Name: Revenue leadership in an AI-first world
Uploaded: 2026-06-03T17:30:51.257Z
Duration: 41 min 20 s
Description: Revenue leadership in an AI-first world

Transcript for "Revenue leadership in an AI-first world": Alright. Hello, and welcome today. My name is Elizabeth Hayes, and I'm on the marketing team here at Jackstory. And I am joined by two other gentlemen today, but I wanted while we're introducing today's panelists, we wanted you to drop in the chat where you're joining us from today. So first, I'll introduce Chad O'Connor. He runs revenue at Backstory. He spent his career solving one problem knowing what's actually happening in the pipeline, and he has real opinions on what it takes for AI to do anything useful for your teams. And And then we have Nick Kiesler who leads rev ops and enablement at AIDOC, and he's the person CROs call when things don't quite add up. So if and he's thinking about the hard gap between what AI promises and what it actually delivers for stakeholders. The other quick housekeeping item is drop any questions in the q and a and comments in the chat, and we will be sure to get to them at the end. So with that, welcome, Chad and Nick. Let's go ahead and get started. Alright. Thanks everyone. for joining. We appreciate it. Alright. So before we go into what's, I guess, the more negative side of things, I'll I'll shoot this to both of you. But, Nick, we can start with you. So where is AI genuinely working for revenue teams right now? Where are you seeing it work for your teams? I think the the first thing that's working quite well is being able to, automate or at least create a lot more efficiency around account research, the speed in which it takes rather than clicking through several LinkedIn profiles and finding which podcast or publications. So I think we're finding an immense amount of speed around, account research. I think the second thing I would point out is I think the where it's working well is context to a deal. I think, AI tools today, much like backstory and others have become quite good at being able to synthesize what's happening inside a deal and provide bubble up where the risk may be, bubble up what may be going on. So I think those are two areas today I see where, we're realizing some of the potential that's that's been committed. Awesome. And, Chad, what do you think? No. Mine's really similar. I think, you know, Yeah. Nick and I were were joking before we start how everyone has a different level of of, I would say, AI competency. And and. depending where they're at, you know, we're we're trying to make it really easy for people. And so, I agree with Nick. It's like, hey, helping them do the account research, but actually making it so easy that either information is pushed to them, or they just really push one button and and it provides that information to them. I think, one of the other things I find is is when we're we help them give them specific sort of next steps, meaning, here's the specific action. Here's the contact or stakeholder we want you to to actually, get a hold of, and here's what, you know, we're asking you to do. The more easy we make it for them, I think the better they are about taking up the action on on the advice we're giving them. Otherwise, it's just it's too hard for them. I mean, Totally. Nick, what's what's kind of your opinion on that? Yeah. I think it's I think it's also it's e it's ease, but it's also relevancy, and I think that's one of the most challenging elements to get right, because nothing is worse than, you know, promising that the AI is going to be correct or help. And and the first time they interact with it, it's it's like, well, I already called Chad. I called him actually yesterday. We had a conversation. Right? And then you kinda have that, oh, man. Like, coming back from that is gonna be tough. Right? So I think, I think a lot about relevancy, before we think about ease, they're both really important. They go hand in hand. But if we're not relevant and we're just over tilted on how easy can we make it, sometimes. we then lose the kind of pat the punch. So I think there's a there's a healthy blend, but I I like to give the Disneyland analogy to my team. Like, below the the concrete, right, where everything's going on at Disneyland, it could be chaos, which is where we're building the agent, testing the agent, tweaking the AI, all that. But it's how it's gotta be a really, really, phenomenal experience for the end user who's engaging with the AI back to your point of ease. Right? It should just feel naturally planned, naturally able to integrate. If we're forcing folks to to radically change their behavior, to radically change their day to days, I'm not sure we're actually achieving the the desired output we probably want. Yeah. No. I I would agree with that. It it does make me think about, the next component is, like, the accuracy of the data. Right? The richness of the data. And, I mean, what's your what's your viewpoint on I what I'm finding with with, sellers and frontline leaders and even, you know, other rev ops folks I'm interacting with is if if we're not able to have the data data really accurate and relevant, they're just like, I'm not gonna listen to it or this isn't for me or or I'm not gonna take the advice that it's giving. Yeah. Yeah. I think it's a phenomenally, challenging subject. I think two elements on it. One, I think we spend a lot of our time testing, in in in the rev ops world. Right? So we're building and we're kind of testing over and over again for exactly what you're saying. Right? To understand, do we understand its accuracy? Do we understand the variability in which it may be able to, deliver on? Because there's two things we're trying to achieve, I think, at once. One is, obviously, we're looking for speed and efficiency. But when we have speed and efficiency and the data is inaccurate, we're just basically making bad or mediocre faster, which is also not good. Right? So this sort of obsession about quality, I think, is is key. And and as we hit into it's it's both the quality of the data and completeness of the data. And I think, one of the biggest challenges is how do you get to completeness. Right? It's easy to build tools on top once they have a complete dataset. But getting the completeness of a dataset is, is frankly where I think a lot of the list is for ops ops teams. Yeah. No. I so let me give a couple of examples. So to the audience, we've been leveraging, our data set with with, I would say, different AI platforms, other AI agents to figure out, okay, how do I how do I prioritize, accounts for the rep? Now how do you determine where I think their potential revenue is? How do I understand, what LinkedIn profiles are bringing and go after? How do I understand where this past champions, buyers, or influencers? And and so when you think about that, the complexity and the accuracy of that data really drives, I think, either really productivity for an end seller or, or for a leader when you're trying to understand, okay, are my reps actually penetrating all their accounts? Are they trying to engage with them? What personas are they trying to engage with across the board? Are they actually trying to, you know, pitch all of our our solutions or products that we offer? I mean, these specifics actually really make a difference. And and for me, when we get it right, the productivity or or effectiveness from each rep and my leaders tremendously goes up. When we get it wrong, that's when people disengage, and they just don't wanna take the advice of of what the data is telling them. So I'm I'm I'm curious on your, Nick, your perspective on that as well. Yeah. I mean, I could give you an example. I mean, up until the last three probably three months, our core datasets, were, were were from backstory was the the calendar and the email data. We, of course, have the CRM data that reps were putting into Salesforce. But do we're in a we're in a highly regulated, med device, industry with, sellers who aren't native SaaS sellers. Most of them come from, the Phillips GEs of the world. And so utilizing, deploying, adopting, call recording has been something at the four years I've been at ADOC that's sort of always been a back and forth. We've ops pushing for it, sales leadership pushing back, and we've never really had the agreement to go forward. Well, in the last three months, we have. So imagine four months ago running AI on your datasets. This is where I think completeness and the the fullness of your corpus of data is really important. If you're not actually taking in the recordings of calls, like, you're missing a really key piece of the sales motion. So then how can your accuracy and your relevancy of maybe the insights or the, outputs you're deriving? And so I think this is where the corpus becomes really important. It's not to say that this is one of the best things about AI. Completeness doesn't necessarily need it needs to be structured perfectly in a in a great database, and it's really easy to query. It's actually more about, do we actually have the data? Yes or no. If we don't have call recordings as an example, we're missing a massive piece. Right? An another our our sales cycle happens to be highly, in person on-site at the health system. Well, I I'm not recording those. I'm I'm highly relying on a sales rep to give me their secondhand point of view of what happened on that call. So that's just a good example, I think, where this the the corpus being really, really important to mine that is, is is is where the impact starts. And if that's not clean, it's really hard to make everything else, relevant and accurate. Yeah. Really good points. Totally. I was gonna say too, I think, like, you kinda talking about getting that data right before going into this, like, personalized enablement AI agent kind of world. So what how do you talk to leaders who wanna skip ahead? Or how do you kind of I don't wanna say tell people to slow down, but how do you help people understand the importance of getting the sequencing right? It's a good question. I'm sorry. I don't maybe I stumped you. No. I'm just kidding. I'm sure you got a great answer. I I think I think it's I think it's it's balancing two things. Look, first and foremost, as rev ops leaders, Yeah. the answer can never be slower. That's not a good enough answer to to back to the CRO. But I think what it has to be is framing, framing it as kind of two swim lanes we're trying to we want them to basically move at the same speed, but they don't always. Swimlane number one is building that corpus of data. Right? That has to be a. core strategy. Swimlane number two has to be the tooling you're building alongside of it. And so the way I think a lot about it is, look. And up until three months ago, we were missing that big piece of corpus of data. Did we wait? No. We we worked with the agents and the AI to harness as much of the data that we had as possible to drive as much value. So I think it's it's like anything. Right? It's explaining and presenting the trade offs that exist. What are the potential risks? Yeah. Highlighting the value of still moving forward with the risks, but you're always demonstrating progress. I recently was talking to someone around the hardest part right now in rev ops is anxiety. There's so much anxiety that we're not moving fast enough because the AI is moving so fast. Right? Right. And so I think those two swim lanes and focusing on them uniquely and making sure both are always pushing forward, both also gives the CHAS, the CROs of the world confidence for making progress, but it also hopefully relieves reduce a little bit of anxiety as as rev ops leaders as. this thing just moves so fast. And getting behind is is is frankly a bit of a death sentence, in terms of it's moving too fast to catch up. You you're making me think about, a conversation I was having yesterday, which is, I think, for me, in my relationship with my rev ops folks is, one of the things, like, you're always counseling me is, hey. The direction the data is always directionally accurate. Right? It doesn't have to be perfect. It's always directionally accurate. The second thing is, you know, one of the things I've learned is, hey, we gotta evolve every 90 days because it's changing so fast. It's okay to fail. It I mean, try different things. It's okay to fail. Learn from what we failed and then try something else. And, I think that's, you know, one of the things when I think about the relationship, like, you and I are having, it's this constant evolution and and it's okay not to get it right right the first time. Right? But to keep iterating, iterating, iterating. And I'm finding as I'm sort of going through that that journey with, you know, you and the rev ops space, my I'm starting to get answers to questions I never got I never got answers before. Or instead of waiting for a week for someone to actually talk to the sales leader, talk to the rep, put something in writing, do the research, it's like this story starts getting, convoluted by the time it gets back to me versus something you can literally give me in a matter of minutes, which is directionally accurate, which is, I would say, less emotional, more data, fact based is really, really powerful. But I've had to kinda let go of my, preconceived notion of what I think the answer is. or what I. believe is what's happening. So it it is I'm I'm curious if you can talk about that a little bit because our relationship you and I relationship is definitely changing. Right? Yeah. Well, I I'll comment on that. I'll also comment on I think there's a key piece here. Right? So when you look at how the anthropics open AIs of the world, they work. Right? You know, you have they have the lab component where they're they're. testing, they're building. They're all that. I mean, that's a that's the same model we're running in RevOps. Right? We have these sets of tools that. are AI tools, and the lab is RevOps. Right? And my my my the folks on my team and their and their, colleagues are are the lab. We we're we're building. We're the ones sort of testing, validating. Is what we're getting out of the agent correct? Is it good enough to your point? All of that. And so this idea around it's not buy it off the shelf, plug it in, step away, move on. Right? This is about building the lab in in your own, commercial organization. Right? And sometimes that's bringing in a seller. Right? I had a phenomenal use case. A seller had built a really cool a, their own agent, inside Glean, how to to validate his own deal quality. Great. Let's bring him in the lab. So we brought him in the lab, and we've we've worked to build and improve that, agent on top of what he's already built. So I think it's this idea that it's not plug and play. It's not, buy something off the shelf. You're ready to go. You have to have that, alignment up and down the org of the same thing. We're gonna put it in the lab. We're gonna build it. We're gonna test it. We're gonna tweak it. We're gonna launch it, and it's gonna go back in the lab, and we're gonna do the same thing over again. And we're constantly gonna do this over and over again. And I think one of the misconceptions may be, hey. We built this agent. We put it out in the environment. We're good. Right? Like, we're talking a lot right now about upkeep. Right? How do we go back and test the agent consistently? How do we go make tweaks to it as it's changing? So I think, this is an example where I think the relationship between, rev ops and CROs are changing because I think you did the in back in the day, to your point, it was a lot about how fast can you get me reports. Are the reports accurate? Give me a dashboard. And now it's, like, thinking through, that's table stakes. I should just be able to open a open a what LLM chat, ask you questions. That's not where it's at. Right? Like, I'm looking for the actual innovation that comes through using, the AI, and and that's really where we've had to shift our mindset as a team to be focused in this kind of lab building environment. I everyone from enablement to ops on my team has an expectation through their OKRs to be building as part of the lab, and and different different agents for different use cases. But the expectation is that you're building and contributing, because that's actually how we make the lab, work better. That's actually how we come with better outputs. Right? So I think that's where a lot of the change is coming. It's a different it's a different expectation. on the ops leader. And frankly, for you, you're I believe a lot of the change there is your speed and accuracy is possible now. So. the question then becomes, since that's always been the challenge, what is the next obsession of the of the modern CRO? What is the next big, kind of boulder we have to move? And I think that's kind of ever ever evolving over the last twelve to eighteen months. So you you made me think about, some of the some of the things you just comment on on about the lab and and and actually experimenting on things. So at our company, and I would encourage other companies to do this too, we we actually we basically did, like, a talent show once a quarter of how you leverage AI in anything you want. Right? How do you get answers to things that you haven't been able to get before and and come and present? And and I mean, all sort of areas of our company, and I would encourage anyone in the audience to do something like this. Because what I, Nick, when you started talking about, like, experimenting in the lab, we we literally have had all these different personalities with these different backgrounds, with these different jobs coming and experimenting. And what we've had some great ideas out of it that we didn't even think about before. Great use cases. Awesome. And so that's, I like the idea that you folks are doing that as well. And and for those in the audience, I think the more you do that, the more it helps in this evolution of, like, every 90 days, enhancing or changing or improving. But it's but it's crazy some of the use cases that that people are leveraging, you know, a data set with AI to help them be either more productive or efficient, more effective, sort of in their day to day, I would say, at home life that can come into the work world. So Absolutely. if any if any of you, have tried that, we'd we'd love to, you know, hear any comments in the in the chat as well. Yes. I feel like you just have to create that safe space, which we have that the hackathon here at backstory. I. even participated. It's fine. Okay. I kinda wanna double click into the relationship piece, the CRO rev ops relationship. You kinda were getting there, but I know you both had really funny ways to describe your relationships in our prep call, so I wanna make sure we share those with the audience. So I guess we can start you with you, Nick. Like, what what do you what is your ideal relationship with the CRO, or what does yours look like, and what makes a good one different? Yeah. I think in the prep, I I my comment was if if you're, not aligned with your CRO from a rev ops perspective, you should probably find a new job. And. I believe it. Right? Like so, I could tell you a story. When I started ADOC, our former, CRO during the interview process, the way he described the role was, the right buy my right bicep. And it was an interesting way to describe it at first. I didn't really you know, I was like, yeah. Yeah. I've never heard it started that way. But now than a minute and and and and and in this way, the type of commercial motion we have, it's really true, right, in the sense of I feel that way, and I feel like that interconnectivity is really important. I can tell you one of the things that with with our chief commercial officer at ADOC and I, what's really important for us is it's it's I was Steve Nash, one of my favorite basketball players ever, is on the Phoenix Suns. And what made him incredible was he didn't even have to look where he was passing because he already knew the player would be there. That's the relationship expectation now between rev ops and CRO. CRO, as soon as they look and they see that's we should already be there. We should have already had it solved. Right? We're already be thinking about it because we should be anticipating where the CRO is going next, whether that's a potential new market or whether that's, a change to the operating model. So I think a lot about, the the relationship has to create a lot of trust. There has to be a lot of sharing between the two parties, good and bad and ugly. But when you do that, something magical happens where this anticipation exists. And I could tell you what I feel at ADOC, been here now four years, knowing the business well, but also having that ingrained trust is we're anticipating now at a at a way we've never done before. And and it's fun. Right? It it creates a lot more dynamic, but I think, ultimately, those are kind of the the the couple things that, resonate with me. I think the last thing I would say is that it's about it's always about effective communication. And I think that the more open we are about reality, and setting the right expectations, the the better we can we can be. And oftentimes I find in in other roles I've been at in ops orgs or other places, there's kind of a mismatch of expectation of how fast we can move or what how fast we can complete things or execute on things based on size of team and resource. And I think that honest dynamic of having that conversation is really, really important. And it boils down into that kind of trust, which is back to the where. we started. Right? If if you don't have that trust, then it's probably time for a new job, frankly, because it's not working. out. You can't have that dynamic between the two of us. So, Jack, curious how you think about us, but, I think we're we're trying to be we're trying to be the Steve Nash for you all. Yeah. I I was, I was smiling when you were we're talking about relationship because I look at you as, like, my work wife, you know, work wife, husband. And And and sometimes we bicker and sometimes we disagree and sometimes we argue, but but I but I agree with you. You know, one of the things I I think I've learned is the more vulnerable I am with you and I'm sharing with you daily I mean, it's kinda interesting that there's so much that happens in a day across the organization and where we're looking for the future to drive. But if I don't share my thoughts with you as those are happening so I'm I'm talking to you multiple times a day. I'm being vulnerable at sharing what's what's going well, what's not going where, where I want the future direction. What I'm finding when is when I do that, the our our our relationship was way more productive. And you start answering questions for me that frankly, sometimes I just don't have time to do because I'm back to back to back to back to back. Right? And then you end up going out to, you know, dinner with a customer. Next thing you know is nine or 10:00 at night. And and and me talking to you often and explain where we have challenges and you helping in the same time of the day, coming in with suggestions or or different ideas is really, really productive. But that means you and I are really well connected, kinda like kinda like a relationship of a of a of a wife and a husband. And and when and I'm I think it's awesome, but it's definitely, you know, for me, a little more set. we're laughing to the audience. We're laughing, at the beginning of this is, you know, Nick and I come from different backgrounds, but also different generations. And so, you know, it's it's required me to change and to, to be open to having different relationship. And when I do that, the benefits are immense. Right? It's it's it's much better for us as I think, from a work life balance and from our productivity of the job. So I think to to build on that, I one thing I'd like to, talk a lot about, I think one of the blended elements of being in this leadership role of ops is also having the ability to kind of put on a chief of staff hat. Yep. And and I think that that that sort of mindset helps a lot in what you're kind of talking about. Because sometimes the sharing, with with my boss is sometimes therapeutic. Maybe it's not always things I can help control, right, or solve. But then I kinda know as the chief of staff, like, what are some of the priorities that maybe, he's thinking about or in your case, Chad, you're thinking about that I can influence. Right? At the end of the day, ops' role, we don't manage the sellers, the CS people, like so so we don't have accountability for them executing. What we do have, which is potentially even more, impactful is influence. And I've talked to my team. a lot about influence. And so this idea of having that chief of staff hat on as you're engaging with your CRO, CCO, and thinking about their priorities and where else in the org can you influence? Oh, we you just made a comment to me. This is the third time you said something about our product wasn't working here. Clearly, it's something stuck in your head. Like, let me take the action and go hit with product and figure out if we can fix it and come back to you. So it's taking those cues that may not be in your ops and, role, but that chief of staff hat. And I think those are the little things much like you would do in a relationship with, with someone else. Right? You're trying to find those little hidden gems you can do for them in the day to bring a smile to their face. That's the same thing here with that chief of staff hat. What's that little thing I can do? And that's it's about relationship building. It's not doing it because I'm looking for the for the accolades and the recognition. It's because that's how you build strong relationships. That's how you build trust, within the within the org. And, I think that's a key key piece as as as a rev ops leader. If you don't have that second kind of, side of your hat on, you're missing a big, area of potential in terms of generating trust and and building a stronger relationship. No. No. I agree. You know, the other thing I think about is, one of the things I've learned from my rev ops, and you and I have talked about this, is letting the data be another viewpoint. You know, the the results that you're giving me, you know, look at the data as, unemotional facts and make decisions based more on the facts versus your emotions. You talked a little bit about that before. I think that's sort of that other third element is like, the more, I'm open to reviewing the facts, and I think the the better I'm able to adjust the organization to where we should be going or what changes we should be making, and sort of taking my emotions out of it. Right? And I and I I don't know if you you struggle with your leaders getting them to, like, just review the facts without the emotions. I'm curious, you know, your viewpoint on that. it's funny. In the past, I would say yes. And, Aslan, our chief commercial officer, which is kind of fascinating, is is a is an MD. So before he got. into commercial as an MD. So, Oh, wow. my brains have to shift massively. Right? It used to be exactly what we're talking about, like, emotional, your typical, like, sales background. And, like, my head had to flip two years ago to someone who's, like, science first. Like, everything is unemotional. Everything's through the lens of an MD. And so I I agree with you in terms of, like, that's a kind of a typical piece is how do you kind of let the the the data and the facts speak for for itself. It's almost now the the in my world, it shifted even further, which is, like, the data completeness and and sort of quality has to even be at even a higher level because you know the decision making, tendencies of of your CRO and how do they make decisions. I think that's another key piece around the relationship. Right? Not every. CRO has the same way to make decisions or the same things they need facts on. And so I know, and I've shifted my style to better support someone who comes at it from a little bit more of a scientific kind of, traditional MD, point of view. But I think that's where the all relationships, CRO rev ops is no, is is no exception. It's understanding each other. It's being able and being open to kind of shifting our our approaches. It doesn't mean we're changing the outcomes. It doesn't mean we're sacrificing the way we wanna run our teams. It just means that we're open eyed and open eared into the way we like to communicate, and we adjust our structure. So it's been a fun process for me at ADOC. I've had two separate, chief commercial officers, one coming from a more classically trained sales and and and much more emotional and, you know, kind of your big energy. And then working with Austin, who's phenomenal as well, but just comes from a different line of thinking, and his his decision making frameworks are very different, both producing results. So it's kind of a it kind of is a good proof point too. I think that when you nail the relationship, you can kind of insert different, points of view, and it still works. Yeah. That's great. That's interesting and that you've been through both because I'm definitely more on the emotional energy, you know, side like your prior one than, than your present, which sounds like he's more, you know, science based. Right? Yep. Are there of the tale of two leaders. I was gonna say, brands, yeah, are we need we do we have time for a few questions? I know we're at the bottom. of the mean, I was gonna ask just one of you all too. Okay. So for folks on the call today who are, a, either sorry. I'm gonna kinda give you a loaded question. But maybe they're struggling with this relationship a bit, or they are struggling with how to get started not even get started, but push AI forward within their organizations. Do you have any kind of advice or, help them helping folks do one thing differently tomorrow to drive change, if you had any advice to give? And I was kinda summarizing all the things we talked about. So I could I could tell you on the AI front, I think the thing to do tomorrow is sit down and review what your plan looks like. And I think, again, tools, data, those tend to be at the top of every plan. And. they're really important. Don't get me wrong. But back to where we were talking about influence and relationships across the org. Oftentimes, it takes more than just the CRO to bless, you know, hey. We're doing AI and go. Right? You have an IT or you may have a security or a diagnostic of a highly regulated, med device. So we've got to regulate. So what matters actually before data and AI and all that is what are my personal relationships with the leaders of those functions? And are they at a quality level in which I can go have very open dialogues around? What does it take? Right? Like, hey. We wanna bring in a tool like Avant, which is an LLM tool specifically built for RevOps as an example. I wanna build that, but we already have all these tools IT's approved. And IT, I was gonna go like, well, why do you need another? But that. relationship and that influence really matters in that piece. And so I think you could start that tomorrow. I think it's sitting down with the folks you know that are key to the decision or they can say no and saying, okay. Let's have a discussion. Let's under understand because the best plans can exist, all these great tools, and I'm gonna do this with the data and all. That's really, really important. Don't get me wrong. But in this environment with AI being, one, so powerful, two, prevalent everywhere, three, the amount of risk it it brings into orgs, your reliance on those relationships are unlike any other tools that we've dealt with as rev ops leaders. Right? Like, Salesforce kind of sat in a box. Right? I mean, not really. It's it's touching. But, like, but, like, one AI tool installed on all your data, all of a sudden opens the entire org up for for, risk. Right? So I think I think the piece I would start with in the Monday morning is sitting down and saying, are my relationships there with the key people? Because oftentimes, if they're not the best plan, the best tool, all the budgets align, kinda doesn't matter, right, Right. for for whatever reason. So I think that's that's really helps from my lens, and and something I think that's often undervalued as we think about AI adoption and and sort of the exploitation of AI in every facet of the business. I I would agree with that. The the second thing I would add is, try to simplify in basically maybe two two areas for each role. So for a frontline salesperson, what are two areas or two things I can do actually make them more productive? For a leader, what are two things? It could be two or three for, my customer support or, or the VP level or or even people my position is, you know, take, I would say, small, simple steps and put those in motion and then continue to refine those, as you evolve. And so to me, it doesn't have to be this large effort. It's like, let's take simple steps and put them into workflows where people are interacting today, and you will start seeing that. From my view is productivity and efficiency starts happening. Right? But it's but it's actually small steps. Totally. And it's interesting that you said that because I feel like this relates to one of the questions we just got about, you know and I think, Nick, you kinda talked about this at the beginning, but just the varying skill sets of the teams that you're working with and building for and how you're able to accommodate or build for each of these different levels of proficiency with either AI or technology. So, I think you kind of are both getting at at how to overcome that or kinda how you approach that. But I don't know if you have anything else to add that was a question from from the audience, but I think you kind of just gave a pretty good spiel there too. I think the big piece is to remember that you're building our CCO has the the the phrase you're building for ordinary to achieve extraordinary. And? I think AI is even more important about that is you're building for the average. This doesn't mean our whole teams are average. We have wildly talented. folks, but you have to build for the average user, of AI. And if your workflow or the what you're asking them to do with AI is just so complex. Right? You sort of have kinda missed the whole idea of AI tools. So, it's something I think a lot about, right, is is as we're in the lab, as we were referring to, like, how do we bring average into the lab to test and use to make sure it's palatable when we get there? But I think that's the the key piece when we think about all these wildly diverse backgrounds and places we, folks different folks come from. It's really just solving for the the the ordinary. If we do that across the business, whether that's AI or anything else, our sales process documentation or an LMS enablement, like, whatever it is, if you're kinda keeping the mindset focused on that, the likelihood you're going to see that leverage point build is is quite high. Yeah. An an example of that, I think, is, so some of the things that we've been trying. So you mentioned sort of this digital chief of staff. So we've we've built out sort of the easy button for the digital chief of staff where, you know, you just push on instead of you building a prompts, the the rep or the leaders building prompts, it's prebuilt for them. They can just push the button and up comes the results. One of the things I've learned from that is, that it needs to still be put in their workflow. So if you're an organization that's, you know, communicating through teams, or you're using Slack and email, What I found really productive from an easy perspective is if I want, information to flow to a rep or a leader, I can send it to them or push it to them in Slack or Teams and an email. Sometimes they'll do the combination of both. So it's kinda in the workflow that they're working each day. And what I found started to happen, and I think you might see the same sort of benefit is, reps or leaders start, instead of doing one activity with one contact, people are doing two or three activities with two or three different, you know, contacts or stakeholders. And so what starts to happen is, at least from a rep productivity is that the sales start cycle starts shrinking because their productivity is going up. And the other thing that's interesting is when they're being, suggested in in a, in these different workflows of what to do, they're less emotional. They have a less emotional response, maybe is the way I'm saying it. Because it's. not like the leader telling me to do things. The data is suggesting or telling them to do something. And what starts happening is they they start taking up those suggestions and acting on it because the there's no emotion around it. And so, you know, the the more I think we try to do things like that in their workflow, I think you're gonna see really an increase in productivity and effectiveness from them. So I would encourage everyone. to try. different things. was, yeah, I was talking to an early stage, tool that that kind of helped with account research recently. And, one of the things I kind of asked was like, hey. Or do you see yourself as a push or a pull system? And I think, like, the the nomenclature is that you're talking about, thinking about the workflow is really important because I think the challenge with a lot of what's being built in the AI space is is it's another pull system. And what do I mean by that? It means like, okay. It's another browser or another thing I've gotta go open. Right? And we've already asked our teams to be in several different tools. And so the question is constantly, what is the push strategy and where does that go? Right? Yep. And. and so there's also Slack's always the, you know, kind of the one that we all like. But then, again, you gotta start that brings in a whole another piece. Like, right? Like, Slack can get really noisy. How many alerts. are we bringing? So this I I what I like about what you're talking about around workflow is something like we actually do inside of a health system. I think about it a lot is, like, we're constantly going in and looking at the workflow as it is and making small adjustments that add up after a year, two, three years to to big improvements. But the idea that tomorrow you're gonna take the average seller, switch the whole workflow up, and now you're gonna work like this is, like, it's crazy. Right? We have to remember as rev ops leaders for the better part of the last ten years, we've been hammering our teams, Salesforce, Salesforce, Salesforce. Yeah. Right? Like, that's all we've cared about. Right? Is everything's happening in Salesforce. It's all going to integrate. I'm not saying that's going away, by the way, but I think we just have to be cognizant of the fact that we put a lot of effort into this, and we've gotta think that same way again. Like, what is that new interface? If it's not Salesforce, what is it? Because the the alternative of five, ten different it's just not gonna work. It's. Yeah. not it's they're never gonna go. Go. Right? So that workflow, I think, is really important. I think over the next three to five years, I think every the average seller's workflow, it it will be massively different. It won't Yeah. all these small tweaks will end up with something radically different, but, we're in for something in the next, you know, I I think it might be faster than three years, but I we're in for something radically different. The day to day for an average seller inside a SaaS company, in terms of the tools they're using, how they're interacting, I think is we we might not even be able to imagine exactly what it looks like today, but it's it's gonna be radically different. Wow. Okay. Dropping some knowledge. But I agree. I think, you know, there's just so much out there and, have to have to work where work where people wanna work or where they need to work, not in everybody else's platforms. So, alright. I was gonna say with that, we are at time. So I wanted to just say thank you both very much for the candid discussion today. I feel like we got some hot takes and some really good advice for our audience. So, thank you to everyone for joining, and thank you, Nick and Chad, for a lively discussion. I hope everybody has a great rest of your day. Thanks, Paul. Awesome. Appreciate it. Thank you. Bye. Appreciate it.