TechEDGE Endeavor

As insurance agencies quietly experiment with AI tools, carriers are confronting a growing governance gap they cannot fully see, monitor or trust. The future of insurance AI may depend less on automation itself and more on the systems needed to govern, orchestrate and audit it. Insurance leaders are hearing increasingly bold claims about artificial intelligence transforming distribution.

The days of relegating cybersecurity to an IT problem are behind us. Cybersecurity practitioners agree that the risk is a pressing operational problem that demands immediate attention and budget. Here’s what industrial leaders need to know with insights from ABS Consulting, a safety, risk and compliance management firm with frontline expertise managing some of the nation’s most critical cyber programs. How should business leaders think about today’s evolving cyber risk?

Skip to main content As AI adoption grows, inference costs can rise faster than business value. CIOs must treat inference economics as a governance challenge by measuring cost per outcome, controlling demand, assigning ownership and continuously reassessing workloads before spending becomes unsustainable. Key Highlights Successful AI adoption can create unsustainable costs unless IT leaders connect spending directly to business value.

Diverse infrastructure and economic factors are at play as executives and IT leaders look to transition from AI experimentation and training to functional production systems. Capitalizing on these investments requires moving beyond model benchmarking to inferencing at scale; that is, using trained models to process new data and achieve practical business results in real time.

In this episode of the TechEDGE Podcast, Abby White and Theresa Houck discuss five of the month’s most-read stories and what they reveal about enterprise IT right now. The conversation looks beyond implementation to the operational consequences of technology decisions, including workforce trust, security exposure, sustainability and third-party risk.

Changing the cybersecurity landscape as we know it, researchers from the University of Toronto, Vector Institute and University of Cambridge have built a proof of concept, AI-powered computer worm that autonomously analyzes each target, adapts its attack strategies and builds custom exploits in real time.

Skip to main content Intel’s recently announced 18A design intensifies competition with TSMC as AI-driven chip demand accelerates. For IT leaders, this rivalry could reshape infrastructure strategy by expanding supply options and accelerating innovation while also influencing cost and access to advanced compute for AI, cloud and workloads. Key Highlights IT leaders may be able to diversify chip sourcing strategies as Intel challenges TSMC in chipmaking, reducing reliance on a single foundry.

AI is helping manufacturers forecast demand, optimize inventory and respond faster to disruptions. Yet many companies are discovering that the more digital their supply chains become, the more vulnerable they are to something else: unreliable data. Poor data quality, fragmented enterprise systems, geopolitical shifts, supplier visibility gaps and AI acting on inaccurate information all create new points of failure.

Steve Lasky has been a professional journalist for 45 years and a 35-year veteran of the security media industry and a multiple-award-winning journalist. He is currently the Group Content Director for the Endeavor Business Security Media Group, the world’s largest security media entity, serving more than 190,000 security professionals in print, interactive and events.

The first 50 automations usually save money. The next 500 can quietly destroy ROI. The problem isn't automation. It's governance debt. As automation scales across the enterprise, unmanaged workflows, fragmented ownership and poorly governed AI systems can create costs, complexity and risk that erode the value automation was supposed to deliver. For technology leaders, that may become one of the defining challenges of the AI era.