Only 8% of AI agents work - can $63M fix enterprise AI workflow?
Skan AI has raised $63 million in Series C funding, betting that the biggest obstacle to a working enterprise AI workflow isn’t a smarter model — it’s a company’s inability to see how its own employees actually get things done. The round, co-led by Cathay Innovation and Dell Technologies Capital, arrives as enterprises grapple with a stark reality: most of the AI agents they’ve deployed so far simply haven’t worked.
Citi Ventures, Bloomberg Beta, State Farm Ventures, and Wipro Ventures also joined the round, pushing the seven-year-old company’s total funding to roughly $120 million. Alongside the raise, Skan AI announced general availability of two new products, Skan AI Blueprint and Skan AI Agents, which join its existing Skan AI Intelligence offering to form what the company describes as a full platform for discovering, modeling, and automating enterprise workflows.
Key takeaways
- Skan AI secured $63 million through Series C funding jointly led by Dell Technologies Capital and Cathay Innovation, bringing total funding to roughly $120 million.
- The company launched two new products, Skan AI Blueprint and Skan AI Agents, alongside its existing Intelligence platform.
- Gartner data cited by the company shows only 8% of enterprises have AI agents in production, and 95% of early implementations require a redesign.
- Skan claims more than $500 million in cumulative identified customer value, with customers including seven of the ten largest U.S. banks, Unum, and Mitie.
- The platform runs on Nvidia AI Enterprise and NIM microservices and aggregates anonymized data inside the enterprise firewall to address privacy concerns.
A funding round built around a failure rate the industry can’t ignore
Enterprise AI adoption has run into a wall that money alone hasn’t been able to fix. Gartner research cited by Skan AI found that only 8% of enterprises currently have AI agents running in production, while 95% of early implementations will need a complete redesign before they deliver value. Those numbers track with an MIT report from during the previous year, as reported by Fortune, which documented that approximately 95% of enterprise generative AI pilots were not succeeding in produce measurable returns.
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