The CRO’s Game-Changing Move: Agentify The Sales Process

Julia Nimchinski:

And we are transitioning to our next session.

I want to welcome Justin Schrber, CEO and co-founder of Terra. Justin is building truly compelling Agentix form tech, and yeah, Justin is super excited to have you here. How have you been?

Justin Shriber:

I’ve been doing great. Thanks for having me on, Julie, I really appreciate it.

Julia Nimchinski:

our pleasure, and you know what I’m gonna ask. Right off the bat, ROI, case studies, anything and everything tangible that you’ve seen by identifying workflows and processes for sales, marketing, and, UTM.

Justin Shriber:

Yeah, absolutely. It’s a… I think it’s a burning question right now. I’m definitely seeing a transition from AI being kind of an interesting science project that people are intrigued by to a business mandate.

And now that it’s become a business mandate, people are really looking for ways to not only deploy, but measure the impact that AI is having.

In a… a little bit later in the presentation, I’ve got a matrix that we put together at Tarrett to think about the staged approach that we have to the deployment of agents, and specifically how we’re measuring the impact associated with that.

Julia Nimchinski:

Super cool. Let’s dive into it.

Justin Shriber:

Okay. Let me go ahead and share my screen here.

Julia Nimchinski:

Awesome.

Justin Shriber:

All right, well, first of all, I thought I’d start with a story. I ran an organization, large sales organization, at Oracle for many years, and I always thought that it was ironic, because when I was trying to hire somebody This is the profile I was looking for. Literally, the salesperson that would run through walls, break down barriers, anything they had to do to get the deal done, obviously within ethical boundaries, they would make it happen.

And you would literally sometimes spend months trying to find that person, inordinate amount of times interviewing, putting them through the paces.

Finally, you find that person, and then what I always thought was funny is their first day on the job, you’d throw the policy manual at them and say, alright, here’s all the policies and processes that you guys have to follow, let me know if you have any questions. And then we, like, all throw our hands up and say, why don’t salespeople actually follow the process?

After we tried so hard to hire the people that, don’t want to follow process, and arguably, what makes them great is the fact that they’re out-of-the-box thinkers. that write their own rules. So there’s definitely this discontinuity or this disconnect between organizations and how they need to codify approaches and policies, processes, and what makes great salespeople great.

We actually sat down and we decided it would be really interesting to follow salespeople and figure out how they’re spending their time. where they’re really moving the needle, and what we could do to help them to increase their productivity. And so, obviously, you don’t need to read all of this.

It’s a laundry list of the… daily things that reps get caught up in doing. Some of these things, they’re doing because it’s just part of sales. I mean, you have to fill out a sales forecast.

But there are a lot of these items on the list that actually we make up in an organization.

They’re part of our internal process, and we ask salespeople to do them. What I find fascinating is that the things in these two columns don’t really make a great sales rep great.

And that’s why I call these things the $5 tasks.

These are all of the things that the rep could do, anybody could do, they need to get done, but they’re not really worth a lot. I then ask myself, what is it that I think makes a rep great? And I’ve actually spent a lot of time with various CROs and heads of RevOps.

I’ve asked them the same question. And, unanimously, the same 3 things always make it to the top of the list.

great reps know how to build relationships, they know how to build strategy, and in addition to that, they’re constantly solving problems. Sales is really a problem-solving business. And so, at Terra, we took a step back and we said, okay, if the stuff on the right-hand side is really what matters.

How do we get the stuff on the left-hand side off the plates of the reps, so that they have more time to do what makes them great and what they love? And that really was the inspiration in identifying our go-to-market.

And so we… we literally had several brainstorming sessions as an executive team with our RevOps leader.

And we asked ourselves, what kinds of agents could we and should we be deploying in order to be able to get more productivity out of our sales team?

Now, once we had this list of agents that we wanted to build, we decided that we needed to prioritize, and the next question was, where are we gonna start?

And the one that we picked was the sales process agent.

And there are a couple of reasons for that.

First of all, we had this belief that if you could codify the sales process, automate the sales process. it would have a massive impact on onboarding, it would have a massive impact on your ability to increase conversion rates and win rates, and also the efficiency of the reps. So we’re like, great, let’s start there, and then once we get that under and in hand, we can move on and start to identify some of these other aspects of our business.

So, we started with the question, why don’t reps follow the sales process?

I think every sales leader I’ve talked to asks the same question.

And the reality is that, as I mentioned before, reps just aren’t wired to follow a sales process.

You know, so many times I remember, as a sales executive, getting into a deal review, or a one-on- one with a rep. and pulling up whatever information they’d filled out in CRM, and in the back of my mind, I’m like, I know what happened here.

You knew you were gonna meet with me last night. You probably didn’t fill in any of this stuff until literally, like, 6 hours before the meeting. Then, right before the meeting, you kind of anticipate what you think I want to hear, you selectively remember what’s going on in the deal, you’re an optimist, I get that, because that’s why you’re in sales, and so I immediately discounted anything that was in the system.

And that’s just kind of the game we’ve been playing for the past 20 or 30 years.

Reps are really smart, actually, at figuring out what’s going to move the needle, what’s not going to move the needle. They’re really good at figuring out. what it’s gonna take to actually get a deal done at the end of the day.

All of that can be summarized in a famous line from Taylor Swift. I needed to have a Taylor Swift slide in our presentation. And as Taylor said.

Seller’s gonna sell. They’re not doing anything else if it’s not gonna help them sell. And that, in a nutshell, is why salespeople don’t follow the sales process.

That said, it’s really important to have a sales process, and we believed in it, so we’re like, great, perfect use case for the agents. So here’s the approach we took, and then I want to share with you, we actually took shots of the agents that we built, anonymized the data to protect the innocent, but I’ll show you kind of the framework we used to identify the sales process, and then what that ended up looking like.

So, the first thing we had to do is figure out what are all of the different residuals that come out of a deal.

You’re in email all the time, interacting with customers, prospects. There are amazing transcripts with really rich information, that’s capturing information in detail. Obviously, there’s a CRM and everything.

So he said, great, we need to figure out a way to take all of this information and associate it in a logical way with that, what we call revenue event, which is a closed deal, or a renewal, or an expansion within an account.

And, collectively, what we refer to that entity at is the revenue graph.

This revenue graph puts the revenue event right in the middle, and then it surrounds it with all of the important information that provides color and context for that revenue event. So if I received an email in conjunction with closing a deal, that needs to go into the revenue graph.

If I’ve got information in my data warehouse about product usage, that’s really important when it comes to a renewal expansion, so that needs to go into my revenue graph.

Obviously, I’m going to pull in CRM data to my revenue graph. What’s unique about the revenue graph versus approaches that have been taken in the past is this data lives all over the place, and we’re not trying to bring it all into one system.

We’re putting a layer on top of it that uses AI So, first of all, figure out where the data lives, and intelligently associate it back to the revenue event. Julia, if you were to send me a note and say, hey, let’s get this deal done. I would have no idea, just if I were kind of an innocent bystander, what you’re talking about.

But the agent is smart enough to know, we have contacts, we’ve been working on a deal together, you’re part of such and such an account, this is the deal, and the AI can figure out how to make those Connective lines, that ultimately build the revenue graph.

Once you have the revenue graph, it unlocks so much. And so from there, what we did.

is, first of all, we just use the revenue graph to auto-populate all of our sales process. And then from there, we’re like, great, we have information, but it is a good information.

AI could actually do a really good job of scoring the quality of that information, and so we actually used our agent to assess how high quality the answers were, and then provide feedback to the rep if they needed to get better, or do a better job of questioning on calls. And then lastly, once we had all of this information and the scores.

we turned it into a coaching console. And so now, not only do you know what’s going on in the deals or at an account, but you can also see, over time, how effectively reps are using this process to get deals done.

So it was kind of a three-step process that we took.

in order to identify our wholesaling process. And by the way, this works great if you’ve got a process for renewals and expansion, and you’re interacting with existing customers.

Okay. So, let me click over to our application, and we’re… we’re using what we sell ourselves, so this is our own deployment of Terret, and on the homepage, when I log in, I can see all of my meetings for the day, I can also see all my action items, and then I can really quickly get to, a deal that I’m working on.

So in this case, this is an Acme deal.

And I want to figure out where things are at with the sales process.

So I’m going to scroll down here, and what we’ve done is we’ve laid out all the sales stages along the top, and I can see that the fourth sales stage is the demo.

portion, and the technical validation. And there’s a really important thing, a milestone, that we need to establish during the sales stage.

We need to effectively address the key requirements or pain points using our demo. And so.

one of the milestones is, did you actually accomplish that? Now, you can see here that the agent is saying, yes, we accomplished that, and as a rep, all I need to do is come in and now accept that information, and it’s automatically added into the deal.

Where did that come from? The agent was on the call, and it was listening, and it was smart enough to know, oh, okay, they’re talking about a demo right now, I know what the pain points are, and the AI was able to pull out the relevant information and actually populate it into the deal. So, step one accomplished, we’re auto-populating the sales process, and what was really cool is when we deployed this, we went from maybe 40% completion rate to almost 100% completion rate.

It was… it was awesome. The reps actually loved it because they weren’t getting calls from the boss anymore. Hey, what’s going on in this deal?

The information was well laid out. And for our new folks, they actually ramped a lot faster because they were getting the specific things they needed to accomplish at each stage in the sales process.

So from there, we said, okay, interesting that we have information, but is it good information?

And so, at that point, we decided that what we were gonna do is start to use the AI to actually score the quality of the inputs.

And so you can see here, here’s that Acme deal, and in aggregate, across all of the information that’s been filled into the sales process, the quality score is a 3.2 out of 5.

So, not great, kind of middle of the road.

And if I want, I can actually drill in to the sales process, and I can see each of the different milestones, and each milestone gets its own individual score.

Related to, that particular area. And then I can hover over one of the scores, and it’ll tell me why I got a 3 instead of a 5.

And I’m kind of a Type A personality, I want to get a 5, What do I need to do?

And the AI tells me, hey, on that last call, you didn’t do a good job of asking this question.

So make sure that the next time you get on the call, you do a better job with that.

So now, I’m getting real-time coaching that’s laser-focused on a specific facet of a deal that’s being run.

So we’re like, great, now we have everything auto-populated, we know the quality because we’re actually getting numerical scores.

But what about… what about the reps that are actually, working on these?

How effective are they at, running, running a deal cycle? So, because we have scores on every one of the deals, for every one of the milestones, for every one of the sales stages, we can now aggregate scores for… at a rep level.

So this is an organization, a sales organization, and what we’ve done here is we’ve built out a dashboard using MedPick that shows you the average score that each person gets for each facet of MedPick.

I can see that Tom is really struggling right now, here at the bottom. Tom consistently scores lower than his peers, and I know that, in particular, he has a really hard time at identifying an economic buyer. We’ve also built a dashboard that shows you, how good are reps at objection handling, at qualification, at negotiation.

Because again, the agent is capturing all the information that they’re collecting on calls and in email, and it’s appropriately assembling it.

This is amazing, because now, when Alex, Tom’s manager, gets on the call. he’s able to say, alright, Tom, we’ve got some work to do. Let’s roll up our sleeves and figure out how we can help you to become better at objection handling, and then over time, they can actually track progress, because they’ve got that number there, and they can see that number changing over time.

So again, going back to the original, framework that we used, step number one, figure out where all the data sits.

Step number two, build the revenue graph, and then step number three, start to hang these agents on top of it. And that’s where you really see the unlock starting to happen.

Alright, so Julia, this gets to your question, which is, does this stuff actually work, and can we measure the impact? So, we decided that what we were gonna do is take that initial list of agents that I showed you, and we were gonna divide them into 3 sets.

The first set were kind of the foundational agents that we needed just to get our house in order. The second set of agents would really help us to tackle those productivity issues, and then the third set of agents would help us to think about How we could literally transform the growth rates and the margins associated with our business. So, examples of the… what the agents were helping us to do in Stage 1, it’s basic stuff.

The serum hygiene, just auto-populate the information so I don’t need to do that. research account data for me. And then, as we started to scale, that’s what I just showed you, where we were able to build, deal qualification enforcement mechanisms through the dashboards that I talked about.

the playbook reinforcements, so if you’re scoring low, what do you need to do to improve? We actually use agents to forecast now, which is very cool. The agents are independently coming up with a score on… or a value for what we’re gonna close in that quarter.

And then lastly, and we’re not here yet.

But we are… this is kind of our next step.

We really want to use agents to transform how we’re doing business and the economics associated with the business. So, this idea of literally autonomous prospecting, where we put an agent on an account, and 24-7 they’re just going after the account, reading the signals.

formulating the… the assets, we’re really excited to see how that comes together. So, in terms of the actual numbers, what we’ve seen so far, just out of Stage 1, about 5 to 8 hours of additional selling time, so that’s 15-20% increase in the capacity of our team.

What’s cool about this is we don’t have to hire as many salespeople, because essentially we’re adding another 20% on top of each salesperson that we currently have.

So if we’ve got salespeople, 5 salespeople, that’s literally one incremental head that we’ve added by using these agents.

At the sales stage, that’s where we’re really seeing improvements in win rates. When we implemented the sales process, started to measure the quality of it, and reinforce it through coaching, we actually saw correlation with win rates. That was significant for us.

Forecast accuracy improved as well. We don’t always… go by what the machine forecasts for us, but what it does is it gets us asking really good questions. If my head of sales is calling, 3 million, and the agent’s calling 2.5 million.

there’s a really good opportunity to ask, what’s the discrepancy, and get to a different level of understanding about what’s going on in the business. This next set of metrics have not been achieved yet. These are our targets, but we really want to target lowering the customer acquisition cost, quota attainment.

How much revenue per rep each rep is generating, and then net dollar retention for our install. So definitely, these are the hard metrics that prove that AI can deliver the goods.

At the end of the day, the way I would summarize that, At a rep level, you’re able to give reps larger books to manage, and because you’re able to really focus on high-caliber teams and measure those teams, you’re able to see that each rep should be able to close more revenue. Reps don’t leave if you’re letting them do what they want. If reps see that they don’t have to do all of that minutiae, the sales chores, as I call them, they’re gonna love that.

As Taylor said, seller’s gonna sell. If you let them sell all day, every day, that’s a huge competitive advantage you have relative to people that are trying to poach your people.

And then lastly, if you can get one platform that’s doing all of these different things for you, you don’t need as much technology, and you don’t need as many people to administer and maintain it, and that’s where you drive down the costs.

So, Julia, that, in a nutshell, has been our journey.

Julia Nimchinski:

Thank you so much, Justin. Super impressive. And we are transitioning to a Q&A with the community and myself, and I’d like to start with a question, just given your leadership over the years and decades, really.

In some of the best companies in the world. in sales, marketing, how do you see the AI-native GTM now as a function? Are all of the functions, not all of them, but within the spectrum of GTM merging, diluting?

How do you see it, the future of sales and marketing in CS?

Justin Shriber:

Well, I would say, first of all, there are some things that haven’t changed over the past 30 years. My observation is that the best sales organizations are very disciplined when it comes to defining the process that they expect their teams to follow. They’re metrics-oriented, they know exactly what’s going on in their business, and can identify quickly where there are problems.

What’s changed, though, is Candidly, the organizations that were running efficiently and effectively kind of had a draconian approach.

I mean, if you did not update the systems, somebody was on you, telling you, hey, you gotta make it happen. I mentioned that I was at Oracle. Oracle has a phenomenal sales culture, and it’s very much driven by, you know, Larry, who’s an intense guy.

And if you didn’t do your homework, people let you know.

Not every company is cut out to take the kind of, I would say, heavy-handed approach that Oracle took, and I was at Siebel before that, same culture. What’s great now is that the agents are able to kind of reinforce these processes.

In an environment where the boss isn’t necessarily coming down hard on you, in front of 10 other people, and calling you out and making you stand up in front of folks. And look, if that’s the way you want to run your org, great, more power to you.

But now the agents can kind of be the bad cop, and help you to get where you need to be before you have to actually meet with the other folks.

In terms of your question about, are things coming together, I would say yes. We all now hear the word RevOps frequently, which is a conglomeration of sales ops, marketing ops, and now customer success ops. I see rolling up underneath that.

And the Chief Revenue Officer, I now see not only managing new logo revenue, but also renewal expansion revenue.

And they’re juggling… flavors of revenue.

A lot of companies today have a SaaS version of what they do, and they’ve got a consumption-based version of what they do. that’s all coming together, and I think the agents are starting to become a go- between that thread together the different flavors of revenue, make sure that all of the information is accurate and complete, and are doing the analysis and the synthesis to empower the people that are running the organization with the information they need.

Julia Nimchinski:

Love that. Folks are asking, obviously, the friction points. the biggest friction point in this transition identifying GTM is security and private data.

How do you address that at Terra?

Justin Shriber:

So, the… that’s a… that’s a big challenge, because a lot of the information you need to know what’s going on with revenue is locked away in what I call the personal systems, the email, the calendar.

There’s no hierarchical, OAuth associated with that, meaning I’m the CEO, and there are people in my organization that are receiving email relevant to a deal that I care about. But Outlook or Gmail is not set up hierarchically so that it says, okay, Justin can see the emails of these people, nor would I want to see their personal information.

So one of the really cool things about the Ajentic layer that we’ve built Is that it understands who the user is, where they fit in the hierarchy, what’s personal versus what is business-oriented, and it’s able to respect the access controls as well as the personal business orientation of the data, and pull in the appropriate information.

So now, even though I can’t go into someone’s Gmail account and just read their Gmails.

The agent says, okay, you can see the people that are underneath you in the organization, you can see the emails related to the deals that you’re currently looking at. One of the things that we focus on from an authentication perspective is how the organization in Salesforce is set up, because that gives you the sales hierarchy, and we’ll pass those rules along to the email and the calendar, and use that to guard how we’re accessing the information.

Julia Nimchinski:

Thanks, Justin. I… the graphic that you shared really resonated with me deeply, obviously, working in also sales and marketing for a decade, there is one recurring stat. We always are seeing 80%, 70% with every Salesforce state of sales report.

is being spent on, you know, all sorts of manual tasks, admin tasks, and really helpful that we have now, identified workloads like Terrets. To automate that, but I’m curious about the 20% that you shared on the slide. What are you seeing in terms of the creative work?

Just generally in the industry and within, you know, your customer base. what changes, in methodologies and AI-native approaches are you seeing now and excited about?

Justin Shriber:

So, just as a refresher, that 20% is building relationships, profitable relationships, building account strategy, and solving problems.

The first caveat I would make is that many companies, I think, enter this… this conversation about identifying their enterprise. And they’re thinking about trying to hit those use cases first.

And one of the reasons that you have such a high failure rate, that’s a really big swing.

Those are really ambitious use cases to take on, especially if you’re an organization that doesn’t have a lot of experience with that. So, I would actually say that much easier to go after the low- hanging fruit of just get good quality data into your system with agents before you take that on.

Now, if you are at that kind of third chevron in the journey that I talked about, and you’re ready to go after this, I think there are some really interesting capabilities that agents offer, and this is, as I mentioned, what we’re starting to work on now.

One of the things I love is the level of personalization. Not having to send a generic asset to a particular prospect. The agent knows everything about the deal in flight, it knows about the account because it’s done the research.

Why can’t it take… an asset related to a case study and adapt that to the specific use case that’s been articulated. The challenge is being able to snap that into your agentic workflow so that the asset is generated, it’s deployed appropriately, and then it’s receiving feedback from the customer. But I think that’s one great example of how you can create a more personalized experience for the customer, and it’s kind of that next order of agent.

Julia Nimchinski:

of that. Folks are asking, how does Terra change the role of a manager day to day?

Justin Shriber:

Yeah, so… what I… what I realized when I was a sales manager is that my approach was pretty different than the sales manager that sat next to me. And… In some cases, what I was doing was better, in some cases, it was worse, but there wasn’t a lot of… structure around how we were managing.

I think everybody recognized that you could put structure around how you sell through a sales process, but management, it was like open season in terms of how you wanted to do it.

What’s really cool about agents is that it gives all the managers specific data that they can use to coach their teams.

And so now the head of RevOps and the CRO, by determining what data gets pushed in front of the managers.

Actually is implicitly guiding the conversation that the managers and the reps have during the one- on-one. The other thing is that the agents are not only scoring, but I showed how they’re giving qualitative information about, hey, this is why you got a low score. And again, that gets the managers focused.

So I think that there’s a lot more standardization now in how management is happening.

There’s a lot more efficiency, because you’re really quickly able to zero in, and if you’ve only got 30 minutes, you can immediately jump to the points that you need to help the person with. So you’re spending less time, and what that means is that we’re seeing the ratio of managers to reps actually improve.

In other words, you can manage more reps simultaneously now, just because of what the agents are doing to help you prep behind the scene.

Julia Nimchinski:

And what’s the biggest mistake in agentic deployments?

I mean, obviously.

Justin Shriber:

Yeah, well, I mentioned one of them, which I think… Yeah, trying to be too ambitious is one thing. Number two, not defining in advance what success looks like. And look, the first success could simply be, we want to see our sales process, our medic, filled out.

Like, that is a perfectly good metric to have. Because if you do that, it sets the stage for the next thing.

So, come up with a… an objective metric that you can use to measure success.

And then I think the third thing is, right now, there are a lot of silos.

Especially in larger organizations, you’re going to have the CIO, CTO, leading an AI transformation initiative. You’ll have the RevOps people that are leading an AI transformation initiative. You’ll have all of your salespeople on the front lines deploying their own AI agents, and you create absolute chaos within an organization.

So get on the same page, figure out what the team is, who’s doing what, and what the outcome should be, and you have a much higher Higher likelihood of success there.

Julia Nimchinski:

Of the depth of the session, curious your thoughts on 2026 and a GenTech AI GDM trends and predictions. What’s the biggest one you’re seeing?

Justin Shriber:

I think… I think there are… the big wild card out there is what the LLMs do, and how… how vertical they get.

Right now, there’s a huge land grab happening between OpenAI and Anthropic and Mistral, and… I have my eye on how broad they’re gonna go versus how verticalized are they gonna get, because if they decide that they want to go deep on actually deploying revenue agents, I think that changes the landscape for the rest of us. One of the things that they have an incredible advantage at is, capturing primary intent, if you will. if I have in my brain a question, and I go into Anthropic and I ask that question, Anthropic holds all the cards.

They get to determine how to orchestrate all the processes that sit behind the answer, they get to determine which data to go after, and so really, they’re kind of in the driver’s seat. On the other hand, if Anthropic and OpenAI say. We want to be the LLM on the front end that’s addressing these ad hoc questions, but these workflow-intensive agents, we’re not as interested in those, we want to leave those to other folks.

I think that’ll have a lot of implication over within the enterprise, what technologies you’re using, how you’re deploying.

I will say, overall, this is definitely a consolidation play. We are not going to be running nearly as much tech. as we used to.

It’ll certainly be an LLM and probably a blend of LLMs, and then maybe one platform that handles all of your agents. Whether or not that’s the same, I’m not sure, but you’re not going to have 7 or 8 different technologies anymore.

Julia Nimchinski:

fighting times. How are you addressing all of those points with your roadmap?

Justin Shriber:

Well, we are, First of all, one of the things that we build, and I think we do incredibly well, is that revenue graph. And the beauty of the revenue graph, it’s complicated to build, because not only does it entail interfacing with these different systems, but as you implied, you’ve got to understand access governance controls, you need to know how to translate natural language queries into SQL, And we’ve been able to figure that out.

So, in terms of addressing the future.

We’re obviously deploying a host of agents we call the Virtual Revenue Fleet. We’re also making the revenue graph available to these LLMs so that they can actually ask questions against it. So now, if you’re in Claude, and you say, hey, what’s going on in EMEA?

I just saw that my, win rate dropped by 10%. It can interface with your revenue graph and get an answer to that question. And then the third area is workflow, and we’ve got a workflow, studio.

where you can actually build workflow. So to some extent, we’re kind of hedging our bets, whether it’s agents, workflow build-out, or simply supplying, an LLM with, through an MCP server with answers, we want to create a one-stop shop for our customers.

Julia Nimchinski:

Love that. Great session, Justin. And how can people get a test drive?

Is there a freemium?

Justin Shriber:

We have a free, proof of con- proof of value, that we’re happy to deploy, so reach out to me on LinkedIn, if you’re interested, and You know, actually, let me take a step back. Companies are in different places. Some companies are like, we are a build company.

We have an incredible team of AI engineers, we have the chops, we have the technology, we want to build this stuff. If you fit into that bucket, we have documented our journey and have kind of a blueprint that I’m happy to share with you.

That hopefully would be insightful as you guys build this technology out internally. On the other hand, there are some companies that say. you know what, we want to buy this.

There are experts that are creating this, and we want to go fast, and just get something up and running, and if that’s the case, obviously we can help you out as well with the solution that I just presented. reach out to me, connect with me on LinkedIn, message me, or hit us at tarot.ai, and just let us know that you want to talk, and we’re happy to do whatever we can to help you out.

Julia Nimchinski:

Thanks so much. And, well, that’s… that’s a wrap.

AI practice session just concluded. Reload this page in 24 hours, and you’ll see all the recordings, and we saw a lot of questions about how do you operationalize and just integrate some of the learnings one-on-one with some of the speakers that you saw today. It’s all possible in our marketplace, so go ahead and just Check that out, it’s on the homepage.

And, we are also coming back in a month with another AI Summit.

Just a teaser here. Boom. And Gentex Singularity, we’re gonna be exploring trends and predictions for 2026 with top investors, VCs, CXOs, and analysts, as per usual.

You can find this link on the website as well, and yeah, super excited for this one! Thanks, Justin. Thanks, everyone.

See you soon.