麦肯锡-智能体 AI 能否（最终）推动保险行业核心技术实现现代化 Can agentic AI (finally) modernize core technologies in insurance

April2026

Mcsey

&company

FinancialServicesPractice

CanagenticAI(finally)

modernizecore

technologiesininsurance?

Insurershavelongunderstoodtheneedtotransformtheircoretechnol-ogies.So,whyhaven’tthey?AgenticAImayfinallymakethedifference.

ThisarticleisacollaborativeeffortbyArunGundurao,KrishKrishnakanthan,RoryWalsh,SanjayKaniyar,andTanguyCatlin,representingviewsfromMcKinsey’sFinancialServicesPractice.

Modernizingthecoretechnologiesunderpinning

theinsuranceindustryhaslongbeenviewedas

essential.Yetexcusesforfailingtoactpersist,

fromcomplexitytocosttorisk.Nomore.Agentic

AI,drawingonexperiencefromlarge-scaleAI

transformations,includingworkpioneeredwithin

QuantumBlack,McKinsey’sAIarm,presents

theopportunitytofundamentallytransformhow

modernizationisdoneendtoendbycapturing

legacyknowledgeatscale,compressingrework

loops,andimprovingpredictabilityacrosstesting,

reconciliation,andcutover.

1

Itholdsthepromise

ofagentsperformingandcoordinatingdiscrete

taskswithauditableoutputsandhuman-in-the-

loopcontrols—empoweringleaderstorethinktheir

modernizationportfolio.Andnotonlycouldithelp

insurerssignificantlyimprovefinancialviabilityby

movingfromone-off,bespokeprogramstowarda

repeatable,scalablemodernizationfactory,

2

butearlyadoptersofagenticAImaygainacompetitiveedge.

risksthatforceconservativesequencing.These

dynamicshavedirecteconomicconsequences

thatalsoreducetheappealofmodernizingcore

systems,includingcausingcostly“double-bubble”periodsinwhichinsurerspaytorunthelegacystackwhilefundingthechangeprogramandprolonging

theneedtorunsystemsinparallel,extendingthetimelinefordecommissioninglegacyplatforms.

5

Afinalbutcriticalnuanceundermininginsurers’

motivationisunderstandingwhereeffortactually

goes.Inmostinsurancemigrations,rewritingcodeorconfiguringthetargetplatformisonlyasmallportionofthework.Adisproportionateshareoftimeand

investmentoftensitsinunderstandingandconfiguringtherules,dataconversion,qualitycontrol,reconciliation,operationalreadiness,andpostmigrationstabilization.That’swhyplatformmigrationsoftendisappointwhen

teamsattempttorecreatelegacycomplexityona

moderncore.Thedisciplineisknowingnotonlywhattobuildbutalsowhatnottorebuild.

Theproblem:Insurancecore

modernization’sendlesschallenge

Insurersfailingtomodernizecoresystemsoften

blametheirinactiononspecificstructuralcosts

andrisksrelatedtohowtechnologiesarebuiltandoperated.

3

Andthere’snodenyingthesechallengesarereal:Executivesknowthatlegacycore

modernizationishardbecausethe“core”isalivingsocio-technicalsystemthatmaycompriseseveraldecades’worthofsparselydocumentedembeddedbusinessrules,batchwindows,custominterfaces,anddatasemantics.

Inaddition,inpolicyadministrationmigrations,

severalcommonconstraintsmayrepeatedlycreatescheduleslippageandre-baseliningcycles,

4

fromunderdocumentedproductlogicandactuarial

settingstosemanticgapsthatoftensurfacelateanddrivereworkaswellascutoverandrunbook

Thepotentialsolution:AgenticAIreshapesthecostcurve

Whilethechallengesofcoresystemmodernizationhaveprovedinsurmountableformany

insurers,agenticAImayprovideasolution,

drawingonexperiencefromlarge-scaleAI

transformations,includingworkpioneeredwithin

QuantumBlack,McKinsey’sAIarm.Autonomous

orsemiautonomoussoftwareagentscaninterpret

legacyartifacts,producestructureddocumentation,generateandvalidateconfigurationorcode,createandruntests,andcoordinateworkflowsacrossthesoftwaredeliverylifecycle.

6

Thisisdifferentfromadevelopercopilot:While

copilotsassistausermomentbymoment,agentsaredesignedtopursueagoal,breakitintotasks,usetoolsandcontext,anditeratebasedon

1“AIforITmodernization:Faster,cheaper,better,”McKinsey,December2,2024;SeizingtheagenticAIadvantage,McKinsey,June13,2025.

2Formore,see“AIforITmodernization:Faster,cheaper,better,”McKinsey,December2,2024;SeizingtheagenticAIadvantage,McKinsey,June13,2025;“Theagenticorganization:ContoursofthenextparadigmfortheAIera,”McKinsey,September26,2025.

3Formore,seeKrishKrishnakanthan,SanjayKaniyar,TanguyCatlin,andSophieRu,“HowP&Cinsurerscansuccessfullymodernizecoresystems,”McKinsey,May12,2025.

4Formore,seeKrishKrishnakanthan,SanjayKaniyar,TanguyCatlin,andSophieRu,“HowP&Cinsurerscansuccessfullymodernizecoresystems,”McKinsey,May12,2025.

5Formore,seeKrishKrishnakanthan,SanjayKaniyar,TanguyCatlin,andSophieRu,“HowP&Cinsurerscansuccessfullymodernizecoresystems,”McKinsey,May12,2025.

6Formore,see“AIforITmodernization:Faster,cheaper,better,”McKinsey,December2,2024;SeizingtheagenticAIadvantage,McKinsey,June13,2025.

CanagenticAI(finally)modernizecoretechnologiesininsurance?2

CanagenticAI(finally)modernizecoretechnologiesininsurance?3

feedbackandcontrols.7Inapolicyadministration

migration,thismattersbecausethebiggest

bottlenecksarerarelytypingcodebutratherthe

loopsofdiscovery,mapping,testing,reconciliation,andcutover.Exhibit1outlineswhereagentscan

createefficiencywithineachdomainstepwith,inourexperience,typicalproductivityimprovementsrangingfrom10to90percent,dependingonthestepandthedegreeofautomation.

Mostcoretechnologiesintheinsuranceindustry

useprogramminglanguage,coding,andeven

systemscreateddecadesago.Inmanycases,it’s

difficulttofindpeoplefluentintheselanguagesandsystems—orevenworkersabletounderstandthem.That’swhysomeofthemostsignificantgainsin

enablingcoremodernizationwithagentscomefromdecodingandtranslatingoutdatedprogramming

language,draftingrules,andmappingartifacts.

Exhibit1

AgenticAIcoulddeliverproductivityimprovementsofupto90percentintheinsurance-core-systemmodernizationprocess.

ImpactofagenticAIintheinsurance-core-systemmodernizationprocess

Domain

Discovery,reverse

Target

Datamapping,

Testing,

Cutover,

Program

step

engineering,and

configuration

conversion,

reconciliation,

hypercare,

management

productorrule

understanding

accelerationfordesignand

3rd-partypolicyadministration

platforms

andquality

anddefectcyclecompression

andoperationsreadiness

andgovernance

ReduceSME1

dependencyandearly-cycle

uncertainty

HowAI

canhelp

Supportinformedcustomization

decisionsusingapproved

patternsandconstraints

Surfaceissuesearlierand

compressdatareadiness

timelines

Shorten

feedbackloopsandreduce

SMEe仟ort

Enable

role-based

trainingandpolicyservicingwith

decisionsupportandcontinuouslearning

Provide

continuous

surfacingof

executionandrisksignals

Through

rapidlyreverseengineering

legacypolicy

logicandbatchbehaviorinto

decisiontables,mappings,andexplainable

knowledge

Bygenerating

andvalidating

configurationsof3rd-partypolicyadministration

platforms

andtracing

requirementsendtoend

Through

automated

mapping

analysis,ETL2

generation,

profiling,anomalydetection,

andtest-datacreation

Byautomaticallygenerating,

executing,

diagnosing,

andreconciling

tests,thereby

linkingdefectstorootcauses

andfinancialoutcomes

Byorchestratingcutoverrunbooksandsupporting

hypercareoperations

Through

automating

PMO3reporting,dependency

tracking,andcomplianceartifacts

Typical

productivity

improvement,%

20–50

15–40

20–60

15–90

10–40

25–50

1Subjectmatterexpert.

2Extract,transform,andload.3Programmanagementofice.

McKinsey&Company

7Formore,see“AIforITmodernization:Faster,cheaper,better,”McKinsey,December2,2024;SeizingtheagenticAIadvantage,McKinsey,June13,2025;“Theagenticorganization:ContoursofthenextparadigmfortheAIera,”McKinsey,September26,2025.

CanagenticAI(finally)modernizecoretechnologiesininsurance?4

Agentscanreadcodewritteninarchaiclanguages,reverseengineerthelogic,andconvertitintoplainEnglish.Likewise,agentscanparsecodeand

extractanddocumentthebusinessrulesembeddedwithinit.Inmanycases,anagentcanaccomplish

withindayswhatwouldtakeatrainedsubjectmatterexpertmonthsorevenyearstodo.

Oncetheseagentcapabilitiesareestablished,the

incrementalcostofmodernizingadditionalproductsandsystemscanfallquicklybecausethesame

agents,patterns,andcontextlayerscanbereusedacrosswavesanddomains.Inthisway,agenticAI

createsaportfoliooptionthatinsurancetechnologyleadershavenothadbefore.Inadditiontoenablingthetransitiontoanewcoresystem(whichisoften

asoftware-as-a-serviceplatform),thereisoften

alongtailofadjacentlegacyapplications,utilities,andinterfacesthatneedtobemigrated.Agentic

approachescanmakeselectiverewritesofthislongtaileconomicallyviable,reducingmaintenancecostandlongcyclesofmigrationandintegration.

8

Bestpracticestocapturevalue

Reshapingthewayinsurersmodernizetheircore

systemswithagenticAIrequiresmorethansimply

applyingthetechnology.Itdemandsfundamentalshiftsinworkflows,roles,governance,andsequencing.Inourexperience,threemovesmattermost:

Buildingmodularagentsto

acceleratethewholeprocess

Thehighest-performingtechnologymigrations

decomposeworkintoreusable,composableagentsacrossextraction,validation,transformation,

orchestration,andgeneration.Thisapproach

improvescontrol,makesoutputsauditable,and

enablesreuseacrossdiscovery,data,testing,and

cutover.Inpractice,itmeanstreatingagentsasa

libraryofatomiccapabilities,eachwithclearinputs,acceptancecriteria,andescalationpathstohumans(Exhibit2).Italsomakesiteasiertoupdatespecificcomponentsasmodelsandtoolingevolve,withoutdestabilizingthewholeworkflow.

Shiftingfromasingle-programmindsettoamodernizationportfolio

Agenticcapabilitiesfundamentallychangetheuniteconomicsofmodernizationbecauseoncecore

agentsarebuiltandgoverned,theirmarginalcostofreusesharplydeclines.Thatmeansmodernizationcanbeframednotasasinglelarge-scalemigrationbutasacoordinatedportfolioofopportunities

acrossthefulltechnologyestate.

McKinseyresearchonAI-enabledsoftware

engineeringfindsthatthegreatestimpactcomes

whenAIisembeddedacrossworkflowsandscaled

systematically,ratherthanconfinedtoisolateduse

cases.

9

Inaninsurancecontext,thismeanslooking

beyondmigratingtheadministrationofsinglepoliciestothebroaderlandscape:multipleproductlines,billingandclaimsintegrations,satellitesystems,andhigh-

maintenancelegacyutilities.Thisdoesnotmeanthatvaluecannotberealizedfromtacklingmodernizationinphases,suchasend-to-endprospecting—designchoicescanbetailoredtoachievespecificoutcomesaccordingtoaparticularcadenceortimeline.Buttheidealendstateforoptimalimpactisfullmodernizationofthelegacycore.

Withareusableagentstackinplace,the

incrementalefforttomodernizeadditionalproductsoradjacentapplicationshasthepotentialtofall

materially.Thatallowsleaderstoevaluateplatformmigrationsanddecommissioningdecisions,aswellasselectivelyrewritelong-taillegacyapplications,aspartofanintegratedroadmap.Andthisportfoliolensenablescompoundingvaluecreationwhile

maintaininggovernanceandplatformdiscipline.

Redesigningroles,governance,andriskmanagementforagenticexecution

Insurancemodernizationisregulatedand

operationallysensitive.Agenticworkflowsneed

controlsbydesignsuchashuman-in-the-loop

approvalsatstagegates,traceabilityfrom

requirementstoconfigurationtotestevidence,

andclearmodel-validationpractices.AgenticAI

fundamentallyreshapesrolesandcreatesmore

full-stack“productdefiners”and“productbuilders,”

8Formore,see“AIforITmodernization:Faster,cheaper,better,”McKinsey,December2,2024;SeizingtheagenticAIadvantage,McKinsey,June13,2025.

9CharlotteRelyeaandMartinHarrysson,“TheAIrevolutioninsoftwaredevelopment,”McKinsey,April1,2026.

CanagenticAI(finally)modernizecoretechnologiesininsurance?5

Exhibit2

AIagentscanbedeployedacrossthemigrationworkfiow.

DeploymentofAIagents,bycategory

McKinseyAIagentcategories

IInteractionIReasoningIExtractionIValidationTransformationOrchestrationGenerationLearning

andproductorruleunderstanding

1

Discovery,reverseengineering,

Documentextraction(eg,extractrelevantfields,pullpolicydocsandcontrolmemos)

Tableextraction(eg,converttabularcontent,turndecisiontables)

Entityextraction(eg,identifyentities,products,systems,owners)

Summarization(eg,condenselongtext,produce“ruleintentandedgecasesandopenquestions”)

Classification(eg,clusterrulesbyproduct,transactiontype,complexity,orexceptionpatterns)

Root-causeanalysis(eg,diagnosepatternsor

dependencies,surfacedependencychains,andanomalies)

Interview(eg,conductstructuredQ&A,guideSMEs1throughpromptstoelicitruleintentsandexceptions)

Reportgeneration(eg,producestructuredreportstocreatean“explainablerulepack”perpolicydomain)

3

Datamapping,conversion,andquality

Tableextraction(eg,digitalizemappingspecsandtransformationrules)

Formatconversion(eg,transformdataformats,

standardizemappingartifacts,extracts,andtestdatasets)

Classification(eg,groupfieldsbydomain,criticality,transformationtype,andexpectedqualitycontrols)

Dataquality(eg,assessandfiagissues,detectmissingorinvalidvaluesandinconsistentdomaincodes)

Factchecking(eg,verifyclaimsvstrustedsources)

Scenariogeneration(eg,createrealistictestcases,generaterepresentativetestdatasets)

Monitoring(eg,tracksignalsandalerts,fiaganomalies)

configurationacceleration

2

Targetdesignand

Planning(eg,sequencemultistepplans,draftconfigurationbuildplanperdomain)

Decisiontree(eg,applyconstraintsorpatternstodriveif-thenconfigurationdecisionsandexceptionhandling)

Processvalidation(eg,checkconfigurationsagainst

definedFAST2designstandardsandgovernanceguardrails)

Qualityassurance(eg,testandvalidateoutputsagainstacceptancecriteriabeforepromotingtothenextstage)

Codegeneration(eg,generatecodesnippetsandtemplates,draftrepeatabletemplatesandscripts)

Contentgeneration(eg,createconfigurationnotesandrequirement-to-configurationtracestatements)

Approval(eg,runstage-gatereviewsforconfigurationpackages,logdecisions)

Routing(eg,routeconfigurationquestionsorapprovalstotherightproductSMEs,architects,anddeliveryowners)

4

Testing,reconciliation,anddefectcyclecompression

Scenariogeneration(eg,createtestcasesandregulatoryorcontrolcases)

Reportgeneration(eg,createreconciliationreports,defectpacks,andreadinesssummaries)

Qualityassurance(eg,validateagainstbenchmarks,verifyoutputsvsexpectedresults,preventregressions)

Summarization(eg,condenseevidence,ie,whatfailed,where,likelydriver,andnextaction)

Prioritization(eg,rankbycriteria,sortdefectsbybusinessimpact,operationalrisk,andrelevance)

Root-causeanalysis(eg,diagnoseunderlyingdrivers,connectdefectstodi仟erentdrivers)

Monitoring(eg,watchdefectaging,reconvariancetrendsandregressionspikes)

1Subjectmatterexperts.

2Flexible,appropriate,structured,andtransparent.3Programmanagementofice.

Source:McKinseyAgents@Scale;McKinseyanalysis

McKinsey&Company

CanagenticAI(finally)modernizecoretechnologiesininsurance?6

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Exhibit2continued

AIagentscanbedeployedacrossthemigrationworkfiow.

DeploymentofAIagents,bycategory

McKinseyAIagentcategories

IInteractionIReasoningIExtractionValidationTransformationIOrchestrationIGenerationLearning

6

Cutover,hypercare,andoperationsreadiness

5

Programmanagementandgovernance

Scheduling(eg,orchestratecutoverrunbooktimingandcheckpoints)

Routing(eg,directworktoowners,routeincidentstothecorrectgroupstoresolve)

Approval(eg,loggo/no-godecisions,managecheckpointapprovals)

Escalation(eg,routecomplexcasesupward,trigger

humanescalationforhigh-severityorambiguouscases)

Conversational(eg,guided“opsassistant”interactionforhypercaretriageandrunbookexecution)

Formfilling(eg,captureincidentdetails,decisions,actionstaken,andevidenceinconsistentfields)

Rewriting(eg,tailorupdatesforexecsvsoperators)

Patternlearning(eg,learnrecurringissuepatterns,improveplaybooksanddecisionsupportovertime)

1Subjectmatterexperts.

2Flexible,appropriate,structured,andtransparent.3Programmanagementofice.

Source:McKinseyAgents@Scale;McKinseyanalysis

McKinsey&Company

makingtalentupskillingcritical.Fortechnology

leaders,thisimpliestreatingagentsasanew

productionsystemthatrequiresdefinedprivileged

access,monitoringofbehaviorandoutcomes,and

theuseofauditableartifactsforregulatoryand

internalassurance,withhumansinvolvedthroughout.

theminwaysthatmateriallyimprovecertaintyoncost,risk,andtimeline.Thatrequiresthreeshifts:embeddingagentsa