And let’s not overlook enterprise risk management. Suppose a group of business users asks an LLM, “What are the biggest financial risks for our business next year?” The model might confidently generate an answer based on past economic downturns. However, it lacks real-time awareness of macroeconomic shifts, government regulations, or industry-specific risks. It also lacks the current and actual corporate information—it simply does not have it. Without structured reasoning and real-time data integration, the response, while grammatically perfect, is little more than educated guessing dressed up as insight.
This is why structured, verifiable data are absolutely essential in enterprise AI. LLMs can offer useful insights, but without a real reasoning layer—such as knowledge graphs and graph-based retrieval—they’re essentially flying blind. The goal isn’t just for AI to generate answers, but to ensure it comprehends the relationships, logic, and real-world constraints behind those answers.
The power of knowledge graphs
The reality is that business users need models that provide accurate, explainable answers while operating securely within the walled garden of their corporate infosphere. Consider the training problem: A firm signs a major LLM contract, but unless it gets a private model, the LLM won’t fully grasp the organization’s domain without extensive training. And once new data arrives, that training is outdated—forcing another costly retraining cycle. This is plainly impractical, no matter how customized the o1, o2, o3, or o4 model is.
