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 to go to the source itself. The company deploys observation technology on employee desktops that continuously watches how work moves across applications — the spreadsheet, the CRM, the email client, the 40-year-old mainframe — and abstracts those observations into a living model of the underlying business process. "Think of it this way: if I were to share my screen here, and you were to observe my screen going from Excel sheet, CRM system, email client, in about two iterations you'd build a model of what I do," Misra said. "Except you couldn't do that at scale. You couldn't do it 24/7, and for 1,500 people like me. Now replace yourself with our technology." How screen-level observation captures the work that never shows up in system logs That framing also explains how Skan positions itself against process mining vendors like Celonis, which reconstruct workflows from the data trails left in backend systems. System logs, Misra argues, only capture completed transactions — not the messy human work that produced them. "All backend data, by definition, is a committed state of work. Work is really what happens between those committed states," he said. "Eighty percent of what you're interested in, from an AI point of view, in execution of work, actually lies between those systems." The screen, in Skan's view, is the one place where everything converges. "It brings together human agency, it brings together the entire application landscape, and it brings together the data that matters," Misra said. Two decades of user interface design have quietly buried enormous amounts of process knowledge in the space between a worker's eyes and their monitor; Skan's pitch is to bring that hidden layer back to the surface. But watching, he insists, was never the hard part — a point aimed squarely at the incumbents who might be tempted to copy the approach. "The hard problem is not screen observation," Misra said. "The hard problem is abstraction of what you see on the screen — the intent extraction." A human watching a colleague's screen can instantly tell whether a jump back to step one means a new case or rework on an old one, because humans understand the signature of the work. Teaching a model to make that same judgment, statefully and at enterprise scale, is where Skan believes its seven-year head start lives. The result is a context model that AI can reason over and act on — the raw material for the agents that now sit at the top of the company's product stack, and the foundation for everything else the platform does. Walking the line between operational telemetry and workplace surveillance An approach built on continuously watching employee screens invites an obvious objection, and it is not a hypothetical one. In June, Reuters reported that Meta scaled back an internal tool that tracked employee mouse clicks after workers raised concerns — a sign that even AI-forward companies are wary of the line between operational telemetry and surveillance. Misra says he heard the objection before he wrote a line of code. When he first pitched the concept to Delphine Icart, then chief transformation officer at AXA Mexico, her reaction was blunt. "Delphine's first words to me