‘Data Governance Is Equal to Trust’: Qlik’s Varun Babbar on AI’s Shift From Experimentation to Scale

Why It Matters

Strong data governance turns AI experiments into reliable, revenue‑generating operations, a critical differentiator as Indian enterprises accelerate AI adoption. It also mitigates regulatory risk under India’s Digital Personal Data Protection Act.

Key Takeaways

• Qlik's Answers now queries structured and unstructured data together
• India's AI market focuses on productivity, not full process overhaul
• Agentic AI can auto‑create ServiceNow tickets from insight detection
• Data governance councils and CDO roles now standard in India

Pulse Analysis

The conversation around enterprise AI in India has moved beyond hype to practical implementation, and data governance sits at the center of that evolution. Companies that once scattered large‑language‑model experiments across silos are now re‑architecting their data estates to blend the 80% of unstructured content—emails, PDFs, documents—with traditional structured sources. Qlik’s Answers platform exemplifies this shift, offering a unified query layer that lets business users extract insights without worrying about data provenance, thereby reducing friction between insight and action.

Agentic AI, the next frontier highlighted at Qlik Connect 2026, promises to close the loop between analytics and operational execution. By automatically generating tickets in systems like ServiceNow when an inventory shortfall is detected, these agents eliminate manual handoffs and accelerate decision cycles. The Model Context Protocol further amplifies this capability, enabling heterogeneous AI agents to communicate and collaborate across vendor ecosystems. For Indian firms, where efficiency gains are paramount, such autonomous workflows translate directly into cost savings and faster time‑to‑value.

Regulatory pressure adds urgency to the governance narrative. India’s Digital Personal Data Protection Act, 2023 imposes steep penalties for mishandling personal data, compelling enterprises to formalize AI oversight through governing councils and dedicated Chief Data or AI officers. These structures not only ensure compliance but also provide a disciplined framework for selecting high‑impact use cases. As a result, organizations that invest in robust data stewardship are better positioned to scale AI responsibly, outpacing competitors still mired in ad‑hoc experimentation.