mckinsey -2026把握中欧人工智能发展机遇研究报告 Capturing Central Europes AI opportunity

June2026

CapturingCentral

Europe’sAIopportunity

AIisadvancingtooquicklytoremainasideinitiativeforbusinesses.CentralEuropeancompaniescangetaheadbyredesigningbusinessdomains—andtheorganization—aroundAI.

byBalázsCzímerandLievenVanderVekenwithMatyášZetek

1“

ThestateofAIin2025:Agents,innovation,andtransformation

,”McKinsey,November2025.

2ArtificialAnalysisIntelligenceIndex,ArtificialAnalysis,accessedApril15,2026.

CapturingCentralEurope’sAIopportunity2

Artificialintelligencehasbecomethemostpressingtopicinboardrooms.Companiesare

learninghowtoquicklyredesignworkflowstoincludeAIandconverttechnicalprogressintogreatereconomicvalue.

InCentralEurope,thestakesareparticularlyhigh.AIcouldhelpuncover€280billionto€700

billionineconomicvalueacrosstheregion,equivalentto6to15percentoftheregion’stotalnetturnover.Theurgencyissharpenedbytworealities.First,adoptioniswidespread,butimpactisnot:88percentofcompaniesgloballyhavedeployedAIinatleastonefunction,but94percenthavenotachievedasignificantimpactonEBIT.1Alonglistofpilotsmaysignalthatcompanies

aremakingprogress,butthesesmalleffortsrarelychangeend-to-endperformance.Second,

CentralEuropetrailsWesternEuropeinenterpriseAIadoptionby16percent,andapproximately60percentofCentralEurope’seconomyistiedtosectorswherescalingAIisthemostdifficult.

MuchofthevalueofAIwillcomefromembeddingAIintophysicaloperationsratherthan

layeringitontodigitalchannels.Inindustriessuchasmanufacturing,engineeringand

construction,consumergoods,andretail,AIcanoptimizeproductionplanningtoimprove

factoryutilization,refineinputmaterialstoreduceyieldloss,andincreasesalesconversionratesthroughfaster,morepersonalizedinteractions.Companiescangainadvantagesbychanging

howfactories,projects,servicecenters,andcommercialfunctionsrun,ratherthansimplyusingAIasanaugmentingtool.

CentralEuropeanleadersmustcontendwithabigquestion:Willtheregion’scoreindustriesbereshapedbythecompaniesthatoperatethemtodayorbycompetitorsthatmovedfaster?BybeingintentionalaboutAIadoption,CentralEuropecancatchuptootherpartsofthe

world—andintegratethesetoolsinawaythatcapturesmaximumvaluerightaway.

ThecompoundingadvantageofAI

AIadvancementshaveoutpacedtraditionalplanningcycles.Between2019and2022,leading

largelanguagemodelsfromOpenAI,Google,andAnthropicadvancedatroughlytwoindex

pointsperyearontheArtificialAnalysisIntelligenceIndex,acompositebenchmarkspanning

reasoning,knowledge,mathematics,andprogramming.2From2024onward,gainshave

exceeded20pointsperyearandkeepaccelerating—atenfoldincreasesincetheintroductionofChatGPTinlate2022.Overallcapabilityisnowdoublingaboutevery12months(Exhibit1).

ThepaceatwhichAIhasbeenadvancingoutstripstypicalbusinesscycles.Strategy,capital

allocation,andoperatingmodelredesignsareusuallyplannedovermultipleyears.Whencoretechnologyimprovesmateriallywithinasingleyear,assumptionsembeddedinthoseplanscanloserelevancequickly,andthecompetitiveconsequencescompound.

EarlymoversinAIadoptionlearnwheremodelsfailandwheretheycanbetrusted,build

proprietarydataflowstotrainmodels,redesigndecisionsaroundhuman–machineinteractions,anddevelopinstitutionalroutinestorapidlyadjustworkflows.Delayingadoptionforthesakeof

CapturingCentralEurope’sAIopportunity3

Exhibit1

CapturingCentralEurope’sAIopportunity4

optionalitywillonlysetcompaniesback:Theywilllearnmoreslowlythancompetitorswhilecapabilitycontinuestoimprove,wideningnotonlyatechnologygapbutalsocapability

andtalentgaps.ThegoalnowshouldbetobuildoperatingmodelsthatcontinuouslycaptureAIvalue.

Ashiftinthenatureofhumanwork

Asrecentlyas2019,AIwaseffectiveonlyatnarrow,well-definedtasks,suchasimage

classification,basiclanguageprocessing,translation,andspeechrecognition.Itfellshortin

multitaskunderstanding,advancedmathematics,andcross-modalreasoning.Withinsixyears,thoseboundarieshaddissolved.3Tasksthatpreviouslyrequiredspecializedhumanexpertise,suchasroutineanalysis,documentprocessing,andstructureddecision-making,arenowwithinreachforAIsystems,shrinkingthesetofactivitiesinwhichhumaninvolvementisessential.

Forwhite-collarfunctions—forexample,finance,legal,procurement,andcustomer

service—specializedAIagentswillhandlethebulkofstructuredworkwhilepeople’srolesshifttohandleorchestration,judgment,andexceptionmanagement.Ineffect,asmallernumberofpeoplecanproducesubstantiallybetteroutcomes.Thispatternmirrorswhathappenedin

radiologyaround2016,whencomputervisionbecameabletoprovidequalitydiagnostics.

Smallerteams,supportedbyAI,couldreadmorescanswithgreateraccuracyandfaster

turnaroundtimesthanlargerteamsdidwithoutthistechnology.Thesamedynamicisemergingacrossknowledgeindustries:Leanerteamsareachievinghigherthroughputwithfewererrors.

Thenextpracticalstepistheagenticenterprise.Ratherthanoperatingasstand-alone

assistants,AIsystemsaremovingintohuman-supervisedworkflowsasspecializedagents.Forexample,inbanking,groupsofagentscollaborateinsquads.Onesquadhandlesdocument

ingestionandinsightextraction,whileanothergeneratescreditmemosdrawingonfinancial,

sector,income,collateral,andtransactionanalysis.Othersquadsmanagedocumentchecking,customercontractcreation,policyandcompliancevalidation,internalorchestration,andclientcommunications.Humancreditmanagersandworkflowspecialistsoverseetheflowand

intervenewherejudgmentisrequired(Exhibit2).

AdoptingAItoolsisnotthesameaschanginghowworkgetsdone.Insoftware

development—oneofthemostdigitallyadvancedfields—ourexperienceshowsthat

approximately90percentofdevelopersnowuseAIcodingtools,butonly20to30percentofthattotalhavechangedhowtheyworkasaresult.Thismeansthatoverallproductivity

improvementhasbeenlessthan15percent,signalingshortcomingsinimplementationratherthaninthetechnologyitself.

Movingfrompilotstosystematicintegration

MostorganizationsdeployAIinfragmentedwaysthatdon’tsupportend-to-endeconomics.

Mostcompanies,88percent,havedeployedAIinatleastonefunction,but94percenthavenotachievedafullenterprise-scaledeploymentthathassignificantimpactonEBIT.4Leaders

reportearlyqualitativebenefitsininnovation,customersatisfaction,andcompetitivedifferentiation,buttheirincomestatementsbarelymove(Exhibit3).

3ArtificialIntelligenceIndexreport2025,StanfordUniversityHuman-CenteredArtificialIntelligence,April2025.

4“

ThestateofAIin2025:Agents,innovation,andtransformation

,”McKinsey,November2025.

CapturingCentralEurope’sAIopportunity5

Exhibit2

Closingthegapbetweendeploymentandprofitrequirestwomoves:settingthecorrectscopeandrewiringtheorganization.StartingwithAIadoptionindomainsoftenprovidesthecorrectscope.Businessdomainssuchassales,customeroperations,supplychain,engineering,or

claimsarelargeenoughtomatterfinancially,coherentenoughtoredesignendtoend,andcancompleteafullAIintegrationwithinsixmonths.

Fromthere,rewiringwaysofworkingisessentialtoupholdadoption.Transformingadomain

meansredesigningprocessesendtoend,integratingagenticAIintocoreactivities,andaligning

Exhibit3

CapturingCentralEurope’sAIopportunity6

incentivestowardmeasurableoutcomes.LeadersshouldsetaclearvisionforhowthedomainwilloperatewithAIandcommittotransformingitcomprehensively.Theseconditionscreate

tangiblefinancialimpact,andthisapproachismateriallydifferentfromlaunchingaportfolioofdisconnectedpilots.

Amorethan€280billionprize—andthedistinctivepathtocaptureit

AccordingtoMcKinseyanalysis,AIcouldunlockmorethan€700billionofvalueacrossthe

region(withmorethan€280billionattributabletoautomation)byimprovinghowworkisdone,includingbystreamliningprocesses,raisingproductivity,andacceleratingthedigitalizationof

CapturingCentralEurope’sAIopportunity7

coreoperations.IndustrieswillfindvaluefromAIintwomainways.First,AIautomates,

augments,androbotizesexistingwork,whichreducestheeffortandcostrequiredforcurrent

operations.Inthisway,industriescouldimprovevalueby6to9percent.Second,AIenables

betterproducts,higheroutput,fasterinnovation,personalization,newservices,andbusiness-

modelupsidebeyondefficiency.Thesecapabilitiescouldimprovevalueby3to6percent.UsingAIinbothwaysisessentialtoreapthehighestpossiblevalue.Thelargestpotentialgainsareinadvancedmanufacturing,consumergoodsandretail,andtechnology,withfinancialservices,

engineeringandconstruction,energy,andlogisticsalsocontributingmaterially(Exhibit4).

ThepotentialvaluefromAIdoesnotsitinanarrowdigitalenclave;itisspreadacrossthe

sectorsthatalreadyshapeCentralEurope’sproductivecore.Atthesametime,sectorsthatarecomparativelysmallerintheregion’seconomicstructurealsoexhibithighAIintensity.The

technologysector,forexample,accountsforroughly10percentoftotalAIvaluepotential,indicatingthatgainswillextendbeyondthemostdominantindustries.

Theindustrydetermineshowquicklyitcanreapvalue

ThespeedatwhichindustriescanscaleAIvarieswidely.Digitallymaturesectors,suchas

technology,media,andtelecommunications,willmovefastest,benefitingfromricherdata,

highermargins,andgreaterstandardization.Asset-heavy,operationallycomplexsectors,suchasmanufacturing,construction,andconsumergoods,willscalemoregradually.

CentralEuropeisdisproportionatelyexposedtothelatter.Approximately60percentofthe

region’snetturnoverisinindustrieswherejust17to18percentofAIadoptionisatthescalingphase.Thesearenotmarginalindustries—theyemploythemostpeople,generatethemost

exportrevenue,anddefinetheregion’scompetitiveposition(Exhibit5).

Closingthisgaprequiresstrongerdigitalfoundations,clearprioritizationofhigh-valueusecases,andtargetedcapabilitybuilding.

WhereoperationalAIprovidesthemostvalue

ITandknowledgemanagementindustriesareleadingadoptionglobally,withtechnology,mediaandtelecommunications,andhealthcareandpharmaceuticalsscalingsolutionsfastest.Theseindustrieshavedigitallymatureoperations,richstructureddata,andservice-heavyrevenue

modelsthattranslateeasilyintoagent-readyworkflows.Technologyindustriesareleadersinadoption,withmediaandtelecommunicationsandhealthcarefollowingcloselybehind.ThesesectorsbuiltintegrateddataplatformsandstandardizedprocesseslongbeforegenerativeAIgainedtraction,givingthemafoundationtodeployverticalagentsfasterandcheaperand

makingthesetoolseasiertogovern.

Basedonourexperience,thedomainsinwhichAIcanprovidethemostvalueareIT,knowledgemanagement,marketingandsales,serviceoperations,softwareengineering,andproduct

development.Thesefunctionssharestructuredworkflowsandhavewell-codifieddata,whichprovidesvisibilityintotheconnectionbetweenagentactionandproductivitygain.

Exhibit4

CapturingCentralEurope’sAIopportunity8

CapturingCentralEurope’sAIopportunity9

Exhibit5

ThemostvaluableusecasesforthesedomainsareagenticservicedesksinIT,deep-researchagentsinknowledgemanagement,codegenerationinsoftwareengineering,contentand

campaignpersonalizationinmarketing,andend-to-endcontact-centerautomationinserviceoperations.Basedonourexperiencewithclients,softwareengineeringalonecanachievecostreductionsof10to20percent.Acrosstheseusecases,impactcomesfromembeddingagentsdirectlyintotheworkflow,ratherthanlayeringthemontopoftraditionalwaysofworking.

CapturingCentralEurope’sAIopportunity10

MaximizingthevaluefromintegratingAIintoCentralEurope’sdominantindustriesrequiresa

distinctapproach:ItmustmeetindustrieswheretheyareandensurethatoperationalAI

addressesthespecificcomplexitiesofmanufacturingfloors,constructionsites,andretailsupplychains.

Threemovesthatdistinguishleaders

Bynow,weknowAIworks.Nowthequestionis,“Wherecanitbescaledfastest?”Leadersfollowthreedistinctsteps:IdentifythelargestAIopportunities,buildworkflow-embeddedsolutionstiedtomeasurableoutcomes,andredesigntheenterprisearoundscale.

Moveone:IdentifythelargestAIopportunities

AtthestartoftheAIjourney,thehardestworkisstrategic.Leadersmusttakeanenterprise-

wideviewtoidentifyasmallnumberoftransformativebets—areasinwhichAIcanredesign

end-to-endprocesses,sharpendecisionquality,andmateriallymovetheneedleongrowth,

cost,risk,orcustomerexperience.Thatdiscipline,appliedearly,iswhatseparatesorganizationsthatscaleAIsuccessfullyfromorganizationsthatsimplyaccumulatepilots(seesidebar“Two

casestudies:Identifyingopportunities”).

Twocasestudies:Identifyingopportunities

AssessingAIopportunitiesindifferentcontextsallowscompaniestocommittoinitiativeswithouttakingonmorethantheycanhandle.Here’showitworkedfortwocompanies:

—AbankinCentralEuropesettheambitiontobecometheregion’sfirstfullyagenticbank.Our

diagnostictoolidentified14coredomainsinwhichperformancecouldbesignificantlyimprovedwithAI.Theseareasalsohadthepotentialtogrowrevenuesby30to40percentandoptimizecostsby15to30percent.Aftermorethan50managementsessions,wetranslatedthesegoalsintoaprioritizedtransformationroadmapbasedonthepotentialimpact.

—Wefoundmorethan250genAIusecasesacrossaEuropeanmediaandtelecommunicationscompany,representing£1.4billioninfive-yearnetpresentvalue.Realmomentumcameaftertheseopportunitieswereassessedforeconomicvalue,technicalfeasibility,andrisk.Themostviablecaseswereincludedinarealisticroadmap.

CapturingCentralEurope’sAIopportunity11

MostorganizationslackthevisibilitytochooseAIopportunitieswell.Ideasbubbleupfrom

technologyenthusiasts,usecasesgounevaluated,andleadersarelefttocompare

opportunitieswithoutasharedfactbasetoguidetheconversation.Companiesroutinely

misjudgetheirownreadiness,overestimatingwhattheirdatainfrastructurecansupportor

underestimatingtheorganizationalliftrequiredtooperationalizeAIatscale.Moreover,the

relentlesspaceofAIdevelopmentmakesitdifficulttodistinguishwhatispossibletodayfromwhatwillbefeasibleineventwoyears.

Breakingthroughthenoiserequiresstrategicclarityatthetopoftheorganization.Thatmeansgivingseniorleadersagrounded,example-richviewofwhatAIcanrealisticallydeliver.Itmeanscreatingacommonlanguageacrossbusiness,technology,andriskstakeholderssothatuse

caseprioritizationisasharedactratherthanamandatefromIT.Anditmeansrigorously

evaluatingboththeimpactandthefeasibilityofeachopportunity,sotheorganizationemergeswithafocusedpipelineitcanexecute,ratherthanawishlistthatstallsbeforeitstarts.

Movetwo:BuildAIsolutions

Inthebuildphase,thehardestworkisinexecution.LeadersmustdeliverAIatscalewithclearaccountabilityforresults,requiringaholisticdeliveryapproach,notscatteredpilots.AIuse

casesmustbeembeddedintorealworkflows,testedagainstmeasurableoutcomes,and

iteratedonrapidlyenoughtokeeppacewithtechnologyadvancements(seesidebar“Twocasestudies:Buildingsolutions”).

Twocasestudies:Buildingsolutions

It’sessentialtounderstandwhichusecasesarethemostpromisingandprioritizetheirbuild-outasthoughtheywerebusinessproducts.Here’showitworkedfortwocompanies:

—ACentralEuropeaninsurersoughttolaunchhyperpersonalizedcampaignsacrossmorethan

300microsegments.AnAIknowledgeassistantscannedmorethan1,000policydocuments,andemployeeswereofferedsalescoachingusingvoice-recognitionagents.Asaresult,reachrates

improvedthree-tofourfold,conversionratesimprovedtwo-tothreefold,95percentofcallswereauto-reviewed,andcallandprocessingtimesfellby25percent.

—Aglobaluniversalbankusedanenterpriseagentfactorytoreengineeritssoftwaredevelopmentlifecycleandcenteritonbusinessrules,agent-readyartifacts,andasharedknowledgegraph.Agentsworkedatnight,whilehumanpractitionersworkedduringtheday.Asaresult,thebankimprovedefficiencyby40to80percentandachievedmorethan$50millionintargetedimpact.

CapturingCentralEurope’sAIopportunity12

Inourexperience,mostorganizationsstallatthispoint.Promisingideasremainunrealized

becausenooneownsthetransitionfromconcepttoworkingproduct.Companieswith

departmentsthataretoosiloedorthathaverigidhierarchicaldecision-makingprocesseshaveadifficulttimeprioritizingusecasesacrossbusinessunits.Inthesesituations,leadership

attentiontendstodrifttothenextstrategicprioritybeforethefirstonedelivers,andgoalsandaspirationsdivergeacrossstakeholders.Forexample,technologyteamsfocusontechnical

elegance,businesssponsorspushforspeed,andriskfunctionsraiseobjectionsthatnooneisempoweredtoresolve.Thewiderthescope,themorecomplexthedependencies,andthe

harderitbecomestoshowtangibleprogress.

TobuildAIsolutionseffectively,organizationsneedtotreatAIintegrationsasproduct

developmentinitiativesratherthanprojectmanagement.Leaderscanstartwithquickwinsandimprovevisibilityaroundthem.Theseprojectsshouldbefocusedenoughtodeliverquick

returnsandcredibleenoughtobuildorganizationalconviction.Eachprioritizedideaneedsa

clearminimumviableproduct(MVP)definitionandscopewithexplicitsuccessmetrics,sothat

progressismeasurablefromdayone.Critically,ratherthanlayeringAIontoexistingworkflows,leadersmustredesignorganizationalprocessesaroundthenewtoolsandsystems.Inour

experience,skippingthisstepisthesinglemostcommonreasonthattechnicallysound

solutionsfailtodeliverbusinessimpact.MVPsshouldbebuiltinsiderealworkflowsusing

representativedataandrealusers,andthentestedanditeratedonrapidlytovalidateimpact,

usability,andtechnicalfeasibility.Inaddition,governancemustensureclearaccountabilityandofferasinglesourceoftruth,sothatdecisionsaremadeonceandacceptedacrosscommittees.

Movethree:Redesigntheenterprisearoundscale

Earlywinsbuildconvictionandshowwhichstrategieswork,buttheycannotscaleautomatically.ThenextstageistotransformAIfromasetoflocalimprovementstoanintegralpartofhowthebusinessoperates.RewiringtheorganizationaroundAIallowscompaniestoachievesustainableenterprise-wideimpact,ratherthanplateauingafterthefirstwaveofusecases.

Thebarriersatthisstagearestructural,nottechnical.Manyorganizationsattempttodeploy

advancedmodelswhilekeepinglegacygovernancestructures,siloeddatasets,fragmented

technologystacks,andtraditionalroledefinitions.Inthesecircumstances,AIimprovesindividualtasksbutfailstomateriallychangeperformance.Businessunitspursuefragmentedapproacheswithnocoordinatedscaling,technologyarchitecturelacksthemodularityanddatafoundationsrequiredforcross-domainreuse,andchangemanagementischronicallyunderinvested.Asa

result,solutionsarelaunchedbutnevertrulyadoptedbecauseroles,incentives,anddailyroutinesremainunchanged.

Sustainedvaluerequiresacoordinatedrewiringacrossthreestages,builtonthesixenablersofMcKinsey’sRewiredframework:strategy,talent,operatingmodel,technology,data,andchangemanagement.5Thesesixelementsformanintegratedsystem.Weaknessinanyoneareawill

constraintheothers.OrganizationsthatrethinkallsixwillbeabletobuildthecapacityneededtocontinuallyembednewAIadvancementsintotheirsystemsandtranslatethesetoolsinto

sustainableperformancegains(seesidebar“Twocasestudies:Redesigningtheorganization”).

5EricLamarre,KateSmaje,andRodneyW.Zemmel,

Rewired:TheMcKinseyGuidetoOutcompetingintheAgeofDigitalandAI

,Wiley,2023.

CapturingCentralEurope’sAIopportunity13

Twocasestudies:Redesigningtheorganization

CompaniescanachievebetterreturnsfromtheirAIinvestmentsbyfollowingtheprinciplesoftheRewiredframework.Here’showitworkedfortwocompanies:

—Aviva,theUnitedKingdom’slargestgeneralinsurer,transformeditsend-to-endclaimsjourney.Ithiredmorethan50datascientists,engineers,andleadersanddeployedmorethan80machinelearningmodelsacrossdamageassessment,frauddetection,andrepairrouting.Asaresult,it

reducedassessmenttimesby23days,saw65percentfewercomplaints,andimproveditscustomersatisfactionscorebyafactorofseven.Gainscamefromreshapingthebusiness

strategy,movingtomoreagilewaysofworking,andinvestinginmorethan40,000hoursoftraining.1

—ACentralEuropeansoftwarecompanytookanagenticapproachtocodemodernization.Itused

train-the-trainerprogramstobuildcapabilitiesformorethan1,400employees,anditdevelopedagenAIcenterofexcellencethattrackedimpactacrosstheorganization.Asaresult,thecompanyfreedup20to30percentofdevelopers’timeandimprovedEBITDAby30to40percent.

1“

Aviva:RewiringtheinsuranceclaimsjourneywithAI

,”McKinsey,accessedonApril30,2026.

Stage1:Strategicalignment.AIcannotbeasideprojectownedbyITdepartmentsalone.Seniorleadershipmustdefineasequenced,realisticstrategybuiltaroundprioritydomainsandbackedbyexplicitvaluetargets.Withoutclearownershipandaccountabilityatthe

top,initiativesfragment,momentumdissipates,andthegapbetweenambitionandprofitimpactwidens.

Stage2:Buildingtherightinternalcapabilities.OrganizationsworkingtowardanAI-focused

redesignmustalsohavetherightinternalcapabilitiesacrosstalent(thepeoplewhocandesign,deploy,andmanageAI-enabledworkflows),operatingmodel(shorterdevelopmentcyclesthatmatchthepaceofAIiteration),technology(thearchitectureandvendorecosystemthatcan

supportrapidexperimentation,modulardeployment,andscalingacrossdomains),anddata(foundationsthataregoverned,high-quality,andenterprise-grade).

Stage3:Changemanagementandadoption.ScalingAIrequirescompaniestoredesignroles,buildnewcapabilities,andactivelymanageriskandcompliance.Structuredcapability-buildingprograms,reinforcementmechanisms,andtransformationgovernance—includingKPItracking,escalationmechanisms,andprogressivehandovertobusiness