A recent survey by VentureBeat Research involving 573 technical leaders from enterprises with 100+ employees reveals a striking trend: 86% report that their GPUs for AI workloads operate at 50% capacity or less. This inefficiency coincides with enterprises deploying AI agents faster than they can implement control measures, such as identity verification, output evaluation, cost tracking, context management, and orchestration. Many companies are now retrofitting their systems with control layers and budgeting for vendor changes within the next year. Despite the rapid adoption of AI agents, the majority are single-prompt chatbots rather than true autonomous multi-step agents, impacting necessary control requirements and costs.
The survey also highlights risks, with over half of companies experiencing security incidents linked to credential sharing between agents, and 57% recording errors due to inconsistent or missing business context. Enterprises are increasingly prioritizing agent technology portability, motivated in part by regulatory impacts and new open-weight AI models promising greater control. Vendor lock-in concerns and the dominance of provider-native solutions shape spending and procurement strategies. The report underscores the need for enterprises to optimize hardware utilization, validate AI results against real-world outcomes, and govern the data definitions agents rely on to improve AI deployment effectiveness and security.
