AI readiness in practice: Leading with people, not just tools

All right, everyone. Welcome to today's live program with Achieve Engagement. My name is Deck Doms, president of Achieve Engagement. And as your head of community and here to learn with you, thank you so much for taking some time out of your busy schedules, the craziness out within the world of work to join us for this live program and sharpen your crafts and level up your expertise on these topics so that we can continue to make a bigger impact within the workplaces that we serve. So thank you so much for being here with us. That's what we're all about at Achieve Engagement, is rethinking the ways in which we can do that and building a better world of work. So I'm loving already the activity happening in the chat. Let's keep that up with today. We have amazing leaders with us to unpack this topic of AI readiness, AI adoption, how do we develop our people game momentum, uh, integrate this into our operations. We're gonna unpack a lot of different things. So let's keep up this activity. In the chat, I see Naomi, welcome from California. We got Toronto, Laura Laurel and Richmond, Virginia. We have Southwest Virginia. Lacey, I see you, John from my hometown, Milwaukee in the house. Lori, good to see you in here as well. And Los Gatos, California, Colorado Springs. Wendy, good to see you in here. Autumn, in Dallas, Texas. If you haven't already, add in the chat where you're calling in from. I would also love just to do a pulse check here because we're talking about a lot of things within ai. Its use case, its adoption. How do we drive that buy-in? How can we use it to drive better levels of engagement or performance? I'd be curious, for some of you, if you had to think on like a scale of % to a hundred percent, where are you within your workplace when it comes to AI adoption? Like if you just had to throw a number up, maybe some of you are actually measuring this already, maybe some of it's kind of like, well, people are probably using it, but we're definitely not reinforcing it and we have no idea what tools they're using or how they're using it, which brings in its own risk, which maybe we'll talk a little bit about today, but where's like your level of adoption? I'd be curious of where you are or where you think your organization is. %, %. %, . Okay. , % promoting that customer. Awesome. All right. %. % Laurie, % said our leader. Oh, we got %. Okay, , to % depending on the tools used. All right. I want you to keep thinking about that. And I would also start to think about, okay, if you're at %, think about as you're listening today, like what are some of the levers and things maybe you could do and learn from today that would move that to % or %? Like what's a couple percentage points for you? And where are the use cases or tactics that you could do that? So keep that in mind as we're going through this. And I also really encourage you to post your questions, post your thoughts, whether in the q and a function or in the chat there as well, because these are leaders that have been actively doing this at a high level with multiple organizations as well as internally at their own organizations. So we're very lucky to kind of have this exposure with us as a network today. And then on top of that, if there's certain things that have worked for you and there's certain tactics or things that you've implemented that made a big impact, I think that's also one of the more powerful aspects of today's learning experiences and learning from each other. So also add those in the chat as we're going through the discussion. I would love to hear, you know, were there certain use cases that you focused on first that built a lot of buy-in and momentum? Were there certain things that you did to enable your leaders to guide the way within their teams? I mean, what was that like for you? So that being said, let's get to the fun part of today's program. If you can hit the emoji and put in the chat, welcome for us to these two amazing leaders. Let's welcome Torch's, CEO, Heather Conklin, as well as Zapier's Chief People Officer Brandon, as they come to the stage with us to talk through how they're making real progress with ai. So I'm gonna stop sharing here. Heather and Brandon, thank you so much for joining our network for today. Really excited to learn from both of you. And that being said, I'll, I'll hand it over to you, Heather. That sounds great. Well, thank you so much, Zach, for the setup. I loved hearing all those percentages. You gave me the, um, the, the vibes of, um, like a, what do you call it, the, like a bid you or an auctioneer. That's what it was. That's what I'm looking for. Uh, but it was great. Thank you. Um, so we are so excited to be here with all of you today to talk about the topic that we know is on so many people's minds right now, which is just how do you drive AI readiness and adoption across your organization? And I'm super excited to have Brandon Smoot here from Zapier. And he's someone who I have been following on LinkedIn and just watching what Zapier has been doing on the AI front. And really, I love following what he's saying out in the world because he's someone who's actually doing it. Um, you know, while mo most companies are still in this figure it out mode, uh, about their AI strategy, Brandon has really already like led a team orchestrating real transformation with AI across Zapier. And you know, even with the customers I get to talk to all the time, I hear them referencing what Zapier is doing more and more as a model for how to lead with change and, and change around the people side of this, not just the tooling side. So, um, again, just really, really excited to have you here. Welcome, Brandon. It's good to be with you, Heather. Yes. Alright, so let's dig in. So, most companies today, uh, are still approaching AI through either a mandate, like a top down, you know, CEO says we have to do this AI thing, or really experimentation kind of random. There's a lot of activity happening on the technical side of ai, but, uh, not so much a really holistic and strategic approach to AI that joins the hard skills around the technology with the people skills. And so we want to make sure we're talking today about how do you hold both of those things as being true? Of course, people have to learn the technology that is critically important for this change. But we also need to think about how do we bring people along on this transformation and get them actually thinking differently and working differently day to day. And you know, we really believe that if you're not focusing on both the people side and the technology side, then you're leaving massive competitive advantage on the table when it comes to ai. So hopefully by now everybody has seen the study from MIT that says that % of AI pilots are failing. And that's despite to $ billion being spent on the technology right now. And those are really big numbers and they're alarming. Uh, but one of the things that I have really started to take notice of in, in my role is that there's a lot more being said about what's happening in the % that are succeeding. So HBR actually released an article after the MIT study saying that they looked at the % and, and looked for what is the key differentiator of who succeeds with AI in these pilots and who fails. And the number one differentiating factor that they discovered was leadership. And this also got confirmed. Wall Street Journal had a huge round table with a bunch of CIOs and CTOs. And so these are, these are the technical people, but they came to the exact same conclusion that the real kicker for what was gonna help them succeed or fail with AI was gonna come down to leadership. And so, again, we really want to think about not just the technical side of this change, but actually also the human side of the change. Okay. So Brandon, we're gonna dive in here. Um, but you recently said at an event that AI transformation equals people transformation. And you even said that people could fight you about that. And I'm not gonna fight you about it, but I'm definitely gonna fight with you on it. Uh, but today, really wanna get practical about what does that really mean when you are the one that's actually driving that change across Zapier. So with that, it sounds like Brandon, uh, Zapier got started with a bit of a, a code red mandate from your CEO. Um, and you took an approach to really dive in and say, the people team needs to lead this change. So tell us what happened in that moment. What did that look like for you? Um, and what made you decide that the people team really needed to be the leader here? Well, let's tell that story. We'll take the clock back to March, . It's two and a half years ago already, but that was already, if you can believe it, six months after chat GPT- . was released. And that was the model, uh, that really took hold, captured folks' imaginations. And so, you know, six months pass, we have a variety of folks within Zapier who are starting to use Chacha pt, starting to think about what it might mean, uh, for our work, for our customers. And that's great. But we didn't have anything approaching broad-based adoption really, and way more questions than answers. And so six months later in March of , our leadership team sat down and said, Hey, like we need to provide more clarity and more vision about the opportunity as we're now understanding it. And we need to, um, we need to just ask, not suggest, uh, that we all get our hands on the keyboard and really start understanding the technology. Um, that was a business imperative. 'cause we believe that it was really important for us to continue to be successful as a business, uh, that it was probably the biggest opportunity since the company's first product years ago, uh, to take a step closer to achieving our mission, which is to make automation work for everyone. And then thirdly, we thought it was really important for our team. So this is the human piece, right? It's like we're not doing anyone any favors, um, by shading the reality that, uh, we, we believed, again, this was all the way two and a half years ago, that AI was going to transform how work gets done. Uh, so let's start investing in our people. But that started with being clear and, you know, it's a tops down trigger point. It's woefully inadequate by itself, but it's where a lot of organizations need to start. 'cause folks are, you know, if you, if the, if the mandate or something comes sideways, folks are gonna look up and ask like, I'm hearing that from my manager or maybe a peer. But like, what are we really trying to do as a whole organization? Now, Heather, you know what I'm about to say next, which is that we published this, it was literally called, it was an internal blog called Code Red around ai. And it wasn't universally popular at first. Not everyone knew. Uh, not, not everyone saw the opportunity the same way at first. And a lot of folks had very understandable concerns about what it would mean for their jobs, what it would mean for, uh, their career opportunities, what it would mean for the environment. And the list goes on and on. But two and a half years later, you know, looking back with the benefit of hindsight, I'm glad that we didn't wait for anything near consensus to call the team in to learn and build together. It was really important. Uh, but we had to be okay with some messiness at first. And then to your second question around, well, why was the people team leading the way? Well, I think it gets back to, you know, I think you and I are on the same page about the notion that this opportunity, this AI transformation, it has the same number one success factors. All the other organizational transformations that came before us. This, you know, what you and I are talking about or what that HBR article suggests in the study that they did, that's not a hunch and it's not new. Every other type of organization, transformations, leadership, leadership, leadership, that's what makes the difference. And cultivating leadership, uh, is one of the primary missions of, you know, people, teams in general is certainly true. Exactly. Well, yes, I definitely agree with you on that front. And I'm curious 'cause a lot of the conversations I have with CHROs or other people leaders is that they often have to convince someone else that, that the leadership or the people side of it is important. So did you have to convince anybody or did you have, you know, aligned leadership on this fact that that leadership is and has always been, you know, the critical factor here? You know what I've, I've found, I've been a chief people officer for over nine years now. And one of the things I've learned starting the hard way for years is that, um, you know, much in the same way with our end users, whether, you know, if we have organizations of all kinds represented here today. So whether your end users are, uh, paying customers or, um, students or whomever, uh, or for people teams, our end users oftentimes are our fellow employees at our own companies, whoever our end users are. Um, when we think about, uh, serving them and helping them be successful, it's, it's intuitive to us to translate our know-how, which is usually embedded in some pretty deep subject matter expertise and some jargon. Mm-hmm. Right? Like, let's call that like some jargon. We need to convey the opportunity or whatever it is we're trying to inspire, uh, not through the lens of our language, but through the language of the people that we're trying to team with. And again, I've learned that lesson the hard way for years. I think now I think I've got it roughly speaking now, but that's been really important here too. You know, if we come at this opportunity and I'm in at the, you know, leadership table with our CEO and my peers on our executive team, and I'm talking about it using a bunch of HR speak, it, it's hard for folks to connect, even if at the ground level, they absolutely agree that, uh, the human side of this opportunity is a, just as important as the technical side, and two, the most likely one to get overlooked if the people team is not helping to lead the way. So that's maybe, I mean, my number one like plea for all of us that do, you know, people in HR work is think about the language, uh, that resonates with your audience and, you know, uh, put the jargon on. Yeah, totally. Um, my, my background prior to Torch was all in product leadership. And, you know, when you think about building a product, you're building it for a, an end user and an end customer, and you really need to have a lot of empathy for what they really want, what will be relevant to them. And, and I think that's true of exactly what you said there. I think the only thing I would also add from, from my point of view is, uh, the more that I see leaders connecting to the, you know, what are the business goals that we're trying to drive with this change, you know, that's where you can also get a lot more buy-in rather than just kind of leadership development in, in sort of like a vacuum or in isolation from the business goals and really making sure that that's all tied. So, um, okay. So you said also that, you know, you, you believe that the people team owns the key ingredients, the critical ingredients to driving this change around the, obviously the culture, the psychological safety, things around change management. And I think, again, I see a lot of leaders who like, know this in their mind, but struggle to get it to, um, to come to fruition operationally on the day-to-day basis. So what do you think the, what is the gap there? Well, you know, I, I think anchoring on the notion of change manages as a specific component, you know, it's like, oh gosh, it's like, you know, we hear change management, we all kind of nod our heads and we're like, we know what that is, right? It's like, well, what are the, what are the ingredients? Like, let's get more specific when we're talking about change management. Like what are the specific behaviors or tactics that we think we need to bring to bear, um, so that folks have a, have a shared sense of purpose, um, and, um, mission, right? And you could really flip those. I might start with like mission, like, like why? Like why, uh, why, why, what's the opportunity? What are we trying to do here? And then the purpose part, which is well, and why does it matter? Mm-hmm. And the why does it matter piece, you have a, a couple different altitudes for that. It's like, why does it matter for the organization? Why does it matter for the people we serve? Which again, whoever those stakeholders are. And then, you know, what is, what's my opportunity within all of this? And a lot of change management comes down to, uh, you know, kind of mission and purpose and being really clear about that and being really clear about that for all of your key stakeholder types. And really what I mean, what we do at Zapier and, and what we've done in, in other organizations have worked at is you just map that out and it's like, it's not real until it's on a single sheet of paper. And then you can start converting that shared, you know, kind of like language into specific, uh, forums and opportunities and, and really like one of the, you know, change management , but it's like, like you were saying, Heather, it's like this playbook on like effective change management does not need to be rewritten. The hard part is, um, focus on execution and, uh, beating the drum on the key points over and over and over again in different forums. Folks need to hear it from, you know, multiple, you know, folks, you know, their manager, their CEO, uh, their coach, like whatever the case may be. Uh, and then it starts to sink in. And then the other thing too, when it comes to change managers, like nothing helps, like, um, showing versus telling. You know, the sooner an organization can start sharing examples of what it is we're talking about, even if they're small wins at first, uh, the more practical and tangible that it is for the team, and that starts to create a, a virtuous cycle. Yeah. Yeah. And I think you, you said your team went first, right? Like, that's the other thing I think I've seen with some really strong people, teams, is that they're not just helping to guide everybody else on this change, that you're, you're trying to be a shining example of it for your own organization and teams. So you can talk about it with the rest of the company from a place of real credibility as well. Mm-hmm. Yeah. Yeah. Great. Uh, okay, well, let's see. Let's go now. You know, so I think that that was great. Like setting up, obviously we have this big change. We need to drive, we need to tie it to, you know, the business objectives and the why behind it. We need to make it relevant, you know, down at, at the individual level. And so let's talk a little bit more about where our employees are really at today, um, and think about that individual level a little further. So, you know, I think we all see this day to day and, and probably feel it for ourselves as well, but, you know, people are, are definitely in a state of overwhelm and, and trying to do more with less right now. Um, LinkedIn just released re research recently saying that they've seen a big uptick on the number of people posting about being overwhelmed or navigating a lot of change on LinkedIn. And they said that it's an % increase in that. So, you know, just, and I, I keep joking that that's only the people who are brave enough to post about it on LinkedIn in the first place, right? So, uh, you know, clearly there is, there's a lot happening right now. And then there's the added dimension of AI being something that does make people fear about the future of their job and, you know, their, their professional value at work. Like, if you think AI can, can take your job, then, you know, that does create a different level of fear and insecurity in people as well. And so this isn't exactly like a recipe for success here, um, as far as like, who's there to receive this change. And, and it's not a, not a simple change to drive, you know, we talked about change management, yes. Like the, the concepts of change management are always gonna be the same, but this is also a, you know, a pace of change that we've never seen before. Like what you know today and what you need to know a month from now are gonna be totally different. Like, there's a lot happening. And, and again, we don't have like a, a necessarily people who are fully in the right space to, to even receive this change. So, you know, Brandon, you had said, I think at one point that, um, transformation is this health check for the org, like no other health check. So tell us more about that. Like, when you were thinking about how do you get people in, in the state, like what I was just describing, really onboard with this change. Like what did you find in the health check? How did you overcome some of these barriers? Well, you know, to build on what Heather's saying about this idea of, uh, a health check, what I, what I meant by that is that, um, many of the ingredients for successful AI transformation are kind of core ingredients for healthy organizational cultures in general. And some specific examples, right? Healthy organizational cultures, uh, ensure that everyone knows, uh, what is expected of them. They are cultures where folks, uh, also know what excellence looks like within what's expected of them. Great organizational cultures or cultures where people know where they stand within those expectations at any given point in time. Great organizational cultures do have healthy, um, you know, kind of promotion of experimentation, putting yourself out there a little bit, trying a new thing and knowing that if it doesn't work out perfectly or at first, you won't be punished for, right? These are really important aspects of healthy and high performing for what it's worth, organizational culture. And so as organizations are setting out on their AI transformations, and you think about that MIT study, % of these pilots are failing. I am willing to bet that if you do an org health check along some of the dimensions that we're talking about right now. Oh, by the way, trust and leadership, another big cultural component here. If, uh, some of the pieces of that foundation are shaky, it is hard to lead an AI transformation on top of a shaky foundation. And so it's Zapier to your question, Heather. Um, you know, so here's the good news, by the way. Like, no organization is starting from scratch on its AI transformation. It seems that way a lot of the times because the technology's new. Uh, but these ingredients that we're talking about, these cultural components, well, none of that's new. And I'm willing to bet that a lot of our organizations are already measuring sentiment, at least an experience on many of these ingredients in things like engagement surveys or pulses or skip levels, or whatever we do in our organizations. Like, could be cut and why, like, why have we been doing that for years? Because we understand that those are some of the ingredients for, you know, high performing, um, engaging workplace cultures. And so really, you know, as we kinda embark on this AI transformation opportunity, it's like, well, how can we, um, you know, adapt and reuse, you know, some of what we already understand so that we know the difference between the places where we have wind at our backs, like places within our culture, which are gonna help us as we climb the mountain together, the places that may in fact be a little shaky where we need to shore up in order to be successful. Yeah, absolutely. And I, I think in a lot of ways, I feel like AI is just, it's, it's kind of forcing us to shore up some of these things that we maybe haven't paid as much attention to in the past, but they are critically important now going forward. And so it kind of shines a light on some of those aspects around the, the trust and the culture and the clarity of the strategy and things that people really need. So that is great. Uh, okay, so what about people who didn't, uh, who, who couldn't get on board? You know, I, I know that there probably were people who, um, had ended up leaving. How did you address that? You know, that's a tough conversation for leaders. Like how did you build the muscle to, to have those conversations across the company? Sure. Well, to give you a sense of magnitude, you know, 'cause you're like, okay, what do people leave, you know, as a result of this, this invitation and this call to action? Uh, I think on the edges a little bit, like I actually a little hard pressed to think about specific examples that I am firsthand aware of. I've heard secondhand that may be a handful, and this is an organization of people, and it was about back, uh, two and a half years ago when we did our, our AI code rip. So call it five outta , maybe, maybe. But even that's like a little speculative. And, and, uh, you know, when we're calling a team into something that we know is gonna be, uh, potentially a little stressful and certainly challenging, even if it's not stressful, sometimes leadership teams hold back or they, they shade their guidance, uh, to make it a little bit more palatable at first. And, and that is a mistake. That is a mistake. Um, we need to trust our people to take in new information as challenging and stressful as it may be at first, right? We need to trust our people, and then we need to be accountable as leaders for leading. Well, if we lead well and trust our people, we will figure it out together. We absolutely will. It doesn't mean it won't be hard. It'll absolutely be hard and it will be stressful at times, and we we're not gonna have answers to all the questions that we want answers to at any given point in time either. All of that's true for sure. It's a human endeavor at the end of the day. Uh, but I think it's really important for leaders to be direct and clear, especially when we're talking about something new. And then on top of that, there's no substitute for us. A lot of the, in a lot of folks initial like very understandable concerns. They start to, um, dissolve when you get specific. So it comes back to something we talked about earlier, like the, the sooner teams can you about what is our piece of the AI transformation opportunity? Um, what's the specific way that I can start learning what this means for my job? At first, we had a lot of fud and it stands for fear, uncertainty, and doubt, right? There's a lot of FUD at Zap. You're just like, there was everywhere still isn't a lot of places right? Around like, what is this? Like, I'm reading some stuff about it. It's like, well, we're reading a lot and we're hearing a lot. Are we, are we actually like using it? Mm-hmm. And what we've seen over and over and over again that if you give folks guided opportunities to see firsthand, not see firsthand, the two build firsthand with AI and automation in ways that help them be more awesome at their jobs, at the thing they already care about at the thing they're already accountable for. Well, you know, in some ways, like what's not to like about that right now? I'm, I'm my company, my organization, my manager investing in me, right? They're not just telling me to figure it out. Uh, and in doing so, what we've seen over and over again at Zapier is, uh, it certainly can lead to increases in performance and productivity. It can also lead to increases in the quality of the output, which is really important to us as humans. Like if we spend all this time working every day, it's like fine to be efficient, I guess, right? But it feels really good to know that you're doing it really well, you're having a big impact on a customer or big impact on an employee or whatever the case may be. And then the third piece we've seen, I'm gonna bring this back to a framework in a minute, um, is that the resulting jobs in so many cases, in fact, I can't think of an exception to what I'm about to say, it's accurate at least. Um, the jobs are better, they're more engaging jobs, they have less tedium in the jobs, right? Like less busy work or moving things around on a spreadsheet or moving things from one folder to another, whatever the case may be. There's just less of that in the resulting job. And so to come back to a framework when we're circling opportunities for more automation and ai, Zapier, we really insist that we focus on opportunities where we believe we can improve three things at the same time. Efficiency, quality and employee experience. Great. That's great. And you know, I think it reminds me of the idea of just the, the fastest way out of the fear, uncertainty, and doubt is to start taking action and, and, you know, like move forward in some way. And it sounds like you guys really help people to, to do that, you know, even at the team and individual level. Um, okay, so let's take a quick poll break here, and I wanna hear from all of you of what do you feel like might be getting in the way of readiness in your organization? So I'm gonna launch a poll here, and we would love to see you all chime in. Um, so we have things like the people are overwhelmed and they don't have bandwidth, we have the fear. Um, we haven't equipped them necessarily yet with the right mindset or skills, or maybe there's actually a lack of connection from AI as a tool to their day-to-day work. Or maybe you don't know yet figuring it out. Okay. Responses are rolling in. Thank you all. I'll give you another minute here. Got almost half of the participants. Okay, last call. All right, let's look at the results here. Brandon, can you tell me, can you see the results here now? I sure can. Okay, great. Alright, so let's see what we have. So we have %, the biggest number here at the top of people overwhelmed and don't have bandwidth. Next up is not connecting it to their day-to-day work. And then the third one on here is still figuring it out. And again, super, super common. And I think that, you know, from, from Torch, what we talk about a lot with our customers who are up against this exact same set of, of problems and barriers is that oftentimes people start to say that people are just resistant, you know, get labeled as resistant to AI and, and it's just resistance. Um, but when you really peel that back and, and look a few layers deeper, it's often not actually resistance. And so one of the things that we're working a lot with our customers on is helping them to shed light on what are these things like, maybe you don't know what is, uh, standing in the way of people, you know, being ready in your organization, but through, you know, some of the listening techniques that Brandon talked about before. Um, you know, we serve back a lot of insights to customers to help them understand what, what is really happening on the ground level that might actually be standing in the way of success here. And, you know, we really believe that we have to help people develop the capacity to take on this change. You know, this is, this is really complex, um, situation to have to learn to lead through. It requires a ton of adaptability to keep moving. Um, you know, with, with all the changes that are gonna continue to come at us, and it does require a lot of just big picture thinking. We need people who can step back and actually look at what is there and what is on their plate today and, and start to let go of some of the old things so that they can embrace this new way of working. But that is not easy to people. Um, but you know, really believe that if we don't help people to develop that deeper capacity to more strategically approach this change, then you're only gonna get a fraction of the productivity or value from AI that you would see if you, if you don't invest in, in those kinds of things. So, okay, let's go back to the organizational level now, Brandon. Um, and let's talk about, uh, I, I really am seeing companies a approaching AI in, in three different ways. So first is, is the mandate, um, from the top that we talked about before where CEO says this is what we're gonna do, we're going to ai, um, everybody needs to be on board and, and adopt, but maybe it isn't necessarily connected down into the what do you do about it? So what for me, that kind of thing. Second is experimentation. People are running a lot of technical experiments, pilots, um, lunch and learns, hackathons, all of the activity is there, certainly on the technical side of things. Um, but maybe lacking like a, a north star as far as like what is the actual outcome we're trying to drive here, or the results that we're looking for for ai, it is more, um, you know, just in, in the technology experimentation stage. And then there is a more strategic change where they are recognizing that this is a complex transformation that we do have to rethink how we are working, maybe how we're organized, how do we actually support people at the individual level in order to get through that. So let's break these down together maybe a, a little bit one by one and talk about what works or not about them, um, with some of the practical examples that, that we both have seen. So let's start with the, the mandate. You know, I think you said it in, in, um, one of your earlier responses, like why doesn't a, a mandate on its own really work? And and what did you guys do to tie that mandate to some practical next steps of how to translate that down? You bet. So, so what does that top down mandate do and not do? So what does it do? Uh, one, it provides organizational wide clarity on the overall opportunity in a good top down mandate also conveys a really clear sense of urgency. It's not enough to say, like, why, why should we lean into this? It's really important to say, like, why is it important to do that right now? Because like we saw from the poll, and by the way, we see the same thing in our own data, right? Like, for as much progress as er may have made on this topic, bandwidth, right? Like, it's the number one thing we hear too. 'cause it's hard, right? It's, it's hard to see this amazing opportunity on the horizon. And by the way, that's what a top down mandate can help do. Uh, problem is like you're on the other side of the chasm, right? Someone, someone needs to build a bridge, like from the way we work today and the results we produce as a team to this beautiful future on the other side of the chasm. And meanwhile, there's a lot we're accountable to over on this side right now. So like, who's got the time? Right? You, you name it, it's a big, it's a big deal. We'll get to that when we get to the third pillar of that framework. Um, but the, the top down mandate is, is important. Like, I don't, I don't think you make a lot of progress without it. It just is a very specific job and it's one piece of the bigger puzzle. But the sense of urgency is really important. Importantly, a great tops down mandate is anchored on the company's or the organization's existing mission strategy angles while one, because transformation is supposed to be in service of those things. Like what, what trans transformation for its own sake. I don't even know what that is, right? Mm-hmm. Uh, well, I do know what, it's, it's not durable. If you don't anchor a transformation in, in something that the team already knows deeply matters, you know, for, for its success, for its customer success, you have an un unor transformation. So it's really important to make that connection and that that tops down storytelling is where that, you know, starts. Yeah, Absolutely. Uh, totally agree. I think the, the thing that, um, you know, we'll get to as we move down into this is, is again, how do you then take that, that mandate into real action and, and tie it back to how are we going to actually move forward as an organization? I talk to people all the time that say, my CEO has given me this mandate, and I'm supposed to take it down into the organization, but I, I don't even know exactly what it means myself yet. And, you know, and I'm, I'm trying to figure that out, or we see the opposite where there's a lot of, um, I'll call it scrambling, uh, you know, to try to figure out what do we do about it really fast because, you know, because it's coming from the top. Um, but I think that takes us a little bit into the experimentation bucket where, again, this is important, right? Like we, we have to experiment with the technology, we have to get hands on with it. Um, but, you know, we wanna make sure that it's not just like experimentation for experimentation's sake or, you know, just in isolation from, again, what you were just talking about, like, it, it should be experimentation in the service of what are the goals that we have as a company, or what is the outcome that we're trying to drive and, and really help to, to steer people toward that. So what else have you seen on experimentation, um, at, at Zapier that has like, really worked well, um, and helped, maybe helped the organization kind of learn and, and move forward and, and not just be, you know, random experimentation? Yeah. So like with the tops down mandate, let's talk about like, the job to be done for experimentation and the limitations. And then I guess we'll have some bulletins while we're at. Uh, so, uh, what, what is so useful about experimentation as an ingredient? So, a couple things. One, uh, no one knows what, you know, what the work is really like better than the people doing the work. And so, you know, the, you know, tops, tops down, you know, we're gonna do this, we're gonna do that. You know, don't worry about anything else. Like, I don't know, I'm like, as the leader of our people team at Zapier, for example, like I look at some of the most powerful ways we're using AI and automation on my own team, and how many of those came from me? How many of those even came from my leadership team? My direct reports maybe % generously. And so one of the benefits of experimentation, I think is everyone, the tools and the know-how to translate their subject matter expertise into, uh, use cases and opportunities to improve impact. That's the most useful thing about experimentation. It depends, like we talked about earlier, that you have a healthy culture of experimentation on the team or in the organization, but let's presume that's the case, then you get to build on top of that. Now it's wind at your back. Uh, and two, it's one of the best ways to help folks, uh, kind of, you know, help deal with some of the, the fear, uncertainty, and doubt, because you are making a very tangible investment in people, both in terms of, like Wendy was saying in the chat, making sure people have the tools where they can apply their know-how and do some experimentation and see what's going on, right? See what works, see what's worth scaling up are investing more the limitations of experimentation. Experimentation with, with a, and automation is typically at the use cases level, right? It's, it's, uh, you know, how how might we use automation and AI to, um, do the work that I do, the way it's typically been done better. And for what it's worth, there's a lot of potential for impact in that. And we still believe that in Zapier, uh, at Zapier as well. What's the limitation for some of the biggest opportunities that we might ultimately circle as an organization? Say, oh, wouldn't that be interesting if it typically requires a rethink of way more than like, you know, uh, one person's way of working. Uh, now you're usually talking about role design org design incentives, other things related to leadership, um, and sometimes bigger checks, like bigger investments beyond the ones that are typically required for experimentation. And so when we get to that, you know, that third, uh, pillar, that's probably our segue, uh, that I'm seeing up here. Like that's kind of where that grassroots experimentation hits its limits. It's still a really important ingredient for an AI transformation, and you don't stop promoting that type of experimentation as you shift into circling and getting after some of these bigger macro like gnarlier opportunities. Yeah, I mean, I think, you know, if you work, uh, in a tech company or a a tech team, you're probably more familiar with experimentation. But I think for most companies and, and, um, you know, learning to build this experimentation muscle on the team is actually like a big, a big thing to, to take on. Uh, and, and so it is really great, like you said, to, to do this. I think, you know, you said it, you'd have to assume that the culture supported this kind of risk taking and stuff. And I do think that from what we see, there are a couple of other big barriers there, which is one is not a lot of companies are not set up for a, a risk taking, you know, kind of culture and, and experimentation does require, you know, a level of, of taking a risk and showing where things have failed or where you don't know what you're doing. And, um, and so that, that requires some, some stronger leadership and stronger culture to be able to just foster that. Um, and then the other piece is just the, the coordination, right? Like, you can have a lot of random experimentation, but how are you actually honing the learnings from that? How are you incorporating it and getting into this iterative state of not just the experiments and, and automating a lot of things or, you know, having these different aha moments individually, but how are you actually using that to, to take the, the, at least your team, if not the organization, you know, somewhere somewhere greater. So again, that I think does segue nicely into the strategic change where people do understand that, um, you know, we need to connect the technical experimentation to changing how people are thinking and working. Um, and, and so tell me how you took to, to that next level of, of strategic change. So this what I'd call more like transformational change. Uh, sometimes the, the kernels of that are the, the sparks come from the experimentation. And, uh, I can share a very specific example that connects, uh, this together, the experimentation and the, this kind of more like structural or strategic change. At zapper, we have a customer support team, probably like many of our organizations and customer support, it's one of those places where people kind of like most immediately go when they think about the AI opportunity. Customer support is like purpose-built to be helped by a more AI and automation. And, uh, that's true at Zapier as well. And so naturally, our, uh, support leader, Lauren, she saw this too, and so over about two years ago now, um, but she has a team of nearly a hundred people. And so she's thinking about this too. She's aware of the reality that we saw in the polling we did a few minutes ago that like, bandwidth is limited, people have day jobs, pretty busy. Two, uh, it's probably not necessarily, they're either practical or even like super efficient to call like a hundred people into all experimenting here, there, and everywhere. Um, and so, uh, but she, she did believe there was a big opportunity and she knew experimentation was gonna be a step towards, uh, the new way of working. So here's what she did. First it started with leadership and some of those foundation foundational cultural ingredients. So at Zapier, the customer support team has a mission, and we didn't create that mission for ai. It's a pre-existed Gen ai. What is the mission of Zapier's customer support team? Fast, accurate support. It is a three word mission. Fast, accurate support is very clear. Every single person at in the support team, if not almost everyone at Zapier knows the mission of the customer support team at Zapier. Fast, accurate support. And guess what? It's really two things. Like the third word in the mission. It's just the name of the team. It's just what the team does. The, the operative terms. There are just two of them fast, inaccurate. And the team has measurable, um, performance indicators for each of those two terms fast. Uh, the team goals itself on what we call ticket handle time, that's just how many minutes does it take on average to successfully respond to a customer's question. The second accurate is, uh, customer satisfaction. So it's not just how quickly we resolved the customer's, uh, question. It's how effectively from their perspective did we do that? And this is where the, this is a great example of like how a strong foundation, cultural foundation for a team like helps with ai. Because then when Lauren is thinking about the opportunity, she's like, okay, then, like, really the opportunity, the question we're asking ourselves with AI and automation is how can AI and animation help us take a big step forward, uh, with fast, accurate support as measured by our existing measures of success, right? So it's like really clear, but then she's got this busy team of almost a hundred people. So what does she do? She takes two kind of squads, little groups of like three or four folks who really know their craft, they're great folks and support, and she says, Hey, um, I'm gonna change some things about your job for, you know, a six month period. So you have the time, you, not everyone on the support team, but you, these two pods, you will have time to help us, uh, experiment with these new ways of working. Now, why did she do two pods rather than one? Well, you get twice as much experimentation. Conceivably, Lauren could have done three, who knows, but she did two. And uh, sure enough, about six months later, they had proof of concepts and meaningfully more effective ways of working. And by the way, on all three of the impact dimensions that we talked about earlier, more efficient, higher quality output, and better employee experience like this, the jobs are better as a result. So these, these two pods of experimenters, they showed the way they showed what was possible, and then imagine what happens, right? Instead of Lauren, the leader getting up by herself and saying like, we need to find the new ways of working. Go use AI now. She says, Hey team, as you remember, we've had several of our teammates experimenting with AI around how to have, like, you know, how to, you know, take a big step forward on fast, accurate support. And I'm really excited to share with you, I, they figured it out and now I'm gonna give them the microphone and they're gonna do most of the talking. They're just gonna show you what they tried, what worked and what didn't, and what they believe is now possible for our team. Oh, by the way, and with the measurable results. And then after all that, the team can see firsthand from their peers, not like some hand wavy like leadership thing, uh, that oh my goodness, wow. And then they also get to hear from their peers, by the way, about like, why the job that results from that new way of working is a more interesting job. Mm-hmm. So it's like, don't take it from your leader. Take it from the people who do exactly what you do every day. Then at the very end to like bookend the conversation, Lauren gets to get back up and she says, well, isn't that amazing? Right? Like, huge credit, like so much creativity there. Incredible. Now that we know what is possible, including the benefits to the people doing the work, uh, we're setting a clock. We are now going to invest in a transformation of our team into these new ways of working. We'll invest in every single team member to cross that finish line together, provided you want to row with us towards that destination. And then by the end of that time period, we will take up our performance expectations because we've already shown ourselves that we can do it right. And in doing so, we have a team that is more efficient, produces better outcomes for customers, and results in better jobs for all the people doing the work. Now, the icing on the cake is that support team at, at Zapier in, in part, uh, supported by the fact that they're now so unusually productive. They're paid top %, uh, for customer support folks. That's awesome. Well, Lauren sounds like an amazing leader. So I feel like we can all learn from what Lauren did there, but I love how she took, you know, something that often people are kind of waiting for that top down, you know, um, mandate or, or the push or maybe the enablement on these things. And she really saw it from like the ground up. Like she enabled, she got her team going, and that's really, really cool. I think, you know, a different example that I have also seen of, of just really strategic change on this is, um, one of our biggest customers is LinkedIn. And, and LinkedIn also really wanted to be, you know, at the, at the forefront of how do they build an AI ready workforce. And so to do that, they actually decided to give people coaching, um, and, and help them ask themselves a question, which is, how might AI fundamentally change what success looks like in my role? And gave people that individualized support to go into a safe space, ask those questions, talk about, you know, the, the fud, or talk about how, you know, they, they might need some, some space to process the, the overwhelm and the things that are on their plate. And, and I think it was really cool because it was, uh, one of the first times that I had seen someone really recognize that this is a really individual journey and that there isn't anything, you know, one size fits all about it. Like what Lauren did there is awesome, and, and there's certainly learnings we could take, but as we know, you know, AI affects every industry, every company, every function, every level, every generation really, really differently. And so, you know, to be able to really provide that, that support, to shift that perspective and, and work toward this, that that was something that, um, has really changed things, um, you know, for LinkedIn. And of course it's really helping their teams work differently. But also LinkedIn has learned a lot along the way, way about what else is going on again, at the ground level in the organization that might actually be standing in the way of success. How can they get more clear? How can they communicate better? How can they, you know, uh, provide a stronger like strategy and vision for people to really rally around And so created this, this feedback loop back to the organization about how do they, how do they move forward? So, okay, so I think that the, the last piece here that we wanna chat about in this conversation is, is a bit about what you, you described Lauren doing where Lauren actually said like, and I'm gonna get these people to go and think less about just how do we automate, you know, the, the things that we're doing today, which again, has value, no doubt. Um, but she wanted them to think bigger and what, what could be now. And so, you know, my background in, in product, I've spent years talking about first principles thinking, um, in leadership development we often say like, what got you here won't get you there. Um, and I'm just realizing that this idea of being able to really go to a blank slate and, and really rethink, um, you know, what, what your, the work is that needs to be done, what is the problem that we're even trying to solve in the first place is going to be really critical, you know, for success in, in the AI world. And, and I think if we're only having our team ask themselves like, what work could be faster or easier with ai, then we're gonna miss the bigger picture here. And again, I think that like is what Lauren really saw, um, with her team. So, you know, we wanna help get people comfortable with this idea that they can ask themselves, you know, what could be if we started from scratch today, um, and we let go of some of the old ways of working and the old ways of thinking in order to, to have a, a new, you know, chapter ahead of us here. So I think you, you guys certainly saw this at, at, um, Zapier, but you know, I think you talked about like clearing the lane and like really creating space for this kind of bigger transformative thinking. And again, that's what, what Lauren did, but tell us more. Did you do that in other places across the company, or how did you, how did you help people actually clear some of that overwhelmed, uh, state to be able to do this work Work? Yes. So the two, two best examples I can share. The first one we, we talked about with Lauren's case. And so the way she, and the, and by the way, two different ways to do the same thing, right? Because we're talking about clear value, we're talking about how do we make sure that the right people, enough people have the ability to focus and have the time and bandwidth, not just the skills and tools to, to do the experimentation or when you've kind of circled the big opportunity to get after and help us figure out how to do that. So in in Lauren's example of customer support, you know, she cleared the lane, uh, by basically carving out a fraction of her team to really focus on this, on the people team at Zap, you're the second example. I made it a a, we, we plan in six month periods at Zapier. I made it a priority for us in the first half of this year specifically, I said, Hey, let us, um, we have a lot of programs and practices on the people team and like, like a lot of product teams. You were talking about product earlier, Heather, like a lot of product teams, like it's really easy to like build a new thing, ship a new thing. It takes a lot more focus, uh, and intentionality to like prune back all of that over time. It's like you add, add, add and before you know it, you're like, Ooh, when's the last time we really went through all this stuff and consolidated it? Is it still doing its job as well as it could? And it's like, Hey, it's, we're, we're gonna do that now because a, it's just good hygiene in general, but also, uh, we need bandwidth. Like we're, we're hurting for bandwidth and as a result we're not, um, exploring, you know, as, as fast or as far as we could be with ai. So we inventoried every single people product and practice and program that we have at the company. And I said, Hey, by the end of the six month period, at least % of these need to be either, uh, further automated to save time, either for our team and or for the end user, our, our fellow teammates, or retired altogether. And I, I don't know for sure 'cause you know, as a leader of the team, it's like you can't always get like a total read on how people react to something like that. I wanna believe it was not like universally understood or loved at that time. Uh, but again, like we talked about with the AI transformation opportunity, when we got specific, when we kept like beating the drum and, and when I started sitting down with specific groups of teams, working groups and what have you, and really explaining why this was important, why we needed to do it now, and also like what it is and what it's not really about. Uh, folks got kind of interested and, and sure enough, uh, we, we did it and it had two benefits. One, it did create enough space for us to take, you know, now our piece of experimentation on the people team with AI is noticeably faster. Two, um, our offerings to the rest of the company, our talent programs and offerings are just like easier to understand. 'cause there are fewer of them. They're stitched together more elegantly. Uh, and in many cases we are not just saving my team time, we're saving time for our employees. Mm-hmm. Yeah. Yeah. Well, you know, I think that the hardest part of all that is, is again, this idea of like letting go of what was, you know, we all work really hard on all of those different initiatives and so it can be hard to, to prune that back and, and set down something that you, you probably put a lot of time and energy into. So, I guess last question for you here is just, um, how, how did you create the space for that, you know, like psychologically on your team, as the leader of the team, in order to, to help them know that you know, it, it's okay to, to set some of that down and be able to, to rethink where you go Now? It is a place where cultural foundations helped. Again, we had been working for years to like really anchor the team on impact not activity. Mm-hmm. And so we, we were able to, we were able to like stand on that foundation when we were setting up the clear lane effort and say, Hey, just like we've been talking about for years, right? This is just another manifestation of like, you know, know, living into what we talk about. So we're not, we're not slashing stuff for the sake of cutting stuff out, but let's, let's apply an impact lens on all the work we do and make sure that it's still producing the intended impact. And then let's also, for, even for the things that are, let's figure out, this is the Zapier way, one of our values. We have five values called Build the Robot, which is, you know, don't let the humans do the things that robots can do or do better. 'cause we wanna make sure we have as much time as possible to do the things that humans do best. And there are still many, many, many things that humans do best. Uh, and that was, uh, really what we were able to stand on when we call ourselves into that clear the lane, um, moment. Yeah. That's great. Well, it sounds like the, the cultural, um, foundations, as you called it, that you had in place at Zapier certainly gave a lot of like, wind in your sails as you all took on this change. And, and I love all the, the practical examples that you've shared. Um, definitely taking some notes that I'm gonna bring back, uh, to torch as well. So, um, thank you for that. Uh, I know we're getting at time here, so let me just, uh, wrap up by sharing some of, I think the, the patterns and things that, that we covered today. And, and hopefully there's some key takeaways that you all can take back to, to your orgs and your teams. So, um, first is, you know, really believing that the people team has the critical ingredients to lead in, in the AI transformation space. And, and don't be afraid to, to go first and get yourself out there and, and really, um, think about how can you help people get to this much more like practical, um, uh, you know, state of action around AI and get out of, you know, the, the fear and uncertainty and doubt that exists out there. Second one, obviously we've talked about a lot, but great leadership is going to be really critical for ai. And I agree with what you said, Brandon. It always has been critical for any other transformation, but sometimes I know that can, uh, can get lost in, in all the activity that that companies are doing. But, you know, really think more about how do you build the capacity for people to lead through this and, um, be able to tolerate the level of uncertainty and doubt, not just for themselves, but actually for their teams and, and, and be able to effectively guide people toward this new state of being. Third one, AI is definitely an individual journey. And, and you have to have these systematic frameworks and things that, you know, I think that you've done so well at at Zapier. Um, and the personalized support goes a long way. And, and wanna think about that from both the technical skills as well as, you know, the human skills side of it. And then finally, I think just this idea of building two-way communication between what you're trying to do as an organization and what's really going on for your people is going to be important to actually understand more of what is getting in the way, um, or what, where are the wins even? Um, and how do you actually continue to, to iterate together as the company and the people at this is AI change. 'cause it is gonna require a very iterative state of, of constant experimentation and growth, uh, again, at the technical and the leadership level. So with that, um, wanna leave you all with one final question, which is, when you think about how your companies are approaching AI today, are you thinking of it as an outcome and, and you know, I'm, I'm going to achieve ai. Or are you actually really tying it to the goals of the organization and the mission of your organization and helping people to get clearer about how to drive this change, um, in a really practical way, like what we've heard from Brandon and, and then what becomes possible for you as an organization if you help build the capacity for people to really think and lead differently in this next chapter. Can you embrace, um, this next chapter in a totally different way than maybe you have in the past? So with that, thank you again, Brandon. It was so lovely to talk to you and hear all your examples. And thank you all so much for, for being here with us today. It was great spending time with you. I really admire how you and the church team are helping leaders navigate this big opportunity. Thank you. Thank you very much. All right. Thank you all very much.