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Good morning. Here’s an interesting stat that captures rising corporate demand for AI. The number of forward-deployed engineers, who help companies customize software to their specific needs, is soaring.
Startups embed staff with customers to teach enterprises how to use the AI and to teach AI how to act in specific customer environments, WSJ Pro VC reports. AI requires more hand-holding, especially by the standards of software-as-a-service.
The median number of job vacancies for forward-deployed AI engineers rose 1,066% to 1,050 vacancies through August, according to Job.zip. The median number of companies hiring for the role rose 650% to 450.
“The proliferation of startups using FDEs comes at a pivotal moment in AI adoption,” given that many companies still struggle to get value out of AI investments. In theory, the forward-deployed engineer should help AI companies and their customers get more value from one another.
Marc Vartabedian and Yuliya Chernova of WSJ Pro VC provide an example of how it works:
Jordan Chin changed his role at New York-based startup Regal to a forward deployed engineer earlier this year. Regal, which raised $83 million in venture capital from Emergence, Homebrew, Founder Collective and others, helps businesses build voice AI agents for customer support and sales.
Chin works with Regal’s customers—from roadside assistance services to Medicare Advantage plans—to tailor AI voice agents’ intonations, conversation content, and even names. He takes what he learns back to Regal’s product team to suggest new features.
“There’s this misconception that AI either works or doesn’t work,” Chin said. In reality, “it involves a lot of iteration and testing.”
The use of such engineers makes sense now, but over time, they could add too much cost to AI deployments, according to Renata Quintini, co-founder and managing director of venture firm Renegade Partners. “If every sale needs engineers in the field, you’re scaling people and not software,” Quintini said.
For now, the use of forward-deployed engineers provides a useful measure of demand for AI, and how customers are working through the very real challenges of making sure their investments are actually useful.
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