Name: GTM Connected: Haunted Handoffs and How to Fix Them for the AI Era
Uploaded: 2026-10-28T17:31:27.644Z
Duration: 46 min
Description: GTM Connected: Haunted Handoffs and How to Fix Them for the AI Era

Transcript for "GTM Connected: Haunted Handoffs and How to Fix Them for the AI Era": Good morning, everyone. Welcome to our GTM Connected webinar. We're just gonna give it a couple minutes before we get started. But while we're waiting, if you wanna share what you're gonna be for Halloween, since we're going with the spooky theme today, share it in the chat with us or what your kids are gonna be or what your dog is gonna be or cat. My my daughter is one, so she's gonna be a cow because all she can say is moo. So I think she'll be a good good character for this Halloween. John, you said you had a good Halloween... Oh, a minion? Yep. Mike, do you have... Are you a Halloween guy? Yeah. We are gonna do Halloween. I have two young kids, so they wanna be dinosaurs. So I think I'm gonna be doctor Grant, who is the Jurassic Park, one of the main protagonists. We're going to do some Jurassic Park dinosaur type of theme. Nice. Love a family theme. I didn't get my get my stuff together for that this year, so my daughter is just a solo cow. But I don't I don't think she'll notice. No. Awesome. Well, let's go ahead and jump in. We're one minute past the hour. Welcome everyone to GTM Connected. Happy almost Halloween. Today, we're talking about the spooky stretch between closed won and kickoff and how to fix it for the AI era. Let's go ahead and jump in. A couple quick housekeeping items. If you have a question at any point, type it into the chat and we will take them as we go. And also we'll have some time dedicated for questions at the end. We are also recording, so you'll get the replay afterward in your inbox. So now let's take a look at what we have planned for today. So we have about forty five minutes. The... First, we'll take a look at kinda where this topic sits in the revenue life cycle and everything that LeanData does. Then we'll talk about, what happens when a deal closes and why handoffs get haunted. After that, we'll show how LeanData closes the gap, and John will do a demo to showcase the solution live. We also have a preview of something, new that's coming with LeanData soon at the very end. And, we have some queue... Time for q and a. So I'll go ahead and introduce our team for today here. My name is Sarah Cablano. I'm on our product marketing team here at LeanData, and I'm joined by Mike and John. So they're gonna be my experts for the day. Mike, do you wanna go ahead and introduce yourself? Yeah. Thanks. So hi, everyone. I'm Mike Madsen. I lead the revenue operations function here at LeadData. I've been in rev ops for over a decade now. And so my core function at our company is overseeing all of our operational processes across marketing sales and CX, our tech stack, our our data, our our strategy. And so hand off and the and the process between, our new business team and our customer and information teams is very much something that me and the group, manage every day, and so really excited to chat more and share with everyone how we do here at LeanData. John? Awesome. Thanks, Mike. And then I'm John Demas, presale solutions consultant for commercial and mid market segments, and I'll be running your demo. Awesome. Thanks. So, before we kind of get into handoffs, I wanna do a quick level set on what LeanData is, what we do, because we do have a a mix of customers and some newer faces here. So, LeanData sits across your entire revenue life cycle from the first inbound lead all the way through renewal and expansion. It's the orchestration layer for your AI go to market motion. So we're the connective tissue between all the tools and your GTM stack, making sure the right signal reaches the right person at the right time. And how that works? Signals flow in from all of those tools in your stack. So your AI agents, your intent data, your enrichment, and champion changes. And lead data sits in the middle and handles all the matching, the routing, the scheduling, the workflow automation. On the right hand side of this diagram, those signals turn into predictable actions, like the right rep getting notified, a meeting getting booked, the onboarding kickoff happening. So, those people know us for that first half of the journey, but, there's a lot more that LeanData can do. And so that's what this webinar series is really designed to show you is kind of the art of the possible with everything that LeanData does. To do that, we use this solution framework map to kind of show our customers and map the use cases that our customers run across all the stages of the revenue lifecycle from acquisition to expansion. So, you can see that these are, all the use cases that we've mapped that our customers are doing. And today, we're really focusing in on adoption. So the pre to post sales handoff and customer onboarding workflows that LeanData can help with. The part where the handoff can either go smoothly or turn spooky and make a big impact on the customer experience. So now let's dive in and talk about the problems that we see with handoffs and getting from that closed one to the kickoff and beyond. So the moment a deal closes, the clock starts ticking. So maybe your customer... Your deal closes on a Monday, and then your customer doesn't hear anything from your team for, you know, five days, eight days, thirteen days. And that gap between closed one and hand off is what I'm calling here the old haunting of silence. So if we don't reach out right away, we have a we have a problem. It's a rough start for the customer, and it puts the relationship on pretty shaky ground before it even begins. I think today, though, we can agree... All agree that AI has made the speed of this a bit more table stakes than it was even just a couple of years ago. So automating that email that goes out the moment a deal closes is actually pretty easy. But even if we do have AI here and it's closing that lag between closed one and the first intro email, it hasn't solved everything. So there's a new haunting in the process. So When AI joins the handoff, we're risking the opposite of silence and that's noise. We're seeing two things driving this. The first one is what I'm calling here the crowded house. Everyone is deploying agents across the revenue lifecycle. We've got AI, SCR agents, demo agents in presale. On the post sales side, maybe a scaled CS team run by agents that help kick off onboarding. With that many agents and people in the mix, the question of who acts first gets a lot more complicated and matters even more than ever. And secondly, there's more context than ever. So it's building up on all sides with all those agents, all those people, and all of it needs to travel when the deal closes. So when people and agents can all act and there's more context, than ever before, we really need those rules that decide who goes first. So that's why deterministic action becomes even more important. It means that the customer will hear from one owner one time and gets a great experience after close one. I want to bring Mike in here. You've worked in RevOps for a long time. What are you seeing that looks different in the handoff now that we're in this AI era? Yeah. So a little bit of history. I think most of rev ops or go to market ops or sales ops, whatever you wanna call them, historically in this space was working through taking the go to market strategy. So how do we want to assign accounts to people in this kind of onboarding post sales kind of process flow in building that in our systems to make it as automated as possible. I think that's where, historically, a lot of us have been focused on, is getting that kind of right to making sure that the the account is going to the right people, and LeanData, you know, has actually helped support this in in the market. I think the piece that has historically been challenging still is how do you then take that and get it to the next phase, which is getting onboarding documentation kind of generated, building a project plan of the PS team, kicking off that kinda intro email to the customer and making sure they know who their onboarding team is and all those kind of things. Historically, it's been pretty manual. And now given that there's AI and agents and there's a lot more data and context that we have, I e, like, call recordings. Like, there's so much rich data that we're now recording all of our deals through the cycle. There's a ton of more information that we can use to essentially now supplement that deterministic rules that we've built around who gets the account and why and start layering on top of that that that last mile of generating an actual onboarding document that is designed around what the pain points were the customer told us during the kind of deal cycle, kicking off the actual email that isn't just generic, hey. Welcome to LeanData or welcome to whatever. It's actually tailored directly towards the value they're gonna get in what they're trying to solve for. So I think that's what's the the new piece is, like, that is so much more available now that we have agents to to do that work. The flip side to what you're kind of alluding to here is, a lot of people have not done a good job of solving that first problem still of, like, what are the rules of who gets an account and why? How does the CSM gets assigned to the account? How do you determine what PS person is going to own this? And if you haven't written those down and those aren't really well structured in your process flow, if you then start passing over context to an agent to go send emails, to generate documents, start interacting with customers, and it doesn't know who the right people that should be coming from or who the right people are on this deal, it's gonna cause a ton of noise. And if you're having multiple agents run based on certain use cases, you're gonna have multiple noise to your point that's being sent to customers and interacting with customers. So I think that is the current kind of risk that people are trying to solve for is how do you essentially take this deterministic build, start layering on top of this probabilistic kind of model to be able to kind of generate this context and this, you know, content that kinda streamlines the the onboarding experience. And I think that's the piece that has changed the most, that I think is what's in front of the rev ops community now to to go solve for. I... And so I worry about, like, the people that are just throwing agents at this problem and not really have enough... A strong foundation because it's gonna cause way worse experience for your your onboarding team, if it's not anchored on a source of truth based on your your go to market rules. So that's kinda my take right now of of, like, where this kinda has shifted, based on where we started and kinda where we're going right now. Yeah. Thanks for for sharing. And I think that's a perfect segue into to our next slide here. So, like, why these break down and the the four ghosts we're seeing haunt the handoff. You alluded to probably the the first one and the second one the most. So, but let me kinda paint a picture. So let's say a deal closes at, like, 04:30 on a Friday afternoon. The customer is really excited. They've spent months with your presales team, working with the AE, the SE, maybe a PS consultant who came in for scoping and they've seen lots of demos. They've talked to a lot of people and now they're ready to get started. Once that deal closes, there's a few different ways that the ghosts can come and haunt the handoff, right, and haunt the engagement. So the first one Mike talked about is is the the playbook. So if there's no playbook, nobody knows whose account it is. Maybe that consultant they worked with during the sale looks busy next week, so the account goes to someone else even if that person isn't the best fit. Or maybe that consultant thinks they're supposed to take this one and they reach out, so they cause, you know, confusion. The second ghost, again, that we just alluded to is the context loss. So all of that, you know, documentation we have, the customer's goals, their tools, the promises they made during the sale, it can live with the AE. It can live in, you know, call recordings, a lot of different places. Maybe it's in sitting in Salesforce fields that no one is reading before the kickoff call. So, you know, how do we make sense of all that context, especially now that we have the tools that are gonna help us do that much easier than we ever before? The third is is more for our internal teams in this endless chase and the manual work. So, you know, maybe on Monday morning, someone in ops spots the closed deal in Slack, then they have to check a capacity spreadsheet and ping a few people to confirm. Once the owner is picked, the emails start flying to find a kickoff time. I've lived this at past companies. I'm sure many of you have too. At one company I was recently at, we had a spreadsheet of kickoff slots, and you had to sign up for one manually before the CS team would assign your owner and then reach out to them. And that was only two years ago. So, people could still be doing that today, and it's a lot of manual work for for internal teams, obviously. And then maybe the scariest ghost here and the newest one is what we're calling the poltergeist. So what if the AISDR agent doesn't know that the deal was handed off for some reason or... And it keeps sending follow-up emails or the AE checks in, the CSM introduces them... Themselves, and then another automation kicks off. So now the customer has, you know, four different emails and no idea who to respond to. So, Mike, you've obviously seen this done a lot of different ways, both pre AI, post AI. Which one of these do you think is the most costly? Yeah. I think all of them, what I was kind of alluding to in my previous, comments is, like, the playbook itself. Like, you have to have your defined rules or none of these other kind of scenarios are gonna really be effective, to solve for. Like, you can have really great context, really understand who the customer is, what their pain points are. But to your point, if it's coming from five different people, that's a horrible experience for the customer to be onboarding. So you really have to have a defined, deterministic kind of rule book around how you're going to essentially run your onboarding, your hand off process itself. So that to me comes first and has the highest risk. If you don't have that, then everything else is going to fail. It's it's pretty foundational. I think the other part to this that has a huge amount of risk in it is the lack of using the context you do have. I think that's something that's a huge unlock for everyone. An example that I'll use is I'm a buyer. People try to sell me software all day long. Anyone out there, I'm sure you've maybe sent me an email or two. The worst thing I've experienced, and I won't name any any vendors that are out there, but one... Some of the biggest ones in the world, some of the fastest AI native ones in the world that that we bought and brought on internally at LeanData started off our implementation call asking me what my tech stack was even though I kinda told you this through the deal cycle. Like, re... Essentially doing, like, a discovery call over again when you pass over to PS, I think that is the, one, horrible buying experience for me as a buyer. And then two, again, the whole point of this kind of, you know, process flow is to speed up time to value that a customer just spent x amount of dollars. They invested their their money, their time. In some cases, they're investing their they're investing their brand. Like, some leaders are making a bet on this tech to be their, you know, marquee thing that they're making their their stand on. And time to value is extremely important to me and and to and to the buyer. So When we're wasting time having to go through essentially discovery all over again and figuring out who the right owner is and what is your tech stack and what problems you're trying to solve and tell me again, do you have any hard dates we need to get this done by and all of this stuff. That wastes so much time. To me, that's all in context that we have access to. Call recordings, notes, previous discovery discos. I think that's the other one that I personally feel like is a huge risk to you be able to have successful experience with your customers is not really capturing that context and deploying it effectively to your onboarding experience. Right. It's it's almost surprising that we have all these tools that can help us make sense of the context, but we're not, not everyone is able to to make use of it, as well as they should. So, with both of those or all of those ghosts haunting are are handoffs, There are a lot of... There are costs on both sides of the coin. Right? So the first one is customer... Of course, on the customer relationship, which we've already talked about a lot. So customers, you know, they're gonna form their opinion in the first few few weeks. Customers like Mike who, you know, on that kickoff call when they're asking for his tech stack again and he's having to retell his story, not a great first impression. So, or if they get a slow start, you know, if we're still in that, you know, problem where we haven't reached out to the customer right after the deal closes, they all... That's also a poor experience. So any of those is gonna put the relationship on the wrong foot and raise the risk of losing that customer from the start. Earning trust back is an uphill battle, and there's a lot of upfront work that, you know, we can do as ops teams that are gonna help your team avoid having to fight that battle. And then on the internal side, the cost to your own team, obviously, if we're still doing this manually, that's not gonna scale. It's eating time. You know, there's more pressure on ops than ever. So, like, doing these manual processes, is is not the way we wanna be spending our time. And then, of course, for, Brina, the the post sales team, when context doesn't travel, having to chase down what the customer needs, that's hours lost to to guessing, and chasing down. So when we want them focused on building that relationship with the customer. None of these things shows up, you know, right when the deal closes or even sometimes they don't show up, you know, within a few weeks. They show up in that renewal conversation or in an escalation. So makes them... The cost easy to miss and expensive once we finally see it. So what does fixing it look like? Well, the good news is everything you need to fix this already exists in LeanData today. So how do we bust the ghosts? LeanData knows who should own the account, what they need to know, and when the customer should hear from you. So that's, you know, deterministic action, full context, and the right timing. When we say deterministic action here, we really mean that clear ownership, that clear playbook. So once you have that playbook defined, you know, the right CSM, the right account manager, the services team, all those different people that need to be in the mix. Once this deal closes, get assigned based on your business rules, whether that's a segment, product, complexity, and it works the right way every time because it's deterministic. Full context really means everything that's captured, especially in Salesforce along the way travels with the account. Your customers aren't repeating themselves. Your post sales team has what they need to start the relationship off right. And then the right timing, of course. You know, SLAs track everything. You know, that intro email goes off within... Right away with the kickoff scheduling link to really kick things off on the right foot. So, Mike, we've talked a lot about AI today, and I would guess some people are wondering, you know, couldn't I just build this myself with, you know, some agents? So maybe before we go deeper on the LeanData solution, you're building a lot of agents day to day or working with our ops team to build agents. What is your honest take on, you know, when you trust an agent with this process or parts of it and when you would turn to our own solution, LeanData? Yeah. I think I get this question a lot, and I'm sure a lot of folks out there do as well as the build versus buy, and and I'm all for building. You know, I think the rev ops or go to market ops community as a whole has had this renaissance with Cloud Code and codecs, and it becomes significantly more easier for non developers who know what a process is, that know a strategy, that know what the data workflow should be, but now can actually build it using these these tools. So I... I'm all for it. I think the piece that gets lost sometimes is, can't we just build it? The answer generally is, like, yes. You you probably could just build this. I think the more important question that I usually go back to is, should you actually build this? Like, what's the actual value prop of you building this yourself? There's all the conversation around maintenance and, you know, sustainability, and is it really something that's gonna drive incremental value in your business? Like, all of those things are real. And I think the other part then that gets lost too is is the agent actually... Typically, when you're building something, it's just gonna have an agent do it instead of a process. Is the agent actually gonna outperform the deterministic typical kind of legacy if then kind of models? In in a lot of cases, it doesn't. And so what I personally believe is, like, in the deterministic space of rule based, you need to make sure that an agent would fire on your business process a 100% accurate every single time. That account doesn't get incorrectly assigned to the wrong PS person or your NQL doesn't get routed to the, you know, wrong person in in your in your business. Like, you want those things to be firing every single time, and by nature, these models are probabilistic. So there's gonna be a percentage of time where they get it wrong. And so I think that's the piece that gets lost sometimes is you can build it, but can it outperform the actual kind of deterministic workflows? In a lot of cases, it doesn't. And so where I typically build is things on how do you take data, aka context, and use it in a way that agents are really good at. So agents are really strong at unstructured data. Call recordings is a very easy example a lot of people anchor on. So how do you kinda have your deterministic workflow that typically would run-in our product, which, you know, in my my point of view is the best in the business, and then supplement it with how do you then generate an onboarding guide or generate that email or generate any type of insights that you wanna pass your team? AI is really good at kinda building and come scrubbing the information and then populating that content or notification or whatever that output is to a user or to a customer. But it's anchored on your source of truth, which is your deterministic guardrails. So that's my my general take is if you need something to run a 100% accurate all the time, you know, the deterministic models outperform. If you have something that's more, like, insight based, generating, like, something like content based, I think the models and building that is where I invest my time. And that's where we internally kind of, you know, are are are building a lot of stuff is on top of our prebaked, kind of determined models that we know are gonna work every single time. Right. That's that's great to hear. And I think John will show a little bit of how, you know, we can still bring the models into lean data so that you're not, you know, it's not a 100% a choice of deterministic or probabilistic. We can take that context and and use the models to apply reasoning where we need to, but still use deterministic rules in LeanData. Obviously, Mike works here at LeanData, so you don't just have to take his word for how this is a great process to do in LeanData. Last year, we actually surveyed some of our customers who use LeanData for more than lead routing. And a couple of our customers shared some quotes about how they're doing know, customer routing and onboarding. So demand based, one of our customers, uses LeanData to automate customer routing. So every onboarding request goes to the right person instantly. They say it cuts delays, removes those manual handoffs, and creates that smooth onboarding experience that speeds up time to value. So I think that's a great metric if you're thinking about, like, well, how do I measure that this is actually, you know, making an impact on my business? Time to value is a great one to track, and Cofense, said something similar. So they use routing to cut the delay in assigning onboarding owners as well so the customers onboarding sooner, and they say it's reduced that lag time between purchase and first engagement. So that's that silence that we talked about at the start of, the session today. So before... We're about to get into the demo. So just a quick, like, simplified example of how this could look in Clean Data. Yours obviously can look quite different. But, so first, the deal closes, the account type changes to customer, and that's the trigger that, the Lean Data Graph takes over and kicks off your business processes automatically. Then LeanData can assign the owners. And one thing that, we always like to, you know, mention about LeanData versus, like, doing this somewhere else potentially is that your team will always own those rules. So the ops team is gonna be the one that can update them when a territory changes or round robin changes. So owners can be assigned, the right way every single time, and then the context travels with that assignment. So John will show our AI Inference node. That's where I was talking about that we can bring reasoning in to this process as well with clean data so we can summarize the deal details from Salesforce and make sure that they get passed along to the new owners and personalized. And then the kickoff starts. The personalized intro goes out right away with a scheduling link that's going to bring in all the right people and at the same time, the onboarding tasks are created automatically. Throughout all of this, every step will land in the audit logs and LeanData. That's another really important part to remember. If someone is asking why a certain professional services person was assigned, the log is going to be able to tell you exactly why. John will show that as well today. Mike, I guess I'd love to hear maybe a little bit before we jump into the demo of how we run this at LeanData. Maybe what are some key steps or interesting points that you would call out? Yeah. I guess going back to my experience of having generic onboarding as a buyer or having to do rediscovery calls, we try to solve for that, ourselves. So I think our our goal is to make sure that three kinda core things carry over when we're onboarding a customer. The first one is, what is the core problem or the core, you know, goal that that customer is trying to solve for? So having that really clear. The second one, you know, is kind of a no brainer, but it feels like it gets lost a lot out there is what is the actual tech stack that this customer had, given that a lot of this is implementation and integrations and, you know, how these things all work together. So tech stack is is another big one. And then what is the actual implementation plan of how to get to time to value as quickly as possible? So all three of those kind of core things is where we use kind of the probabilistic kind of agents to essentially help capture, populate, and synthesize information for our internal teams. And, again, that's where unstructured data exists in notes, in call recordings, in previous conversations. Agent is really good at that. So that's kind of a a big bucket of work that we kinda capture that. The second one is then how does this all then get translated downstream to the actual business processes of assignment? So making sure that the CSM gets assigned to the account based on our hierarchy of the rules of engagement on, like, who owns what accounts on segmentation in an industry, if it's a premium customer, as an example, is a different type of, model, for example. So making sure that the CSM assignment kind of aligns to that. Similarly is the the AM. So we have a new business side of the house and a customer side, which a lot of people have out there. How does the AM get kinda assigned to discount as well based on segmentation, based on kind of their books? So that's something we also kinda build in. And then the last one is for the PS team. And so kicking off the PS team's workflow, happens in a couple ways. One, based on segmentation, goes to either our enterprise or kind of our our mid market business. It looks at their capacity, understanding who's active on projects now and how they essentially get assigned that capacity, and then it kicks off actual... The project plans downstream into their project management tools to make sure they have the actual documented kinda track on how we're gonna run this project. So all of that is kinda happening through our our processes. And then the last one I'll add in that's another kinda unique thing that we we also do is, the AE stays on the account for a period of time while the customer is actually onboarding before it goes over to the customer base AM team, and we have some rules around when that transfers over. There's a time period that, you know, the... Holds onto the accounts, and then, again, later down the road, we fire another kind of rule that essentially transfer that over to the AM based on, again, their books and the segmentation, all those types of things. All of this is kind of managed through our process flows. And what we're doing now is essentially then automating as much as we can that front end part of the customer problems, their tech stack, their implementation plan downstream to these users. So we have a really strong way we show up to the customer. And then again, the whole point of this is time to value. That me as the buyer, I'm getting value out of this product invested in as quickly as possible. And so that's kind of our goal here. Awesome. Thanks, Mike. So I know we've talked a lot about LeanData and hand off, but now John is gonna show a little bit of it live in the product. So, John, I'll hand it over to you. Yeah. Thank you. Let me get the... Go. Entire screen. Setting it up right now. Everyone can see my screen? Yep. Yep. Yep. Okay. Alright. So to give you a little context of what I'm gonna show you, I am a operations lead for Kata Solutions. Kata Solutions is essentially our demo website that I built. Well, obviously, we do lead routing, account matching. I just got off a meeting with the CSM team, and the CSM team had told me that the handoff from, post sales to them is kind of manual. The the assignments are manual. And when we get into meetings with the customer, it's like an... Another discovery call. But that discovery call, all that information has already been done. My job is to use lean data to get all that necessary data to have one seen this hand off to us so the customer has a white hand glove experience and, you know, trust us that we're gonna be the right, solution for them. So how I'm gonna build this is I'm gonna go into lean data. So it's another tab in your in, Salesforce instance. I would go into routing. This would actually be under opportunities, and then go to flow builder. And then I will open up the one that I've been building, which is haunted handoffs. So this is what, handoff, a whole handoff process is going to be all in one particular canvas. Let me zoom in here for you guys. So what happens in this, canvas is a deal moves to a closed one. First question is, is it a customer or if it's expansion? Expansion just gives the current team that's responsible for the account, a heads up that there's something, that there has been an expansion in their book of business. If it's a new customer, like we were talking about, it goes through an AI node, which reads the deal and then writes the handoff brief to the CSM. Then the rules take take over, and then they segment that by the account based on company size, assigned to CSM. And then the CSM has a kickoff task, which is here. And the the customer gets a personal intro with a booking link, and the team gets the brief in Slack. And and, what I like to do here is have an SLA agreement or service level agreement if that hand off meeting doesn't happen within two days, send a, SLA miss Slack to then reach out to the customer to book it from here. Something new that my... You might not be aware of is that I didn't act... Building this as an operations is gonna take a lot of time, but I didn't actually drag a single note into the Canvas. After the meeting, I took notes on what exactly are the... Some of the highlighted points that my CSM team needs. And I opened the LeanData AI assistant and started building from there. And I described all the processes that the CSM team needs through plain English. I can show you what that looks like right now. I prompted. So, basically, what I had is I have a series of rules that I want my particular flow graph to, adhere to. So I want a trigger. I want AI summary. I wanna be able to segment it out based on employee size and assign the CSM. So, essentially, as I'm building, right, I'm prompting. I'm taking all the planned actions, proposed changes from the AI assistant, changing it, editing it, and I'm working on the segmentation piece that I have right here on the... On this potential graph. So after AI deal summary decision, name segment by employees, fields, and opportunities account, I wanna be able to segment it out based on commercial mid market and enterprise companies. After a couple of, seconds, it will then propose me particular changes. So I'm gonna... So what the AI node essentially is saying is I'm gonna segment it out based on employees. I'm gonna give you three different nodes based on round robeting on enterprise, mid market, and commercial. And then I'm gonna modify your AI deal summary node to essentially fit what you're looking for. As a builder, I accept, and then it will, then it will populate that with all the necessary fields based on my, variables inside, Salesforce. Okay. I want to concentrate on the... So that's, building within, the AI assistant. I wanna concentrate on, the AI inference node. So what I'm doing in the AI inference node, once that opportunity becomes into a closed one, is I'm using my own LLM, which is Claude Sonnet. I'm prompting it. Basically, it's a... It's basically my hand off assistant. Right? I want it to then listen to these particular variables from already, stamped data on our Salesforce of the opportunity description, what the next steps should be, what the tech stack is for the particular customer, their employee size, and, the... Basically, the call summaries for the handoff. I wanna be able to write into Salesforce, basically, a a summary for the CSM, a three to five, short sentences basically telling me more about the account. I wanna be able to know exactly what their tech stack is even before I get into the introduction call. Some of the quick wins that I can have within the next thirty days that can get me closer to my customer and try to find other opportunities for me to grow into the account. And then I wanna be able to segment it out even before the decision node, giving me a better better routing option when it goes into the round routing system. So let me put some data in here. So for the particular description, it is... I just closed with a CFO. Some of the next steps is the CSM needs to introduce themselves here and the AM. Some of the tech stack already, on the variable fields will then pop up and populate here. The number of employees is about twenty five zero one. And from the original call summaries, this is some of the data coming here. So I'm testing the AI node, and what the response is gonna come out to be after it fires is usable data here. So handoff summary. The account purchased a hundred and two three solution... A 102... 32 k solution to eliminate need assignment delays. The tech stack already in, is already, laid out for me from CRM all the way to data warehouse. Some of the quick, wins that me as the CSM and AM team can take upon ourselves, It already... And it already segment... Segments it out before the decision node is even there. Good. It's working correctly. Once it's segmented out, it's, the account is about an enterprise account. It will go into the round robining for enterprise picking a CSM and AM. It will then automatically kick off a task for me and the AM to reach out. It will update all those variable fields from the AI node and send a intro email automatically from there. In the intro email, what I have here is a booking link for my, particular schedule for the prospect to book time for themselves already. Alright. So that will look like... Oop here. Oops. Yes. So this is what, essentially, the booking link would then do. As a prospect, I can book time for myself here. After that, automatic Slack handoffs, basically having the rep briefs. I know who... As a CSM, I know what their, what the handoff summary is is. I know what the tech stack is, those quick wins. And then I can track the SLA. I can track exactly what's going on if, the the handoff meeting is booked or not. But what does that look like within the audit logs per se is essentially every single decision for that handoff is then stamped on the record, giving you as the ops team exactly why it's been routed, where it's been routed, and if it's been followed up on a a, fairly quickly. And then if you wanna do track or if you wanna track SLAs, for example, we can give you dashboards to see exactly if we are adhering into and how quickly we are turning over that close one deal into a successful customer relationship from there. And there you go. Pass it over to Sarah. Thanks, John. Really cool to see the AI assistant in action since that's been one of our newer, releases. So if you haven't turned on AI in your Lean data, that was a good example of something you can do and how much easier it'll make it to build these flows. So, we've got just a couple of minutes for Q and A here. Feel free to drop additional questions in the chat. I see one in the q and a, for for John. So, this question's about the AI Inference node. Can we only pull information from Salesforce into the AI Inference node? No. So there is all... Great quest... Question, David. There's also a web search, toggle that you, put in on as well. And then from there, it would take public information from, you know, the Internet to get those particular, data points, that you need. Awesome. Thanks, Great question. John. No problem. Yeah. Great question. If anyone has other questions, feel free to, drop them in. Otherwise, we will, move on to just a couple last slides here. So I'll just give it a second for any more questions to funnel in. As people ask questions or or not, I'll just, like, add in, like, what John just showed is exactly the idea of, like, having a deterministic build. Like, these are the rules of how we wanna run our business, And then using agents to supplement those pain points we've always had around, like, how do you summarize all of the disco information we've just spent, you know, three, six, sometimes a year on an account to qualify. And getting all that information over to the implementation team has always been this manual exercise. And so this is, like, the perfect example of how you blend those two of this hybrid approach. Your deterministic rules, leveraging agents on the unstructured data, bringing those things together, and just making the time to value significantly better for our customers. So just wanted to recap that, John, that, yeah, that was amazing. Thank you. Awesome. Well, I think people are just taking in how amazing that was, so we'll keep it moving. And switching gears a little bit before we wrap up, I wanted to share like a quick preview of something that's coming soon for people that maybe missed this if you weren't able to join us at OpStar. So, what we keep hearing from ops teams is that they're being charged to do all this stuff with AI that is, really exciting work, but then they're getting stuck still fixing all their old data issues and requests, instead of getting to spend their time building. So we are building our own agentic product, our first agentic product, which is called Run by LeanData. It has three jobs. It picks up those ops requests from your ticketing system or Slack, wherever they live, checks them against your your playbook, your policies, and proposes the fix so you can finally get relief from that ops request queue. Next, is finding those issues. So it scans Salesforce. It spots the issues early, ranks them by business impact, and trace... It traces each one to a root cause so you can learn about problems before someone's complaining about them in a forecast meeting. And then finally, define, defines the playbook. So it keeps your data dictionary, your business policies, your procedures in one place so that all of your agents and all of your people are working from the same the same facts. So, this is our new agentic product that we are currently building. And so just wanted to take a moment to encourage people to sign up for early access. So what this means is that you'll get updates as we, are building this. You'll be on the list to get access when we start, inviting customers to use it in December. And you'll... We're also planning some great launch events for this new community that we're building. So just wanted to plug this here. But please, yeah, sign up for early access if this is a pain that you're you're dealing with. With that, thank you all so much for joining. Don't let the ghosts haunt your handoffs this Halloween. You'll get the recording in your inbox and you can also request a personalized demo of LeanData. Let me just drop our link in the chat real quick. Just give me one second. If you want to learn more, this link, John got it. Perfect. This will route you to the right team, of course, so you'll get your correct team on the line for a demo and they can show you more of how to build this in your own Lean data. Thank you again and have a great rest of the week. Happy Halloween. Thanks, everyone. Cheers.