AI Agents Are Reinventing Business Analytics

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Ariel Katz is the CEO of Sisense with 30 years of experience in technology, cloud products and leading innovation at Microsoft.

The era of traditional business intelligence (BI)—with its static dashboards and quarterly reports—is coming to an end. As AI evolves from simple automation to sophisticated autonomous agents, companies face a critical inflection point in how they leverage their data.

This shift isn't just about better visualizations or faster reporting; it's a fundamental transformation in how businesses derive value from their analytics investments.

We're seeing this transformation accelerate as AI shifts from general-purpose to vertical-specific applications, exemplified by OpenAI's collaboration with Clay to create a specialized sales agent . These AI agents are increasingly targeting high-volume workflows in customer support, sales operations and supply chain management, analyzing patterns and executing decisions autonomously—provided they can access enterprise data directly without waiting for traditional BI dashboards to update.

BI platforms have relied heavily on human intervention to interpret data, identify insights and execute tasks, introducing inefficiencies and delays. Now, AI agents—which are sophisticated, autonomous entities capable of managing complex workflows, making informed decisions and continuously adapting by learning—can enable businesses to shift from merely accessing tools to directly achieving results.

Traditional BI tools were designed for human consumption, generating reports and visualizations that require manual interpretation and action. AI-driven operations will require a different kind of BI ecosystem.

This transformation from pre-scheduled data analysis workflows to real-time analytics is already happening across industries.

In manufacturing , agentic AI is monitoring production lines in real time, predicting maintenance needs and automatically adjusting workflows. In financial services , they're detecting fraud patterns and executing risk mitigation strategies without human intervention. In healthcare , they're optimizing patient scheduling and resource allocation based on real-time demand patterns.