A new research by SambaNova Systems is showing a critical readiness gap as enterprises face escalating power demands with AI. While 49.8% of leaders are concerned about the energy and efficiency challenges posed by AI, only 13.0% monitor the power consumption of their AI systems. The survey exposes a lack of infrastructure preparedness amidst the rise in AI deployments across the United States and Europe.
“The findings reveal a stark reality: businesses are rushing to adopt AI, but aren’t prepared to manage its energy impact,” said Rodrigo Liang, CEO of SambaNova Systems. “Without a proactive approach to more efficient AI hardware and energy consumption, particularly in the face of increasing demand from AI workflows, we risk undermining the very progress AI promises to deliver. By 2027, my expectation is that more than 90% of leaders will be concerned about the power demands of AI and will monitor consumption as a KPI that corporate boards will track closely.”
Key findings from the research include:
AI Inference Will Drive Power Demand Growth: While 70.0% of leaders recognize the energy-intensive nature of training large language models, only 59.7% are aware of the significant power demands of inference. This gap is critical as inference workloads are set to dominate AI usage with the scaling of Agentic AI.
Energy Efficiency is a Strategic Priority: Only 13.0% of organizations currently monitor AI power consumption, yet 56.5% acknowledge that energy efficiency will play a crucial role in future strategic planning. The need to address rising energy demands is being driven by both cost management imperatives and operational scalability concerns.
Scaling Agentic AI Brings New Challenges: The roll-out of Agentic AI is amplifying power concerns for enterprises. For 20.3% of companies, rising power costs are a pressing issue. Furthermore, 37.2% are experiencing increasing stakeholder pressure to improve AI energy efficiency, with a further 42.0% expecting these demands to emerge soon.
Few Enterprises Are Proactively Addressing AI’s Energy Impact: Among organizations that have widely deployed AI2, 77.4% are actively seeking to reduce power usage. Popular approaches include hardware and software optimization (40.4%), adopting energy-efficient processors (39.3%), and investing in renewable energy (34.9%). However, these measures remain insufficient compared to the rapid pace of AI adoption and scaling.
The rise of agentic AI marks a turning point for the industry. As businesses scale AI and deploy more advanced workflows, addressing energy demands will be crucial to managing costs and achieving efficiency goals. Bridging the awareness gap with education and strategic planning will be key to ensuring AI’s growth remains both financially and commercially sustainable.
The excessive power consumption and prohibitive costs associated with current GPU-based solutions are likely to compel many enterprises to seek more efficient alternatives. This shift will fundamentally change the landscape of AI hardware, favoring solutions that deliver high performance without unsustainable energy demands.
The rapid pace of AI adoption underscores a critical need for enterprises to align their strategies with the power requirements of AI deployment,” stated Liang. “As businesses integrate AI, addressing energy efficiency and infrastructure readiness will be essential for long-term success. Customers are turning to SambaNova for help deploying energy efficient solutions.”
Source: Sambanova Systems
