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

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 were, ‘This sounds like a great idea, but you are dead on arrival,'» Misra recalled. «‘You are observing things that you shouldn’t be observing — the privacy of my operators, and the sovereignty of my data on those screens.'»

That conversation, he says, shaped the architecture. Skan aggregates rather than individuates: the system surfaces statistical patterns across hundreds of workers performing the same process, not the behavior of any one of them. «We’re not interested in what John is doing at 10 hours and 43 seconds,» Misra said. «We are interested in what hundreds of Johns put together — what are the statistical and the semantic decisions that they are making in that business process?»

Organizations control what the technology can see through an opt-in scoping model — specific applications and URLs, nothing else — and the data Skan produces never leaves the enterprise firewall. A three-tier architecture sends only anonymized metadata to the cloud. Misra points to deployments approved by European works councils, among the most privacy-protective labor bodies in the world, as evidence the model holds up under scrutiny — and credits it for clearing security review at institutions where most AI tools cannot operate.

Whether aggregation fully defuses the concern is likely to remain contested. The same telemetry that reveals a broken process can, in principle, reveal an underperforming team, and Misra acknowledged that the technology has led some customers to reduce headcount in certain processes.

What $500 million in claimed customer value actually measures

Skan claims more than $500 million in cumulative customer value to date, a figure worth unpacking. Pressed on whether that represents realized savings or projections, Misra was direct that it is an envelope, not a bank balance.

«The number comes from the cumulative, across all our customers, of the quantified savings that we have brought to them — the savings that they have expected they would save,» he said. «Now they are on the roadmap of recouping those savings through a variety of interventions,» including process redesign, technology changes, and, increasingly, AI agents. In other words, $500 million is identified opportunity, some portion of which has been captured.

The more concrete evidence comes from individual deployments. At one top U.S. bank, according to the company, Skan observed 11.2 million context switches across 1,500 finance professionals and uncovered $37 million in operational friction. Turning those observations into agent-executable context cut cost per transaction by 32%, lifted throughput by 41%, and delivered $18 million in annualized savings.

Misra pointed to an anti-money-laundering operation at one bank where «60% of the cases are now being run by AI agents,» adding that the results surprised even him: «The accuracy of those agents surpasses many times over the accuracy of humans. It’s not just an argument of efficiency; it has also become an argument of quality.» Among insurers, he said, Skan typically delivers roughly 25% productivity uplift in core claims processes; one customer doubled its case volume over the past year without adding a single claims specialist.

Skan’s publicly referenceable customers include Unum, the $13.8 billion employee benefits provider, and Mitie, the U.K. facilities management company, whose chief technology and digital officer, Cijo Joseph, said Skan’s technology «gives us unprecedented operational visibility that has dramatically accelerated our AI transformation.» The company declined to share revenue but said it grew more than 300% year over year — for the second consecutive year — with net dollar retention around 150%, and now counts seven of the ten largest U.S. banks and a quarter of the Fortune 50 as customers.

Can AI models learn good work from imperfect employees?

Skan’s thesis rests on observing how work actually gets done — which raises an uncomfortable question. Real employees make mistakes, take shortcuts, and entrench inefficiencies. What happens when the context graph faithfully encodes bad process?

Misra’s answer reaches for the most famous precedent in modern AI. «Think for a moment what OpenAI did,» he said. «OpenAI took the totality of the world’s text and fed it into a transformer architecture, and semantic understanding emerged. OpenAI’s model has seen bad language and has seen good language, and yet it is able to have semantic understanding.»

Skan, he argues, does the analogous thing with work: treat business process execution as a language, where process steps, screen features, and handoffs stand in for words and sentences. Fed enough end-to-end executions, the model learns the full distribution of paths — efficient ones, slow ones, compliant ones — without assuming any single path is best. «The longest path may be the best path, because it is more compliant,» Misra said. An organization then constrains the model along the axes it cares about, and the model returns the path that satisfies them.

«It is not record and play — and that’s the fundamental difference between us and a lot of our competition, UiPath and so on,» he said. «It is fundamentally creating an AI model that understands work, and then constraining that model.»

He offered a concrete illustration of what that unlocks: at one large bank, Skan’s telemetry continuously compares live case execution against a 600-page controls inventory, with agents that trigger alerts when cases miss required compliance steps — turning a document no human could hold in their head into a real-time enforcement layer. It is the kind of application that only becomes possible, Misra argues, once a model genuinely understands the work rather than merely replaying it.

The race to own the context layer of enterprise AI

Skan sits at the intersection of several crowded categories, and its answer to each competitor is a variation on the same theme: scope. Process mining vendors see only what the logs record. RPA incumbents replay tasks without understanding them. And the platform giants — ServiceNow, Salesforce, Microsoft — are shipping capable agents whose vision ends at their own walls.

«The context that these agents have access to is limited to ServiceNow, limited to Salesforce, whereas work spans processes across the board,» Misra said. «Creating a customer entry is a task. To receive an email and decide whether a customer entry has to be created, or something else — that is the process, and that’s what we are after.»

The deeper strategic argument, and the one that seems to resonate with Skan’s regulated customer base, is about differentiation in a world where every enterprise has access to the same frontier models. «If every insurance company, every bank had access to the same models, then the outcomes will asymptotically decay to the outcome of the model,» Misra said. «Historically, you have competed and differentiated in the way you have organized work. That old word — process — now comes back as context for AI. But that context is protected by you. It’s not part of the model.»

That logic explains both the company’s posture toward the model makers — «the more they are successful, the more power we have,» Misra said, disclaiming any ambition to compete with them — and the Nvidia partnership featured prominently in the announcement. Skan runs on Nvidia AI Enterprise and NIM microservices, and Misra described growing demand for private appliances that can observe work, hold the context model, and execute agents entirely inside a customer’s own infrastructure. It also fits the market’s direction: venture investors surveyed by TechCrunch at the end of last year predicted enterprises would spend more on AI in 2026 but through fewer vendors — a consolidation that favors Skan’s decision to ship discovery, intelligence, and agents as a single closed loop.

Misra argues that loop matters more, not less, as automation scales, because agents demand oversight in a way humans never did. «It is an irony of sorts,» he said, «that you’ll probably need much more observation and much more understanding of work in an automated way than you would with humans.» The bet embedded in this round is that work context becomes foundational infrastructure for enterprise AI the way CRM became the system of record for customers — a comparison Cathay Innovation partner Simon Wu made explicitly, calling Skan «one of the defining platform companies of the next decade.»

Misra put the stakes more simply. «You cannot retrieve context that you do not capture,» he said. «The battleground is shifting from the smartest model to knowing how your company actually works — because everyone will have access to the smartest model.»

The frontier labs, in other words, can keep their arms race for the better driver. Skan just raised $63 million on the conviction that the money is in the map.