Agentic AI: Why Enterprise AI Is Not Delivering on Its Promise

Enterprise AI systems often fail to deliver impactful results despite their promise of improved decision-making and automation, primarily due to a lack of context rather than a deficiency in models, ambition, or data. These systems struggle to operate outside of demo environments because they lack the comprehensive context required for reliable decision-making, which includes transactional data, customer history, and internal documents. This challenge is further exacerbated by the adoption of smaller language models (SLMs), which, while cost-effective, require precise context to function effectively. Enterprises often have vast amounts of unstructured data that remain unused because they are siloed and difficult to access, creating a paradox where AI systems operate with outdated business views. To succeed, AI workflows must integrate context, applications, and data in a continuous loop, which is made possible by modern real-time databases like SingleStore. These databases provide low latency, high concurrency, and the ability to handle complex queries, enabling the assembly of rich context on demand and facilitating AI applications without moving data between slow silos. The real constraint on enterprise AI is not the capability of the models but the system's ability to provide the necessary context swiftly and consistently for informed decision-making.