Skan AI raises $63 million for AI that maps how employees actually work

Skan AI, a startup focused on enterprise AI challenges, announced it has raised $63 million in Series C funding. The round was co-led by Cathay Innovation and Dell Technologies Capital, with participation from Citi Ventures, Bloomberg Beta, State Farm Ventures, and Wipro Ventures. This brings the seven-year-old company's total funding to approximately $120 million.

The new capital will support the launch of two new products: Skan AI Blueprint and Skan AI Agents. These, alongside its existing Skan AI Intelligence offering, form a comprehensive platform for discovering, modeling, and automating enterprise workflows. The company notes that many enterprise AI initiatives face high failure rates, with only a small percentage successfully deploying AI agents into production, according to research from Gartner and MIT.

Skan AI's co-founder and CEO, Avinash Misra, suggests the industry's problem lies not with AI models themselves, but with a lack of accurate understanding of how businesses actually operate. He believes the focus has been too much on building "better drivers" rather than a "better navigation system" for AI.

In contrast to traditional methods relying on official process documentation—which often diverges from reality—Skan AI employs observation technology on employee screens. By analyzing how work flows across different applications, it creates a dynamic model of actual business processes. This approach aims to capture the nuances of human decision-making, exceptions, and the institutional knowledge that defines how an organization functions.

The company positions itself against process mining vendors like Celonis, arguing that system logs capture only completed transactions. Skan AI focuses on the often-overlooked interactions between these transactions, which it believes contain crucial data for AI in work execution. Observing the screen allows Skan AI to capture the full scope of work, including the human element that system logs miss.

An approach involving continuous observation of employee screens naturally raises privacy and surveillance concerns. Skan AI acknowledges these challenges, emphasizing that the core difficulty lies not in data collection, but in extracting and understanding intent at an enterprise scale.