Crunchbase CRO Ann Davis on the pressure to show ROI from AI

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Speaker C: hi, I'm Greg Kilstrom, host of the Agile Brand, and here's a question for you.

Speaker D: What if the biggest obstacle to AI driven ROI isn't the AI itself, but everything you're feeding it? Agility requires not just the speed to adopt new technologies, but the clarity to recognize when foundational elements like your data strategy need to be fixed first to unlock true potential. Today we're going to talk about the intense pressure on revenue and marketing leaders to demonstrate ROI from AI. We're going to explore the counterintuitive idea

Speaker C: that simply chasing better AI is a

Speaker D: distraction and that real gains come from addressing the foundational data gaps that plague most organizations. Welcome to season eight of the Agile Brand Podcast. This season we're going all in on Expert Mode, MarTech, AI and Customer Experience, talking with the people and platforms behind the brands you know and love. Again, I'm your host Greg Kilstrom and I help Fortune 1000 companies make sense of martech, AI and marketing ops. Hit, subscribe or follow to make sure you always get the latest episodes and leave us a rating so others can

Speaker C: find us as well.

Speaker D: And make sure you check out our sponsor, TechSystems, an industry leader in full stack technology services, talent services and real world adoption. For more information, go to techsystems.com now let's dive in. To help me discuss this topic, I'd like to welcome Ann Davis, Chief Revenue Officer at Crunchbase. Ann, welcome to the show.

Speaker E: Thanks so much Greg. It's wonderful to be here.

Speaker C: Yeah, looking forward to definitely a timely conversation for all, I would say for ah, at least very, very many. Um, but before we dive in, why don't you give a little background on

Speaker D: yourself and your role at Crunchbase?

Speaker C: Sure.

Speaker E: Um, so I've been in tech sales for about 30 plus years. Um, I'm a serial startup person. I've done seven startups, um, taking them from everything from zero to a hundred million dollars and there being some form of an exit and then I go off and do it again. My last startup was Looker that I got acquired by Google in 2020, uh, for 2.6 billion, which was a 26x multiple of our run rate. So I did spend five years at Google Cloud. First large company I worked for probably in the history of my career. Um, learned a lot and decided that wasn't the best spot for my personal skill sets because I'm such a builder that when Crunchbase, um, started pursuing me pretty hard and I took a look at their value proposition and where they sat in the market with proprietary data, um, I decided to come on board. Um, I joined as the SVP of sales last year and was just recently promoted last month to CRO.

Speaker D: Nice.

Speaker C: Well, congrats on the, on the promotion. That's.

Speaker E: Thank you.

Speaker C: Nice, nice. Well, yeah, let's, let's dive in here then and want to start, uh, looking at this from the, from the strategic standpoint and to kind of tee off what I talked about in the intro. Uh, you've talked about that asking for better AI is not the right approach to the ROI problem. What foundational issues do you see sales and revenue leaders overlooking when they jump straight to advanced AI solutions?

Speaker E: Yeah, I think that there's this perception that whatever tool they use, that the data and the responses they get are going to be inherently right. And I think to a certain extent, whatever you're using, whether It's Claude or ChatGPT or Gemini, I, um, think they're only as good as the data set that they're built upon. Right. So if you're asking, you know, them to do very general type, you know, Q and A, they're gonna go out and search, you know, everything that's available on the Internet, you. And that's probably okay. I think it's more when you start to try to get into, you know, what we call, um, expert type of requests, that you're only going to get responses based upon what data they have access to. Right. And I was talking to a leader at Databricks recently and she shared that, you know, I'm really good at writing prompts and I will take a prompt and go to like all five of the top providers and I get very distinctly different answers with each one. So, so I think it's like, I think it can speed things up for sales so fast on a lot on the research side of their companies, but you still have to Apply your brain to look at the response and be like, is this reasonable? Like you can't just assume what it spits out is, you know, gold every time without sort of examining it first. And that's just what I caution my team to do because you know, our salespeople use this stuff all day, every day to help them. Again, mostly on the research side, breaking into new companies, understanding, you know, how to position things and it's fantastic. But it's just not the be all, end all I guess.

Speaker C: Well, and there's, there's some gaps there too regardless, right? I mean you correct, you touched on some of them already, you know, because the, these LLMs, they're only trained on what they're trained on, right. And as quick, as quickly as they can be trained, there's still gaps, but there's. Are there other types of data gaps that, you know, when we're talking about enterprise go to market that leaders should be aware of?

Speaker E: Um, yeah, I think depending upon, you know, what sort of your, your business is, I'm seeing a lot of companies because obviously we work with a lot of AI companies that are looking to incorporate our data into their sol. Um, so we're talking with these companies and I think that making sure that the data set that the company has is incorporated into whatever they're using is a huge gap. Right. So if the CRM data is not very complete and it's pointing to that, or your er, system doesn't talk to the sales systems, um, if you've got information that's living in silos and it doesn't really have that connective tissue, that's really where I think once companies sort of get a handle on their own data estate, making that available to, you know, an enterprise version of these AI solutions to use internally is going to be extremely valuable for companies. But I just think that people are approaching it in a multitude of ways and there's really no right or no wrong and just the understanding that your system is only going to perform as well as the data that you have in it, whether that be from a completeness, from an accuracy standpoint and, or from a value standpoint.

Speaker C: And so you know, these gaps that, you know there's, there's certainly there's external gaps. But you uh, know, a big thing that I know that I see a lot in the work I do with enterprises is those internal data silos that you mentioned, uh, as well. And you know, you could have access to great data, you could even have it within the quote unquote four Walls of the organization. And yet if one team can't access all the things that they need to, then uh, there's uh, a lot of drag on things like revenue and efficiency and things like that. Where are you seeing some of the most significant areas that are kind of suffering from this?

Speaker E: Yeah, I think um, for our particular business, when you look at um, the companies that we are selling to, a lot of them are just super reliant on public data and they don't understand that their internal data and the things that they have um, can affect how their sales cycle's going to develop. You can't expect that public data and talking about what SaaS companies, you know, sort of do is going to fit everything for your own, you know, organization. So for us we spend a lot of time sort of defining what the sales process has to look like from the buyer's experience side and then try to map to that. So we provide a lot of um, non public information about private markets for example. So if people are only trying to search what's on the Nasdaq um, for information about private markets, they're not going to get that. Right. So trying to make strategic decisions about where your sales team is going to invest their time, you need to seek out data sources that are aligned to your ICP and who you're looking to sell to. Because everybody you have to assume is going to be using that same public data. So you've got to find what's your edge going to be and how can you drive more pipeline and more conversion into revenue because you're seeking out data sources that other people just don't have or they haven't learned to use effectively. Um, and I think that's what we're seeing a lot specifically in financial services and go to market companies.

Speaker C: Yeah, I mean uh, what I'm hearing is a lot of it has to do with context. Right? So there's, you know, there's not only the internal again company context that you know, chat GPT, if you just ask it questions, it's, it's not going to have, but it's also some of the things like that you provide which is, it's not just you know, polling the, you know, the NASDAQ every, every day at 5pm or whatever. It's, it's a lot of things and putting the uh, putting, putting the public data, putting the internal company data and putting that information that is not just publicly available about external sources. That seems to be to me getting the right context in place.

Speaker E: Right, well exactly. Because if, if you know, what you're seeing right now is, is a lot of FOMO in the AI space. Right. Executives all are, are rushing to deploy AI so that they can, you know, make that big announcement and you know, us too sort of thing. And, and it. So the issue is so pervasive in organizations. I mean, I saw this all the time when I was at Google is because they either haven't connected their data sources before trying AI, so they're throwing AI on top of what they have, and then they're not getting the best results from it, which it's because they're just focused on that, you know, exciting new, you know, AI tool that they get to, you know, announce versus doing the sort of unglamorous work of really data wrangling and infrastructure. Like, I spent an exorbitant amount of time talking to CDOs because these are the guys that are responsible for getting the right data sets together to solve the right business problems. And if you can't look at that from a strategic perspective to say, okay, what is it that I'm trying to get to? What's the business outcome? I'm trying to drive and then work backwards to, to what the data you need. Pretty soon the AI agent that sits on top is gonna be agnostic, right?

Speaker C: Yeah.

Speaker E: So it's gonna become more about how are you going to differentiate your internal data set, whether it be from stuff that you already have internally or data that you're gonna go procure to give yourself a competitive edge in the market.

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Speaker C: So how do you, how do you know that you're on the right track? I mean you know, every business is different. So you know the company, you know, the KPIs at a healthcare company are going to be different than financial services and so on and so forth. But how do you measure that? Okay, now we're using AI in the right way. We're using our data better. Like what, what are some ways to, to know that you're on the right track?

Speaker E: I guess, um, I think for us teams um, that are winning with AI have sort of made that fundamental mindset shift. Right. And how do we get our reps using the tools, but how do we just do it from the stance of eliminating um, sort of the research burden. So I constantly, one of my euphemisms is always like if they're not doing revenue generating work, I don't want them doing it. Right. Because the most finite um, asset is time. Right. And as a salesperson if you're not using your time accordingly. So I feel like so much of the pre meeting prep and understanding of particular companies has been completely eliminated. Like I know when I was an IC, which I was one for 16 years, I would spend an exorbitant amount of time getting ready um, for a meeting. I think people can do that now extremely fast.

Speaker B: Yeah.

Speaker E: In terms of getting up to speed. Right. But is that going to help them close the business? We don't know yet. We have, we haven't seen a lot of end to end. And in fact, because I have a company that's buying our data right now Simply for targeting AI companies, we have 31,000, over 31,000 um, AI companies in our database. Um, so they're popping up all over the place. What their specific um, angle is or the um, like we're rolling out our market insights which is going to have like lots of subcategories of what these guys are in, so we can further and more granularly slice and dice this industry. Um, but I think that's where we still have a little bit of a wait and see, you know, mindset is it's like we've got to allow for deployment of AI where it makes operational and efficiency sense, but we can't let it take over what a salesperson has to do. Because that face to face or real time interaction, that's not going to go anywhere, um, in the near term. And when I say near term, I'm saying like at least five years because I don't know how you get away from, you know, the person to person, the negotiating the deal. I can't really use AI to negotiate the deal.

Speaker D: Right.

Speaker C: I mean, I don't, I don't think that large organizations are going to make multimillion dollar decisions agent to agent anytime soon. To your point. Yeah, yeah, definitely. So you've, you've talked a little bit about the, the, the mindset shift here, but I wonder if you could elaborate a little bit on that because you know, it sounds like, you know, certainly there's the data silos, there's the, there's some of the process things, but there's also a culture of um, you know, either, you know, I've seen, I've seen it always, you know, there's the reluctance, there's the embracing and maybe running a little too fast, you know, to do it. But you know, what, what is, what is good culture and process look like in AI adoption?

Speaker E: Well, speaking strategically from um, sort of a revenue organization, which is, which is my area of expertise, I would say that this is really about um, where our go to market leaders, rev ops leaders. We're really thinking about how we can take the busy work out of the AE's job with AI, like including pre building dashboards and pre mapping territories and preloading account intelligence. Um, rather than sort of handing the reps the tools and saying okay, you know, like we usually do in January, here's your territory, like go figure out your territory plan. Right. Like my team can upload their territory list of accounts if they've got 5,000 accounts or 50,000 accounts into um, Crunchbase and they can get a territory report based upon who's growing and who's gonna raise money versus who's the ones that are declining and likely going to have a layoff. Like just prioritizing it by growth or decline is a huge time saver. Right. Um, I have a rep that works for me that had worked for me before at both Looker and Google and he said this, this exercise that would take me five weeks at the beginning of the year, I can get it done in like 15 minutes.

Speaker B: Wow.

Speaker E: Um, now so those are like sort of the time saving things that I think rev ops teams um, really need to start looking at is how can you pre do a lot of this for your reps? Because in the past it's always been handing them the tools and expecting them to self service. Right. And to your point that's where you're going to have some reps that are going to be early adopters and they're going to see value in it and they're going to keep doing it and you have other reps that are going to be like I'm not touching that with a ten foot pole.

Speaker C: Right.

Speaker E: Um, so it's like anything you can pre do for the AES is always um, I think a good idea. And using AI, um, is no exception to that. Um, giving them sort of like this is what we've seen. I want to give my team roadmaps to success on how other people have used it and been successful. So there are still going to be areas where they need to use the tools directly. So I'm not saying that won't happen but I think also making sure that you're culminating all the successes and, and sharing it across the board because at the end of the day right, wrong or indifferent salespeople are coin operated. So we're going to do whatever it takes to get us where we need to get as fast as possible. So I think adoption in you know, a sales organization is going to be much, much higher. But you got to show them the way and you got to give them the roadmap to success.

Speaker C: Well and I think it also sounds like going back to your, you know, getting, keeping people focused on revenue generating activities. It kind of where in the past creating a PowerPoint from scratch and copying and pasting all this that was, you could make the argument that's a revenue generating activity because eventually the client, you know, the customer's gonna see the PowerPoint but it kind of changes the definition of those things because you can get that PowerPoint generated or you can get the research done and, and you know, on your desk at 9am when you're ready to start and stuff like that. So it kind of, it's kind of a shift in, in definitions, right?

Speaker E: Yeah, absolutely, absolutely. Like I remember, gosh, this is going back like Five years. But when I was at Looker, which was a BI tool, but it was a little bit more than just, you know, your run of the mill one. It was a little bit more for, for developing products. But our partner at Slack figure out a way that they, uh, developed a Slack channel that all of a CS person or a salesperson had to do is enter a company name and, and in two minutes they would get like this whole deck that showed all of the usage, etc. That a company had. It was like 50 pages long that they would get it in two minutes. Like that thing, the ROI on that was insane because they said that they were saving like 1000 hours a month.

Speaker B: Wow.

Speaker E: Uh, people's time building that and taking screenshots from the tool and plugging it in and stuff like that. So, so that's where all of these time saving sort of efforts that AI is going to help us with. This is where salespeople are just going to have more time to actually do the selling.

Speaker C: Yeah, yeah, love that. Well, and thanks so much for joining today. Got a couple of last questions for you as we wrap up here. The first one, um, if we were having this interview one year from today, what is one thing that we would definitely be talking about?

Speaker E: Oh, that's a good question. Um, one year from today, I think it would be talking a little bit more on the ROI that people are getting for a variety of AI solutions. We would really be quantifying it more.

Speaker C: Yeah, I definitely, that conversation is starting now, but yeah, definitely. Well, hey, we'll have to talk about that in a year then.

Speaker E: Yeah, exactly, exactly. Because I know we're doing it right now, um, and we're constantly looking at that because I think that is the ultimate equalizer. Right?

Speaker C: Yeah, yeah.

Speaker E: Is what is the return on this investment going to be? And you know, where I live and where I sit, I'm not in a position where people are going to be replaced by AI. So it's more just about like, how can we use it as efficiency tools to get us where we need to get so that we can build more pipeline and close more deals.

Speaker C: Yeah, yeah, love it. Well, and last question for you, uh, what do you do to stay agile in your role and how do you find a way to do it consistently?

Speaker E: Oh my gosh. That's just inherent to who I am. I, um, think that, um, you know, constantly looking at the core tenants, um, of the business and the progress and making changes quickly. I'm not, um, a person that I don't believe hope is a strategy. And I'm not a person that wants to give things too much time to see if it can turn around. I'm, uh, more action oriented where you gotta make decisions, whether it's people, process or technology. And you gotta make them, um, you know, faster than ever now. Because again, reiterate, hope is not a strategy.

Speaker C: Yeah, love it. Well, again, I'd like to thank Ann Davis, Chief Revenue Officer at Crunchbase, for joining the show. You can learn more about Ann and

Speaker D: Crunchbase by following the links in the show notes. And thanks again for listening to the Agile Brand podcast. If you like the episode hit, subscribe and drop a rating so others can find the show too. And if you're interested in consulting, advisory work, or if you need a speaker for your next event, feel free to remember reach out. Just visit GregKilstrom.com that's G R E G K I H L S T R o m m.com the Agile brand is produced by Missing Link, a Latina owned, strategy driven, creatively fueled production co op. From ideation to creation, they craft human connections through intelligent, engaging and informative content. Until next time, stay curious and stay agile.

Speaker E: The Agile Brand

Speaker B: this podcast is brought to you by Thomson Reuters. The best don't just do their work, they change what's possible. Cases won, Audits completed, Jobs saved. Behind every one of those moments is a professional who needed to get it right and did. Thomson Reuters builds the technology that sharpens insights, speeds up decision making, and powers the outcomes that matter. So when professionals act, the impact is felt by everyone. Be a changemaker visit tr.com changemakers could

Speaker A: AI help you do more of what you love? Workday is the AI platform for HR and finance that actually knows your business. We help you handle the have to dos so you can focus on the can't wait to do's. It's a new workday.