Next in Enterprise AI: Controlling the Cost of Scale
Key Takeaways
- •A Harness survey of 700 FinOps and engineering leaders found that approximately one quarter of enterprise AI spending is wasted.
- •More than half of surveyed organizations do not have a dedicated owner responsible for tracking and managing AI costs.
- •Companies frequently deploy larger and more expensive AI models than necessary for routine tasks, even as per-token costs continue to decline.
- •An EY survey released the same week confirmed that organizations are scrutinizing token costs and return on investment more closely while maintaining AI investment levels.
- •Hyperscalers including Microsoft, Alphabet, and Meta have shifted their earnings narratives toward deployment execution and infrastructure efficiency rather than sheer spending increases.
A new report finds that one in four AI dollars is wasted, highlighting a growing reality for enterprises: the challenge is no longer simply deploying AI but controlling the cost of running it at scale. As AI spending spreads across organizations, cost management is becoming as critical as adoption itself.
The finding comes from AI software development platform vendor Harness and its 2026 State of AI in FinOps report, based on a survey of 700 FinOps and engineering leaders. FinOps — short for financial operations — emerged as a discipline during the shift to cloud computing, when organizations realized they needed dedicated practices to manage variable infrastructure spending; it is now being applied to AI. According to the report, more than half of organizations lack a dedicated owner for AI costs, making it difficult to track spending or identify waste. Organizations also frequently use larger, more expensive models than necessary for routine tasks, while growing AI adoption drives token consumption — the volume of text fragments that models process and that providers bill on — higher even as per-token costs decline.
Related: OpenAI Cuts Model Prices Amid Enterprises' Concerns About AI Spend
The Harness findings reflect a broader shift in enterprise AI. For the past two years, the focus has been on deploying generative AI across the business. Now, the challenge is ensuring those deployments remain financially sustainable as usage expands.
The findings were not isolated. An EY survey released this week found that organizations are continuing to invest in AI while paying much closer attention to token costs and ROI. Separately, Schneider Electric and AMD unveiled a blueprint for AI factories, another sign that enterprises are shifting from proving AI works to building infrastructure capable of supporting it at scale.
The shift was also evident in hyperscaler earnings, with Microsoft, Alphabet and Meta emphasizing deployment and infrastructure execution over simply increasing AI spending. Together, the week's developments suggest the next phase of enterprise AI will be defined less by how much companies spend and more by how effectively they manage those investments.
Enterprise AI appears to be following a familiar pattern. Much like cloud computing before it, the early race to adopt the technology is giving way to a greater focus on managing costs, measuring returns and scaling deployments sustainably. Organizations that treat AI as an operational capability rather than an open-ended experiment may be better positioned going forward.
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