AI Boom Reveals Energy Gaps in Organizations, Survey Finds

In a recent survey conducted by Pure Storage® in collaboration with Wakefield Research, alarming findings underscore the unpreparedness of organizations across diverse industries to meet the energy requirements and data challenges posed by the widespread adoption of artificial intelligence (AI).

Key Survey Highlights:

1. Skyrocketing Demand for Computing Power: The survey, encompassing 500 IT buyers from companies with 500 or more employees, highlights that 88% of those who embraced AI witnessed a substantial surge in the need for computing power. Notably, 47% reported a doubling or more of their computing power since the adoption of AI.

2. Underestimating Energy Demands: A staggering 73% of IT buyers admitted to being inadequately prepared for the energy demands associated with AI integration, revealing a critical oversight in planning.

3. Diverse Upgrades Needed: For 73% of respondents, AI necessitates or will necessitate various data management upgrades, including tools (48%), processes (46%), and storage infrastructure (46%).

4. IT Infrastructure Overhaul: A significant 96% of organizations have either already undertaken or plan to update their IT infrastructure, with 29% indicating that AI has necessitated or will necessitate a complete overhaul.

5. Impact on Sustainability Goals: The challenges posed by AI integration have set back 89% of businesses in achieving their Environmental, Social, and Governance (ESG) goals. Despite this setback, 60% of those adopting or planning to adopt AI in the next year are investing in more energy-efficient hardware to align with ESG objectives.

Rob Lee,

Pure Storage’s Chief Technology Officer.

“Investing in and deploying the right AI-ready data infrastructure is not only essential to effective deployment and energy efficiency but also to driving the most value out of AI projects.”

The survey sheds light on the widespread adoption of AI across industries. However, the lack of adequate infrastructure capable of handling the escalating data demands and energy prerequisites jeopardizes the successful implementation of AI. This limitation not only hinders critical corporate initiatives but also poses challenges to achieving environmental goals, with nearly all IT buyers feeling the pressure to reduce their carbon footprint.

As AI continues to gain momentum, the call for smarter infrastructure becomes more urgent. Legacy systems struggle to support the colossal AI data pipelines required for optimal machine learning outcomes. To keep pace with AI’s rapid evolution, IT teams are increasingly recognizing the need for efficient, reliable, and high-performance infrastructure to ensure effective deployment and maximize the benefits of AI projects.