Tarek Nseir is co-founder of Valliance and a senior value partner, focused on delivering leading AI time-to-value for clients across Europe.gettyWith new AI models, agents and capabilities emerging at a relentless pace, it’s easy to believe the enterprise advantage lies with building more advanced automation. Yet as organizations deploy AI at scale, their attention needs to shift toward a different challenge—giving agents the context they need to be effective, a bit like onboarding a new joiner.Most enterprises are trying to solve this in the wrong order: buying and piloting agents first, and treating the underlying context as something to backfill later—yet that’s exactly why so many pilots stall. Agents can only reason about the information, processes and business logic they understand, but for many enterprises that knowledge remains fragmented, limiting the value even the most advanced models and tools can deliver. Enter ontologies, knowledge graphs and reasoning frameworks: the foundations that allow both humans and AI systems to make better decisions. As enterprises strive to derive value from their AI investments, these are a nonnegotiable or else they will remain stuck in "pilotitis"—throwing new ideas at the wall and wondering why nothing quite sticks. A Market In Search Of Context In many ways (and without realizing it), the whole industry is beginning to converge on the same search for context and understanding. Funnily enough, we’ve seen the seeds of this idea before. Palantir's core bet, years before anyone was discussing AI or agents, was that decisions only get better once scattered data, processes and business rules are turned into one coherent model first; everything else, human judgment included, gets layered on top of that.Fast forward to today and startups are developing new knowledge graphs of their own, like Databricks' Genie Ontology. Built from the company's heritage in data and analytics, it’s geared toward helping organizations make better sense of information spread across multiple systems, creating richer business understanding for both human users and AI systems. At the same time, enterprise vendors are embedding AI into existing business applications, and frontier model providers are improving memory, orchestration and reasoning capabilities. Although these efforts appear distinct, they are all addressing the same underlying challenge—helping AI systems better understand the organizations they’re serving and the people they are designed to support. The Emergence Of The Enterprise Loop Industry leaders are already describing the future of AI in terms of ontologies—the same argument Palantir made a decade earlier—they just frame it as a "loop" for the AI era. In a recent essay on X, Microsoft CEO Satya Nadella argued that competitive advantage will not be determined solely by access to frontier models, it will come down to how well an organization's knowledge, workflows and decisions work together as a single system. Intelligence becomes far more powerful when it operates within a connected system rather than in isolated applications or tasks, but the "loop" doesn’t create itself. The onus is on firms and their partners to build it. Viewed through this lens, ontologies and enterprise context layers take on greater strategic significance. They give people, systems and AI a common reference point for how work gets done, how information moves and how decisions get made. As enterprises continue to navigate growing volumes of information, their attention must turn toward the connective layers that provide operational knowledge across systems, teams and workflows. Making this shared understanding easy to act on is the key to getting more value out of the information that enterprises already hold. A Long-Term Transformation The direction of travel is becoming clearer, even if the destination remains some way off. Microsoft is approaching the opportunity from the enterprise platform layer, while SAP is approaching it through systems of record. AWS and Google bring their infrastructure strengths, while OpenAI, Anthropic and other frontier model providers continue to expand their model’s capabilities.Together, they are contributing to a broader transformation that is likely to unfold over many years, but building effective enterprise context layers requires organizational change as much as technical implementation. It demands new ways of thinking about systems, workflows, governance and team collaboration. The organizations that navigate that transition successfully will be best positioned to capture AI's long-term value. As the market matures, the winners won't be the ones with the best models—they'll be the ones that make information, decisions and action move together across the business, and ontologies are the only answer.Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?