The Era of Context

The Knowledge Economy

Peter Drucker, one of the most important thinkers on management and business, predicted in the 1990s that “knowledge has become the key economic resource and the dominant—and perhaps even the only—source of competitive advantage.”

He couldn't have predicted the AI era more accurately. As AI progress continues unabated, AI models will get more and more capable of augmenting or automating a variety of knowledge worker tasks. Just as we've seen in coding, these models will morph into AI agents that are expert lawyers, healthcare professionals, business strategists, scientific researchers, and other roles in almost every domain of work.

The AI Context Problem

But these models, by default, don't know anything about your particular team or organization. In an instant, they might be asked to review a legal contract for one company, and then in the next second be writing code for a completely different organization. They are fully general-purpose superintelligence systems that can take on any task for anyone that's asking.

“As a consequence, that means your company is getting the same expert lawyer as another company, the same engineer, and so on. The question we will have to wrestle with is: in a world where everyone has access to the same intelligence, how does a company differentiate?”

The answer lies in context—the proprietary information, decision patterns, and institutional knowledge that makes your organization unique.

Context as Differentiation

Certainly how teams and employees use AI agents effectively will matter, but the ultimate force-multiplier will be the context that the agents receive:

• ●Context about the right products to build
• ●The ways to serve customers
• ●The markets to go after
• ●The specific details of client interactions
• ●The tribal knowledge in an organization
• ●Endless amounts of other proprietary information

This problem has always plagued companies well before agents existed. Companies have always been a collection of various forms of context they try to extract the most value from: their processes, intellectual property, unique ideas and roadmap, ways they make decisions, information about their customers.

“If HP knew what HP knows, we would be three times more productive.”

Context Engineering

Context engineering has emerged as a critical discipline to tackle just this challenge, and it's not an easy problem. Imagine taking an expert lawyer or engineer that by default knows absolutely nothing about your organization, and you only have a single document's worth of space to describe:

• ●Their entire job responsibilities
• ●Every system they have to leverage
• ●All the data they are supposed to work with for that particular task
• ●What their objectives are

Getting the right information to them becomes a critically important requirement to drive their productivity. This is where context graphs come into play.

Metcalfe's Law of Data

AI Agents finally make the vision of leveraging organizational knowledge possible. We can, for the first time, begin to tap into this wealth of knowledge and information sitting inside of our organizations.

This means that in the 21st century, one of the most critical forms of competitive advantage will be a company's ability to capture, manage, and build processes around the right context:

• ✓The real estate firm with better insights on market pricing will get more clients
• ✓The pharmaceutical company that can develop drugs using reams of data will generate more revenue
• ✓The marketing agency that can generate better campaigns will differentiate more

Organizational Change

Companies will have to drive a substantial amount of change management to make this work. We imagined that AI systems would adapt to how we work, but it turns out due to their extreme power (and inherent limitations) we will instead adapt to how they work.

This means we will have to optimize our organizations and workflows to best enable context for agents to be successful. The core tenant of this change is that the user is now responsible for directing and guiding agents on how to do their work, ensuring it gets the right context along the way.

The Future of Context

The overall trend is clear: we are entering the era of context. The teams and companies that can accumulate and best utilize context will drive the greatest productivity and highest output. Those that don't will find it harder and harder to serve customers competitively.

Just as Peter Drucker predicted about knowledge becoming the key economic resource, context is now becoming the foundation of competitive advantage in the AI era.