Arun Pratap Singh

FreshWorks, Lead Agentic AI Engineer - Analytics & BI

San Francisco, California, United States

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Arun Pratap Singh is an IEEE Senior Member and Fellow of the Institution of Engineering and Technology (IET). Distinguished judge and speaker on agentic AI & analytics adoption in enterprise analytics at MIT, Harvard, Yale, Duke, Georgia Tech, the University of California, San Diego, and various IEEE conferences.

He is a contributing author to CIO Magazine, where he writes on analytics maturity frameworks, agentic AI & analytics adoption, and digital transformation. His recent research focuses on multi-agent AI architectures and autonomous decision systems, examining how organizations can scale AI from experimental pilots to production-grade infrastructure.

Area of Expertise

Business & Management
Consumer Goods & Services
Finance & Banking
Information & Communications Technology
Real Estate & Architecture

Topics

Artificial intellince
AI Agents
analytics engineering
Business Intelligence
Process Optimization
Information Tehnology

Why Analytics Maturity, Not Just AI Tools, Will Define Agentic AI Adoption in 2026

Why Analytics Maturity, Not Just AI Tools, Will Define Agentic AI Adoption in 2026:
Agentic AI exposes what dashboards hide: conflicting definitions, unclear ownership, insights without execution paths. Drawing from enterprise implementations, discover why the competitive advantage in 2026 won't come from deploying the most AI—but from building analytics mature enough to make autonomy trustworthy.

Why Analytics Maturity, Not Just AI Tools, Will Define Agentic AI Adoption in 2026

Organizations rushing to deploy agentic AI often discover a harsh reality: advanced tools can't compensate for immature analytics foundations.

In 2026, the gap between AI adoption and business value will widen—not because the technology fails, but because enterprises lack the analytical infrastructure to support it.

This piece examines why analytics maturity—defined by data governance, stakeholder alignment, and decision-making processes—is the true prerequisite for successful agentic AI implementation.

Companies with mature analytics frameworks will unlock exponential productivity gains through agent orchestration, while those chasing shiny AI tools without foundational capabilities will see minimal ROI and mounting technical debt.