Context Graphs Are About Judgement

Part of the Judgement Layer series on decision infrastructure.

A veteran supply chain planner retires on a Friday. He knew which safety stock formula the division had settled on after the last major disruption, and why. He knew which supplier relationship required careful handling, and the history behind that care. On Monday, his replacement starts guessing.

This happens at every enterprise, constantly. The data stays. The judgement walks out the door.

Twenty-five years working at the intersection of enterprise systems and supply chain operations, at Syngenta, Pfizer, AstraZeneca, and BASF, and the pattern never changed: organisations capture what happened, but never systematically capture why. The reasoning behind decisions evaporates when the meeting ends or the person leaves. Now the broader industry conversation is arriving at the same problem.

Jaya Gupta and Ashu Garg at Foundation Capital recently identified something important: the next trillion-dollar enterprise platforms won’t be built by adding AI to existing systems of record. They’ll be built by capturing what enterprises have never systematically stored, the reasoning behind decisions. They call this layer the context graph.¹

The context graph framing clarifies an important shift: decisions are no longer isolated events. They live inside structured memory.

What “Context” Actually Means (And Why It’s Three Things)

When people talk about context in enterprise AI, they’re usually conflating three distinct things.

The first is memory. What happened before. The decision traces Foundation Capital describes: who approved a discount exception, which vendor was chosen last quarter, why a product launch was delayed. This is institutional history, and most of it lives in people’s heads or buried in email threads.

The second is situation. What’s happening right now. Current inventory levels, open customer commitments, capacity constraints, market conditions. This is the live data that any decision needs to reference. Most enterprises have this in their systems of record, though accessing it across systems remains hard.

The third is working state. What’s active in this specific decision process. The scenarios being compared, the tradeoffs on the table, the assumptions being tested. This is the most ephemeral and the least captured. It evaporates when the meeting ends.

Building a context graph from memory alone gives you a record of what happened. It misses the reasoning that connected the situation to the choice, and the working state that shaped how people weighed their options.

A useful record captures all three. It connects institutional memory to the live situation to the active reasoning process.

From Data to Judgement: Adding Direction

The difference is structural.

Data describes a state. Inventory is at 12,000 units. The customer’s churn risk score is 74. Last quarter’s revenue missed forecast by 8%. These are points in space. Essential, but static.

Judgement adds direction and force. “We’re going to increase safety stock by 15% because the supplier concentration risk in Southeast Asia exceeds our tolerance, even though it raises working capital by €2M.” That’s a decision with direction, magnitude, reasoning, and an implicit bet about the future.

A database stores the points. A decision carries the forces between them.

The Anti-Atrophy Effect

There are two kinds of atrophy. Both happen quietly. Both compound.

The first is organisational. Every enterprise has experienced the quiet catastrophe: a senior architect leaves, a veteran supply chain planner retires, a PM who held the institutional memory of why a system was designed a certain way moves on. And with them goes judgement that took a decade to build. The organisation keeps its data. What it loses is the reasoning that made the data meaningful. There is then a silent tax on every subsequent decision in that domain. The replacement doesn’t know which constraints are sacred and which are negotiable. They don’t know which supplier relationships have history behind them, or why a particular safety stock formula was chosen over the textbook alternative. So they guess. And guessing at constraints that someone else understood deeply is how organisations make locally rational decisions that are globally incoherent. A record of the reasoning is organisational memory that doesn’t walk out the door, not because it replaces the expert but because it captured the structure of their thinking while they were still making decisions.

The second is individual. When leaders accept system recommendations without engaging with the reasoning behind them, their own decision-making muscle fades. The muscle that used to weigh tradeoffs, sit with discomfort, and form independent views weakens with disuse. Technology should make human judgement structurally better, not structurally unnecessary.

The Trillion-Dollar Shift

Enterprises have never systematically stored the reasoning behind their decisions. That reasoning is their least captured asset.

The companies that capture it will have something no amount of data can replicate: an organisation’s record of not just what was decided, but why it was decided.

Data tells you what the world looks like. Judgement tells you which direction to push, how hard, and why. One is a photograph. The other is a navigation system.

Sources

1. Jaya Gupta and Ashu Garg, “AI’s trillion-dollar opportunity: Context graphs,” Foundation Capital, 22 December 2025. Frames decision traces as the next platform layer; argues that the companies that capture and structure the reasoning behind enterprise decisions into context graphs will own the most valuable layer in enterprise AI.