The Era of Context

Summary

Peter Drucker predicted decades ago that knowledge would become the key economic resource. In the AI era, raw expertise is commoditizing — models can approximate expert-level work across domains. What differentiates organizations now is context: the proprietary information, decision traces, and institutional knowledge only your company possesses.

The shift

AI agents are becoming capable lawyers, strategists, researchers, and operators — in the abstract. But an agent that knows nothing about your customers, products, compliance constraints, or tribal knowledge is useless in production.

The binding question is no longer "can we get expert intelligence?" It is "can we get the right context to the agent at the right time?" HP's Lew Platt captured this decades ago: "If HP knew what HP knows, we would be three times more productive." Agents finally make operationalizing that latent knowledge plausible — if the infrastructure exists.

Context engineering is now a discipline. Too little context and agents hallucinate or miss constraints. Too much irrelevant context causes "context rot" — models attend to the wrong details. The design problem is precision: task-specific, accurate, interoperable access to enterprise data.

Why it matters

When everyone has access to similar foundation models, model choice stops being a durable moat. Advantage accrues to teams that capture decision patterns, customer history, workflow state, and domain-specific data — and wire it into agent systems reliably.

Organizations that treat context as an afterthought will find agents impressive in demos and disappointing in production. Those that invest in data readiness, context graphs, and cross-system interoperability will compound productivity as agents take on more work.

What to do

• Inventory proprietary knowledge that agents need but isn't digitized yet (decision traces, informal process knowledge)
• Design context delivery per workflow — not one giant prompt dump
• Guard against context rot: curate what agents see per task
• Ensure agents can interoperate on shared enterprise data (documents, CRM, code, records)
• Treat institutional knowledge capture as infrastructure, not a one-time documentation project