Why Context Is Capital In Enterprise AI

Every week, we are inundated with headlines about the newest, most powerful large language model (LLM). But does that mean everything built on last week's or last year's LLM is suddenly obsolete? According to Anand “Andy” Logani, executive vice president and chief AI officer at EXL, the answer should be no. Models are getting more capable and cheaper every quarter, which is exactly why access to the “best” model is no longer an advantage.

The more important business question is what your AI spend leaves behind when the next model arrives. For many enterprises, the answer is “not enough.” McKinsey estimates that just 37% of businesses can attribute any enterprise-level financial impact to their use of AI, and Gartner projects that more than 40% of agentic AI initiatives will be canceled by the end of 2027.

The symptoms vary, but the pattern is consistent: The AI model works well in the pilot, but struggles when applied to complex, real-world scenarios. The enterprise AI systems that improve over time are not necessarily using better models than everyone else. They are capturing what their business learns every time a decision is made.

Context Capital

Logani calls it context capital, the AI era’s counterpart to the software estates of the client-server era, the digital channels of the internet era and the data estates of the cloud era. The smartest enterprise AI systems treat LLMs as a rental, temporary and depreciating, while the context that informs them becomes an appreciating enterprise asset.

Building The Context Layer

Business leaders do not need advanced degrees in computer science to understand the value of context capital. It makes their business their business—the decision data, company-specific ontologies and unique approaches to problem solving that define their operations. Many AI integration projects fail because a foundational LLM was never designed to address company-specific protocols or incorporate critical feedback loops.

Consider the challenges insurance and financial services leaders face integrating AI into underwriting and compliance workflows. While these repetitive, process-heavy functions should be natural fits for AI-powered automation, many initiatives stumble when encountering missing data points or edge-case decisions requiring institutional knowledge. An LLM does not know which claims specialist can locate a missing accident report, or which compliance expert has the final word on a KYC anomaly.

The Path Forward

Context capital demands its own architecture. The context build path forms the capital by drawing on enterprise metadata, business definitions and operational signals to build and maintain a context graph. The context serving path puts that capital to work by assembling a working set for each agent, user and task, drawing on the context graph and accessing additional source-system data as needed.

These paths converge when company-specific data and accumulated enterprise insight are available across the organization to support complex decision making. For example, in wealth management, one firm developed a context graph across wealth-management data that gives agents a shared understanding of clients, products and opportunities, with decision-histories built into the loop.

Every enterprise running AI is already accumulating context capital. The question is whether it's being built deliberately or by accident. Business leaders need to ask three key questions: Where does our decision context live today? Who owns what we are accumulating? What would a swap cost us? If the answers come back scattered, that's where to start.