Traditional finance software automates specifics, defining workflow, policy, approval thresholds and exception paths, but AI the only constraint is verifiability, not capability, explains Konstantin Dzhengozov, CFO and co-founder at Payhawk
In April, Uber’s chief technology officer, Praveen Neppalli Naga, revealed that the company had exhausted its entire 2026 AI budget in four months, driven largely by engineering’s adoption of AI coding tools. By June, Uber had capped spend at $1,500 (£1,100) per employee per month for each agentic tool, tracked on an internal dashboard.
Uber’s response to the overrun is more instructive than the overrun itself, because it did not slow adoption; it made consumption visible and set a limit on it. In essence, the smarter AI gets, the more tokens employees utilise, and the more expensive it may become to use at scale.
For CFOs, this challenges a core assumption that AI technology reduces costs in a linear, predictable way. In reality, several factors, including inference costs, usage spikes, and rework linked to poor data quality, can quickly erode expected gains.
