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Skan AI secures $63 million to analyze employee work patterns for efficiency

Skan AI has raised $63 million in Series C funding to enhance enterprise efficiency by analyzing real employee work patterns. This investment, totaling about $120 million since its founding, aims to โ€ฆ

Skan AI raises $63 million betting that watching how employees actually work is the missing layer of enterprise AI
VentureBeat โ€” 12 August 2026
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Skan AI, a startup focused on enhancing enterprise efficiency, has secured $63 million in Series C funding. The funding round was co-led by Cathay Innovation and Dell Technologies Capital, with additional participation from Citi Ventures, Bloomberg Beta, State Farm Ventures, and Wipro Ventures. This latest investment brings the company's total funding to approximately $120 million since its inception seven years ago.

This funding comes at a crucial time for the enterprise AI sector, which has faced significant challenges recently. Companies have invested heavily in generative AI initiatives, yet many have reported disappointing outcomes. According to Gartner, only 8% of these projects have yielded satisfactory results. Skan AI aims to address this gap by creating a "context graph of work" that analyzes how employees perform their tasks in real-time across various enterprise software platforms.

In conjunction with the funding announcement, Skan AI unveiled two new products: Skan AI Blueprint and Skan AI Agents. These tools complement the existing Skan AI Intelligence solution and together create a comprehensive platform for discovering, modeling, and automating enterprise workflows. By focusing on the actual work patterns of employees rather than abstract models, Skan AI hopes to provide organizations with actionable insights that can transform their operations.

The implications of this funding and product launch are significant. As businesses continue to grapple with the effectiveness of their AI investments, Skan AI's approach could offer a new path forward. By leveraging real-world data and behavior, the company aims to provide a more accurate and useful framework for understanding and improving enterprise processes, potentially reshaping how companies implement AI solutions in the future.

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