Skan AI raises $63 million betting that watching how employees actually work is the missing layer of enterprise AI

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Skan AI, a startup that builds what it calls a "context graph of work" by observing how employees actually perform their jobs across enterprise software, has raised $63 million in Series C funding co-led by Cathay Innovation and Dell Technologies Capital, the company announced Wednesday. Citi Ventures, Bloomberg Beta, State Farm Ventures, and Wipro Ventures also participated in the round, which brings the seven-year-old company's total funding to roughly $120 million. Alongside the raise, Skan is announcing the general availability of two new products — Skan AI Blueprint and Skan AI Agents — that, together with its existing Skan AI Intelligence offering, form a complete platform for discovering, modeling, and ultimately automating enterprise workflows. The announcement lands at a moment of deep frustration in enterprise AI. Companies have poured billions into generative AI pilots, but the results have been dismal: Gartner research cited by the company finds that only 8% of enterprises have AI agents in production, and 95% of early implementations will require a complete redesign. Those figures echo an MIT report last year, covered by Fortune, which found that roughly 95% of enterprise generative AI pilots were failing to deliver measurable returns. Avinash Misra, Skan's co-founder and CEO, believes the industry has misdiagnosed the problem. The models are fine, he argues. What they lack is an accurate picture of the businesses they are being dropped into. "Everyone is obsessed with building a better driver," Misra told VentureBeat in an exclusive interview ahead of the announcement. "We think the bigger opportunity is building a better navigation system." Why enterprise AI agents keep failing when they rely on official process documentation The standard playbook for grounding AI agents — feeding them process documentation, standard operating procedures, and system logs — is built on a fiction, Misra argues. The way work is documented and the way work actually happens inside a large enterprise are two different things, and the gap between them is precisely where agents fail. That gap is what sent Misra and co-founder Manish Garg down this path seven years ago, long before agents were a boardroom obsession. "Why is it so difficult for an organization, and a large enterprise especially, to understand how its own work actually gets done?" Misra said. "Why does it need to fly in McKinsey consultants for that?" The question has only grown more consequential as enterprises race to operationalize AI. Frontier models arrive at the company door brilliant but blind, with no knowledge of the exceptions, decisions, handoffs, and institutional habits that define how a claims department or a compliance team actually operates. Every company now stuffing agents with documentation and logs, Skan contends, is discovering the same uncomfortable truth: the source data was never the whole story. And a source data problem cannot be fixed downstream. Skan's answer is t…