Crunchbase CRO Ann Davis on best practices for AI implementation

Ann Davis, the newly minted CRO of predictive company intelligence platform Crunchbase, is what you might call a “data dawg.” Boasting over 30 years of experience in enterprise SaaS and tech sales—most recently with Google Cloud and Looker—Davis is using these skills to make effective revenue decisions on everything from AI to untapped growth opportunities.

After picking her brain on the state of sales orgs last year, we checked back in with her on how she’s cutting through the AI noise to deliver effective decisions for her team, while always keeping data top of mind.

This conversation has been edited for length and clarity.

What are some of the common mistakes companies might be making when implementing enterprise AI?

Our revenue is not coming from that solution. Our revenue is coming from the data…The UIs are so simple because it’s all AI, that it’s all about the data sources that they have, and it’s about finding ways to leverage your customers’ existing data sources. Whether you’re talking about bringing your own key or bringing your own license; those are becoming legit big businesses for people such as ourselves that are proprietary dataset providers.

Is it fair to say those finding success with AI products are the ones seeking narrow solutions?

In financial services, the difference of .002 on a number is a big deal if you’re a VC or a corporate lending bank; the numbers really matter. Therefore they have to go seek out the appropriate data sources that are going to give them that accurate information. It’s almost like it’s a use case. I’m very focused on go-to-market, so everything I do is in relation to AI and GTM.

It feels like you know your operations so well that you know how to go find the tools that’ll make them work better. How do you get to that point?

Almost every single CRO I talk to, they’re saying, “We have got to get rid of some of these tools. We’re spending so much money on all these tools.” Some of them are different flavors of another one; some have AI, others don’t.

I’m fortunate because we’re a smaller company, and my team can trial and error stuff…That’s where it puts more work on the sales operations and the Rev Ops teams, because they got to vet this stuff out, and they got to understand what the sales stack is, and they got to bring their recommendations to the CRO.

How do you make sure you’re using your data correctly? And how important is that?

I can just simply ask the question, and [AI] takes a couple minutes, and produces the results and the table and all the information that used to take me hours to try to get that right. The sheer ability in which AI tools are able to process data—all types of data—and perform the analyzes that you want to do.

Given how important it is for Crunchbase, how do you balance what AI and other tools can do with data, while also ensuring precision and accuracy?

When you’re doing mergers and acquisitions, or funding of new companies, a small decimal point move is a big deal.

We’re just using it to get the collective data of what is being said about this company, and what’s great for us is that we have 5.2 million companies, so we know we have more insights and predictions about where a company’s going, if they’re growing or if they’re going to fund. Are they going to do a layoff? Are they going to go out of business? That becomes a great tool again from an efficiency standpoint.

Is AI just another thing making the CRO more of a cross-functional leader?

Because of the world we’re living in, and people’s access to data, it does require [more]. I run a handful of cross-functional teams on everything from website design to product development to product marketing…The sales org is one pillar of revenue that I drive, but there’s three or four that are all sort of in the mix, and because of AI and technology and data, the whole buying process has changed over the last 32 years that I have been in tech sales.

You didn’t have access to the types of information that you have access to, and now with AI, it’s like at your fingertips. Finding the new ways of revenue stream for your particular org—and how AI fits into supporting and or driving that—are critical for companies.