A Clay GTM engineer on why the world needed the role to exist

The proliferation of generative AI has spawned a slew of roles that didn’t exist before: AI product manager, chief AI officer, and even chief AI revenue officer (if this is you, I’m dying to talk).

As these leaders decide the technological trajectory of an organization, another pivotal role is actually constructing revenue-generating engines: Meet the go-to-market engineer. This is usually a pure data dog who focuses not on using AI for volume, but for precision. At least that’s the case with Daniel Johnson, GTM engineer at popular sales intelligence tool Clay. The platform services high-profile clients including Canva, HubSpot, and Anthropic.

Revenue Brew spoke with Johnson to figure out how GTM engineering came to be, what it does for sales teams, and what it means for how teams approach the sales cycle.

This interview has been edited and condensed for clarity.

I want to start by talking about the origins of GTM engineering. Why was this role created?

Two or three years ago, there were multiple different names for [GTM engineering], but it was being defined as stringing together multiple different tools and hacking systems internally that weren’t fully connected. This could be any sort of sequencer that you’re using to send out messages in your CRM, and building out these custom scripts to be able to send data between these different platforms.

Was the advent of AI and the increased number of tools it brought into the tech stack part of the reason this position needed to exist?

How sales really works is you have two main options: using all these tools to either mass send out the same message to every single person, or you could use the tools to send very hyperpersonalized messages to a handful of people. Before, to do this you would need multiple different data providers for different regions.

What has your experience been like being on those first GTM engineering teams? How has it evolved over time?

The things we’re building out are becoming more status quo. People are now hiring their own go-to-market engineering teams where before they didn’t really have that.

Before there was a lot more change and you’re selling more change management. Now, go-to-market engineering, at least with a lot of tech companies, is clearly defined; they understand this is something they need. Building that out has become easier because we’re really fitting an industry that has basically formed to a certain degree.

How does AI play into all of this, and what does the actual application look like?

The most powerful piece that AI has done here is [it has] basically allowed us to move away from traditional segmentation based on [a] kind of firmographics.

When AI came out people thought, “Okay, this is a great way that we can use it for spamming these personalized messages,” but it really has helped with more segmentation.

How has AI changed lead orchestration and market research?

Being able to do this account research at scale, and being able to filter and segment based on those hyperniche, specific criteria has really helped a lot. It’s this idea of fishing in ponds or puddles, instead of your entire town.

Even finding the right information about a company, or generating account briefs, or generating premium call notes, or doing all these different pieces that before took hours and hours of manual research, a lot of that is being taken away. So, I think you’re seeing higher volume of sales go through just because people are actually able to spend more time focused on the selling side.

Because of the scale that AI is allowing, where do the human aspects of the sales cycle become most important?

The human connection is now more important than ever with a lot of these sales pieces because people are reached out to and a lot more frequently.

If done well, it’s actually a lot less volume of accounts and contacts that you’ll be outreaching to, and it should be a lot more hyperfocused and hyperpersonalized. The days of picking up a phone and cold-calling an account you think is going to be a good fit—it’s not really there anymore.

Because of AI’s role in early parts of the sales cycle, and with the trend of usage-based contracts, do you think post-sale employees will become even more valuable?

You’re definitely seeing the rise of post-sales being a lot more important. I think this is especially prevalent in any AI company with any consumption-based product, because ultimately a lot of SaaS of the past, particularly go-to-market tech, was seat based.

How much someone could pay with Clay is basically boundless because there are so many different ways you can use it. The main challenge is: How do you get someone in the seat? How do you get them to use it? Having a post-sale team that is very focused on implementing the product and using it becomes a lot more important.