- Most CFOs say they still can’t make money on AI yet
- Traditional pricing fails in a utility -driven artificial intelligence economy
- AI -Intention Generation is now stuck on the Board of Directors Priority List
Artificial intelligence transforms every industry, but a new report has claimed that many companies fail to capture its financial value.
A global survey of 614 financial officers conducted by Digital Route found that nearly three -quarters (71%) said they were struggling to make money from AI effectively, despite almost 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 is experimenting or “flying blind”, according to the data, and over two-thirds (68%) of tech companies say their traditional pricing strategies are no longer applicable in an AI-driven economy.
Other Digital Gulrush?
“AI is in the second digital gold rush,” said Ari Vanttinen, CMO at Digitalroute. “But without visibility at the level of use, companies with pricing, profitability and even product dividend play. Our data shows CFOs that urgently need real -time interior design and revenue management to transform AI from a cost line to a true profit engine.”
Board rooms notice -almost two -thirds (64%) of the respondents say that AI earnings are now a formal board priority, but still one in five companies can trace individually AI consumption, leaving financial teams with limited tools for accurate invoicing, forecasts or margin analysis.
70% of CFOS cites pricing of pricing as the biggest barrier to scaling AI, and more than half reports incorrect adjustment between finance and product teams.
Legacy systems are also a challenge: 63% of companies are investing in new revenue management infrastructure, recognizing that traditional quote-to-cash systems are not suitable for using AI prices.
The study also highlights regional differences. Nordic countries lead to implementation but are struggling with profitability, while France and the UK show stronger early commercial returns. The United States is still a global leader in AI development, but the data suggests a slightly more cautious approach to organizational revenue generation.
Although US companies clearly understand the importance of AI, many are still developing the internal frameworks needed to scale effectively.
The United States scores a lot on the perceived meaning, but hangs a little behind Britain with regard to perceived criticism, indicating a broader, more experimental AI culture that is not yet fully transmitted to commercial execution.
The report recommends three steps to success: First, AI consumption measures at functional level; Secondly model Value -based and use -based pricing before launch; And thirdly, adjust product, finance and income teams around shared data.
As Vanttinen puts it, “Every prompt is now an income event. When companies can see, price and bill for real-time AI-use, they lock the margins that the market expects.”
