Building an Agentic AI Strategy That Delivers Real Business Value
Agentic AI can redefine industrial operations, but that is only if it is deployed with discipline and can scale across the organization.
Agentic AI can redefine industrial operations, but that is only if it is deployed with discipline and can scale across the organization.
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.
In this week's real-time analytics news: This week’s Confluent Current 2025 conference focused on bringing real-time data and AI together.
If the data reflects inequality or bias, the AI’s outputs will mirror it, potentially resulting in an individual being denied a job or loan, or, even more concerning, the correct medical treatment.
As AI continues to reshape data centers, market competition will intensify, driving up energy and resource costs. The demand for quieter, more energy-efficient, and sustainable facilities will only grow.
By integrating GenAI, automation, and now agentic AI capabilities into hybrid IT strategies, organizations can eliminate key adoption barriers, unlock the full potential of their data, and future-proof their 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.
While the vision for adaptive edge intelligence is clear, the execution requires technology that can combine real-time data processing, low-latency decisioning, and scalability.
Manufacturers are entering a phase where AI is less about exploration and more about embedding it into the core fabric of operations.