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

Skan AI, a seven-year-old startup that builds a "context graph of work" by observing how employees perform their jobs across enterprise software, has secured $63 million in Series C funding co-led by Cathay Innovation and Dell Technologies Capital. The company now boasts a total funding of approximately $120 million. Skan is also unveiling two new products, Skan AI Blueprint and Skan AI Agents, alongside its existing Skan AI Intelligence offering, creating a comprehensive platform for discovering, modeling, and automating enterprise workflows.

The funding comes at a time when enterprise AI has faced significant challenges. Despite billions poured into generative AI pilots, only 8% of enterprises have AI agents in production, and 95% of early implementations require a complete redesign. MIT research has shown that roughly 95% of enterprise generative AI pilots fail to deliver measurable returns.

Avinash Misra, Skan's co-founder and CEO, believes the industry misdiagnosed the problem. He argues that while models are fine, they lack an accurate picture of the businesses they are dropped into. Misra contends that the standard playbook for grounding AI agents, which relies on process documentation, standard operating procedures, and system logs, is built on a fiction.

He notes that the way work is documented and the way work actually happens in large enterprises are two different things, and this gap is precisely where agents fail.

Misra and co-founder Manish Garg embarked on this journey seven years ago, before AI agents became a boardroom obsession. They recognized the difficulty in understanding how work is actually done in organizations and the growing importance of operationalizing AI. Frontier models, while intelligent, lack knowledge of exceptions, decisions, handoffs, and institutional habits that define how specific departments operate.

Skan contends that relying solely on documentation and logs for AI agents is insufficient, as source data is never the whole story.

To address this issue, Skan deploys observation technology on employee desktops, continuously watching how work moves across various applications like spreadsheets, CRM systems, email clients, and legacy mainframes. The company abstracts these observations into a living model of the underlying business process. Misra compares this to observing a screen, moving from an Excel sheet, CRM system, and email client, which allows one to build a model of what a person does.

However, this cannot be done at scale or continuously for 1,500 employees. Skan's technology replaces the human observer, capturing the work that happens between system logs and bringing together human agency, the entire application landscape, and the crucial data that matters in execution.