AI systems are probabilistic; they generate answers based on likelihood, not fixed logic. That works well for summarisation and exploration but breaks down without quality and reliable data as a foundation in environments where decisions must be repeatable, explainable and defensible.

Not only that, but as more data is scraped from the web, it becomes clear that curation and context are incredibly important to decision-making for technology leaders. AI can identify signals across environments, but it cannot reliably determine what software is, how it maps to licensing, whether it is end of life, vulnerable or even approved for use.

For example, if a CIO asks an AI assistant how many Oracle databases are deployed, the answer may change as new signals are inferred. That variability is acceptable for exploration. It is not acceptable when that same answer drives a licensing true up, a board report or a regulatory disclosure.

AI may also detect Apache Tomcat running on a server, but it cannot determine the version, bundled dependencies or associated risk without curated data and enrichment. Many of those datapoints aren’t discovered via AI search. Without normalisation and human-curated intelligence, discovery remains incomplete and decisions cannot be trusted.

AI produces fast answers. Enterprises still need durable ones.