For the last couple of years, enterprises have doubled and tripled down on investments in AI-related initiatives. The board-level leadership at the Global 1000 enterprises has high expectations of the value that can be unleashed through the power of AI in growing the business as well as driving efficiencies. CIO and CAIO organizations have spent a lot of time and energy on addressing constraints such as data availability, governance and change management to enable value realization through AI initiatives. For the most part, enterprises have assumed unconstrained access to AI infrastructure through hyper-scalers. The exponential reduction in token costs has enabled broader adoption of AI use cases within enterprises.
As generative AI, agentic systems and real-time inference move from pilots into production, CIO’s and enterprise AI leaders would be well advised to be cognizant of another constraint: energy availability and its impacts AI cost, scale and risk. Power, cooling and physical infrastructure and have not been commonly discussed within CIO organizations as a potential constraint.
This article outlines AI’s energy wake-up call and why CIOs should pay attention to the impact of energy on AI while building their longer-term AI strategy.