This is the second post in our series on building enterprise AI agents. Read part one: Process Mining to Process Intelligence: The Architecture That Defined the Last Decade Won't Power the Next
In every enterprise, there's no shortage of data—only a shortage of signal.
This isn't a data volume problem. It's a distillation problem. And it's the difference between agentic AI that transforms your business and AI that just creates more noise.
Enterprise work happens across applications, humans, and time. It's noisy, fragmented, and constantly changing.
Consider a single insurance claim. It might touch twelve different applications. Five different people might work on it over three days. Each person makes dozens of micro-decisions—which screen to check first, what data to verify, when to escalate, how to interpret ambiguous information.
Traditional process documentation captures none of this. Your standard operating procedures show the idealized happy path. Your process diagrams show boxes and arrows that bear little resemblance to actual work execution.
You can't build intelligence on that chaos unless you distill it.
That's where Skan AI started—not with copilots or LLMs, but with distillation.
This wasn't an obvious choice in 2019. The market was excited about robotic process automation. Every analyst report focused on automation rates and ROI from eliminated FTEs. The promise was simple: replace human work with bots.
But we saw a different problem. Before you can automate intelligently, before you can train agents that make good decisions, you need to understand what makes good decisions in the first place.
That requires distillation: taking messy, fragmented reality and extracting clear, reusable intelligence.
Skan AI's Observation-to-Agent (O2A) platform observes billions of work events: mouse clicks, keystrokes, application switches, and screen transitions. From these messy, human interactions, we distill clear, reusable intelligence.
This isn't simple data capture. It's sophisticated interpretation.
Every click happens in context. Opening a browser might mean starting a new case, verifying customer information, or checking reference data. The action is identical, but the business meaning is completely different.
We compress all this behavioral data into a model of how work actually flows—not how the stale process diagram or SOP document says it should. Our AI algorithms, trained specifically for enterprise work patterns, identify:
This becomes your process intelligence. It's a living model of actual work execution that updates as your business evolves.
That model becomes our customer's competitive moat.
Here's why: every enterprise has unique process DNA. The way your claims team handles complex cases isn't the same as your competitor's approach. Your underwriters make decisions based on institutional knowledge that exists nowhere else. Your customer service team has developed workarounds that reflect years of learning.
This intelligence is trapped in human execution. It's not in your systems. It's not in your documentation. It's in the collective behavior of your workforce.
When you distill this intelligence into a reusable model, you create something competitors can't easily replicate. They can buy the same AI platforms you use. They can hire similar talent. But they can't instantly acquire your process intelligence—the accumulated wisdom of how work gets done in your specific business context.
It's the difference between an AI agent that mimics random actions and one that understands intent.
It's why our customers can automate with confidence while others are still experimenting.
Without distillation, you're stuck with imitation. Your AI agents learn from examples, but they don't understand the underlying logic. They can replicate observed actions, but they can't adapt to new situations or handle exceptions.
With distillation, you get understanding. Your agents learn the patterns that separate successful execution from failed attempts. They understand when to follow standard procedures and when to deviate. They recognize when a case requires human escalation versus autonomous handling.
Think about how you onboard new employees. You don't just show them one example of how to process a claim. You teach them:
Distilled process intelligence gives your AI agents this same foundation. They're not just copying behaviors—they're operating from a model of how work actually works.
Skipping this step is like trying to have productive employees without ever onboarding them.
We've watched organizations try. They deploy agents trained on limited examples or generic industry data. The agents perform adequately on simple, high-volume tasks. But they fail on anything requiring judgment, adaptation, or domain expertise.
The problem isn't the AI model. GPT-4 is remarkably capable. The problem is the foundation. Without distilled process intelligence, these agents lack the context to make good decisions.
In AI, you can't spray cognition. You have to distill it.
This is especially true as organizations move from simple automation to genuine agentic AI. Agents must:
None of this works without a foundation of distilled process intelligence.
Once you have this foundation, agent training becomes dramatically more effective.
Traditional approaches require months of data labeling, example curation, and iterative testing. Our customers compress this timeline because they start from distilled intelligence that already captures:
The O2A platform automatically converts this process intelligence into agent training data. The agents learn not just what to do, but why—the logic and context that separates successful execution from failure.
This is the architectural difference we described in our previous post. Process mining tools can't create this foundation because they don't observe actual work execution. They can only map what happens within integrated systems.
Process intelligence isn't static. As your business evolves, the model evolves. New best practices emerge. Processes adapt. Exceptions become routine.
Organizations with distilled process intelligence capture these changes continuously. Their AI agents get smarter over time, learning from every interaction. The moat widens.
Organizations without this foundation are stuck in a cycle of manual retraining. Every process change requires new examples, new testing, new deployment. They can't keep pace with business evolution.
If you're evaluating enterprise AI strategies, the distillation question matters more than most vendors acknowledge.
Ask your potential partners:
The answers reveal whether you're building on a foundation of distilled intelligence or just layering AI onto chaos.
We built Skan AI's O2A platform around distillation because we understood a fundamental truth: before you can execute work intelligently, you must understand it deeply.
This isn't the fastest path to market. It's not the easiest story to tell. It requires deployment at scale to capture enough behavioral data. It requires domain expertise to interpret what you observe.
But it creates something durable: a competitive moat built on understanding how your specific business actually works.
As enterprises move from experimentation to production with AI agents, this foundation becomes the difference between incremental improvement and transformational value.
The shortage isn't data. It's signal. And distilling that signal is where competitive advantage begins.
Learn more about how Skan AI enables enterprise AI agents with real human work intelligence.