Key Takeaways Integrating humans into automated AI processes helps avoid costly errors. Reserving humans for high-impact decision-making can create new challenges. By positioning AI tools as assistants rather than replacements, organizations can increase internal tool adoption and maximize the value of AI. The rapid adoption of AI tools and agents in the workplace has caused
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
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 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
Until recently, “more” was the default answer to almost any artificial intelligence (AI) challenge. More data, more parameters, more compute. But now, many organizations are realizing that raw scale comes with a price: spiraling infrastructure costs, slower inference times, and rising energy use. That raises a question: Do we need a 70-billion-parameter model to summarize
Artificial intelligence (AI) is accelerating down the enterprise highway. But according to new research, governance is idling in the slow lane, and the gap is already raising breach costs, fueling shadow AI adoption—when employees use generative AI tools outside official oversight—forcing regulators to wave the caution flag. Global average data breach costs fell in 2025
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