Here is a number worth sitting with: Gartner says only 8 percent of enterprises have AI agents actually running in production. Of the early implementations that do exist, 95 percent will require a complete redesign.

That is not an adoption problem. That is a context problem. And Skan AI just raised $63 million to fix it.

The Series C round, announced August 13, 2026, was co-led by Cathay Innovation and Dell Technologies Capital, with participation from Citi Ventures, Bloomberg Beta, State Farm Ventures, and Wipro Ventures. The raise brings Skan AI’s total funding to roughly $120 million over its seven-year history, and comes alongside the general availability launch of two new products: Skan AI Blueprint and Skan AI Agents.

Why Agents Keep Failing in the Real World

Every business runs on two versions of itself. There is the version in the process documentation, the org chart, the ERP diagrams. And then there is the version where people actually work: switching between six applications, routing approvals through someone’s personal inbox, working around a legacy system that was never updated.

AI agents trained on the documentation version get deployed into the real version and fall apart. They hit edge cases nobody documented. They skip steps that exist only in institutional memory. They escalate or fail because they were never shown how work actually flows.

This is what Skan AI calls the “context gap” and it is the company’s central thesis: you cannot build agents that work in production without first building a living, accurate map of how your business actually runs.

Skan AI’s technology observes real employee workflows across every system people use, including legacy environments and regulated workflows that most tools cannot reach. It builds what the company calls an enterprise context graph: a continuously updated operational record drawn from thousands of real work patterns, not static documentation.

The Three-Part Platform

The newly launched platform has three components designed to work together.

Skan AI Blueprint is the discovery layer. It maps every process across the enterprise, identifies which workflows are actually being followed versus which ones exist only on paper, and surfaces the highest-value opportunities for automation. Critically, it covers the regulated and legacy environments that most process intelligence tools skip over.

Skan AI Intelligence is the management layer. It gives operations and technology leaders process benchmarking, workforce productivity data, and an ongoing view of automation opportunities as the business changes.

Skan AI Agents is the execution layer. These are agents built and tested against the actual context from Blueprint and Intelligence. They are trained on thousands of observed real cases from that specific business, tested against reality before deployment, and continuously updated as workflows evolve. Human oversight and full audit trails are built in throughout.

The pitch is that each layer feeds the next: you can only build agents that work when you know how work actually happens, and you can only know that through continuous observation.

Why This Matters for Businesses Deploying AI Now

The 95 percent redesign figure from Gartner is a signal the market should take seriously. Most enterprise AI agent pilots are being built against idealized process documentation rather than observed reality. They pass controlled tests and fail in the field.

The businesses getting real production value from AI agents have one thing in common: they understand their actual workflows at a granular level before they automate anything.

Skan AI’s Gartner Magic Quadrant recognition in process intelligence platforms and its inclusion in the Gartner Market Guide for Task Mining Tools suggests the approach is gaining institutional credibility beyond just the funding community. Dell Technologies Capital backing this round is notable too: Dell is one of the largest enterprise infrastructure providers in the world and they are betting this kind of workflow intelligence becomes standard infrastructure for any enterprise AI deployment.

The investor mix is worth noting. Citi Ventures and State Farm Ventures are not software-focused funds. They are the innovation arms of two of the world’s largest financial services companies. Both operate in highly regulated environments where the stakes of an AI agent making a wrong call are significant. Their participation signals that highly regulated enterprises, not just tech-forward companies, see this problem as urgent enough to fund solutions.

What This Means for Business

The enterprise AI market is splitting into two camps. One is chasing agent capability: smarter models, faster reasoning, lower inference costs. The other is chasing agent reliability: the infrastructure needed to make agents work consistently in the messy reality of actual business operations.

Skan AI is firmly in the second camp, and the $63 million suggests investors believe the reliability layer is where durable value gets built.

For any business that has tried to deploy AI agents and hit unexpected failure modes, the Skan AI thesis is worth understanding. The agents probably were not the problem. The missing piece was a clear picture of how the work those agents needed to do was actually happening in the first place.

If your organization is evaluating AI agent deployments, the question to ask before you evaluate any agent platform is simple: how well do you actually know your own workflows? If the answer is “reasonably well, based on our documentation,” that is probably not well enough.

The businesses getting this right are starting with observation and letting the agents come second. That sequence matters more than the technology choice.

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Enterprise DNA’s Omni Ops service deploys AI agent workforces for businesses, with implementation work that includes workflow mapping before automation. Book a discovery call to explore what an AI agent workforce could look like for your operations.