Skan AI Raises $63M Series C to Build AI Agents by Watching How Employees Actually Work

Skan AI Secures $63M to Transform Enterprise Work Into AI Agents

Skan AI announced $63 million in Series C funding on Wednesday, co-led by Cathay Innovation and Dell Technologies Capital1

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. The process intelligence company builds AI agents by observing how employees actually perform enterprise work across applications. Citi Ventures, Bloomberg Beta, State Farm Ventures, and Wipro Ventures also participated in the round, bringing total funding to approximately $120 million since the company's 2019 founding2

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How Skan AI Records and Analyzes Employee Workflows

The platform deploys software on employee desktops that captures screenshots and processes them locally on the machine1

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. The actual images never leave the device. Instead, only anonymized metadata transmits to Skan's analytics platform, including application usage patterns, time allocation by process, workflow sequences, and decision paths1

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. Employee identifiers are swapped for tokens before any data moves anywhere, and the company states that personal messages and passwords are never captured1

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Co-founder and CEO Avinash Misra explained the core insight: "Everyone is obsessed with building a better car. We think the bigger opportunity is building a better navigation system"1

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. The platform has logged upward of 25 billion work signals across its customer base3

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Creating a Map of Enterprise Work From Direct Observation

Skan offers three products that form an AI-driven platform for discovering, modeling, and automating enterprise workflows2

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. Blueprint maps how processes move across systems and teams, Intelligence identifies where time and money leak from operations, and Agents deploys automation modeled on what top performers actually do3

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Misra argues that standard approaches to operationalizing AI in enterprises fail because they rely on documentation rather than reality. "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"2

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. The screen becomes the convergence point where human agency, the entire application landscape, and relevant data come together2

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Major Financial Institutions Deploy the Platform at Scale

A quarter of the Fortune 50 are customers, along with seven of the ten largest US banks and three of the five largest US insurers1

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. At one large US bank, Skan observed 11.2 million context switches across 1,500 finance professionals, surfacing $37 million in operational inefficiencies1

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. AI agents built on those observations cut cost per transaction by 32% and lifted throughput by 41%, delivering $18 million in annualized savings1

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The company reports revenue growth exceeding 300% year-over-year, with net dollar retention averaging 150%1

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. Skan claims cumulative measured customer value exceeds $500 million, with average operational savings between 30% and 40%3

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Why Enterprise AI Agent Deployments Keep Failing

The funding arrives amid widespread frustration with generative AI implementations. Gartner research cited by the company finds that only 8% of enterprises have AI agents in production, and 95% of early implementations will require complete redesign1

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. These figures echo an MIT report covered by Fortune that found roughly 95% of enterprise generative AI pilots failed to deliver measurable returns2

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Misra frames this as a data problem rather than a model problem. "You cannot fix a source data problem downstream. Better models will not solve it. Better prompts will not solve it"1

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. He argues that process mining vendors like Celonis only capture completed transactions from backend systems, missing the messy human work between those committed states. "Eighty percent of what you're interested in, from an AI point of view, in execution of work, actually lies between those systems"2

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Workplace Monitoring Raises Privacy Questions in Europe

Two investors attached to the round are European. Cathay Innovation, one of the co-leads, is French, and UK facilities group Mitie with 75,000 staff provided a customer testimonial1

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. Mitie's Chief Technology Officer Cijo Joseph credited Skan with "unprecedented operational visibility"1

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Europe presents both opportunity and scrutiny for AI-driven workflow automation involving workplace monitoring. Skan's privacy guide cites a deployment at Allianz in Munich that won full works council approval1

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. However, TD Bank in Canada scaled back a monitoring rollout after workplace surveillance objections from staff, and Meta paused a program collecting keystrokes for AI training in June1

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Cathay Innovation partner Simon Wu argued that enterprise work context is becoming infrastructure for corporate AI similar to how CRM software became the system of record for customer data, calling Skan the only company building that context graph of work from direct observation rather than documentation or system logs3

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. Skan plans to deploy the new capital toward product development and expansion in financial services, insurance, healthcare, and technology sectors3

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