bcg - 扩展人工智能需要新流程而不仅仅是新工具 Scaling AI Requires New Processes,Not Just New Tools

BCG

AIAGENTS

ScalingAIRequiresNewProcesses,NotJustNewTools

By

EricJesse

,ZeeshanShah,and

RajeevSingh

ARTICLEJANUARY20,20268MINREAD

Twodecadesofautomationtechnologies,fromroboticstoguideduserinterfacestomachine

language,haveenabledcompaniestoachievesignificantcostsavingsbyreducingtheamountofarduoushumanlaborrequired.Freedfrominefficienttasks,teamshavebeenabletorefocusonmorevalue-addedworkthatdemandsjudgment,higher-levelreasoning,orinterpersonal

relationships.

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Nowcompaniesareturningtheirattentiontothe

customerexperience

.Theemergenceofsemi-autonomousandautonomous

AIagents

nowpromisescompaniesastepchangeinproductivity

and

valuecreation

by

reducingcosts

andincreasingrevenues.Butseizingthisopportunityrequiresabroaderandboldermindsetarounddesignandimplementation.Insteadofseekingincrementalgainsbyautomatingdiscretestepswithinaprocess,organizationsshouldredesignentireprocessesend-to-end,withAIagentsassumingdifferentrolesalongtheway.Someoftheseagentswill

executepurelytransactionalwork—rule-based,highvolume,lowvariance—ontheirown.Otheragentswillprovideanunprecedentedlevelofsupporttohumanteams,whowillstillmanage

complex,high-stakesrelationships.

HowItWorksinPractice

One

industrialgoods

companyrecentlyredesigneditsquote-to-orderprocesstoimproveefficiencyandboostrevenuebydeployingAIagentsend-to-end.Bystandardizingprocessesandlinking

discretesystems,itisreducinglaborcostsbybetween30%and40%.Atthesametime,improvedquoteturnaroundtimeandgainsfromunmanagedrequestsforquote(RFQs)aregeneratingtensofmillionsofdollarsinadditionalrevenue.

Thenewprocess,designedtoaccommodatelocalneedsacrossacomplexglobalfootprint,reliesonfourAIagents(seeExhibit1):

.AssessmentandClassification.Automatesfront-endintakebyevaluatingandsortinginboundrequests.Itclassifiesemails,assistsquoting,checksconfigurations,andsuggestsproducts.

.Recording.Streamlinesorderentryandprocessingacrosssystemsbybookingorders,

supportingRFQchanges,enablingdirectshipping,andimprovingpricingandescalations.

.Status.Enhancesvisibilityandcustomercommunicationbyautomatingacknowledgments,enablingself-service,andboostingquoteconversions.

.Lead-TimeGeneration.Supportsbothquotingandorderfulfillmenttimelinesbydeliveringaccurateleadtimesusingunifiedplanningdata.

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ThecompanyhasdesignedthisprocesstohaveAIagentsresolvearound70%ofRFQswithout

humanintervention.ThoseRFQsinvolvesmallertransactionvaluesandsimplerproductswith

establishedengineeringspecifications.Around20%ofRFQswouldrequiresomehuman

interventionincollaborationwithAIagents.Theremaining10%wouldcomprisethemost

sophisticatedorcomplextransactionsandrequireintensivehumaninterventionwiththesupportofAIagents.

Todevelopandplantheimplementationofthismulti-agentapproach,thecompanyfirstassessedwhetheritssystemsanddataweresufficienttoprovidetheagentsthenecessarysupportcontext.Itisimplementingthenewprocessintworeleasesover15to18monthstoallowfor

change

management

effortsandtoensureitisreadytoadoptthestructuralchanges.ItisalsosettingupitsoperatingmodeltosustaintheAIagentplatform,includingtheformationofagent-based

solutionteamswithcross-functionplatformcapabilities.

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FourKeyDecisionsforanEnd-to-EndProcessTransformation

Leaderswillneedtomakeseveralbusinessandtechnologydecisionsastheyredesignprocessesend-to-endandembedAIagents.

PlatformVersusProduct.Leadersmustdecidewhethertoadoptacentralizedinfrastructure

(platform)orusedecentralizedagents(products).Arobustagenticplatform,ownedbyplatform

teams,providesasharedinfrastructureformemory,orchestration,toolregistries,andgovernance.SuchaplatformenablesAIagentstoworkseamlesslytogetheracrossfunctionsandbusinessunits.AIagentproducts,meanwhile,focusondeliveringtargetedcapabilitiesandoutcomes.This

separationoftheagents,whichcanbeownedbyabusinessunitwithsomesupportfromacross-functionalITteam,ensuresscalability,reusability,andvalue-drivenexecutionatspeed.

AIAgentsAcrossorWithinBusinessUnits.Similartopreviouswavesofautomation,companiesbeganthedeploymentofsingleAIagentstosolveaspecificissueorautomateasinglestep.Manyorganizationsarenowprogressingto

AI

toolsconnectedacrossasharedinfrastructure

orchestratedbyanAIagentwithindefinedguardrails.Thefinalstageofthisprogressionfeaturesmultiplenetworksofagentscollaboratinginanecosystemthatcanfacilitateprocessesacrosstheorganizationthroughagent-to-agentinteractions.Theorganizationneedstodecidewhich

deploymentistheoptimalfit.(SeeExhibit2.)

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ServerlessVersusClient-Server.Architectsandproductleadsshouldweightrade-offsinscalability,latency,integration,andoperationstodeterminewhichapproachbestsupportstheorganization’stransformationobjectives.

Aserverless-nativeapproach,suchasBedrockfromAmazonWebServices,treatsAIagentsason-demandserviceswithnoinfrastructuretomanage.Theseagentscanspandiversesystemswithgreaterflexibilityandcanautomaticallyscaletoaccommodateunpredictablespikesinworkloadwithoutmanualprovisioning.Thetrade-offisthatserverlessagentsmayfacelatencyand

executionconstraints.

Theclient-serverapproachsetsupAIagentsinacloudvendorenvironment.Thisgivesthe

companymoreflexibilityandcontroloverthegovernanceoftheAIagents,includingworkloads

andperformance.However,scalingandextendingagentsmayrequirecarefulcapacityplanningorrelianceonthevendor’sinfrastructure.Integratingbeyondthenativestackcanaddcomplexity.

BuildVersusBuy.Withanunderstandingofyourrequirementsforthechoicesabove,thefinaldecisioniswhethertobuildyourownagenticplatformortoutilizeanddeployavendorplatformsuchasSem4AIorn8n.Exhibit3summarizesthekeycriteriaforthisdecision.

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Thedecisiontobuildorbuywillhaveasignificantimpactontheeconomicsofthetransformation.Leadersshouldweightheshort-termspeedandconvenienceofbuyingagainstthelong-term

scalabilityandcostefficiencyofbuilding,especiallyasagentusagegrowsacrosstheorganization.VendorplatformsofferingprebuiltAIagentcapabilitiescanacceleratedeploymentbuthavehigherannualruncosts.Factoringinlicensesandimplementationservices,thosecostscanreachupto

$1.5millionperusecaseorfunction.That’saroundthreetimesanin-houseplatform’stypicalannualruncosts,whicharedrivenprimarilybytokenusagefromfoundationalmodels,cloudinfrastructure,andinternalengineeringtalent.

Regardlessofthedecision,leadersmustalsoconsidertheriskofrapidobsolescenceasAI

technologiesevolve.ThismeansdesigningAIplatformsasplug-and-replacesystemssothatthe

organizationcanswapoutcorecomponentslikeLLMs,memorymodules,orchestrationlayers,andtoolregistriesastheybecomeoutdated.Leaderscanconsidertaskingplatformteamswith

scanningemergingcomponentstoensurethattheyconsiderandadoptthelatestparadigms—suchasModelContextProtocolandagent-to-agent(A2A)protocols—intheirreleases.

HowtoMeettheChangeManagementChallenge

ThroughmanyAItransformationsacrosssectors,BCGhasestablisheda

guidingprincipleof

10/20/70

forresourceallocation.Thatis,companiesshoulddevote10%oftheireffortsto

algorithmsand20%totechnologyanddata;theremaining70%oftheireffortsshouldfocusonpeopleandprocessestomakesurethatthechangesstick.

Withinprocesses,thefirststepistoopenupthethinkingaroundallvalue-creatingstepstofindtheoptimalend-to-endprocessdesign.Awell-designedagenticprocessshouldsignificantlyreducethenumberofchecksbecauseiteliminatesthehumanuncertaintythatpromptsquestionssuchas

“DidIhearthatcorrectly?”or“DidIforgetsomething?”Itcanalsomakereviewsdefinitiveandfinalinsteadofiterative,thisreducingreviewtimefromdaystominutes.

Theorganizationalimpactwilllikelybefar-reaching.Weanticipatesignificantlyfewerfrontline

employeesandacorrespondingreductioninthemanagementlayers.Thiswillleadtoareworkingofspansofcontrol.Managerswillhavesmallerteamsfocusedonhigher-level,higher-valuetasks

wherehumansstillexcel,augmentedbyAItoolstheyknowhowtouse.Therequiredskillsetswilldemandgreaterfluencyintechnology,withconsequencesforlearninganddevelopment

programs.

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TwoPrerequisitesforanAgenticAITransformation

Datareadinessandtherightteamstructureareprerequisitesformakinganend-to-endprocess

transformationsucceed.Onecommonmisconceptionisthatanorganizationmustwaituntil

enterprisedataisfullyclean,structured,andintegrated.Inourexperience,waitingforperfectdatao允enleadstounnecessarydelays.ThelatestmodelsandAIagents,especiallythoseusing

retrieval-augmentedgeneration(RAG)andexternaltoolAPIs,canworkeffectivelywithsemi-structured,decentralized,andevenmessydata.Adoptinga“buildwithwhat’sgoodenough”

mindsetletsAIusagedrivedatamaturity,nottheotherwayaround.

Whethertheorganizationbuildsorbuysitssolution,itshouldcreateplatformandproductteamswithclearlydefinedresponsibilitiesacrossAIagents.Theplatformteamownsthesharedmodularinfrastructure,whichincludesLLMorchestration,memoryservices,toolregistries,agent

evaluation,governance,andobservability.Theoptimalsolutionistohavecommonteamsthatscaleacrossbusinessunitsandenablemultipleagents.Butdiscreteplatformsteamsmaybenecessarytomeetregulatorydemandsorotherneedsofabusinessunit.

TheproductteamsfocusondesigninganditeratingAIagentsthatsolvedomain-specificproblems.Theyembedagentsintoprocessessuchasquote-to-order.TheseteamsshouldincludeAIproductmanagers,userexperiencedesignersforhuman-agentinteraction,andbusinessprocessowners

whocanframeoutcomesandnotjustfeatures.Youcanhaveasingleproductteammanagingallyouragentsorsetupmultipleproductteams,dependingonnumberofprocesseshavingagenticsupport.

DeployingAIagentsatscaleisanambitiousandpresentopportunity.Thetechnologyisproven,

themodelsareevolvingrapidly,andthewindowforfirst-moveradvantageisopen.Nowisthetimeforleaderstoshi允frompilotingagentstoredesigningthework,notjustthetools.

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EricJesse

ManagingDirector&Partner

Den