Agentic Analysis: 48% of Useful Insights, 60% Focus on Governance

Key Takeaways

148% of agent insights lead to useful actions on average, compared to 37% last year.

255% of executives cite a lack of context; 52% face an overload of insights.

360% prioritize governance and 45% add a semantic layer.

Why it matters : these organizational and architectural choices aim to enhance the quality of insights and increase the real impact of analytical agents.

The performance of analytical agents is improving but remains heterogeneous. On average, 48% of insights lead to useful actions, while obstacles such as lack of context and insight overload still weigh heavily. Some organizations are focusing on governance and a semantic layer, while a small group exceeds 70% of actionable insights.

Lack of Context and Overload Hinder Evaluation

According to 55% of surveyed executives, the main cause of low-quality insights is the absence of business context. For 45%, fragmented or inaccessible data also poses a barrier. Insight leaders report less frequently difficulties in accessing context. In 52% of the organizations surveyed, agents generate too many insights to be properly evaluated and acted upon. Only 24% manage to review everything; most proceed on a case-by-case basis, through a currently labor-intensive process.

Governance and Semantic Layer at the Forefront of Solutions

To improve the quality of outcomes, 60% of surveyed companies prioritize strengthening AI and data governance. Meanwhile, 45% are adding a semantic layer to their data architecture. Experts interviewed believe that this semantic layer is essential to ensure effective agents.

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An Average of 48%, Leaders Beyond 70%

On average, 48% of insights produced by analytical agents lead to actions that improve business outcomes, compared to 37% reported last year. Despite this progress, more than half of the insights still do not help companies. A small group of insight leaders significantly outperforms: over 70% of their insights lead to beneficial actions, and they rate the overall quality higher, with a particularly marked gap in prescriptive insights. Most respondents consider descriptive insights solid, but opinions remain divided on prescriptive ones.

Strong Promises Amid Quality and Risk Challenges

Generative AI has already broadened access to analysis within organizations, and agentic AI aims to automate entire workflows and take actions based on insights. However, the quality of outcomes remains a weak point. This deficit deprives companies of the expected return on investment, and erroneous insights can lead to reputational or financial damage. Companies are assessing the perceived quality of the results produced by their analytical agents and seeking to improve the quality of insights so that these systems serve the business more effectively.