麦肯锡 -人工智能如何变革汽车行业质量管控工作 How AI is transforming automotive quality operations

November2026

McKinsey

&Company

Automotive&AssemblyPractice

HowAIistransformingautomotivequality

operations

YoavLevy,cofounderandCEOofUpstream,explainshowAlcanreducewarrantyandrecallcoststhroughearlyissuedetection,accelerated

investigations,andtargetedcountermeasures.

Forautomakers,qualityproblemscanquicklyescalate.Adefectthatgoesundetectedortakestoolongtodiagnosecanleadtounnecessaryrepairs,broadrecalls,disgruntled

customers,andmountingwarrantycosts.Inmanycases,OEMsmaynotbecomeawareofqualityissuesuntiltheyaredelugedwithsimilarwarrantyclaims,bywhichpointit’sveryexpensivetocontaintheproblem.

AIcanhelpautomakersescapethistrapbymonitoringconnected-vehicledataandsurfacingissuesbeforetheybecomecostly,widespreadfailures.Itcanalsobringgreaterspeedand

precisiontoroot-causeanalysis(RCA)andseverityandscaleestimations,allowingOEMstofocustheirresponseonthevehiclesthatactuallyneedattention.Theresultislowerwarrantycosts,improveduptime,andlessdisruptionforcustomers.

Upstream,abehavioral-intelligenceplatformforautomotiveandphysicalAI,isputtingthese

capabilitiesintopracticewithProactiveQualityDetection(PQD),itsAI-powered,cloud-based,qualitymonitoringanddetectionplatform.ThissystemhasalreadyhelpedmanyOEMsreducewarrantycosts—inonecase,by98.5percent.

McKinsey’sEikeReus,apartnerintheBerlinoffice,recentlyspokewithUpstreamCofounderandCEOYoavLevyabouthowAIischangingthewayautomakersapproachafter-salesquality,andwhatittakestoturnthegrowingvolumeofconnected-vehicledataintofaster,more

effectivedecisions.

Catchitbeforeitcostsyou

EikeReus:WhatisthemostsignificanteffectthatAIhashadonqualitymanagementandwarranties?

YoavLevy:AIchangedwhat“early”means.Earlydetectionusedtomeancatchingaproblemafterafewhundredclaimsinsteadofafewthousand.Itwasstillareactiveresponse,justlessdelayed.Nowyoucatchitbeforeasingleclaimisfiled,fromsignalsalone.

Butdetectionwasnevertheonlybottleneck.Investigationusedtotakeweeksasengineers

piecedtogetherevidencebyhand.Now,thatinformationgetscompiledautomaticallybyAI.

OEMresponsesusedtodefaulttorecalls,whichweretimeconsuming,butnowtheyresolveagrowingshareofproblemsovertheair[OTA]thenmonitorthefleettoconfirmthattheissuesdon’tresurface.

Qualitycontrolisasingleloopnow:Detect,investigate,resolve—allaheadoftheclaiminsteadofafterit.Oncethatloopcloses,thewholeeconomicsofwarranty,howmuchyouspend,howwidearecallhastobe,theimpactonvehicledowntime,howmuchtrustyoupreserve,also

improve.

EikeReus:HowmuchcanAI-poweredsolutionshelpcompaniesaccelerateRCAandreduce

HowAIistransformingautomotivequalityoperations2

HowAIistransformingautomotivequalityoperations3

warrantyandrecallcosts?

YoavLevy:Inourexperience,RCAcanhappenroughly30to90percentfaster,andwarranty

andrecallcostscandropby5to20percent.Themainimprovementdriversarevelocityand

scale:cuttingtimetoRCAandtimetocountermeasure,usingmachinelearning[ML]toprioritizeproblemsandpreciselyidentifytheaffectedvehiclessothatyou’renotreplacingparts

unnecessarilyorrunningcampaignsthataretoobroad,andmakingmoreOTAupdatesratherthanpullingvehiclesintoservicebays.

OnemorethingworthsayingtoaCFO:Almostalloftheimprovementrelatestocostavoidanceratherthanhardsavings.Sincecompaniespreventthecostfrommaterializing,weexpect

significantreductioninwarrantyandrecallcosts.

Anewsolutionforautomotivequality

EikeReus:HowdoesyourPQDplatformenableearlyproblemdetection?Whatsignalsdoesitreadacrossafleet?Andhowdoesitseparateagenuineemergingdefectfromordinarynoiseacrossmillionsofvehicles?

YoavLevy:PQDispoweredbyOceanAI,Upstream’ssuiteofML,AI,andagenticworkflows.Itisspecificallytrainedonautomotivequalitydata.Itmonitorsvehiclebehavioratthevehicle,

component,andfleetlevels,lookingattelematics,diagnostics,andqualitydata.Italso

determineshowdiagnostictroublecodes[DTCs]correlatewithtelemetry,repairorders,

customersupport,andevensocialmedia,ratherthanrelyingonanysinglesignal.Thesystemiscloud-basedandagentless,withnohardwareinstalledinthevehicle.

Theplatformusestwodetectionapproachesthatworkintandem.Oneidentifiesknownfailurepatterns,andtheotherusesMLmodelstocatchanomaliesthatengineershavenotyetdefined.Resultsfrombothanalysesfeedintothesamepipelinewellbeforearepairorderorclaimexists.

PQDfindstrueproblemsbylookingforpatterns.Afaultcodefromasinglevehiclemightmeannothing,butapatterninagroupofvehicles,checkedagainstasimilarcontrolgroupwithno

issues,isadifferentstory.Throughpatterns,wecanfindarootcausegroundedinreal

evidence,ratherthanmakingaguess.Qualityissueswithnoexistingfaultcodesaremoredifficult;wehavetosurfaceproblemswhenthereisnoprogramorpotentialvisibility.

Thesystemclustersemergingpatternsbeforetheclaimandranksthembyscale,severity,cost,andwhetherafixcangoovertheair.Itdoesn’tmakethefinalcall;itputsevidenceinfrontofanengineer,whodecidesonitssignificance.Ratherthanneedingmonthsofaccumulatingclaimsdatatoidentifyproblems,wecansurfacethembeforethefirstclaimisfiled.

EikeReus:Whatsortofqualityissueshasyoursystemcaughtearly?

HowAIistransformingautomotivequalityoperations4

YoavLevy:I’llgiveyouthreeexamples,andtheyprovethreedifferentthings.Inonecase,therewasahigh-voltagebatterycellimbalanceonanewlyreleasedmodel.TheOEMidentifiedthe

rootcausequickly,butmorethan10,000vehicleshadalreadyshippedbythetimeacountermeasurewasready.

Thissituationwouldnormallyrequireablanketrecall,withbatteryreplacementsrunningover

$10,000pervehicle.Instead,theOEM’sownqualityteambuiltcustomtelematics-based

detectorsonourPQDplatform,anditdeterminedthatonlyafewdozenvehiclesweredefectiveandneededafix.Thatrepresenteda98.5percentreductioninwarrantycosts,resultingin$150millionto$200millioninsavings.SincetheOEM’sengineersbuiltthemodel,itwillalwaysbe

availableforfutureanalysis.

Thesecondexamplerelatestoafuelpumpdrawingtoomuchcurrentfromdebrisinthe

assembly.Normally,itwouldtakearound45daystoconnectscatteredcomplaintsaboutsuch

problemstoarootcause.Inthiscase,ourPQDplatformflaggedtheissueearlyandcorrelateditwithanotherexistinginvestigation.TheOEMfoundtherootcauseineightdays.That’sagood

exampleofresponsevelocity.

Finally,therewasacasewhenanodometerspikedandnevercamebackdown,eitherbecauseoftamperingorasensorfault.Normally,youwouldn’tfindoutaboutanissuelikethatuntila

buybackclaim.Wecaughttheproblemwithin24hours,beforeanyonehadcreatedaprogramtodetectit.Theissuewasn’tonanywatchlists;itwasjustbehaviorthatdidn’tmakesense,andourPQDplatformisbuilttonoticethat.

Thecommonthreadthatunitestheseexamplesisthatnoneofourcustomersstillwaitforclaimstopileupbeforetheyact.I’mveryproudofthat.

TransformingtheOEMqualityculture

EikeReus:WhenusingAItoshiftfromareactivequalityoperationtoaproactiveone,whatarethebiggestchangesanorganizationanditsqualityteamsmustmake?

YoavLevy:Thebiggestchangeisbehavioral,nottechnological.Qualityteamshavelongreliedoninstitutionalmemory,oftenintheformofaseniorengineerwho’sseenapatternbefore.Thatworkswelluntilthatpersonretires,andtheirknowledgegoeswiththem.Movingtoaproactivequalityapproachmeansaskingthousandsofpeopletostarttheirdaydifferently.Theywillopenabriefingbuiltfromlivesignalsinsteadofaclaimsreport.Thathabitchangetakeslongerthanimplementinganymodel.Therulethatmakesitstick:AIproposes,theengineerdecides.

Alotofuntappedvaluesitsinunstructureddata,techniciannotes,repairorders,andcall

transcripts,butmostOEMscan’tuseanyofitatscale.WithUpstream’sOceanAI,oneagent

replaysacar’sfullhistory,anothersurfacespatternsacrossacohort,andathirdpulls

multivehicleevidenceintoasinglehypothesisinsteadofhavinganengineerstitchittogetherby

HowAIistransformingautomotivequalityoperations5

hand.Engineerscanstillaskquestions,buttheheavyliftinghappenedbeforetheyloggedin.OceanAInevermakesthefinalcallabouthowtoresolveaproblem,andthat’sexactlywhy

engineerstrustitenoughtouseit.

EikeReus:Ourresearchfoundthat85percentofOEMsarestillintheearlyorintermediate

stagesofconsolidatingqualitydata,andonly18percentuseAIinproductiontopredictissues.Whenyoubeginworkingwithacompany,howmuchoftheworkinvolvesanalyticsandhow

muchinvolvesfixingtheunderlyingdataandgettingfunctionsandsupplierstoshareit?

YoavLevy:Withanewcustomer,ourworkis80percentfoundationaland20percentanalyticalinthefirstfewmonths.Thattrackswithwhatyourresearchfound.Therefore,thefirstphase

typicallyinvolvesbringingexistingtelematics,DTCs,repairorders,andclaimstogether.Wedon’tforceorganizationstorebuildtheirsystemsbutratherbuildontopofexisting

technologies.

Duringtheearlyphase,sortingoutwhoownswhatdatainvolvesalongerconversationthan

anythingrelatedtomodels.Dataisfragmentedbecauseownershipisfragmented.Nobodybuiltthreeincompatiblesystemsonpurpose,butthreefunctionseachbuiltasystem,focusingonthemetricsthatweremostimportanttoit.

Toolingisalsooftenachallenge.Mostorganizationsarerunninghomegrown,siloedsystemsthatdon’ttalktoeachother.Weconnectintoexistingdatabasessothatteamsdon’thavetocheckfiveseparatesystems.

MyadviceforbeginninganAIinitiativeisalittlecounterintuitive:Don’tsettletheownership

questionfirst.Thedataalmostalwaysexists;youneedtobringittogether,evenimperfectly,andpickoneproblemtosolve.I’vewatchedasingleresultdomoretosettleanownershipdispute

thansixmonthsofmeetingsevercould.

EikeReus:Three-quartersofOEMssaytheyareexperimentingwithAIforquality,butonly

aboutoneintenhasasolutiontheyconsidersatisfactory.WhatseparatesthecompaniesthatscaleAIqualitymeasuresacrossproductionfromthosestuckinpilots?

YoavLevy:Atcompaniesthatscalequalityefforts,somebodyownsthedecision,notthemodel;pilotsarescoredonwhethertheirpredictionswereright,andproductionisscoredonwhetheradecisionchanged.Thefirstusecasegetschosenbasedonitsabilityforreplication,notjust

impact.

SuccessfulorganizationscanalsotelliftheirAItoolsgetsomethingwrongduringaparticularquarterandthenmakeadjustmentstostrengthentrust.Teamsthataren’tabletoseeatool’sshortcomingswon’ttrustitandwillworkaroundit.

Companiesthatwanttoscalemustwatchoutforapatternthatdeceptivelylookslikeprogress:astrongpilotfromacentraldatateam,builtwithoutinputfromthequalityorganizationthat

HowAIistransformingautomotivequalityoperations6

wouldactuallyuseit.Thepilotmayproduceimpressiveinitialresults,butitwillnotpromote

adoption,anditdoesn’tprovethatthenewprocesscanscale.Overall,thebiggestreasonpilotsfailisthatthey’renotwiredintoanengineer’sexistingworkflow.Apilotmayyieldausefullistofanomalies,butifthatlistsimplyaddstotheworkloadofanengineerwhoalreadyhasafull

queue,nothingwillchange.

EikeReus:Whatproblemsarecompaniesencounteringwhentheyscaletheirqualitysystemstocoverentirefleets?Whatarethekeystosuccess?

YoavLevy:Companiesaregenerallygoinginthedirectionofusingagentic,closed-loopqualitysystemsthatdetectproblems,scopeexactlywhichvehiclesareaffected,investigatetheissues,andtriggerafix,allwithlimitedhumaninterventionateachstep.Applyingthesesystemsatfleetscalerequiresacoherentdataenvironment,modelspeopletrust,cleardecisionrights,and

humanoversightonanythingsafety-critical.

DecisionrightsisthefactorthattendstogetunderestimatedandthatismostdifficultforOEMstoaddress.Atpilotscale,ambiguityaboutwhosignsoffissurvivable:Theremaybeten

decisionsamonth,andeveryonerelevantisintheroom.Atfleetscale,therearethousandsofpeopleinvolved—andambiguitydoesn’tjustslowthingsdown,itstopsthem.

Focusisalsoessential.Weprioritizequalityissuesbypredictingtheirscaleandseveritybeforecommittingresources.Teamscanthusbemoreconfidentthattheireffortswillhaveanimpactinsteadofchasingeveryanomalythesameway.

Andweincreasetheoddsofscalingbybuildingqualitysystemswithalllayersinplace:data,a

livedigitaltwin,AI,andagenticexecution.Whileeveryonewantsagenticexecution,itonlyworksifthethreelayersunderneathitarereal.Andwhenselectingourinitialqualityfocus,webegin

whereverimpactwillbemostvisibletocreatemomentum.

Lessonslearnedandboldpredictions

EikeReus:What’sthehardestlessonyou’velearnedaboutgettingaconservative,safety-firstindustrytoadoptagenuinelynewwayofworking?

YoavLevy:Automotiveisrightlyasafety-firstindustrythatadoptsnewmethodsbasedon

proof,notpromises.Inonecase,PQDfoundsomethingthataverygoodteamhadmissed,

basedontheirowndata.Afterthat,thevaluewasclear.Thatexperiencetaughtustostartwithasinglelighthousewinandlettheresultpersuade.

WebuiltthePQDplatformforautomotivebecausethissectorfacesmultiplequalityissues.Therearemillionsofconnectedvehicles,andtheindustryisbothsafety-criticalandheavilyregulated.Butthesamedetect-and-correlatecapabilityappliestoanyconnectedphysicalsystem,whichiswherewe’reheadednext.

HowAIistransformingautomotivequalityoperations7

EikeReus:Asvehiclesbecomesoftware-andAI-definedandgetupdatedorrepairedthroughOTA,howwilltheeconomicsofvehiclequalitychange?Doesadefectbecomecheapertofix,ordoesitsimplygetaddressedearlier?

YoavLevy:Both.WithOTA,ameaningfulshareoffixesinvolveasoftwareupdateratherthanaphysicalrecall,whichlowerscosts.Butsoftwarecomplexitycreatesitsownfailuremodes,andabadupdatepropagatesacrossafleetinhoursataspeednotpossibleinthephysicalworld.Theriskaccelerates.

WithOTA,afixmaycostafewdollarsinsteadofafewhundreddollars.Whenthathappens,

OEMsmightstartfixingthingsthattheywouldhavejustmonitoredbefore.Butcanyoutell

preciselywhichvehiclesneedrepairs?That’sadetectionproblem,whichiswhyOTAraisesthevalueofdetection.

EikeReus:Ifproactivequalitymeasuresworkasyouenvision,wherewilltheautomotiveindustrybeinfiveyears,andwhatroutineprocesseswilldisappear?

YoavLevy:Dat