Raising the quality of insights in agentic analytics

The report also identifies a cohort it calls "insight leaders," organizations where agents perform far above average. At these firms, more than 70% of agent insights lead to business-improving actions, and they rate their agents' insight quality higher than the overall respondent pool — with the gap widest on prescriptive insights. That spread suggests the technology is capable, but that most deployments have not yet closed the gap between what agents can do and what they deliver in practice.

When insights fall short, the report attributes it primarily to missing business context, cited by 55% of surveyed executives, followed by fragmented or inaccessible data at 45%. Fewer insight leaders report their agents struggling to access context, implying that well-organized enterprises see better output. A second problem is volume: at 52% of surveyed organizations, analytics agents generate too many insights to be properly evaluated and acted upon. Only 24% are able to evaluate all agent insights; most handle them case by case, a process the report describes as currently human-intensive. That bottleneck undercuts the automation case for agentic analytics — agents that generate more than teams can review simply shift the labor rather than removing it.

The open questions center on whether the trajectory holds and whether the fixes scale. Can the 48% success rate keep climbing toward the 70%+ that insight leaders already report, and will semantic layers and governance programs actually close the context gap the survey flags? Just as important: as agent-generated volume grows, whether organizations can move beyond case-by-case human review to automated evaluation — or whether insight overload becomes the binding constraint on agentic analytics returns.