A global DigitalRoute report reveals that nearly 71% of CFOs face challenges in generating revenue from AI, highlighting gaps in pricing models, legacy systems, and interdepartmental alignment despite AI’s critical importance for business growth.
A global report by DigitalRoute reveals that a significant number of Chief Financial Officers (CFOs) are struggling to effectively monetise artificial intelligence (AI), despite recognising its importance in their business strategies. The study surveyed 614 CFOs and found that almost 71% reported difficulties in capitalising on AI financially, while nearly 90% identified it as a critical priority for the next five years.
At present, only 29% of organisations have implemented a viable AI monetisation model, with the majority either experimenting or lacking a clear strategy. The findings suggest that technology firms are grappling with traditional pricing strategies, with 68% acknowledging that these models are ill-suited for the shift towards an AI-driven economy.
Ari Vanttinen, Chief Marketing Officer at DigitalRoute, stated, “AI is in the second digital gold rush. But without the usage-level visibility, companies are gambling with pricing, profitability and even product viability.” This highlights the pressing need for companies to adopt real-time metering and revenue management to transform AI from a mere expense into a viable profit source.
In boardrooms, AI monetisation is becoming a formal priority, with 64% of CFOs indicating its significance. However, only one in five businesses can monitor individual AI consumption, limiting the capacity of finance teams to conduct accurate billing, forecasting, and margin analysis. Complexity in pricing structures emerged as the predominant challenge, cited by 70% of CFOs. Furthermore, misalignment between finance and product teams was reported by over half of the respondents.
The report also identifies legacy systems as a significant barrier, with 63% of companies investing in new revenue management infrastructures as traditional quote-to-cash systems have proven inadequate for the emerging usage-based AI pricing models.
The study underscores regional disparities in AI implementation and monetisation. While Nordic countries are at the forefront of AI integration, they are experiencing challenges in achieving profitability. Conversely, France and the UK are showcasing stronger early commercial returns. The United States, a global leader in AI development, appears to be adopting a more cautious approach to monetisation, with many businesses still constructing the internal frameworks necessary for effective scaling.
Although American enterprises recognise the importance of AI, their perceived criticality scores indicate a broader, exploratory culture around AI that has yet to translate into comprehensive commercial execution.
To enhance monetisation strategies, the report recommends three key steps: first, metering AI usage at the feature level, second, modelling value-based and usage-based pricing prior to launches, and third, fostering collaboration between product, finance, and revenue teams through shared data.
As Vanttinen emphasised, “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.” This underscores the evolving landscape of AI monetisation, as companies strive to navigate the complexities of pricing and profitability in an increasingly competitive environment.
Source: Noah Wire Services
- https://www.coupa.com/blog/finance-ap/nearly-90-cfos-lack-confidence-business-targets-ai-strategies – This article discusses how 90% of CFOs are concerned about their organization’s ability to implement AI strategies, highlighting the challenges in monetizing AI effectively.
- https://www.gartner.com/en/newsroom/press-releases/2024-05-20-gartner-says-cfos-must-address-four-enterprise-ai-stalls – Gartner identifies four common stalls hindering AI adoption, including cost overruns and misuse in decision-making, which CFOs need to address to capitalize on AI financially.
- https://the-cfo.io/2023/11/13/the-slow-march-of-ai-in-finance-a-contrast-to-potential-and-reality/ – This article highlights that 61% of finance functions are not planning or are only in the initial stages of AI implementation, indicating a lack of viable AI monetization models.
- https://fortune.com/2024/03/18/cfos-productivity-generative-ai-finance-employees/ – The article discusses how CFOs are exploring generative AI but face concerns about the technical skills of finance teams, emphasizing the need for real-time metering and revenue management.
- https://www.cfodive.com/news/costs-top-finance-automation-hurdle-AI-machine-learning/650352/ – This survey reveals that budget constraints are the top obstacle to new technology investments, including AI, affecting the ability to implement viable AI monetization models.
- https://kpmg.com/ca/en/home/insights/2024/10/the-cfo-and-ai-shaping-the-future-of-finance.html – The article notes that only 4% of finance professionals have an advanced level of AI adoption, indicating that many organizations are still in the early stages of implementing AI monetization strategies.
- https://www.techradar.com/pro/security/global-bean-counters-are-struggling-to-find-value-for-money-in-anything-ai-and-that-is-a-big-big-problem – Please view link – unable to able to access data
Noah Fact Check Pro
The draft above was created using the information available at the time the story first
emerged. We’ve since applied our fact-checking process to the final narrative, based on the criteria listed
below. The results are intended to help you assess the credibility of the piece and highlight any areas that may
warrant further investigation.
Freshness check
Score:
8
Notes:
No indications of recycled content found. The narrative references a current report and recent business strategies, with no outdated references to roles, events, or systems. The discussion of AI monetisation challenges aligns with known industry trends, though the specific report date is unverified.
Quotes check
Score:
7
Notes:
Ari Vanttinen’s quotes are specific to the report’s findings and lack publicly traceable prior use, suggesting original sourcing. No earlier references found, indicating potential first use.
Source reliability
Score:
8
Notes:
TechRadar is a reputable publication in technology reporting. The narrative cites a named report (DigitalRoute) and directly attributed executive quotes, supporting credibility.
Plausability check
Score:
9
Notes:
Claims align with known AI monetisation challenges and industry sentiment. Regional disparities (Nordic vs. US approaches) and cited statistics (e.g., 70% citing pricing complexity) are consistent with current discourse.
Overall assessment
Verdict (FAIL, OPEN, PASS): PASS
Confidence (LOW, MEDIUM, HIGH): HIGH
Summary:
The narrative demonstrates credible sourcing, original quotes, and alignment with industry trends. While the exact report date is unspecified, the content’s technical coherence and lack of red flags support its reliability.
