Global bean counters are struggling to find value for money in anything AI and that is a big, big problem
- Most CFOs say they still can’t make money from AI yet
- Traditional pricing fails in a usage-driven artificial intelligence economy
- AI monetization now sits firmly on the boardroom’s priority list
Artificial intelligence is transforming every industry, but a new report has claimed many companies are failing to capture its financial value.
A global study of 614 Chief Financial Officers conducted by DigitalRoute found nearly three-quarters (71%) said they were struggling to monetize AI effectively, despite nearly 90% naming it a mission-critical priority over the next five years.
Only 29% of companies currently have a working AI monetization model, and the rest are either experimenting or “flying blind,” according to the data, and over two-thirds (68%) of tech firms say their traditional pricing strategies are no longer applicable in an AI-driven economy.
Second digital gold rush?
“AI is in the second digital gold rush,” said Ari Vanttinen, CMO at DigitalRoute. “But without the usage-level visibility, companies are gambling with pricing, profitability and even product viability. Our data shows CFOs urgently need real-time metering and revenue management to turn AI from a cost line into a genuine profit engine.”
Boardrooms are taking notice – nearly two-thirds (64%) of those surveyed say AI monetization is now a formal board priority, yet just one in five businesses can track individual AI consumption, leaving finance teams with limited tools for accurate billing, forecasting, or margin analysis.
70% of CFOs cite pricing complexity as the biggest barrier to scaling AI, and more than half report misalignment between finance and product teams.
Legacy systems are also a challenge: 63% of companies are investing in new revenue management infrastructure, acknowledging that traditional quote-to-cash systems aren’t fit for usage-based AI pricing models.
The study also highlights regional differences. Nordic countries lead in implementation but struggle with profitability, while France and the UK are showing stronger early commercial returns. The US remains a global leader in AI development, but the data suggests a slightly more cautious approach to monetization at the organizational level.
Although American businesses clearly understand the importance of AI, many are still developing the internal frameworks needed to scale effectively.
The US scores highly on perceived significance but lags slightly behind the UK in terms of perceived criticality, indicating a broader, more experimental AI culture that has yet to fully transition to commercial execution.
The report recommends three steps for success: first, meter AI consumption at the feature level; second, model value-based and usage-based pricing before launch; and third, align product, finance, and revenue teams around shared data.
As Vanttinen puts it, “Every prompt is now a revenue event. When businesses can see, price and bill for AI usage in real-time, they unlock the margins the market expects.”
