ibm -AI 智能体值得投入吗？智能体经济学与现代运营模式 Is that AI agent worth it Agentic economics and the modern operating model

July2026

Mckunsey

Quarterly

IsthatAIagentworthit?

Agenticeconomicsandthemodernoperatingmodel

AscompaniesfocusonspiralingAIcosts,theyneedtomakesurenottolosesightoftherealprize.

ThisarticleisacollaborativeeffortbyLariHämäläinen,MarkPatel,SvenBlumberg,TanguyCatlin,andWasimLala,representingviewsfromQuantumBlack,AIbyMcKinsey.

Forallthenewsaboutfallingtokenprices,manyenterpriseleadersareexperiencingstickershock

aroundAIagents.WhiletheStanfordHAI2025AIIndexreportedthatinferencecostofGPT-3.5-level

capabilityfellfrom$20permilliontokensto$0.07through2024,1enterpriselargelanguagemodel

(LLM)spendingtripledovera12-monthperiodbytheendof2025.2Some93percentofrespondents

toaMcKinseysurveyreportexceedingtheirAIbudgets,3whileone-fifthofrespondentstoMcKinsey’s

forthcomingglobalStateofAIsurveyreportedthattheirorganizationshaveconstraineduseofAIbecauseofAI-relatedoperatingcosts.4

Whatgives?TherearemanyreasonsforthespikeinAIcosts:EnterprisesarescalinguptheirAIefforts;LLMprovidershavepivotedfromsubscriptiontoconsumption,whichhascreatednewincentives(for

example,answerlengthhasincreasedtodrivetokenusage);andexpensivemodelsareoftenusedforsimpletasks.

Wegointomoredetailontheseandothercausesbelow,butit’sworthwavingabrightredflagbeforethattohighlightacriticalpoint:CEOsshouldnotfocusjustontokencostreductionconversations.As

Davi

d

Tepper,

CEOofPay-i,framesit:Tokensarenotvalue;tokensarethebill.Thefocus,instead,needstobesquarelyonhowtouseAIagentstocreatevalue.

Alotgoesintowhatdeterminesthatvalue,suchaswhethertheagentoutputiscorrect,whetherhumansmustsuperviseorrepairit,howmuchcomputeitconsumeswhilereasoning,andwhetherthevalueofthecompletedworkexceedsthefulloperationalcostofgeneratingit.Inessence,forCEOsthisboilsdowntoansweringabasicquestion:AretheAIagentcapabilitieswe’rebuildingandrunningworththevaluewe’regettingfromthem?

AnsweringthatquestionisbecomingevermorepressingforCEOsasAIsystemsmovefromagentic

coworkerstoagentscapableofmorecomplextaskslikereasoningandorchestratingworkflowsacross

theenterprise.Furthermore,AIspendingisexpectedtorisetoroughly25percentofenterpriseIT

budgetswithinthenextseveralyears.YetmanyorganizationsstillcannotclearlyexplainwhichAIsystemsaregeneratingvalue,whattheytrulycosttooperate,orhowthoseeconomicschangeasusagescales.

Gettingclearonthe

supply

and

demand

forcesshapingAI’scostcurve,andtheirimplicationson

competitivedynamics,willdeterminehowwellcompaniesmanagethisphaseoftheAIrevolution.OurexperienceworkingwithcompaniesandonourownAIprogramdemonstratesthatleadersneedto

managemachineworkdifferently,fromallocatingintelligencelikecapitaltorecalibratingthebalancebetweeninsourcingandoutsourcing(seesidebar“WhatMcKinseyhaslearnedfrommanagingAI

economicsatscale”).Thatmanagementdisciplinedoesnotyetexistinmostenterprises.

1

NestorMaslejetal.,“TheAIIndex2025annualreport,”AIIndexSteeringCommittee,InstituteforHuman-CenteredAI(HAI),StanfordUniversity,April2025.

2

3

4

TimTully,JoffRedfern,DeedyDas,andDerekXiao,“2025:ThestateofgenerativeAIintheenterprise,”MenloVentures,December9,2025.

EnterpriseAIFinOpsSurvey,May2026;120enterpriseparticipants,75qualifiedrespondentsacrossfivemajorindustries.

Theforthcoming2026StateofAIsurveywasinthefieldfromMay4–June8,2026,andgarneredresponsesfrom1,719participantsrepresentingthefullrangeofregions,industries,companysizes,functionalspecialties,andtenures.

2IsthatAIagentworthit?Agenticeconomicsandthemodernoperatingmodel

3IsthatAIagentworthit?Agenticeconomicsandthemodernoperatingmodel

WhatMcKinseyhaslearnedfrommanagingAIeconomicsatscale

AsAIadoptionhasexpandedacrossMcKinsey,wehaveexperiencedmanyofthesameeconomic

challengesourclientsarebeginningtoface.Whilewearestilllearning,severalkeylessonshaveemerged:

—Growthisexponentialandshowslittlesignofabating.AsofMay2026,McKinseyisprocessingroughlyfivetrillionAItokensmonthly.AIusagehasgrowninwaves,expandingtheuserbase,increasing

consumption,andintroducingnewpatternsofdemand.Intheseinitialphases,therewerenolimits

placedonusageinordertoaccelerateAIskilldevelopment.Wearenowshiftingtoinstituting

guidelinesandguardrailstobettermanagecostsaheadofthenextwave,whereautonomousagentswillconsumeintelligenceonbehalfofusersratherthansimplyrespondingtoprompts.Demand

managementhasbecomeapermanentcapabilityratherthanaone-timeexercise.

—Consumptionfollowsapowerlawofdistribution.AIusageishighlyconcentrated.Roughly

10percentofusersaccountforabout65percentoftotaltokenconsumption.Softwareengineersandconsultantsareamongthetopusers,generallydrivenbypersonalorworkingteaminterest,though

thereisaverylongtailofusers.

—Poolingconsumptionspendisthebestwaytodistributecosts.LLMprovidersaretransitioningfromseat-basedtoconsumption-basedlicensingmodels(combinedwithadhocusertransparencyabouttheseconsumptioncosts).Poolingconsumptioncostsacrosstheenterpriseallowsustosupport

legitimatepoweruserswhilesmoothingdemandacrossthebroaderenterpriseandavoidingunusedcapacitylockedintoindividuallicenses.Understandingwheredemandconcentratesisoftenmorevaluablethanunderstandingaverageusage.

—Oldermodelsarebetter(andcheaper)formostusecases.Userstendtogravitatetowardthebest(andmostexpensive)model,eventhoughmanyoftheirtaskscanbedonewithmuchlesssophisticated

models.Wehavefoundmeaningfulsavingsthroughintelligentmodelrouting(therightintelligence

attherighttime)andmatchingdifferentmodelstodifferentstagesofaworkflow—forexample,usingafrontiermodeltogenerateaninitialhypothesisbeforehandingsubsequentrefinementtoasmallermodel.

—Pricingflexibilityisincreasing.Thesamemodelmaybeavailablethroughmultipleproviders,eachofferingdifferentpricing.Enterprisediscountscanoftenbestacked.

—Automatecostdecisionsasmuchaspossible.McKinseyperformspromptoptimizationandhas

investedinaninternalAIgatewaytorouteLLMvendors(withplanstoperformmodelroutinginthefuture)andotheroptimizationoptionstransparentlybeforerequestsreachmodelproviders.

—Simpletipsandupdatescanhavedramaticeffects.Providingtransparencytoeachuseraboutthe

costofaprompthelpspeoplemakemoreeconomicaldecisions.Givingusersspecificguidelinesand

tipsatthepointofusagecanhaveasignificanteffect.Encouragingmoreconcisepromptsandoutputs(“cavemanlanguage”),forexample,canreducetokenconsumptionforsomeworkflowsby30to40

percentwithoutmateriallyaffectingquality.

—Therearesignificantcostsbeyondthemodelitself.Someofthefastest-growingAIcostssitoutside

theLLM.Securitygateways,orchestrationplatforms,monitoringtools,andevensoftwarevendorsareincreasinglypricingservicesbasedontokenconsumption.Whenyouconsiderthatthesametokens

maybeinspectedorprocessedmultipletimesastheymovethroughtheenterprisearchitecture,thatcanhavebigcostimplications.

4IsthatAIagentworthit?Agenticeconomicsandthemodernoperatingmodel

Whytheoperatingexpenditurebillkeepsrising:Thesixdriversofagenticeconomics

Sixpatternsexplainwhyagenticworknegatestoken-pricedeflation:

—Long-livedcontext.LLMsarestateless,whichmeansagentsoftenresendpriorcontextaswork

progresses,compoundingthetotalcost.Agentictaskscanconsumeroughly1,000timesmoretokensthancodereasoning(single-turnproblem-solvingwithouttoolinteraction)orchattasks(multi-turn

dialogueaboutacodingproblem).5Contextbecomesanoperatingassetandaccountsforasignificantportionofagenticcosts.Poorlymanagedcontextturnseverystepofaworkflowintoarecurringcost.

—Refinementisthesink.Inagenticworkflows,theexpensivepartisnotthefirstanswergeneratedbut

thechecking,repairing,andreverifyingthatfollows.About60percentofanagentictask’scosts,infact,aretiedtorefininganswers.6Leadersneedtoaddressandfactorinqualitycontrol,exceptionhandling,andreworkaspartofprocesscostsacrossallworkflows.

—Autonomycreatesvariance.Agenticsystemscanproducemateriallydifferentcostsforthesametaskbecausetheymaytakedifferentpaths,calldifferenttools,orretryindifferentways.Inprogramming,forexample,thesametaskcanhaveafactor-of-30variationbetweencompletions.7Costbehavesasadistribution,notafixedunitprice.Average-costbudgetswillnotbeenough.

—Expensivereasoningisusedforbasictasks.Extendedthinkingpaysforitselfmanytimesoveron

hardtasks.Foreasyones,itisexpensiveoverhead.Modelroutingiscriticaltoensurethatexpensive

reasoningisreservedforworkwhereitcanchangetheoutcome,andthatcheapermodelsareusedforbasictasks.

—Agentchoiceorchestrationcancompoundcosts.Howanagentcallsatoolmattersasmuchaswhatitcalls.Thewayworkisdecomposed,coordinated,andhandedoffacrossagents,tools,andmodelscanchangecostsdramaticallywithoutchangingthebusinessoutcome.

—Informationstructurecandriveinefficiencies.Howefficientlyinformationispresentedtoorbyamodel(forexample,promptdesign,contextlength,language,formatting,anddatastructure)affectstoken

consumption.Non-Englishtext,forexample,getsfragmentedintomoretokenspermeaning,sothesameconversationcostsmoreinsomelanguagesthanothers.

ThesesixdrivershelpexplainwhyonlymakingcheapermodelcallsdoesnotautomaticallyproducelowerAIbills(readmoreonthistopicinourupcomingarticle“Thecostofintelligence:HowCIOscanmanageAIdemandatscale”).

Theimplicationsforcompetitivedynamics

WhileAIiscreatingcomplexitiesintermsofcostmanagement,itisalsoforcingsignificantrecalibrationsaroundcompetitivedynamics.AsCEOsdeterminewhereAIwillcreatevalueandhowtodeveloptheir

business’s

strategicadvantages

,theyneedtopayattentiontofourimplicationsinparticular.

5LongjuBaietal.,“HowdoAIagentsspendyourmoney?Analyzingandpredictingtokenconsumptioninagenticcodingtasks,”arXiv2604.22750,April2026;“Howwebuiltourmulti-agentresearchsystem,”Anthropic,June13,2025.

6

7

MohamadSalimetal.,“Tokenomics:Quantifyingwheretokensareusedinagenticsoftwareengineering,”arXiv2601.14470,January2026.

LongjuBaietal.,“HowdoAIagentsspendyourmoney?Analyzingandpredictingtokenconsumptioninagenticcodingtasks,”arXiv2604.22750,April2026.

5IsthatAIagentworthit?Agenticeconomicsandthemodernoperatingmodel

Thefirstimplicationisthatprocessadvantagebecomeshardertodefend.Fordecades,companiescreatedadvantagethroughsuperiorexecution,suchasbetterunderwriting,fasterclaimshandling,moreefficientoperations,strongercustomerservice.Agenticsystemscompressthoseadvantagesbecausethesame

capabilitycannowbescaledacrossthousandsofworkflowsthroughsoftwareplatforms.Whatoncerequiredyearsofprocessredesignandimplementationcanincreasinglybedoneinmonthsorweeks.

Thesecondimplicationisthatdataandcontextbecomemorevaluablethanmodels.Asfoundationmodelsconverge,thedifferentiatorincreasinglyshiftstowardproprietarysourcesofadvantage.Thesame

commercialagentwillproduceradicallydifferentoutcomesdependingonthecustomersignals,operationaldata,decisionhistory,andworkflowcontextitreceives.Inanagenticworld,thecontextualdatathe

enterprisecapturessystematically,suchascalltranscripts,decisionprecedent,andprocesstelemetry,isbecomingthelayerthatcreatesacompetitiveadvantage.

ThethirdoneisthatAIgovernancebecomesanewbasisofcompetition.Theeconomicsofmachinework

exhibitenormousvariation.Thesametaskcancost30timesmoredependingonhowitisexecuted.Similarly,thesameinfrastructurecanproducedramaticallydifferentlevelsofoutputdependingonhowintelligence

isallocatedandrouted.Theorganizationsthatlearntogovernmachineworkeffectively—measuringit,

allocatingit,improvingit,andintegratingitintooperations—willcreatemorevaluethantheircompetitorsfromthesameintelligencespend.

Thefourthimplicationisthatagenticeconomicsareredefiningfirmboundariesintermsofwhatshouldbein-oroutsourced.Foryears,sourcingdecisionswerelargelydrivenbylaborcosts,scaleeconomies,andcoordinationoverhead.Buttheeffectivenessofagenticsystemsdependslessonthemodelandmoreonaccesstocontext,aswementionedabove.Enterprisesneedtotreatthiscontextcapabilitylikeacrown

jewelandkeepitinsidethebusiness,whileothertasksthatarenotcoretoitscompetitivefootprintcanbeoutsourced.

TheCEOto-dolistforbuildingtheagenticoperatingmodel

TheCEOagendaistodesigntheenterprise’sagenticoperatingmodeltomaximizevalue,nottooptimize

tokenusage.Itrequiresamanagementdisciplineformachinework:whatoutcomesitismeasuredagainst,

whereandwhattypeofintelligenceisallocatedagainstthoseoutcomes,howmuchautonomyagentsare

granted,andwhichworkloadsjustifydifferentdeploymentmodels.ThefollowingimperativesarewhatCEOsshouldrequirebeforeagenticsystemsscale:

—Allocateintelligencelikecapital.Oneofthemostexpensivemistakesorganizationsmakeisassuming

everyproblemrequiresfrontierintelligence.Inreality,manyenterpriseworkflowscanbeperformed

effectivelyusingsmallermodels,open-weightmodels,deterministicsystems,ortraditionalsoftware.

TheCEOshouldrequirethateachmajorworkloadhaveaclearanswerforwhyitrunswhereitruns,whatwouldcausethatdecisiontochange,andwhoreviewsitasprices,regulation,usagevolume,andmodelqualitymove.

—Setthecompetitivestrategybeforethetechnologystrategy.AIshouldforceeverycompanytoaskitself:“Whatistheessenceofourbusiness?”and“Whereareour

sourcesofadvantage

?”Thehighest-returnAIinvestmentsarerarelyspreadevenlyacrosstheenterprise.Theytendtoconcentrateinahandfulof

economicallycriticalcapabilities.Ininsurance,claimsandunderwritingmaydominate.Inpharma,the

largestreturnsmaycomefromR&Dandclinicaldevelopment.Insoftwarecompanies,theymaycome

fromengineering.Thefirstexecutivetaskisthereforeidentifyingthehandfulofdomainswheremachineworkcanmostmateriallychangetheeconomicsofthebusiness.

6IsthatAIagentworthit?Agenticeconomicsandthemodernoperatingmodel

AsCEOsdeveloptheirstrategytobuildcompetitiveadvantage,itwillbeimportanttocreatea“glide

path”thatcombinesboldaspirationswithpracticalintermediategoals.Clearmilestonestiedto

outcomes,budgetdevelopmentandallocationrequirements,andsufficientflexibilityforteamstomeetgoalsarecoreelementsofaneffectiveplan.

—Buildagenticoperationsasamanagedenterprisediscipline.Enterpriseshavespentdecadesbuildingdisciplinesforfinancialcontrol,leanoperations,supplychainproductivity,cyberrisk,andcloudcost

management.Theynowneedtheequivalentdisciplineforagenticwork.Toolarchitecture,model

routing,evaluation,andautonomyshouldbegovernedasmanagementdecisions,notmerelytechnicalones.Noautonomoussystemshouldoperatewithoutadefinedmandate,budget,andstoppingrule.

Fundthisasamultiyearcapabilitybuild,notaprojectlineitem.Thecapabilityshouldnotbeoutsourcedtovendorsbydefault.

—Answerthe“whoownsthecapability”question.Thetechnicalandoperationalleversrequiredtogovernmachinework(suchascontextmanagement,modelrouting,evaluation,autonomy,andworkload

placement)don’tsitcleanlywithinthemandatesoftoday’stechnology,finance,operations,orhumanresourcesleaders.Thatlackofclaritytendstoleadtomistakes,redundantactivities,andwaste.TheCEOwillneedtodeterminewholeadsAIoperations(seesidebar“Whatmachine-workeconomics

meansforkeyleaders”).

Whatmachine-workeconomicsmeansforkeyleaders

Chiefinformationofficer(CIO):Buildthesystemtomanageintelligence.TheCIO’sroleextendswellbeyondmanagingAIinfrastructure.Itistoensurethateveryagenthasaccesstotherightenterprise

contextattherighttimebyconnectingsystemsofrecord,operationaldata,unstructuredcontent,and

enterprisesemanticsintoacoherentinformationlayer.Thatrequiresmodernizingdatagovernance,

managingbothstructuredandunstructuredinformation,andbuildingtheenterpriseontology,metadata,andcontextservicesthatallowagentstoreasonreliablyacrosstheorganization.

Chieftechnologyofficer(CTO):Improvetheeconomicsofmachinework.TheCTOincreasingly

ownstheproductivityofintelligenceitself.Decisionsaboutarchitecture,modelselection,context

management,routing,caching,evaluation,andautonomydirectlyaffectthecostandperformanceofmachinework.Thekeyquestionishowtodeliverthesamebusinessoutcomewithtechnologiesthatdeliverlessintelligenceconsumption,lowerlatency,higherquality,orgreaterreliability.

Chieffinancialofficer(CFO):TurnAIintoaneconomicassetclass.FortheCFO,AIisbecominganewcategoryofenterprisespendthatrequiresitsownmeasurement,forecasting,andcapital-allocation

frameworks.Thefocusshouldshiftfromtechnologybudgetstoeconomics—forexample,costperoutcome,returnonintelligence,andtheproductivityofmachineworkrelativetohumanworkand

traditionalautomation.Theobjectiveistoensurethatspendingisdirectedtowardthehighest-valueopportunities.

7IsthatAIagentworthit?Agenticeconomicsandthemodernoperatingmodel

WhethertheanswerisachiefAIoperationsofficer,anewoperatingcommittee,oranothermodelmatterslessthanbeingexplicitaboutwhereownershipandaccountabilitylie.TheyshouldhaveexplicitresponsibilityforcriticalKPIs,suchascostperoutcome,vendorperformance,learning

cadence,andtheoperatingeffectiveness.Theyalsoneedtostayontopofmarketdynamicstoanticipatechanges,andbuildsufficientflexibilityintotheoperatingmodeltoadjust.

—Measuremachineworkwithmetricsthatmattertothebusiness.Theunitofgovernanceisthe

completedbusinessoutcome,notthetoken,modelcall,ortechnologylineitem.CEOsshould

requirethateverymaterialagenticworkflowbemeasuredagainstbusinessoutcomesratherthansolelytechnicalmetrics.Thespecificmetricmatterslessthantheprinciple.Leaderswillneed

visibilityintotailcosts,escalationthresholds,andacceptableoverallcostlevels,ratherthansingleexecutioncosts.Theobjectiveistocreateacommoneconomicunitthatallowsmanagementto

comparehumanwork,machinework,andhybridwork.

—Rethinkyoursourcingboundaries.UnderstandingthevaluedynamicsofAIshouldforceamajorrecalibrationoftheexistingoutsourcing/insourcingmodel.Someactivitiesthatoncemade

economicsensetooutsourcemaybecomemorevaluabletokeepclosetotheenterprisebecausemachineworkperformsbestwhentightlyintegratedwithproprietarycontext.Atthesametime,

otheractivitiesthatpreviouslyrequiredsignificantinternalcapability(suchaslegalresearch,

financialanalysis,orcontentgeneration)mayincreasinglybedeliveredthroughagenticserviceswithrelativelylightinternaloversight.

Performanactivity-by-activityreviewtodeterminewhichshouldbemanagedinsidetheenterpriseoroutsidebasedonsourcesofvalue.Thedefaultdirectionisnolonger“outsourcemore.”Atthe

sametime,reviewcontractsandrenegotiatetermstoprivilegesourcesofcompetitiveadvantageandprovidemorebenefitsbasedoneconomicdynamics.Buildthemuscletomoveactivitiesbackin-housewhenthecasecallsforit.

—Reshapethebuyversusbuildversuspartnerportfolio.Treatworkloadplacementasaportfolio

review,notasinglemomentofbuildversusbuy.Therightdecisionforanygivenworkloaddependsonvolume,predictability,sensitivity,qualitythreshold,andoperatingmaturity(table).Overtime,

mostAIcapabilitieswilllikelybepurchasedthroughcommercialsoftware,models,andservices.

Internaldevelopmentincreasinglybelongsinonlythreeareas:proprietary“gluelayers”thatconnectenterprise-specificdataandworkflows,differentiatedcapabilitieswheretheagentitselfisasourceofadvantage,andstrategiclearninginvestmentsthathelptheorganizationbuildexpertiseand

shapefuturedecisions.

Commercialagent-nativesoftwareinmanycategorieswilllikelytaketwotofouryearstomature.

Enterprisesthatbuildselectivelyduringthiswindowwilllearnthingsthattransferacrosstherestoftheirportfolioandthatbuy-onlycompetitorscannotreplicate.

8IsthatAIagentworthit?Agenticeconomicsandthemodernoperatingmodel

Table

Fiveenterprisescenariosandtheirlikelyworkloadplacements

Scenario

Volume

Predictability

Sensitivity

Quality

threshold

Likelyplacement

Expertreasoning

(legal,strategy,research)

Low

Volatile

Medium

Frontier

required

APIflagshiptier(capabilitybeatsunitcost)

High-volume

classification(supporttriage,fraudsignals)

High

Stable

Low-medium

Validatable

Managedopenorprivate(unitcostdominates)

Regulatedworkload(banking,healthca