mckinsey -智能的成本：首席信息官如何规模化管控人工智能需求 The cost of intelligence How CIOs can manage AI demand at scale

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Thecostofintelligence:

HowCIOscanmanageAIdemandatscale

AsAlcostsspiral,CIOsneedtomanageenterpriseAldemandtooptimizeforoutcomes,notjustcost.

ThisarticleisacollaborativeeffortbyPankajSachdevaandWasimLala,withAvinashJavaji,KaaviniTakkar,andPurvaArora,representingviewsfromQuantumBlack,AlbyMcKinsey,andMcKinsey'sTechnologyandAlgroup.

July2026

Atarecenttechleadershipmeetingforatechservicescompany,ateamenthusiastically

approvedanewAIcapability.Asthediscussionturnedtoimplementation,oneexecutiveaskedasimplequestion:“Howmuchisthisgoingtocost?”

Theroomwentquiet.Nooneknew.

Thatlackofclarityisnotunusual,anditisbecomingoneofthedefiningrisksofenterpriseAI.

Acrossindustries,organizationsarediscoveringthatAIcostscanspiralfarfasterthantraditionaltechnologyspending.Insomecases,companieshaveexhaustedannualAIbudgetsinamatterofmonths,forcingemergencycontractrenegotiationsandunexpectedfundingrequests.

Muchofthisspendremainsinvisible.BusinessunitspurchaseAIcapabilitiesindependently.

EmployeesbuildAI-poweredworkflowsoutsidecentralIT.Citizendevelopersand“vibecoders”canunintentionallycreateautonomousagentsorapplicationsthatconsumemillionsoftokenseveryday.

Asorganizationsmovefromisolatedusecasestoenterprise-wideadoption,AIspendincreasesnearlyfourfold,accordingtoaMcKinseysurvey.1While62percentoforganizationshavemovedbeyondexperimentationintoactivedeploymentofAI,93percentofrespondentsreport

exceedingtheirAIbudgets(Exhibit1).2

Thisproblemispoisedtogetmorepronounced,withamajorityoffirmsinoursurveyexpectingtheirAIspendtoincreasebyatleast25percentoverthenext12months.3Manyorganizations

arerealizingtheylackthevisibilityandcontrolsneededtomanagethatspend.“Tokenmaxxing”isbecomingadirtyword,withsomecompaniestakingdowntheirAIuserleaderboards.The“go,go,go”energydrivingAIoverthepasttwoyearsisyieldingtoa“waitasec”moment.

WhilesomecompaniesmaybetemptedtohalttheirAIprograms,thoughtfulchiefinformationofficers(CIOs)arelookingtoscaleAIwhilemanagingdemandbymasteringtheemergingfieldofenterpriseAItokenomics(seesidebar“Whatistokenomics?”).ThinkofitasFinOpsforAI—awaytoactivelyandcontinuouslypredictandmanageAImodelusagetomaximizeROI.

Whatistokenomics?

Tokenomicsextendsbeyondpromptandinferencecosts.ItencompassesthebroadereconomicsofAIconsumption:modelselection,routingdecisions,orchestrationpatterns,agentbehavior,workflowdesign,infrastructureutilization,andtheeliminationofwastethroughoutAI-enabledprocesses.

1McKinseyEnterpriseAIFinOpssurvey,May2026;120enterpriseparticipants,75qualifiedrespondentsacrossfivemajorindustries.

2McKinseyEnterpriseAIFinOpssurvey,May2026;120enterpriseparticipants,75qualifiedrespondentsacrossfivemajorindustries.

3McKinseyEnterpriseAIFinOpssurvey,May2026;120enterpriseparticipants,75qualifiedrespondentsacrossfivemajorindustries.

Thecostofintelligence:HowCIOscanmanageAIdemandatscale2

Thecostofintelligence:HowCIOscanmanageAIdemandatscale3

Exhibit1

ForCIOs,thisisanimportantopportunitytoshapehowtechnologydriveseconomicbenefitforthebusiness.Inourexperience,companiesthatarethoughtfulintheirAIconsumptioncansave20–30percentontheirAIcosts.They’refindingthesesavingsbydevelopingthecapabilitiestooptimizespend,improveaccountability,andredirectsavingstowardthehighest-value

opportunities.

WhyAIcostsaresodifficulttomanage

ManyCIOsinstinctivelycomparethechallengeofmanagingAIspendtotheearlydaysofcloudcomputing.Thecomparisonisusefulononelevelbecausemanyofthegovernanceissuesare

similar.ButtheuniqueissuesregardingAItiedtotokens,APIcalls,andinfrastructureusageaddanewarrayofchallengesforCIOstomanage:

—AIusagepatternsarenotpredictable.Thesametaskcangeneratedramaticallydifferenttokenvolumes,invokedifferentmodels,triggerdifferentagentchains,andproduce

significantlydifferentcosts.Agenticworkflowsmultiplymodelcallsperinteraction,rapidly

Thecostofintelligence:HowCIOscanmanageAIdemandatscale4

outpacingbudgetassumptionsbuiltonpriorusagebaselines.

—Consumptionpricingexposescompaniestotrueusagecosts.Bundledseat-basedpricingactedasa“safetynet”becauseithidtheusagecosts.Withlargelanguagemodel(LLM)

providersshiftingtoconsumptionpricing,companiesnowhavetopaydirectlyforhigh-usagescenarios.

—Governancelevelsarenotmature.Thereisnoplaybook,notagging,nosourcingstrategy.

WithoutmatureFinOps,tagging,andsourcing,organizationscannotforecast,allocate,orcontrolAI-drivenconsumptionatscale.

—Teamsdonotknowwhichmodel,tool,orpricingtiertouse.Usersoftendefaulttopremium

modelsorfamiliartoolsbecausetrade-offsacrossquality,latency,risk,andcostareunclear.

—CitizendevelopersandAI-assistedapplicationdevelopmentareacceleratingconsumption.

EmployeescannowrapidlybuildAI-poweredapplications,workflows,andautonomous

agentswithlittleengineeringeffort.Whilethisdemocratizesinnovation,italsodramaticallyincreasesunmanagedtokenconsumptionandmakesforecastingsignificantlymoredifficult.

HowtomanageAIconsumption

OuranalysisandexperiencesuggestfouractionstobettermanageAIdemand.

Knowwhatyou’respendingonAItomanageandforecastbetter

Accesstoanexpandingecosystemofoff-the-shelfAIcapabilitieshasledtoAIsprawlwith

limitedvisibilityintotheircumulativecost.Organizationscannotoptimizewhattheycannotsee.Acrossmanyenterprises,AIspendingremainsfragmentedacrosscloudproviders,foundationmodelvendors,softwareplatforms,experimentationenvironments,andbusinessunits.This

makesithardtohaveaviewofenterprise-wideconsumption,forecastfuturedemand,or

understandcostdrivers.Infact,ourexperiencehasshownthat20–30percentofAIspendisoftenunaccountedforbecauseAIinvestmentsarefragmentedacrossvendors,tools,and

commercialmodels.

Inoneorganization,anefforttoestablishaconsolidatedviewofAIspendinguncoveredcostsspreadacrossenterprisecopilots,foundation-modelcontracts,AI-enabledsoftwarefeatures,API-basedservices,experimentationenvironments,andbusiness-unitpurchases.What

appearedtobeastraightforwardtechnologybudgetexercisehadevolvedintoafragmentedportfolioofAIexpenditureswithnosinglesourceoftruth.

Thislackoftransparencyoftenmeansthatcostoverrunsfrequentlybecomevisibleonlyafterconsumptionhasalreadyoccurred.Furthermore,ithampersthedevelopmentofreliable

forecasts,aparticularchallengegiventhenondeterministicnatureofAIagents.Tokenusagecanvarybyupto30timeswhenexecutingthesametask.4

4LongjuBaietal.,“HowdoAIagentsspendyourmoney?Analyzingandpredictingtokenconsumptioninagenticcodingtasks,”

StanfordDigitalEconomyLab,April14,2026;MattySmith,“HowareAIagentsspendingyourtokens?,”StanfordDigitalEconomyLab,May5,2026.

Thecostofintelligence:HowCIOscanmanageAIdemandatscale5

Asorganizationsmovefromcopilotstoagenticworkflows,CIOswillincreasinglyneeddemandforecaststhatmodelmultiplescenarios,includingadoptiongrowth,workflowexpansion,routingchanges,andshiftsbetweenfrontierandopen-weightmodels.Infact,organizationswithhigh

forecastingmaturitysave10percentmoreonAIspendthantheirpeersonaverage.5

MeetingthatdemandrequiresstrongAIFinOpscapabilities.Todate,however,only20–25percentofcompanieshavematureAIFinOpspracticesinplace(Exhibit2).

Exhibit2

AsCIOsbuilduptheirFinOpsmusclesforAI,theyneedtoprioritizeestablishingacentralizedAIcontrolplanethatprovidesasinglesourceoftruthforspend,usage,performance,andbusinessoutcomesasanimportantsourceofinsightforscenarioplanning(seesidebar“WhatisanAI

controlplane?”).

ThistransparencycreatesaccountabilitythroughshowbackandchargebackmechanismsthatconnectAIconsumptiondirectlytothebusinessactivitiesgeneratingdemand.Thiscapabilityiscritical,sincetheunitofgovernanceshouldbethecompletedbusinessoutcome,notthetokencost.

AIisemergingasapowerfultoolformanagingspend.OrganizationsareturningtoAIfora

wideningrangeoftasks,includinganalyzingconsumptionpatterns,recommendinglower-costmodels,forecastingdemand,detectinginefficientpromptdesigns,identifyingunnecessary

5McKinseyEnterpriseAIFinOpssurvey,May2026;120enterpriseparticipants,75qualifiedrespondentsacrossfivemajorindustries.

Thecostofintelligence:HowCIOscanmanageAIdemandatscale6

WhatisanAIcontrolplane?

AnAIcontrolplaneistheenterprisemanagementlayerthatsitsbetweenusers,applications,agents,andtheAImodelsorplatformstheyconsume.Ifenterpriseresourceplanning(ERP)systemsbecamethesystemofrecordforfinancialtransactions,AIcontrolplanesmaybecomethesystemofrecordforintelligenceconsumption.ItspurposeistomakeAIusageobservable,attributable,andgovernableatscaleacrossthreevectors:

—Visibilityandattribution.Acontrolplanecapturesrequest-leveltelemetryacrossusers,

applications,workflows,models,prompts,agents,tokens,APIcalls,latency,andcost.Bytaggingusagetoabusinessunit,product,usecase,workflow,owner,andcostcenter,organizationscanmovefromaggregatevendorinvoicestomeaningfuluniteconomics,suchascostpertask,case,codereview,orcustomerinteraction.

—Policyandgovernance.Acontrolplaneenablesorganizationstoenforcerulesinrealtime,

includingapprovedmodelaccess,role-basedpermissions,tokenbudgets,spendthresholds,

context-windowlimits,premium-modelapprovals,andexceptionworkflows.Thishelpspreventavoidablespendandunmanagedriskbeforetheyoccur.

—Routingandoptimization.Acontrolplanecanrouteworkloadstotherightmodelorprovider

basedoncost,quality,latency,risk,andavailability.Itcanalsoidentifyoptimizationopportunitiessuchascaching,promptstandardization,agent-loopreduction,output-lengthcontrols,and

modelright-sizing.

agentloops,andrecommendingwhereopen-weightmodelscouldreplaceproprietarymodelswithoutcompromisingbusinessoutcomes.Overtime,AIsystemswillbeabletocontinuouslyoptimizeotherAIsystems.

Optimizespend

MostorganizationsinitiallyapproachAIoptimizationasamodelselectionproblem.The

opportunityissignificantlybroader.Thelargestsavingsoftencomefromoptimizingthesystemsthatconsumetokens.

AIcostmanagementiscomplexbecauseitcanfluctuatedramaticallybasedonmodelchoice,tokenconsumption,routingdecisions,agentbehavior,orchestrationpatterns,anduser

adoption.Ouranalysishasshownthatthereareabout40leversthatgeneratethebest

outcomesinoptimizingspend.Promptcaching(reusingstaticpromptcontext),forexample,reducesrepeatedinput-tokencostsbyuptoabout90percent,especiallyforretrieval-

augmentedgeneration(RAG)andagentswithlarge,stableprefixes.

Thecostofintelligence:HowCIOscanmanageAIdemandatscale7

AI-assistedoptimizationcanalsoimprovepromptefficiencybyrecommendingshorterprompts,reducingunnecessarycontext,andstandardizingprompttemplates.Aboutathirdof

organizationssurveyedhavealreadyachievedsavingsof20to30percentthroughactiveoptimizationactions(Exhibit3).

Exhibit3

ThefollowingactivitieshavethemostimpactonoptimizingAIcosts:

—Selectingtherightmodeltype.Fewtaskswarranttheuseofexpensivefrontiermodels.

Companiesshouldrouteeachtasktothelowest-costmodelthatcandelivertherequired

Thecostofintelligence:HowCIOscanmanageAIdemandatscale8

quality.

—Reducingbloatedtokeninputs.Shortenprompts,limitcontextwindows,passonlyrelevantsectionsofdocumentsortooloutputs,andsummarizelongconversationhistoriesbefore

sendingthembacktothemodel.Reusingstaticpromptcontextcanreducerepeatedinput-tokencostsbyuptoabout90percent,especiallyforRAGandagentswithlarge,stable

prefixes.

—Controllingoutput-tokenconsumption.Defineexpectedresponseformats,usestructuredoutputswhereappropriate,andcapresponselengthsbyusecasesomodelsdonot

generateunnecessarytext.

—Managingagentandworkflowvolume.Setlimitsonretries,toolcalls,agentloops,and

escalationpaths;redesignrepetitiveorpoorlystructuredworkflowsupstreamratherthansimply“throwingagentsattheproblem.”

—Batchingandcaching.Batchnonurgent,high-volumerequestswherelatencyisless

important,andcacherepeatedprompts,stablecontext,andreusableintermediateresultstoavoidpayingrepeatedlyforthesameinference.

—Usingopen-weightmodelsselectively.Consideropen-weightmodelsasalternativesto

frontiermodelsforsimplertasksthatdonotneedthesmartest,mostexpensivemodels(seesidebar“AcloserlookatAImodels”).

Modernizesourcingandestablishfinancialaccountability

Traditionalsoftwareprocurementmodelswerebuiltaroundpredictablelicensestructures,

annualcommitments,andseat-basedpricing.AIintroducesadifferentrealitythatisdefinedbyconsumption-basedpricing,rapidlyevolvingmodelecosystems,fluctuatingdemandpatterns,andcontinuousinnovation.Asaresult,companiesneednewmechanismstobothgovernAI

consumptionandmanageprovidersandaccess.

LeadingorganizationsfromaMcKinseysurveyareseeingunitcostreductionsof10to20

percentthroughtwosourcingandnegotiationlevers.6ThefirstistodevelopanAIaccountabilitymodelthatprovidesvisibilityintowhoisconsumingAIresources,whatvalueisbeingcreated,

andhowcostsareincurredacrosstheorganization.SimilartocloudFinOps,leading

organizationsareincreasinglyallocatingAIcosts—includingtokenusage,APIcalls,

infrastructureconsumption,andmodelexpenses—tothebusinessunits,products,orusecasesgeneratingdemand.

Effectiveaccountabilitymodelscombinetransparentchargebackorshowbackmechanismswithautomatedguardrailsthatmonitorusage,enforcepolicies,andidentifyinefficiencies.ThishelpsorganizationsmovebeyondviewingAIspendasacentralizedtechnologycostandinstead

manageitasabusinessinvestmenttiedtomeasurableoutcomes.

6McKinseyEnterpriseAIFinOpsSurvey,May2026;120enterpriseparticipants,75qualifiedrespondentsacrossfivemajorindustries.

Thecostofintelligence:HowCIOscanmanageAIdemandatscale9

AcloserlookatAImodels

EnterprisesincreasinglyhavemultiplewaystoaccessAImodels.

Proprietarymodels(includingfrontiermodels):Thesearethemostadvancedmodels.They

typicallydeliverthestrongestperformanceforcomplexreasoning,coding,multimodaltasks,and

agenticworkflows.Forthatreason,thesetendtobethemostexpensivemodels.TheyaretypicallyaccessedthroughAPIsormanagedcloudservicesandpricedbasedonconsumption.Agentsusingthesemodelscanoftenmanageanentireworkflow.

Open-weightmodels:Open-weightmodelsareAIsystemswhosecorecapabilities(or“weights”)aremadepubliclyavailable.Theyprovideenterpriseswithgreaterflexibilityandcanoftenofferlower-

costalternativesformanyusecases.Organizationscanaccessopen-weightmodelsthroughseveraldeploymentapproaches:

—Provider-hosted.Thesemodelsarehostedbyacloudorinferenceprovider,allowingenterprisesaccesstothemwithouthavingtomanagetheunderlyinginfrastructure.Thetrade-offisthat

companiesstillrelyonanexternalproviderforhosting,scaling,uptime,andsomeaspectsofdatagovernance.

—Enterprise-hosted.Someorganizationsrunopen-weightmodelsoninfrastructuretheyprocure,configure,andmaintain.Thiscanprovidegreatercontroloverdata,customization,latency,and

uniteconomicsatscale,butitrequiresstrongerengineering,MLOps,security,andinfrastructurecapabilities.

—Edgeorlocalmodels.Smallermodelscanalsorundirectlyonemployeedevices,suchaslaptops

orphones.Thesemodelsaretypicallylesscapablethanfrontiermodelsbutcanbeusefulfor

lower-risk,localworksuchassummarization,low-volumeclassification,andtranslation.

Thisvisibilityisincreasinglyimportantbecauseithelpsidentifyhiddencoststhatdrivespendatscale:repeatedfailedretriedrequests,contentbloatonlargeprompts,conversationhistories,agenticchains,andmodelproliferation.

ThesecondleverinvolvesdevelopinganAIthatgoesbeyondthetraditionalbuy-versus-buildquestions.Thegrowingavailabilityofoff-the-shelfAIsolutionsisincreasing,andnotreducing,thecomplexityofenterpriseAIeconomics.Asorganizationsgainaccesstomoremodels,

copilots,AIapplications,anddeploymentoptions,thechallengeshiftsfromacquiringacapabilitytogoverningwhen,where,andhowthatcapabilityisconsumed.

That’swhythebuy-versus-buildsourcingquestionisnolongervalid.Increasingly,itisaquestionoffindingthebestmixofbuy,build,host,route,andswitch.Organizationsmustcontinuouslyevaluatewhichmodelsorarchitecturesareneededforeachworkload

basedonmodelperformance,cost,risk,andbusinessvaluetooptimizetheirAIsupplymixovertime(table).

Thecostofintelligence:HowCIOscanmanageAIdemandatscale10

Table

AIoutsourcingdynamicsaresignificantlydifferentfromtraditionalsourcing.

TraditionalsoftwaresourcingAIsourcing

Seat-basedlicensing

Consumption-basedpricing

Single-vendorstrategy

Multimodelecosystem

Fixeddemandforecasts

Dynamicdemandmanagement

Long-termcommitments

Flexiblecommercialstructures

Periodicbenchmarking

Continuousbenchmarking

Embedgovernancedirectlyintothearchitecture

CIOsfaceanunusualchallenge:Forthepastthreeyears,theyhaveencouragedemployeestousemoreAI.NowtheymustencourageemployeestouseAImoreintelligently.Cost

managementneedstobepartofabroaderchangemanagementeffort,ratherthanexpectingpeopletolearntobecost-efficientintheirAIuse.

OnewaytoaddressthisissueistoautomateandembedgovernanceandspendmanagementpracticesdirectlyintotheAIarchitecture.AIgateways,controlplanes,policyengines,and

automatedguardrailshelpenforceapprovedmodelusage,monitorconsumption,automaticallyapplyorganizationalpolicies,andenableresponsibleAIcontrols.

OnetoolbeingpilotedinternallywithinMcKinsey,forexample,coachespeopleonhowtowritebetterpromptsandthoughtfullyselectmodelsastheyuseLLMs.Educatingandguiding

peoplewhenthey’reactuallyusingtheproduct,insteadofonlyrequiringseparatetrainings,hasproventowork.

WhatCIOsshouldprioritizenow

ThetemptationinthefaceoftheseAIcostpressuresistosimplycutspend.Thatwouldbeamistake.Thebetterapproachistofocusonshapingdemandtocreatethemostvalueforthebusiness.Inourexperience,themostsuccessfulCIOsfocusonthefollowingactions:

—Buildforecastingcapabilitiesbeforecostserupt.AsAIadoptionexpands,organizationsneedaclearunderstandingofwhereconsumptionoccurs,whatdrivescosts,andwhichusecasesaregeneratingvalue.CIOsshouldthereforeinvestinforecastingcapabilitiesthatmodel

demandunderdifferentadoption,pricing,andworkloadscenarios.AIbusinesscasesshouldincludeprojectedconsumptioncurves,sensitivityanalyses,andexpectedcost-per-outcomemetrics.ThegoalistomoveAIeconomicsfromabudgetingexercisetoaplanningdiscipline.

—MoveAItoanAIbusiness-valueaccountabilitymodel.CIOsshouldworkwithfinanceandbusinessleaderstoestablishcost-attributionmodelsthatconnectAIusagedirectlytotheproducts,workflows,andbusinessunitsthatgeneratedemand.Theobjectiveistocreatetransparencyaroundwhichusecasesaregeneratingvalueandwhichareconsuming

Thecostofintelligence:HowCIOscanmanageAIdemandatscale11

resourceswithoutmeaningfulreturns.Overtime,organizationsshouldevolvetoward

measuringAIinvestmentsusingbusinessmetricssuchascostperclaimprocessedorrevenuegeneratedperAI-enabledworkflowratherthansimplytrackingtokenconsumption.

—AnchorAIeffortsaroundtheenterprise’smostvaluableworkflows.CIOsshouldworkwith

businessleaderstoidentifytheworkflowswhereAIcancreatedisproportionatevalue,

whetherthroughproductivitygains,revenuegrowth,improvedcustomerexperience,orriskreduction.TheseworkflowsshouldbethefocusofinitialAIconsumptioncapabilities

becausethat’swheretheimpactwillbegreatest,generatingsavingsthatcanbereinvestedinothertechnologypriorities.

—Maintainflexibilityinsourcingandtechnologychoices.Giventhepaceofinnovation,today’soptimalmodelorvendormaynolongerbeoptimalsixmonthsfromnow.CIOsshouldavoidlockingtheorganizationintorigidcommercialstructuresornarrowtechnologystacks.

Instead,CIOsshouldworkwithenterprisearchitectstodesignarchitecturesandsou