The Startup Gold Rush Hiding in Plain Sight: Context Engineering Context, data, rules, workflows and tacit tribal knowledge is becoming the real raw material of enterprise AI and the place where advantage lives.

Over the past year, the enterprise AI conversation has revolved around which model to use, which cloud to choose and how quickly pilots can be shipped into production. As systems go live, a quieter realization is spreading: lasting advantage will come less from the model and far more from the context wrapped around it.

Context, the data, rules, workflows and tacit tribal knowledge that make each organization unique, is becoming the real raw material of enterprise AI and the place where advantage lives. That shift opens up a large, underappreciated entrepreneurial opportunity.

Think of context engineering as the craft of making models behave as if they were hired into a specific business. Instead of maintaining long lists of rules in code, teams curate everything that expresses how the organization really works, from policy manuals and workflow logs to tickets and chat histories. They then design the pipelines and guardrails that let models use that context safely and reliably.

Done well, the same base model starts to behave like a seasoned underwriter in insurance or an expert dispatcher in logistics. Done badly, it behaves like a generic chatbot with a good vocabulary and terrible judgment. The difference is not the model. It is the quality of the context and how thoughtfully it has been engineered.

For entrepreneurs, this is a rare moment. The surface area is enormous, because every function and every industry runson its own context. The work has real defensibility, because governed context, workflows and feedback loops are hard to copy. And the impact is measurable, because better context moves metrics like days to cash, audit coverage, risk losses and customer satisfaction.

This is already visible in early projects. A bank wires credit agents into rich histories of customer interactions and internal memos rather than simple scorecards. A manufacturer feeds maintenance agents years of machine logs and field service notes, not just a parts catalogue. In each case, the headline is not a breakthrough in modelling. It is a breakthrough in how context is collected, structured and governed.

That is why it helps startups to think of four archetypes of opportunity for this moment. The first is the vertical context native product. A company picks a domain where the stakes are high and the context is messy, such as claims adjudication or cross border tax, and builds a product with a purely commercial promise: fewer write-offs, faster audits, safer plants. Its intellectual property lies in the context packs, workflows and governance created for that niche and then extended to adjacent ones.

The second archetype is context infrastructure and tooling. Large organizations are discovering that simply plugging a model into a data lake does not work. They need catalogues of context, clear access policies and continuous evaluation of how agents behave. This is the DevOps of context, with version control and observability not for code but for prompts, policies and domain knowledge. Founders who make it easier to catalogue, test and deploy context will become essential partners to serious AI programmes.

The third archetype is the context studio that turns into a product company. Many ventures will begin life as high touch specialists who sit with operations teams, map workflows, capture tacit knowledge and stand up agents. Over time, the best of them will standardise what they learn into reusable blueprints, license those on a recurring basis and start to look more like product companies than consultancies.

The fourth archetype is the partner play with incumbents. Software vendors and IT services firms already sit on enormous troves of client context but cannot productise every niche. Specialist startups can co develop agents, co-own context IP in a vertical or provide opinionated context layers on top of horizontal platforms. Often the incumbent brings relationships and distribution, while the startup brings speed and depth in domains the larger player would never have prioritized.

The context opportunity matters just as much for IT services firms as it does for founders. Traditional growth engines such as application development, maintenance and lift and shift cloud migration are maturing as automation and platforms take on more of the routine work. At the same time, clients are looking for clear business outcomes from AI rather than one more proof of concept. Services firms that continue to define themselves only as people plus billable hours businesses will feel growing pressure to evolve their value proposition.

This shift is not theoretical. In August 2025, Cognizant announced a strategic initiative to train and deploy 1,000 context to industrialize agentic AI for clients worldwide, a clear signal that context engineering is moving to the core of the services business.

Context engineering opens up a new growth chapter. Instead of competing only for CIO budgets, services firms can create context platforms that matter to risk, finance, operations and HR leaders and that unlock larger, cross functional budgets. When a firm develops a distinctive context layer for areas such as trade finance or supply chain planning, it becomes harder to dislodge and easier to scale without a linear increase in staff. For entrepreneurs, that makes IT services players natural customers and distributors.

Every computing era has had a raw material that made fortunes. In the microprocessor era it was code. In the cloud era it was workloads. In the model era it is context.

We do not need yet another slightly better model. We need companies that know exactly which context solves which problem in which industry, and can turn that knowledge into repeatable products and services. For the next wave of entrepreneurs, and for the services firms that support them, context is not just a technical detail. It is the business.