Dell Technologies Capital Co-Leads $63M Skan AI Funding

What happened

Dell Technologies Capital co-led a $63 million Series C funding round for Skan AI alongside Cathay Innovation. Citi Ventures, Bloomberg Beta, State Farm Ventures, and Wipro Ventures also participated, bringing Skan's total funding to roughly $120 million. Alongside the raise, Skan launched two new products - Skan AI Blueprint and Skan AI Agents - which join its existing Intelligence offering to form a complete platform for discovering, modeling, and automating enterprise workflows. The company claims revenue growth exceeding 300% year-over-year for the second consecutive year, with average net dollar retention of 150%. Skan's customer base now includes seven of the ten largest U.S. banks and a quarter of the Fortune 50, with the platform having processed over 25 billion work signals. Dell Technologies Capital previously led Skan's $40 million Series B in March 2022.

Why it matters

Dell Technologies Capital's repeat leadership in Skan AI's funding underscores the firm's strategic focus on enterprise AI infrastructure. For Dell Technologies shareholders, the investment reflects the company's capital allocation toward AI platforms that address a critical gap in enterprise adoption: translating generative AI capabilities into measurable operational value. Skan's technology tackles a persistent challenge highlighted by Gartner research, which found that only 8% of enterprises have AI agents in production and 95% of early implementations require complete redesign. By capturing how work actually happens - rather than relying on documentation or system logs - Skan claims to deliver 30–40% operational savings and has generated over $500 million in measured customer value. One deployment at a major U.S. bank identified $37 million in operational friction and delivered $18 million in annualized savings with 32% lower cost per transaction and 41% higher throughput. Dell Technologies Capital managing director Raman Khanna emphasized that enterprise leaders now face a mandate to identify where AI can create measurable operational advantage, positioning Skan's observation and context layer as foundational infrastructure for that goal.

Bigger picture

The funding highlights growing investor conviction that enterprise AI adoption depends less on model capabilities than on solving the context problem. While frontier models from OpenAI, Anthropic, and others have become widely accessible, enterprises struggle to ground them in the messy reality of how work actually happens inside large organizations. Process mining vendors like Celonis reconstruct workflows from backend system logs, but Skan argues that approach misses 80% of execution that occurs between committed transactions. Robotic process automation incumbents like UiPath replay tasks without understanding intent, and platform giants such as ServiceNow, Salesforce, and Microsoft ship capable agents whose vision ends at their own application walls. Cathay Innovation partner Simon Wu framed enterprise work context as becoming foundational infrastructure for corporate AI the way CRM became the system of record for customer relationships. The broader market context is sobering: an MIT report found that roughly 95% of enterprise generative AI pilots fail to deliver measurable returns, echoing Gartner's findings. Skan's approach - observing desktop activity across applications, abstracting intent from screen-level behavior, and feeding that context to AI agents - represents a bet that differentiation in enterprise AI will come from proprietary process knowledge rather than model access. The company's growth trajectory and customer concentration in highly regulated industries (financial services, insurance, healthcare) suggest the observation-based model has cleared privacy and compliance scrutiny, including approval by European works councils.

What to watch

Monitor whether Dell Technologies Capital's continued investment leads to tighter product integration with Dell's own enterprise infrastructure offerings, particularly around private AI deployments. Skan emphasized growing demand for private appliances that observe work, hold context models, and execute agents entirely inside customer infrastructure - an architecture that aligns with Dell's hardware and systems business. Watch for customer adoption metrics beyond revenue growth, particularly whether the 8% enterprise AI agent production rate cited by Gartner improves as Skan's observation layer matures. Key proof points will include whether the 30–40% operational savings Skan claims hold up at scale and whether competitors in process mining, RPA, or enterprise platforms respond by adding screen-level observation capabilities. Regulatory scrutiny around workplace monitoring technology remains a wildcard; while Skan claims its aggregated, anonymized approach satisfies privacy requirements, the same telemetry that reveals broken processes can identify underperforming teams. Finally, track whether Skan's claim that AI agents trained on observed employee behavior achieve higher accuracy than humans in domains like anti-money laundering - where one customer reportedly runs 60% of cases through agents - proves replicable across industries.