Executive RoundtableAgentic GTM

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Julia Nimchinski: Our community was really excited about this one agentic. Gtm, welcome, Alina.

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Julia Nimchinski: co-founder and co-CEO of Chili Piper are super pleasure.

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Julia Nimchinski: And yeah. An all-star panel here. Welcome to the show.

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Julia Nimchinski: How about we just start with a quick, quick round of introduction. Everyone.

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Alina Vandenberghe: Thank you, Julia, for having me. I’m very excited to have Lisa Lisa and Matt and Kevin, Jonathan and Kelly we gonna talk about use cases that are working, that we’ve implemented internally that have produced results. And I’m very excited about that.

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Alina Vandenberghe: What I would love to do as part of the introductions as well with everyone. If that’s okay, is just name and what the company does in a short in one short sentence. And I would also be super curious what everybody understands by AI agents, how you look at it and like just this short definition, because I feel that different people understand different things. And we can start with that. Does it work? Yeah.

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Alina Vandenberghe: all right, I’m gonna maybe start. So I’m Alina Vandenberg. I’m co-founder. I’m the technical co-founder at Chili Piper. I love building and breaking things. That’s where my most joy comes. And as soon as I saw that we can do so many cool with AI. I got everybody in the company excited as well. I made it more of a game. Everybody can volunteer and build AI agents on our Gtm. Flow, and there are all sorts of rewards for it.

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Alina Vandenberghe: Our company is all focused on demand and conversion. How to optimize the buying funnel for better conversion rates, and for me personally and an agentic flow is different than a regular workflow that you’ll create with Zapier or something like that. Well, now, with that zapier, you can also create a agentic. Workflows is the ability to

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Alina Vandenberghe: make decisions in the in the process and learn in order to get to a goal. So the there’s more to the automation because there’s also decision making in the process that’s independent from a human I would love. Maybe, Jonathan, you want to go next, because that’s who I see next on my screen.

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Jonathan Kvarfordt: Sure, Lena, big Fan, and thank you, Julia, for letting me be a part of this really exciting. So my name is Jonathan Carford. Some people call me coach. I am the CEO of Gtm. AI Academy, which is all about on demand and live education for how to use AI, and I’m also the head of Gtm. Growth for momentum, which is a what we call enterprise listing platform. As I’m sure we’ll get into that here in a second, I would actually agree with you as far as your

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Jonathan Kvarfordt: your definition of agents. Because I know in the email I sent you like some people say agents. It’s like a chat Bot by lean more towards the actual agency where you have this technology that can make decisions for itself and execute. So I agree with you on that one.

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Alina Vandenberghe: All right, Kelly.

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Alina Vandenberghe: I love your microphone, Jonathan. It work. It sounds so much better than mine.

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Jonathan Kvarfordt: Thank you.

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Kelly Hopping: Hi, I’m Kelly hopping. I’m the Cmo at demand base. And certainly agents are a big part of that. So demand base is account based. Go to market platform which is all you know, data and insights.

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Kelly Hopping: it’s AI powered to to drive the right recommendations on audience and message and and next steps and things as you’re going after target accounts.

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Kelly Hopping: I think the agent space on ours. We’ve been talking so much about this lately in terms of what is really the value prop. We went through a bunch of messaging pillars about agents yesterday and we said, Is it really like, What’s the hook. Is it about being faster? Is it about being better? Is it about being getting too strategic? Faster, more efficiently? Is it about like just the mundane going away and getting sort of burdensome, tedious tasks out of the way.

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Kelly Hopping: And I think what we kind of came down to is it’s really about unlock for us. It’s about unlocking the value of

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Kelly Hopping: of the platform. So, for example, account base can be super complex to put into place, to get adoption, to get the whole team running on it, and if you can get to value faster, meaning adoption is faster. Your account lists are set up faster, your customer journeys are set up, your campaigns are done faster all of that, because they’re all powered by

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Kelly Hopping: these AI powered insights. Then you can actually benefit and drive revenue faster because you’ve you’ve unlocked more value in the platform. So to me, that’s where agents really come in is about, how do we take the things that usually get in the way of productivity and getting those automated so that we can focus on on the really strategic bets. We want to make.

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Alina Vandenberghe: Okay, Matt, you want to go next.

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Matt Darrow: Sure, thanks, Alina, and great to be part of this panel. Today I’m Matt Dara. I’m the co-founder and CEO of Vivin.

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Matt Darrow: We’ve raised over 130 million from excel Menlo and Salesforce ventures. And our customers are enterprises that you guys know in the audience, Adp Snowflake Dayforce Docusign. We’ve built the world’s 1st AI sales engineer to give 24 by 7 coverage to your entire go-to-market team so they can move a whole lot faster on their own.

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Matt Darrow: So my definition of an agent is actually pretty simple. An agent can reason like an expert to actually complete work without being prompted. That’s how we see it. That’s what we’re building toward as well. And we’ve deployed all sorts of agentic use cases at Vivin. And I’m really excited to share some of those learnings today with the rest of the group in real tangible sessions about what worked and what didn’t. And hopefully, people can learn from it.

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Alina Vandenberghe: Kevin.

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Kevin White: Hey? I’m calling from the airport so hopefully

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Kevin White: doesn’t sound like garbage. We’re at an offsite.

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Alina Vandenberghe: We can’t tell. We can’t tell.

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Kevin White: Okay, great, great, great. So yeah, I leave the go to market strategy at Common Room common room is a is a platform for for go to market efficiency or go to market pipeline creation. Essentially, we capture a bunch of buying signals. Tell you the person in the account behind the signal, and then I’ll enable action on top of that. You can see how it kind of a place to AI as capturing all this data as the foundation for AI agentic things to work on top of all that

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Kevin White: and then, as far as the definition of agentic, I feel like the jury’s still out. I really like Matt’s simplistic definition, or simplistic in a good way.

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Kevin White: and we’re workshopping this this analogy at Common Room, where we think of an agent or

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Kevin White: AI as like a chat bot where it’s like the analogy is

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Kevin White: how you would tell a teenager to clean a room versus a housekeeper and a teenager. You say you know, clean the fridge vacuum. This like do all these things is like one by one, whereas you tell a housekeeper like, Oh, just clean the house, and it’s it’s there, and it’s done so. It’s like agentic is a form of. And now the analogy is like, it’s doing a job for you and reasoning. So I think everyone’s kind of like had a similar response. So yeah.

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Alina Vandenberghe: Right. We can hear you. Well, we can’t tell. You’re in an airport. So, all good. And last, but not least, the Queen of using agents for strategy. Liz is. I want to go next.

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Liza Adams: Lina, you’re so kind, so humbled, so, Lisa Adams, I’m an AI and executive advisor, and also fractional. Cmo with growth path partners. And I inspire, go to market teams with what’s possible with AI and help them with their transformation. And with regard to the definition of an AI agent in its most simplistic form. I believe that it is AI that does things on our behalf autonomously.

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Liza Adams: but the purest would say that it does things on our behalf autonomously in 5 different layers. So it sets goals, it plans, it executes, it learns, and it analyzes.

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Liza Adams: So if it does all those things. That’s a pure agent. However, in today’s environment, you can find different companies and different groups that do a subset of that, so they might primarily be doing the execution part or execution, and a little bit of planning, and that’s still called an agent. So I believe that the agent term now has a spectrum of capabilities along those 5 things that I mentioned.

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Alina Vandenberghe: Okay.

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Alina Vandenberghe: all right. So my hope is that at the end of the session we all come up with new ideas to implement in our Gtm. Funnel, and things that are working and things that we’ve tried, and they we already flopped on them. So it’s not worth trying again, at least not in the next 3 months. So on. Practical use cases for me, the biggest difference

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Alina Vandenberghe: has made. And last year we only had 2 marketers that booked 1,400 sales, qualified opportunities for our team, which is a mind blowing number for coming from just 2 people, and the reason why we were able to do. That is because we put all our data into Snowflake. So all our Crm data or our marketing data.

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Alina Vandenberghe: and we were able to query and personalize a lot of our funnel. And I’m going to give example of 2 workflows. I have like an entire notion, Doc, with what we’ve built. But I’m just going to give 2 examples, and I’m going to go around and get 2 examples from each one of you.

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Alina Vandenberghe: The 1st one was around account scoring. So for us being able to go to someone with their website and see what they’re buying. Funnel looks like, what the who they’re selling to whether they have a contact. Us form and all the details that show us whether there’s leakage in the funnel would allow us to create an account score. That was

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Alina Vandenberghe: that allowed us to focus all our efforts to specific ones, that we knew that they’re gonna convert fast. And then the second piece was providing an Sdr. With all the detail information to reach out. They say they would take screenshots of the form. They would say what what the message would look like what the chat would look like. So the Sdr. Would they be able to craft super personalized messages based on what the agent, or had already discovered, that was broken on their on their buying journey. So that was also a big amplification for for Sdrs.

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Alina Vandenberghe: Alright, I’m going to go next. Maybe, Jonathan, you, since we go in the same order, you give us your 2 examples also.

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Jonathan Kvarfordt: Sure. So the 1st one is with with momentum. So we have an agentic workflow where?

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Jonathan Kvarfordt: when we have identified the exit criteria that salespeople are supposed to meet at a certain point of the sales stage. AI has the ability to analyze the conversations real time, so we can get all the information from the from the conversations, analyze the the exit criteria to go through stage by stage, and then, once the things are already met, all the Crm data

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Jonathan Kvarfordt: and all the exit criteria for whatever stage it will automatically move the stage as it goes. So it’s going through the process as the salesperson goes through.

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Jonathan Kvarfordt: and for those people who, you know don’t trust the AI to do what needs to do. There’s a human in a loop factor where it will send a message to the opportunity owner saying, Hey, you’ve met all the opportunities. Do you want to move this forward? Press this button.

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Jonathan Kvarfordt: you know. I’ll just click that button in, slack and move the move the deal forward. So really nice to be able to let the salesperson kind of focus

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Jonathan Kvarfordt: the other one’s kind of similar to what you said, Alina. And that’s using implur with various amounts of tools implarai where we can use things like we have like a data coming into slack. And then we have data from clay and other enrichment sources. And then this agent understands when someone comes in from our b 2 b, for example, we get that slack channel. We can understand what’s going on. It can analyze when it’s visit the website from our b 2 b.

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Jonathan Kvarfordt: And then analyze that with Clay. Did any of the Richmond 3rd party stuff, and then use that information to then go through a sequence of creating the messaging, reaching out on Linkedin, doing the whole process, using all the data points they they require, and then the next level of that is, they can analyze the person title in industry or company, and then look out and say, Okay, what other companies are like this? And are other people who are like this to then cross over to

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Jonathan Kvarfordt: similar look like type companies, which is kind of cool. So those are my 2.

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Alina Vandenberghe: Alright. So it’s also similar top of the funnel. Also for the account scoring and the optimization of the of the close rates. Kelly, you want to go next.

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Kelly Hopping: Sure. Yeah, ours probably similar. I think. Clay is a big one for us. For our Sdr. So similar to what Jonathan talked about just supplementing and augmenting the data gaps that we have that not only help our Sdrs be more effective and get them to the right data that they need to go after the accounts. But also like.

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Kelly Hopping: what do we kind of say builds our own data. So it updates within our own contacts and updates within our own sort of data hygiene to make sure that we’re kind of cleaning the data in the same at the same time, so it both helps enable them, but also make sure that the next time it sort of cycles through. So that’s been really helpful for us, because our sellers have been able to to get to contact information faster, which is always sort of the goal. So that’s a big one. Another one that we have is I don’t know if I’d call it.

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Kelly Hopping: I’m I’m interested to see, by the way, and how the industry moves on. Agents versus say, AI powered workflows. Are they different, are they? The same thing is everything. Just gonna kind of get rebranded as an agent when that’s just because it’s cool and sexy. But at the end of the day it’s just AI driving a workflow. So we use Jasper on the content team.

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Kelly Hopping: But we’ve actually ended up using it across the organization. We run about 50 different workflows that come sort of prepackaged with Jasper. But we use it for everything from taking.

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Kelly Hopping: you know, a single thought leadership. We don’t use it for original content creation as much because we sort of like owning the voice and things. But we use it to take a piece of content like an article, and we can use jasper does these workflows. It automatically, converts it to a Linkedin post, or automatically converts it to a blog or to a social post, or a quote, or anything else it’s able to extract, and the brilliance of it that’s different than, say, a Chat Gpt

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Kelly Hopping: is that it has all of our demand base data in it. So it’s got our Icp definition. It’s got all of our buying group messaging. It’s got all of our buying guides for customers. It’s all the things that you need that are proprietary. So it’s probably got 100 documents that are all about our own messaging, our own positioning, our own tone of voice, our own brand standards, whatever it is.

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Kelly Hopping: and they have trained the answers back, and so everything is well informed on our own products and our own messaging that’s able to to populate all of our content. So that’s been a big one. And then, of course, we’re rolling out a whole bunch of agents from Demand base just to help you get up and running on demand base faster. So

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Kelly Hopping: that’s just a few.

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Alina Vandenberghe: I’m really happy to hear that Jasper does some amazing things with AI on their own Gtm. As well, and they usually piper to optimize all the ways in which they get prospects to buy faster, and I’m so happy that you’re using them. I would be super curious, and I’m sorry to put you on the spot that we can get that later. I’d be super curious if you can have an example of what you have created with Jasper like a post, so that

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Alina Vandenberghe: I’m I’m curious on the structure, and like the format of it.

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Kelly Hopping: Yeah. What do you mean on how we, how we’ve used.

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Alina Vandenberghe: Over Linkedin Post over Link, over Linkedin Post that was created with Jasper. If you can post it in the comments, if not, that’s that’s fine. I’m just.

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Kelly Hopping: Give me a sec.

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Alina Vandenberghe: The other format.

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Kelly Hopping: Find one for sure. The what what we did which was kind of crazy is that we loaded in like, I have a podcast so we loaded in, like every episode of the podcast, and it was used to create my voice

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Kelly Hopping: from that, like just the way that I talk and the way that I communicate, and then I overlay that, too, with my post. So sometimes I’ll say, Hey, here’s an outline of what I want to talk about use Kellyai voice to actually write this thing and then create an article out of this. And it’s just fascinating. So yeah, I’ll pull. I’ll drop an example in. If I can find one.

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Alina Vandenberghe: Okay, Matt.

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Alina Vandenberghe: I know that you’ve done a ton also.

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Matt Darrow: Yeah.

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Matt Darrow: Well, I’ll well, I could talk for like an hour on this. I’m not going to monopolize it. I’ll give you guys 3 examples, Alina, and then, if you want to dig deeper, go for it, and I’ll stick to like where we focus on the outcome. And then we could talk, talk about like the tech behind it. Demand gen sales, engineering, legal, and and each one of the 3 like on the demand, Gen. Side, like we had a traditional sort of Sdr structure that you guys might have come across in the other b 2 b company.

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Matt Darrow: where there’s an Sdr leader. There’s pods of Sdrs that’s rolling up to the sales team. And we basically dismantled that whole function. And we’re able to reduce that down to a handful of folks that work directly with marketing, and a whole host of different systems that actually have allowed us to blow all those historical pipeline numbers out of the water with a very, very different composition of like people and technology. So it’s like one thing we could go down. The second thing sales engineering.

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Matt Darrow: This isn’t a Vivin commercial. Yes, we’ve built an AI sales engineer. That’s our innovation. But the thing of like well, why, you’d care about this is like our reps don’t go to enablement tools to ramp anymore.

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Matt Darrow: They’re always ramped because the AI sales engineer knows everything about our products, our competition, our customers, all the new use cases so they can move a whole lot faster on their own. That’s just been like a crazy dynamic where you don’t need the same ratios anymore, and your reps can be more independent. And the 3rd thing is legal.

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Matt Darrow: We do a host of different things with tools there, where our turnaround time on red lines like our sales team has never experienced that before, and that actually like when it’s crunch time at the end of a quarter as much as we all know in b 2 b. How much you you know you want some level of linearity that never happens. It always hockey sticks at the end, and being able to like quickly turn and move through. Contracts, have given us a big edge where we can pull forward deals because of how quickly we can work through the legal side.

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Matt Darrow: Those are things we’ve already deployed across the board, and we have a whole host of other things in recruiting and support that we’re looking at, too. But those are big areas that we’ve invested in some of the changes we’ve made and happy to talk about how we make any of it work, too, when the time comes.

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Alina Vandenberghe: I love the legal use case. I use it a lot myself for legal red lines and for enterprise. It makes a lot of sense that you’re using that for the reps. For the 1st use case, I’ve always had a love hate relationship with the idea of removing Sdrs from the flow, because on one side you’re removing people, and that has a

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Alina Vandenberghe: my, the mom in me feels bad whenever we’re talking about that but on the positive side. The way I always envision is that Sdrs can get into roles that are fulfilling like account executives and and

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Alina Vandenberghe: it just means that they want to do low, level work, and and it’s more fulfilling.

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Alina Vandenberghe: we can. We can dig further into that. But I would like to go 1st to Kevin, and then we can go back to the tech stack. And then all of that. So, Kevin, you want to walk?

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Alina Vandenberghe: Yeah, some. It’s a okay, sir.

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Kevin White: Yeah. Nice segue into into my section here, too, because, all of our our use cases are more around like the the Sdr. And ae side of things. And I think to your point of you know, taking away the mundane, tedious tasks like that’s what we’ve been focusing on, enabling our Sdrs with with agentic workflows, or whatever you want to call it. The 1st thing we do

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Kevin White: is take away all the the process of process and hard work of

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Kevin White: doing an account research plan. And so we kind of cut. We were able to cut that time from an hour to like 60 min, just using AI and and workflows come room for for that and the other thing. And I don’t know if this is actually like Agentic or AI. Maybe it’s just a powerful workflow. But it’s kind of slick how we do it where we have

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Kevin White: account. IP tracking on our website. Someone, some account hits our website and we don’t have the person the contact behind it. And so what we’ll do is prospect the 5 use AI to prospect the 5 most relevant titles and then

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Kevin White: sync that with data in our salesforce, and then and assign an owner to it and all that kind of stuff, and then send it out just an automated outbound email. And so that whole thing just happens in the background without any humans in the loop, and it’s assigned to the right owner in when when we get replies from it just lands the the sdra’s inbox, and so that whole workflow is is automated where?

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Kevin White: You know in the past, like a year ago, it would take, you know, a slack, alert, reviewing the account. All this kind of stuff. And now it’s all fully automated. So pretty

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Kevin White: select use case, I would say, and

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Kevin White: come, we use cover for pretty much the whole thing. Not to be the annoying guy to be slinging my own deck here.

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Alina Vandenberghe: I’m happy that you’re removing friction.

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Alina Vandenberghe: not making your buyers work harder to get to your tech. Lisa, you have a different kind of you have different kind of examples than the rest of us, because you were working with with different clients. Do you want to give us like a couple of examples.

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Liza Adams: Yeah. And I just saw Julia’s question here about she’s noticing that the panel has noted.

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Julia Nimchinski: Calendar.

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Liza Adams: For the sales process right? And my use case actually is more on the right hand side of the bow tie. So rather than the acquisition phase. It’s really more on the use retention upsell, cross sell, because we know how hard it is to acquire, and we are dealing with less resources, less budget.

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Liza Adams: and it’s a lot cheaper to grow our existing base than to get new customers. So my use case is really more around Upsell and Cross sell where we. We have a client couple of clients that are really leaning in on that right side of the bow tie, where we’re looking at the usage of different features on a Saas platform.

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Liza Adams: and depending on the features that they use, and the amount of usage of those features we try to determine which ones are the best opportunities for an upsell which ones are the best opportunities for cross sell. So all that data is that fed to AI. AI then makes a determination which accounts are better suit are best suited for an upsell cross sell, or simply an awareness of our roadmap.

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Liza Adams: and then depending on whether they’re an upsell or a cross sell opportunity for us. We then customize emails

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Liza Adams: and an outreach essentially for them to do that motion. We also use AI to do an in-app message for those cross sell and upsell opportunities. So I think it’s slightly a different use case, because we’re dealing with clients that don’t have a lot of resources to acquire new business. So they’re trying their best to keep who they have

831
02:52:07.110 –> 02:52:22.570
Liza Adams: and improve market wallet share with those customers. And I think my second use case is a bit more broader than what I just said, where we’ve actually transformed a really lean marketing team absolutely wanting to do more with less.

832
02:52:22.570 –> 02:52:46.729
Liza Adams: That pretty much is the mantra nowadays more with less, but still achieve the goals where we’ve transformed this team into a 45 Member organization where 25 are humans and 20 are AI teammates. So the AI teammates were actually built and are being managed by the humans to do very specific tasks. So they are actually on a

833
02:52:46.730 –> 02:53:09.480
Liza Adams: on an org chart. And these AI assistants are reporting to the humans that built them. So in product marketing. We have a pitch deck creator. In campaigns. We have a performance analyzer in content marketing, we have a topic generator. So there’s about 20 of those that are in support of the humans doing the mundane and repetitive tasks.

834
02:53:12.550 –> 02:53:35.340
Alina Vandenberghe: I love the cross, sell and upsell use case, for now we’re also doing a lot there because our expansion use cases is big, but I find that the account scoring, modeling that we’ve had for the top of the funnel is sufficient for us to do the automatic plays. So if we identify certain accounts that fit certain criteria, we put them into advertising. We actually run advertising campaigns against our customer base. Also.

835
02:53:35.340 –> 02:53:48.489
Alina Vandenberghe: I don’t know if some of you are already. Customers have already seen my campaigns, maybe, but we actually multi-thread several buyers into that funnel as well, because our user is not our buyer. So it’s

836
02:53:48.500 –> 02:53:58.789
Alina Vandenberghe: very important for us to do those multi-threaded plays. Not only sales like Julia is asking. We run a lot of advertising campaigns into the funnel. You wanted to say something, Matt.

837
02:53:59.670 –> 02:54:21.359
Matt Darrow: Yeah, I wanted to maybe continue on that question, too, around, why do we all focus on sales? Or at least this discussion for it. And to Liza’s point, I don’t think that’s the case, like even for us on the engineering side, like, if your engineers aren’t using cursor right, they’re being left behind like, that’s like another great example of an AI driven tool that’s going to give them massive productivity and development.

838
02:54:21.360 –> 02:54:50.669
Matt Darrow: And I was talking to Julie about this yesterday. I have like a simple matrix that talks about like how quickly AI is going to impact your role and effectively, maybe put you out of a job as a human, or teach you that you need to learn something different. And if it’s just a simple grid between the complexity of your role and the level of live interaction that you have with somebody that’s like a really simple matrix that you can just think about. So if you’re really high complexity, really live interaction. You’re probably using these agents to like augment and help you do things.

839
02:54:50.670 –> 02:55:10.480
Matt Darrow: But if you’re low complexity, low live interaction, you can just sort of live up to what Gen. AI is all about, which is disruptive labor, like getting rid of some of these roles. And I think that’s why there’s so much noise in the AI. Sdr. Space, because it is a perfect zone for that sort of that bent of disruption so like for us.

840
02:55:10.570 –> 02:55:16.909
Matt Darrow: you know, this might sound a little complicated, but when we made that change from the big Sdr Org to what we run now.

841
02:55:17.060 –> 02:55:41.659
Matt Darrow: you know, we’ll have warmly help us with interesting intent signals that feeds into common room. That helps us out with segmentation. That common room hooks into hubspot or gong engage that allows us to then go and do the really type of targeted outreach. And thanks, Julia. That was what I was mentioning before, and then, ultimately, that helps us drive traffic to our website, which is webflow.

842
02:55:41.770 –> 02:56:01.479
Matt Darrow: which we’ve had. Chili Piper embedded in webflow, that this whole consortium allows us to take what we were doing all this inbound and outbound for, and just sort of automate the hell out of this whole stuff. Now, I think what’s interesting. And you know, Alina and Kevin. I’m customers of your guys and happy with your service. But I think what’s interesting. Now

843
02:56:01.480 –> 02:56:22.949
Matt Darrow: you look at where Openai is going with their sales agent, and what 11 X is trying to do. And there’s people in that space are trying to then consolidate and wrap all this stuff up. And we didn’t feel like those tools is like the one stop shop to handle. Everything in that sense was really there yet. But I could definitely see, like next renewal cycle, like those things, are going to be more mature potentially when the time comes to.

844
02:56:24.610 –> 02:56:49.769
Alina Vandenberghe: There’s for sure there’s a lot of in this space. There’s a lot of disruption, and there’s a lot more gray area between the tools, and what does what and who does it? Well, I think that as it comes to the Sas industry, Openai is going to disrupt many companies, especially for simple use cases, especially for smbs. And that’s a good thing. I think that AI is here for intelligence, not yet for wisdom.

845
02:56:50.040 –> 02:56:56.650
Alina Vandenberghe: but for intelligence definitely, and it helps us do our jobs better. Kevin, you wanted to add something.

846
02:56:57.410 –> 02:57:22.386
Kevin White: Yeah, I just wanted to add something to Matt’s point of you know, maybe the foundational models, or maybe like anthropic or or open I will get. There is where we don’t need that a platform or a tool like common remark, Chili Piper, or whatever. But I think one part that’s missing from those things fully taking over Sas right now is just like they don’t have the connectivity to all the data points like like your sales. Sorry. The

847
02:57:22.940 –> 02:57:35.299
Kevin White: data warehouse snowflake is what the word I was looking for, or your Crm or all these different like data points. And so being able to aggregate that and connect to all those different things. The extensibility issue is, you know, certainly something that is like

848
02:57:35.310 –> 02:57:58.589
Kevin White: required to get the get the right data in, because, like the the quality of the outputs is just as good as like the the data that you feed into the model. So like, I think that is a really key place of the work where you can actually make a difference versus you know, versus like an ineffective AI, Sdr. Is not going to work. If it’s just like, you know, Crawling Linkedin, for what’s public when it’s not like in not using your internal 1st party proprietary data.

849
02:57:59.930 –> 02:58:05.506
Kelly Hopping: Yeah, I think the data makes a huge difference. Right? I mean, if you’re not powering with the right

850
02:58:06.400 –> 02:58:27.370
Kelly Hopping: I would guess if you’re not unifying your data together and using it to power these agents. Then, you know, it’s just. I mean, it’s like any part of AI, right? It’s garbage in garbage out, and the automation doesn’t help if it ends up being crap. So I think that’s interesting. I did, was thinking about Matt’s comment. Do y’all think of these agents as being. It sounds like.

851
02:58:27.620 –> 02:58:29.753
Kevin White: Cost savings, and when when

852
02:58:30.220 –> 02:58:50.130
Kelly Hopping: They were talking about the the mix of sort of human and AI org chart and things like that like in my head. Yes, I’m sure there’s cost savings that will happen. But I don’t. I never. I don’t want to think of agents as replacing human jobs. Instead, I feel like I want to use the humans to get the more strategic work out of you.

853
02:58:50.130 –> 02:59:00.289
Kelly Hopping: And so that we can actually just move faster because we’re able to spend more time doing the good meaty strategic stuff. And we’re just sort of.

854
02:59:00.860 –> 02:59:10.740
Kelly Hopping: you know, outsourcing, or whatever to these agents, the more mundane. So I never think of it as cost savings. I think I think of it as revenue, acceleration, but I don’t know what your thoughts are on that.

855
02:59:10.740 –> 02:59:26.860
Alina Vandenberghe: I love that reframing. And I do imagine that at some point, if Openai gets access to the data, so it has access to your Crm. And it has access to Snowflake or anything like that. They could still do many of the things that do.

856
02:59:26.860 –> 02:59:42.290
Alina Vandenberghe: But there is an extra layer of expertise, because these agents can only produce as well as the wisdom behind the people that program and structure the agents. And when you’ve been in the industry for like 10 years. It’s hard to replicate that by the Openai team

857
02:59:42.290 –> 02:59:53.360
Alina Vandenberghe: on what the best way to structure this workflow is but the best way to get most out of it. And that expertise and wisdom of humans is still, we’re still better at it, especially in certain spheres.

858
02:59:53.671 –> 03:00:05.200
Alina Vandenberghe: I don’t know how things are going to evolve past the 10 year mark. But I would imagine that for the next 5 to 10 years that wisdom is going to remain for us to have an

859
03:00:05.220 –> 03:00:23.869
Alina Vandenberghe: to be able to get the most out of it. I would also be curious now to get into some of the flops as well some of the things that you’ve tried. And you thought, Oh, my gosh, this is going to work, and it completely failed. And I remember one of you had the interesting examples before I think it was you, Jonathan, on the chat flow. Or maybe it was you, Matt. I don’t remember.

860
03:00:26.580 –> 03:00:27.010
Matt Darrow: You want to go.

861
03:00:27.010 –> 03:00:27.630
Jonathan Kvarfordt: Good job.

862
03:00:27.630 –> 03:00:28.260
Matt Darrow: Go for it.

863
03:00:28.260 –> 03:00:28.960
Jonathan Kvarfordt: Go ahead, Matt.

864
03:00:28.960 –> 03:00:53.640
Matt Darrow: Oh, okay, fine. Yeah. Like 2 2 ones everywhere. We’re always experiment with this stuff as well, and also just to double back on Kelly’s point, which I liked on the agent value prop case, like, I always found it’s going to spike on one of 3 dimensions. It’s either faster, cheaper, better like. And it’s okay which one of those is like, the agent really, gonna highlight. Now, we’re always in search for these things. So 2 things came to mind.

865
03:00:53.810 –> 03:01:17.949
Matt Darrow: one was like, we’re always trying to figure out on the video and image creation side, like, can we off board a lot of the creative work that we do, and we haven’t really found anything that we love there yet, because what we found is there’s ways that you can do things internally faster. But when it comes to like the actual creative production asset that you want to put out in the world, we just find ourselves like toiling in the last 10%

866
03:01:17.950 –> 03:01:25.890
Matt Darrow: like God. This would have just been a lot faster to do this, to end, to end with a human to begin with. And then the secondary thing that we had tried was

867
03:01:25.890 –> 03:01:48.869
Matt Darrow: some of those a little bit more out there, like AI website, agents and bots where a website visitor will just go and like sort of converse with this thing that’s sitting on your website. And the problem with that is, they’re just like wildly inaccurate. So whenever we had somebody like come in in that realm, and then get sent to sales like the salesperson is coming into that conversation like worse off than they would have otherwise, because

868
03:01:48.870 –> 03:02:11.590
Matt Darrow: this sort of inaccurate bot, whose brain was the basis of the Llm. Which is the wrong brain to put in an agent, is actually sort of skewing the initial conversation. So those are 2 things that were like those didn’t really work for us in terms of trying to get like full production image video marketing assets, and then also thinking that somebody could like interact and get a good leg up.

869
03:02:11.925 –> 03:02:15.619
Matt Darrow: For a sales conversation by interacting with something on a website.

870
03:02:15.970 –> 03:02:39.160
Alina Vandenberghe: It depends a lot on the industry and the use case. And I can see, Matt. Why, if in your case it didn’t work, there are certain Llms that do perform really well, especially on B, 2 CB 2 c websites. It can do really well on B, 2 B, there’s been a lot of hit. And, miss, we’ve refrained back from releasing our own AI agent on chat because of that. There’s still a lot more rag experimentation that needs to be done before it’s like.

871
03:02:39.160 –> 03:02:48.790
Alina Vandenberghe: okay, I trust it. We’re not at that point yet, but I think in the next 3 or 6 months is going to get better. Any other flops? Maybe Jonathan.

872
03:02:49.340 –> 03:03:18.700
Jonathan Kvarfordt: Yeah. So mine was, I was making an SEO agent team. So I hooked up relevance to make and to semrush semrush. I was just working on experimenting to see what kind of content I had this agent team where I had a manager agent and then a back leak agent, and then a research agent and a keyword agent and a writing agent and an editing agent. They all work together, and and it broke several times because it’s just creating content. That number one was not even close, remotely. What I was trying to get to it was just accessing the wrong data. So it’s just.

873
03:03:18.700 –> 03:03:35.289
Jonathan Kvarfordt: you know, one of those things. A few months ago I was experimenting with all this of trying to get into work, that it’s it’s obviously really important to tinker into play, but also iterate and test like crazy, because all my good successes have come from failing many, many times before I get to the right result. But as an example, our flop as the output was

874
03:03:35.310 –> 03:03:47.249
Jonathan Kvarfordt: with the name of momentum. There’s a lot of different momentums. There’s like this momentum medical thing. And so it made this article about medical. I was like, what in the world what in the world is this? And so I just started loosening on the topic which obviously wasn’t good. So yeah, that was mine.

875
03:03:48.670 –> 03:03:53.409
Alina Vandenberghe: It was brave of you to try to do your entire team of agents talking to each other right?

876
03:03:53.750 –> 03:03:59.530
Alina Vandenberghe: It’s eventually it’s gonna work. But there’s more tinkering for sure, Kelly, some flops from you.

877
03:04:00.230 –> 03:04:01.129
Alina Vandenberghe: I’ll skip that.

878
03:04:01.130 –> 03:04:22.569
Kelly Hopping: I mean other than you said creative I’ll say I don’t know if you’ve ever done headshots with AI, but I look like boardroom, Barbie, or something like it’s so bad like like I look like I weigh like 97 pounds, and they’re horrible. So that’s the only thing I will say is that that one is my biggest flop.

879
03:04:23.070 –> 03:04:45.050
Kelly Hopping: Outside of that I think it’s more of the change management side. I would say that we have the challenge on. It’s not that the AI use case doesn’t work sometimes is that the org isn’t ready to use it. So, for instance, where, when we added the clay piece to the Sdrs. The behavior of getting them to look at the the full account data differently.

880
03:04:45.050 –> 03:04:58.460
Kelly Hopping: or to know like way to get the full value of what the the account data was showing, I think, is a change muscle. And so adoption of that agent running in the background, I think, has been the bigger challenge

881
03:04:58.778 –> 03:05:13.409
Kelly Hopping: versus the agent itself flopping. So that’s kind of the big one. Sometimes we have a little bit on sort of voice and tone we have to play with that that gets us there. But I think we’ve seen a ton of success running it in

882
03:05:13.440 –> 03:05:18.900
Kelly Hopping: inside like our platform and things like that we’ve seen. I think it’s a 3rd party integrations we’re still trying to work through.

883
03:05:19.220 –> 03:05:20.119
Kelly Hopping: But yeah, stay away from me.

884
03:05:20.540 –> 03:05:30.819
Alina Vandenberghe: For sure. The buy in from the other teams to use the agents to in their favor takes a while. They have to see the benefits, and you have to spend the time with each. Sdr to to adopt. Yeah.

885
03:05:31.560 –> 03:05:32.240
Alina Vandenberghe: Or Kevin.

886
03:05:32.240 –> 03:05:33.200
Kevin White: I tried.

887
03:05:33.480 –> 03:05:41.040
Kevin White: Yeah, yeah, I was. Gonna say, I tried one of the AI Sdr tools that I won’t name but and I remember.

888
03:05:41.040 –> 03:05:42.216
Alina Vandenberghe: Many of them too.

889
03:05:42.510 –> 03:05:47.610
Kevin White: I’m a total believer in the technology. I just don’t think it’s quite there yet. What I found is that

890
03:05:47.740 –> 03:06:09.139
Kevin White: you know it’s I had to prompt it to death so that it wouldn’t like hallucinate or say like, Oh, I hope this email finds you well and stuff like that, and so essentially like the amount of prompting that I had to put into it, it would come up with the same exact template every single time, and like, like 2 or 3 differentiated best messages that go out to the same exact person are the same exact, like prospects.

891
03:06:09.490 –> 03:06:35.320
Kevin White: And so I was like I should have probably just wrote this email myself, or wrote the sequence myself. And then, like, use the sequence, or like outreach or something. So yeah. And I tried it. I think I sent like 3,000 emails from it. We’re targeting like a smaller, smaller, unrisky audience for us. It wasn’t square in our Icp and just like didn’t get any responses or anything like that. So I think we’ll it’ll evolve and we’ll get there. But like it just

892
03:06:35.760 –> 03:06:37.509
Kevin White: did. That didn’t work for me.

893
03:06:37.960 –> 03:06:50.790
Alina Vandenberghe: It didn’t work for me yet, either. My best performing emails are still our templates that I know that they’re true and tested, and I still experiment on low score accounts. And it’s just I, yeah. Still still more work to do.

894
03:06:51.120 –> 03:07:21.029
Kelly Hopping: It’s 1 of the challenges we have. We had considered whether we want to build like an AI Sdr capability into our product. Our competitor has it, and we were like losing some deals on it. And then what ended up happening was because they had it and we didn’t. And then what would happen is they would go over, and they would choose the competitor because of the availability of that feature. And then they boomerang back not too long after, because they’re like, well, we chose for this thing. But that thing kind of stinks. And we’re like, yeah, I mean, so there’s also like

895
03:07:21.040 –> 03:07:26.630
Kelly Hopping: the ability like to just figure out like which of this AI like which of these are really

896
03:07:26.720 –> 03:07:40.520
Kelly Hopping: going to stick around? I read A. Stat the other day that said like 35 or 40% of AI companies won’t be around in a year. I’m sure a whole new batch will be doing something else, but of the current batch that are available. And I think it’s because the

897
03:07:40.520 –> 03:07:57.280
Kelly Hopping: the concept is there. I think the quality is where is where they’re going to kind of make or break. And so just interesting to kind of follow that journey, and seeing like how much you can believe in the marketing of AI versus the actual delivery of really solid content.

898
03:07:57.510 –> 03:08:08.059
Alina Vandenberghe: I feel the same pressure in the Banks cycle their competitors that have certain things with AI. And I know for a fact from their customers that it doesn’t work. But it’s hard to convince someone that

899
03:08:08.210 –> 03:08:13.630
Alina Vandenberghe: what I’m saying is true unless they actually experiment, experience it. And I have a lot of boomerangs. In the same way.

900
03:08:13.900 –> 03:08:16.310
Alina Vandenberghe: Liza, you want to give us some flops.

901
03:08:16.810 –> 03:08:41.150
Liza Adams: Oh, my gosh! I love this topic because I have many flops, but I won’t tell you about my many flops, you know, as they say with success. We win and with failures we learn. And that’s how I learn. Sorry, many, many failures. But I’m going to plus 100. What Kelly said about change management. Right? Like AI, I think the AI adoption is less about

902
03:08:41.220 –> 03:08:54.720
Liza Adams: technology, less about innovation. It’s truly more about the humans. The humans are the toughest part of this, and if we can’t meet people where they are, and be respectful and graceful about how we bring them along

903
03:08:54.720 –> 03:09:13.859
Liza Adams: that is going to backfire tremendously. I think trust building is so critically important, but more specifically on the flops. My initial thought on AI. This was about a year ago is, oh, this thing should be able to do math super. Well, right? So heck, you know it’s AI should be able to do math.

904
03:09:13.880 –> 03:09:31.169
Liza Adams: but it is a large language model, and it’s good in language, and it’s not good in math. So you know, for very simple math, it’s okay. But you get to 30,000 rows of excel based on customer feedback at a huge event.

905
03:09:31.220 –> 03:09:47.670
Liza Adams: And you try to analyze all that data. It will give you really good insights, but for it to give you very precise calculations and graphs and stats that are accurate.

906
03:09:47.860 –> 03:10:13.670
Liza Adams: it’s not going to go very well. So that was a lesson learned. And I think we just need to really reset our expectations, that if it’s something more nuanced and it’s more around language, then I think AI is really good at it today with math, really complex calculations, big spreadsheets. It’s not today. And the qualification is today because it continues to move forward in advance. So at some point this will get super good.

907
03:10:14.140 –> 03:10:39.646
Alina Vandenberghe: It’s progressing a lot faster. But on the simplest things around, math is definitely challenging. I’m gonna go to the last topic, which is, what’s a tech stack that’s enabling you to have best results. We’ve covered some of the tools already, but I would love to dig a little bit further for me. The most valuable in my tech stack right now is Snowflake and Gemini. I have everything in Snowflake. I don’t even need salesforce anymore. I don’t even need hubspot anymore.

908
03:10:39.970 –> 03:11:09.020
Alina Vandenberghe: it’s becoming my truth. And on we create most of our workflows with Gemini. So, for instance, right now, I have all my customer data in Snowflake, and the queries that I do around what messaging is winning, what’s winning against competitors? Looking for customer testimonials, looking for customer data. Everything is in Snowflake, and I query it, and the kind of insights that everybody gets in the team, the product team, the design team. It’s like mind blowing to me that we get access so fast. This would have taken us like.

909
03:11:09.020 –> 03:11:17.860
Alina Vandenberghe: I don’t know the data team months and months of research. And now we have that access super fast. So for me, these are the 2 tools that are most valuable in my tech stack. Right now.

910
03:11:18.361 –> 03:11:21.330
Alina Vandenberghe: All right. We go next, maybe to Jonathan.

911
03:11:22.750 –> 03:11:49.350
Jonathan Kvarfordt: Oh, man, I got a humongous list. But I want to go back to Google, release a white paper talked about the 3 main components of agents want to make sure I call those out, which is, you need an Llm. You need a software. You need some sort of orchestration layer. So for me, I mean, obviously, I use the main 3, obviously Openai Claude, and then Gemini for my Llms. For orchestration. Momentum. Max is like an orchestration layer. So obviously, that’s that’s 1 employers, another one. I use relevance make for automation

912
03:11:49.450 –> 03:12:07.360
Jonathan Kvarfordt: and then tapping into things to perplex your other Apis. And I’m seeing more and more tools becoming more available with giving their Apis open. So you can use things like semrush and other tools. And I use our team’s. A customer of Chili Piper as well. So we use some of the data bank and and things we get from Chili Piper, which is awesome.

913
03:12:07.360 –> 03:12:27.169
Jonathan Kvarfordt: But I would also suggest, if I can, it’s really easy to get overwhelmed. I would say, kind of like you, Alina, of just picking 2 or 3 core and pushing those as much as possible, because it’s really easy to get overwhelmed with all the technology out there, and just kind of limit yourself to the the ones you get really familiar with and get powerful and then add them on, carefully based on the outcomes you’re trying to achieve.

914
03:12:27.170 –> 03:12:39.320
Alina Vandenberghe: Oh, my gosh! I completely agree. So overwhelming like it feels like every minute. There’s a new tool that does something, and every second something else that does something else better. And it’s like, Oh, it’s very debilitating, Matt. You want to go next.

915
03:12:40.720 –> 03:13:03.780
Matt Darrow: Yeah, I’ll keep this one short because I shared with you guys. Well, the tech stock on the demand Gen. Case and the sales engineering case. The one that I’ll just put out there to Jonathan’s point is, I mean, everybody in the company has access to Enterprise Openai. That’s just a given. It was a big part of the culture change like Liza and Kelly. What you’re saying about change management. It’s really hard to drive change management, not everybody have access to the tools. And even if

916
03:13:03.780 –> 03:13:14.129
Matt Darrow: everybody has a very, very different use case, it just gets people in the motion that using this tool is, gonna just be part and parcel for your job, moving forward as well. So that’s that’s been critical for us for a variety of reasons.

917
03:13:18.000 –> 03:13:34.549
Alina Vandenberghe: Seems like you’re doing a lot. I think we would all of more in depth implementation from you, Matt, especially on the on the external support, Jonathan also on the all the things I see. As soon as he nails down, the agents talking to each other. Kevin, you want to go next, or maybe.

918
03:13:34.550 –> 03:13:38.785
Jonathan Kvarfordt: I was. Gonna say, it works now. It didn’t then, but works. Now, how about that?

919
03:13:40.230 –> 03:13:42.660
Kevin White: Yeah, maybe I can provide like a

920
03:13:43.050 –> 03:14:03.969
Kevin White: different take on tech stacks. I mean, I feel like there’s just there’s so much out there, and you can get over overwhelmed on like connecting all this stuff, and then also having to maintain and manage it all. And I see some of the the charts and flows that people are using. I’m just like, Wow, that is like way too complex. For like a small early stage startup to to be putting all these

921
03:14:04.080 –> 03:14:12.580
Kevin White: these tools together. We call it. We have a term that we use called Frankenstack, where you know, you see that. And it’s like, well, you could just do this with, I think I think getting back to the

922
03:14:12.820 –> 03:14:35.819
Kevin White: the the dream actually is just the simplify, incredibly would be to just like like you, said Alita. Just dump everything into Snowflake, or dumping everything into a system of record, and then just have the AI work on top of that like, maybe that’s where we’ll get in like 5 years. As like, there’s our 2. There’s a there’s a text package like 2 different tools. But yeah, I mean, we use typical stuff common room, because we use our own product. We use salesforce use hubspot

923
03:14:38.760 –> 03:14:43.319
Kevin White: For analytics. We use like metadata meta meta, base stuff like that.

924
03:14:44.649 –> 03:14:58.380
Alina Vandenberghe: We we’ve if we have all our gong transcripts in there, it’s so different, right? When you have this kind of intelligence and emails and call recordings for each one of accounts, especially kind of understanding which ones are converting better. Why, what are the characteristics? You can do all sorts of inferences super fast

925
03:14:58.480 –> 03:15:03.700
Alina Vandenberghe: next week, mind blowing to me. It feels like I have done extra brains Kelly.

926
03:15:03.700 –> 03:15:14.560
Kevin White: I should also give a plug. Oh, sorry! I should also give a plug for Chili Piper. I didn’t mention it, but we we use Chili Piper for for like it’s been a game changer for our Sdr team for managing inbound self.

927
03:15:15.020 –> 03:15:17.589
Alina Vandenberghe: I’m so happy to hear I’m so happy to hear.

928
03:15:18.359 –> 03:15:25.070
Alina Vandenberghe: Kelly, you are saying that you’re using Jasper. Is there anything else in your tech stack that you’re very happy with.

929
03:15:26.109 –> 03:15:47.859
Kelly Hopping: I mean, certainly. We run demand base on demand base. We run it internal. But for our sellers and our marketers, which is a big piece, because that’s all AI powered. We use ricado, which has some AI components into our Rev. Ops capabilities. We also use stencil on that side for marketing Ops. And the way that we communicate emails out integrate with

930
03:15:47.980 –> 03:16:00.279
Kelly Hopping: Slack and all of those things as well, we use momentum is one that I really like. I don’t know if you guys use it with oh, it says that. Look at that moment demand base uses momentum. Somebody told me that.

931
03:16:00.526 –> 03:16:29.850
Kelly Hopping: Yes, we do. Which is great for me, because it basically sort of pulls all those gong calls together into a summary and says, Hey, your sellers are doing great crystallizing this value prop, but they’re really struggling with pricing. Or they’re really struggling to talk about data insights. Or they’re really, you know. And so we it helps us kind of troubleshoot at a thematic level in a really quick way. And I get that slack update all the time. And I love it, because then I can kind of figure out where to point with a product marketing team to kind of help enable our sellers. So that’s 1 that’s worked really well for us.

932
03:16:31.130 –> 03:16:32.329
Alina Vandenberghe: Very happy to hear.

933
03:16:32.820 –> 03:16:37.400
Alina Vandenberghe: And what about you, Lisa? Some? Oh, Matt, you wanted to add something on top of that.

934
03:16:37.400 –> 03:16:38.959
Matt Darrow: I don’t know. Keep going first.st It’s okay.

935
03:16:39.640 –> 03:16:40.450
Alina Vandenberghe: Okay.

936
03:16:41.770 –> 03:17:02.940
Liza Adams: Yeah. So on the tech stack side. It’s not an easy answer, because the answer for the right answer for one company is not the right answer for another right? Because we have different goals. We have different existing tech stacks. We have different strengths and weaknesses. So what I generally guide people to do is start with what you have.

937
03:17:03.390 –> 03:17:12.179
Liza Adams: and for the most part what you have will most likely have some AI capabilities in it, and if it doesn’t, it probably has it in the roadmap.

938
03:17:12.320 –> 03:17:35.830
Liza Adams: So whether it’s your Martech stack sales tax Cs tech, take a look at what you have, because to Kelly’s point. A huge part of this is change management. So if people are already familiar with the technology, the likeliness that they will use it is a much, much higher. So start with what you have. And then the second thing is.

939
03:17:36.160 –> 03:17:50.500
Liza Adams: have at least one or 2 foundational models paid versions of it. So whether it’s copilot chat, Gpt, Gemini cloud, whatever you’ve got because it will allow you to see what is possible

940
03:17:50.690 –> 03:18:03.480
Liza Adams: in a lot of the innovation are happening in those foundational models. And then, thirdly, I guide people towards all right. You you’re looking at your existing tech stack. You’ve got one or 2 foundational models.

941
03:18:03.480 –> 03:18:26.039
Liza Adams: If those still don’t address the need, then start looking at new technologies out there. New capabilities, startups, perhaps. But be very mindful about this 14,000 vendor tech space that we’re in right. There will be consolidation. There will be mergers, and some of them will not be around.

942
03:18:26.040 –> 03:18:42.510
Liza Adams: So we need to really be mindful of evaluating them to ensure that it aligns with our needs, evaluating the leadership team, the financial viability of that company, so that we’re doing business with a company that will be around longer term

943
03:18:42.510 –> 03:18:50.449
Liza Adams: and then, more personally, I shared on the chat. My role largely is in strategy. It’s a lot of

944
03:18:50.450 –> 03:19:15.259
Liza Adams: analytics, a lot of thought, a lot of thinking. So my tech stack tends to lean on thought partners. So the large language models like Claude Chatgpt Gemini and I actually use them simultaneously, pit them against each other. When one tells me one thing, I feed it into the other and get its opinion, and it kind of feels like I’ve got a team of 10 instead of just me. And you know a couple of people that I brainstormed

945
03:19:15.260 –> 03:19:16.420
Liza Adams: with so.

946
03:19:18.240 –> 03:19:37.639
Alina Vandenberghe: I like that. You’re making them compete for for your attention. We have 9 min. Matt wants to add something, and I wanted to talk about just one last word on the type of use cases that you’re excited to try next. Some of the workflows that you’re that you’re planning, Matt. You wanted to. Yeah.

947
03:19:37.640 –> 03:19:56.690
Matt Darrow: Yeah, it was. It was kind of actually a lead into that, Alina, because I didn’t want to end this discussion without going there. Because actually, we could use some help, too, from this panelist. Maybe expert point of view is, there’s like the dark side of AI as well. And 1 1 place that we felt that ourselves is in hiring and recruiting.

948
03:19:56.690 –> 03:20:19.389
Matt Darrow: and what will happen is like, you open a Rec. And a roll, and there’s so much of an inundation on AI applicants and resumes that it’s becoming very, very challenging to sift through candidate pools and profiles because people are using AI to apply to jobs in mass and then effectively cook up skill sets that mimic your job descriptions.

949
03:20:19.390 –> 03:20:33.540
Matt Darrow: And where we’ve seen this even I wouldn’t have expected this. Even in certain interviews folks are able to use AI to have a digital doppelganger representation of themselves, and they’re not even on camera. They’re behind the scenes

950
03:20:33.540 –> 03:21:00.950
Matt Darrow: talking through the digital doppelganger researching questions as they go. So this is one of our areas of like where we want to go and use AI next is actually in this entire applicant screening candidate hiring pool to actually start to cut through. I would say some of these malicious cases that we’re seeing in some of these cases. And I’m wondering if you guys also, if there’s preferred tools you’ve already run across, because we’re just starting that investigation. But I think, on the hiring side, everybody’s going through it.

951
03:21:00.950 –> 03:21:04.250
Matt Darrow: and it’s getting really, really messy, really quickly.

952
03:21:04.250 –> 03:21:16.769
Alina Vandenberghe: Yeah, for sure. I was on some calls also, where I could tell that the candidate was just using AI to respond my questions, and since then, and that happened maybe like 8 months ago, and since then, every time I get on a call with a candidate I asked them to show me their hands.

953
03:21:17.780 –> 03:21:31.069
Alina Vandenberghe: I was like hands showing. I’m I’m I’m with my hands. You’re with your hands. Everybody’s open. It’s only our brains that we’re gonna use, because I want to hear what you are about, not what AI is about. Yeah.

954
03:21:31.400 –> 03:21:35.189
Alina Vandenberghe: I I’m curious what others have done on this.

955
03:21:38.160 –> 03:21:48.920
Jonathan Kvarfordt: I was just saying in the chat, this is a new tech called spark hire. It’s awesome. It does exactly what you’re talking about, as far as analysis and understanding both resume tones of speech. All the analysis you need to have to help you hire the right people and have

956
03:21:50.290 –> 03:21:59.800
Jonathan Kvarfordt: assessments on the other side. So it’s not just conversation based, but also assessment based. You can get a better idea of what they are and what they’re capable of so really, really good tech. I’m not affiliated with them. I just like it.

957
03:22:02.180 –> 03:22:07.500
Alina Vandenberghe: All right. What other things are you all are you all curious and excited to try.

958
03:22:19.020 –> 03:22:21.120
Kevin White: I I’ll I’ll go. I’ve been

959
03:22:22.060 –> 03:22:27.030
Kevin White: I’ve been tinkering with the on the creative side on the like, hey.

960
03:22:27.030 –> 03:22:30.099
Kevin White: gen, videos. Actually use.

961
03:22:30.440 –> 03:22:40.089
Kevin White: I I use it. I use it for promoting these events that I join it’s kind of fun I haven’t found like an actual core use case for it. It’s more just like, Wow, this is

962
03:22:40.320 –> 03:22:46.820
Kevin White: cool tech that like, it’s like an avatar of yourself, essentially. And so I think there, you know.

963
03:22:47.160 –> 03:22:54.350
Kevin White: there might be some unlocks there. I feel like the dream that I have. This is also kind of a flop, but like the dream that I would love to get to is like

964
03:22:54.480 –> 03:22:58.369
Kevin White: upload my brain or my content into

965
03:22:58.774 –> 03:23:02.889
Kevin White: into some sort of like data repository, and then say, like, Oh, write a

966
03:23:03.020 –> 03:23:17.140
Kevin White: post or write, you know, write something about this and like it actually output something that sounds like it’s in my voice but haven’t quite got it to that level yet. And it’s always like diminishing returns trying to like go in and edit with the output of AI. So

967
03:23:17.350 –> 03:23:22.470
Kevin White: I’m very excited for something that actually works like that. But tbd, if it’s gonna happen or not.

968
03:23:22.470 –> 03:23:46.050
Alina Vandenberghe: Yeah, my avatar is super weird. It doesn’t have my accent at all. I’ve tried a million things, and it doesn’t make the mistakes that I make. And the it’s like super weird. And the other thing is that I have a book, and like a million posts that I’ve created, and I try to rag it and try to create it in my own voice. And it just sounds like a robot, you know. It just doesn’t sound like the human I am yet yet.

969
03:23:46.250 –> 03:23:49.799
Alina Vandenberghe: Liza you want. And, Jonathan, you wanted to add something.

970
03:23:50.564 –> 03:23:51.580
Jonathan Kvarfordt: Lisa! 1st go ahead.

971
03:23:51.840 –> 03:23:54.819
Liza Adams: All right. Go ahead. Well, okay, I’ll go.

972
03:23:56.041 –> 03:24:00.730
Liza Adams: I really want to try chat. Gpt operator.

973
03:24:01.348 –> 03:24:24.299
Liza Adams: You know, we saw the demo of it, you know, ordering pizza it, reserving, you know, restaurants and and buying tickets at games. The reason I want to try it is, I think it will change the way we market, because now we no longer just need to market to human beings. But we will need to market to

974
03:24:24.300 –> 03:24:49.079
Liza Adams: the AI agents that serve the human beings. So this is so. You kind of look at it as a mirror, right? We as go to market leaders where our teams are using AI to create content. But that same content will be judged by people that are using AI and the Ais that serve them. So it’s like AI on both sides. So I think there’s a lot of implications to the go to market engine.

975
03:24:49.080 –> 03:25:11.990
Liza Adams: and how we will cater to both humans and AI, because what’s important to humans like personalization and experience and empathy and authentic stories will not be relevant to AI agents. What’s going to be relevant to them will be quick access to the information. Really structured information, metadata.

976
03:25:11.990 –> 03:25:21.189
Liza Adams: all sorts of things. So I’m looking forward to testing it to see how it how we might adapt with that kind of environment.

977
03:25:22.730 –> 03:25:28.960
Alina Vandenberghe: Hey? I relate a lot with that. I hope that in the future bots only talk to bots for the boring stuff.

978
03:25:29.150 –> 03:25:56.000
Alina Vandenberghe: And as to humans, we keep the interesting topics. I have a personal anecdote. So I have an aura ring that measures my stress, and I have a therapist, and whenever I talk to my therapist you can see that my stress is actually decreasing when I’m talking to him, whereas if I talk to chat, gpt about my traumas, my stress remains the same. So I think that humans are still good. And I hope that in the future again, bots only talk to bots for the boring stuff.

979
03:25:58.450 –> 03:25:59.390
Kelly Hopping: I like that.

980
03:26:00.540 –> 03:26:08.150
Julia Nimchinski: Alina, what a treat! Thank you so much for the deep dive, and let’s use it as a shameless plug.

981
03:26:08.260 –> 03:26:14.990
Julia Nimchinski: Tell us more about Chili Piper. What’s the evolution of the platform? And what have you been building all this time?

982
03:26:15.500 –> 03:26:42.430
Alina Vandenberghe: Oh, my gosh! I’m building so many things, and I get so excited, but my team hates me for it. Right now the thing that I’m most passionate about is all the things that we’ve built for ourselves in Snowflake that we have access to that enables us to accelerate the personalization of accounts at scale. I want to give back to our customers. And some of the 1st things are things that I need myself very badly organize a lot of events. And I want to put that on automatic pilot.

983
03:26:42.908 –> 03:26:58.429
Alina Vandenberghe: I’m also creating a lot of personalized landing pages for all my accounts based on insights that I get on gone calls because different segments react differently. Different personas react differently to the messages. Different industries react different different sizes, different

984
03:26:58.712 –> 03:27:19.057
Alina Vandenberghe: and creating all of these things at scale is like super exciting to me. It’s going to be interesting, because the space is kind of getting all everybody’s like on everybody else. My hope is that we can, I don’t know. Be one Happy family, and all the tech companies can like. I don’t know, maybe consolidate at some point, and just we all have one simple option as buyers. But

985
03:27:20.080 –> 03:27:23.470
Alina Vandenberghe: yeah, my my roadmap is full of AI agents.

986
03:27:25.130 –> 03:27:32.770
Julia Nimchinski: Super cool. What are your thoughts on Openai, releasing all the sales agents in Tokyo or Whatnot?

987
03:27:33.210 –> 03:27:57.279
Alina Vandenberghe: I’m really excited that they’re pushing this, and they’re showing what can be done. And they’re making more mainstream, because otherwise I would have to do the education all by myself, and the fact that Openai shows that this is a possibility to others. It opens up the gate for for us. I think that for us, on the smbs, and on those simple use cases we’ve never been able to help anyway. So the fact that they’re aiding on that for me is just.

988
03:27:57.420 –> 03:27:59.449
Alina Vandenberghe: It’s an accelerator.

989
03:28:00.500 –> 03:28:02.460
Julia Nimchinski: And what’s your prediction for this year.

990
03:28:03.680 –> 03:28:05.219
Alina Vandenberghe: Prediction on what the Openai.

991
03:28:05.220 –> 03:28:07.432
Julia Nimchinski: Gdm Gdm agents

992
03:28:09.780 –> 03:28:11.570
Alina Vandenberghe: I tend to be.

993
03:28:13.350 –> 03:28:23.979
Alina Vandenberghe: because I I guess because I’m so technical. And I get excited about all these things. I imagine that everybody else does, and everybody else is building things, and everybody else is getting optimized workflows. But

994
03:28:24.428 –> 03:28:52.789
Alina Vandenberghe: I realize that I’m in the minority. I hope that people can get excited about it as as much as I am, and understand all the possibilities and create more efficiencies in their team, so that they can spend more time doing things that are rewarding, and they’re bringing them joy. And that is to me a catalyst to our best work is to keep us focused on the type of things that we’re really good at, and the type of things that get us excited. And that’s a rare

995
03:28:53.310 –> 03:28:57.500
Alina Vandenberghe: rare thing to to find, but I hope that’s what AI gives us.

996
03:28:58.380 –> 03:29:11.880
Julia Nimchinski: Thank you so much again and last question, where can our community go to support you? What’s the next big thing for Chili Piper? When’s the back next big release? Or, yeah, where should we go?

997
03:29:12.040 –> 03:29:32.959
Alina Vandenberghe: I’m not on Tiktok. I’m not on red note. I am not on blue sky. I am not on X. I’m only on Linkedin right now, and I’m finding that the discussion is very civil there. And I’m responding to everybody who interacts with me there. And I learn a lot, and I appreciate it when people do it, even with like negative feedback, or

998
03:29:33.260 –> 03:29:38.280
Alina Vandenberghe: anything that helps me learn. And I open. I welcome those kind of conversations. There.

999
03:29:38.840 –> 03:29:44.589
Julia Nimchinski: Thank you so much, Kelly, Jonathan Kevin, Matt, Lisa, and Alina. Thank you again.