智能体式自动化：转型领导者指南+Agentic+Automation：A+Transformation+Leaders+Guide

Agentic

Automation

ATransformationLeader’sGuide

b

AgenticAutomation:

ATransformationLeader’sGuide

AgenticautomationemploysAIagentsthatcanreason,planandmake

decisionsautonomously.Insteadofmorepilotsandisolatedbots,it

orchestratestheseintelligentagentsalongsidedigitalworkersandhumanteamsaroundspecificjourneysandmetricssuchasstraight_through

processing,exceptionagingandcosttoserve.Thepromiseispragmatic:

usewhatyoualreadyhave,addcontext_awareintelligenceontopandprovevalueinmonthsratherthanyears.

Thispaperpositionsagenticautomationas:

•Aleadershipchallengemorethanatoolingchoice

•Aframeworkthatacknowledgesrealbarriers:misalignedstakeholders,changefatigue,regulatorypressureandlegacyconstraints

•Agovernance_firstapproachastheonlycrediblepathtoscale

•Apracticalguidethathelpsyoumovefromexperimentationtoarepeatablepatternofhigh‑value,high‑trustautomations

•Awaytobuildautomationthatboards,regulatorsandfront‑lineteamscanallstandbehind

AgenticAutomation:ATransformationLeader’sGuide|2

Introduction

Thispaperequipstransformationandinnovationleaderswithapracticalframeworktoimplementagenticautomationasagovernance_first

operatingmodelthatdeliversmeasurablebusinessoutcomesatscale.

Afteradecadeofroboticprocessautomation(RPA)andisolatedartificialintelligence(AI)pilots,manylargeorganizationshavenumerousseparateautomationtoolsandprojectsthatworkinisolation,withoutdelivering

meaningfultransformationacrosstheorganization.

AccordingtoMcKinsey,mostenterpriseshave“notyetscaledAIbeyond

pilots”,andonlyasmallminorityhavemanagedtoembeditacross

theoperatingmodelinawaythatdrivesmaterialbusinessimpact.

[1]

Agenticautomationisgainingattentionacrosstheindustry,butmost

implementationsfocusnarrowlyonagent‑drivenautomationalone.SS&C|BluePrism®WorkHQtakesamorecomprehensiveapproach:itcombinesAIagents,digitalworkersandhumanteamsingoverned,orchestrated

workflowsthataredesignedaroundbusinessoutcomes,nottools.

Fortransformationandinnovationleadersinlarge,regulatedenterprises,

thismattersnow.BoardsareaskingwhatAIwilldotooperatingmodels,

regulatorsaresharpeningexpectationsondata,modelsandexplainability,

andinternalstakeholderswantproofthatautomationwillmoverealmetrics,notjustproducepresentations.

AgenticAutomation:ATransformationLeader’sGuide|3

INTRODUCTION

Whenyouselecttherightbusinessprocessestoautomate,youcan

achievesignificantimprovementswithin12‑18months:amajorincreaseinstraight‑throughprocessingrates,andreductionsof10%ormoreinbothexceptionagingandmanualhandlingtime.Donewell,agenticautomationoffersapracticalpathtoscaleexistingautomation,raisestraight‑throughprocessing,reduceexceptionagingandcreateaclearerROIstoryinthattimeframe,withoutarip‑and‑replaceofcoresystems.

Ifyougetthisright,youcanexpectoutcomessuchas:

•Higherstraight‑throughprocessing(STP)intargetedjourneys,withcorrespondingreductionsinexceptionqueuesandrework

•Significantreductionsininvestigationorcase‑handlingtimeasAIagentsautonomouslycompletetasksandtakeactionswithindefinedguardrails,escalatingonlytrueexceptionstohumanreviewers

•Betterbusinesscasesforbudgetrenewal,withmeasurable

improvementsincapacity,costpertransactionandriskexposurethatfinanceandriskteamscanvalidate

•StrongerauditabilityandmodelgovernancethatmakeregulatorsmorecomfortableasAIisembeddeddeeperintohigh‑stakesworkflows

Theopportunityissignificant,butsoaretherisksofungoverned,automatedexperimentation:

•DataleakageandPIIexposure:Withoutpropercontrols,sensitivedatacanbeexposedtoexternalAImodels.

•Lackofexplainability:Inabilitytoanswerauditandregulatoryquestionsaboutdecisionsandmodelrecommendations.

•Modeldrift:AImodelscandegradeovertimewithoutdetection.

•Fragmentedgovernance:Differentfunctionsimplementingtheirownpatternsincreasescompliancerisk.

•Inabilitytoscale:Withoutframeworks,organizationsremainstuckinone‑offpilots.

AgenticAutomation:ATransformationLeader’sGuide|4

•Lossofaudittrails:Missinglogspreventthetraceabilitythatregulatorsexpect.

TheNewRealityforTransformationLeaders

Asatransformationorinnovationleadinalarge,regulatedenterprise,youare

operatingunderconvergingpressures.TheboardandC‑suitewantcleardirectionandpilotsonAI,butbudgetrestraintsandrisingexpectationsmakethischallenging.Thetechnologylandscapeitselfisincreasinglycomplex,withlegacyplatforms,fragmenteddata,overlappingtoolsandvendorecosystemsthatrarelylineupcleanly.Yourautomationestateissimilarlyfragmented,spanningRPA,BPM,workflowtools,analyticsandnowawaveofAIand“agent”offerings,oftenownedbydifferentteams.

You’renotbeingmeasuredontooladoption,you’rebeingmeasuredon

whetherautomationchangescosttoserve,cycletimes,riskpostureandexperience,andwhetheryoucanproveit.

WhyTraditionalAutomationJourneysStall

Mostenterpriseswentthroughsomeversionofthesamejourney:

1.InitialRPAwave:Ahandfulofhigh‑volumeback‑officeprocesseslikereconciliations,simpleclaimsorinvoicehandling

2.CenterofExcellence(CoE):Governance,standards,reusablecomponentsandademandpipeline

3.Plateau:After30‑100automations,thenextwaveofusecasesisharder,morecross‑functionalandmoreregulated

Industryobserversdescribethisasthe"automationplateau":initialRPAsuccessthatdoesnottranslateintosustained,end‑to‑endtransformation.

[2]

Thisiswheremanyprogramsstall.Commonfailurepatternsinclude:

•Islandsofrobots:Automationsworklocally(forexample,infinanceoroperations)butarenotconnectedendtoend

•Pointsolutions,notjourneys:Teamsautomatestepsratherthanoutcomes,socustomersandemployeesstillexperiencedelaysandrework

•Underpoweredgovernance:Process,dataandmodelgovernanceareboltedonlate,makingregulatorsandriskteamsskeptical

•Weakvaluenarrative:Benefitsaretrackedas“hourssaved”orbotcounts,notasmovementinmetricsthatmattertothebusiness

Theresultisanautomationestatethatistechnicallyimpressivebut

politicallyfragile.Agenticautomationisemergingasawaytobreakoutofthatplateau,solongasit'streatedasaframeworkthatshouldbeadoptedandnotanothershinytool.

AgenticAutomation:ATransformationLeader’sGuide|5

Capability

Datahandling

AgenticAutomation

Canworkwithfree

Worksbestonstructureddata

text,documentsand

andstableinterfaces

semi‑structureddata

Decision‑making

Deterministic:Follows

scriptedrulesstepbystep

Proposesdecisions,

summariesandrisk

assessments

Taskscope

Excelsatautomating

well‑understood,repetitive

tasksinasinglesystemor

smallclusterofsystems

Candecidewhichworker(humanordigital)shoulddowhatnextbasedoncontext

Adaptability

Modelscanberetrainedor

tunedaspatternschange,

withinagovernedframework

WhatIsAgenticAutomation?

AgenticautomationusesAI‑poweredagentsthatunderstandcontext,makerecommendationsandcoordinateactionsacrosssystems.

AccordingtoIBM,thenextstepbeyondclassicRPAandisolatedgen‑AIpilotsisagentsthatcanplan,decideandacttowards

goals,orchestratingtoolsanddatasourcesratherthansimplyrespondingtoprompts

.[3]

Inpracticalterms,anagentmightreadandsummarize

unstructuredinputssuchasemails,documentsandcallnotes.Itcanclassifyworkbypriority,risk,productorcustomersegment,anddraftnext‑bestactionsorresponses.TheagentcantriggerdeterministicstepsviadigitalworkersorAPIs,andescalatecasestohumanswithadecision_readyfileandexplanation.Thinkofit

asmovingfrom“robotsthatclickbuttons”todigitalcolleaguesthatcanreasonaboutworkwithinguardrailsyouset.

HowItDiffersFromTraditionalRPA

TraditionalRPA

Static:Requiresmanual

updateswhenprocesses

change

AgenticAutomation:ATransformationLeader’sGuide|6

ThegoalisnottoreplaceRPA,buttolayerAIcapabilityontopofyourexistingexecutionfabricsothatmoreofyourjourneycanbeautomatedsafely.Agentswillalsoopenupentirelynewusecasesthatwerepreviouslynotfeasiblewithtraditionalautomationalone,whilesimultaneouslysupplementingandenhancingexistingautomations.

WHATISAGENTICAUTOMATION?

HowItDiffersFromGenericGenAIPilots

ManyenterprisesexperimentedwithgenericgenerativeAIpilotslikechatbots,documentsummarizationorcodeassistants.Thesepilotsoftenstayedinthelabbecausetheywerenotconnectedtorealworkflows,hadnoclearguardrailsforsensitivedecisions,andlackedKPIstiedtobusinessoutcomes.

Agenticautomationisdifferentinthreeways:

1.Embeddedinrealprocesses:Agentsaredeployedwithinend‑to‑endworkflowsforclaims,onboarding,payments,sanctions,fraud,underwritingandmore,notasstandalonepilots.

2.Governance‑first:PIIcontrols,approvals,modelloggingandaudittrailsarepartofthedesign,notanafterthought.

3.Outcome_led:Werecommendframingeveryusecasearoundmeasurablechangesinstraight‑throughprocessing,exceptionaging,cycletime,lossratiosorriskindicators,thoughthisrequiresdeliberatedesign.

AgenticAutomation:ATransformationLeader’sGuide|7

WHATISAGENTICAUTOMATION?

AgenticAutomation:ATransformationLeader’sGuide|8

KeyBuildingBlocks

Tomakethelandscapeconcrete,thetablebelowoutlineshowdifferenttechnologytypesfit

togetherinanagenticoperatingmodel.

TechnologytypePrimaryroleStrengthsTypicallimitations

Rule_basedautomation

(RPA/scripts)

Executedeterministic,

repetitivetasksacross

systems

Highaccuracyonstabletasks;goodaudittrail;

extendslifeoflegacy

systems

Designedforpredefined,

rule‑basedworkflowsand

structureddata

Workflow/BPMplatforms

Orchestratetasks,

approvalsandhand‑offs

acrossteams

Clearprocessvisibility;SLA

andqueuemanagement;

goodforcomplianceand

standardization

Designedprimarilyfor

structuredprocess

orchestration

StandaloneAI/genAIpilots

Classify,summarize

orgeneratecontentin

isolation

Fastexperimentation;

strongontextanddocument

understanding;canboost

individualproductivity

Oftendisconnected

fromcoresystemsand

governance;hardtotieto

end‑to‑endvalueandrisk

Agenticautomation

(SS&CBluePrism

WorkHQ)

CombineAIagents,digital

workersandhumansin

governed,outcome_led

journeys

Worksacrossstructured

andunstructureddata;

embedsgovernanceand

observability;linksdirectly

toSTP,agingandriskKPIs

Requiresclearoperating

model,sponsorshipand

changemanagement;nota

“switchonandforget”tool

AgenticAutomation:ATransformationLeader’sGuide|9

KeyBuildingBlocks(cont.)Thinkoffivecorebuildingblocksyouneedtoassemble:

AIagents

AIagentsreasonover

bothunstructuredand

structuredinputs,classify

andsummarizeinformation,

recommendnextsteps,

prioritizeworkandoperate

withinclearlydefinedpolicy

andmodelgovernance

frameworks.

Digitalworkers

(RPAanddeterministicautomation)

Digitalworkershandle

repeatabletasksacross

legacysystemsandAPIs,

providingconsistency,

speedandauditabilitywhile

extendingthelifeandvalue

ofcoreplatformswithout

requiringreplatforming.

Integrationandconnectivity

Integrationandconnectivity

enableagentsanddigital

workerstocommunicate

withlegacysystems,

APIsanddatasources,

ensuringthatautomation

canaccesstheinformation

andexecuteactionsacross

theenterprisetechnology

landscapewithoutrequiring

wholesaleplatform

replacement.

Governance

Governanceframeworks

enforcePIIpolicies,

role‑basedaccessand

segregationofduties,and

logeverydecision,inputand

modelusedforlaterreview.

Human_in_the_loop

Human‑in‑the‑loop(HITL)defineswhenandhow

humansparticipateinautomatedworkflows:

approvingoroverriding

agentrecommendations

whenrequired,providing

inputsandcontextthat

agentscannotinferontheir

own,andhandlingtasksthat

remainmoreeffectivewhen

performedbypeople.

Governance‑FirstAgenticAutomation

WhyGovernance,PIIControlandExplainabilityAreNon‑Negotiable

Inahigh‑complianceenterprise,youcan’tscaleanyAI‑ledautomationwithoutbeingabletoanswerbasicquestions

fromrisk,auditandregulators:Whomadethisdecision?

Ahuman,anagentorboth?Whatdatadidtheysee?Was

anysensitivedataexposedtoexternalmodels?Whydidthemodelmakethisrecommendation?Canweexplainitinplainlanguage?Howdoweknowthemodelhasnotdriftedor

degradedovertime?

AccordingtoEY’sguidanceonresponsibleAI,agenticAI

introducesnewautonomy,dataleakageandexplainabilityrisksthatgobeyondtraditionalAI,andtheserequire

strongerguardrails,testingandgovernancebakedintothedesignofworkflowsratherthanboltedonlater.

[5]

Governance‑firstagenticautomationaddressesthesequestionsbydesign.

PIIcontrolsensurethatpromptsandoutputsarefilteredandmasked;sensitivefieldsareredactedorheldbackfromAImodelsunlessexplicitlyallowed.

Policyenforcementmeansthatonlyapprovedmodelsandconfigurationscanbeusedforspecificworkflows;deviationsareblockedorflagged.

Modelloggingandlineageensurethateachagent

interactionlogsthemodel,parameters,inputfeatures,outputandanyhumanoverrides.

Explainabilityisbuiltinsothatagentsareconfiguredtosurfaceinterpretablereasons(forexample,riskfactors)insteadofopaquescoresalone.

AgenticAutomation:ATransformationLeader’sGuide|10

GOVERNANCE‑FIRSTAGENTICAUTOMATION

GovernanceasanEnablerofScale,NotaBlocker

Donewell,governancedoesnotslowyoudown.Itremovesfrictionby

givingriskandcomplianceteamsastructuredwaytoassessandapproveusecasesinsteadofreactingcasebycase.Modelriskfunctionsgetthelogsandlineagetheyneedwithoutinventingnewprocessesforeach

project.Businessownersgainconfidencetorolloutautomationstomoreregions,productsandsegmentsbecausetherulesareclear.

McKinsey’sworkonscalingAIsuggeststhatenterprisesthattreatgovernanceasaproductwithclearstandards,reusablepatternsandtoolingtendtomovefromone‑offpilotstoportfolioroadmapsinside6‑12monthsandincreasetheproportionofhigh‑value,high‑riskusecasestheycantackle.

[4]

AgenticAutomation:ATransformationLeader’sGuide|11

CommonChallengesandHowToOvercomeThem

Behindeveryautomationroadmapisasetofveryhumanandorganizationalchallenges.Formosttransformationandinnovationleaders,theday

joblookslesslike“rollingoutAI”andmorelikemanagingfrictionacross

vision,people,riskandexecution.Below,weoutlinethefivemostcommonchallengesandpracticalstrategiestoaddresseachone.

AgenticAutomation:ATransformationLeader’sGuide|12

COMMONCHALLENGESANDHOWTOOVERCOMETHEM

Theproblem:

Operationsteamsarefocusedonclearingbacklogsandreducingmanual

workload.CIOsareconcernedaboutlegacysystemsandmountingtechnicaldebt.Riskandcompliancefunctionsarepreoccupiedwithexposureand

CHALLENGE1

regulatoryscrutiny.Meanwhile,businesslinesarepushingforfastergrowthandinnovation.Withoutasharedvisionofsuccess,everyautomationinitiativeendsupbeingnegotiateddowntoacompromisethatsatisfiesnoonefully.

MisalignedVisionandFragmentedStakeholders

AccordingtoMcKinsey,manyorganizationshavenotyetmanagedtomovebeyondthispatternofpilotsandlocaloptimizationstoasharedoperatingmodelandroadmap.[

1

]

Thismisalignmentiscompoundedbylocaloptimization.IndividualfunctionstendtooptimizefortheirownKPIs,toolsandpreferredvendors,which

makesitdifficulttoprioritizeinvestmentsthatspanmultiplejourneysorrequirecross‑functionalcoordination.

Howto

overcomeit:

Startfromjourneysandoutcomes,notplatformsorfunctionalsilos.

Defineasharedoperatingmodel:whoownsroadmaps,howdemandisprioritized,howriskandmodelgovernanceareembedded.

AlignwithyourCOO/CIOonthetoptwoorthreemetricsyouneedtomoveandensurestakeholdersacrossoperations,ITandriskagreeonthesepriorities.

Positiontechnologyastheexecutionlayerofthatoperatingmodel,notthecenter.

AgenticAutomation:ATransformationLeader’sGuide|13

COMMONCHALLENGESANDHOWTOOVERCOMETHEM

Theproblem:

Colleaguesoftenfearthatautomationwillmaketheirrolesredundantor

erodetheirexpertiseandcontrol.Aftermultiplewavesof“transformation”,front‑lineteamscanbeskepticalthatthistimewillbedifferent,leading

CHALLENGE2

tochangefatigue.EvenmotivatedteamsmaynotfeelequippedtoworkeffectivelywithAI‑enabledworkflowswithouttargetedsupport,creatingskillsandconfidencegaps.

InsightsfromMITSloanandsimilarresearchonAI‑enabledorganizationssuggestthatmanagementreadiness,changesupportandclearrole

ResistancetoChangeontheGround

definitionsmattermorethanindividualwillingnesswhenitcomestoscalingagenticAI.

[6]

Howto

overcomeit:

Involvefront‑lineteamsearlyinjourneydesignandpilotphasessotheyshapethesolutionratherthanreceivingit.

Communicateclearlyhowroleswillevolve(notdisappear)andwhatnewcapabilitiesteamswillgain.

ProvidetargetedtrainingandsupporttobuildconfidencewithAI‑enabledworkflows.

Celebrateearlywinsvisiblytobuildmomentumanddemonstratetangiblebenefits.

AgenticAutomation:ATransformationLeader’sGuide|14

COMMONCHALLENGESANDHOWTOOVERCOMETHEM

CHALLENGE3

The

problem:

Manyorganizationsstilllackconsistentpoliciesondatause,modelapprovalandhuman‑in‑the‑loopdecisioning.Audit,complianceandregulators

expectexplainabilityandtraceabilitythatmanyearlyAIpilotscannot

provide.Differentfunctionsandvendorsimplementtheirowngovernancepatterns,whichincreasesoperationalandcompliancerisk.

Governance,RiskandRegulatoryPressure

Howto

overcomeit:

Agreeonminimumgovernancestandardsupfront,evenforpilots.

Workwithriskandmodelgovernanceteamstodefinereusablepatterns(forexample,standardprompts,loggingandapprovalflows).

Treatgovernanceassets(policies,templates,dashboards)asfirst‑classdeliverables

informedbybest‑practiceframeworkssuchasEY’sguidanceonAIrisk.

BuildPIIcontrols,modelloggingandexplainabilityintothedesignfromdayone,notasanafterthought.

AgenticAutomation:ATransformationLeader’sGuide|15

DifferentteamsoftenmanageseparatetoolsforRPA,BPM,analytics,CRM,coresystems,andnowAIandagentofferings,creatingislandsoftooling

withoverlappingcapabilitiesandnounifiedviewofwork.Criticalcontext

remainstrappedinemails,documentsandline‑of‑businesssystemsthat

donoteasilyintegratewithoneanother.Meanwhile,legacysystemscreatedrag:modernizingcoreplatformsisslowandexpensive,yetmaintainingthestatusquolimitswhatautomationcanrealisticallyachieve.

Theproblem:

ChooseanorchestrationlayerthatcanconnectAIagents,digitalworkersandpeopleintoend‑to‑endworkflowsacrossexistingsystems.

Preferjourneysthatare:

—High‑volumeandrepeatable

—Painfulenoughthatstakeholdersaremotivated

—Boundedenoughtoshowresultsin90days

Avoidoverlyambitiousfirstjourneysthatrequireextensiveintegrationorreplatforming—startwithwhatyoucanconnecttoday.

Examplesthatoftenworkwell:

—Paymentsreconciliationandsettlements

—KYConboardingforaclearsegment

—Claimsorcasetriage,whereagentscanpreclassifyandpreparefiles

Howto

overcomeit:

AgenticAutomation:ATransformationLeader’sGuide|16

COMMONCHALLENGESANDHOWTOOVERCOMETHEM

CHALLENGE4

Technology

FragmentationandLegacyConstraints

Threekeychallengesemergewhentryingtoprovevalueinwaysthat

executivesandregulatorstrust.First,hours‑savedmetricsoftenfailto

resonate:botcountsandtheoreticalFTEsavingsrarelyconvinceCFOs,

COOsorboardsofrealimpact.Second,attributionbecomesdifficultwhenmultipleinitiativesrunsimultaneously,makingithardtoisolatethespecificcontributionofagenticautomationtokeyperformancemetrics.Third,

thereisapersistentneedforevidencethatwithstandsscrutiny—tosecuresustainedsupport,transformationleadersmustdevelopvaluestories

thataligncrediblywithfinancialreportingstandards,riskframeworksandregulatoryexpectations.

Theproblem:

Anchortothreeorfourmetricsthatmatterforyourstakeholders:

—STP,exceptionaging,TAT

—Lossratiosorleakage

—Costtoserve,FTEcapacityreleased

—Auditfindingsorregulatoryremediationitems

Buildbefore/afterviewsandsimplevisualsthatcanbereusedinexecutivepacks.

Baselinemetricsbeforeyoustartandtrackthemrigorouslythroughoutthepilotandscalephases.

Equipyoursponsorswithshort,crediblenarratives,notjargon.

Documentgovernance,controlsandmodelconfigurationssoyourvaluestorywithstandsauditandregulatoryscrutiny.

Howto

overcomeit:

AgenticAutomation:ATransformationLeader’sGuide|17

COMMONCHALLENGESANDHOWTOOVERCOMETHEM

CHALLENGE5

ProvingValueinaWayLeadersandRegulatorsTrust

90_Day

ImplementationRoadmap

forAgenticAutomation

Weeks0‑2

FrameandAlign

Focus:Clarifystrategicintentandselectflagshipjourneys

KeyActions:

•Confirmstrategicintent:Rankpriorities(costtoserve,riskreduction,CXorregulatoryconfidence)

•AlignwithCOO/CIOonthetop2‑3metricsyouneedtomove

•Pick1‑2flagshipjourneyswithclearpain,realisticdataaccessandwillingstakeholders

•Baselinekeymetrics:STP,exceptionaging,cycletime,backlog,costpercase,errorrates

•Agreeongovernanceprinciples:PIIboundaries,humanapprovalpoints,loggingrequirements,sign‑offauthority

Deliverables:

•One‑pageproblemandvaluestatementperjourney

•Agreedbaselinemetricsandtargetranges(e.g.,“+20pointsSTP,”“–30%aging”)

AgenticAutomation:ATransformationLeader’sGuide|18

•Namedsponsorsinbusiness,ITandrisk

90‑DAYIMPLEMENTATIONROADMAPFORAGENTICAUTOMATION

AgenticAutomation:ATransformationLeader’sGuide|19

Weeks3‑6

DesignandPilot

Focus:Provethepatternonacontainedscopewithtightgovernance

Deliverables:

•Workingagenticworkflowinproductionforalimitedslice

•EarlydataonSTP,cycletime,exceptionmixanduserfeedback

•Clearrecordofcontrolsandmodelconfigurationsused

KeyActions:

•Assembleasmall,cross‑functionalsquad(operations,IT,risk,data)aroundeachjourney

•Maptheend‑to‑endflow,identifyingcandidatestepsforAIagentsanddigitalworkers,plusHITLcheckpoints

•DesignanMVPworkflowwithclearentrypoints,exceptionpathsandsystemboundaries

•DesignanagenticworkflowwithclearHITLcheckpointsandPIIboundaries

•Configuregovernance:PIImaskingrules,modelchoicesandconstraints,logginganddashboards

•Buildandrunapilotonasubsetofvolume(e.g.,oneregion,productline,orchannel)

•Implementacontainedpilotandstartcollectingdataandfeedback

AgenticAutomation:ATransformationLeader’sGuide|20

90‑DAYIMPLEMENTATIONROADMAPFORAGENTICAUTOMATION

Weeks7‑12

ScaleandProve

Focus:Movefromasuccessfulpilottoacrediblestoryandrepeatablepattern

KeyActions:

•Useearlyresultstorefinethresholds,routingrules,promptsandHITLpoints

•Expandcoverage:morevolume(channels,products,regions)andadditionalexceptiontypes

•EmbedKPItracking:real‑timedashboardsforSTP,aging,TAT,capacityreleasedandincidents

•Documentthepattern:whatworked,whatcontrolswereneeded,andwhatskillsandteamswereinvolved

•Codifythepattern:architecture,metrics,governance,rolesandskills

•Builda12‑18monthroadmapthatreusesthispatternacrossadjacentjourneyswithexplicitvaluetargets

ExampleKPIstoPresenttoSeniorStakeholders:

•STPincreasefrom52%to78%fortargetedflows

•Exceptionaging≥7daysreducedby45%

•25‑35%reductioninmanualhandlingtimepercase

•Zeronewauditissuesraisedontheautomatedflowoverthefirstthreemonths

Bytreatingagenticautomationasagovernedoperatingmodelratherthanastandalonetool,youcanturnscatteredpilotsintoaportfolioofoutcomesthatyourboard,regulatorsandteamscanseeandtrust.

References:

1.McKinsey&Company.“ThestateofAIin2025:Agents,innovation,andtransformation.”

2.A

.“TheAutomationPlateau:WhyRPAProgramsStallandHowtoBreakThrough.”3.IBM.“WhatareAIagents?Thenextevolutionofenterpriseautomation.”

4.McKinsey&Company.“Theagenticorganization: