Clay is perhaps one of the most talked about tools in the modern GTM stack.
Founded in 2017, the GTM platform now operates at +$100M ARR and was valued at $3.1B following a $100M Series C in August 2025.
In addition to building a product people love, they’re championing a new approach to GTM. One that helps companies win in the AI era.
We spoke to Head of GTM Engineering Everett Berry to understand what this kind of growth journey looks like behind the scenes.
Below are the key takeaways, from how Clay works with customers, to how it finds (and keeps) a competitive edge, and what actually works when it comes to AI automation.
The rise of GTM engineering
The role of GTM engineer didn’t exist a few years ago. In mid 2025, there were 1,400 ads for GTM Engineer roles live on LinkedIn.
At a base level, a GTM engineer automates parts of the go-to-market process: prospecting, enrichment, routing, campaign execution, and messaging.
At Clay, GTM engineers also help its customers build these automated GTM workflows, Everett explained:
“We go into customers and we help them automate their processes in Clay… these new AI strategies and these high-leverage, automated tasks.”
Even senior leaders like Everett spend time getting into the weeds with customers:
“You won’t succeed at Clay if you start to lose track of what’s happening with the product, the functionality, and what customers are doing with it. I try to stay really close to the metal.”
GTM Alpha: Why creativity defines winning teams
Staying close to the customers is one way Clay stays ahead, the other is what Everett referred to as “GTM Alpha”. It’s a concept borne from Everett’s disappointment in previous growth marketing roles, he explained:
“Most go-to-market tactics have a shelf life. Even if it is super productive, it will stop working over time…The software will catch up, the data will catch up, other people will start doing it, and the market will saturate itself.”
One example is alma mater emails. Where, previously, including someone’s school or college in an email was attention grabbing, now it’s dismissed as AI.
GTM Alpha is the response to this cycle and it refers to building a system that protects you when tactics expire:
“It is the concept of continually finding new tactics and experimenting… to maintain a permanent edge against your competitors.”
In practice, it involves allowing teams to be creative and try new channels, Everett added:
“Creativity is the key. Without that, and without a muscle to experiment and be wrong, and also double down on things that are working, GTM Alpha won’t work.”
Everett shared a working example at Clay, where the team built a workflow around an X account that leaks VC funding rounds before they’re public.
Clay pulls those posts into its demand gen systems automatically and routes them to the right owners.
“That tactic probably has a very short shelf life… but that’s the kind of creativity and niche you can find if you’re able to experiment.”
The initiatives driving growth at Clay
A big part of GTM Alpha is experimentation, the other is doubling down on what works. For Clay, that’s customer hackathons.
What started as an organic request from a customer now forms a key part of Clay’s top of funnel GTM:
“A customer said, ‘Can we just come to the office and build these with you?’ And in a couple of hours, we knocked out automation workflows that would normally take weeks.”
Today, Clay runs hackathons with customers all over North America to help them build GTM workflows in real time.
In addition to hackathons, Clay also uses in-person events as a way to get in front of customers and prospects directly. For Clay, it’s a way of tapping in to that creativity needed for successful GTM:
“The creative go-to-market, designing, and building, it just really works well in person.”
How Clay uses AI internally
One of the reasons they can prioritise face time with customers is because of the automation in place behind the scenes.
The biggest AI automation win for Everett is automating Salesforce updates. Sales calls are analysed through Clay and internal LLMs, with deal stages updated automatically.
“The ability of LLMs to understand the context and quality of the calls has made a huge difference for us.”
Importantly, reps can still review the outcome, and override it if necessary. The rep gets a message explaining why the system thinks a deal should move stage, giving them the option to accept or override it. If there’s no response, the update happens automatically.
“Our CRM is the cleanest it’s ever been… I haven’t opened Salesforce in months.”
Where AI falls short
One area where AI hasn’t yet hit the mark for Clay is AI SDRs, Everett explained:
“Automating that full cycle is just not possible right now. There’s way too much context and creativity required.”
For now, he’s thinking instead about the opportunity for AI to support more structured areas like renewals, sales engineer support, and voice AI agents for inbound and outbound.
Go deep on GTM at SaaStock USA
Clay’s approach reflects how AI first teams are running go-to-market: GTM engineering, fast experimentation, and value-adding use of AI.
We’ll be going even deeper on GTM strategy at SaaStock USA in April. Join us and hear from leaders from Freshworks, Go Nimbly, Make, Datarails, Legora and more on what’s accelerating growth.
