Accelerating SQL and BI Analytics (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.

Running AI experiments as ventures, such as distinct, startup-like entities with their own mandates, teams, and freedom to imagine, is one of the few mechanisms that gives a scaled company a real shot at genuinely new thinking.

Organizations that treat AI as a standalone initiative see incremental results. Those that treat it as a data architecture transformation, however, unlock sustained performance improvements.

Drone programs have already proven their value as a data collection tool across construction, agriculture, energy, and infrastructure. The next frontier is making that data work harder.

For AI to work in production, models are not enough. Systems need access to current, external information that can be used directly in decision-making.