The Race for a Unified Analytics Warehouse (White Paper)

The shift toward real-time decision-making at the edge is an evolution in how businesses operate. Closing the latency gap means smarter, safer, and more resilient operations.

AI agents represent an opportunity to go beyond isolated analytics projects and move toward self-improving, autonomous workflows that directly drive operational performance.

Scaling AI in industry requires the right technology, a practical understanding of industrial operations, and open interoperability with an organization’s existing data infrastructure.

The enterprise data stack is at an inflection point. Storing and streaming data is no longer enough. Businesses need a combination of streaming technologies, high-performance in-memory databases, and adaptive intelligence to support real-time enterprise operations.

In this week’s real-time analytics news: Qlik introduced a sovereign AI initiative and an expansion of its trust and governance capabilities for AI.

Digitizing Leadership Standard Work (LSW) is not the same as deploying a new tool and expecting behavior to change. Organizations that get real value from this approach are the ones that treat digital LSW as a management system, not a software rollout.

Building AI operations is a journey. The companies that succeed treat it as an ongoing operational capability, not a one-time technology project.

The next real shift with agentic AI will come from changing how work is done. Humans will move away from clicking through systems and toward commanding outcomes, with agentic layers coordinating execution across tools on behalf of the user.