In just a few years, generative AI has changed the face of enterprise analytics, notably by making analytics accessible to users across the organization. Agentic AI promises to do even more, by automating entire workflows and taking actions based on agent insights. But there is a problem: The quality of insights that agents currently generate leaves much to be desired.
Fewer than half of agent-generated insights at the organizations surveyed for this report lead to business improvement. And while most respondents praise the quality of their agents’ descriptive insights, reviews of their prescriptive insights—those on which actions are based—are decidedly mixed. Failure to produce insights that improve the business deprives organizations of their desired return on investment in agentic analytics.
Worse, flawed insights that trigger incorrect actions can cause reputational or financial damage. This report gauges how companies view the quality of the outputs produced by their analytics agents and explores how organizations are seeking to boost insight quality and get their agents working harder for the business. Some key findings from the report include the following: Agentic analytics is only just starting to deliver.
On average, 48% of insights generated by analytics agents at the surveyed companies lead to actions that improve the business. That is a marked advance from the 37% average the respondents say generated such actions one year ago. But it means that more than half of the insights generated by analytics agents today are still failing to help businesses.
Better quality of insights leads to clear business advantage. At a small cohort of organizations, which we term “insight leaders,” AI agents perform much better than the average. In this group, more than 70% of agent insights lead to actions that improve the business.
The insight leaders also give the quality of their agents’ insights higher marks than do the total respondents overall. The quality gap is particularly wide when it comes to prescriptive insights. Insufficient context reduces insight quality.
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