From AI Pressure to AI Impact: What Every Revenue Leader Needs to Do Now

You're absolutely right. AI is great. Thank you. (audience laughing) You know, one more thing, one more thing.

Now I kid, I kid. That was actually Opus 4.7's attempt at this talk. If only it were that easy, right? Then again, if it was, we wouldn't be sitting here together preheating in Las Vegas.

So let's get into it. Another show of hands. How many of you here have a clear and comprehensive AI strategy for your customer success organization? One that you could hand a new hire today and they'd know exactly how AI fits into how you work.

Okay, well, how about this? Keep your hand up if you have clear and measurable results to show for it. NRR or retention move, something that you can put in front of the board. Huh, is this thing on?

So it's a pretty clear gap in this room that aligns with what we're seeing in the market right now. And the funny thing is, is that this isn't a room full of skeptics. We all believe in AI. Everyone here is paying attention.

The gap isn't engagement or belief. The gap is execution. And by the end of this session, you'll have a concrete action to take on Monday to help you quickly and effectively narrow that gap. Here's what that gap looks like at scale.

91% of CS organizations say AI will have an impact on their strategy. That's all of us except the 9% apparently living under a rock. Yet only 52% have actually incorporated it into how their teams work. A 39 point delta between believing and achieving.

And that 52%, that's not all organizations seeing transformational results. Most of them are using AI for meeting summaries and maybe churn retros, which is good and useful work, but it doesn't really move the line on the things that matter. In fact, IBM released an enterprise AI ROI dataset at Think 2026 on May 5th. And they reported that only 25% of AI initiatives deliver the expected ROI.

Only 29% of executives are seeing significant returns from generative AI specifically, and only 23% from AI agents. IBM frames this as a widening gap in the AI divide and not between organizations spending more or less on AI, but those that have built a coherent four layer operating model. One of agents, real time data, automation, and hybrid governance. And those still treating AI as a collection of individual tools.

Enterprises without that operating model are increasingly unable to make AI work reliably enough to scale into regulated or mission critical workflows at all. AKA, the areas where you'll see the most impact. And again, this gap is an awareness. It's not belief, it's not adoption, it's execution.

And execution gaps do have specific causes. So let's go through some of the primary ones impacting our ability to show ROI on our AI investments. Start with what you've already done. The board mandate made it clear, AI is not optional.

So budget got approved. You bought a bunch of shiny new AI tools. Someone wrote a strategy deck with a roadmap slide. You did all the things that you were supposed to do.

And yet if I asked most of you if anything is fundamentally different about how your CS organization operates today versus two years ago, the honest answer for most teams is not really. The tools are there, the CSMs are using them for something, but the outcomes, things like retention, NRR, those are the metrics that matter, and they haven't moved in the way that the mandate implied that they would. And very few people wanna say that out loud, so let's say it, something isn't working. It's not the board, it's not the budget, it's not even the tools.

Let's look about what it actually is. So there are three places that this breaks, and most teams aren't just hitting one of these, they're hitting all three, and not to brag, but at the same time. And as per usual, it starts at the top. Leadership made a mandate, not a decision.

Do AI is not a strategy. It doesn't tell anyone what AI will be responsible for, where human judgment still matters, or what success looks like in 18 months. Without that decision, every team fills the vacuum differently and you get fragmentation. In fact, you get a layer of AI over the same silos that create challenges for us today.

In the middle, the foundation work gets skipped. Teams went right to tooling, because before redesigning the workflow, before getting the data right, before knowing what they actually needed the tool to do. So you end up with a powerful software, executing a broken or outdated process. We can't simply automate the status quo and expect different results.

And finally, on the ground, the humans didn't change. And if a new workflow was built or introduced, it may have gotten announced, but it wasn't embedded. Managers didn't reinforce it, incentives still rewarded old behaviors. Too long, didn't listen.

The tools are there, but the habits aren't. The good news is that all three of these layers are fixable, and I'm about to teach you the sequence that fixes them. But before we get to the sequence, let's first orient ourselves with the AI readiness curve. There's five stages, and this is the map.

It starts at ad hoc, where AI is used experimentally, inconsistently, there's no real strategy, no measurable impact, but someone on your team probably found a cool tool. And then we move to foundational, where early signals exist, some adoption is happening, but it's uneven, insights surface, but they aren't consistently acted on, the technology is doing something, but the organization isn't yet. Then we move to operational, where we see AI embedded in select workflows. It starts to influence decisions, but it's not yet transforming how your revenue and your post sales organization works.

And then we get to transformational. This is where AI becomes proactive. Your CS teams are finally ahead of the customer. Renewal signals, or renewal conversations start from signals and not gut feel.

And then lastly, we get to optimized, where AI is at the core of our operating model. It's continuously learning and continuously shaping how our teams engage with our customers. Most CS orgs are sitting here, somewhere between foundational and operational. That's the distance that we wanna talk about closing today.

Now I want you to use this to place yourself. You don't need to raise your hand, just answer it honestly. And answer this question to do that. When was the last time AI changed what your team is working on?

Not help them work faster, but actually change the priority. Changed what account a CSM touched first. Changed what got flagged in an account review. Changed the conversation a CSM walked into.

If you can answer that with a specific example from the past 30 days, you're in good shape. You're probably at operational or beyond. But if you're struggling to think of one, that tells you exactly where you are on the curve. And that's a starting point, not a failure.

It means that now you know what you're solving for. And knowledge, as they say, is power. So for the past couple of years, the AI promise in customer success was save your CSM's time. Meeting notes in 30 seconds.

EBR prep cut from 20 hours to two. That's real work that's been done by successfully leveraging this new technology. But here's the question few people are asking. What is your CSM doing with those hours that you just gave back?

Because if the answer is more of the same, more emails, more check-ins, more of the same conversations at a higher volume, again, all you've done is automate the status quo. You haven't changed anything. So saving time used to be the win, now it's the floor. It's just what you get for showing up with an AI tool, but it's not a strategy.

The teams actually closing this gap are the ones asking that harder question. If we're giving our CSM's back hours every week, what should they be doing differently with that time? What does the role look like when it's been redesigned around what AI can now own? That's the question that this session and framework is built around.

I was recently working with a CS team that had done everything right. They rolled out AI for meeting summaries, EBR prep, data entry, they measured it carefully. CSM's were getting back eight to 10 hours a week. Real time, real numbers.

They were proud of it and they should have been. But six months later, they pulled their retention numbers. Flat, no movement, and they were baffled. AI was working, the time savings were real, but when they actually looked at how CSM's were spending those 10 hours, they were just touching more accounts with the same old approach.

They hadn't changed what the role was doing. They just given that old role more runway. That's the trap. Efficiency is not impact.

It is a starting point. Right now, most CS teams look like the top image. A rowing crew where every person is pulling hard, but not in the same direction. Some CSM's are using AI for meeting notes, some for risk scoring.

Someone on your team discovered a new tool last week and they're running their own experiment. Everyone's working, but nobody is moving together. We're on the boat, but it's not going anywhere. And I wanna be clear, I'm not saying that anyone is doing anything wrong.

They're trying, they're curious, and those are really good things. It means that they have appetite. But appetite without direction doesn't get the boat to the finish line. As I mentioned before, it's typically not an adoption problem.

You don't need to mandate harder or train more. It's a strategy and governance problem. The goal isn't to get everyone rowing even harder. It's to get everyone rowing toward the same North Star.

And that only happens when a leader decides on the direction and owns the sequence to get there. Which brings us to the sequence. Now that you know where you are on that readiness curve, the next question becomes, what do we work on first? And this is where I see one of the second big mistakes and it kinda shows up in a predictable form.

First, when they try to do everything all at once. Risk scoring, meeting summaries, health scoring, EBR prep, expansion signals, all running simultaneously, but nobody owns any of it, nothing gets done. And six months later, you have seven half-built things instead of one thing that actually works. The second is chasing the most sophisticated sounding use case.

Predictive churn modeling at scale sounds way more impressive than we flag at-risk accounts before the renewal conversation. But the second one is actionable in 90 days or less on the data that teams have today. The first requires infrastructure that most teams don't have yet. So impressive is not the same as effective.

And third, letting a tool selection meeting substitute for a strategy conversation. You sit down to evaluate vendors and you walk out having decided what AI will be responsible for based on what a demo showed you. That's not a strategy, that's a subscription. And we do love you for it.

(audience laughing) Here's what works. Pick one use case, do it well, and let that become the proof point that pulls the rest of the organization forward. One Lighthouse initiative, proven, and then expand. Okay, Kate, but how do we select that Lighthouse initiative?

And that's a really good question. There are three principles to picking the right first use case. The first is the start where the signal is the clearest. Retention use cases like risk detection or renewal management have the fastest feedback loop.

AI surfaces a signal, your team acts on it, and then you see if the outcome changes. That speed of feedback is what builds trust in AI across the organization, and trust is what you need to sustain the sequence. The second is the start where the data is already good enough. And I wanna be specific about why this matters, because a failure mode that I see most often is an organization deploying predictive churn modeling on fragmented and inconsistent data.

It makes outputs that are noisy. A CSM acts on a high-risk flag when the account is perfectly healthy or vice versa. They act on a few more, and then they stop trusting it. And that trust is very hard to rebuild once it's gone.

And it also poisons the next use case before it starts. So pick a use case where you have the data today that is sufficient. And finally, start where you can prove it to the room. Your first AI initiative is not just about the outcome.

It's about creating organizational permission and momentum for everything that comes next. That requires a visible win for leadership to point to. If the impact is invisible or measured by a metric that takes two quarters to two years, you won't get the reinforcement that you need. The practical test and the one I want you to leave here with today is to look at your highest friction CSM workflow right now.

Where is the human making a judgment call that AI could inform? Where's your CSM deciding what account to touch first based on gut feel and last week's notes? That's almost always your starting point. Here's the sequence.

Five unlocks. Not five options. Not five parallel work streams. Five sequential steps.

Where each one is a prerequisite for the next. Strategy first. Workflow redesign second. Data third.

Tooling fourth. Behavior change fifth. Now I've spent the past five years working with CS organizations across every size and vertical on their CS and AI strategy. And one thing that I see all the time is organizations starting at tooling.

They skip to what feels like action and they build on a foundation that isn't there. Powerful software, again, executing a process that was designed before AI existed on data that's incomplete with a team that doesn't know what to do with the outputs. And then again, six months later, the question is why isn't it working? Well, it's working exactly as it was designed and the design is the problem.

The sequence matters. So let's walk through each step. The first unlock is strategy, or strategically if you're an SNL fan. And I wanna be specific about what I mean because a lot of what I see labeled as AI strategy is actually a tool list with a roadmap slide.

Those are not the same thing. A real strategy starts with one decision, a decision that only a leader can make. What will AI be responsible for in this organization? Not what tools you'll use, what outcomes will AI own?

And conversely, what will humans be responsible for? Where is human judgment irreplaceable? In complex customer conversations, high stakes renewal moments, the trust building that requires a real person in the room. And where is human judgment actually a bottleneck?

Slowing down the work that AI could do faster and better. Without those decisions explicitly made at the leadership level, again, every team fills that void differently. Your CSMs use different tools or skills or prompts. Your managers reinforce different behaviors.

Your CS ops team builds whatever CSMs are asking for that week. You end up staying reactionary, creating fragmentation from the absence of a shared direction. Here's the question that starts your strategy. If you're standing here in 18 months and this worked, what is actually different about how your CS organization operates?

Start there and work backward. Everything else will follow. Yep. Unlock two, workflow redesign.

And I'm gonna emphasize this heavily because this is the step that gets skipped most consistently and it's also the reason that most AI programs don't deliver. So this is what we usually see. An organization buys the tool, they skip this step, and then they ask why the outcomes that they expected aren't happening. The tools are running, but again, they're running on a process that was designed in 2018 or earlier, before AI was even a twinkle in our eyes.

Faster execution of the wrong process is not progress, and it certainly isn't impact. Here's the thing. Most CS workflows were built around the constraints of what humans could do. The handoffs exist because humans needed them.

The approval layers exist because humans needed oversight. The manual synthesis steps exist because there were no other alternatives. When you add AI to that workflow without asking whether that workflow still makes sense, you've automated the bottleneck. You haven't eliminated it.

You just made the old way more efficient, but you haven't changed anything that moves the line. So this exercise requires divergent thinking and a hard left turn away from the way we've always done it. You have to think from infinite capacity, no restraints on headcount, budget, technology, data. Ask yourself, if I was designing this CS motion from scratch today, knowing what AI can own and do, and knowing what my humans are best at, what would that look like?

Where does AI intervene? At what point does a human engage? What steps no longer need to exist at all? These answers tell you exactly what data you need and what tooling will serve you.

Every organization that skips this step ends up rebuilding their data infrastructure in six months, replacing those tools because they chose tools for a workflow and not the other way around. All right, let's get into everyone's favorite topic, data. And unfortunately, as we all know, you cannot strategize or redesign your way past the data problem. Remember, AI outputs are only as good as the inputs and the context it's provided.

But if you followed the sequence properly by the time you get here, you know exactly what data you need and where it lives. Most organizations that struggle with data are struggling because they went looking for it before they knew what they needed it for. Most CS organizations have a challenge where a significant portion of their customer interaction is completely invisible to AI. It's in email threads, calendar entries, meetings that weren't analyzed, notes that a CSM wrote but never made it into a system.

Your customers are communicating with you every day in the language that they use, in the concerns they raise, in their engagement patterns. And if that data is not being captured and structured so that AI can see it, no model is going to surface it. So can you tell me right now what percent of your customer interactions are making it into a system where AI can see them? And if not, that's a solid place to start when you focus on this step.

The key here is that the data layer is not a technology problem, it's a discipline problem. What gets captured, how consistently, and by whom. And this step is not a set it and forget it. Sorry, George.

You'll want to continuously revisit and ensure hygiene adherence. Unlock 4, tooling. And I want you to notice where we are in the sequence. You know what AI will be responsible for.

You know what the redesign workflow looks like. You know what data signals AI needs to do that job. Now you can evaluate tools against real requirements. Not demo scenarios, not vendor positioning, not what product your board read about in a newsletter.

You can walk into a tool evaluation and say, "Does this tool do what we actually need?" at the decision points we identified on the data that we have. And that is a completely different alternative to sitting in a demo, getting excited about a capability, buying the tool and then figuring the rest out from there. When a tool decision drives your strategy, you let the vendor decide what AI will be responsible for in your CS org. By the time you reach this unlock, tool selection is the most straightforward decision in the sequence.

And not because tools don't matter, but because you're finally in a position to evaluate them correctly. Behavior change is a lock that everybody underestimates. New workflows don't stick because they were announced. They don't stick because of a well-run training or a launch event with good energy.

I've seen beautifully designed AI programs announced and received excitedly to only be ignored eight weeks later. Here's what actually changes behavior. Managers making new workflow non-optional in their rituals. When a manager opens every account review in the same way, what did AI flag on this account and what did you do about it?

Not in launch week, not sometimes, but every time. That is when behavior changes. When early wins are celebrated loudly enough that the whole team understands, "This is how we work now," not an experiment that we're running. That changes behavior.

When incentives align with the new workflows instead of rewarding old ones, that's when it holds. Ask yourself, "What has to change about how my managers lead for this to stick?" Not what you will announce, not what training you'll run, but what has to change about what your managers ask about, what they reward, and what they make non-negotiable and how they run their teams. The answer to that question is the beginning of your change management plan. So now that you know the sequence that's required to actually create impact with AI, let's talk about how you know it's working.

Every executive wants to measure AI, ROI. The problem is that ROI is a lagging indicator that can sometimes take quarters to move. And by the time it shows up in that retention or NRR number, you've already missed the window to course correct. So here's three questions that you can ask to tell whether you're on track before that quarterly number moves.

The first is, "Are risk signals being consistently acted upon across the whole team, or does it depend on which CSM saw it that day?" If you have a few individuals who are great at acting on AI signals and others that consistently ignore them, you don't have an AI program. Is AI changing what CSMs are actually working on? Or is it just giving them smarter confirmation of what they already plan to do? Because if every AI output confirms what the CSM already thought, it's not changing the priority.

It's reducing friction or saving time, which is great and useful, yes. But it's not transformational. And lastly, "Are your managers coaching to AI outputs in account reviews, or are they running those conversations based on relationship history and gut feel just like they always have?" If the answer to all three is yes, ROI follows naturally. That's not a hypothesis.

It's what happens to every organization that actually closes the gap. And if the answer is not yet, then you don't have a technology problem. You likely have a strategy and a behavior change problem. And this is where leaning back into the sequence is your best bet.

So let's bring it all together. Strategy, workflow design, data, tooling, behavior change. In that order every time. Most organizations are running all five simultaneously or not at all, and then wondering why nothing is holding.

Or they started at tooling, and now they're working backward on a foundation that they've never built. So the question I want you to answer right now is not, "Are we doing AI?" You are. Everyone in this room is doing something with AI. The question is, "Which five of these unlocks is your actual blocker?

Where is the chain breaking? Is it that no one has made a real strategy decision? More than a mandate, an actual decision about what AI will own and what humans will own. Is it that a workflow was never redesigned and you're executing an old process on new tools?

Is it a data quality program that's a problem that's quietly producing outputs that nobody trusts? Is it tooling that was chosen before the workflow existed? Or is it that everything is in place but the behavior hasn't changed because nobody made it non-optional? You don't need to raise your hand, just answer that.

I'll give you a few seconds. That answer is your starting point. AI transformation in a CS organization doesn't happen because it was mandated, it doesn't happen because the right tool got purchased, or because a smart, well-resourced team built a good strategy deck. It happens because a leader decides to own it.

The strategy, the workflow design, the data discipline, the tooling decisions, the behavior change, all five unlocks in order without short-cutting. That leader does not have to have all of the answers. They don't need to be a tech-savvy superhuman. What they need is the willingness to hold the sequence when the organization tries to skip it and they will.

There's always a moment, usually around unlock two or three, where it feels easier to jump to tooling or to declare victory after a launch. The organization successful at AI transformation had someone who held the line at that moment, someone who said, "We're not skipping this step." Someone in this room can be that person. So here's your Monday action. Before your next meeting, before you open email or Slack or Teams, answer one question in writing.

What high-value work will my human team be responsible for in my customer success organization in 18 months? Not what tools you'll use. What outcomes will humans own? The delta between where you are today and that vision is the job of AI.

It will help you define where AI makes the decisions, surfaces the signals, drives the action, and where humans focus their judgment and relationships because that's where they're irreplaceable. Write it down, even a paragraph, even a draft that you know will change. And then share it with your team, because that's the first step of a real AI strategy. A decision in writing shared with the people who have to act on it.

And if you already have this answer, good for you, if you walked in here today knowing that you have a clear 18-month picture, then your Monday question becomes, "Which of the five unlocks is the actual blocker right now?" Name it and assign it one owner. Not a committee, not a working group, one person this week. And don't forget, you can do that. All right.

Well, I hope this session was helpful in giving you a starting point with your AI strategy. On the Gainsight Advisory Services team, we do this kind of work daily with customers to help transform their CS organizations, and we have many different sessions and workshops that you can take part in, no matter where you fall on that maturity curve. So if any of that sounds like something your organization could benefit from, scan the QR code and we'll get back to you. And also, if that workflow redesign piece sounded interesting and you're looking for a framework that you can use to take back and start to rework those CS motions, tomorrow is the very last session.

I'm doing a workshop to give you a framework, it's called the GAIN framework, on how you can go redesign those motions. So, thank you so much for your time and attention. [applause] And come find me on the dance floor at the party later. There you go.

There you go. Questions? Keep the conversation going, yes. I see a couple.

Do you want to take it or do you want me to just-- Why don't you come up here, girl? Come on. Come up there. Oh, my God.

Yeah. Nice to you. You know. All right.

Kalpana, everybody. [applause] That was an awesome session. And as you already know, this track is all about making it happen, truly happen, and not just like lip service, just not giving it any words, but actually making it happen. And the roadmap that Kate laid out is real.

It is a real roadmap. So, with that, our first question here is, how do you make a case to a board that's mandating AI when your team is still building the foundation? Oh, very good question. So, I would highlight for them where you are today, right, with whatever they're pushing for, in terms of setting that baseline, and then share with them some of the anecdotes.

They're all over social media. They're all over LinkedIn. They're all over everywhere of people implementing AI, starting at tooling, and then pulling it back, getting a new tool, right? And that time that they took to get to that point between buying that tool and then realizing that they need something else is all time that you've lost.

Whereas, if you spent that time building the foundation, going into something that you know you're building for in a future-proof way, you're still spending that same amount of time, right, to get to where you want to go. One of them is just effective and efficient, and one isn't. So, I would just try to give them that narrative. I would help them understand, you know, share with them that one lighthouse initiative and the impact that you expect it to drive, and then kind of let that push everything forward.

Yeah, I love lighthouses, too. They really guide you. So, and the next question here is, what signals... Wait.

I know these always... No, hold on. The question that is even more interesting is, what's the most common reason... So, you see AI initiatives as stall?

I mean, I think it's not that there's one common reason. There's a few, right? It's skipping right to tooling. It's not redesigning the workflow.

It's not getting your data right. It's moving into doing AI without understanding what that AI is supposed to do and also what your humans are supposed to do with that input. And so, I think, you know, the common reason that I see them stall is that they're not following the sequence. Dang it.

And then they have to go back and start over. Right. Thank you. So, another question we have here is, what do you do when the organization started at tooling?

So, instead of following that roadmap, they actually started somewhere in the middle, and you have to work them all over like you were saying. So, you do have to go back to the drawing board. So, is there a way that you can, you know, elegantly backtrack and... Elegantly?

Or not, clumsily backtrack? You know, I think that that's fine. If you're in that boat, so is everybody else. Don't sweat it.

But I would still kind of take a tool agnostic approach to going through the sequence to get to the strategy, to get to the workflow. Right. What do you need the AI to do? What data do you have?

What data do you need to feed that to get the right signal? And then pull your tech stack and your ecosystem back in, because now you can at least evaluate those things and understand what features, what functionalities, what workflow each of those tools will be a part of. Right. We get questions all the time from you guys about, "Hey, we have all these products and we're using it here, we're using it there, but how does it all work together?" Well, the answer to how it all works together starts from your strategy.

Right. We can't, I mean, we can help you. Right. But we can't do that for you.

And so, you need to kind of partner with us or other vendors on, "Okay, here's what we want to have happen. What can you make work?" Then you can identify the gap and go back and see if there's, you know, something else that you can throw into the stack or something that you can vibe code to fill the gap. Good advice. And our next question, which has gotten a lot of votes, is, "Many of my execs are looking for us to save time and be more efficient with AI." Yes.

All of us do. I love the idea that this is really the bottom. What are other activities we can implement as a result of saving time, like customer visits? The high-value activities, right?

So, I don't know if you guys are seeing the same thing, but we're sort of seeing this bifurcation of the CSM role in the market right now, where we've got on one hand this sort of technical FDE, right, someone who can really roll up their sleeves, get in there, you know, help them set up a tool, help them customize it, translate their workflows into it so that they can get the outcomes that they wanted to achieve. And then the other side is more commercial, right? They want to be farming that account for growth. They want to have that relationship building.

And so I think those are the two paths that we're seeing CSM start to take. So when we think about high-value activities for those two personas, you know, it's making sure that we do deep, deep discovery with the customer from a technical perspective so that what we're spending the time setting up actually does what they want it to do, and then we can point to the outcomes that we're driving. And then on the other side, being able to articulate that value back to the customer, and not just articulate it to them, but ask them to articulate it with you as well, right? They have to have that buy-in.

We can't just tell them this is what we've done. We need their partnership in confirming that. And so I think, yeah, like customer in-person stuff is really impactful, depending on the level of the account. EBRs, right, run, in my opinion, correctly, can yield the right results.

If you're going in from a defensive position trying to prove your value, that's not going to really move the needle as an EBR. Or if you're going in with like just a vendor point of view, that's not going to move the needle. But if you go in using it as a continuous discovery with the customer and uncovering other areas that you can partner together over the next couple quarters or a year, that's going to have the kind of impact that you want. Awesome.

This question is probably one of our last questions. What signals tell you an organization is actually ready for AI adoption culturally, not just technically? Yeah, I mean, as weird as it sounds, I think if we go back to the boat image, if that's happening, right, even if the boat's spinning in circles, that's a good sign. Because they're trying, they're experimenting, they have that appetite for it.

If you have a team that's totally against AI, which, you know, there are people with valid reasons for that, that's probably not like culturally the right thing and you may need to usher them to, you know. Don't use it in your personal life, but here at work, we do AI. So, yeah, I think also one of the things that I've created on the advisory services team is an AI maturity assessment and there is a cultural element to it. So ask one of your execs to reach out to us because we can set that up for you and it's something that we don't charge for.

But at least it can give them a reading back of on the five pillars that we assess of which one is cultural behavior change, that kind of thing. Where does your team fall? And then that gives them a roadmap of, you know, where can I go beyond that? Thank you.

And I'm really proud at the way that Gainsight has created that culture for us, pushed us constantly on utilizing AI. So a lot of things also come from the top, like your leadership, like how do they perceive AI? Is it just another buzzword that they want to get on the bandwagon just like any other company in their peer network? Or do they really want this to start making that move, right?

That moving the needle. So a great question. So we have another minute. Okay, sure.

Let's do it. Let's do it. I feel AI will require me to completely change how CS operates. So AI is blowing up everything that we have done to date.

And that's that's the truth. Do you feel like transformative change can take place without a big shift from today's operating model? Transformative change? No, probably not.

I mean, here's the thing. Even if we take AI out of the picture, how many of us can honestly say that the way that we've been doing CS all these years is actually working? How many times have you had a green customer account churn out of the blue or a red account renew? Right?

I just I think it's okay for us to be honest with ourselves. And AI is exposing the things that we've done as CS practitioners that felt good and, you know, developed relationships but didn't actually do anything meaningful. And so I think that we're really lucky to be at this inflection point where we can redesign what that looks like without losing our humanity and the things that are that makes CS, you know, professionals, great people and easy to work with people. But we can pair that with impactful and effective.

Great answer. Thank you. And I feel like the word transformative in this question actually answers everything. Transformative means you're blowing up what is existing today and you are recreating and recreating it with intention.

So I think this is so thank you so much. Thank you.