Key Takeaways Teams with supervisors who embrace AI use AI-based tools at much greater rates—regardless of how good the tools are, how effective the training was, or how great the incentives to utilize the new capabilities are. Incorporating employees into the design process around how AI will be integrated into daily operations has consistently been
Key Takeaways AI scalability is now constrained by energy availability, not only compute. AI costs are increasingly tied to electricity markets, grid stress, and regional demands. Vendors can win over customers by offering predictable, energy-efficient, and flexible AI. When a Virginia data center went offline for 90 minutes last summer amid a heatwave, it wasn’t
Key Takeaways AIOps—AI Intelligence of IT Operations—allows teams to stay a step ahead of problems by identifying them before they happen Teams can take advantage of AIOps to help them correlate and prioritize alerts, which streamlines operations and reduces noise Data is critical to AIOps implementations; inconsistent, disconnected, or out-of-date information can degrade system outputs
Key Takeaways AI solutions need to gain user trust to succeed; capabilities alone are not enough Manager adoption plays a key role in ensuring teams use AI tools regularly Including end users in how AI capabilities are designed and incorporated into workflows can help enhance outcomes Think about the last time a new system was
Your marketing group just signed up for an AI tool that scrapes customer data. Your sales team is feeding proprietary deal terms into ChatGPT. Your developers are using AI coding assistants that may be training on your intellectual property. And your procurement team? They just approved three new SaaS contracts with “AI-powered features” buried in
AI doesn’t lie, it hallucinates. And attackers can exploit this weakness. Organizations need to understand what hallucination is, what it means for cybersecurity, and how to approach it within their Security Operations Center (SOC). This understanding becomes critical as more organizations deploy AI in their security operations. How AI Accuracy Training Creates False Confidence AI
Cloud evolved from packaging and elasticity toward stateless, autoscaling, and managed building blocks that reduced operational effort. AI is now shaping the next phase of that evolution, turning cloud into an AI runtime platform where hardware, scheduling, data movement, and governance are designed around latency and token-based economics—not just workload size and power. Inference, running
Artificial intelligence (AI) dominated 2025—from boardroom strategies to campaign brainstorms—and it’s reshaping how every business operates. Across industries, we’ve seen remarkable innovation, but also some recurring assumptions about what AI can (and can’t) do. Like every new wave of technology, AI adoption brings learning curves and course corrections. Our new eBook, Top Six B2B AI
The global enterprise software market is expected to reach $280 billion this year, on its way to more than $760 billion by 2034. The real question isn’t how much you’re spending. It’s whether you’re investing in code that ages or code that learns.1 Artificial intelligence (AI) isn’t just about improving enterprise software—it’s about transforming the
The next evolution of GenAI will be agentic AI: large language models (LLMs) that not only provide answers to user queries but also take specific actions that can include writing new data and creating new files. Businesses are abuzz at agentic AI’s potential to extend code generation activities to enable LLMs to connect new data
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