Harnessing AI Expertise: How Context Will Define Competitive Advantage in the Future

AI expertise is becoming a commodity
Artificial Intelligence is rapidly reshaping how companies operate, and one of the biggest shifts is that “expert intelligence” is no longer confined to a small set of specialists. As AI models improve, they are increasingly able to handle complex, high-level tasks across industries—work that once required years of training in fields like law, medicine, finance, and operations.
Box CEO Aaron Levie has argued that this change will make advanced expertise far more accessible than it has ever been. If powerful AI capabilities become widely available—embedded into products, platforms, and everyday workflows—the traditional advantage of simply “having the best experts” starts to weaken. The tougher question becomes: when everyone can access strong AI reasoning, how do businesses still stand out?
Levie’s view is that differentiation will shift away from raw intelligence and toward something companies already possess but often underuse: the unique information that sits inside their organizations. In other words, the model may be smart, but the advantage depends on what the model is allowed to know and how effectively it can use that knowledge.

Why context becomes the advantage
Levie’s central point is that the competitive edge won’t come from owning the “smartest model” in a world where capable models are everywhere. Instead, advantage will come from providing AI systems with the right context—especially proprietary context that competitors cannot easily replicate.
That context can include several layers of company-specific material, such as: Internal business data and records that reflect what has worked (and failed) over time. Customer histories that show preferences, buying patterns, churn signals, support issues, and long-term relationship details. Operational workflows that describe how work actually moves through the organization, including approvals, dependencies, and handoffs. Decision-making patterns that capture how leaders have historically weighed trade-offs, handled risk, and prioritized outcomes. Institutional knowledge: the accumulated “how we do things here” wisdom that rarely lives in one place, but strongly shapes execution.
In this framing, AI becomes less like a standalone tool and more like a force multiplier. The more relevant and precise the context, the more useful an AI agent can be—because it can produce responses and decisions that match the organization’s real environment instead of generic best practices. When AI agents can “see” what the business knows, how it operates, and what it values, they can act with far more relevance and speed.

Context engineering is emerging as a core skill
This idea of turning context into leverage is increasingly being discussed in tech circles as “context engineering.” The emphasis here is important: it’s not only about writing clever prompts, but about building systems that reliably deliver the right information to AI at the right time, in the right structure.
Several industry leaders have echoed the theme that context delivery will become a major part of making AI work in production environments. The argument is that companies will need to get better at designing workflows, data pipelines, access controls, retrieval methods, and “AI-ready” knowledge systems—so that AI agents can operate with real business awareness rather than shallow or incomplete inputs.
In practical terms, that means the winning organizations won’t just adopt AI—they’ll operationalize it. They will connect AI tools to the underlying reality of the business: customer interactions, internal documentation, product rules, compliance constraints, and the way decisions are actually made. Over time, building this contextual layer can become as strategically important as building the product itself, because it determines whether AI is merely helpful or truly transformative.

The risk: too much context can backfire
While context is the differentiator, Levie also warns that feeding AI systems unlimited information is not the same as feeding them the right information. A major risk is what he describes as “context rot”—a situation where models are flooded with excessive or poorly organized input. When that happens, the AI can become less accurate, less focused, and more likely to latch onto irrelevant details.
This is a real operational challenge: organizations often assume that “more data” automatically leads to “better output.” In practice, messy context can create confusion, degrade relevance, and weaken decision quality—especially when the AI struggles to identify which signals matter most for the task at hand.
To reduce this risk, companies need to treat context like a curated asset, not a dumping ground. Practical safeguards include: Curating task-relevant information so the AI receives what it needs, not everything that exists. Structuring context delivery through clear templates, workflows, and retrieval logic so AI agents can process information efficiently. Updating context continuously so the AI isn’t acting on stale assumptions, outdated policies, or old customer realities.
Ultimately, the stakes are high. Businesses that successfully capture, organize, and activate what they know can unlock major productivity gains—faster execution, better decisions, and improved customer outcomes. Companies that fail to do this may find themselves with the same AI tools as everyone else, but without the internal clarity needed to use them well. In an AI-driven economy where expert intelligence becomes widely available, the organizations that win are likely to be those that build smarter systems for supplying context—not merely those that adopt the latest model.