Brief

Many companies struggle with AI strategy due to a diffusion of responsibility, where no single leader wants accountability for potential failures. This "hot potato" game is fueled by AI's cross-functional impact, opaque supply chains, and rapid external innovation, making it politically risky. To address this, CEOs should lead AI strategy as part of the overall business strategy, with boards ultimately approving it, ensuring AI is treated as a core business driver rather than just infrastructure. AI

A new study by IBM's Institute for Business Value reveals that as enterprises increasingly deploy artificial intelligence, Chief Information Officers (CIOs) and Chief Technology Officers (CTOs) are struggling with a growing "AI control gap." This gap highlights challenges in managing and governing AI systems effectively within organizations. The research indicates a need for better strategies and tools to ensure responsible and controlled AI adoption. AI

Companies are facing unexpected and rapidly escalating costs associated with AI tokens, with many exceeding their initial budgets. This surge in token usage is driven by the adoption of agentic AI systems, which require extensive context to function effectively. Consequently, CFOs are experiencing significant budget overruns, as the variable nature of token costs makes them difficult to predict and manage, unlike traditional fixed-cost SaaS models. AI

Databricks co-founder Arsalan Tavakoli-Shiraji argues that most enterprises struggle to derive real value from AI initiatives due to architectural shortcomings. He highlights that selecting a foundation model is the easiest part, while the real challenges lie in data integration, robust governance, and ensuring agents have a deep semantic understanding of the business. Agentic systems often fail in production when data is siloed, governance is an afterthought, and the underlying infrastructure is not designed for action-oriented tasks. AI