EY survey finds C-suites pivoting from AI adoption to unlocking value as token costs trigger fiscal scrutiny
ey.com · 28 July 2026
98% of senior leaders using AI tools that require tokens say token usage costs caused their organisation to reconsider their approach. 82% are concerned about AI token usage and costs. Only 64% actively monitor token usage and have clear budgetary guardrails. 76% say off-the-shelf software solutions no longer meet their needs. 91% view in-house AI-built software as critical. 87% have fully deployed or are piloting in-house AI-built software development. 82% anticipate traditional per-seat SaaS pricing model will become less relevant in their industry within five years. Actual AI spending fell short of projections. 35% of leaders projected spending 10 million dollars or more, and only 23% report that spend today. 18% planned 50% or more of total budget to AI, and only 3% have done so.
This is the observable fact the cited source supports, stated without interpretation.
Enterprises actively building in-house autonomous systems in response to consumption cost structures incompatible with traditional SaaS licensing. Per-seat pricing model anticipated to lose relevance. Spending projections missing targets suggest commercial structures are not sustaining projected revenue.
Token consumption costs are driving enterprises away from vendor commercial software toward in-house autonomous systems, and consumption-based cost drivers are making traditional per-seat licensing incompatible with autonomous activity structures. Value capture is restructuring around cost governance of autonomous throughput, and less around access.
Token consumption costs are driving enterprises away from vendor commercial software toward in-house autonomous systems, and consumption-based cost drivers are making traditional per-seat licensing incompatible with autonomous activity structures. Value capture is restructuring around cost governance of autonomous throughput, and less around access.
This is the framework's reading of the fact above. The source does not endorse it and is not cited as doing so.
The move to in-house development could reflect a general enterprise preference for customisation, with no structural change driven by AI. The gap between projected and actual spending could indicate that enterprises are risk-averse or cautious, without the commercial model failing. Token cost concerns may be temporary pain points in a new market, and may not point to durable repricing.
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