A (2:35)

Yeah, I think there's, if I could expand it to three takeaways. You know, one is just generally speaking, I try to encourage everyone who I have a conversation with to really challenge themselves to think beyond an incremental approach, beyond a wrapper interface when they consider how to use AI at work. And Oracel of course is, is an AI native platform and we'll talk about that later. And I don't mean this in a self serving way. I truly mean it in a way that most of the people that I speak to in business are still thinking about the application of AI through the lens of chat. Ask it a question, get an answer and that's fine because that was the first application and interface for which AI generally was exposed to us as a public, as a platform shift. The power of AI is so dramatically impactful beyond question and answer. And, and I would challenge everybody as they turn internal to their organizations and think about how to unlock productivity, how to out compete their market, how to better serve their customers. Just take, throw away that scared incremental approach of putting chat wrappers on this and that and connecting it to Slack and homegrown systems and wiring it up and get past that. Like it's cool that you could take a little bit more data and replicate a consumer experience in chat. We really, we're past that. Nice products are past that technology is ready. I would challenge everybody, jump in with both feet and pay to, to an AI native approach everywhere you can love it.

B (4:25)

So that's number one.

A (4:26)

Okay, it's number one. And then hopefully somewhere in this conversation I'll have a chance to talk a little bit more self serving about Oracel for two things that I'm excited about that we are announcing tomorrow. And so a little preview here and you know, by the time this gets out it probably will come out but you know, fresh off the press is we have taken our AI native platform originally built for CRM plus about 15 other products on top of it all on a single platform. And we're now offering, we're taking the power of that AI native platform, decoupling it from the, the dependency of a CRM and making it available to sit right on top of legacy architectures like Salesforce and HubSpot. So large enterprises have a path to harness the power of AI native intelligent automation for all of the go to market through Oracel GTM operating system and they can sit it right on top of their existing CRM. That's number one. And number two, we're also shipping an agent, a custom agent builder inside of Oracel that will unlock the power of AI native agentic workflows for all go to market nops teams out there with just simple natural language prompt. They can execute agentic workflows and build their own agents to really with very few limitations. I mean this is one of those moments where it's like your imagination is your limitation.

B (6:00)

Yeah.

A (6:01)

And it's an exciting time.

B (6:03)

Awesome. Well, you know what? I think that's going to give me a lot to chew on here. I want to start with your number one. I'm with you like I'm drinking the Kool Aid. I know what's possible now and I don't quite understand why. I mean I get there's risk concerns. We don't understand the risk associated with it or does the budget make sense? I get all that stuff. But you're. That's a ballsy comment. And just say guys, jump in. There's how are you helping executives that you're talking to, prospects, peers that aren't all in like you and I, how are you helping them understand like the urgency and the importance of it and like how to do it right? What are you telling them?

A (6:44)

I think the importance of it is directly tied to the reason for the urgency. And I think you could boil the importance down to very simple, a few simple statements. You can unlock at least a 2x productivity gain with this capability. With AI native intelligent automation powering true execution, true automation, not just serving up insights. And those who unlock that opportunity for a 2 to 3x gain in productivity first will have a tremendous advantage. And as the gains continue to be exponential in the technology platforms that underpin B2B use of AI, they will by default be the first movers for those other. The other cohort of people that are thinking about this incrementally and trying to stitch together. You know, you take a fragmented tool stack and you try to stitch together, you know, chat chatbot like communication across 15 to 20 different vendors. And every time there's an incre, there's a. Every time there's a step function gain in underpinning AI capabilities. It's just like anything else in infrastructure. You're going to be stuck trying to manage all of those versions, all of those interconnections all of the different flavors of niche agents and your homegrown bots that you've tried to build. And it's just like the spaghetti infrastructure of the past.

B (8:24)

Sure, yeah.

A (8:26)

When you went from scripts to full automation or on premise to the cloud, I mean, it's just another version of that evolution. And everyone was scared of those technology shifts in the beginning too.

B (8:40)

Yep.

A (8:40)

And I guess maybe it's because I've been around for 25 years doing this stuff that, you know, I can remember all those conversations when people went from building servers by hand to automating workloads that built data centers to not needing data centers. And to me, there's a lot of similarities here in the sense that the people that jump in and adopt the full power of AI native capabilities first will continue to stay out in front of those that take an incremental approach that gets stuck in the tangled web of complexity. And I just think it's a hard thing to outrun. And now's the moment that you have to create that step function shift in your business along with that, that disruptive platform shift that's already occurred. Now a bit of urgency. I think the importance really just comes down to you truly can transform the productivity model of your business. When we talk about go to market, the traditional B2B sales teams are still in a place that's upside down. I mean just last year, Forrester and there's all kinds of reports out there. This is easy to find the data, but still 80% of the revenue is coming from the top 25% of sellers. That means that organizations are spending 75% of their go to market expense envelope to get 20% of the business. And that's just an unsustainable productivity model. When everyone's doing that, you have small levers for gains. But when some portion of the competitive landscape starts to to garner a 2x productivity gain and through excellence and execution at scale with consistency provided through automation, you take those bottom performers and you move them up to look more like the elite performers of your org. Yeah, those, those, those competitors, those companies that do that first will simply just outpace the ones that are still here spending 75% of their expense envelope on 20% of productivity. In a market that is unrelenting around what we've seen recently in value slides, there's going to be pressure. Anytime you have a market shift like that, there's going to be natural pressure that flows through on efficiency. And so whether you think about it through the lens of gaining a productive edge or gaining an efficiency edge. Either way, whether you want to drive more top line and you want to do it in a more productive way, I just think the opportunity is there and those winners will be the ones that jump in. Right now, the other side of this is, you know, I was talking, by the way, just to share a customer story and I'll keep the, keep the names out of it, but I was talking to one of the world's largest hardware and technology services companies yesterday and they've been around for decades and decades and decades. And this is not a company that, if I, if I said the brand name to you, you would think, absolutely, they're going to be on the top of the adoption curve of AI.

B (11:51)

Yeah.

A (11:52)

But they are, they are challenging themselves. They are carving off a portion of their business, you know, 150 users out of 2,000 sellers and saying, hey, we're going to take this podcast and we're going all in. We're throwing out all the 20 year legacy rules and we're going to pretend that this is, we're going all in right now. How would we build this, go to Market Motion today for this division of the company if they were a new company? And they are, we're going on that journey with them. And they are benchmarking all of the metrics, all the productivity gains. Yeah. All of the expense, all of all the additional insights and automation intelligence. They are benchmarking it and they're just going to put it to the test. And that's the kind of thing that I would encourage people to do. When you see companies that are 100 years old doing this, how could you be a younger company and not.

B (12:45)

Yeah, so a couple of things. I like this because I was actually trying to explain this to someone this morning. This idea that you had about the exponential gains that are going to occur from the individuals who adopt now, like they're going to pull away from the pack and it won't, you will not be able to catch up with them. If you are, if you delay three months, six months, and these people are in stealth mode unintentionally, but they're going AI native. So those who wait will not be able to catch up. Which is a, like, that's a paradigm that doesn't happen that often in business where somebody's like, oh, we just worked twice as hard, we'll catch up. No, like the distance of time, performance, capability, resource requirement, minimization, like all of that will be. Anyway, you, you, you verbalize what I was thinking this morning. And that's an unusual place. And I can see how that ties to the importance and the urgency. They're, like you said at the very beginning, they're very much tied together. And then I like this idea a lot about an incumbent saying, hey, let's peel off a little bit of the business and let's go AI native. Let's go off the reservation, go all AI. How would we do it? They're going to learn some stuff that will be translated to the rest of the 2,000 sellers and it will be lights out. That's, that's amazing. That's a fantastic approach. Would love to hear more about that data when you can, if, if you can ever share that. Okay. And then now the second thing was you were talking about the, this kind of overlay that you guys. I mean, when you and I first spoke about being on the podcast, it was probably, you know, before the holidays and just in that short period of time, it sounds like there's been some developments, lessons learned and enhancements that have like aura sells a different product than it was 90 days ago. So tell me more about that overlay. It's basically natural language search and retrieval from all of my legacy systems.

A (14:39)

Okay, so you're referring to our second point. Yeah. Okay, so not something that we've said on camera yet. So let's bring the audience up to speed. So you're referring to our customers agent.

B (14:52)

No, that was the third thing that you mentioned just now. It sounded like you, you were indicating that this, this environment of having this cluster of systems that people used to need is going away and that. Yeah, yeah, let's dig in on that a little bit.

A (15:07)

Okay, sure.

B (15:08)

Sorry.

A (15:08)

So I guess let's, let's bring the audience on the journey. So we started Oracel in summer of 2024 out of a place of frustration and technical opportunity. You know, I was, I've been an operator for over 20 years, building sales, marketing and CS teams for multiple startups. My co founder and CTO Ran was SVP of engineering with me at Harness for five years where we last worked together. Prior to that, built big products like the cloud offering at Nutanic and Nutanix and built a lot of product for VMware pre and post IPO. So worked together for five years. We were talking a lot about the opportunity with AI being a platform shift and settled in on just a shared concern that we both have, which is the, the customer journey and how fragmented the existing go to market tooling landscape is and how go to market teams really have like three CRMs and tool stacks inside of go to market. You know, you have your, your, your martech tool, tool stack anchored by, you know, marketo or HubSpot marketing or whatever kind of as the CRM of marketing and Salesforce, HubSpot et cetera for and the sales CRM you have, you know, plan plansight et cetera as kind of these CSM products. I would call that like the quote unquote CRM of, of CS and sure, yeah. For from a customer's perspective, someone who's buying a solution off somebody, you, you know, that's just a single customer journey. You go going through different phases of that customer journey. Why should you be, why should that intelligence about that customer journey be spread across three different systems? Why should there be fragmentation in the technology landscape that requires 15 to 20 products? When I was in harness, our go to market tooling stack was 22 products sitting on top of Salesforce and I had a team of 11 ops people stitching that stuff together manually. We had three products we had custom built to fill the gaps. On top of it I had engineers, I had data engineers building data pipelines for analytics on top of all this mess. And it's like a Jenga stack, you know, it's like it just leans over and you have problems all the time and integrations break and you don't have a single lens for analytics. Metadata is trapped in 22 different databases. And when you think about applying that legacy architecture and to the go to market workflows in the era of AI, it just doesn't make sense. In the era of AI, what we've built is an AI native CRM platform. That was the original product that we built at Oracel, an AI native CRM platform. It included the CRM built on an AI native architecture with a unified data model supporting structured typical CRM data, structured data and unstructured data with a, with a data lakehouse and having knowledge graphing and time series and all of these, you know, rag models and all these AI native architectural components allowed us to build an agentic layer on top of it. You know, being backed up across five of the world's best known LLM models and drive the surfacing insights that were relevant to the different Personas and at the different phase of the prospective buying journey from, you know, contact to contract and then driving intelligent automation through an agentic workflow model built within the platform. That's the first product we took to market and we have customers on today. What we are announcing now are two different products. One, we're taking that the power of that entire AI native platform and decoupling it from our CRM and allowing it to just plug and play right on top of Salesforce or HubSpot, still getting rid of those 14 other products that you have to plug in on top of your legacy CRM to make them useful. You know, we still ship with 85 million accounts and 850 million contacts. Our platform still ships with the operating system, still ships with 10,000 agents in the background that are doing deep research, AI enrichment, surfacing, automated AI enrichment, extending custom AI enrichment and then putting all that to work and automated agentic pipeline workflows, personalized outreach at scale, AI forecasting, et cetera. So all these capabilities that sit across all the internal and external conversation signals that are being enriched to unlock intelligent actions, that capability is what we're shipping in our go to market operating system. But now large enterprises and customers that want to coexist with maybe other workflows that they've built into their CRM system that can coexist and it can either be a bridge for adoption from a legacy tool stack into an AI native go to market platform as you kind of take a crawl walk, wrong approach or, and or it can coexist forever. Now what does this mean for the day to day work of your sellers or marketers or CS teams? It's simplified, it's automated, it's enriched. You take the the user and you put them in oracel, allow them to have a single place for all of those signals, driving contextual awareness across all the internal and external conversations and unlocking intelligent actions for them, making them twice as productive, getting rid of 80% of their manual toil time, eliminating the need for another 14 products on top of your legacy CRM. That's what we're shipping in the go to market operating system.

B (21:29)

So as a user, as a participant in those departments, I have one place that I go. I'm not getting a report here, extracting that data, uploading it here and playing that whole game.

A (21:39)

Humans don't need to be in the business of copy and paste anymore, right? That's an incredibly unproductive use of a human's capacity. Our belief is that so what we do is we design all of our automation, all of our insights, all of our enrichment, all of our automation that sits on top of it with the notion of what would a human do? What are the next three actions that the human we're serving, the Persona we're serving in that moment, what would they do? And can we automate that intelligently for them to further free them up for conversations like this one. That's how you make operators superhuman operators and ours to free them up to do what they do best.

B (22:24)

And I would imagine that in the sales environment there's churn as in any department. But sales can get pretty churny. That means probably onboarding for new reps is pretty quick because there's one tool that they're dealing with primarily so they're not having to go and figure out all the proprietary stack that was built. Interesting.

A (22:45)

Yeah. We're seeing on the enablement front or on the onboarding front, you know, our customers on the average are cutting their onboarding time by 50%.

B (22:54)

Wow. That's internal onboarding or that's onboarding clients.

A (22:59)

That's their internal onboarding to productivity for their go to market teams. I mean we of course use our own product. Our SDRs are booking meetings and productive on their third day. Yeah.

B (23:17)

As compared to what industry standard? A couple of weeks maybe.

A (23:20)

Yeah, it's about three to four weeks. Yeah, yeah. And then, and if you think about, you know, a 50% reduction to productivity time when you're scaling on the back of a productivity model led by sales one it's costly and you always have to over hire because you're chasing a six to nine month product, you know, ramp time. And so if you can save that by 50% and get to max productivity faster while you're also increasing the the productivity average productivity of a seller you org by 50, 50% to 100%. So somewhere between 50% to doubling your productivity per head on the average exponential. When you get those two levers for, for productivity.

B (24:04)

Yeah.

A (24:04)

You really are, you dramatically reduce the amount of hiring you have to expense, you have to lay out to reach the same or better top line goals.

B (24:14)

And I would imagine that just you.

A (24:16)

Think about it, it just kind of makes sense. It's the, you know, all the intelligent automation is there to, to feed them signals so they spend time with the right prospects, you know, dynamically driving ICP territories, dynamically filling up those accounts with the right contacts, automatically showing them the moment that they should contact that person. Yeah, because we see the external signals and then really feeding them the personalized outreach and all the intelligence on the account, automating the value, the enriching the value hypothesis and giving them a ready made pitch on what to do and what, how to say it in that moment that they're supposed to reach out to them. You just remove a lot of the toil and you Maximize a lot of conversion. I'm thinking about all these searching is unlocked at every interaction along the cell site.

B (25:06)

I'm thinking about all these sales books that you know, all these salespeople have read out their career that give them systems on follow up and you know all that like out the window, whole.

A (25:15)

Different paradigm that's all automated, right? Well, it's all automated and by the way, it's not. Those things are still great. I mean the.

B (25:23)

Sure.

A (25:23)

We have built, we have built the sales frameworks into the system and so as our customers set up Oracel and choose the frameworks that matter for their organization. And by the way, you can choose different frameworks and different sales processes for different motions. So PLG motion can be different from high velocity sales leg, commercial motion different from a large enterprise, very complex motion. All those things can coexist and it dynamically applies at the right moment. And then it's coaching like the value based selling frameworks. The coaching is derived from the best practices of those sales frameworks that our customers are choosing. So it's like having your best trained sales leader on the shoulder of every sales rep.

B (26:10)

This is incredible. So one of the stats that always struck me, I'm not from a sales background, but it was how little amount of time a salesperson spent on the phone and it was surprisingly low. You think, okay, it's a salesperson, they're on the phone a lot. No, they're updating the CRM, they're sending the email and preparing the proposal or whatever this is. That that increased productivity you're talking about is because the salesperson isn't doing the things that usually were the parts of the job they didn't like, but that the sales manager was always like, dude, you got to get this done, update the CRM, put your notes in the. Yeah. And now this is all being handled automatically. So.

A (26:48)

So I imagine that yeah, the industry stat, by the way of what you're talking about is 20 is 24 to 30%. So an average B2B seller will, will, will be in a productive selling activity talking to a prospect, either in a meeting, on a call, whatever, 24 to 30% of their life, that means you're paying them. So whatever you're expending per head on your sales team, you're wasting 70% of it with regard to productivity models.

B (27:17)

And this is going back to that stat you shared earlier. Yeah.

A (27:20)

So the objective is to free them up to do what they do best as close to 100% of the time as you can and you really touched on another aspect of this, which is the emotional unlock. That toil, that manual toil, that manual activity. Those are the parts of the job that every seller hates the most.

B (27:38)

Friction.

A (27:39)

Every marketer hates the most. Every SCR hates the most. Every CSM hates the most. You know who hates it just as much as they do? The managers have to chase them to do it. And you're wasting the cycles of those managers too. Yeah, and the execs above them who have to chase the managers to get it done. The ripple effect of toil on productivity and emotional drag just goes through the organization like a tidal wave.

B (28:10)

Interesting. The whole paradigm of the sales environment is going to change. You need fewer people, they're going to be better supported at higher momentum, higher speed. Incredible. Now let's move on to the third thing that you talked about, which was this kind of breakthrough that you guys are having with the agentic. I don't know if you can share the example you were telling me earlier, but like it's kind of mind blowing as you as a listener, as you guys pay attention what he's about to say, like I want you to think about how many people would have been involved and how much time would have been necessary to, to execute something that's now natural language initiated. Go grab a cup of coffee.

A (28:46)

Yeah, sure. So I'll give a couple examples. Chris, what you're referring to is our conversation ahead of this meeting which. So Oracel, we are about to ship our agent builder. And to put that into context, what does it mean? Well, because we're an AI native platform and the architecture already has embedded within our platform services an agentic workflow engine. You already can go into the Oracel platform and build workflows that have agentic properties. And that is powerful. It's very powerful. There's deep web research married together with AI logic research and actions that are templated and it's great. What we're shipping now is different though. What we're shipping now is the ability. I'll give you a couple examples so it's a concrete for your listeners. One example which I just walked into a room at 8 o' clock last night here in the, in the office and a couple engineers and my co founder and CTO were you know, asking what they're working on and they demoed it to me and it was fantastic, you know, and with two lines of just natural language prompts meaning hey, yeah, so this is the actual prompt. Hey Oracel, tell me which of my users, which which users are the top three users of my product, how they use the product and rec and recommend to them something that they might get more value additional value out of in the platform and then build an automated sequence to share that information with them Expo and expose knowledge videos to teach them how to use it. So you know a couple sentences natural language like you might ask me to go do something from there. Oracel's agent builder pulled in post hog feeds, evaluated all the users use usage, ranked it by power users against features, derived the the the logic and reasoning to understand what value would be unlocked in context of that that user's business of our platform with those features, created a message around that an outreach message around message. That was a sequence that Oracel executed to to send that user a message. It then looked at what they're not using again married it against the value hypothesis of that our customer's core business and then derived the reasoning for how they might benefit from understanding another capability in the platform, explained it to them and then from there grabbed a knowledge based video and embedded it. Now the other thing that happened here was and we watched their run on the screen in Oracel in the editor mode is in the middle of all that our Oracel agent builder started writing code to go out and discover other fragmented data sources that were relevant to answering the question. So think about the fact that we pulled in post hog data. We have value hypothesis information and other structured data in our CRM of course. So it's using structured data from two different sources and then based on that user it went out and looked at the Persona and then it went out and used our agents to go gather information about external signals that would further inform it on what that person might find valuable. And then it wrote a connector into a data warehouse that's separate from our unstructured lakehouse where additional data was stored. And it wrote that connector on the fly, gathered the user information prompted where it didn't have it, established the connection, pulled in other unstructured data as part of its its research and reasoning and then came to a conclusion, executed it in eight step sequence and with no with zero user interaction.

B (33:15)

And while that's happening, the competition is saying hey guys, on Tuesday we need to do a meeting. Okay? We need to plan this thing out, make sure the devs are going to be there because we're going to have some stuff for them. Queuing it like you're talking about whatever just happened while you guys sat at that conference table. The competition is taking a few Weeks just to get off, like get started. Incorrect.

A (33:35)

Yeah, I mean think about the old way of doing that. You would, you would go on a journey probably for a data engineer to ask them to pull together three different data, three or four different data sources.

B (33:47)

But they've got a queue, they can't stop and do like I'll get to it later kind of thing. Yeah.

A (33:52)

And then you, you know, that would be served up to an exec somewhere. Some list would get handed to a CS team and a sales team. They would then write out outreach based on that. Maybe they'd put it in a sequencer, maybe they wouldn't. Probably half of the org would actually execute the request.

B (34:10)

Yeah.

A (34:11)

And then they'd get busy with context switching on something else and you know, go do something else. And by the way, when that message goes out, it's not just about the outreach of the message. The interaction continues. So when that that message is responded to, our agent continues the dialogue for as long as, until it derives an action that it has to involve a human. All of that hits notifies the human attached to the account. All of that hits a timeline in the account. So anybody involved in the team can see it at any point. A human can step in and take over. But if the human doesn't, it's going to continue to work and engage in this way. It kind of goes to that like context switching, drop balls, lack of follow up kind of.

B (34:58)

Yeah, yeah.

A (34:59)

Now like human has the power to supersede at any point in time but if you want to let it continue to go, it will like you know and like go like. Another example of extending that, that workflow out is scheduling. It's just simply like, you know, can we, would you like to have a meeting with one of our field deployed, four deployed engineers to learn more about it in real time beyond this video. And if they say, they come back and they say sure, I'd love to, great. And it, and it takes over the calendaring action interaction again personalized. It's going to feel like the human interaction to the end user on the other side and then it'll go tap the right resource in our organization with that meeting happens and needs a human and off we go. And so that was one example. Another example which is pretty cool, which I felt like was really interesting was is a rev Ops example. And in this case we told the agent because, because Oracel knows about the profile of the customers in our platform. We know we've sucked in, you know, their case studies. We know what they sell what problems they solve, who they sell to, their buyer, Personas are ICP competitors, et cetera. Because, because we know all this information already. You know now in Auracel. And if you were setting it up for the first time, like if a Rev Ops person wanted to establish a sales process, Auracel has an opinion on what sales process best practices would look like based on your company. And so now, instead of thinking about going through this long design process and setting everything up in your system, a RevOps person, we did this demo last week. A RevOps person simply goes into Oracel and says, design a sales stage process and recommend sales frameworks that would best optimize outcomes for my company. That's it. You give that prompt, we run the research logic and show you a visual of the sales stages and the sales frameworks. And it might be one and it might be three. Depends on your business that coexist. And then if you say, great, go, Auracel goes to work building it and configuring it in the platform for you. That's it.

B (37:22)

Dude, this is just, it's not fair.

A (37:27)

It's really. This is what I mean by unleashing the power of an AI native platform. The productivity just whipples throughout.

B (37:35)

So for the listeners, I mean, obviously this type of, you know, experience is occurring across other departments, but, you know, the easiest place to get big buy in is show me the numbers. Right? Like, is it impacting the revenue? And this is obviously mechanisms that will certainly do that. So for the people who are listening, because aren't our audience is all strata, obviously. But we do have a lot of lower middle market executives that listen to this who probably hear this and this sounds like magic to them. How do they get a peek behind the curtain and see some of that magic with Oracel?

A (38:09)

Yeah, I mean, it can feel like magic sometimes. So we, you know, just reach out and we're happy to give you a demo. We, we do have, you know, there's a, there's a short explainer demo that's on the new website coming up.

B (38:25)

We'll have that in the show notes. Okay.

A (38:26)

Yeah, if you want to check that out, you can hit the homepage and click on it and you can schedule a meeting with any of our Orcel team right there live. And. Yeah, well, we still send Human Star meetings. We do use our. Another agent we're about to ship soon in March is our autonomous SDR agent. So we're using that internally. And our SDRs are just 100% on the phone now through our voice dialer.

B (38:54)

Wow.

A (38:55)

We removed all the other work from them.

B (38:57)

Yeah, I'm interested in that for our, our endeavors as well. So we'll have that.

A (39:03)

If you book a demo, a human will show up, I promise.

B (39:06)

Yeah, so we'll, we'll have links to that certainly in the show notes. But as far as, like, you sharing perspectives, do you, like, do you have time to even post on any of the social platforms or blog or anything for the company?

A (39:19)

Man, I could be better at this, you know. You know, I try, but. So, yes, I'll make a commitment to be better at it. What's best in cloud?

B (39:29)

Okay. Where could people pay attention to it? Because we know what's happening in the marketplace here at Chief AI Officer, and what you guys are doing is advanced but still accessible to companies that aren't, you know, all in on AI. The way that you've explained things, I get it. I could not even know how all this stuff is working as a business owner, you yet still have access to what I would consider cutting edge, you know, capabilities of generative AI for my sales environment. So, like, as a, as a listener who may not be as deep into it as you or I, I think that if they were to get more of your perspective, it would be easier for them to translate that to the rest of the team and say, guys, we got to do this right? So where do they pay attention to kind of how you're looking at this and how you're explaining it and the experiences that you're having talking to other businesses about it.

A (40:18)

Great question. I am Most active on LinkedIn. That would be the short answer.

B (40:25)

We'll put that, we'll put your LinkedIn handle in the show notes as well. Jason, this is, this is awesome. Like, I'm kind of like the kid in, in at Christmas kind of vibe, because every time I talk to somebody who's doing something cool on the podcast, I'm like, oh, I want to do that too. I mean, not, not build the product, but use the product. Right. So I'm actually going to have our head of enterprise sales reach out and just go through the process as a customer and it'll be great. Yeah, what you're doing is pretty cool stuff. So thanks for taking the time out. I know that with as much travel as you're doing, with both of us having startups that are starting to take off, it's tough to get people on the. On for an hour on a podcast. But I appreciate you sharing this with the community and any closing remarks or anything that you think we need to wrap this up with.

A (41:07)

I think that I would just underscore, again, if you're not doing this in a big way, assume that everybody's playing with AI at some point.

B (41:17)

Yes.

A (41:18)

You go to a dinner party and everybody wants to tell you they find out that you're in the industry and they want to tell you about how they use ChatGPT. Everybody's playing with AI, so of course you have to assume that your competitors are. And again, I would just underscore that now is the opportunity, now is the opportunistic time to really jump in and consider AI native approaches across the business and all of your workloads and workflows. Because I do believe it is the moment to create a gap, a really unfair gap in whatever it is that you do with your business. And you're right. Our technology and the way that we've built it is meant to deliver kind of this magical moment for all of the Personas that we serve in their respective workflows. And it's intended to be automated in a way that it doesn't require you to think about the infrastructure behind it, and that makes it accessible to everyone. So that is part of our design practice. It's part of the way we've built the platform. So I'm glad to hear you say that, and I hope that part of this conversation makes it a little less intimidating for those listeners who are considering doing more but nervous about jumping in or, you know, taking an incremental approach to get started. I would just challenge themselves. Like that company, that big company that I talked about before is challenging themselves. You know, you can do that at any stage, whether you're a small, medium or large business, young or old. And other than that, I just say thank you, Chris. Thank you for having me on. It's always a pleasure to have a conversation with you. I think we could just kick around all of these stories and ideas for hours. And so this is a, you know, I very much enjoy joining you.

B (43:10)

Awesome. Well, thank you again, Jason, and safe travels. I know you've got a bunch of business travel coming up, and for all of our listeners, my advice, as always, go use AI. Thanks, everybody. We'll see you on the next. Thanks for tuning in to Using AI at Work. Don't forget to subscribe for more conversations about how to use AI at work. And a special thank you to our sponsor, Chief AI Officer for empowering businesses with AI education and training. Visit their website for a free AI Readiness Assessment and AI Strategy Guide to help you get started. Using AI at Work. That's www.chiefai officer.com. follow us on Twitter at the handle Using AI at Work and visit www.usingaiatwork.com for free resources to help you harness AI in your role.