Agentic analytics empowers humans to work collaboratively with AI agents, transforming data analysis and insight discovery from a manual task to an automated, personalized, and proactive experience.

Agentic analytics represents a significant evolution in the business intelligence (BI) space, moving beyond traditional data analysis and visualization to autonomous, AI agents that augment and accelerate every stage of the journey from data to insights to action. Instead of merely assisting, AI agents go beyond simply presenting information; together with humans, they engage in dynamic, conversational interactions, anticipate user needs, and automate complex analytical workflows, all with people remaining in control.

Traditional BI is manual, time-intensive and complex, defined by disconnected tools and siloed workflows; reactive decisions and slow response times; and heavy reliance on data analysts and institutional knowledge. In contrast, agentic analytics is conversational, proactive, action oriented, self learning, and always available.

Unlike traditional BI, agentic analytics provides:

• Conversational analytics with intelligent business and user context.
• Adaptive learning and recommended actions for improved decision making.
• Continuous monitoring and autonomous actions, where applicable, with intelligent agents.

Agentic analytics can help organizations enhance decision making, increase operational efficiency, and improve business outcomes. With agentic analytics, organizations and teams are empowered to:

• Automate data connectivity and preparation.
• Proactively identify patterns and anomalies.
• Generate contextual insights and explanations.
• Automate insight delivery.
• Assist with advanced analysis.
• Enable actionable recommendations.
• Automate actions.

Agentic analytics can democratize data across an organization by giving everyone access to contextual, actionable insights and proactive actions.

• Data foundation and semantics: A data platform with data orchestration and harmonized and consistent input data. A robust semantic layer for consistent data definitions, quality, and lineage is necessary.
• Transparency and trust: Agentic analytics must not be “black box” - it must have transparency into how insights and recommendations are generated.
• An action framework integrated with business systems to automate workflows.
• An API-first approach:Discoverable, reusable data components and APIs.

Traditional BI tools served as monolithic data repositories with static visualizations. Agentic analytics accelerates the data to insights to actions journey, making it possible for every user across the organization to engage in faster insight discovery with the help of AI agents.

Agentic analytics is a fundamentally new approach to BI, one that transcends the limitations of current BI tools by infusing them with the autonomy and adaptability of AI agents. Powered by LLMs and new generation semantic models, these agents can orchestrate tasks autonomously with humans in the loop. Together humans and agents can achieve stated goals, execute multi-step analyses, provide explanations, and even trigger automated actions based on the insights, enabling a level of data-driven decision-making that was previously unattainable.