Key Takeaways

  • Clay secured a $115 million Series D led by Wellington at a $7.1 billion valuation.
  • Clay’s growth agents combine internal business data with external signals to identify prospects and coordinate personalized campaigns.
  • Clay also launched a $1 million scholarship fund for GTM engineers ahead of its Sculpt conference on October 8.

Clay has raised $115 million in Series D financing at a $7.1 billion valuation, giving the New York-based AI go-to-market business fresh capital to expand its vision of a self-learning revenue engine.

Wellington led the round, with participation from Sequoia, StepStone, Andreessen Horowitz (a16z) Perennial, Meritech, DST, CapitalG, BoxGroup, Boldstart, Bloomberg Beta and Evolution. The investment comes as Clay reports more than 17,000 customers, including 80% of the Forbes AI 50. Anthropic, Google, OpenAI, Stripe, ElevenLabs, Workday and Siemens are among the named users.

That customer mix matters. It gives Clay access to demanding organisations already familiar with deploying AI in operational settings, rather than businesses merely testing isolated chatbots.

“AI is unleashing the biggest wave of company creation in history, and Clay’s goal is to be the engine those companies use to grow to their full potential,” said Kareem Amin, Clay co-founder and CEO.

Clay began by aggregating internal company data and outside signals, then added infrastructure for running personalized campaigns. Its next stage centres on growth agents that help teams decide what action to take, identify suitable customers, monitor buying signals and deliver campaigns based on changing market information.

Clay calls the people operating these systems GTM Engineers. The comparison is deliberate: just as software engineers increasingly supervise coding agents, GTM Engineers can configure and oversee agents responsible for revenue workflows. The role may draw on RevOps, growth marketing, software engineering and design rather than belonging neatly to one established department.

While automating a single email is straightforward, building a revenue system that remembers prior interactions, understands product usage and adapts based on outcomes presents a much deeper technical challenge.

Clay addresses this complexity by combining CRM records, campaign engagement, calls, emails and product usage with external signals such as funding announcements, hiring activity and job changes. Teams can then describe a commercial objective in plain language. Clay converts the request into a workflow that users can inspect and modify, either through deterministic rules or AI agents.

For example, a team could ask Clay to arrange meetings with fintech marketing leaders. Clay could identify relevant people, research their businesses, prepare tailored presentations and establish an outreach sequence. If prospects repeatedly request pricing clarification, Clay could detect the pattern and revise future materials. Human oversight remains part of the operating model, an important point when communications directly affect customer relationships.

Broader market data underscores the demand for these revenue-generating systems. McKinsey, Gartner and the Stanford AI Index offer different measures of that momentum. McKinsey estimated that generative AI could contribute $2.6 trillion to $4.4 trillion annually across business functions, with marketing, sales and customer operations among the largest opportunities. Gartner projected global AI spending of $644 billion for 2025, while the Stanford AI Index 2025 reported that 78% of organisations used AI in 2024, up from 55% in 2023 (source).

Still, adoption does not automatically equal transformation. Research from AWS and Strand Partners found that AI adoption reached 38% of Malaysian businesses in 2026, up from 27% in 2025. Yet 67% of adopters remained focused mainly on basic applications, and only 19% had a formal strategy for scaling AI across functions.

Could agent-led revenue operations help close that gap? Potentially, but businesses will need clean data, measurable objectives and clear accountability. Governance approaches such as the NIST AI Risk Management Framework and ISO/IEC 42001 can also inform how organisations document oversight, monitor outcomes and manage AI-related risk.

Clay is supporting the emerging profession with a $1 million scholarship fund intended to train more GTM engineers. Founded in 2017 by Kareem Amin and Nicolae Rusan, with Varun Anand joining as co-founder in 2021, Clay will preview upcoming product launches at Sculpt, its second annual user conference, in San Francisco on October 8. The funding sets a high valuation; execution will now determine whether Clay can turn its category language into a durable enterprise operating model.