麦肯锡：生成式人工智能的经济潜力

Theeconomic

potentialof

generativeAI

Thenextproductivityfrontier

June2023

Authors

MichaelChui

EricHazan

RogerRoberts

AlexSingla

KateSmaje

AlexSukharevsky

LareinaYee

RodneyZemmel

iiTheeconomicpotentialofgenerativeAI:Thenextproductivityfrontier

Contents

Keyinsights

3

Chapter1:GenerativeAIasatechnologycatalyst

4

Glossary

6

Chapter2:GenerativeAIusecasesacrossfunctionsandindustries

8

Spotlight:Retailandconsumerpackagedgoods

27

Spotlight:Pharmaceuticalsandmedicalproducts

30

Chapter3:Thegenerative

AIfutureofwork:Impactsonworkactivities,economicgrowth,andproductivity

32

Chapter4:Considerationsforbusinessesandsociety

48

Appendix

53

Spotlight:Banking

28

TheeconomicpotentialofgenerativeAI:Thenextproductivityfrontier1

2TheeconomicpotentialofgenerativeAI:Thenextproductivityfrontier

Keyinsights

1.GenerativeAI’simpactonproductivitycouldaddtrillionsofdollarsinvaluetotheglobaleconomy.OurlatestresearchestimatesthatgenerativeAIcouldaddtheequivalentof$2.6trillionto$4.4trillionannuallyacrossthe

63usecasesweanalyzed—bycomparison,theUnitedKingdom’sentireGDPin2021was$3.1trillion.Thiswouldincreasetheimpactofallartificialintelligenceby15to

40percent.ThisestimatewouldroughlydoubleifweincludetheimpactofembeddinggenerativeAIintosoftwarethatiscurrentlyusedforothertasksbeyondthoseuse

cases.

2.About75percentofthevaluethatgenerativeAIusecasescoulddeliverfallsacrossfourareas:

Customeroperations,marketingandsales,softwareengineering,andR&D.Across16businessfunctions,weexamined63usecasesinwhichthetechnologycanaddressspecificbusinesschallengesinwaysthatproduceoneormoremeasurableoutcomes.ExamplesincludegenerativeAI’sabilitytosupportinteractionswithcustomers,generatecreativecontentformarketingandsales,anddraftcomputercodebasedonnatural-languageprompts,amongmanyothertasks.

3.GenerativeAIwillhaveasignificantimpactacrossallindustrysectors.

Banking,hightech,andlifesciencesareamongtheindustriesthatcouldseethebiggestimpactasapercentageoftheirrevenuesfromgenerativeAI.Acrossthebankingindustry,forexample,thetechnologycoulddelivervalue

equaltoanadditional$200billionto$340billionannuallyiftheusecaseswerefullyimplemented.Inretailandconsumerpackagedgoods,thepotentialimpactisalsosignificantat$400billionto$660billionayear.

4.GenerativeAIhasthepotentialtochangetheanatomyofwork,augmentingthecapabilitiesofindividualworkersbyautomatingsomeoftheirindividualactivities.

CurrentgenerativeAIandothertechnologieshavethepotentialtoautomateworkactivitiesthatabsorb60to70percentofemployees’timetoday.Incontrast,wepreviouslyestimatedthattechnologyhasthepotentialtoautomatehalfofthetimeemployeesspendworking.1TheaccelerationinthepotentialfortechnicalautomationislargelyduetogenerativeAI’sincreasedabilitytounderstandnaturallanguage,whichisrequiredforworkactivitiesthataccountfor25percentoftotalworktime.Thus,generativeAIhasmoreimpactonknowledgeworkassociatedwithoccupationsthathavehigherwagesandeducationalrequirementsthanonothertypesofwork.

5.Thepaceofworkforcetransformationislikelytoaccelerate,givenincreasesinthepotentialfortechnicalautomation.

Ourupdatedadoptionscenarios,includingtechnologydevelopment,economicfeasibility,anddiffusiontimelines,leadtoestimatesthathalfoftoday’sworkactivitiescouldbeautomatedbetween2030and2060,withamidpointin2045,orroughlyadecadeearlierthaninourpreviousestimates.

6.GenerativeAIcansubstantially

increaselaborproductivityacross

theeconomy,butthatwillrequire

investmentstosupportworkers

astheyshiftworkactivitiesor

changejobs.GenerativeAIcould

enablelaborproductivitygrowth

of0.1to0.6percentannually

through2040,dependingonthe

rateoftechnologyadoptionand

redeploymentofworkertime

intootheractivities.Combining

generativeAIwithallother

technologies,workautomation

couldadd0.2to3.3percentage

pointsannuallytoproductivity

growth.However,workerswillneed

supportinlearningnewskills,and

somewillchangeoccupations.If

workertransitionsandotherrisks

canbemanaged,generativeAI

couldcontributesubstantivelyto

economicgrowthandsupporta

moresustainable,inclusiveworld.

7.TheeraofgenerativeAIisjust

beginning.Excitementoverthis

technologyispalpable,andearly

pilotsarecompelling.Butafull

realizationofthetechnology’s

benefitswilltaketime,andleaders

inbusinessandsocietystill

haveconsiderablechallengesto

address.Theseincludemanaging

therisksinherentingenerative

AI,determiningwhatnewskills

andcapabilitiestheworkforcewill

need,andrethinkingcorebusiness

processessuchasretrainingand

developingnewskills.

TheeconomicpotentialofgenerativeAI:Thenextproductivityfrontier3

1

GenerativeAIasa

technologycatalyst

TograspwhatliesaheadrequiresanunderstandingofthebreakthroughsthathaveenabledtheriseofgenerativeAI,whichweredecadesinthemaking.ChatGPT,GitHubCopilot,StableDiffusion,andothergenerativeAItoolsthathavecapturedcurrentpublicattentionaretheresultofsignificantlevelsofinvestmentinrecentyearsthathavehelpedadvancemachinelearninganddeeplearning.ThisinvestmentundergirdstheAIapplicationsembeddedinmanyoftheproductsandservicesweuseeveryday.

ButbecauseAIhaspermeatedourlivesincrementally—througheverythingfromthetechpoweringoursmartphonestoautonomous-drivingfeaturesoncarstothetoolsretailersusetosurpriseanddelightconsumers—itsprogresswasalmostimperceptible.Clearmilestones,suchaswhenAlphaGo,anAI-basedprogramdevelopedbyDeepMind,defeatedaworldchampionGoplayerin2016,werecelebratedbutthenquicklyfadedfromthepublic’sconsciousness.

ChatGPTanditscompetitorshavecapturedtheimaginationofpeoplearoundtheworldinawayAlphaGodidnot,thankstotheirbroadutility—almostanyonecanusethemtocommunicateandcreate—andpreternaturalabilitytohaveaconversationwithauser.

ThelatestgenerativeAIapplicationscanperformarangeofroutinetasks,suchasthereorganizationandclassificationofdata.Butitistheirabilitytowritetext,composemusic,andcreatedigitalartthathasgarneredheadlinesandpersuadedconsumersandhouseholdstoexperimentontheirown.Asaresult,abroadersetofstakeholdersaregrapplingwithgenerativeAI’simpactonbusinessandsocietybutwithoutmuchcontexttohelpthemmakesenseofit.

4TheeconomicpotentialofgenerativeAI:Thenextproductivityfrontier

Howdidwegethere?Gradually,thenallofasudden

Forthepurposesofthisreport,wedefinegenerativeAIasapplicationstypicallybuiltusingfoundationmodels.Thesemodelscontainexpansiveartificialneuralnetworksinspiredbythebillionsofneuronsconnectedinthehumanbrain.Foundationmodelsarepartofwhatiscalleddeeplearning,atermthatalludestothemanydeeplayerswithinneuralnetworks.DeeplearninghaspoweredmanyoftherecentadvancesinAI,butthefoundationmodelspoweringgenerativeAIapplicationsareastepchangeevolutionwithindeeplearning.Unlikepreviousdeeplearningmodels,theycanprocessextremelylargeandvariedsetsofunstructureddataandperformmorethanonetask.

Foundationmodelshaveenablednewcapabilitiesandvastlyimprovedexistingonesacrossabroadrangeofmodalities,includingimages,video,audio,andcomputercode.AItrainedonthesemodelscanperformseveralfunctions;itcanclassify,edit,summarize,answerquestions,anddraftnewcontent,amongothertasks.

Continuedinnovationwillalsobringnewchallenges.Forexample,thecomputationalpowerrequiredtotraingenerativeAIwithhundredsofbillionsofparametersthreatenstobecomeabottleneckindevelopment.2Further,there’sasignificantmove—spearheadedbytheopen-sourcecommunityandspreadingtotheleadersofgenerativeAIcompaniesthemselves—tomakeAImoreresponsible,whichcouldincreaseitscosts.

Nonetheless,fundingforgenerativeAI,thoughstillafractionoftotalinvestmentsinartificialintelligence,issignificantandgrowingrapidly—reachingatotalof$12billioninthefirstfivemonthsof2023alone.VenturecapitalandotherprivateexternalinvestmentsingenerativeAIincreasedbyanaveragecompoundgrowthrateof74percentannuallyfrom2017to2022.Duringthesameperiod,investmentsinartificialintelligenceoverallroseannuallyby29percent,albeitfromahigherbase.

TherushtothrowmoneyatallthingsgenerativeAIreflectshowquicklyitscapabilitieshavedeveloped.ChatGPTwasreleasedinNovember2022.Fourmonthslater,OpenAIreleasedanewlargelanguagemodel,orLLM,calledGPT-4withmarkedlyimprovedcapabilities.3Similarly,byMay2023,Anthropic’sgenerativeAI,Claude,wasabletoprocess100,000tokensoftext,equaltoabout75,000wordsinaminute—thelengthoftheaveragenovel—comparedwithroughly9,000tokenswhenitwasintroducedinMarch2023.4AndinMay2023,GoogleannouncedseveralnewfeaturespoweredbygenerativeAI,includingSearchGenerativeExperienceandanewLLMcalledPaLM2thatwillpoweritsBardchatbot,amongotherGoogleproducts.5

Fromageographicperspective,externalprivateinvestmentingenerativeAI,mostlyfromtechgiantsandventurecapitalfirms,islargelyconcentratedinNorthAmerica,reflectingthecontinent’scurrentdominationoftheoverallAIinvestmentlandscape.GenerativeAI–relatedcompaniesbasedintheUnitedStatesraisedabout$8billionfrom2020to2022,accountingfor75percentoftotalinvestmentsinsuchcompaniesduringthatperiod.6

GenerativeAIhasstunnedandexcitedtheworldwithitspotentialforreshapinghowknowledgeworkgetsdoneinindustriesandbusinessfunctionsacrosstheentireeconomy.Acrossfunctionssuchassalesandmarketing,customeroperations,andsoftwaredevelopment,itispoisedtotransformrolesandboostperformance.Intheprocess,itcouldunlocktrillionsofdollarsinvalueacrosssectorsfrombankingtolifesciences.WehaveusedtwooverlappinglensesinthisreporttounderstandthepotentialforgenerativeAItocreatevalueforcompaniesandaltertheworkforce.Thefollowingsectionsshareourinitialfindings.

TheeconomicpotentialofgenerativeAI:Thenextproductivityfrontier5

Glossary

Applicationprogramminginterface(API)isawaytoprogrammaticallyaccess(usuallyexternal)models,datasets,orotherpiecesofsoftware.

Artificialintelligence(AI)istheabilityofsoftwaretoperformtasksthattraditionallyrequirehumanintelligence.

Artificialneuralnetworks(ANNs)arecomposedofinterconnectedlayersofsoftware-basedcalculatorsknownas“neurons.”Thesenetworkscanabsorbvastamountsofinputdataandprocessthatdatathroughmultiplelayersthatextractandlearnthedata’sfeatures.

Deeplearningisasubsetofmachinelearningthatusesdeepneuralnetworks,whicharelayersofconnected“neurons”whoseconnectionshaveparametersorweightsthatcanbetrained.Itisespeciallyeffectiveatlearningfromunstructureddatasuchasimages,text,andaudio.

Earlyandlatescenariosaretheextremescenariosofourwork-automationmodel.The“earliest”scenarioflexesallparameterstotheextremesofplausibleassumptions,resultinginfasterautomationdevelopmentandadoption,andthe“latest”scenarioflexesallparametersintheoppositedirection.Therealityislikelytofallsomewherebetweenthetwo.

Fine-tuningistheprocessofadaptingapretrainedfoundationmodeltoperformbetterinaspecifictask.Thisentailsarelativelyshortperiodoftrainingonalabeleddataset,whichismuchsmallerthanthedatasetthemodelwasinitiallytrainedon.Thisadditionaltrainingallowsthemodeltolearnandadapttothenuances,terminology,andspecificpatternsfoundinthesmallerdataset.

Foundationmodels(FM)aredeeplearningmodelstrainedonvastquantitiesofunstructured,unlabeleddatathatcanbeusedforawiderangeoftasksoutoftheboxoradaptedtospecifictasksthroughfine-tuning.ExamplesofthesemodelsareGPT-4,PaLM,DALL·E2,andStableDiffusion.

GenerativeAIisAIthatistypicallybuiltusingfoundationmodelsandhascapabilitiesthatearlierAIdidnothave,suchastheabilitytogeneratecontent.Foundationmodelscanalsobeusedfornongenerativepurposes(forexample,classifyingusersentimentasnegativeorpositivebasedoncalltranscripts)whileofferingsignificantimprovementoverearliermodels.Forsimplicity,whenwerefertogenerativeAIinthisarticle,weincludeallfoundationmodel

usecases.

Graphicsprocessingunits(GPUs)arecomputerchipsthatwereoriginallydevelopedforproducingcomputergraphics(suchasforvideogames)andarealsousefulfordeeplearningapplications.Incontrast,traditionalmachinelearningandotheranalysesusuallyrunoncentralprocessingunits(CPUs),normallyreferredtoasacomputer’s“processor.”

Largelanguagemodels(LLMs)makeupaclassoffoundationmodelsthatcanprocessmassiveamountsofunstructuredtextandlearntherelationshipsbetweenwordsorportionsofwords,knownastokens.ThisenablesLLMstogeneratenatural-languagetext,performingtaskssuchassummarizationorknowledgeextraction.GPT-4(whichunderliesChatGPT)andLaMDA(themodelbehindBard)areexamplesofLLMs.

6TheeconomicpotentialofgenerativeAI:Thenextproductivityfrontier

Machinelearning(ML)isasubsetofAIinwhichamodelgainscapabilitiesafteritistrainedon,orshown,manyexampledatapoints.Machinelearningalgorithmsdetectpatternsandlearnhowtomakepredictionsandrecommendationsbyprocessingdataandexperiences,ratherthanbyreceivingexplicitprogramminginstruction.Thealgorithmsalsoadaptandcanbecomemoreeffectiveinresponsetonewdataandexperiences.

Modalityisahigh-leveldatacategorysuchasnumbers,text,images,video,andaudio.

ProductivityfromlaboristheratioofGDPtototalhoursworkedintheeconomy.Laborproductivitygrowthcomesfromincreasesintheamountofcapitalavailabletoeachworker,theeducationandexperienceoftheworkforce,andimprovementsintechnology.

Promptengineeringreferstotheprocessofdesigning,refining,andoptimizinginputpromptstoguideagenerativeAImodeltowardproducingdesired(thatis,accurate)outputs.

Self-attention,sometimescalledintra-attention,isamechanismthataimstomimiccognitiveattention,relatingdifferentpositionsofasinglesequencetocomputearepresentationofthe

sequence.

Structureddataaretabulardata(forexample,organizedintables,databases,orspreadsheets)thatcanbeusedtotrainsomemachinelearningmodelseffectively.

Transformersarearelativelynewneuralnetworkarchitecturethatreliesonself-attentionmechanismstotransformasequenceofinputsintoasequenceofoutputswhilefocusingitsattentiononimportantpartsofthecontextaroundtheinputs.Transformersdonotrelyonconvolutionsorrecurrentneuralnetworks.

Technicalautomationpotentialreferstotheshareoftheworktimethatcouldbeautomated.Weassessedthetechnicalpotentialforautomationacrosstheglobaleconomythroughananalysisofthecomponentactivitiesofeachoccupation.WeuseddatabasespublishedbyinstitutionsincludingtheWorldBankandtheUSBureauofLaborStatisticstobreakdownabout850occupationsintoapproximately2,100activities,andwedeterminedtheperformancecapabilitiesneededforeachactivitybasedonhowhumanscurrentlyperformthem.

Usecasesaretargetedapplicationstoaspecificbusinesschallengethatproducesoneormoremeasurableoutcomes.Forexample,inmarketing,generativeAIcouldbeusedtogeneratecreativecontentsuchaspersonalizedemails.

Unstructureddatalackaconsistentformatorstructure(forexample,text,images,andaudiofiles)andtypicallyrequiremoreadvancedtechniquestoextractinsights.

TheeconomicpotentialofgenerativeAI:Thenextproductivityfrontier7

2

GenerativeAIuse

casesacrossfunctions

andindustries

GenerativeAIisastepchangeintheevolutionofartificialintelligence.Ascompaniesrushtoadaptandimplementit,understandingthetechnology’spotentialtodelivervaluetotheeconomyandsocietyatlargewillhelpshapecriticaldecisions.WehaveusedtwocomplementarylensestodeterminewheregenerativeAIwithitscurrentcapabilitiescoulddeliverthebiggestvalueandhowbigthatvaluecouldbe(Exhibit1).

8TheeconomicpotentialofgenerativeAI:Thenextproductivityfrontier

TotaleconomicLaborproductivitypotential

potentialof60-plusacross~2,100detailedwork

organizationaluseactivitiesperformedby

cases1globalworkforce

Revenue

impactsof

usecases1

Costimpacts

ofusecases

Exhibit1

ThepotentialimpactofgenerativeAIcanbeevaluatedthroughtwolenses.

Lens1Lens2

1Forquantitativeanalysis,revenueimpactswererecastasproductivityincreasesonthecorrespondingspendinordertomaintaincomparabilitywithcost

impactsandnottoassumeadditionalgrowthinanyparticularmarket.

McKinsey&Company

ThefirstlensscansusecasesforgenerativeAIthatorganizationscouldadopt.Wedefinea“usecase”asatargetedapplicationofgenerativeAItoaspecificbusinesschallenge,resultinginoneormoremeasurableoutcomes.Forexample,ausecaseinmarketingistheapplicationofgenerativeAItogeneratecreativecontentsuchaspersonalizedemails,themeasurableoutcomesofwhichpotentiallyincludereductionsinthecostofgeneratingsuchcontentandincreasesinrevenuefromtheenhancedeffectivenessofhigher-qualitycontentatscale.Weidentified63generativeAIusecasesspanning16businessfunctionsthatcoulddelivertotalvalueintherangeof$2.6trillionto$4.4trillionineconomicbenefitsannuallywhenappliedacrossindustries.

Thatwouldadd15to40percenttothe$11.0trillionto$17.7trillionofeconomicvaluethatwenowestimatenongenerativeartificialintelligenceandanalyticscouldunlock.(Ourpreviousestimatefrom2017wasthatAIcoulddeliver$9.5trillionto$15.4trillionineconomicvalue.)

OursecondlenscomplementsthefirstbyanalyzinggenerativeAI’spotentialimpactontheworkactivitiesrequiredinsome850occupations.WemodeledscenariostoestimatewhengenerativeAIcouldperformeachofmorethan2,100“detailedworkactivities”—suchas“communicatingwithothersaboutoperationalplansoractivities”—thatmakeupthoseoccupationsacrosstheworldeconomy.ThisenablesustoestimatehowthecurrentcapabilitiesofgenerativeAIcouldaffectlaborproductivityacrossallworkcurrentlydonebytheglobalworkforce.

TheeconomicpotentialofgenerativeAI:Thenextproductivityfrontier9

13.6–22.1

2.6–4.4

11.0–17.7

Someofthisimpactwilloverlapwithcostreductionsintheusecaseanalysisdescribedabove,whichweassumearetheresultofimprovedlaborproductivity.Nettingoutthisoverlap,thetotaleconomicbenefitsofgenerativeAI—includingthemajorusecasesweexploredandthemyriadincreasesinproductivitythatarelikelytomaterializewhenthetechnologyisappliedacrossknowledgeworkers’activities—amountsto$6.1trillionto$7.9trillionannually(Exhibit2).

Exhibit2

GenerativeAIcouldcreateadditionalvaluepotentialabovewhatcouldbeunlockedbyotherAIandanalytics.

AI’spotentialimpactontheglobaleconomy,$trillion

17.1–25.6

6.1–7.9

~15–40%

incrementaleconomicimpact

~35–70%

incrementaleconomicimpact

Advancedanalytics,

Newgenerative

Totaluse

Allworkerproductivity

TotalAI

traditionalmachine

AIusecases

case-driven

enabledbygenerative

economic

learning,anddeep

potential

AI,includinginuse

potential

learning1

cases

1Updatedusecaseestimatesfrom"NotesfromtheAIfrontier:Applicationsandvalueofdeeplearning,”McKinseyGlobalInstitute,April17,2018.

McKinsey&Company

10TheeconomicpotentialofgenerativeAI:Thenextproductivityfrontier

WhilegenerativeAIisanexcitingandrapidlyadvancingtechnology,theotherapplicationsofAIdiscussedinourpreviousreportcontinuetoaccountforthemajorityoftheoverallpotentialvalueofAI.Traditionaladvanced-analyticsandmachinelearningalgorithmsarehighlyeffectiveatperformingnumericalandoptimizationtaskssuchaspredictivemodeling,andtheycontinuetofindnewapplicationsinawiderangeofindustries.However,asgenerativeAIcontinuestodevelopandmature,ithasthepotentialtoopenwhollynewfrontiersincreativityandinnovation.IthasalreadyexpandedthepossibilitiesofwhatAIoverallcanachieve(pleaseseeBox1,“HowweestimatedthevaluepotentialofgenerativeAIusecases”).

Box1

HowweestimatedthevaluepotentialofgenerativeAIusecases

ToassessthepotentialvalueofgenerativeAI,acustomerserviceusecasebutnotinause

weupdatedaproprietaryMcKinseydatabaseofcaseoptimizingalogisticsnetwork,wherevalue

potentialAIusecasesanddrewontheexperienceprimarilyarisesfromquantitativeanalysis.

ofmorethan100expertsinindustriesandtheir

ofthesegenerativeAIusecasesiftheywere

generativeAItechniques(primarilytransformer-

technologies.

Weanalyzedonlyusecasesforwhichgenerativemarketingexpenditures.

AIcoulddeliverasignificantimprovementinthe

estimatesoftheprimaryvaluethetechnology

couldunlockdonotincludeusecasesforwhich

language.Forexample,natural-language

capabilitieswouldbethekeydriverofvaluein

1“NotesfromtheAIfrontier:Applicationsandvalueofdeeplearning,”McKinseyGlobalInstitute,April17,2018.

problemsnotwelladdressedbyprevious

deliverbyincreasingtheproductivityofsalesand

Ourestimatesarebasedonthestructureofthe

basedneuralnetworks)canbeusedtosolvecasesaimedatincreasingrevenue,suchassome

outputsthatdrivekeyvalue.Inparticular,ourglobaleconomyin2022anddonotconsiderthe

businessfunctions.1OurupdatesexaminedWethenestimatedthepotentialannualvalue

usecasesofgenerativeAI—specifically,howadoptedacrosstheentireeconomy.Foruse

thesolebenefitwouldbeitsabilitytousenaturalentirelynewproductorservicecategories.

valuegenerativeAIcouldcreateifitproduced

Inthischapter,wehighlightthevaluepotentialofgenerativeAIacrosstwodimensions:businessfunctionandmodality.

TheeconomicpotentialofgenerativeAI:Thenextproductivityfrontier11

500

400

300

Impact,$billion

200

100

0

Valuepotentialbyfunction

WhilegenerativeAIcouldhaveanimpactonmostbusinessfunctions,afewstandoutwhenmeasuredbythetechnology’simpactasashareoffunctionalcost(Exhibit3).Ouranalysisof16businessfunctionsidentifiedjustfour—customeroperations,marketingandsales,softwareengineering,andresearchanddevelopment—thatcouldaccountforapproximately75percentofthetotalannualvaluefromgenerativeAIusecases.

Exhibit3

UsinggenerativeAIinjustafewfunctionscoulddrivemostofthetechnology’simpactacrosspotentialcorporateusecases.

Represent~75%oftotalannualimpactofgenerativeAI

Sales

Marketing

Softwareengineering

Software

(forproductdevelopment)

(forcorporateIT)engineering

Customeroperations

ProductandR&D1

Supplychain

Manufacturing

FinanceRiskandcompliance

Talentandorganization(inclHR)

Procurementmanagement

CorporateIT1Legal

Strategy

Pricing

0

10

20

40

30

Impactasapercentageoffunctionalspend,%

Note:Impactisaveraged.

¹Excludingsoftwareengineering.

Source:ComparativeIndustryService(CIS),IHSMarkit;OxfordEconomics;McKinseyCorporateandBusinessFunctionsdatabase;McKinseyManufacturingandSupplyChain360;McKinseySalesNavigator;Ignite,aMcKinseydatabase;McKinseyanalysis

McKinsey&Company

Notably,thepotentialvalueofusinggenerativeAIforseveralfunctionsthatwereprominentinourprevioussizingofAIusecases,includingmanufacturingandsupplychainfunctions,isnowmuchlower.7ThisislargelyexplainedbythenatureofgenerativeAIusecases,whichexcludemostofthenumericalandoptimizationapplicationsthatwerethemainvaluedriversforpreviousapplicationsofAI.

12TheeconomicpotentialofgenerativeAI:Thenextproductivityfrontier

GenerativeAIasavirtualexpert

InadditiontothepotentialvaluegenerativeAIcandeliverinfunction-specificusecases,thetechnologycoulddrivevalueacrossanentireorganizationbyrevolutionizinginternalknowledgemanagementsystems.GenerativeAI’simpressivecommandofnatural-languageprocessingcanhelpemployeesretrievestoredinternalknowledgebyformulatingqueriesinthesamewaytheymightaskahumanaquestionandengageincontin