The Agentic Paradox: Why More Autonomy Demands More Control

Why scaling agentic AI means pairing more autonomy with stronger control, not less.

In this session from the Big Data & Analytics Summit Canada, Josh Good, Vice President of Strategy and Market Development at Qlik, unpacks the central tension every enterprise hits as AI agents proliferate: the agentic paradox.

Systems that reason, plan, and act autonomously are boosting individual productivity at unprecedented speed, yet many organizations struggle to turn that autonomy into scalable business value. Centralization brings trust, governance, and control but slows innovation. Decentralization unlocks speed and creativity but fragments accountability, data quality, and outcomes. Agentic AI exposes the limits of both. Josh shares practical ways to resolve that tension.

Inside this talk:

• Designing a core operating model that centralizes trust, governance, and data foundations while enabling autonomy at the edge where agents actually operate
• Establishing clear control mechanisms for agentic systems, including context inheritance, permissions, and accountability, without constraining innovation
• Aligning data, platforms, and operating models so agentic AI can scale consistently across teams, use cases, and markets

If you work in AI strategy, data governance, enterprise architecture, or platform ownership, this is a clear look at how to give agentic AI the trust, context, and control it needs to deliver impact at enterprise scale.
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Видео The Agentic Paradox: Why More Autonomy Demands More Control канала Big Data & Analytics