As AI shifts from a technological novelty to a foundational organizational driver, the traditional “pyramid” hierarchy is under immense pressure. In this exclusive session, Dan Sommer, Global Lead for Market Intelligence at Qlik, sits down with industry veterans to discuss why the next 24 months will be defined by a shift from oscillation (the pendulum swing between centralization and decentralization) to orchestration.

Expert panelists:

• Gopal Subramanian, Chief Data and AI Officer, Focus
• Sarang Deshpande, VP of Data and Analytics, Franciscan Health
• Sharad Kumar, Global Solutions CTO of Data and AI, Qlik

The transformation of the data org

The panel explores how the role of the data professional is evolving from a “task-doer” to a “system designer” and “orchestrator”.

1. From reports to insights: The traditional “dashboard developer” role is fading. Future success lies in building interactive, agentic applications that provide real-time answers rather than static visualizations.
2. The Rise of the “Translator”: A critical emerging skill set is the Analytics Translator — the bridge between business leaders and AI technical experts.
3. Platform over Projects: Organizations are moving toward a Platform Engineering model. A successful platform makes it easy for “producers” to share data and “consumers” to extract value without needing to understand the underlying complexity.

“Without a trusted data foundation, you’re not scaling AI—you’re really scaling risk.” — Sharad Kumar, Global Solutions CTO, Qlik

Building a “Trust Intelligence” layer

A recurring theme throughout the discussion is that an AI strategy without a solid data foundation is merely “slideware.”

Key pillars for AI readiness:

Featured use cases:

• Healthcare (Franciscan Health): Utilizing AI to predict hospital length-of-stay and optimize patient discharge planning.
• Financial Services (Focus): Deploying “Independent AI” for meeting summarization to save advisors 10 minutes per client, and “Strategic AI” for advanced lead scoring in CRM systems.
• M&A Integration: Developing dual agents — a Data Analyst Agent for source-to-target mapping and an Engineering Agent for automated ingestion—to accelerate company integrations.