenges and opportunities organizations encounter when rolling out AI. Toni Van Winkle is also here with us. Toni is the vice president of the digital employee experience team at Adobe. She's also the cochair for the AI at Adobe. Toni is a distinguished leader in digital transformation with extensive experience in strategy development, program execution, and organizational change management. She has been honored as digital workplace leader of the year. She has received the CIO 100 award, and she was recognized as one of the top 50 women leaders of San Francisco by women we admire. I have to say I admire Tony and Emily so much, and they are the perfect people for our session topic today, which is about intentional change management and helping you navigate the human side of AI adoption. So whether you're just starting your AI journey or you're already scaling your efforts across the enterprise, today is focused on actionable strategies, and we're also gonna pull back the curtain and share our experiences and best practices through our own AI transformational journey at Adobe. Alright. So here's how we're gonna roll. We'll get started with Emily. She'll set us up with some current enterprise trends and AI adoption. Then Emily and Tony will take us through their guiding principles and tactical applications for navigating organizational transformation with AI. K. Let's get our AI transformation journey started. Emily McReynolds is here with us. Welcome, Emily. Hi, Lindsay. It's so good to see you. Thanks for being here. Great to see you too. We are all so excited to learn from you and Tony today, so I'm gonna just hand it over to you. Here we go. You know, this is one of my favorite topics. We talk about this so much. I think it's really important as we're hearing all these headlines and seeing all this information about how to use these technologies, where they fit, how immediate and urgent it is that you learn them. You know, if you're inside a company really figuring out how you set up your teams and your colleagues for success, focusing on this AI transformation really being rooted in people, process, and culture. Those are big words. So we're gonna go through one by one and talk about what those mean. First, we're gonna share some of the things we're seeing today. So what we have in terms of research. I come from a long research background. I I started doing research in a university context over twenty years ago now. I think it's really helpful to understand where people are coming from and where they're at in their journey. So this research is from January. It was done with an organization called eConsultancy looking at how executives and decision makers are thinking about the implications for change management for bringing on this new technology. You know, it's it feels still new. It's, it's been two years since, generative AI really hit the consciousness. But in terms of enterprise adoption, we still see people who are just getting started, and don't think of that as being behind. We still see a lot of people who are piloting, figuring out what it looks like to scale this, to roll it out. But if we look at these numbers across the different stages of adoption, we're seeing a pretty flat graph. The challenges as you move through these are similar, which is part of the reason I think even if you're just getting started today, it's not coming from a position of being behind. So what are we looking at? A lot of considerations around governance and compliance, part of the reason we asked that question of you, thinking about what are the best use cases. First, we were talking about generative. Now the big thing this year is agentic. What do those things mean if you, have the ability to, like, look at these different technologies? You know, agentic combining all these generative systems gives us even new considerations. And if we're talking about what it looks like in your day to day work, what is that process? Where does it fit? A big consideration is how we manage this change, how we foster a culture where people feel comfortable with experimentation and curiosity. So I think these numbers are really helpful to see that no matter where you are in your AI journey as an organization or individually, there are real places you can get started and keep moving. So how do we think about this? We really like to talk about it in terms of reimagining new ways of working with AI. I know it's changed my day to day process, being able to ask questions in a different way, to have help in a new way. So what are the skills we individually need? We've there's a lot of research out there on what AI literacy looks like, what particular skills we should focus on, and how we do that training to really transform our workplaces. Figuring out what that specific training is can be really challenging. We'll talk a little bit about what it looks like in terms of roles, but also in terms of how we use AI. You're gonna hear us talk a little bit more about personas. So thinking about what that transformation looks like, you know, there was a great report earlier this year from the World Economic Forum, talking about what the future of jobs might look like. There's so much data out there and so many different stats you can have, but I think these are really impactful. Being able to understand that a majority, 59% of the global workforce will need training. Even me who's been in this space for fifteen, twenty years, I'm constantly learning new things, new tools. There are technologies that we didn't have three months ago. It's a really exciting, evolving space. And so making sure our skill sets keep up with that, we're we know that we want to keep learning. Part of the reason I love being in this space is because things are constantly changing. So making sure that we're ready for that required transformation that's, you know, 39% of the global workforce needing to change their skill sets, but also really understanding this is this is a tool. We really want these technologies to help people be more creative, to give them time to do more strategy. So I've talked a little bit about my experience. In a moment, we're gonna have, Tony join us as an expert in how Adobe has done this. But first, we wanna share with you a few examples from around the company from different people. AI is changing all aspects of the way that we work. I believe that AI will be an unlock to help us create better outcomes for our business. Refactoring the way we work with AI is just a critical foundational piece for how we can deliver breakthrough innovation to our customers. We created the AI ambassador program, and they're chartered with basically identifying manual tasks that they do today and how can they augment or improve those with AI. One of the ways that I use Acrobat AI assistant in my role in finance is summarize the document. AI helps me work smarter and not harder. It doesn't do the work for me, but allows me to do my work faster, more efficiently, and more strategically. I use AI in my role as a software engineer. We use coding assistance. We use AI in express to help us reach employees with creativity. I think AI at Adobe is imperative for Adobe's future success. When we think about the future of work, AI is definitely a part of the equation. And I believe it's going to help elevate the outcomes for all of us. So after seeing that, I'm really excited. We're gonna bring Tony in to have this conversation. You saw her a little bit in the video, but she's had played a huge part in how Adobe has thought about integrating these technologies, not just the ones we build ourselves, but really how do we pick which ones we're gonna bring in, which ones we're gonna leverage, and what it looks like, to do that with vendor AI. So, Tony, I'm so excited to have you here. I can't say that enough. Thank you so much for joining us. Yeah. Thank you, Emily. As you know, I always love chatting with you on this topic. We both kind of get on fire, and we can go on for hours, but I'll try to behave today. I appreciate that. We're usually in a limited time situation, but people don't get to see us go on in for hours and hours about all the different aspects of this. You know, one of the ones we talked about starting with is this adoption curve that we see as these new technologies come out. I like to remind people this isn't this isn't new new, but it's a really helpful repeated framework. So can you talk to us about why you like to use this? Yeah. I I really turn to, the technology adoption curve, and many of you may have seen this when Jeffrey Moore introduced it in his book Crossing the Chasm. You may have seen it in other places. But, basically, what it's telling us is that we all approach, new and novel technologies in different ways. If you're, someone like me and, my my architect, we're always tinkering. We're like the first people to buy the new phone. We're the first people that get the Roomba and so on and so forth. Well, there may be other people who are a little bit more reluctant. They're waiting for, like, you know, the warranty recalls on the on the technology before they make that commitment to change. And so it's a way for us to be mindful that, we need to understand where people are, when it comes to adopting new technology. And it can be kinda, you know, it could be kind of intimidating and scary if you're not, on that, you know, kind of bleeding leading edge, type of, place when it comes to technology. Probably one of the biggest things to think about as you think about people who are more to the right of this chart is is the WIFM. And you know what that is? That's that's the what's in it for me. And so we need to understand what is the thing that will be attractive to people to get to the other side of the chasm so that we can bring the whole organization along on this journey of transformation. I love that because, you know, we see this and they're skeptics and we have, you know, some terms for for people who might not be as comfortable getting started with technology. I grew up in a space where, you know, we always had to have the latest thing, and that was I recognize that a lot of people don't. You know? I have I have a sibling who's more interested in doing art and those kinds of things. And so being able to understand where people are starting from and what the best approach for them is and being okay with wherever they are, not just playing to the people who are the techies. And And I think, you know, that can be an impression that comes off because of being inside a tech company. But we have people we need to show the value of these technologies for wherever they are on the spectrum. I think one of the things that we found helpful you know, I talk to a lot of companies about this. We do a lot of research. Having these pillars of success, I think, are emblematic of ways that companies can think about this, that individuals can think about this for their teams and what they need to do. So we have these five pillars. We're gonna walk through each of them. But talking about executive sponsorship, making sure you have partnership across the company, which I've seen you lead. Lindsay mentioned you're the cochair of our AI at Adobe committee. Metrics. Inside a company, we're always thinking about metrics. What are the KPIs and incentives that we need to do? And then there's a really popular term right now, upskilling. We talked about train but training isn't new. We've been doing training for employees on new technology for a long, long time. And then, of course, we wanna think about what are the guardrails? How do we make sure this is done in the right way? I often talk about the evolution we saw with employee guidance, you know, right when the technologies came out to now thinking about how we do this in a responsible way, that meets our individual values and our company values. So now you know, I'm gonna ask you a little bit about Adobe's approach. We have this big focus on amplifying the human potential. You know, when we look at this image when we're talking about this image, you said I want I want the person facing the future looking forward. Yeah. It was it was interesting. We the there was the flip of this image, and, you know, I was really struck by the fact that, the the woman in this picture was not looking forward. And I think that's what we need to help people do is kind of flip, their perspective on how they're thinking about work. And, honestly, we talked about people, process, and culture, and, of course, technology. And today, the technology is, artificial intelligence, but you really need to think about your own culture. And at Adobe, we have a culture of innovation, of curiosity, and involvement. And so that that plays to, making sure that we can cocreate the future together, keeping in mind that even at Adobe, we're in different parts of that technology adoption curve. But we wanna make sure that we're thinking about this differently. Now I know that you're really thinking about agents and agentic, workflow, Emily. And so for years, we've thought about, refactoring work through a lens of strict automation. In other words, I'm just gonna take this task. I'm gonna give it to the machines. I'm just gonna automate it. What I find fascinating about this technology is you need to think about two things in my mind. One is, yes, the automation. What can the machines do to help offload work from you? But in this world, we can think about augmentation. Because what about this technology as a thought partner before you get into task execution? And so when you're developing strategies for either your business, your operations, or your products, you can use this type of technology for, you know, thought partnership, design concepts, writing, so on and so forth. So we wanna start meeting people in this place where they're cocreating what the future of their work is so that we can unlock, that capacity. In my own organization, you know, our number one, priority is to elevate business outcomes, through, you know, integrating AI into our daily work. But that includes, you know, making sure that we're also elevating and growing in our knowledge and, proficiency with AI so that we can be the change champions that the organization needs. Well and I love what you've said about having, you know, two specific things because I think amplifying human potential, it can seem like this big broad thing just like when we say people process and culture. You know, there's there's so many ways to think about those things. So what we're really trying to do is make sure we have the practical, what is in it for me, so to speak. Right? Like, how how do we understand these specific things? And I think, you know, we at Adobe, you and others led this AI at Adobe committee formation and thinking about how we elevate the impact of individuals, teams, and the company. And I think that's been part of the key to our success. You know, we have some examples here, and I'd love for you to talk us through how we enable these experiments and created this community. Yeah. So, thank you for that. Most companies have some type of technology governance in place if you're a larger corporation like ours. What we ended up doing is leveraging that framework to make sure that we can create the scaffolding to help people experiment, a faster velocity so that they could learn, about how to apply this technology, fail, start again, and, wherever there were learnings that we can start to scale, kind of take those nuggets and pull them out. So first of all, we needed this ability to enable experimentation at velocity so that we can learn fast. Because, again, as you said at the top of this, the world is changing every single minute. We also wanted to be able to provide some strategic framing and visibility to our c suite. So what are you learning? How are you codifying things that need to scale? Where is it most effective in our business? And so being able to do that. We also wanted to foster community because we find, at least in our culture, that people really, really love to learn from each other. So we set up showcases, and, honestly, it makes it real for people. Right? And we're showcasing not only where we have those, quote, unquote, successes. I I kind of include the failure successes also because you learn something. So important. Right? But but right. Where we may have found something that didn't really work. And that helps us not make those same mistakes again or approach it slightly differently. So this community of learning was super critical. And now we have communities of communities because, and we'll talk about personas, but different personas have, created communities. And then we have cross persona communities. And then we have larger learning communities. And then you take all of that, and you need to put it to action. And so you start to marry that that strategic framing to how you can move this task optimization into transformation, into value stream transformation for your organization. I I love that we move into action here because I think, you know, when we talk about these these five pillars and start start with executive sponsorship, what that looks like, I think what you're what you bring up when we talk about this really has a tendency to surprise people in terms of who are the key players for that executive sponsorship. Who helps us make that action happen? Yeah. It it's so interesting because, we we had c c c suite sponsorship, and, and excuse me. I can't for whatever reason, I can't speak today. But we had, you know, our most senior leaders in the organization. However, we still manage this as a movement that employees were empowered to drive, and this allowed us to go in between those two levels. And what I found just really interesting, this is just Tony's brain, I found it interesting that our CFO and our chief people officer were really, really leaning into this. And what's interesting to me is because I'm also thinking of governance and all this other stuff around the kind of data that they have, which is highly confidential, highly regulated, very, very sensitive. However, these two leaders really looked at our organization and said, hey. We're very interested in how this could transform the way that we work. Now most of us think, hey. You know, just get your chief technology officer and your CIO involved, which they were. Of course. But you don't want to rely on this just being a technology change. This change is about people, and this change is about knowing and navigating your business where you can have the most impact. And so I want people to really take that away from this is that this is not a technology play in itself. That change management is fundamentally human centered. And we say that a lot. Right? We're gonna talk a little bit more about the partnership that has happened. I think the people involved in this, it's roles, but it's also the willingness of the people to engage. You know, one of the things that I think surprises, customers and others when I'm talking to my colleagues and fellow experts is how we have this core team that meets weekly Yeah. At a at a pretty high level. Can you can you talk about who's there? You've talked about, you know, our chief people officer and our CFO, but they all send people to this weekly meeting as well. Right. In in in the CICE, we we we have, you know, we have our chief legal officer engaged and and others engaged. But in this particular meeting, we have representation from, ethics. We have our chief security officer. We have, you know, different members of our legal team, including privacy. We have a technology leader, myself. We have a business leader, my partner and cochair, Emmy Hong. And, we have various leaders across multiple personas that are represented in the governance, cycle. What's important about this, especially if you're just getting started, that whole thing about, you know, the scaffolding for governance and foundation, it's really, really important that you have the leaders that create policy and governance across the org. And those are generally in your legal groups, your security groups, your IT groups, etcetera. Make sure that they are a part of this committee because that's gonna allow you to create the the safe place for people to experiment at an accelerated pace. I I think this has been the sort of the secret sauce for how fast we've been able to experiment and how quickly we've been able to try out, say, new startup or other, companies' different technologies as they come out. We really see you know, every week, we're seeing a new company or a new, model that's available. And I think, you know, with the core team, we have these working groups. And I wanna dig a little bit more into these working groups because I I I think people are used to doing the role types, but we have a conversation about these different personas, which really speaks to, you know, people are like, what do you mean by process? And it's not just, you know, the process that's set up from the higher levels, but, really, you talked about this community. And so having people who use the technology similarly. And I'd love to have us talk through a little bit, not just the personas, but the examples of what they've been able to do with different agentic technology. Yeah. Thank you. Thank you so much. So so we took a, you know, kind of a page out of my book as a digital, employee experience leader to say, you know what? We all work differently in the enterprise, and it may seem obvious, like, we have different jobs, of course, in different organizations and different outcomes we're trying to drive. But when we apply technology, we really need to think about the work to be done and the jobs that are doing that work. And so we look at four primary persona based group or work type groups, and that's the builders. These are generally our engineers, our architects, our creatives that are doing design, our customer facing, those folks that have the empathy to take care of our customers. They could be in sales positions, support positions. Even someone like you, Emily, is really thinking about our customers every single day. We have our enterprise catalyst, and these are strategic leaders that are in our supporting corporate functions. So that could be marketing, finance, legal, HR, IT, and so on and so forth. Even our admins that support executives across, the organization have been involved and a part of the transformation. And then we look at, general productivity. So how do we think about stitching together the the the different personas across the enterprise? Because, you know, we all have things, that we do, like collaboration or managing our teams that are very similar. And so when you think about engineering, what are their care abouts? Building the best products, right, for our customers. So how can we unlock their world through engineering and coding tools? Tools that help to elevate the quality, of the code that's being produced, writing test scripts, for example. If we think about customer facing, how could I understand my customers better and present them with, you know, things like ROI calculations for utilizing Adobe products so that we can start to talk about the impact of their business versus, speaking about always, you know, where we're coming from from our products. It's kind of the with them for our customers, if you will, what's in it for them. Thank you. And then, you know, there's so many catalysts. I mean, so many unlocks for enterprise catalysts, be that analyzing financials, creating the best of course, we're a creative company that creates marketing, you know, capabilities. How could we create the best content, and support the creative process, through our tools and using AI as a component of that. And so, yeah, these are real life examples and experiments. I think I think this was taken in April. We have over 700 experience experiments at this point, that are in the enterprise, and that allows us to focus on themes of those experiments again so that we can start to scale and get the goodness across multiple teams. Yeah. That it's so helpful to think about how people use the technology in the day to day. You know, one of the things that I think we see a lot of headlines about are about coding agents and and how that's helping engineers. But those aren't the only examples. You you've you've mentioned some examples, of agents that are that are in use, say, it for our salespeople or, for our marketing folks. Can you talk a little bit about some of the choices you guys made there? Mhmm. Yeah. So, you know, an example and some of some of these are agents and, you know, workflows that were created internally. So if you think about, from a sales, perspective, one of the themes that we saw is almost 50% of our experiments were around unlocking knowledge and information in the enterprise. And so, the teams were able to create, agents that help, the organization through conversational interfaces unlock knowledge that can help them through their sales and support processes. And so I think that that is, you know, an example that most people, can, can really attach themselves to. In the finance group, they created a an ability to generate an analysis, moving it from hours, perhaps even days of work, to really minutes of work where they can run, scenario analysis through, through agents and create a generative analysis for financials that help them make decisions better. Now remember, always human in the loop. Right? And so I always talk about, agentic and conversational interfaces as the human plus machine augmentation. However, there are things that we can fully automate. And so, for example, in the IT organization, creating requirements and, of capabilities that we might wanna develop. So you can generate those requirements and then take it from there to be the human in the loop to start to build new capabilities. Okay. So we have all these experiments, and we have different tech that we've tried. How do we measure this? You know, I think this is the number one thing that comes up in enterprise. Yeah. What are the the key performance indicators, the the OKRs, and maybe maybe you can even tell us a little bit about what OKRs are if we have, a non enterprise audience. Yeah. Exactly. So OKRs stand for objectives and, and key key, results. And so, what we wanna be able to do is set an an objective and then have kind of a a key metric or a KPI or even, other types of metrics that actually generate a particular outcome or result. And we do this at multiple tiers of the organization. So if your organization doesn't have it yet, probably one of the things that's pretty important if you're gonna drive AI across the enterprise is having an o o level, meaning a corporate level objective that says we're going to transform the company. And this is where you get that c suite sponsorship, and it's also the place where you start to get partnership, which are two things that you have in your, requirements for success, is by establishing an objective at that level. Then you come down to, what we call the business unit level. How are the business units starting to say, I'm gonna manage what or measure what's matters in my organization and set goals there, and then cascading that through different, categories and functions into different teams. The one that I love by the builder group, is fantastic because it infuses not only, like, create more better code, but it also has this human factor. So make software development more creative, impactful, and joyful, and accessible. I just I just love this. We talked about our approach being the thing that's gonna elevate outcomes for humans so that they can do more of what matters, and I think it's encapsulated in what the builders are saying their key, OKRs are. And, you know, I I I talk about even at the team level. So, you know, my my particular team, you know, we're trying to increase our usage of some of these tools by 10%, see what we can do, at this level. Because we talked you know, we saw the the the creation of the ambassador program at Adobe, and I've seen that be champions in other places or even heard one recently, the influencers inside of the company. Because we we need that top down, bottom up perspective. I've seen it without the top down, without the key executive buy in, and that's really hard. But, you know, when we're talking about the adoption curve, Getting people excited with their colleagues is really helpful. You know, we talked about this experimentation and what the metrics were, but I I think you have this great way of describing why we do experimentation and how we can give people the opportunity to try so many different things. Mhmm. Yeah. And just one word about the ambassador network. I cannot, emphasize more that, you know, some people use the word change champions, but, readying your organization or preparing them by having embedded change champions or ambassadors that I believe are culture carriers and their change carriers that can really help in those moments that matter when you're grappling with what to do with this technology. And we have ambassadors across the entire company that are helping to drive specific change around those OKRs, that you saw in the previous slide. Emily, you know me well, and you know I'm a woman about analogies. And I'm also a and I'm also a gardener. I'm a gardener. And, as an innovative company, we love to bloom a thousand flowers. And that's how I think about experimentation. We are blooming a thousand flowers all the time with the intent to get to this next phase, which is planting intentional gardens. And this is why we have these working groups because the builders and the customer facing and our strategic, catalysts that are within our supporting functions, And even across general productivity, we need to plant specific, gardens, intentional ones, and that helps us to start to standardize. But when you get to the right to these designed ecosystems, that's where I believe you get scale and you get a lot of value. Because now you've moved from, task optimization into strategic transformation. Now you're starting to transform value streams across your business, and you're also starting to transform the type of investments in this technology you have. Yeah. And I know it's crazy. Right? Well, I love it. It gives us it gives us a fun, pretty, picture. You know, I think we technology can seem maybe a little bit cold. We're always talking about machines, and what you really bring into this conversation is that community and, honestly, that beauty as we we think about the people involved in this. I think, you know, those experiments and that blooming a thousand flowers, there are so many ways to pick these technologies. Yeah. And so, you know, one of the things you've said to me is our ability to leverage these existing systems. I've spent, I think, a year now talking about how companies have a choice, many choices. There's so many technologies to choose from, but there's also the choice of, are you gonna build it yourself? Are you gonna buy it? How do you how do you think about which one and when to do it and where that combination is? Because I don't think it's an either or. 100%. So, you know, first, I wanna tell you that picking the right tool is an unlock in itself. So so through our experimentation, we used, you know, different tools. And using the right tool, and having the motivation to use that technology is completely unlocked when it is the right tool for the job. So let me just say that on the top of this. But the way that I process, the investments is I ask my team, you know, these three questions so that we can decide if we're gonna leverage, buy, or build. And so, on the leverage piece, your existing platforms have probably already started to infuse technology into it. We happen to be a Microsoft shop, for example, and we all know that technology the the AI technology has been embedded into Microsoft. And so that was easy for us. We're not going to build a tool to transcribe meetings. We're going to leverage that within the application. So regardless of what your ERP system is or your collaboration tool base, that I would say, think about leveraging what already exists. Then there's cases where there's there's I call them boutique shops. Right? If you have a mall, you might have Macy's and Nordstrom's, but you might have boutique shops in between. And leveraging a boutique for a specific type of task or goal is something that we would consider because it's not something that's within the suite of products that we already own. So I might buy before I build something. And this helps me with the cost of ownership. This helps me with technical debt. This helps me, of course, give the right tools to the people at the right time. And then if none of those exist, then I build. Right? And, boy, this is so hard for a builder, because because, you know, we like to build things. And, but we need to think about both the short term and the long term view of investments and how we enrich to get to that right, that that managed ecosystem. Because I will tell you, having been in technology for a while, that will be disrupted also. Well, you were you were talking about how everybody's gonna be able to build their own agent. We keep seeing, you know, conversations about what my agent is gonna look like or what yours is gonna look like or something I might even build, you know, to make my work path a little bit better. And and so as we we enter this future world where anybody can build, you know, you hear the term vibe coding right now. Yeah. But if anybody can build, what do you do as an enterprise? Yeah. So this is, yeah. And so thank thank you for that question because, you know, this is at the the larger investment level that you're thinking about leverage by build. But also know that you can have platforms for agents to be built that also allow you to govern how the agents' fall is happening. And so this ability to democratize, the way that we unlock work through AI is incredibly powerful. Again, you need to help your organization learn how to do it, and you also need some kind of framework that allows people to share, govern, manage because well, my agent might talk to your agents. You know, not all agents are created equal. They are not. And I think that's a great segue to our conversation about how we do this training. So if everybody's building agents, really important to have people understand what the guidelines are for those. Right? How how we're how we're doing that the right way. You know, I can't I I can't even remember how many times I've edited or written guidelines about how people should use these technologies, what responsible use looks like. And we've all sat through many, many trainings if we're sitting inside of a company. We have them annually or every six months. And then there's there's the ones you can get excited about, like, when your friends are there in in some of these live learning sessions. Yeah. But one of the ones that we found really successful that I think is of the moment is this this podcast system that we have. So you've done a few of our podcasts. Can you Yeah. Talk about why they've been successful and how some of the other education opportunities compare in terms of meeting people where they are in that adoption curve? Yeah. So, so we start as a part of AI at Adobe. One of the first things we did is we started this unprompted podcast. And, yes, it is a play on words. And what it allow us allowed us to do is reach a broad base of our employee community, in a very approachable way. A short podcast that delivers information about the program, but also delivers information about experiments and projects happening across the enterprise. Remember, we were talking about how you bring people along, and part of this is creating community. This is really manifested in this podcast, as well as other learning opportunities. We all learn differently. And we all, I'm I'm kind of like a multimodal learner. I will need to sit down and read something in some cases to to understand really deep research. But to be inspired to action, you know, me watching a video or participating in a live learning session where I'm cocreating with my colleagues or listening to a podcast could be the way that you meet me in that particular moment. So we wanna make sure that we have multiple places for learning. The other the other challenge that enterprises have with learning is time. When do you have time on your calendar to go to the class, to do the thing, to learn the thing? Right? And so we created a calendar where people can schedule learnings in and, you know, the podcast again as an example, take it on a walk for you with you, you know, and, do it on your time. Yeah. I love that. You know, we we've we've talked a lot about the community and building the ambassador. I think part of that community as well is some of the hands on learning that you can do, having kinds of workshops and those kinds of things. You know, there's just one more thing we wanna make sure we say about experimentation. You've heard me say guidelines. We talk about guardrails. Adobe has really taken a perspective with our AI ethics principles of accountability and responsibility and transparency as the foundation for not only how we build our products, but also how we think about our experimentation. I know this has been a big part of the conversation, and I often talk a lot about, the risk management, the risk tolerance people have in terms of new technologies and figuring out where those fit into our organizations. We have just one more, you know, takeaways. If you had to pick just a couple things to remind people of as we wrap up and open it up for questions. Let's take let's take, the the community, because I think it it it assumes that people are involved, in in kind of the catalyst for change. Yeah. Yeah. So creating community and making space. Yeah. I love that. Because, of course, this whole session is about change management and how we adopt to the new technology. And so being able to really focus on the people aspect and the community, you know, I I'm gonna use that as our transition into hearing from the people who joined us here today with their questions. Lindsay's coming back on to help us with that. Tony, Emily, thank you so much. You gave us so much to think about. You know, things are changing so quickly, and it can feel overwhelming, I think, no matter your role, no matter your organization. And so, listening to you outline these strategies and these adoption frameworks, they felt really empowering to me. And I think our audience felt that too. It's I felt excited to be in this crazy time with everyone learning together. Right? Like, this is an exciting time, and there's a lot to consider. So, it made me feel like let's go. And the audience had a lot of questions too. So if you are good, we're just gonna jump jump into them. Let's go. Okay. Let's go. Okay. So what are we got a question. It was really interesting. What are Adobe's best practices for validating AI output? Oh, I love it. Emily, you wanna start or you want yeah. Go for it. Okay. Great. So, depends on the output. What I what I would say is that, of of many cases, let's say that you're searching through a knowledge base and you get a summarized answer. My hope is that, the technology that you have gives attribution to where it's getting information from. And if it does have some attribution, go to those links and actually read through where it's deriving information. As we know, AI, and some of the training models do have hallucinations. So they may, be grounded in different information, and therefore, they give you different responses. So my hope is that you have some, attribution. If you're thinking about something like, coding outputs, we wanna make sure that we are using our discernment and our expertise to challenge AI and ask different questions. So sometimes, I love to have my arguments with AI and say, where did you get that information from? How did you derive it? And by being able to go to your IDE, for example, if you're coding and look at the logic for how that particular code snippet was built is a way that you can do validation. And so we could probably go on and on with this question, but go ahead and look at the math, I would say, and philosophically look at the math, whether that be from writing, financial analysis, coding, testing. Go and look at the math and how it derived, that output. Well and I think you've you've raised some really interesting points around what human in the loop needs to do. Mhmm. I think we're also thinking about how we can trust these technologies. Yeah. Really knowing what the people who are building the technology are doing. So, you know, the the questions about outputs could also be, you know, what filters are on there to make sure something bad doesn't come out. How are you detecting if something harmful is there? And really understanding how how the system works. I've been working on, you know, machine learning and AI explainability for many, many years and figuring out how people understand what they're seeing and what the decision was in the system to show you what you're seeing. Those kinds of filters and techniques are really important as we think about that, seeing how we're using it as a partner and being able to trust it, but also having that critical thinking skill to know when to question it. I'm I'm gonna start having arguments with our AI now from your encouragement, Tony. Me too. Like, for sure, that was yeah. Yeah. That really resonated with me. Tony, we got questions. You know, you actually mentioned looking at successes and building on them, but also failures or learning opportunities or, you know, failures might even be a strong word. They're learning opportunities. We had questions around, if you had any sort of surprises during your transformation, or do you have an example of a pivot that you made when you had to adjust course? Right. You know, I I think probably, some of the biggest, surprises have come with how fast the technology is changing. And, you know, I have this thing with my team. I live about fifteen minutes away from the office, and, by the time I leave my house and get to the office, there's probably five new start ups, that have come up with the solution that's going to cure, you know, the world's biggest problems. And we test with some of these new technologies. And it's just what's surprising is how fast the technology is changing. Even if it's in one of those existing, ERP or, large enterprise systems that you have, that they're innovating, a very fast clip. So one of the things we need to do is make sure that we're staying, you know, in pace or ahead of those changes so we can evaluate it before we release it to the public, or our to our community at Adobe. And so there have been surprises that there's been capabilities integrated into our environment, And we're like, hey. Wait. Wait. You know, pull that one back because it's not ready for prime time. So that has happened more than anything. I think the one of the biggest, you know, from a human change management perspective is how vulnerable people have been and transparent about, successes and failures. And I've said, hey. You know, I've gone down this path. Don't go down that path because it doesn't work. I I think that emphasizes your great point throughout this conversation about what I would actually call the importance of failure, being able to to learn from that change. You know, we have we have technologies deployed inside the company that are maybe three or four months old. We're seeing these companies just pop up with new options and new opportunities, and at the same time, figuring out which ones. That's why I love the culture of experimentation and trying things. You know, two years ago, we thought there was gonna be this whole new role about prompt engineering and how the technology has evolved that you just ask the system. Can you help me improve my prompt? And here we are, you know, thinking about how people are thinking to make sure that we're, as much as we can, preparing for those surprises, preparing for this rapid pace of change. You know, the the the cliche of get comfortable with being uncomfortable. It's a time of of really rapid, I I don't even know how which five started today while we were driving to work. Yeah. Yeah. Okay. There's you know, we got a few questions we always do with these topics about, you know, the concern with AI replacing people in their workplaces. And so the question that we got here was how do we best move forward showing the aid of the products and tools without sort of raising these fears between coworkers? I was thinking WIFM WIFM. Did I, like, use that right? Right? Like got it. What's good for me? Yeah. Yeah. Can you speak a little bit to how you're navigating that? Yeah. You know, I first of all, you know, automation of our work and processes has been going on for for decades, for centuries, honestly. And so since the industrial age. And, the best, antidote for that is to bring people along in the process of reinventing the way that they work. And there's two ways. One is to involve them in the actual automation design and process. And, the other piece is to, you know, involve them into a community that is also, surfing the wave of change. What you know, it it's interesting. There was a question about momentum. You know, how do you drive the momentum? And we've instituted, you know, learning Tuesdays. And I talked about the struggle of, like, where do you put it on someone's calendar? But if there's something that I can join on a Tuesday morning or Tuesday evening to, to get all the time zones where my colleagues are there learning about this new technology, and we're all, like, learning together about how we can unlock capacity and time and get some more joy out of our work, it it starts to help people turn towards a future possibility, like that image. Let's not look at the past. Let's have the the mindset, the growth mindset to start to look for the future. So let's give people opportunities in their discomfort, to help us, transform towards the future. Really inspiring. Another question. There's been so many. How has your organization effectively balanced AI literacy training with governance and compliance guidelines within these upskilling initiatives. So they're kind of, like, thinking about it altogether Thinking about it altogether. All together. It altogether. How are you doing it? Closing thought. Yeah. All of it at once. Yeah. So a term that I've been using and sorry if this kinda makes everybody kind of gross out, but I always talk about putting spinach in the chocolate cake. And so it's like, what are you talking about, Tony? Spinach and chocolate cake, You know, for those of you who like chocolate cake, hopefully. But as you're thinking about, you know, putting forward the the message of the goodness, you also need to put the message of what are the risks and the threats around this. And I believe that can be along the same learning curve curve that we're or learning journey that we have with folks. Look. These tools are gonna help unlock, insert with them here, what's in it for me here. And then, also, we need to act in a certain way to protect our organization, put the impact to our organization here, and then we, together, will have an outcome that moves our business to a better place. And that's how we elevate our organizations and transform them. Spinach and chocolate cake. Here we go. Yeah. Here we go. I'm doing it. I'm doing it so my kids will eat spinach. Spinach. Alright. Emily, Tony, we are at time. There is never enough time, especially with you two. For the audience, if we didn't get your questions, we'll do our best to get back to you. We also have resources. We have the presentation deck linked. If you'd like to follow-up if you'd like us to follow-up with more information about Adobe's AI capabilities, please let us know in the survey questions on the right of your screen. Also, you will get the on demand courting after we wrap. Alright. Big, big thank you, Tony and Emily, for being here and for sharing your insights with us. Thank you, Lindsay, for having us. Absolutely. Huge, huge thank you to our audience for being here today. AI is best driven by people like you. Thanks for being here, and have a great rest of your day.