We have all seen it happen. A high-performing leader leaves. They hand over the passwords, the project files, the process documents. On paper, the transition is seamless.
Three months later, the department is in chaos.
Why? Because the Process Document describes what happens when everything goes right. It doesn't describe reality.
Most critical decisions happen in the gray areas.
The tribal knowledge passed down over coffee. The "it depends" side conversations. The Slack threads at 5 PM on a Friday. The judgment calls that never make it into any system.
Your ERP shows the discount was approved. Your CRM logs the deal closed. But neither captures why it was allowed to happen (eg: "We approved it because the client is a strategic partner for the Q3 expansion, despite the margin hit").
We keep automating tasks while the decision-making behind them remains invisible.
AI agents are hitting the exact same wall new employees do. They have no memory of precedents, exceptions, or tradeoffs. They can follow the rule, but they cannot understand why.
Humans provide nuanced judgment at the edges: the ambiguous deals, the tricky escalations, the compliance tradeoffs.
If we don't capture how we reason through these moments, AI can only ever automate the easy stuff.
The next trillion-dollar opportunity in AI isn't better models or more data. It's capturing why decisions get made.
When your best leader walks out the door, their judgment walks out too. Unless you've found a way to encode it.
That’s the Context Graph, and it will be the single most valuable asset for companies in the AI era.
THE SHIFT
Idealized Process → The 'It Depends' Reality
A process map is static. A Context Graph is a living record of how your organization actually operates. It turns every meaningful decision into a structured record: what inputs were consulted, which policies applied, where exceptions were made, and who approved them.
Automating Tasks → Encoding Judgment
The opportunity isn't replacing human judgment. It's encoding it so future humans and agents can build on it.
Information → Wisdom
Information is knowing that the deal closed. Wisdom is knowing the three specific concessions that made the deal close. The Context Graph captures the wisdom.
THE STRATEGY
You cannot buy a Context Graph off the shelf yet, but you can start building the dataset for one today.
You need to reframe the question from "What can we automate?" to "How do we preserve and scale how our best people decide?"
The Exercise: Identify a recurring high-stakes decision in your team (e.g., approving a non-standard contract, prioritizing the product roadmap, hiring a VP).
The Prompt: Whenever this decision is made, use this framework to capture the logic. Over time, this creates a dataset of reasoning, not just results.
We are making Decision [X].
- The Outcome: [Approved/Denied]
- The Constraints: What rules did we follow?
- The Exception: Did we break a rule? If so, why? (e.g., 'Approved because of strategic partnership potential').
- The Precedent: What past decision did we reference?
Summarize this logic so a new AI agent (or new employee) could replicate this judgment in the future.
THE STACK
Tool: Glean
Why it matters: Most companies have amnesia. You solve a problem in January, and someone else solves the same problem from scratch in June because the context is buried in a private email chain.
The Use Case: Tools like Glean connect across your Slack, Google Drive, Jira, and Salesforce to create a search index of your company's brain. Instead of asking "Where is the file?" (System of Record), you can ask "How did we handle the pricing objection for Client X last year?" (System of Context).
If your best decision-maker left tomorrow, would they take the "Why" with them?
Until next time...stay curious!
Cheers,
Nikki
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