AI hasn't reduced the need for analysts, but it has changed what makes one valuable. The role of the analyst is moving from producing the analysis to interrogating it and standing behind the decision. With AI taking care of speed, trust has become the real bottleneck. The platform can't be held accountable for a wrong number, but the analyst can. Judgement, not speed, is now the scarce skill. AI can be confidently wrong in ways that look polished. A clean summary or a tidy forecast can hide a wrong assumption, a misclassified category or numbers that simply don't reconcile. I've seen AI generated dashboards that looked flawless, with every chart populated and every total appearing sensible, until I traced the data connection and realised it was pointing to the wrong source system. I've reviewed year on year rates that returned perfectly believable percentages, only to discover the prior year figure was counting the wrong field entirely. Those moments aren't failures of the tool, but rather they show where the analyst's real value now sits. You can only catch that if you understand the business and the data underneath it. Domain knowledge is what separates "looks plausible" from "is correct." More often than not, the more valuable question isn't "does this check out?" but "what is this output not telling me?"