CFOs need AI that pays: actual metrics, not advertising and marketing demos

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

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

In accordance with a KPMG survey of 300 U.S. monetary executives, investor stress to exhibit ROI on generative AI investments has elevated considerably. For 90% of organizations, investor stress is taken into account “vital or essential” for demonstrating ROI in Q1 2025, a pointy enhance from 68% in This autumn 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 at the moment measuring AI worth. Those that have adopted gen AI instruments report seeing preliminary returns primarily by means of effectivity features.

“We created a customized workflow that automates vendor identification to shortly 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 Monetary, echoed this effectivity focus: “Throughout the board, [the biggest benefit] has been the flexibility to extend velocity of study. Gen AI hasn’t changed something, but it surely has made our present processes and folks higher.”

However CFOs are actually trying past easy time financial savings towards extra strategic functions. 

The Bain information reveals CFOs are most enthusiastic about making use of AI to “long-standing ache factors that prior generations of expertise have been unable to resolve.” Cosmin Pitigoi, CFO of Flywire, defined: “Forecasting developments primarily based on giant information units has been round for a very long time, however the difficulty has all the time been the mannequin’s capacity to elucidate the assumptions behind the forecast. AI might help not simply with forecasting, but additionally with explaining what assumptions have modified over time.”

These latest surveys counsel that CFOs have gotten the first gatekeepers for AI funding; nonetheless, they’re nonetheless growing the monetary frameworks needed to judge these investments correctly. Those that develop strong analysis methodologies first will probably acquire important aggressive benefits. Those that don’t could discover their AI enthusiasm outpacing their capacity 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 centered predominantly on effectivity metrics, with CFOs prioritizing measurable time and price financial savings that ship quick returns. This deal with effectivity represents the low-hanging fruit of AI implementation — clear, quantifiable advantages which might be simply tracked and communicated to stakeholders.

Drip Capital, a Silicon Valley-based fintech, exemplifies this strategy with its AI implementation in commerce finance operations. In accordance with chief enterprise officer Karl Boog, “We’ve been capable of 30X our capability with what we’ve performed up to now.” By automating doc processing and enhancing danger evaluation by means of giant language fashions (LLMs), the corporate achieved a outstanding 70% productiveness increase whereas sustaining important human oversight for advanced selections.

KPMG analysis signifies this strategy 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 refined 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 group members shift from handbook information processing to higher-value analytical work.

Nonetheless, main CFOs acknowledge that whereas effectivity metrics present a strong 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 — transferring properly 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 features, they’re growing new monetary metrics that extra comprehensively seize AI’s enterprise affect. This evolution displays a maturing strategy to AI investments, with finance leaders adopting extra refined analysis frameworks that align with broader company aims.

The surveys spotlight a notable shift in main ROI metrics. Whereas effectivity features stay vital, we see productiveness metrics are actually overtaking pure profitability measures because the chief precedence for AI initiatives in 2025. This represents a elementary change in how CFOs assess worth, specializing in AI’s capacity to boost human capabilities slightly than merely cut back prices.

Time to worth (TTV) is rising as a important new metric in funding selections. Solely about one-third of AI leaders anticipate having 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 initiatives 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 circumstances, recognizing that even essentially the most promising AI functions will fail with out high-quality information inputs.

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

“The most important profit has been the flexibility to extend velocity of study,” famous Jason Whiting of Mercury Monetary. This angle represents the bridge between easy effectivity metrics and extra refined worth assessments — recognizing that AI’s worth typically comes not from changing present processes however enhancing them.

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

ROI = (Web Profit / Whole Price) × 100

The place web profit equals the sum of direct monetary advantages plus an estimated worth of oblique advantages, minus whole funding prices. This strategy acknowledges that AI’s full worth encompasses each quantifiable financial savings and intangible strategic benefits, akin 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. Probably the most refined 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 properly past the straightforward time and price financial savings that dominated early adoption phases.

Amortization timelines: Recalibrating funding horizons

CFOs are essentially rethinking how they amortize AI investments, growing new approaches that acknowledge the distinctive traits of those applied sciences. Not like conventional IT programs with predictable depreciation schedules, AI investments typically yield evolving returns that enhance as programs study and enhance over time. Main finance executives now consider AI investments by means 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 immediately 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 rookies and implementers. This correlation is prompting CFOs to develop extra refined amortization fashions that anticipate accelerating returns as AI deployments mature.

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

To deal with this problem, main finance groups are implementing pilot-to-scale methodologies to validate ROI earlier than full deployment. This strategy permits CFOs to assemble 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 functions, akin to improved forecasting, could have longer, much less sure payback durations. Progressive CFOs are growing differentiated amortization schedules that mirror these variations slightly than making use of one-size-fits-all approaches.

Some finance leaders are adopting rolling amortization fashions which might be adjusted quarterly primarily based on precise efficiency information. This strategy acknowledges the dynamic nature of AI returns and permits for ongoing refinement of monetary projections. Relatively than setting mounted 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 routinely generates executive-ready alerts. Whereas the quick ROI stemmed from effectivity features, the CFO developed an amortization mannequin that additionally factored within the system’s growing accuracy over time and its increasing software throughout numerous enterprise items.

Many CFOs are additionally factoring in how AI investments contribute to constructing proprietary information belongings that respect slightly than depreciate over time. Not like conventional expertise investments that lose worth as they age, AI programs and their related information repositories typically turn out to be extra priceless as they accumulate coaching information and insights.

This evolving strategy to amortization represents a major departure from conventional IT funding fashions. By growing extra nuanced timelines that mirror 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 transferring past operational metrics to combine AI investments into broader frameworks for creating shareholder worth. This shift represents a elementary evolution in how monetary executives consider AI — positioning it not merely as a cost-saving expertise however as a strategic asset that drives enterprise development and aggressive differentiation.

This extra refined strategy assesses AI’s affect on three important 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 deal with how AI enhances top-line development by bettering 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.

Threat 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 exhibit how AI investments cut back earnings volatility and enhance enterprise resilience — elements that immediately 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 strategy acknowledges that AI typically 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 repeatedly function 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 quick monetary returns and long-term strategic benefits. Relatively than treating AI as a specialised expertise funding, these frameworks place it as a elementary enterprise functionality that drives sustainable aggressive differentiation.

By growing these built-in strategic worth frameworks, CFOs make sure that AI investments are evaluated not solely on their quick operational affect however their contribution to the corporate’s long-term aggressive place and shareholder returns. This extra refined strategy 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.

Threat-adjusted returns: The danger administration equation

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

The danger panorama for AI investments is multifaceted and evolving quickly. Latest surveys point out that danger administration, notably 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 elements. Relatively than treating danger as a binary go/no-go consideration, these frameworks assign financial values to totally different danger classes and combine them immediately into ROI calculations.

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

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

Past these exterior dangers, CFOs are quantifying implementation dangers akin to adoption failures, integration challenges and technical efficiency points. By assigning probability-weighted prices to those eventualities, they create extra life like 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 consists of 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 strategy applies a modified weighted common value of capital (WACC) that includes AI-specific danger premiums. Others use risk-adjusted web current worth calculations that explicitly account for the distinctive uncertainty profiles of various AI functions.

The transportation sector supplies an illustrative instance of this evolving strategy. 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 immediately into their monetary fashions slightly than treating them as non-compulsory add-ons.

By incorporating these refined danger changes into their monetary evaluations, CFOs are creating extra life like assessments of AI’s true financial worth. This strategy 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 initiatives to enterprise-critical programs, CFOs are growing extra disciplined, complete frameworks for evaluating these investments. Probably 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 incorporates a number of key parts that differentiate leaders from followers.

1. First is the implementation of multi-dimensional ROI frameworks that seize each effectivity features and strategic worth creation. Relatively than focusing completely 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 section. This strategy acknowledges that AI investments typically comply with a J-curve, with worth accelerating as programs mature and functions increase.
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 price range critiques, capital allocation selections and investor communications. This normalization alerts AI’s transition from experimental expertise to core enterprise functionality.

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

As traders 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 reviews 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 means 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 expertise and enterprise groups to determine high-value use circumstances, 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 following 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 important to organizational success.

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