**Our Take: The Missing Map for the Age of AI Agents**
For years, the enterprise narrative has been dominated by the search for a better algorithm. We've been obsessed with the raw horsepower of foundation models, treating them as the intelligent drivers of the digital economy. Yet, as the staggering failure rates of AI pilots make painfully clear, a brilliant driver is useless without a road. The real crisis isn't a lack of intelligence; it's a profound lack of context. Skan AI's recent funding and strategic shift signal a necessary maturation in our approach, pivoting from the model to the messy, undocumented reality of how work actually happens. The industry has been trying to automate a process it doesn't even understand, and Skan's bet is that the only way forward is to finally capture the map of that work.
The core insight here is both elegant and disruptive: we have been feeding our most sophisticated models on a fiction. We hand them standard operating procedures and system logs, expecting them to master the nuances of a claims department or a compliance team. But as Skan's leadership astutely points out, work is what happens *between* the committed states. It's the judgment calls, the exceptions, the context switching between a legacy mainframe and an open email client. This is the "context" that a 600-page manual could never capture. By observing the screen, the one place where human agency, application landscapes, and data converge, Skan is extracting the tacit knowledge that has remained invisible to the backend systems. It's not about surveillance; it's about abstraction, teaching a model to understand the "language" of work, complete with its dialects, shortcuts, and even its flawed grammar.
This brings us to the most provocative and intellectually honest part of their thesis: the willingness to learn from imperfection. The immediate reaction is often, "Why encode bad habits?" But this perspective flips the script on traditional process mining. We don't need to automate the "ideal" path; we need to understand the distribution of all paths. By treating business execution as a language, Skan's model learns the full spectrum of human behavior, the efficient, the slow, the compliant. It doesn't judge; it understands. This is a profound leap from the record-and-play logic of RPA. It allows an organization to constrain the model to find the path that meets its specific needs, whether that's speed, cost, or compliance. In a world where every bank has access to the same models, the only defensible moat is the unique, proprietary context of how a business organizes its work.
We are entering a new era where the winners won't be those who build the best model, but those who build the best navigation system for their own enterprise. Skan's positioning is a powerful reminder that in the rush to adopt AI, we cannot skip the foundational step of understanding the human processes we seek to augment. The context graph is not just another tool; it is the missing infrastructure layer for the agentic enterprise. The challenge ahead is not about the models themselves, but about the quality of the map we build to guide them. The data is there, waiting to be captured. The question is no longer if we should look, but how quickly we can start building the maps that will define the next decade of work.