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

This text is a part of VentureBeat’s particular situation, “The Actual Value of AI: Efficiency, Effectivity and ROI at Scale.” Learn extra from this particular situation.

Latest surveys and VentureBeat’s conversations with CFOs counsel the honeymoon part of AI is quickly drawing to an in depth. Whereas 2024 was dominated by pilot applications and proof-of-concept demonstrations, in mid-2025, the strain for measurable outcomes is intensifying, whilst CFO curiosity in AI stays excessive. 

In response to a KPMG survey of 300 U.S. monetary executives, investor strain to display ROI on generative AI investments has elevated considerably. For 90% of organizations, investor strain is taken into account “essential or crucial” for demonstrating ROI in Q1 2025, a pointy enhance from 68% in This fall 2024. This means a powerful and intensifying demand for measurable returns.

In the meantime, in line with 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 not less than one finance exercise. This reveals a telling sample in how CFOs are presently measuring AI worth. Those that have adopted gen AI instruments report seeing preliminary returns primarily by way of effectivity good points.

“We created a customized workflow that automates vendor identification to rapidly put together journal entries,” mentioned Andrea Ellis, CFO of Fanatics Betting and Gaming. “This course of used to take 20 hours throughout month-end shut, and now, it takes us simply 2 hours every month.”

Jason Whiting, CFO of Mercury Financial, echoed this effectivity focus: “Throughout the board, [the biggest benefit] has been the power to extend velocity of research. Gen AI hasn’t changed something, nevertheless it has made our current processes and folks higher.”

However CFOs at the moment are wanting past easy time financial savings towards extra strategic purposes. 

The Bain information reveals CFOs are most enthusiastic about making use of AI to “long-standing ache factors that prior generations of know-how have been unable to resolve.” Cosmin Pitigoi, CFO of Flywire, defined: “Forecasting tendencies primarily based on giant information units has been round for a very long time, however the situation has at all times been the mannequin’s capability to clarify the assumptions behind the forecast. AI may help not simply with forecasting, but in addition with explaining what assumptions have modified over time.”

These latest surveys counsel that CFOs have gotten the first gatekeepers for AI funding; nevertheless, they’re nonetheless growing the monetary frameworks obligatory to judge these investments correctly. Those that develop strong analysis methodologies first will possible acquire vital aggressive benefits. Those that don’t might discover their AI enthusiasm outpacing their capability to measure and handle the returns.

Effectivity metrics: The primary wave of AI worth

The preliminary wave of AI worth seize by finance departments has targeted predominantly on effectivity metrics, with CFOs prioritizing measurable time and value financial savings that ship speedy returns. This give attention to effectivity represents the low-hanging fruit of AI implementation — clear, quantifiable advantages which can be simply tracked and communicated to stakeholders.

Drip Capital, a Silicon Valley-based fintech, exemplifies this method with its AI implementation in commerce finance operations. In response to chief enterprise officer Karl Boog, “We’ve been in a position to 30X our capability with what we’ve finished thus far.” By automating doc processing and enhancing danger evaluation by way of giant language fashions (LLMs), the corporate achieved a outstanding 70% productiveness enhance whereas sustaining vital human oversight for advanced selections.

KPMG research signifies this method is widespread, with one retail firm audit committee director noting how automation has improved operational effectivity and ROI. This sentiment is echoed throughout industries as finance leaders search to justify their AI investments with tangible productiveness enhancements.

These effectivity enhancements translate on to the underside line. Firms throughout sectors — from insurance coverage to grease and gasoline — report that AI helps determine course of inefficiencies, resulting in substantial organizational value financial savings and improved expense administration.

Past easy value discount, CFOs are growing extra subtle effectivity metrics to judge AI investments. These embody time-to-completion ratios evaluating pre- and post-AI implementation timelines, cost-per-transaction analyses measuring reductions in useful resource expenditure and labor hour reallocation metrics monitoring how staff members shift from guide information processing to higher-value analytical work.

Nonetheless, main CFOs acknowledge that whereas effectivity metrics present a stable basis for preliminary ROI calculations, they symbolize only the start of AI’s potential worth. As finance leaders acquire confidence in measuring these direct returns, they’re growing extra complete frameworks to seize AI’s full strategic worth — shifting effectively past the effectivity calculations that characterised early adoption phases.

Past effectivity: The brand new monetary metrics

As CFOs transfer past the preliminary fascination with AI-driven effectivity good points, they’re growing new monetary metrics that extra comprehensively seize AI’s enterprise affect. This evolution displays a maturing method to AI investments, with finance leaders adopting extra subtle analysis frameworks that align with broader company aims.

The surveys spotlight a notable shift in major ROI metrics. Whereas effectivity good points stay essential, we see productiveness metrics at the moment are overtaking pure profitability measures because the chief precedence for AI initiatives in 2025. This represents a basic change in how CFOs assess worth, specializing in AI’s capability to boost human capabilities fairly than merely scale back prices.

Time to worth (TTV) is rising as a vital new metric in funding selections. Solely about one-third of AI leaders anticipate with the ability to consider ROI inside six months, making fast time-to-value a key consideration when evaluating totally different AI alternatives. This metric will assist CFOs prioritize quick-win tasks that may ship measurable returns whereas constructing organizational confidence in bigger AI initiatives.

Information high quality measurements will more and more be integrated into analysis frameworks, with 64% of leaders citing information high quality as their most important AI problem. Ahead-thinking CFOs now incorporate information readiness assessments and ongoing information high quality metrics into their AI enterprise instances, recognizing that even essentially the most promising AI purposes will fail with out high-quality information inputs.

Adoption price metrics have additionally turn out to be commonplace in AI analysis. Finance leaders observe how rapidly and extensively AI instruments are being utilized throughout departments, utilizing this as a number one indicator of potential value realization. These metrics assist determine implementation challenges early and inform selections about further coaching or system modifications.

“The largest profit has been the power to extend velocity of research,” famous Jason Whiting of Mercury Monetary. This angle represents the bridge between easy effectivity metrics and extra subtle worth assessments — recognizing that AI’s worth usually comes not from changing current processes however enhancing them.

Some CFOs are implementing complete ROI formulation that incorporate each direct and oblique advantages (VAI Consulting):

ROI = (Web Profit / Whole Value) × 100

The place web profit equals the sum of direct monetary advantages plus an estimated worth of oblique advantages, minus whole funding prices. This method acknowledges that AI’s full worth encompasses each quantifiable financial savings and intangible strategic benefits, corresponding to improved determination high quality and enhanced buyer expertise.

For corporations with extra mature AI implementations, these new metrics have gotten more and more standardized and built-in into common monetary reporting. Essentially the most subtle organizations now produce AI worth scorecards that observe a number of dimensions of efficiency, linking AI system outputs on to enterprise outcomes and monetary outcomes.

As CFOs refine these new monetary metrics, they’re making a extra nuanced image of AI’s true worth — one which extends effectively past the easy time and value financial savings that dominated early adoption phases.

Amortization timelines: Recalibrating funding horizons

CFOs are basically rethinking how they amortize AI investments, growing new approaches that acknowledge the distinctive traits of those applied sciences. In contrast to conventional IT techniques with predictable depreciation schedules, AI investments usually yield evolving returns that enhance as techniques study and enhance over time. Main finance executives now consider AI investments by way of the lens of sustainable aggressive benefit — asking not simply “How a lot will this save?” however “How will this remodel our market place?”

“ROI straight correlates with AI maturity,” in line with KPMG, which discovered that 61% of AI leaders report higher-than-expected ROI, in contrast with solely 33% of inexperienced persons and implementers. This correlation is prompting CFOs to develop extra subtle amortization fashions that anticipate accelerating returns as AI deployments mature.

The issue in establishing correct amortization timelines stays a big barrier to AI adoption. “Unsure ROI/problem growing a enterprise case” is cited as a problem by 33% of executives, significantly these within the early levels of AI implementation. This uncertainty has led to a extra cautious, phased method to funding.

To deal with this problem, main finance groups are implementing pilot-to-scale methodologies to validate ROI earlier than full deployment. This method permits CFOs to collect correct efficiency information, refine their amortization estimates, and make extra knowledgeable scaling selections.

The timeframe for anticipated returns varies considerably primarily based on the kind of AI implementation. Automation-focused AI sometimes delivers extra predictable short-term returns, whereas strategic purposes, corresponding to improved forecasting, might have longer, much less sure payback durations. Progressive CFOs are growing differentiated amortization schedules that replicate these variations fairly than making use of one-size-fits-all approaches.

Some finance leaders are adopting rolling amortization fashions which can be adjusted quarterly primarily based on precise efficiency information. This method acknowledges the dynamic nature of AI returns and permits for ongoing refinement of economic projections. Relatively than setting fastened amortization schedules on the outset, these fashions incorporate studying curves and efficiency enhancements into evolving monetary forecasts.

One leisure firm carried out a gen AI-driven instrument that scans monetary developments, identifies anomalies and robotically generates executive-ready alerts. Whereas the speedy ROI stemmed from effectivity good points, the CFO developed an amortization mannequin that additionally factored within the system’s growing accuracy over time and its increasing utility throughout numerous enterprise models.

Many CFOs are additionally factoring in how AI investments contribute to constructing proprietary information property that recognize fairly than depreciate over time. In contrast to conventional know-how investments that lose worth as they age, AI techniques and their related information repositories usually turn out to be extra worthwhile as they accumulate coaching information and insights.

This evolving method to amortization represents a big departure from conventional IT funding fashions. By growing extra nuanced timelines that replicate AI’s distinctive traits, CFOs are creating monetary frameworks that higher seize the true financial worth of those investments and help a extra strategic allocation of sources.

Strategic worth integration: Linking AI to shareholder returns

Ahead-thinking CFOs are shifting past operational metrics to combine AI investments into broader frameworks for creating shareholder worth. This shift represents a basic evolution in how monetary executives consider AI — positioning it not merely as a cost-saving know-how however as a strategic asset that drives enterprise development and aggressive differentiation.

This extra subtle method assesses AI’s affect on three vital dimensions of shareholder worth: income acceleration, danger discount and strategic optionality. Every dimension requires totally different metrics and analysis frameworks, making a extra complete image of AI’s contribution to enterprise worth.

Income acceleration metrics give attention to how AI enhances top-line development by enhancing buyer acquisition, growing the share of pockets and increasing market attain. These metrics observe AI’s affect on gross sales velocity, conversion charges, buyer lifetime worth and value optimization — connecting algorithmic capabilities on to income efficiency.

Danger discount frameworks assess how AI enhances forecasting accuracy, improves state of affairs planning, strengthens fraud detection and optimizes capital allocation. By quantifying risk-adjusted returns, CFOs can display how AI investments scale back earnings volatility and enhance enterprise resilience — components that straight affect valuation multiples.

Maybe most significantly, main CFOs are growing strategies to worth strategic optionality — the capability of AI investments to create new enterprise potentialities that didn’t beforehand exist. This method acknowledges that AI usually delivers its most important worth by enabling solely new enterprise fashions or unlocking beforehand inaccessible market alternatives.

To successfully talk this strategic worth, finance leaders are creating new reporting mechanisms tailor-made to totally different stakeholders. Some are establishing complete AI worth scorecards that hyperlink system efficiency to tangible enterprise outcomes, incorporating each lagging indicators (monetary outcomes) and main indicators (operational enhancements) that predict future monetary efficiency.

Govt dashboards now frequently characteristic AI-related metrics alongside conventional monetary KPIs, making AI extra seen to senior management. These built-in views allow executives to know how AI investments align with strategic priorities and shareholder expectations.

For board and investor communication, CFOs are growing structured approaches that spotlight each speedy monetary returns and long-term strategic benefits. Relatively than treating AI as a specialised know-how funding, these frameworks place it as a basic enterprise functionality that drives sustainable aggressive differentiation.

By growing these built-in strategic worth frameworks, CFOs be certain that AI investments are evaluated not solely on their speedy operational affect however their contribution to the corporate’s long-term aggressive place and shareholder returns. This extra subtle method is quickly turning into a key differentiator between corporations that deal with AI as a tactical instrument and those who leverage it as a strategic asset.

Danger-adjusted returns: The danger administration equation

As AI investments develop in scale and strategic significance, CFOs are incorporating more and more subtle danger assessments into their monetary evaluations. This evolution displays the distinctive challenges AI presents — balancing unprecedented alternatives towards novel dangers that conventional monetary fashions usually fail to seize.

The danger panorama for AI investments is multifaceted and evolving quickly. Latest surveys point out that danger administration, significantly in relation to information privateness, is predicted to be the most important problem to generative AI methods for 82% of leaders in 2025. This concern is adopted carefully by information high quality points (64%) and questions of belief in AI outputs (35%).

Ahead-thinking finance leaders are growing complete risk-adjusted return frameworks that quantify and incorporate these numerous danger components. Relatively than treating danger as a binary go/no-go consideration, these frameworks assign financial values to totally different danger classes and combine them straight into ROI calculations.

Information safety and privateness vulnerabilities symbolize a major concern, with 57% of executives citing these as prime challenges. CFOs at the moment are calculating potential monetary publicity from information breaches or privateness violations and factoring these prices into their funding analyses. This contains estimating potential regulatory fines, litigation bills, remediation prices and reputational harm.

Regulatory compliance represents one other vital danger issue. With many executives involved about making certain compliance with altering laws, monetary evaluations more and more embody contingency allocations for regulatory adaptation. An aerospace firm government famous that “advanced laws make it tough for us to attain AI readiness,” highlighting how regulatory uncertainty complicates monetary planning.

Past these exterior dangers, CFOs are quantifying implementation dangers corresponding to adoption failures, integration challenges and technical efficiency points. By assigning probability-weighted prices to those situations, they create extra practical projections that acknowledge the inherent uncertainties in AI deployment.

The “black field” nature of sure AI applied sciences presents distinctive challenges for danger evaluation. As stakeholders turn out to be more and more cautious of trusting AI outcomes with out understanding the underlying logic, CFOs are growing frameworks to judge transparency dangers and their potential monetary implications. This contains estimating the prices of further validation procedures, explainability instruments and human oversight mechanisms.

Some corporations are adopting formal risk-adjustment methodologies borrowed from different industries. One method applies a modified weighted common value of capital (WACC) that comes with AI-specific danger premiums. Others use risk-adjusted web current worth calculations that explicitly account for the distinctive uncertainty profiles of various AI purposes.

The transportation sector supplies an illustrative instance of this evolving method. As one chief information officer famous, “The information acquired from AI requires human verification, and this is a vital step that we overlook.” This recognition has led transportation CFOs to construct verification prices straight into their monetary fashions fairly than treating them as non-obligatory add-ons.

By incorporating these subtle danger changes into their monetary evaluations, CFOs are creating extra practical assessments of AI’s true financial worth. This method permits extra assured funding selections and helps organizations keep applicable danger ranges as they scale their AI capabilities.

The CFO’s AI analysis playbook: From experiments to enterprise worth

As AI transitions from experimental tasks to enterprise-critical techniques, CFOs are growing extra disciplined, complete frameworks for evaluating these investments. Essentially the most profitable approaches strike a steadiness between rigor and suppleness, acknowledging each the distinctive traits of AI and its integration into broader enterprise technique.

The rising CFO playbook for AI analysis accommodates a number of key components that differentiate leaders from followers.

1. First is the implementation of multi-dimensional ROI frameworks that seize each effectivity good points and strategic worth creation. Relatively than focusing solely on value discount, these frameworks incorporate productiveness enhancements, determination high quality enhancements and aggressive differentiation right into a holistic worth evaluation.
2. Second is the adoption of phased analysis approaches that align with AI’s evolutionary nature. Main CFOs set up clear metrics for every improvement stage — from preliminary pilots to scaled deployment — with applicable danger changes and anticipated returns for every part. This method acknowledges that AI investments usually comply with a J-curve, with worth accelerating as techniques mature and purposes broaden.
3. Third is the mixing of AI metrics into commonplace monetary planning and reporting processes. Relatively than treating AI as a particular class with distinctive analysis standards, forward-thinking finance leaders are incorporating AI efficiency indicators into common finances opinions, capital allocation selections and investor communications. This normalization alerts AI’s transition from experimental know-how to core enterprise functionality.

Essentially the most subtle organizations are additionally implementing formal governance constructions that join AI investments on to strategic aims. These governance frameworks be certain that AI initiatives stay aligned with enterprise priorities whereas offering the required oversight to handle dangers successfully. By establishing clear accountability for each technical efficiency and enterprise outcomes, these constructions assist stop the disconnection between AI capabilities and enterprise worth that has plagued many early adopters.

As buyers and boards more and more scrutinize AI investments, CFOs are growing extra clear reporting approaches that clearly talk each present returns and future potential. These studies sometimes embody standardized metrics that observe AI’s contribution to operational effectivity, buyer expertise, worker productiveness and strategic differentiation — offering a complete view of how these investments improve shareholder worth.

The organizations gaining a aggressive benefit by way of AI are these the place CFOs have moved to turn out to be strategic companions in AI transformation. These finance leaders work carefully with know-how and enterprise groups to determine high-value use instances, set up applicable success metrics and create monetary frameworks that help accountable innovation whereas sustaining applicable danger administration.

The CFOs who grasp these new analysis frameworks will drive the subsequent wave of AI adoption — one characterised not by speculative experimentation however by disciplined funding in capabilities that ship sustainable aggressive benefit. As AI continues to rework enterprise fashions and market dynamics, these monetary frameworks will turn out to be more and more vital to organizational success.

The CFO’s AI analysis framework: Key metrics and issues