In this episode
Ann Davis, Chief Revenue Officer at Crunchbase, joins Greg Kihlström to confront the intense pressure on revenue and marketing leaders to show ROI from AI — and argues that chasing “better AI” is a distraction from the real problem. Drawing on 30-plus years scaling enterprise sales teams, including Looker and Google Cloud, Davis makes the case that AI outputs are only as good as the data beneath them, that the biggest gains come from fixing internal data silos and layering in proprietary data competitors don’t have, and that AI’s near-term ROI in sales is about reclaiming reps’ time for revenue-generating work rather than replacing the human close.
Key takeaways
- “Better AI” is the wrong ask — the data is the constraint. Whatever tool you use, whether Claude, ChatGPT, or Gemini, the responses are only as good as the dataset they’re built on, so the ROI problem starts with data, not the model.
- The same prompt returns different answers across providers, so keep humans in the loop. Davis cites a Databricks leader who runs one prompt through the top five providers and gets distinctly different answers each time — output has to be examined, not assumed to be gold.
- The biggest gaps are internal data silos. When CRM data is incomplete or ER and sales systems don’t talk to each other, AI bolted on top underperforms, because a system only performs as well as the completeness, accuracy, and value of its data.
- Proprietary data is the edge; public data is table stakes. Everyone can reach the same public sources, so differentiation comes from nonpublic data aligned to your ICP — like private-market intelligence you can’t pull from the NASDAQ.
- AI FOMO drives poor results. Executives rush to bolt AI onto disconnected data so they can make an announcement, skipping the unglamorous data-wrangling and infrastructure work that actually makes it perform.
- Work backward from the business outcome to the data. As the AI-agent layer commoditizes and becomes “agnostic,” Davis says the real differentiator becomes how you build or procure a distinctive internal dataset.
- Reclaim reps’ time for revenue-generating work. Winning teams make a mindset shift, using AI to eliminate the research burden — Davis’s rule is that if it isn’t revenue-generating work, she doesn’t want her team spending their most finite asset, time, on it.
- Pre-do the work for reps instead of handing them tools. Rev-ops should pre-build dashboards, pre-map territories, and pre-load account intelligence rather than expect self-service — a territory exercise that once took five weeks now takes a rep about 15 minutes.
- AI won’t close the deal — for at least five years. Large organizations aren’t going to make multimillion-dollar decisions agent-to-agent anytime soon, so AI stays an efficiency tool and the person-to-person negotiation remains human.
Chapters
- 01:09 — Cold open: is your data the real obstacle to AI ROI?
- 02:52 — Ann’s background: seven startups, Looker, Google Cloud, Crunchbase
- 04:04 — Why “better AI” isn’t the ROI fix
- 06:17 — The data gaps behind enterprise go-to-market
- 08:10 — Internal silos as a drag on revenue
- 08:53 — Public data vs. the proprietary edge
- 11:22 — AI FOMO and the unglamorous data work
- 15:14 — How you know you’re on the right track
- 18:17 — Culture and the AI mindset shift
- 21:38 — Redefining revenue-generating work
- 23:25 — One year out, and staying agile
Why “better AI” is the wrong ask
Davis pushes back on the instinct to solve the ROI problem by reaching for a more advanced model. The perception, she says, is that whatever tool a team uses, the data and responses will be inherently right — but every model is only as good as the dataset it’s built on. General Q&A against the open internet is usually fine; the trouble starts with expert-level requests, where answers are constrained by whatever data the model can actually access. Her practical caution to her team: the same prompt across the top five providers returns distinctly different answers, so reps have to apply their own judgment rather than treat any output as gold.
The real gap is internal data silos
Beyond the limits of what an LLM was trained on, Davis points to the gaps inside the organization. When CRM data is incomplete, or an ER system doesn’t talk to the sales systems, information lives in silos with no connective tissue — and AI layered on top of that inherits the mess. Once companies get a handle on their own data estate and make it available to an enterprise-grade AI solution internally, she argues, the value is enormous. But the governing rule is simple: the system performs only as well as the data in it, measured by completeness, accuracy, and value.
Why proprietary data is the edge
For Crunchbase’s customers, Davis sees over-reliance on public data as a strategic weakness. Everyone can search the same public sources, so if a team is only looking at what’s on the NASDAQ, they’ll miss the private-market picture entirely. The edge comes from seeking out data aligned to your ICP that competitors don’t have or haven’t learned to use — nonpublic information that lets a sales team decide where to invest their time and drive more pipeline and conversion. As she puts it, everyone is working from the same public data, so your differentiation has to come from somewhere else.
How AI FOMO produces bad results
Davis describes a pervasive pattern she saw at Google and still sees now: executives rushing to deploy AI so they can make a “we’re doing it too” announcement, throwing AI on top of data sources they never connected. The result is disappointing output, because the exciting new tool got prioritized over the unglamorous work of data wrangling and infrastructure. Her fix is to start from the business outcome and work backward to the data required. As the agent layer on top becomes commoditized and agnostic, she says, the durable differentiator becomes the internal dataset a company builds or procures.
What good AI adoption looks like in a sales org
Teams winning with AI, in Davis’s view, have made a fundamental mindset shift: use the tools to eliminate the research burden so reps focus only on revenue-generating work, since time is the most finite asset. The rev-ops job is to pre-do that work — pre-built dashboards, pre-mapped territories, pre-loaded account intelligence — rather than handing reps tools every January and telling them to self-serve. She points to a rep who compressed a five-week territory exercise into about 15 minutes, and a Looker-era Slack integration that saved roughly a thousand hours a month of deck-building. Because salespeople are “coin-operated,” adoption runs high once you show them the roadmap to success.
What AI still can’t do
For all the efficiency gains, Davis draws a firm line at the close. She doesn’t believe large organizations will make multimillion-dollar decisions agent-to-agent in the near term — which she defines as at least five years — because the real-time, person-to-person work of negotiating a deal isn’t going anywhere. In her seat, AI isn’t about replacing people; it’s an efficiency tool that frees reps to build more pipeline and close more deals. A year out, she expects the conversation to shift toward quantifying that ROI far more precisely across AI solutions.
FAQ
Why isn’t “better AI” the answer to the ROI problem? Because AI outputs are only as good as the data they’re built on. Davis argues that chasing a more advanced model is a distraction from the foundational data gaps — incomplete, siloed, or non-proprietary data — that actually limit returns.
What data gaps do revenue leaders most often overlook? Internal silos. When CRM data is incomplete or ER and sales systems don’t connect, AI layered on top underperforms, because the system only performs as well as the completeness, accuracy, and value of the underlying data.
Why does proprietary data matter if everyone has access to AI? Because public data is table stakes — everyone can reach it. The competitive edge comes from nonpublic data aligned to your ICP, such as private-market intelligence, that competitors don’t have or haven’t learned to use effectively.
How do you know AI is being used the right way? When teams make the mindset shift to use AI for eliminating research burden and free reps for revenue-generating work, and when the time savings are measurable — like a five-week territory exercise compressed into 15 minutes.
Will AI replace salespeople? Not in the near term. Davis expects the person-to-person work of negotiating and closing multimillion-dollar deals to remain human for at least five years, with AI acting as an efficiency tool that helps reps build pipeline and close faster.
About Ann Davis
Ann Davis is the Chief Revenue Officer at Crunchbase, where she leads global sales strategy and drives adoption of the company’s AI-powered predictive intelligence solution. With more than 30 years of experience scaling enterprise sales teams at high-growth SaaS companies, Ann brings deep expertise in data analytics, customer engagement, and revenue growth. She joined Crunchbase from Google Cloud, where she led sales for data analytics solutions—including BigQuery and Vertex—across multiple U.S. regions. Prior to that, she was Vice President of Sales at Looker, playing a key role in expanding its enterprise business ahead of its acquisition by Google. At Crunchbase, Ann is focused on helping customers unlock the power of AI-driven market insights to anticipate shifts and act faster.
Ann Davis on LinkedIn: https://www.linkedin.com/in/anndavis3/
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Transcript
[00:01:09] Greg Kihlström: Hi, I’m Greg Kihlström, host of The Agile Brand, and here’s a question for you. 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 gonna explore the counterintuitive idea that simply chasing better AI is a distraction, and that real gains come from addressing the foundational data gaps that plague most organizations. Welcome to Season 8 of The Agile Brand podcast. This season, we’re going
[00:01:55] Greg Kihlström: 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 Kihlström, 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 find us as well. And make sure you check out our sponsor, TEKsystems, an industry leader in full stack technology services, talent services, and real-world adoption. For more information, go to teksystems.com. Now, let’s dive in. To help me discuss this topic, I’d like to welcome Anne Davis, chief revenue officer at Crunchbase. Anne, welcome to the show.
[00:02:36] Ann Davis: Thanks so much, Greg. It’s wonderful to be here.
[00:02:39] Greg Kihlström: Yeah, looking forward to definitely a timely conversation for, for all, I would say, or at, at least very, very many. Um, but before we dive in, why don’t you give a little background on yourself and your role at Crunchbase.
[00:02:52] Ann Davis: Sure. Um, so I’ve been in tech sales for about 30-plus years. Um, I’m a serial, um, startup person. You know, I’ve done seven startups, um, taking them from everything from, you know, zero to $100 million and there being some form of an exit, and then I go off and do it again. Um, my last startup was Looker that got acquired by Google in 2020 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
[00:03:37] Ann Davis: best spot for my s- personal skillsets, ’cause 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.
[00:04:00] Ann Davis: I’m happy where I’m at.
[00:04:01] Greg Kihlström: Well, congrats on the, on the promotion. That’s, that’s amazing.
[00:04:03] Ann Davis: Thank you.
[00:04:04] Greg Kihlström: (laughs) Nice, nice. Well, yeah, let’s, let’s dive in here then and wanna start, uh, looking at this from the, from the strategic standpoint, and, 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?
[00:04:32] Ann Davis: 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-
[00:04:43] Ann Davis: … to a certain extent, whatever you’re using, whether it’s Claude or ChatGPT or Gemini, um, I think they’re only as good as the dataset that they’re built upon, right? So if you’re asking, you know, them to do very general type, you know, Q&A, they’re gonna go out and search, you know, everything that’s available on the internet, 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 gonna get responses based upon what data they have access to, right? And, um, I was talking to a leader at Databricks recently, and, 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
[00:05:28] Ann Davis: of, of the top providers, and I get very distinctly different answers with each one.” So-
[00:05:35] Greg Kihlström: Mm-hmm.
[00:05:35] Ann Davis: … 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-
[00:05:49] Ann Davis: … you can’t just assume what it spits out is, you know, gold every time without sort of examining it first. And that’s-
[00:05:57] Ann Davis: … 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 is the point.
[00:06:17] Greg Kihlström: Yeah. Well, and there’s, there’s some gaps there too regardless, right?
[00:06:20] Greg Kihlström: I mean you, you, you touched on some of them already. You know, because the, these LLMs, they’re only trained on what they’re trained on, right?
[00:06:28] Greg Kihlström: And ev- as quick, as quickly as they can be trained, there’s still gaps. But there’s, uh, are there other types of data gaps that, you know, when we’re talking about enterprise go-to-market, that, that, uh, a leader should be aware of?
[00:06:42] Ann Davis: Um, yeah. I think dep- depending upon, you know, what sort of your, your business is, I’m seeing a lot of companies, ’cause obviously we work with a lot of AI companies that are looking to incorporate our data into their solutions.
[00:06:56] Ann Davis: Um, so we’re talking with these companies, and I think that, you know, making sure that the dataset that the company has is incorporated into whatever they’re using is a huge gap, right? So if, if the CRM data is, is not very, you know, complete and it’s pointing to that, or, you know, your ER system doesn’t talk to the sales systems, um, if you’ve got information that’s living in silos and it’s, 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
[00:07:41] Ann Davis: enterprise version of these AI solutions to use internally is gonna 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, just the understanding that your system is only gonna 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.
[00:08:10] Greg Kihlström: And so, you know, the, these gaps that, you know, there, there certainly, there’s external gaps. But, uh, you know, a, a big thing that I know that I see a lot in, in the work I do with, 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, you know, the, the, quote-unquote, “four walls of the organization.” And yet if one team can’t access, you know, all of the things that they need to, then, you know, there’s, uh, th- th- there’s a lot of, uh, a lot of drag on things like revenue and efficiency and, and things like that. Wh- where are you seeing some of the, the most significant areas that are kind of suffering from this?
[00:08:53] Ann Davis: Yeah. I think, um, for our particular business, when you look at, um, you know, the companies that we are selling to, a lot of them are just super reliant on, on public data.
[00:09:06] Ann Davis: And, and they don’t understand that, you know, their internal data and the things that they have, um, can affect, you know, what, how their sales cycle is going to ro- develop. They, you can’t, you can’t expect that public data in talking about what SaaS companies, you know, sort of do is going to fit everything for your-
[00:09:29] Ann Davis: … your own, you know, organization. So for us-
[00:09:32] Ann Davis: … 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, so we, we provide a lot of, um, nonpublic information about private markets, for example.
[00:09:49] Ann Davis: So if people are only trying to search what’s on, you know, the NASDAQ, um, for information about private markets, they’re not gonna get that, right?
[00:09:59] Greg Kihlström: Mm-hmm. Mm-hmm.
[00:09:59] Ann Davis: So, 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, you know, your ICP and who you’re looking to sell to. Because everybody, you have to assume, is gonna be using that same public data. So you’ve got to find what’s your edge gonna 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.
[00:10:32] Ann Davis: Um, and I think that’s what we’re seeing a lot specifically in financial services and, and go-to-market companies.
[00:10:40] Greg Kihlström: Yeah. I mean, I, what I’m hearing is, uh, uh, 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, ChatGBT if you just ask it questions it’s, it’s not gonna have. But it’s also some of the things, like, that you provide, which is it’s not just, you know, uh, polling the, you know, the NASDAQ every, every day at 5:00 PM or whatever. It’s, it’s a lot of things. And putting the, uh, 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, right?
[00:11:22] Ann Davis: Well, exactly. Because if, if, you know, what you’re seeing right now is, is a lot of FOMO in the AI space, right?
[00:11:31] Ann Davis: 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.
[00:11:40] Greg Kihlström: (laughs)
[00:11:40] Ann Davis: And, and it, it’s 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, and infrastructure. Like we-
[00:12:11] Ann Davis: I spent an exorbitant amount of time talking to CD- CDOs because these are the guys that are responsible for getting the right datasets together to solve the right business problems. And, and if you can’t look at that from a strategic perspective to say, “Okay. What is, 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 what the data you need, pretty soon the AI agent that sits on top is gonna be agnostic, right?
[00:12:43] Ann Davis: So it, it’s gonna become more about, how are you going to differentiate your internal dataset, whether it’d 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?
[00:15:14] Greg Kihlström: So, how do you, how do you know that you’re on the right track? I mean, you know, ev- every business is different, so, you know, the company, you know, the KPIs at a healthcare company are gonna 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, I guess?
[00:15:40] Ann Davis: Um, I think for us, um, teams that are winning with AI have sort of made that fundamental mindset shift, right?
[00:15:50] Ann Davis: And, 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, the most finite, um, asset is time, right?
[00:16:13] Greg Kihlström: Yeah. Mm-hmm.
[00:16:13] Ann Davis: And as a salesperson, if you’re not using your time accordingly… So I, I feel like so much of the, like, 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.
[00:16:43] Ann Davis: In terms of getting up to speed, right? But is that gonna help them close the business? We don’t know yet. We have, we haven’t seen a lot of end-to-end. And it’s, in fact, ’cause 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.
[00:17:05] Ann Davis: Um, so they’re popping up all over the place. What their specific, um, angle is, or the, um, like we’re, we’re rolling out our market insights, which is gonna 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 gonna go anywhere,
[00:17:51] Ann Davis: um, in the near term. And when I say near term, I’m saying, like, at least five years. ‘Cause I don’t know how you get away from, you know, the person-to-person, the negotiating the deal. You can’t really use AI to negotiate the deal.
[00:18:05] Greg Kihlström: I mean, I don’t, I don’t think that large organizations are gonna make multi-million dollar decisions agent to agent anytime soon, to your point.
[00:18:14] Greg Kihlström: Or maybe someday.
[00:18:17] Greg Kihlström: So, you’ve, you’ve talked a little bit about the, the, the mindset shift here. But I, I wonder if you could elaborate a little bit on that. Because, you know, it sounds like, you know, certainly there- there’s the data silos. There’s the, the, there’s some of the, the process things. But there’s also a culture of…
[00:18:33] Greg Kihlström: … you know, either… You know, I’ve seen, I’ve seen it all ways. You know, there’s the reluctance, there’s the embracing and maybe running a little too fast (laughs), you know, to do it.
[00:18:44] Greg Kihlström: But, you know, what, what does, what does good culture and, and process look like in, in AI adoption?
[00:18:51] Ann Davis: Well, speaking specifically from, um, sort of a revenue organization…
[00:18:56] Ann Davis: … which is, which is my area of, 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, you know, pre-building dashboards and pre-mapping territories and pre-loading 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.”
[00:19:31] Greg Kihlström: (laughs) Right.
[00:19:32] Ann Davis: 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-“
[00:20:14] Greg Kihlström: Wow.
[00:20:15] Ann Davis: “… 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 gonna have some reps that are gonna be early adopters and they’re gonna see value in it and they’re gonna keep doing it, and you have other reps that are gonna be like, “I’m not touching that with a 10 foot pole.”
[00:20:41] Ann Davis: Um, so it’s like, anything you can pre-do for the AEs is always, um, I think a good idea. And, and using AI is, is no, um, is no exception to that. Um, giving them, you know, sort of, like, this is what we’ve seen. Like, I wanna give my team roadmaps to success on how other people have used it and been successful. So there are still gonna 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 sharing it across the board. Because at the end of the day, right, wrong or indifferent, salespeople are coin-operated, so we’re gonna do whatever it takes to get us where we need to get as fast
[00:21:27] Ann Davis: as possible. So I think adoption in, you know, a sales organization is gonna be much, much higher, but you gotta show ’em the way.
[00:21:35] Ann Davis: And you’ve gotta give ’em the roadmap to success.
[00:21:38] Greg Kihlström: Well, and I think it, 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 cli- 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 9:00 AM when you’re ready to start and, and stuff like that, so it kinda, it’s kind of a shift in, in definitions, right?
[00:22:14] Ann Davis: 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 figured out a way that they de- developed a Slack channel that all of the CS person or salesperson had to do was enter a company name and in two minutes they would get, like, this whole deck that showed all of the usage, et cetera- that a company had. It was, like, 50 pages long, that they would get it in two minutes. Like, that thi- the ROI on that was insane, because they said that they were saving, like, a thousand hours a month-
[00:22:59] Ann Davis: … of 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 gonna help us with, this is where salespeople are just gonna have more time to actually do the selling.
[00:23:18] Greg Kihlström: Yeah. Yeah. Love that. Well, Anne, thanks so much for joining today. Got a couple last questions for you as we wrap up here.
[00:23:25] Greg Kihlström: 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?
[00:23:33] Ann Davis: 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, that people are getting for a variety of, of AI solutions.
[00:23:51] Ann Davis: Like, we would really be quantifying it more.
[00:23:55] Greg Kihlström: Yeah. I definitely… The, it’s, it’s, that conversation is starting (laughs) now, but to your, yeah, definitely. Well, hey, we’ll have to, we’ll have to talk about that in a year then, but…
[00:24:05] Ann Davis: (laughs)
[00:24:06] Greg Kihlström: (laughs)
[00:24:06] Ann Davis: ‘Cause I know we’re, we’re doing it right now, um-
[00:24:09] Ann Davis: … and, and we’re constantly looking at that. Because I think that is the ultimate equalizer, right?
[00:24:15] Greg Kihlström: Yeah. Yeah. Definitely.
[00:24:16] Ann Davis: Is, is, is how, what is the return on this investment gonna be? And, you know, where I live and where I sit, I’m not in a position where people are gonna be replaced by AI, so, so it’s more just about, like, how can we use it as efficiency tool to get us where we need to get so that we can build more pipeline and close more deals?
[00:24:39] Greg Kihlström: 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?
[00:24:47] Ann Davis: Oh, my gosh, that’s just inherent to who I am. (laughs) Um, I, I 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, 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 ’em- You know, faster than, than ever now because again, reiterate, hope is not a strategy. (laughs)
