Bruce Kelley is CTO and SVP of NetScout, leading technology strategies for product and service solutions.getty​For years, organizations have treated data volume as an advantage. They built data lakes, collected logs, stored telemetry and preserved records from every asset they could. The goal was to analyze that data to yield new insights or, better yet, operationalize it to improve automation. Many are applying the same logic to AI. Feed the model more data and expect better answers.But asking AI to infer operational truth from raw data is expensive, complicated and prone to errors.This is especially true for large, heterogeneous networks like those run by telecoms, hospitals and enterprises. The volume of data created within these organizations (and many others) is so great that it is nearly impossible for AI to consume it without costly consequences.The core issues are data architecture and the limits of general-purpose AI models. AI systems are probabilistic. Most operational questions are deterministic. Did a transaction fail? Did a call drop? Which site saw the largest change? How many users were affected? What was normal last month compared with now? These are questions that should be computed before a model enters the workflow. Smart data—operational data validated into trusted facts—makes those answers usable by AI.In my most recent article, I discussed the way MCP would alter the business landscape. Standards like MCP are making it easier for AI models to connect with operational tools. That is an important first step. But connection alone does not solve the harder problem: whether the data exposed through that connection is useful, trusted and can be analyzed at a reasonable cost.Raw Data Creates Accuracy, Speed And Cost ProblemsThe wrong answer, no matter how inexpensive or how fast it has been delivered, is useless. Facts and accuracy are paramount; optimizing for cost or response time comes after getting the desired business outcome or answer.But when LLMs are used to calculate facts from massive raw data sets, the potential for error and hallucination is huge. Stanford’s Human-Centered Artificial Intelligence Center’s 2026 report found hallucination rates across 26 top models range from 22% to 94%. Meanwhile, when McKinsey surveyed executives who said they had suffered negative consequences from AI deployment, inaccuracy was the leading driver of bad outcomes.Even if LLMs eliminated the possibility of hallucinations, it would not solve the cost and speed challenges that a reliance on raw data presents. The more unprocessed operational information an AI system must ingest, the more work it must do before it can begin to reason. That burns tokens, slows response times and increases the chances that the model will miss or misread something important. As agentic AI systems take on more complex workflows, organizations will need data pipelines that reduce unnecessary processing and give models compact, trusted evidence to work from.A smart data pipeline improves accuracy. It improves speed because the model is not plowing through raw records to establish the basics. It improves cost because less unnecessary data moves through the AI workflow. The goal is not simply to give AI more data. It is to give AI better evidence.Multi-Vendor Environments Make This HarderAI systems do not automatically understand how an organization works. They need a usable map of the environment: which systems are connected, which assets matter, how services depend on the network and what counts as normal behavior.That is difficult in large, multi-vendor environments. A telecom provider may rely on different hardware makers across its radio, core and transport networks. A hospital may connect clinical applications, patient records, imaging systems, security tools and facility systems. Each platform may use its own schema, naming conventions and performance metrics.The goal is not simply to expose data through a connector. The goal is to expose facts in a form that supports reasoning.Where AI Enters The PictureOrganizations need curated, normalized and enriched intelligence that turns raw signals into trusted facts. Once those facts exist, AI can do what it does best: reason across context, identify patterns, summarize tradeoffs and recommend action.The single best source of network truth is the packet. Every session, transaction and performance change is recorded there.The key is making that truth consumable. By generating intelligent metadata from packets, organizations turn the richest source of network evidence into facts that both AI and humans can use. That metadata shows what happened: whether a session started, whether it failed, how long it took, where performance changed and which users were affected. From there, AI gets exactly what it needs: a common operational context that connects assets, services, users, locations and time periods.AI’s real value may not be as a standalone analytical engine. In many enterprise settings, it will be more useful as a natural language interface to trusted systems that already calculate, classify and validate what happened. That distinction matters. AI can help teams interrogate evidence, reason through patterns and move faster. It should not be the first and only system responsible for determining whether the evidence is true.AI does not need more noise. It needs better evidence. To achieve this, it requires a thoughtful deployment. The payoff is both practical and sensical: better answers, faster investigations and a more efficient path from problem to action.​Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?