By: Michael Sauter Enterprise IT leaders face a critical challenge: despite investing millions in digital transformation, their organizations remain trapped in data silos. Customer information lives in one system, operational data in another, and...
2025 was a defining year, marking one of the most pivotal periods for IT trends as AI, automation, and infrastructure pressures converged. AI advanced at an unprecedented pace, often outstripping governance, controls, and the operational know-how needed to use it...
By: Brian Sabzjadid In the ever-evolving landscape of artificial intelligence, a new paradigm is emerging that promises to redefine how we get work done: “Agentic AI”. Picture this: It’s the near future, and you’re at your favorite café, sipping on a cappuccino,...
By: Jonathan D. Gough, PhD The integration of artificial intelligence (AI) into business operations is not just a technological leap—it’s a strategic necessity. For organizations beginning their AI journeys, one essential question arises: How should they approach...
By: Greg Berard Building Momentum for What’s Next Change can feel intimidating, but at Pellera, we see it as an incredible opportunity to rethink, reimagine, and reinvent. That’s the mindset driving us forward every day. With the launch of Season 2 of our podcast,...
By: Darren Livingston From training billion-parameter models to handling real-time inference at scale, AI workloads are pushing data centers to the limit. Legacy infrastructure simply can’t keep up with the compute density, heat output, and performance demands of...
By: Jonathan D. Gough, PhD Agentic AI is quickly emerging as business technology’s next big thing. It’s predicted that by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024. Agentic AI distinguishes itself from current...
By: Jonathan D. Gough, PhD Healthcare is in the midst of a data revolution, and predictive analytics is leading the way. Artificial intelligence (AI) and machine learning technologies are being used to analyze large amounts of patient data to anticipate health risks...
