波士顿咨询：GenAI不仅能提高生产力还能扩展功能

GenAIDoesn’tJustIncreaseProductivity.ItExpands

Capabilities.

SEPTEMBER05,2024

By

DanielSack

,

LisaKrayer

,EmmaWiles,MohamedAbbadi,UrviAwasthi,RyanKennedy,CristiánArnolds,andFrançoisCandelon

READINGTIME:12MIN

ThisisthesecondmajorfieldexperimentledbytheBCGHendersonInstitutedesignedtohelpbusinessleadersunderstandhowhumansandGenAIshouldcollaborateintheworkplace.Ourpreviousstudy

assessedthe

valuecreated—anddestroyed—byGenAI

whenusedbyworkersfortaskstheyhadthe

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capabilitiestocompleteontheirown.OurlatestexperimenttestshowworkerscanuseGenAItocompletetasksthatarebeyondtheircurrentcapabilities.

Anewtypeofknowledgeworkerisenteringtheglobaltalentpool.Thisemployee,augmentedwith

generativeAI

,canwritecodefaster,createpersonalizedmarketingcontentwithasingleprompt,andsummarizehundredsofdocumentsinseconds.

Theseareimpressiveproductivitygains.Butasthenatureofmanyjobsandtheskillsrequiredtodothemevolve,workerswillneedtoexpandtheircurrentcapabilities.CanGenAIbeasolutionthereaswell?

Basedontheresultsof

anewexperiment

conductedbytheBCGHendersonInstituteandscholarsfromBostonUniversityandOpenAI’sEconomicImpactsresearchteam,theanswerisan

unequivocalyes.We’venowfoundthatit’spossibleforemployeeswhodidn’thavethefullknow-howtoperformaparticulartaskyesterdaytouseGenAItocompletethesametasktoday.

“

Employeeswhodidn’thavethefullknow-howtoperformaparticulartaskyesterdaycanuseGenAItocompletethesametasktoday.

Withthatinmind,leadersshouldembraceGenAInotonlyasatoolforincreasingproductivity,butas

a

technology

thatequipstheworkforcetomeetthechangingjobdemandsoftoday,tomorrow,andbeyond.TheyshouldconsidergenerativeAIanexoskeleton:atoolthatempowersworkersto

performbetter,anddomore,thaneitherthehumanorGenAIcanontheirown.

Ofcourse,thereareimportantcaveats—forexample,employeesmaynothavetherequisite

knowledgetochecktheirwork,andthereforemaynotknowwhenthetoolhasgottenitwrong.Ortheymaybecomelessattentiveinsituationswheretheyshouldbemorediscriminating.

Butleaderswhoeffectivelymanagetheriskscanreapsignificantrewards.TheabilitytorapidlytakeonnewtypesofworkwithGenAI—particularlytasksthattraditionallyrequirenicheskillsthatare

hardertofind,suchasdatascience—canbeagame-changerforindividualsandcompaniesalike.

HowGenAICanEquipKnowledgeWorkers

Inthepreviousexperiment,wemeasuredperformanceontasksthatwerewithintherealmoftheparticipants’

capabilities.1

(SeetoprowofExhibit1.)FortaskswhereGenAIishighlycapable,we

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foundthataugmentedworkersperformsignificantlybetterthanhumansworkingwithoutthe

technology.However,whenthetechnologyisnotcapableofperformingthetaskatexpertlevel,

humanstendtoover-relyonGenAIandperformworsethaniftheyhadcompletedthetaskontheirown.

:

Butwhathappenswhen,insteadofusingGenAItoimproveperformancewithintheircurrentskillset,peopleuseGenAItocompletetasksthatareoutsidetheirowncapabilities?DoesbeingaugmentedwithGenAIexpandthebreadthoftaskspeoplecanperform?

Forourlatestexperiment,morethan480BCGconsultantsperformedthreeshorttasksthatmimicacommondata-sciencepipeline:writingPythoncodetomergeandcleantwodatasets;buildinga

predictivemodelforsportsinvestingusinganalyticsbestpractices(e.g.machinelearning);and

validatingandcorrectingstatisticalanalysisoutputsgeneratedbyChatGPTandapplyingstatistical

2

metricstodetermineifreportedfindingsweremeaningful.

Whilethesetasksdon’tcapturetheentiretyofadvanceddatascientists’workload,theyare

sufficientlyrepresentative.Theyweredesignedtopresentasignificantchallengeforanyconsultant

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andcouldnotbefullyautomatedbytheGenAItool.

TohelpevaluatetheperformanceimpactofGenAI,onlyhalfoftheparticipantsweregivenaccesstotheGenAItool,andwecomparedtheirresultstothoseof44datascientistswhoworkedwithouttheassistanceofGenAI.Whenwedivedeeperintotheresults,threecriticalfindingsemerge.

TheImmediateAptitude-ExpansionEffect

WhenusingGenAI,theconsultantsinourstudywereabletoinstantlyexpandtheiraptitudefornewtasks.Evenwhentheyhadnoexperienceincodingorstatistics,consultantswithaccesstoGenAI

wereabletowritecode,appropriatelyapplymachinelearningmodels,andcorrecterroneous

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statisticalprocesses.(SeeExhibit2.)

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:

Weobservedthebiggestaptitude-expansioneffectforcoding,ataskatwhichGenAIishighlyadept.

Participantswereaskedtowritecodethatwouldcleantwosalesdatasetsbycorrectingmissingorinvaliddatapoints,mergingthedatasets,andfilteringtoidentifythetopfivecustomersina

specifiedmonth.

ParticipantswhousedGenAIachievedanaveragescoreequivalentto86%ofthebenchmarksetbydatascientists.Thisisa49-percentage-pointimprovementoverparticipantsnotusingGenAI.The

GenAI-augmentedgroupalsofinishedthetaskroughly10%fasterthanthedatascientists.

Eventhoseconsultantswhohadneverwrittencodebeforereached84%ofthedatascientists’

benchmarkwhenusingGenAI.Oneparticipantwhohadnocodingexperiencetoldus:“IfeelthatI’vebecomeacodernowandIdon’tknowhowtocode!Yet,IcanreachanoutcomethatIwouldn’thave

beenabletootherwise”ThoseworkingwithoutGenAI,ontheotherhand,o代endidnotgetmuch

furtherthanopeningthefilesandcleaningupthefirst“messy”datafields;theyachievedjust29%ofthedata-scientistbenchmark.

It’simportanttonotethatmostconsultantsareexpectedtoknowthebasicsofdatacleaningando代enperformdata-cleaningtasksusingno-codetoolssuchasAlteryx.Therefore,whiletheydidnothaveexperiencedoingthecodingtaskinPython,theyknewwhattoexpectfromacorrectoutput.

ThisiscriticalforanyGenAI-augmentedworker—iftheydon’thaveenoughknowledgetosupervisetheoutputofthetool,theywillnotknowwhenitismakingobviouserrors.

APowerfulBrainstormingPartner

Forthetaskthatinvolvedpredictiveanalytics,ourparticipantsfacedachallengingscenario:neithertheynortheGenAItoolwerehighlyadeptatthattask.Here,thetechnologywasstillvaluableasabrainstormingpartner.

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WhileallthetasksinourexperimentweredesignedsuchthattheGenAIcouldnotindependently

solvethem,thepredictive-analyticstaskrequiredthemostengagementfromparticipants.Theywereaskedtocreateapredictivemodel,usinghistoricaldataoninternationalsoccermatches,todevelopaninvestmentstrategy.Theirultimategoalwastoassesshowpredictable,orreliable,theirmodel

wouldbeformakinginvestmentdecisions.

“

ManyparticipantsusedGenAItobrainstorm,combiningtheirknowledgewiththetool’sknowledgetodiscovernewmodelingandproblem-

solvingtechniques.

AsshowninExhibit2,thiswasthetaskonwhichtheGenAI-augmentedconsultantwasleastlikelytoperformonparwithadatascientist,regardlessofpreviousexperienceincodingorstatistics.ThisisbecausetheGenAItoolislikelytomisunderstandtheultimategoalofthepromptiftheentiretaskiscopiedandpasteddirectlyintothetoolwithoutbreakingthequestionintopartsorclarifyingthe

goals.Asaresult,participantswithaccesstoGenAIweremorelikelytobeledastraythantheirnonaugmentedcounterparts.

Evenso,wefoundthat,withthesupportofGenAI,manyparticipantswereabletostepoutsidetheircomfortzone.Theybrainstormedwiththetool,combiningtheirknowledgewithGenAI’sknowledge

todiscovernewmodelingtechniquesandidentifythecorrectstepstosolvetheproblemsuccessfully.TheGenAI-augmentedparticipantswere15percentagepointsmorelikelytoselectandappropriatelyapplymachine-learningmethodsthantheircounterpartswhodidnothaveaccesstoGenAI.

Reskilled,butOnlyWhenAugmented

Participants’aptitudeforcompletingnewandchallengingtaskswasimmediatelyboostedwhen

usingGenAI,butweretheyreskilled?Reskillingisdefinedasanindividualgainingnewcapabilitiesorknowledgethatenableshimorhertomoveintoanewjoborindustry.Wefoundinourstudythat

GenAI-augmentedworkerswereinasense“reskilled,”inthattheygainednewcapabilitiesthatwerebeyondwhateitherthehumanorGenAIcoulddoontheirown.ButGenAIwasonlyanexoskeleton;theparticipantswerenotintrinsicallyreskilled,because“doing”withGenAIdoesnotimmediately

norinherentlymean“learningtodo”

Whileeachparticipantwasassignedjusttwoofthethreetasksintheexperiment,wegaveeveryoneafinalassessmentwithquestionsrelatedtoallthreetaskstotesthowmuchtheyactuallylearned.Forexample,weaskedacodingsyntaxquestioneventhoughnoteveryonedidthecodingtask—andthereforenoteveryonewouldhavehadachanceto“learn”syntax.Yetthepeoplewhoparticipated

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inthecodingtaskscoredthesameontheassessmentaspeoplewhodidn’tdothecodingtask.

Performingthedata-sciencetasksinourexperimentthusdidnotincreaseparticipants’knowledge.

Ofcourse,participantsonlyhad90minutestocompletethetask.Withrepetition,morelearningmighthaveoccurred.Wealsodidn’tinformparticipantsthattheywouldbetestedattheend,soincentivizinglearningmightalsohavehelped.Thisisimportant,becausewefoundthathavingatleastsomebackgroundknowledgeofagivensubjectmatters.

“

WefoundthatcodingexperienceisakeysuccessfactorforworkerswhouseGenAI—evenfortasksthatdon’tinvolvecoding.

GenAI-augmentedparticipantswithmoderatecodingexperienceperformed10to20percentage

pointsbetteronallthreetasksthantheirpeerswhoself-identifiedasnovices,evenwhencodingwas

5

notinvolved.Infact,thosewithmoderatecodingexperiencewerefullyonparwithdatascientistsfortwoofthethreetasks—oneofwhichhadzerocodinginvolved.

Basedonthis,wepositthatitistheengineeringmindsetthatcodinghelpsdevelop—forexample,havingtheabilitytobreakaproblemdownintosubcomponentsthatcanbeeffectivelycheckedandcorrected—thatultimatelymatters,moresothanthecodingexperienceitself.

Theriskoffullyautomatingcode,then,isthatpeopledon’tformthismindset—becausehowdoyoumaintainthisskillwhenthesourceofitsdevelopmentisnolongerneeded?Thisispartofalarger

discussion:Whatotherseeminglyautomatableskillshavesuchimportance?WilltheseskillsbecomethenewLatin,taughtmostlytocultivateaparticularmindset?

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:

ManagingtheTransition

Whilewehaveuseddatascienceasacasestudy,webelievethatourfinding—thataugmentedworkerscanskillfullyperformnewtasks—canbeappliedtoanyfieldthatiswithinthetool’s

capabilities.We’veidentifiedfivecoreimplicationsforcompanyleaders.(SeeExhibit3.)

TalentAcquisitionandInternalMobility.Theresultsacrossourworkforceexperimentshave

shownthatwhatanindividualcanperformonhisorherownbynomeansapproacheswhatcanbeaccomplishedwhenaugmentedbyGenAI.Thissuggeststhatthetalentpoolforskilledknowledge

workisexpanding.

RecruitersshouldthereforeincorporateGenAIintotheinterviewprocesstogetamorecompletepictureofwhataprospectiveemployeemightbecapableofwhenaugmentedbythetechnology.

Leadersmayalsofindthatanunlikelypersoninsidetheirorganizationcanfillanopenrole.We’re

notsuggestingthatnontechnicalgeneralistscanimmediatelybecomedatascientists.Butageneralistmarketercould,forexample,takeonmarketinganalysttasksorroles.

LearningandDevelopment.Whatdoesthismeanforemployeesseekingpathstoseniorroles

and/orleadership?HowshouldmembersoftheGenAI-augmentedworkforce,whocanflexiblytakeonvariousroles,cultivatetherightskillsforcareeradvancement—andwhatarethemostimportantskillsforthemtoretainlongterm?

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WhileGenAIhasanimmediateaptitude-expansioneffect,learninganddevelopmentremainthemostimportleverforcultivatingadvancedskillsandsupportingeachemployee’sprofessional

trajectory.Leadersthereforemustensurethatemployeeshaveincentivizedandprotectedtimetolearn.

Otherresearch

hasshownthatwhenspecificallyusedforlearning(and,unlikeour

participants,peoplearegenerallyincentivizedtolearnintheirjobs),GenAIisaneffectivepersonalizedtrainingtool.

“

LeadersshouldensurethatfutureimplementationsofGenAItoolsincludethefunctionalitytoinformtheuserifataskisoutsidethetechnology’s

capabilityset.

Ouranalysisalsosuggeststhatdevelopingsometechnicalskillsleadstogreaterperformance,evenfornontechnicalworkers.Regardlessofthetrainingemployeesreceive,companyleadersshould

ensuretheirfutureimplementationsofGenAItoolsincludethefunctionalitytoinformtheuserifataskisoutsidethetechnology’scapabilityset—informationthatshouldbecompiledfromregularbenchmarking.

Companiesarelikelytofindcompetitiveadvantagefromdevelopingtoolsandprocessesthat

preciselyassessthecapabilitiesofGenAImodelsfortheirusecases.AsshowninExhibit1,howaworkershoulduseGenAIgreatlydependsonunderstandingwhereatasklieswithintheirownskillsetandwithinthecapabilitiesofthetechnology.

TeamingandPerformanceManagement.Althoughourresultsshowitispossibleforageneralisttotakeonmorecomplexknowledgework,itwillbecrucialtomanagetheirperformanceandensurethequalityoftheiroutput.Thiscouldmeandesigningcross-functionalteamstoprovidegeneralistswitheasyaccesstoanexpertwhentheyneedhelpandestablishingregularoutput-review

checkpoints—becauseanoverconfidentgeneralistmaynotalwaysknowwhentoaskforsupport.

Leaderswillneedtorunpilotstoensuretheirteamingconfigurationsleadtothebestoutcomes.Thismaybeanopportunitytobreaksilosandintegrateteamsofgeneralistswithexpertsfromvarious

centersofexcellence.

StrategicWorkforcePlanning.Giventheimplicationsfortalentandteaming,howshould

organizationsthinkaboutspecializedexperttracksandthestructureoftheirworkforce?Whatdoesstrategicworkforceplanningforknowledgeworkmeaninaworldofconstantjobtransformation

andtechnologicaladvancement?Wedon’thavealltheanswers.Butwedoseethattheskillsneeded

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foragivenroleareblurring,andworkforceplanningwillnolongerbesolelyfocusedonfindingacertainnumberofpeoplewithaspecificknowledgeskill,suchascoding.

Instead,planningshouldincludeafocusonbehavioralskillsandenablersthatwillsupportamoreflexibleworkforce.WhileknowledgeworkersmaybetechnicallycapableoftakingonnewroleswiththehelpofGenAI,noteveryoneisequallyadeptatembracingchange.

ProfessionalIdentity.TheimpactofGenAIonprofessionalidentityisanimportantandcontentioustopic.Buta

recentsurvey

suggeststhatnegativeimpactscanbemitigatedwhenemployeesfeelsupportedbytheiremployers.

Infact,inourstudy,wefoundthat82%ofconsultantswhoregularlyuseGenAIforworkagreewiththestatements“GenerativeAIhelpsmefeelconfidentinmyrole”and“IthinkmycoworkersenjoyusingGenAIfortheirwork,”comparedto67%ofworkerswhodon’tuseitonaweeklybasis.Morethan80%ofparticipantsagreedthatGenAIenhancestheirproblem-solvingskillsandhelpsthemachievefasteroutputs.

Thissuggeststhathighlyskilledknowledgeworkersgenuinelyenjoyusingthetoolwhenitallows

themtofeelmoreconfidentintheirrole—whichalignswithourpreviousfindingsthat

mandating

theuseofAI

canactuallyimproveemployeeperceptionofAI.However,thisisonlytrueif

employees

believethatAIisbeingdeployedtotheirbenefit

.

WeareonlyatthebeginningoftheGenAItransformationjourney,andthetechnology’scapabilitieswillcontinuetoexpand.Executivesneedtobethinkingcriticallyabouthowtoplanforthisfuture,

includinghowtoredefineexpertiseandwhatskillstoretaininthelongterm.

Buttheyarenotalone:

Skilldevelopment

isacollaborativeeffortthatincludeseducationsystems,corporateefforts,andenablementplatformssuchasUdemyandCoursera.Eventheprovidersof

GenAImodelsshouldbethinkingabouthowtheirtoolscanfurtherenablelearningand

development.PreparingfortheGenAI-augmentedworkforcemustbeacollectiveendeavor—becauseourcollectivefuturedependsonit.

⃞BCGHENDERSONINSTTTUTB

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exploringanddevelopingvaluablenewinsightsfrombusiness,technology,andsciencebyembracing

thepowerfultechnologyofideas.TheInstituteengagesleadersinprovocativediscussionandexperimentationtoexpandtheboundariesofbusinesstheoryandpracticeandtotranslate

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Authors

DanielSack

MANAGINGDIRECTOR&PARTNER

Stockholm

LisaKrayer

PRINCIPAL

Washington,DC

EmmaWiles

ASSISTANTPROFESSOROFINFORMATIONSYSTEMS,

BOSTONUNIVERSITY’SQUESTROMSCHOOLOFBUSINESS

MohamedAbbadi

CONSULTANT

Washington,DC

UrviAwasthi

DATASCIENTIST

NewYork

RyanKennedy

AIENGINEER

Boston

CristiánArnolds

CONSULTANT

NewYork

FrançoisCandelon

ALUMNUS

©2024BostonConsultingGroup10

1ThatexperimentwasconductedusingthefirstversionofGPT-4.

2Oftheconsultantswhooriginallysigneduptoparticipate,480

completedtheexperiment.Participantswererandomlysplitintoa

controlgroupthatwasnotallowedtouseGenAIforthetasksanda

“treatment”groupthatwasaskedtouseGenAI.Each