Board members and senior executives increasingly recognize data as critical to successful AI adoption. Accordingly, data centers that support AI models have become a core part of their operations, bringing a growing list of complex and interconnected challenges. In response, boards and C-suites are adopting more holistic governance practices to reduce risk and increase data-center effectiveness.
The role of data in AI
Board members and senior leaders report that the data required for AI models to operate effectively and the data centers that manage them often are misperceived as commodities. Data availability, ownership rights, regulatory and governance rules and cyber/data security remain challenges, as does securing the energy required to access, ingest, clean and analyze data. According to the PEX Report 2025/26, 52% of organizations cite data quality and availability as the primary barriers to AI adoption. Further, these challenges are expected to become more frequent and acute in 2026.
The role of energy in AI
Board members and senior leaders increasingly find electricity and water to be constraints on AI development and application. Goldman Sachs Research forecasts that by 2030, overall power consumption from AI data centers will jump 175% from 2023 levels. AI consumes significant energy because training and building large-scale AI models requires immense computational power as data must run through models thousands of times on increasingly more powerful and energy-intensive chips. And data centers are always on.
With energy demand increasing faster than capacity and alternative solutions requiring years to develop and implement, data centers risk becoming “maxed out” according to Goldman Sachs and others. JLL reports that accessible power (rather than location or cost) could become the primary factor in data center site selection due to multi-year wait times for a grid connection. The Willis Research Network writes about approaches companies can take to solve the AI energy dilemma.
Key elements of AI governance
We previously covered an updated AI governance model from the National Association of Corporate Directors (NACD) as reported by Dylan Sandlin, Dr. Helmuth Ludwig and Dr. Benjamin van Giffen. It includes four elements of AI governance: strategic oversight, capital allocation, AI risk and technology competence. As global demand for data center capacity has expanded, organizations face a widening array of interconnected challenges. A 2026 report by WTW’s George Haitsch covers key actions to reduce risk and enhance effectiveness of data centers, which align with the four NACD AI governance elements.
Effective AI governance incorporates material elements of data center risk and resilience, ensuring sites can withstand operating costs, energy constraints and downtime exposures while supporting robust business continuity and scenario planning.
A version of this article originally appeared on Forbes on February 27, 2026.
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