2025年人工智能成本治理状况报告 2025 State of AI Cost Governance

RESEARCH

2025Stateof

AICostGovernance

TableofContents

Introduction

ResearchOverviewTopFindings

3

ParticipantProfile31

Conclusion:Preparingfor202634

Cloud&AIInfrastructure

6

CFOTakeaways

AICostGovernance

11

PracticesandProcesses

TheRevenueAccountabilityEffect

MaturityLevels

Visibility&Attribution

CFOTakeaways

AICosts:FinancialManagement

21

&Metrics

MeasuringFinancialImpact

ForecastAccuracy

GrossMarginImpact

UsageOverageDetection

MonitoringTools

CFOTakeaways

2Mavvrik+Benchmarkit|2025StateofAICostGovernance

Introduction

AICostCrisis:FinanceLeadersFaceMarginErosionandForecastChaos

AIinfrastructurecostsarealreadyreshapingcorporateprofitability,butmostfinanceteamslackthevisibilityandcontroltomanage

theimpact.ThisresearchrevealsfourcriticalchallengesthatdemandimmediateCFOattention:

·ForecastFailure:85%ofcompaniesmissAIcostforecastsbymorethan10%,withnearly25%missingbyover50%,creatingmassivegrossmarginriskasAIspendingscales.

·MarginHemorrhaging:84%ofcompaniesreportAIcosts

erodinggrossmarginsbymorethan6%,withoveraquarterseeinghitsof16%ormore.Forexample,aproductat80%

grossmargincoulddropto74%onceAIcostsarefactoredin.

·VisibilityBreakdown:Only35%includeon-premisecostsinAI

reporting,andhalfofcompanieswithAI-coreproductsaren't trackingtheirLLMAPIexpenses—creatingdangerousblindspotsincost-to-servecalculations.

·InfrastructureComplexity:61%operatehybridAIenvironmentsspanningpubliccloud,privateinfrastructure,andthird-partyservices,fragmentingcostvisibilityandgovernanceacrossmultiplevendorsandbillingsystems.

consistentlydemonstrate2-3xbettercostdisciplinethanthose

Theaccountabilitygapisreal:CompanieschargingforAI

givingAIfeaturesawayforfree,suggestingthatrevenuepressure

drivesthegovernancerigormostfinanceteamsdesperatelyneed.

3Mavvrik+Benchmarkit|2025StateofAICostGovernance

ResearchOverview

ForCFOswatchingAIexpensesballoonwhilegrossmarginsshrinkbydoubledigits,thisisn'tjustaforecastingproblem,it'sastrategic

crisishidinginplainsight.

Thenumberstellastarkstory:Acrossthefullsample(N=372),84%reportAIcostserodingproductgrossmarginsbymorethan6

percentagepoints(600bps),withoveraquarterseeinghitsof16+points(1600bps).

Yetmostfinanceleadersareflyingblind:unabletopredictnext

quarter'sAIspend,attributecoststospecificproductsorcustomers,orevenseewhat'shappeningacrosstheirhybridinfrastructure

environments.

85%

ofcompaniescannotforecastAIcostswithin10%.

Thisresearch,conductedbyMavvrikinpartnershipwith

Benchmarkit,surveyed372companiestounderstandhow

organizationsarebuilding,running,andfinanciallygoverningAIworkloads.Whatwefoundrevealsamarketintransition:AIhasmovedfromexperimentalbudgetlinetomaterialcostdriver,butthefinancialdisciplinehasn'tcaughtup.

Thestakescouldn'tbehigher.AsAItransformsfrom"nicetohave"to"musthave,"thecompaniesthatmastercostvisibilityand

controlwillprotecttheirmarginswhilecompetitorswatchprofitsdisappearintountrackedinfrastructurecosts.

**Note:Grossmarginimpactfindingsreflectproductdelivery(COGS).About70%ofrespondentswereSaaSandAI-nativevendors,whereinference,GPU,andAPIcostsdirectlyaffectgrossmargin.ForenterprisesusingAIinternally,financialimpacttypicallyflowsthroughOPEXandoperatingmargininstead.

4Mavvrik+Benchmarkit|2025StateofAICostGovernance

Top

Findings

AIcostsarealreadyerodinggrossmargins

84%ofcompaniesreportmorethana6%hittogrossmarginfrom

AIcosts.Withinthat,58%seea6–15%reductionand26%report16%+erosion.Thefinancialimpactiswidespreadandimmediate,makingcostvisibilityandcontrolastrategicimperativeforbothfinanceandproductleaders.

Forecastaccuracyisalarminglylow

Only15%ofcompaniesforecastAIcostswithin±10%.Amajority

(56%)missby11–25%,andnearlyoneinfour(24%)missbymore

than50%.ForCFOsandbudgetowners,thislevelof

unpredictabilitymakesithardertoprotectgrossprofittargetsasAIgrowsasashareofCOGS.

Hybridcomplexityisthedefault

61%ofcompaniesrunAIworkloadsacrossacombinationofpublicandprivateenvironments.Thispatternspansallcompanysizes,

includingsmallbusinesses,andcreatesgreaterdifficultyinachievingunifiedcostreportingandgovernance.

Repatriationisbecomingmainstream

67%ofcompaniesareactivelyplanningtorepatriatesomeAI

workloadstoownedinfrastructure,andanother19%areevaluatingthemove.Thetrendismostactiveinmid-marketcompanies,whilelargeenterprisesaremoreoftenintheevaluationstage.

TheAIcostsurfaceisbroaderthantokens

Dataplatformusageisthe#1sourceofunexpectedAIcosts(56%),followedbynetworkaccesstomodels(52%).LLMtokencostsrankfifth(37%).ThisdiversityofcostdriversmakesAIspendhardertoforecastandcontrol.

Visibilityandattributiongapsblockaction

Only~35%ofcompaniesincludeon-premcomponentsinAIcostreporting,andabouthalfincludeLLMAPIcostsevenwhenAIisacoreproductcomponent.Teamssaythe#1tactictoimprovecostmanagementisunifiedvisibilityacrossenvironments;clearcostattributionis#2.

ChargingforAIcorrelateswithstrongercostdiscipline

OrganizationsthatchargeorpackageAIseparatelyareconsistentlymorelikelytotrackcost-to-serveprecisely,usereal-timeusage

alerts,andattributecostsbycustomer,product,ormodelthanthosewhoincludeAI“forfree.”

Azureiswinningintheenterprise

AWSleadsoverallcloudusage(77%),butamongcompanieswithmorethan$250Minrevenue,Azureadoptionclimbsto82%,

surpassingAWSinthissegment.GoogleCloudholdsthirdat65%,andIBMCloudmaintainsnichestrengthinspecificindustries.

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01Cloud&AI

Infrastructure

Multi-cloudisthenewstandard,withAzuresurgingintheenterprise

ThearchitecturepoweringAIworkloadsisgrowingmorecomplex,blendingpublicclouds,privateenvironments,andspecializedAIserviceproviders.Whilethisdiversitycreatesflexibility,italso

fragmentsvisibility,increasesbillingcomplexity,andintroducesawiderrangeofunpredictablecosts.

Theresearchshowsthathybridisnowthedominantmodel,multi-

cloudusageisstandard,andMicrosoftAzureisrapidlygaininggroundintheenterprise.Theseinfrastructuredecisionshavedirect

consequencesforcostgovernanceandtheabilitytoprotectmargins.

AWSremainsthemostwidelyusedcloudprovideroverall(77%),

followedcloselybyMicrosoftAzure(71%)andGoogleCloud(65%).

Butthedynamicchangesintheenterprisesegment:among

companieswithmorethan$250millioninrevenue,Azureadoptionjumpsto82%,overtakingAWS.IBMCloudranksfourthinusagebynumberofcompanies,withstrongpenetrationinspecificverticals.

Mostcompaniesnowoperateinmulti-cloudenvironments—oftenleveragingdifferentprovidersforspecificworkloads,performancecharacteristics,orgeographicneeds.ForCFOs,thismeansmore

contracts,moreinvoices,andmoreopportunitiesforspendtoescapetraditionaloversight.

CSPUsagebyRevenueSegment

61%61%60%60%

55%

42%40%40%41%44%42%

36%

29%

AmazonWebServicesMicrosoftAzureGoogleCloudPlatformOracleCloudInfrastructureAlibabaCloudIBMCloud

<$10M$10M-$20M$20M-$50M$50M-$100M$100M-$250M>$250M

82%

72%

68%67%67%

100%

80%

60%

40%

20%

0%

33%33%

83%84%

76%77%

79%

65%

9%5%

69%70%

20%

22%

23%

27%

67%

11%

7Mavvrik+Benchmarkit|2025StateofAICostGovernance

ThirdPartyAIServicesUsed

80%

60%

40%

20%

0%

Third-partyAIservicesaddcapabilityandcostdiversity

76%

60%

46%

47%

Third-partylargelanguagemodels(LLMs)arethemostcommonAIservice(76%adoption).Dataplatforms,suchasDatadog,arethesecondmostcommon(60%)andtheyarethe#1sourceof

unexpectedAIcosts.GPUinfrastructureproviders,suchasCoreWeave,areusedby46%ofcompanies.

EvencompaniesthatdonotchargeforAI-enabledproductsareheavyusersofthird-partyLLMs(73%),meaningtoken-basedcostsare

LLMsviaAPIDataPlatformGPU

SaaStoolspoweringAI

InfrastructureProvider

quietlyreducinggrossmarginswithoutbeingoffsetbydirectrevenue.ForCFOs,thisisaprimeexampleof“hiddenCOGS”—coststhatarerealbutunaccountedforinprofitabilitymodels.

UnexpectedAICosts

8%

CPUutilizationLLMtoken/APIcostsDataplatformusageNetworking/egresschargesEngineeringresourcedrainNone/Notsure

56%

45%

37%

60%

50%

40%

30%

20%

10%

0%

45%

52%

8Mavvrik+Benchmarkit|2025StateofAICostGovernance

Hybridcomplexitydominatesworkloadplacement

Hybrid,runningworkloadsacrossbothpublicandprivatecloud,isthemostcommonmodel,usedby61%ofcompanies.Only34%runentirelyinpubliccloud,and21%usethird-partyGPUproviders.

Hybridisnotjustanenterprisepattern.Smallercompanies(<$10M)showa44%splitbetweenpublicandprivatecloud,provingthat

hybridcomplexitycanstartearly.Fromafinancialperspective,hybridenvironmentsoftencomewiththehighestcostvisibilitychallenges,especiallywhencostreportingbetweenpublicandprivatesystemsisn’tstandardized.

Repatriationismovingfromplantopractice

Cloud-basedtrainingoflargeAImodelscanbeprohibitivelyexpensiveatscale,withadditionalchallengesaroundsecurity,control,andperformanceconsistency.

Yes,underevaluation19%

67%ofcompaniesareactivelyplanningtorepatriateatleast

someAIworkloadstoownedinfrastructure,andanother19%are

evaluatingthemove.Mid-marketcompanies($10M–$250M)showthehighestplanningrates,whilelargeenterprisesaremoreoftenintheevaluationphase.CompaniesthatchargeforAIaremorelikelytoplanrepatriation,linkingmonetizationwithtightercontroloverinfrastructure.

61%

39%

21%

Privatecloudonly(on-premise)

Third-PartyGPUInfrastructureProvider

AIWorkloadLocationsByTotalPopulation

PubliccloudonlyHybrid(on-premise&cloud)

34%

AIRepatriationPlans

Yes,activelyplanning67%

Notsure3%

No

11%

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CFOTakeaways

Infrastructuredecisionshavedirect

AIcostsaren’tjust

infrastructure,they’rebusinessriskshidinginyourmargins.

marginimpact.Hybridandmulti-cloudchoicesincreaseflexibilitybutmultiplybillingandvisibilitychallenges.

TheAIcostbaseisdiverseand

growing.Dataplatforms,network

access,GPUrentals,andLLMtokenseachrequiretheirownforecastingmodels.

Repatriationisafinancialstrategy,notjustatechnicalone.It’sadeliberate

movetoreshapethecoststructureofAIworkloads.

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02

AICost

Governance

Practices&Processes

ForCFOs,themostpressingAIcostquestionsarerarely“Howmuchdidwespend?”Instead,wehear:“Doweknowwherethemoney

went?”and“Canwepredictwhat’scomingnext?”

OurresearchshowsthatwhilemostcompaniestracksomeformofAIcosts,gapsinprocessdiscipline,maturity,andvisibilityarekeeping

financeandproductleadersfrommakingfullyinformeddecisions.

ChargingforAIconsistentlycorrelateswithstrongergovernance,yetalargesegmentofthemarketstilldeliversAIfeatures“forfree,”andwithfarlesscostcontrol.

Trackingiscommon,butdepthandtimingvary

94%ofcompaniessaytheytrackAIinfrastructurecosts,butthe

scopeandgranularityofthattrackingdifferwidely.Thekeyquestionisnotifcostsaretracked,butwhat’sincluded,howearlysignalsare

captured,andwhoisaccountableforactingonthem.

Evenamonglargeenterprises(>$250Mrevenue),3%admittheydonottrackAIinfrastructurecostsatall,surprisinggiventhescaleof

spend.Smallercompanies(<$10M)trackathighrates(90%)butmaylackthesystemstomeasureatthesamelevelofdetailaslargerpeers.

94%

ofcompaniestrackAI

infrastructurecosts,but

fewcapturethemearly

enoughttoprevent

budgetsurprises

12Mavvrik+Benchmarkit|2025StateofAICostGovernance

Topchallenges:visibility,forecastaccuracy,hybridcomplexity

WhenaskedfortheirtopthreechallengesinmanagingAIinfrastructurecosts,respondentsmostoftencited:

1.Lackofvisibilityintocosts(34%)

2.Inaccuratecostforecasts(16%)

3.Difficultymanaginghybridcloudenvironments(13%)

Forfinanceteams,thesechallengestranslatedirectlyintohighergrossmarginriskandvolatileforecasts.

Tacticsforimprovement

ThemostcommontacticcitedforimprovingAIcostmanagementisunifiedvisibility(33%):asingleintegratedviewacrossall

environments,services,anddatapipelines.Clearcostattributionrankedsecond(22%),followedbybettercollaborationbetweenteams(17%)andimprovedforecastingtools(15%).

Budgetsexist,butdon’tguaranteecontrol

94%ofcompaniesthattrackAIcostsalsoassignanAI

infrastructurebudget,thoughbudgetingisslightlylesscommoninthesmallestcompanies(88%).

Top3AIInfrastructureCostChallengesRanked

ByTotalPopulation

LackofvisibilityInaccurateforecasts Unclearallocation Noreal-timealertsGovernancegaps

Difficultymanaginghybrid/multi-cloud Unpredictabletoken-basedpricingDifficultyforecastingusage-basedcosts

Rank1(%)Rank2(%)Rank3(%)

16%

34%

19%

14%

15%

12%

7%

18%

21%

7%

10%

9%

7%

11%

9%

13%

12%

10

%

7%

8%10

%

9%

8%

15%

0%10%20%30%40%50%60%

13Mavvrik+Benchmarkit|2025StateofAICostGovernance

TheRevenueAccountabilityEffect

WhyChargingforAIDrivesBetterGovernance

Oneofthemoststrikingpatternsinourresearchisn'tabout

technology,it'saboutincentives.CompaniesthatchargeforAIconsistentlydemonstratesuperiorcostdisciplineacrosseverymetricwemeasured.Thisisn'tcoincidence;it'sthepowerof

revenueaccountability.

Considerthestarkdifferences:

70%ofcompanieschargingforAIcantrackcost-to-serveprecisely,comparedtojust29%ofthosegivingAIaway

71%usereal-timeusagealertsforoverages,versusmuchlowerratesamongfreeproviders

They'retwiceaslikelytoattributecostsbycustomer,product,orAImodel

14Mavvrik+Benchmarkit|2025StateofAICostGovernance

They'resignificantlymorelikelytoincludeAIcostsinstrategicdecision-making

1

Whydoeschargingcreatethisdiscipline?Threeforcesareatwork

CustomerPressureCreatesOperationalRigor:When

customerspayforAIfeatures,theyexpectvalueand

reliability.Thisexternalpressureforcesinternalteamsto

understandexactlywhatthey'redeliveringandwhatitcosts.Everysupportticketaboutslowperformanceorunexpectedchargesbecomesaforcingfunctionforbettercost

attribution.

2

P&LOwnershipChangesBehavior:WhenAImovesfromacostcentertoaprofitcenter,someone'sbonusdependsonmanagingthosemargins.Productmanagersstartasking

"What'sourcostperinference?"Financeteamsdemandreal-timedashboards.Engineeringteamsoptimizeforefficiency,notjustfunctionality.

3

PricingDecisionsRequireCostTruth:Youcan'tpricewhatyoucan'tmeasure.CompanieschargingforAIareforcedtodevelopgranularcostmodelstostaycompetitiveand

profitable.Thisrequirementdrivesinvestmentintheverysystemsthatenablebettergovernance.

TheHiddenCostof"Free"AI:Meanwhile,companiesprovidingAIfeaturesatnochargeoftentreatAIcostsasoverhead—a

dangerousblindspot.Withoutrevenuepressure,thesecostscanballoonunchecked.

TheGovernanceParadox:Interestingly,companieschargingforAIalsoshowthehighestratesofearly-stagecostmanagementmaturity(34%),suggestingthatmonetizationtriggersgovernanceinvestment,evenifmanyarestillbuildingthefoundation.It'sproofthatrevenueaccountabilityaccelerateslearning,evenwhensystemsaren'tperfect.

ForCFOs,theimplicationisclear:ifyou'regivingAIawayforfree,you'renotjustmissingrevenue,you'remissingtheaccountabilitymechanismsthatdrivecostcontrol.

MaturityLevels

Mostcompaniesarestillinearlyordevelopingstages

Only34%ofcompaniessaytheyhavean“advanced”AIcost

managementprogram,definedashavingtracking,costattribution,andgovernancepolicyinplace.

·Earlystage:30%arejuststartingtotrack,budget,andallocatecosts

·Developing:36%havesomevisibility,butmostlymanualprocesses

Industryplaysabiggerrolein

maturitythancompanysize.

Manufacturingleads(50%

advanced),whileFinancial

Services(40%earlystage)andAgenticAIcompanies(38%earlystage)lag.

·Advanced:34%haveautomatedtrackingandattributionwithgovernanceinplace

AICostManagementMaturity

ByIndustry

AINativeSoftwareAgenticAIB2BSaaSFinancialServicesManufacturingOther

50%

40%38%41%

31%32%30%29%30%34%34%30%28%

21%

50%

40%

38%

30%34%

28%20%

10%

0%

EarlyStageDevelopingAdvanced

25%

15Mavvrik+Benchmarkit|2025StateofAICostGovernance

ChargingforAIalsocorrelateswithhighermaturity

CompaniesthatchargeforAIproductsorfeaturesaremore

likelytobeadvancedinmaturity(34–36%).Surprisingly,theyalsoshowthehighestpercentageofearly-stagematurity(34%),suggestingthatmonetizationtriggersgovernanceinvestment,althoughmanyarestillbuildingthefoundation.

AICostManagementMaturityByPricingModel

50%

40%

30%

20%

10%

0%

47%

44%

36%

34%

34%

30%

29%

24%

22%

ChargeExtraPackage&ChargeSeparatelyIncludedforFree

EarlyStage

DevelopingAdvanced

16Mavvrik+Benchmarkit|2025StateofAICostGovernance

Visibility&Attribution

On-premcostsareofteninvisible

Only35%ofcompaniesincludeon-premiseAIinfrastructureintheircostreporting.Cloudandthird-partyproviderstypicallyofferbetternativereporting,butgapsinon-premdatacreatemajorblindspots,especiallyinhybridmodels.

EnvironmentsIncludedinAICostReportingByTotalPopulation

67%

70%

60%

50%53%

48%49%

80%

60%

40%

20%

0%

40%

30%35%36%

20%

10%

1%

PublicCloud

OnPremise/PrivateDataCenters

GPUInfrastructureProviders

LLMAPIs/HostedModels

DataPlatforms

SaaSToolsSupportingAIWorkloads

NotSure

0%

LLMAPIcostsnotalwaysincluded,evenwhencoretoproduct

Only~50%ofcompaniesusingAIasacorepartoftheirproductincludeLLMAPIcostsintheirAIcostreporting.Thisomission

makestruecost-to-serveandgrossmargincalculationsunreliable.

EnvironmentsIncludedinAICostReporting

ByAIPricingModel

Chargeextra(%)Package&chargeseparately(%)

Includedforfree(%)

68%

79%

65%

62%

50%

55%

50%

57%52%

49%45%

37%

34%

38%

34%35%

44%

24%

PublicCloud

On-Premise/PrivateDataCenters

GPUInfrastructureProviders

LLMAPIs/HostedModels

DataPlatforms

SaaSToolsSupportingAIWorkloads

17Mavvrik+Benchmarkit|2025StateofAICostGovernance

Precisecost-to-servetrackingisfarfromuniversal

While62%ofcompaniescantrackcost-to-serveprecisely,34%canonlytrackapproximately,andasmallsegment(4%)cannottrackitatall.CompaniesthatchargeforAIfeaturesorpackageAIasa

separatepaidproductandAI-nativeprovidersarefarmorelikelytohaveprecisiontrackinginplace.

Precisecost-to-serve

trackingseparatesmarginleadersfromlaggards.

Yes,preciselyYes,approximateNo

51%

47%

<$10M$10-20M$20-50M$50-100M$100-250M>$250M

TrackCosttoServeByRevenue

70%

60%

50%

40%

30%

20%

10%

0%

11%

2%2%5%

69%61%

35%28%

65%61%

34%

34%

27%

63%

2%

3%

18Mavvrik+Benchmarkit|2025StateofAICostGovernance

Unexpectedcostsgobeyondtokens

Thetoptwounexpectedcostdriversare:

1.Dataplatformusage(56%)

2.NetworkaccesstoAImodels(52%)

LLMtokencosts,oftenassumedtobethemainculprit,rankonlyfifth(37%).

UnexpectedAICosts

56%45%

52%

45%

37%

60%

50%

40%

30%

20%

8%

10%

CPUutilization

LLMtoken/APIcosts

Dataplatformusage

Networking/egresscharges

Engineeringresourcedrain

None/Notsure

0%

Decision-makingsufferswithoutfullvisibility

26%ofcompaniessayAIcostsdidnotimpactanymajordecisionsinthepastyear:asignthatcostdataisn’tbeingusedstrategically.

CompaniesthatchargeforAIarefarmorelikelytofactorcostsintopricing,packaging,andinfrastructuredecisions.

AICostsImpactedDecisions

InLast12Months

NotSure

1%

No

26%

Yes

73%

19Mavvrik+Benchmarkit|2025StateofAICostGovernance

CFOTakeaways

Budgetsexist,butattributionlags.

94%ofrespondentsassignAIbudgets,yetonly35%includeon-premcostsandjusthalfreportLLMAPIusage,leavingmajorblindspots.

Governancematurityvariesby

industry,notsize.Sectorslikefinancialservicesarefurtherahead,whileothersstruggletomovebeyondbasiccost

tracking.

Monetizationdrivesdiscipline.

CompaniesthatchargeforAIaremorelikelytotrackcost-to-serveprecisely,applyreal-timeusagealerts,and

.

attributecostsbyproductormodel

Youcan’tgovernwhatyoucan’tsee;andwithoutfullvisibility

andattribution,eventhebest-intentionedAIbudgetsare

leavingworrisomeblindspots.

20Mavvrik+Benchmarkit|2025StateofAICostGovernance

03AICosts:FinancialManagement&

Metrics

.-s

MeasuringFinancialImpact

ForCFOs,thenumberstellthestory

AIisnolongeranexperimentalbudgetline:it’samaterialcostdriveraffectinggrossmargins,profitability,andfinancialpredictability.

ThissectioncaptureshowcompaniesmeasureAI’sfinancialimpact,howaccuratelytheyforecastspend,andwhattoolstheyrelyontomanageusage.

MostcompaniesmeasureAIcostsasapercentofrevenue

59%ofcompaniesmeasureAIinfrastructurecostsasapercentageofrevenue.

Whilethisisalignedwithcommoncloudcostreporting,only29%measureAIcostsagainstCOGS,whichisthemetricmostcloselytiedtogrossprofit.

AsmallergroupmeasuresAIcostsasapercentageofR&D,whichmaysignalashiftinhowR&Distreatedontheincomestatement.

AICosts:FinancialImpactMeasurements

Totalcosts($)

%ofRevenue

%ofProductRevenue

%ofCOGS

%ofR&D

NotSure

51%

29%31%

60%

50%

40%

30%

20%

10%

0%

59%46%

3%

4

22Mavvrik+Benchmarkit|2025StateofAICostGovernance

ChargingforAIsharpensprofitabilitytracking

CompaniesthatpackageorchargeforAIproductsaremorelikelyto

measurecostsagainsttotalrevenue,product-specificrevenue,andCOGS.ThoseprovidingAIforfreeriskmissingkeyprofitabilitysignals.

AICosts:FinancialImpactMeasurements

ByAIPricingModel

ChargeextraPackage&chargeseparatelyIncludedforfree

40%

35%

26%

9%

2%2%

NotSure

37%

32%28%

3%1%

Other

0%

TotalCosts($)

67%

59%

40%

30%

20%

%ofProductRevenue

70%

60%

%ofRevenue

%ofCOGS

%ofR&D

50%

46%

54%

29%

65%

55%

10%

21%

31%

23Mavvrik+Benchmarkit|2025StateofAICostGovernance

ForecastAccuracy

Forecastmissesarewidespread

Only15%ofcompaniesforecastAIcostswithin±10%.Amajority,

56%,missby11–25%,andnearlyoneinfour(24%)missbymorethan50%.

Thislevelofinaccuracyputsgrossprofittargetsatrisk,especiallyasAIcostsbecomealargershareofCOGS.

ChargingforAIdoesnotguaranteeaccuracy

Interestingly,companiesthatchargeforAIaremorelikelytohavethelargestforecastmisses(>50%)comparedtothosethatdonot.Thispointstoagapbetweenmonetizationstrategyandoperational

forecastingcapability.

AISpendForecastAccuracyByTotalPopulation

+/-