Full Transcript for SaaStr 884: The AI-First Seller: How Databricks Rebuilt Its Sales DNA at $190B with CRO Ron Gabrisko

It's now official. Today's guest, Ron Gabrisco, is the greatest CRO to ever live. He joined Databricks, its sub -1 million ARR as CRO. Databricks is now a $200 billion company, many billions of dollars of ARR. This is a special episode in that Ron is the sales leader that all sales leaders look up to, and Databricks is one of the most successful technology startups of all time.

Ron, welcome to the show. Excited for the conversation. That is a great introduction, Sam. Excited to be here. Thanks for having me. All right. I don't mean to over -promise. Actually, we will both over -promise and over -deliver on this episode, I'm sure of it. Yeah. So, let's see. I looked at my calendar. Last we recorded, we've done this pod once, you and I.

We recorded on October 24, 2024. And for folks listening, that was a great episode if you want to go back. But man, we live in a different world than we did exactly two years ago. You know, there's the saying that's something like, some decades, nothing happens, and some weeks, entire decades happen. It feels like every week over the last two years has been a version of that.

So then, that's where I want to spend much of today's time. So there are a few categories I'm seeing major changes in sales and go -to -market relative to two years ago. So maybe we start with people. If you look at your leaderboard at Databricks today, top sellers, the things that they're doing, the characteristics or profiles of those people, and you go down the leaderboard.

What, if anything, has changed from two years ago when we last recorded? You're listening to the official Sastra AI podcast brought to you by... Today's episode is brought to you by StackAdapt, the AI -powered marketing platform that allows you to plan, execute, and analyze both owned and paid media campaigns from a single platform. As the world of advertising continues to evolve, catch up on StackAdapt's Innovation Forum event to hear from its co -founders, OpenAI, and other industry leaders about the shifts reshaping digital advertising, the AI innovation striving them, and what they mean for marketers.

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That's NorthwestRegisteredAgent .com slash Sastra Free. S -A -S -T -R -F -R -E -E. First of all, a lot of things are the same and a lot of the things are different, right? So a lot of the things that are the same are you still need to do amazing prep for your customers, develop amazing relationships, research your account, have tremendous grit and EQ and IQ and all those kind of things.

The things that have changed are you have to be AI first in this world. You need to use AI to understand the customer, to develop demos. A lot of our AEs were pretty technical as far as a sales team, as a field organization. But every single one of them, we run Databricks on Databricks. They use Genie on their phones. Genie's our product.

And we're selling Genie to every single customer, right? So they need to be able to develop demos themselves, be able to use the products, be very technical. But also, AI now is a CEO -level initiative, right? And so be able to talk to CEOs and talk about business outcomes. And so you end up having to up your game tremendously in this world.

And it's just, it's moving crazy fast. I've never been more excited to be at Databricks and be in this market. But those are some of the things that I see are different is you got to be AI first. It's awesome. All, it certainly reinforces our experience here, Monaco. My experience here is something like the top performers, because we have a bunch of the folks that were at the very top of the leaderboard at my prior company, Brex, and others that we've hired from.

And it's oftentimes some of the same people. And those people have evolved, but there are also some new people who are seeing at the very top of the leaderboard that maybe in a world, even just two years ago, might not have been at the very top. And it is this, I would describe it as almost go -to -market engineer quality, where they're very AI curious.

And they come into demos, and a lot of what they are showing is the sort of technical guts. You can tell I'm a non -technical go -to -market leader by that language.

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We can be doing a better job on the enforcement side of things there. For enablement, we invest a lot in enablement. We have boot camps up front to make sure. again, we're going through the entire company, products, but we'll certify on the pitch. One of the things I see in today's sales too is we need great storytellers. A lot of people are getting away from the traditional, tell me a big story of the art of the possible CEO level.

So we're certifying on those pitches. We're certifying on these genie demos, these industry -based demos. We're actually doing certification throughout the year on that stuff. So it's really important that just like any other board, I was a baseball player, right? You got to do something 10 ,000 times before you become an expert. So you got to practice 10x more than you play, right? And so I expect our folks to be the best of the best sellers on the planet.

And you got to test for that. You got to enable for that. You got to train for that. You got to mentor for that. So it's a big part of our organization for sure. Makes perfect sense. I think logical progression here. One of the things that I've benefited from and observed evolving is seller time. There's always been an understanding that the highest ROI of a closing reps time would be meeting with customers, building relationships, customer facing.

That's the thing that's the thing that is pretty unique. And the folks that are at the top of the leaderboard oftentimes are exceptional at that activity. One of the things that I've observed that's unique to today's environment at Monaco, again, relative to my time as CRO at Brex, our reps are spending far more of their time customer facing.

If I think back to what were the best reps at Brex doing, they were both exceptional at converting opportunities, but they also controlled their own destiny and created a lot of demand through their own outbound and demand gen activities. I'm curious if you've experienced this as one of the most mature sales organizations, certainly the private company in the world.

Are your reps able to spend more time customer facing today because of the automation that's happening on the demand gen side? What are you experiencing over there? Totally. Yeah, I think, first of all, I think the rule with the more time you spend in front of your customers, the better you're going to do has always been true. I always say sell from your feet, not from your seat.

I think people got away from that when we went to this kind of virtual world. And even our interviews, every single candidate, we need to meet in person, right? I try to push for at least 60% of our time in front of customers. A lot of the automation we're doing, like you can automate a lot of your account research, a lot of your upfront presentation preparation, even our proposals, we're building agents for that.

Again, it's all through our genie app. And so that should increase productivity. And I have seen an increased productivity that frees up time to be in front of customers. So my rule of thumb is if Monday and Friday do all your preparation and all your admin stuff and Tuesday to Thursday, you should be on the road and in front of your customers, if not more.

So that general rule of thumb, I think, shows success. And then upfront pipeline development, same thing, right? A lot of the automation you can do with AI in terms of outreach is amazing. We've been automating and building agents for that. And that's not just the job of the SDR to reach out to new departments or new people or new stakeholders.

That's the job of the AE. You're the CEO of your account. Make sure you're covering every single piece of that account. And so I just think those these tools should make every single salesperson, if they embrace them, even more productive. Has this changed the makeup and or efficiency of your team? Does your sort of SDR or outbound organization from a headcount standpoint look similar to the way that it did last time we got up a couple of years ago? Is the efficiency on a per rep basis doing the types of activities that are demand gen? How has that evolved? I'm curious, again, these more mature businesses that are evolving with the introduction of AI, how is it

manifesting, which might be different from a company that started just two years ago in 2024? Yeah, we still have a really robust SDR team, but the productivity is out the roof, right? Like we continue to add more and more AEs, more and more SAS. But our SDRs, for the most part, are flat. And the reason for that is productivity is going way up, right? We're trying to automate all the outbound, all the signals on certain prospects and contacts, all of our campaigns.

It's pretty sophisticated now where you can get personalization down to the person, right? The productivity is out the roof for us. We happen to be in, I think, one of the hottest markets of all time with data and AI, but lots of people are interested in learning more about Databricks. But productivity based of our SDRs from these tools has gone way up.

It's definitely something, if we started the company, obviously I'd build a demand gen team and I would build these tools, but I'd keep it pretty lean and mean because I think a lot of the tools are out there to be able to scale this pretty fast.

And so maybe takeaway there is, in a mature business, Databricks, we didn't shrink the size of our SDR team. It became far more efficient. So productivity on a per rep basis has gone through the roof for some version of that. And while we have scaled the closing organizations, the SDR org or the demand gen org hasn't scaled linearly the way that, historically speaking, they have.

We're able to add more sellers today with fewer folks dedicated to demand is my interpretation of one of the things that you mentioned there. Our manifestation of this, we have, as a company, we started in 2024. So we're hopefully at the frontier cutting edge of leveraging all of the tools. And certainly we run ourselves on Monaco. We have one SDR that SDR produces 400 outbound opportunities a month.

And this isn't some Mark big marketing organization that centralized. We didn't have anybody in marketing until two weeks ago. And so one SDR who happens to be my nephew, he is producing 400 outbound meetings every month through just like lots of automation and AI that's powering that. And then the other thing that is happening is seller time.

I referenced this thing where at Brex, the top AEs were the best AEs because they controlled their own destiny. They were the best at outbound. Many times there were promotions from SDR. I agree with that. And now the hour AEs, the percentage of their time that they spend in front of a customer is juiced by some massive percent. And many of them, it's certainly the ones that are closing and working with the most customers that don't have time to do their own outbound.

And those are maybe the two things that I've observed very different from 2018 when I joined Brex. The thing you have to think about, though, too, is because a lot of our SDRs graduate into AEs, right? And if you completely get rid of your SDRs, like who's teaching these folks how to sell, who's teaching them how to sell Databricks? So over time, maybe that team gets smaller, but you need some type of, call it Academy, that helps these kind of younger, less experienced sellers learn how to sell.

And so we do that. Obviously, I think we have the best training and mentorship on the planet. But I expect all those folks to graduate into AEs or into management as they stay at Databricks. And if you're going to be lean on that team, you need a way then of how you train AEs, unless you just plan to just go out and just hire folks. But again, then I look at that kind of next generation of sellers and who is mentoring those folks, right? And I think that's a gap.

And some of those sellers that are coming up through the ranks there are some of the best sellers I have because they were born and raised with AI. They embrace technology. They write their own kind of programs and apps using Genie and LLMs. They're some of the best sellers I have. So you still have to have that kind of graduation process and Academy, even if you go lean there.

It's a smart insight. I was an SDR. Were you ever an SDR? Yeah, of course. Okay. So we were SDRs. All of the best sellers at Monaco and maybe Databricks as well start as an SDR. I think there may be like shortcuts you can take if you're more senior to career or something like that. And we have some folks like you who graduated college in like 2024 or something.

Like they entered a world where AI had already existed. And so now these folks two years in, they've progressed their career. Maybe the nuance is like the thing that makes this class of SDR, now many of the best sellers is different from the thing that made maybe folks like you and me, but folks of a sort of prior generation, the best sellers coming out of SDR world.

Yeah, absolutely. Let's talk about a layer deeper on demand gen tactics. I can give my version of this. I gave this like we have one SDR. They schedule 400 meetings. We do have now a person in marketing who's awesome. And then I do some marketing myself. Here's the way that I think about it. And I'm curious how maybe an analogy here would be something like we started AI native.

Databricks is a almost 15 year old company. That's on right. 13, 13, 13 year old, 13 year old company. Databricks is much more like, I don't know, an oil tanker that's trying to turn it because you all about these mature processes and systems in place. I think for us on the demand gen side, the thing, there are things that historically took all of our time.

This was the account research, finding the right buyers, looking for maybe signals, reading information about the companies, about the people, about the people, customizing messages, outbounding through the form of LinkedIn and emails and whatever other channels we're trying to leverage. All of that is automated today. Yeah. And so if you're spending a lot of time on these demand gen activities that historically speaking, folks like you and me and people on our teams were spending their time on, you're really doing a disservice to what we find is the high ROI stuff, which is just things AI can't do.

Yeah. We're doing gifting campaigns that are really manual. We're throwing really high end and fun events. And there is one actually, but even in 2026 has not gone away that still requires a human. It's cold calling. What I think has changed is the efficiency of those cold calls, serving up the right person at the right time for the right reason with the right script.

Yeah. And so we're far more effective at cold calling today, but I think it's too dystopian as we sit here today. We'll do another one of these in two years and we'll see, but it's too dystopian to have a bot cold calling today on your behalf instead of a human. So anyway, love your perspective on how has the transition been talk through maybe how the automation has taken place and then anything that you're able to do today because you have more time that you've been freed up through all of the automation with AI.

Listen, like I'll talk a little bit about kind of a rep first and then about myself, but in terms of demand, Jen, you're right. Everything is automated. You should be able to pick out the right contacts on LinkedIn. It should be able to customize your outreach exactly for that particular prospect, all your account research, all your account background, prep docs for execs, like all that stuff we have agents for now, which makes you incredibly productive in terms of getting meetings and trying to hit those folks versus the old days.

You'd put 5 ,000 contacts in and I call it the automated spammer. And I just spam everybody and hope for one or 2% response rates. Now you can get six, 10% response rates because it's very customized for who you reach out to. Now, that being said, it just changes and up levels what an SDR and AE needs to do, right? I always say, as long as people are buying software, you need people to sell software because it's still about relationships and trust and explaining and coaching and teaching customers how to get value out of your software.

And so we're spending a lot more time on that. How do we make sure we're part of the biggest, most strategic initiatives and objectives of a company? How do we make sure we're getting to the CEO, the CIO? Now we're selling to CFOs, CMOs, right? So a lot of the AI and genie and things we've built allow us to go into the business users, not just IT, because that's the other thing that's changed in this market is the buyer persona.

You used to sell tech a lot of times into IT, and now all the business knows about AI and wants to build amazing things with AI. We spend a lot more time selling to other executives outside of IT that we can deliver real value with. And I think that's a big change in the market. And I think a lot of the automation tools have freed up time for us to be able to take on those new personas.

It's amazing. Okay, so the next thing I wanted to catch up on is servicing. And the way that we service our customers today is very different from the traditional, what I would describe as CSM model, that I've had a bunch of other companies. There were like SaaS companies pre -AI. And the SaaS model was you have a quarterly check -in with the customer, and you monitor their usage, and you tell them about new products and make recommendations and a whole bunch more.

For now, the way that this works at Monaco today is everyone is an FDE. And they come to any calls or messages over Slack or whatever it is, and it's like, hey, I built this thing. Here is how I would deploy it for you. Do you just want me to deploy it? And you immediately start getting value from it. And so we're, in a lot of ways, doing the work for the customer that, historically speaking, you'd go to the customer with this product and be like, you would have to do the work.

So everyone is, Yeah. I expect you to actually build and show them what you can deliver versus PowerPoints. I think the day, the old day of the show up with a PowerPoint is gone. You better show up and show the customer exactly what you expect to deliver and what's the outcome. And listen, it might not be on production data, but it probably needs to be on dummy data that looks and feels like their industry and that particular company.

And so that's the future. And yeah, again, like our closing is like, hey, we built this. This is what we have in mind for this particular use case. Call it supply chain predictions or stock out prevention. And then we'll actually deploy it. We've done that for a lot of our big retailers or big pharma companies. I think when you're selling to the business stakeholders too, that allows them to understand exactly what you're trying to deliver versus it gave you some high level PowerPoint.

Honestly, I think the messaging of a lot of these companies looks identical. And so it's more about like, what can I do for you? What solutions can I solve and how fast can you do it? And how much do I believe in you as a reliable and trusted partner? And so I think it's completely changed in that dimension is we've always been a consumption based company.

So that's constantly what we're doing is pick out the top use cases for that industry, go sell those to the business users and go implement them as fast as possible with an FDE motion. So it's interesting is, as we're having this conversation. We've now talked on several different personas. There's sort of the demand gen or SDR persona, the AE persona, the now host sales could be FDE type persona that there is one trait that is universally applicable and now required, which is you need to be like both AI curious and AI proficient.

In all of these customer facing roles in order to be able to perform. And for some folks that have been customer facing these like sort of charismatic type A profile folks that can be like a hard adaptation to make in today's world. Like it hasn't been historically. And I know Databricks is that technical sellers from day one. Databricks is unique with a highly technical product and highly technical buyer relative to most companies.

Some of these folks are going to struggle adapting. And if you're listening in and you haven't yet done this, you need to force yourself, force yourself on the consumer products, deploy agents yourself, learn about these things so that you can proficiently talk about this with customers. And then again, do all of the work that we're describing on behalf of your customers.

Yeah. Embrace it. It's here. It's here. It's here to stay. It's the future. If you want to continue to build and grow your career in sales, you need to embrace these AI tools. Every single one of my reps has to get certified on Genie, has to be able to create these apps and demos themselves. And like that's the next generation of where sales is going.

So just embrace it. It's going to be really important for the future for sure. Last topic. You touched on this just a moment ago. Databricks is in an interesting position. Most companies that are 13 years old in this world of software, the majority have not benefited from the sort of platform shift or advent of AI. And you see that manifest in a lot of publicly traded SaaS companies who are seat based pricing.

Now, Databricks is a beneficiary from AI for a bunch of reasons. But one of them is this consumption based pricing model that you alluded to. I think for folks today where this is new, Monaco included, you're starting. Most companies today are starting on usage or consumption based. That's a different world than existed even just a few years ago.

Any just learnings that you've experienced through now 13 years of evolution as a company that you could impart on folks that maybe are less proficient in usage based or consumption based pricing? What are just some principles to live by as maybe a market leader in this category? Yeah, great question. I think one of the smartest decisions we made was basing the whole company on kind of usage and consumption, right? Because as data grows, Databricks grows.

As queries grow, Databricks grows. And so I think you need to look at what is your, the fundamental thing is look at what's your base unit of how you provide value. And then how does the market view that unit? Do you think LLMs, they pick tokens? Early days, I spoke with some of the coding folks and I was like, they're all user based pricing.

I'm like, you're never going to be huge unless you go to usage based pricing. Everything went to usage based pricing now on tokens, right? So similar to any other kind of startup or early business, find that unit, whether it's consumption, compute, what have you, that scales over time, that equates to value for your customer, right? For us, it's dollar DVUs, which is consumption or what have you.

And so I think that's really important. And then break down that unit based on how you can add value to it. So if you're adding different products or different features, should have a multiplier on that base unit or an add -on to that base unit. The ultimate pricing algorithm is how do I match price to value? If I overprice value, no one's going to buy it.

If I underprice, I'm going to leave money on the table. Find that base unit and then put incremental multipliers or other products on top. Again, based on that base unit for other things that add value. Could be security. Could be some kind of accelerator. Could be some type of premium support product. All those things are multipliers on that base unit.

And so what that does, and especially if you look at, for us in the world of data and AI, I always say we were doing AI before AI was cool, right? Like we kind of created data science and machine learning and now AI. We partner with all the big AI companies. It's all about context. Like how do you connect AI to data? And so now AI and agents to us, it's a huge tailwind because now there's going to be millions of agents, not just people doing queries and people, you know, trying to develop these algorithms.

They have all kinds of agents that want to query data. We have more data under management than any company in the world. And so that's been a huge tailwind for us in terms of AI and agents. And like I said, I think this will be the biggest data and AI company on the planet. And that's been an amazing journey so far. That's kind of how I think about pricing.

And it's super important to do that early in your company life cycle because it's really hard to switch from like a user -based pricing or the wrong pricing bump. You just give one. Start as early as possible. But any other sort of like pitfalls that either you've seen at other companies or lived at Databricks that could like save somebody, myself included, trouble on this stuff.

Like, again, you just gave one. We talked about this oil tanker that's like trying to shift direction or something that's hard to do. When you're the largest SaaS company in the world that has seed -based pricing, really tough to move to a usage -based pricing model. Other than starting as early as you can, anything that you have seen multiple folks experience that has been pain, that you can save myself and others from going through.

Yeah. Again, you're trying to equate price to value. Early days, one of the mistakes we made was we put some user -based pricing on there. We put a platform fee on there. It turns out, I would talk to customers. I'd be like, why aren't you ramping faster? For every user, it cost me a thousand bucks. I was like, okay, if I just do all the users for free, you'll just let everybody use it? They're like, sure.

Then consumption went out the roof once I did user -based for free. Even if you started on the wrong path, I think you can adjust. I think the biggest mistake is not capturing value from your customers. But I also would be careful. Early stage, you got to get customers on board. It's okay to, like I said, get them ramping at a low cost. You want your entry point as low as possible.

So people try the product, obviously free and all that kind of free trials and all that kind of stuff works. But you want them to get in with the least amount of spend. Pago is a really important model. Let them start to use the product. And then once they start to love it, you grow in these accounts like a mushroom, right? Now all of a sudden, instead of one or two, I got the whole department and I have multiple departments.

Now I can go get a commit. The other mistake I think is if you go in, you demand, hey, you got to do a commit before you can do anything with our software. I think that's a huge mistake. You need to have some kind of Pago type model so people can try out the product, even on their credit card. And go from there. So I definitely see people, they're trying to slam in a big deal right out of the gate.

And the biggest thing for an early company is you need logos. You need new customers. At least three tactical takeaways that I'll repeat back. Start as early as you can on usage -based pricing. Don't mix in other types of pricing. Seats plus usage that can be barriers to maximizing that usage -based pricing. And the third, remove friction from receiving value from the product that you're offering.

Don't ask for really large upfront commitments around spend or usage. Make it simple to get value on your product. You can make it easy to start that. Then it will pay long -term dividends in terms of usage, revenue, customer value, all of the things that create a great partnership. And I think that's true in all cases. Talk to your customers where there's friction in their adoption or ability for them to get value or product.

Remove that friction. It'll go so much faster. Just listen to your customers on that for sure. Awesome. Ron, you're a legend. Thank you for joining us today. It's been a lot of fun. I'm excited to see. Gosh, hard to predict what the trailing 24 months have looked like. Seemingly impossible to predict what the next 24 months are going to look like.

We'll do this one again. Maybe even before then. But it's been awesome. Thank you for your time. And we'll talk sometime soon. Loved it. Thanks, Sam. Appreciate you having me on the show. And yeah, let's talk again in another couple of years, man. I'm sure the entire world would change. So I'm excited about it. But thanks for having me. It was fun.

Sounds great. You're the best. See you, Ron. Take care. All right. See you. You're listening to the official Sastra AI podcast brought to you by... Hey, everyone. StackAdapt goes way beyond just digital advertising. Run and measure the impact of email and direct mail alongside programmatic channels all from one platform. Want to see how StackAdapt can help you spend less time on repetitive tasks and more time on strategic work? Check out their latest virtual innovation forum featuring a walkthrough of Ivy Studio, their newest AI innovation.

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