CFOs want AI that pays: real metrics, not marketing demos

Recent surveys and VentureBeat’s conversations with CFOs suggest the honeymoon phase of AI is rapidly drawing to a detailed. While 2024 was dominated by pilot programs and proof-of-concept demonstrations, in mid-2025, the pressure for measurable results is intensifying, at the same time as CFO interest in AI stays high. 

According to a KPMG survey of 300 U.S. financial executives, investor pressure to exhibit ROI on generative AI investments has increased significantly. For 90% of organizations, investor pressure is taken into account “vital or very vital” for demonstrating ROI in Q1 2025, a pointy increase from 68% in Q4 2024. This indicates a powerful and intensifying demand for measurable returns.

Meanwhile, based on a Bain Capital Ventures survey of fifty CFOs, 79% plan to extend their AI budgets this 12 months, with 94% believing gen AI can strongly profit no less than one finance activity. This reveals a telling pattern in how CFOs are currently measuring AI value. Those who’ve adopted gen AI tools report seeing initial returns primarily through efficiency gains.

“We created a custom workflow that automates vendor identification to quickly prepare journal entries,” said Andrea Ellis, CFO of Fanatics Betting and Gaming. “This process used to take 20 hours during month-end close, and now, it takes us just 2 hours every month.”

Jason Whiting, CFO of Mercury Financial, echoed this efficiency focus: “Across the board, (the largest profit) has been the flexibility to extend speed of study. Gen AI hasn’t replaced anything, but it surely has made our existing processes and folks higher.”

But CFOs at the moment are looking beyond easy time savings toward more strategic applications. 

The Bain data shows CFOs are most enthusiastic about applying AI to “long-standing pain points that prior generations of technology have been unable to resolve.” Cosmin Pitigoi, CFO of Flywire, explained: “Forecasting trends based on large data sets has been around for a very long time, but the problem has at all times been the model’s ability to elucidate the assumptions behind the forecast. AI may also help not only with forecasting, but in addition with explaining what assumptions have modified over time.”

These recent surveys suggest that CFOs have gotten the first gatekeepers for AI investment; nevertheless, they’re still developing the financial frameworks essential to judge these investments properly. Those who develop robust evaluation methodologies first will likely gain significant competitive benefits. Those who don’t may find their AI enthusiasm outpacing their ability to measure and manage the returns.

Efficiency metrics: The first wave of AI value

The initial wave of AI value capture by finance departments has focused predominantly on efficiency metrics, with CFOs prioritizing measurable time and price savings that deliver immediate returns. This give attention to efficiency represents the low-hanging fruit of AI implementation — clear, quantifiable advantages which can be easily tracked and communicated to stakeholders.

Drip Capital, a Silicon Valley-based fintech, exemplifies this approach with its AI implementation in trade finance operations. According to chief business officer Karl Boog, “We’ve been capable of 30X our capability with what we’ve done to this point.” By automating document processing and enhancing risk assessment through large language models (LLMs), the corporate achieved a remarkable 70% productivity boost while maintaining critical human oversight for complex decisions.

KPMG research indicates this approach is widespread, with one retail company audit committee director noting how automation has improved operational efficiency and ROI. This sentiment is echoed across industries as finance leaders seek to justify their AI investments with tangible productivity improvements.

These efficiency improvements translate on to the underside line. Companies across sectors — from insurance to grease and gas — report that AI helps discover process inefficiencies, resulting in substantial organizational cost savings and improved expense management.

Beyond easy cost reduction, CFOs are developing more sophisticated efficiency metrics to judge AI investments. These include time-to-completion ratios comparing pre- and post-AI implementation timelines, cost-per-transaction analyses measuring reductions in resource expenditure and labor hour reallocation metrics tracking how team members shift from manual data processing to higher-value analytical work.

However, leading CFOs recognize that while efficiency metrics provide a solid foundation for initial ROI calculations, they represent only the start of AI’s potential value. As finance leaders gain confidence in measuring these direct returns, they’re developing more comprehensive frameworks to capture AI’s full strategic value — moving well beyond the efficiency calculations that characterised early adoption phases.

Beyond efficiency: The recent financial metrics

As CFOs move beyond the initial fascination with AI-driven efficiency gains, they’re developing recent financial metrics that more comprehensively capture AI’s business impact. This evolution reflects a maturing approach to AI investments, with finance leaders adopting more sophisticated evaluation frameworks that align with broader corporate objectives.

The surveys highlight a notable shift in primary ROI metrics. While efficiency gains remain vital, we see productivity metrics at the moment are overtaking pure profitability measures because the chief priority for AI initiatives in 2025. This represents a fundamental change in how CFOs assess value, specializing in AI’s ability to boost human capabilities moderately than simply reduce costs.

Time to value (TTV) is emerging as a critical recent metric in investment decisions. Only about one-third of AI leaders anticipate with the ability to evaluate ROI inside six months, making rapid time-to-value a key consideration when comparing different AI opportunities. This metric will help CFOs prioritize quick-win projects that may deliver measurable returns while constructing organizational confidence in larger AI initiatives.

Data quality measurements will increasingly be incorporated into evaluation frameworks, with 64% of leaders citing data quality as their most important AI challenge. Forward-thinking CFOs now incorporate data readiness assessments and ongoing data quality metrics into their AI business cases, recognizing that even probably the most promising AI applications will fail without high-quality data inputs.

Adoption rate metrics have also turn into standard in AI evaluation. Finance leaders track how quickly and extensively AI tools are being utilized across departments, using this as a number one indicator of potential value realization. These metrics help discover implementation challenges early and inform decisions about additional training or system modifications.

“The biggest profit has been the flexibility to extend speed of study,” noted Jason Whiting of Mercury Financial. This perspective represents the bridge between easy efficiency metrics and more sophisticated value assessments — recognizing that AI’s value often comes not from replacing existing processes but enhancing them.

Some CFOs are implementing comprehensive ROI formulas that incorporate each direct and indirect advantages (VAI Consulting):

ROI = (Net Benefit / Total Cost) × 100

Where net profit equals the sum of direct financial advantages plus an estimated value of indirect advantages, minus total investment costs. This approach acknowledges that AI’s full value encompasses each quantifiable savings and intangible strategic benefits, corresponding to improved decision quality and enhanced customer experience.

For firms with more mature AI implementations, these recent metrics have gotten increasingly standardized and integrated into regular financial reporting. The most sophisticated organizations now produce AI value scorecards that track multiple dimensions of performance, linking AI system outputs on to business outcomes and financial results.

As CFOs refine these recent financial metrics, they’re making a more nuanced picture of AI’s true value — one which extends well beyond the easy time and price savings that dominated early adoption phases.

Amortization timelines: Recalibrating investment horizons

CFOs are fundamentally rethinking how they amortize AI investments, developing recent approaches that acknowledge the unique characteristics of those technologies. Unlike traditional IT systems with predictable depreciation schedules, AI investments often yield evolving returns that increase as systems learn and improve over time. Leading finance executives now evaluate AI investments through the lens of sustainable competitive advantage — asking not only “How much will this save?” but “How will this transform our market position?”

“ROI directly correlates with AI maturity,” based on KPMG, which found that 61% of AI leaders report higher-than-expected ROI, compared with only 33% of beginners and implementers. This correlation is prompting CFOs to develop more sophisticated amortization models that anticipate accelerating returns as AI deployments mature.

The difficulty in establishing accurate amortization timelines stays a major barrier to AI adoption. “Uncertain ROI/difficulty developing a business case” is cited as a challenge by 33% of executives, particularly those within the early stages of AI implementation. This uncertainty has led to a more cautious, phased approach to investment.

To address this challenge, leading finance teams are implementing pilot-to-scale methodologies to validate ROI before full deployment. This approach enables CFOs to collect accurate performance data, refine their amortization estimates, and make more informed scaling decisions.

The timeframe for expected returns varies significantly based on the style of AI implementation. Automation-focused AI typically delivers more predictable short-term returns, whereas strategic applications, corresponding to improved forecasting, could have longer, less certain payback periods. Progressive CFOs are developing differentiated amortization schedules that reflect these variations moderately than applying one-size-fits-all approaches.

Some finance leaders are adopting rolling amortization models which can be adjusted quarterly based on actual performance data. This approach acknowledges the dynamic nature of AI returns and allows for ongoing refinement of economic projections. Rather than setting fixed amortization schedules on the outset, these models incorporate learning curves and performance improvements into evolving financial forecasts.

One entertainment company implemented a gen AI-driven tool that scans financial developments, identifies anomalies and robotically generates executive-ready alerts. While the immediate ROI stemmed from efficiency gains, the CFO developed an amortization model that also factored within the system’s increasing accuracy over time and its expanding application across various business units.

Many CFOs are also factoring in how AI investments contribute to constructing proprietary data assets that appreciate moderately than depreciate over time. Unlike traditional technology investments that lose value as they age, AI systems and their associated data repositories often turn into more worthwhile as they accumulate training data and insights.

This evolving approach to amortization represents a major departure from traditional IT investment models. By developing more nuanced timelines that reflect AI’s unique characteristics, CFOs are creating financial frameworks that higher capture the true economic value of those investments and support a more strategic allocation of resources.

Strategic value integration: Linking AI to shareholder returns

Forward-thinking CFOs are moving beyond operational metrics to integrate AI investments into broader frameworks for creating shareholder value. This shift represents a fundamental evolution in how financial executives evaluate AI — positioning it not merely as a cost-saving technology but as a strategic asset that drives enterprise growth and competitive differentiation.

This more sophisticated approach assesses AI’s impact on three critical dimensions of shareholder value: revenue acceleration, risk reduction and strategic optionality. Each dimension requires different metrics and evaluation frameworks, making a more comprehensive picture of AI’s contribution to enterprise value.

Revenue acceleration metrics give attention to how AI enhances top-line growth by improving customer acquisition, increasing the share of wallet and expanding market reach. These metrics track AI’s influence on sales velocity, conversion rates, customer lifetime value and price optimization — connecting algorithmic capabilities on to revenue performance.

Risk reduction frameworks assess how AI enhances forecasting accuracy, improves scenario planning, strengthens fraud detection and optimizes capital allocation. By quantifying risk-adjusted returns, CFOs can exhibit how AI investments reduce earnings volatility and improve business resilience — aspects that directly impact valuation multiples.

Perhaps most significantly, leading CFOs are developing methods to value strategic optionality — the capability of AI investments to create recent business possibilities that didn’t previously exist. This approach recognizes that AI often delivers its most important value by enabling entirely recent business models or unlocking previously inaccessible market opportunities.

To effectively communicate this strategic value, finance leaders are creating recent reporting mechanisms tailored to different stakeholders. Some are establishing comprehensive AI value scorecards that link system performance to tangible business outcomes, incorporating each lagging indicators (financial results) and leading indicators (operational improvements) that predict future financial performance.

Executive dashboards now usually feature AI-related metrics alongside traditional financial KPIs, making AI more visible to senior leadership. These integrated views enable executives to grasp how AI investments align with strategic priorities and shareholder expectations.

For board and investor communication, CFOs are developing structured approaches that highlight each immediate financial returns and long-term strategic benefits. Rather than treating AI as a specialized technology investment, these frameworks position it as a fundamental business capability that drives sustainable competitive differentiation.

By developing these integrated strategic value frameworks, CFOs make sure that AI investments are evaluated not only on their immediate operational impact but their contribution to the corporate’s long-term competitive position and shareholder returns. This more sophisticated approach is rapidly becoming a key differentiator between firms that treat AI as a tactical tool and people who leverage it as a strategic asset.

Risk-adjusted returns: The risk management equation

As AI investments grow in scale and strategic importance, CFOs are incorporating increasingly sophisticated risk assessments into their financial evaluations. This evolution reflects the unique challenges AI presents — balancing unprecedented opportunities against novel risks that traditional financial models often fail to capture.

The risk landscape for AI investments is multifaceted and evolving rapidly. Recent surveys indicate that risk management, particularly in relation to data privacy, is anticipated to be the largest challenge to generative AI strategies for 82% of leaders in 2025. This concern is followed closely by data quality issues (64%) and questions of trust in AI outputs (35%).

Forward-thinking finance leaders are developing comprehensive risk-adjusted return frameworks that quantify and incorporate these various risk aspects. Rather than treating risk as a binary go/no-go consideration, these frameworks assign monetary values to different risk categories and integrate them directly into ROI calculations.

Data security and privacy vulnerabilities represent a primary concern, with 57% of executives citing these as top challenges. CFOs at the moment are calculating potential financial exposure from data breaches or privacy violations and factoring these costs into their investment analyses. This includes estimating potential regulatory fines, litigation expenses, remediation costs and reputational damage.

Regulatory compliance represents one other significant risk factor. With many executives concerned about ensuring compliance with changing regulations, financial evaluations increasingly include contingency allocations for regulatory adaptation. An aerospace company executive noted that “complex regulations make it difficult for us to attain AI readiness,” highlighting how regulatory uncertainty complicates financial planning.

Beyond these external risks, CFOs are quantifying implementation risks corresponding to adoption failures, integration challenges and technical performance issues. By assigning probability-weighted costs to those scenarios, they create more realistic projections that acknowledge the inherent uncertainties in AI deployment.

The “black box” nature of certain AI technologies presents unique challenges for risk assessment. As stakeholders turn into increasingly wary of trusting AI results without understanding the underlying logic, CFOs are developing frameworks to judge transparency risks and their potential financial implications. This includes estimating the prices of additional validation procedures, explainability tools and human oversight mechanisms.

Some firms are adopting formal risk-adjustment methodologies borrowed from other industries. One approach applies a modified weighted average cost of capital (WACC) that comes with AI-specific risk premiums. Others use risk-adjusted net present value calculations that explicitly account for the unique uncertainty profiles of various AI applications.

The transportation sector provides an illustrative example of this evolving approach. As one chief data officer noted, “The data received from AI requires human verification, and that is a very important step that we overlook.” This recognition has led transportation CFOs to construct verification costs directly into their financial models moderately than treating them as optional add-ons.

By incorporating these sophisticated risk adjustments into their financial evaluations, CFOs are creating more realistic assessments of AI’s true economic value. This approach enables more confident investment decisions and helps organizations maintain appropriate risk levels as they scale their AI capabilities.

The CFO’s AI evaluation playbook: From experiments to enterprise value

As AI transitions from experimental projects to enterprise-critical systems, CFOs are developing more disciplined, comprehensive frameworks for evaluating these investments. The most successful approaches strike a balance between rigor and suppleness, acknowledging each the unique characteristics of AI and its integration into broader business strategy.

The emerging CFO playbook for AI evaluation accommodates several key elements that differentiate leaders from followers.

1. First is the implementation of multi-dimensional ROI frameworks that capture each efficiency gains and strategic value creation. Rather than focusing exclusively on cost reduction, these frameworks incorporate productivity enhancements, decision quality improvements and competitive differentiation right into a holistic value assessment.
2. Second is the adoption of phased evaluation approaches that align with AI’s evolutionary nature. Leading CFOs establish clear metrics for every development stage — from initial pilots to scaled deployment — with appropriate risk adjustments and expected returns for every phase. This approach recognizes that AI investments often follow a J-curve, with value accelerating as systems mature and applications expand.
3. Third is the combination of AI metrics into standard financial planning and reporting processes. Rather than treating AI as a special category with unique evaluation criteria, forward-thinking finance leaders are incorporating AI performance indicators into regular budget reviews, capital allocation decisions and investor communications. This normalization signals AI’s transition from experimental technology to core business capability.

The most sophisticated organizations are also implementing formal governance structures that connect AI investments on to strategic objectives. These governance frameworks make sure that AI initiatives remain aligned with enterprise priorities while providing the essential oversight to administer risks effectively. By establishing clear accountability for each technical performance and business outcomes, these structures help prevent the disconnection between AI capabilities and business value that has plagued many early adopters.

As investors and boards increasingly scrutinize AI investments, CFOs are developing more transparent reporting approaches that clearly communicate each current returns and future potential. These reports typically include standardized metrics that track AI’s contribution to operational efficiency, customer experience, worker productivity and strategic differentiation — providing a comprehensive view of how these investments enhance shareholder value.

The organizations gaining a competitive advantage through AI are those where CFOs have moved to turn into strategic partners in AI transformation. These finance leaders work closely with technology and business teams to discover high-value use cases, establish appropriate success metrics and create financial frameworks that support responsible innovation while maintaining appropriate risk management.

The CFOs who master these recent evaluation frameworks will drive the subsequent wave of AI adoption — one characterised not by speculative experimentation but by disciplined investment in capabilities that deliver sustainable competitive advantage. As AI continues to remodel business models and market dynamics, these financial frameworks will turn into increasingly critical to organizational success.

The CFO’s AI evaluation framework: Key metrics and considerations