Read more about author Mohan Varthakavi. As agentic AI and autonomous systems transform the enterprise landscape, organizations face a new imperative: Fundamentally reimagining data architecture is no longer optional; it’s required for AI success. Many enterprises are coming to the realization that traditional data architectures, which are built for structured data and deterministic workloads, are ill-equipped to support agentic AI’s demands for diverse, unstructured data and probable reasoning. Traditional data…

Read more about author Dev Nag. The federal government’s proposal to impose a 10-year freeze on state-level AI regulation isn’t happening in a vacuum but in direct response to California. The state’s AI Accountability Act (SB 1047) has been making waves for its ambition to hold developers of powerful AI models accountable through mandatory safety testing, public disclosures, and the creation of a new regulatory body.  For some, California is showing leadership. For others,…

Read more about author Subasini Periyakaruppan. In today’s data-driven business environment, the ability to leverage external information sources has become a critical differentiator between market leaders and laggards. Organizations that successfully harness external data don’t just gather more information – they transform how they understand their customers, anticipate market shifts, and identify growth opportunities. However, the path from recognizing the need for external data to successfully implementing it is fraught with…

Read more about author Derek Ashmore. AIOps, the practice of enhancing IT and DevOps with help from artificial intelligence and machine learning, is not an especially new idea. It has been nearly a decade since Gartner coined the term in 2016. Yet, the growing sophistication of AI technology is making AIOps much more powerful. Gone are the days when AIOps was mostly a way to speed up monitoring. AIOps in conjunction with…