生成式人工智能驱动转型：在各个层面为公司成功做准备+GenAl-driven+Transformation：Preparing+Your+Company+for+Success+with+GenAl+on+Every+Leve

STUDY

GenAI-driventransformation

PreparingyourcompanyforsuccesswithGenAIoneverylevel

MANAGEMENTSUMMARY

GenAI-driventransformation

PreparingyourcompanyforsuccesswithGenAIoneverylevel

GenerativeAI(GenAI)isclearlyheretostay.Notonlythat,itisbringingprofoundchange–transformation,indeed–tovirtuallyeveryaspectofbusinessasweknowit.This

studytracesthemeteoricriseofartificialintelligence(AI)backtotentativebeginningsinthe1930s.Itaffirmsthatevennow,astheinitialhypebeginstoebb,whatremainsistrulyastonishingintermsofitspotentialtoadvance

automation,improvequalityoutputsanddeliverstunninggainsinproductivity.

Thestudygoesontodiscussthoseforcesthatareshapingtheemergenceofthistransformativetechnologytodayandwillcontinuetodosointhefuture.Drawingoninputfrommorethan100topmanagers,ithighlightsareas

andbusinessfunctionswherethegreatestbenefits

areanticipatedbeforeaskingonecriticalquestion:Arecompaniesreadyforwhatliesahead?Andifnot,howcantheybestpreparethemselves?

ThelatterquestiontakesprideofplaceasRolandBergerdelvesintoitsownexperiencewithGenAI,outlines

severalinsightfulcasestudiesandpresentsmultiple

practicalguidelinestohelpcompaniesquicklyadapttheirownorganizationsinordertoreaplastingbenefitsona

coordinated,enterprise-widescale.

2|RolandBerger

Contents

P4

P12

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CoverImageAIgenerated

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1/TheGenAI-poweredtransformation–Keydriversandfactorsthateverycompanyneedstoknow

ThischapterbrieflytracesthehistoryofAIdevelopmenttothepresentdayandoutlinescapabilities,costsandregulationasthreekeydriversthatwillheavilyinfluenceitsfuturetrajectory.

2/ExpectedimpactofGenAI–Insightsfromindustryexperts

ThischapteroutlineswhatbenefitspractitionersindifferentindustriesexpectfromGenAI.Italsoaskswhethertheir

organizationsaresufficientlymaturetoreapthefullrewardsofthistechnology.

3/Transformingyourorganization–Gettingitright

Thischapterintroducesanddiscussesfourkeyprinciplesto

helpbusinessleaderspermeatetheirorganizationwithsuitableGenAIinastrategic,coordinated,enterprise-widemanner.

Casestudies–andourownGenAIjourney–addtextureandpracticalinsightstounderscorethevalueofthisapproach.

4/Practicalrecommendations–Itistimetoactnow!

Thischaptersummarizesthekeytakeawaysfromthisstudy,

providinghands-onadviceonhowtobeginyourGenAIjourney.

GenAI-driventransformation|3

TheGenAI-powered

transformation–

Keydriversandfactorsthateverycompany

needstoknow

ThischapterbrieflytracesthehistoryofAIdevelopmenttothepresentdayandoutlinescapabilities,costsandregulationasthreekeydriversthatwillheavilyinfluenceitsfuturetrajectory.

F

ewtechnologicalinnovationshavepossessedthe

transformativepoweroftheInternetandthepersonalcomputertoreshapeandrevolutionizeourworld.Now,however,aneweraisdawning:anerathatwillbedefinedbyGenerativeAI(GenAI).GenAItoohasthepotentialtoredefineourlivesinwayswecanscarcelyimagine.ProgramsdevelopedusingthelatestAItechnologiescanalreadyemulatehuman-likebehaviorinnumerousways.SomeexpertsevenlikenwhatisunfoldingtodaytotheIndustrialRevolution,whichreplacedmanuallaboronalargescale.YetwhatsetsGenAIapartevenfromsuchepoch-definingadvancesistheincrediblespeedwithwhichitisbringingchange.

ItsexplosivegrowthtodayisrootedinearlyAIconceptsdatingbacktothe1930s.Inthemeantime,thedevelopmentofAIhasbeenaniterativeprocess,witheachnewadvancebeingdevelopedandcommercializedmuchfasterthanitspredecessors.GenAIproductssuchasChatGPT,forexample,markastepchangefromthetraditionalpaceoftechnologicaldiffusion:WhileFacebookandInstagramtooktenand2.5monthsrespectivelytoreachamillionusers,ChatGPTneededjustfivedays–andonlytwomonthstochalkup100millionusers.Suchrapidmarketpenetrationisliterallyunprecedentedinanydomain.Andallthesignsarethatthecurrentfranticpaceofevolutionwillgrowfasterstill,withsuccessivegenerationsofAIandGenAImodelsenteringthemarketwithalmoststaccatofrequency.

YetspeedalonedoesnotexplainwhyGenAIissuchahugeglobaldraw–especiallygiventhatmanybusinessleadersfreelyadmittheydonotyettrulygraspthetechnologyitselforitsprofoundimplications.Whatdoes

getthebusinessworldexcitedisthepromiseofunheard-ofgainsinproductivity–apromiseincreasinglybackedupbyhardevidence:StudiesfromthelikesofStanford,HarvardandMITcitesubstantialproductivityboostsforpeopleusingGenAI(chatbots)overnon-AIusers.IntegratingGenAIinbusinessprocessesclearlyenhancesefficiency,improvesoutputqualityandsupportscreativeproblem-solving.

Tohelpusunderstandwherethiscomplexandattimesmind-bendingtechnologyisheadedanditsramificationsforpracticallyeveryaspectofeverybusinessgoingforward,RolandBergerhasconductedanin-depthsurveyofleadingexpertsandexecutives.ThesediscussionsledustoidentifythreekeyfactorsthatheavilyinfluenceboththegrowthanddisseminationofGenAI:thecapabilitiesofAImodels,thecostsassociatedwiththemandtheregulatoryenvironment.

SURVEYMETHODOLOGY

RolandBergerconductedawidevarietyofextendedinterviewswithhyperscalers,LLMproviders,techstartups,industryexpertsandbusinessleadersacrossvarious

industries.

Wealsosurveyedmorethan100managersandexecutivesfromdiversesectors.Wewantedtoknowwhatimpactsonorganizationscanbeanticipatedfrom

therapiddevelopmentandspreadofGenAIandhowcompaniesshouldrespond.Theinsightsgainedfromtheseinteractionsarediscussedindetailinthisreport.

GenAI-driventransformation|5

Capabilities

MAKINGGENAIWORK–WITHMORECOMPUTINGPOWERANDMORETRAININGDATA

ThecapabilitiesofGenAImodelsrelyheavilyoncomputingpower:Themorepowerisavailable,themoretrainingdatacanbeusedandthebettertheresultantmodeland

outcomes.Thevolumeofdatausedtotrainamodelisthusagoodindicatoroftheprogressbeingmade.FigureAillustratesthepoint,contrastingthetrainingtokens(seeglossary)usedforMeta'sLlama2modelswithitsnewerandmoreadvancedLlama3models,forwhichsignificantlymoredatawasprocessed.ThesameistrueforGoogle'sPaLMmodels.Whileonlyestimatesexistforthenewestclosed-sourceLLMs(seeglossary),itisbelievedthatGPT-4wastrainedonaround13trilliontokens,Google'sGeminiUltraonbetween20and40trilliontokens,andAnthropic'sClaude3Opuson40trilliontokens.A

PoweringthemassivedatacentersneededtoruntheseAImodelsmadeGPUproducerslikeNVIDIAstandoutperformersin2023and2024.Hyperscalerstooarefullycommittedtogrowingthistechnology,withAWS(Amazon)planningtoinvestsomeUSD150billioninnewinfrastructurefortheAI-poweredfuture.Similarly,GCP(Google)andAzure(Microsoft)areinvestinginnewdatacentersaroundtheglobe.

FROMUNIMODALTOMULTIMODAL

Asidefromsheervolume,therehasalsobeenamajorshiftinthekindsoftrainingdataemployed.Traditionally,mostLLMswere"unimodal"modelstrainedpredominantlyonextensivetext-baseddatasets.However,2024hasseentheemergenceof"multimodalmodels",suchasGPT-4o,thataretrainedon(andcangenerate)text,audio,imagesandevenvideos.Combiningdatafrommultiplesourcesinthiswaycandelivermorerelevantandaccurateinformationwithbettercontextualawareness,asthemodelcanbetterdiscernandinterprettherelationshipsbetweendifferent

AImprovementsinmodeltraining

Eachnewmodelorversionistrainedonincreasingamountsofdata

15,0003,600

Trainingtokens[bn]

4.6x

7.5x

780

2,000

PaLMPaLM2

Llama2Llama3

Source:Meta,Google

typesofdata.Italsocreatesamoreengaging,intuitiveandhuman-likeuserexperienceacrossmultiplemodesofcommunication,whichinturnopensthedoortomanycompellingusecasesinareassuchasengineering,sales,marketingandcustomerrelations.

ENLARGINGTHECONTEXTWINDOW

Linkedtotheadventofmultimodality,leadinglanguagemodelssuchasOpenAI'sGPTandGoogle'sGeminihaveshownremarkableprogresswithregardtothe"contextwindow",ameasureofprocessingpowerthatindicateshowmuchinformationamodelcanprocessefficientlyinasingleinteraction.BetweenGPT-3.5Turboandthelatest

6|RolandBerger

GPT-4TurboandGPT-4omodels,forexample,OpenAIrealizedaneightfoldincreaseinitscontextwindowcapacity.YetGoogle'spremierlanguagemodelsleaveeventheseadvancesfarbehind,boastinga63-foldincreaseinprocessingcapability.B

BThelatestGenAImodelscanprocessvastlymoredata

ForpopularLLMs,thecontextwindowisopeningwiderandwider

"LETUSREASONTOGETHER…"

UpscaledtrainingdataandcomputingpowerhaveinturnsignificantlyimprovedthereasoningcapabilitiesoflargelanguagemodelssuchasOpenAI'sGPTandAnthropic'sClaude.Thesemodelsnowdemonstrateasophisticatedlevelofunderstandingandcontextualanalysis,closelyresemblinghuman-likereasoningandevenexceedinghumanperformanceinmanystandardizedtests.Eachnewmodeliterationorupdateshowsaugmentedcapabilitiesinlanguagecomprehension,mathematicsandcoding.AlthoughitisuncertainwhetherfuturedevelopmentswillemulatethesubstantialleapsinlanguageunderstandingobservedbetweenGPT-3.5TurboandtheGPT-4modelfamily,orbetweenClaude2andClaude3Opusincoding,ongoingenhancementsinmodelarchitecturesandtrainingmethodologiesarelikelytobringthenextgenerationofAImodelsevenclosertoachievingperfectscoresbasedoncommonevaluationmetrics.

AIAGENTSASGAMECHANGERS

AImaynotyethaveagencyinthestrictsenseoftheterm,butAIagents(seeglossary)arealreadyaddinganextradimension.Whilelargelanguagemodelscurrentlyexcelatsolvingclearlydefinedproblems(suchassummarizingextensivetexts),theyreachtheirlimitswithhighlycomplexproblems.However,AIagentsarenowdemonstratinganabilitytooptimizepreciselythiskindofperformance.AnAIagentisacomputerprogramthatcanperformtasks(semi-)autonomouslybymakingdecisionsbasedonitsenvironment,inputsandpredefinedgoals.Suchagentscansolvespecificproblemsmoreeffectivelythanthebasic

128

8x

Context

windowsofpopularLLMs['000tokens]

32

16

GPT-432k

GPT-3.5Turbo

GPT-4Turbo&GPT-4o

1Pilotphase

Source:OpenAI,Google

2,000

63x

1,000

32

Gemini1.0Pro

Gemini

1.5Pro

1M

Gemini

1.5Pro

2M1

capabilitiesofLLMswouldallow.AdvancedAIagentsaremanagedusingwhatareknownasagentgraphs(seeglossary),whichareeffectivelystructurednetworksofinterconnected,specializedagentsthatworkcollaborativelytotacklecomplexgoals.Byusinggraph-basedagents,organizationscandevelopmoresophisticatedandadaptableAIsolutionsthatarecapableofhandlingcomplextasksandcanthusbedeployedforawiderrangeofcomplexbusinessprocesses.AgooddesignguidancewouldbetodefinethegraphinawaythateachsubtaskisnottoosmalltoleveragetheGenAIgeneralizationcapabilitiesandnottoobigthattheagentresponsibleforthesubtaskhasnotenoughcapabilities.

GenAI-driventransformation|7

Costconsiderations

CHIPPRICESWEIGHHEAVY

Thehigh-endgraphicsprocessingunits(GPUs)usedbydatacenterstotrainlargelanguagemodels(LLMs)andrunGenAIsolutionsareextremelypowerfulbutalsoveryexpensive,currentlycostingasmuchasUSD40,000apiece.ThisnaturallydrivesupcostsforthetechcompaniesthattrainanddevelopLLMs.However,italsoforceslargecorporateuserstothinkverycarefullyaboutwhethertheycanaffordtotrainandfine-tunemodelsoftheirown.Thatsaid,sincemostcompaniesneithertraintheirownLLMsnoroperatetheirowndatacenters,theyonlyhavetopayusagefees,whicharerelativelylowandfallingasthebigLLMproviderswageabitterpricewar(seenextpage).Itthereforeremainstobeseenwhethertechproviderswillbeableorwillingtopassontheirownhighhardwarecoststodownstreamusers.

Atthesametime,thechipsthemselvesareincreasinglybecomingmuchmoreefficient.SomemanufacturersarenowevendevelopingnewtypesofchipsspecificallydesignedtorunGenAIapplicationsmorecost-efficiently.Accordingly,evenifendusersdoenduphavingtopaymoreforhardware,theyshouldstillgetbettervalueformoneyastheadvanceinchipcapabilitiesfaroutstripsanyincreaseinprice.Demandforchipscertainlyremainsbuoyantand,apartfrominflatingprices,isalsoprojectedtoexpandthemarketforGPUsforuseinGenAIdatacentersfromUSD100billiontodaytoanestimatedUSD3trillionby2040.

INCREASINGSPENDINGONTHECLOUD

Cloudcostsarethesecondmajorcostfactor,asmostGenAIsolutionstypicallyruninpubliccloudsprovidedbyAWS,GCPorAzure,whotogetheraccountforroughly67%ofthetotalcloudmarket.ResearchbyGartnershowsthatglobalspendingonpubliccloudservicesisincreasingby19%everyyear,fromUSD413billionin2021toanestimatedUSD825billionin2025.C

CCloudcostsovertime

Globalspendingonpubliccloudsis

increasingsteadily

+19%

825

Global

spending[USDbn]

675

561

490

413

20212022202320242025

Source:Gartner

WhileusersofGenAIapplicationsmustclearlypreparethemselvesforfurtherrisingcostsassociatedwithcloudservices,thevaluepropositionforendusersonceagaincontinuestoimprovesignificantly.Evenasoverallspendingincreases,thecapabilitiesandfunctionalityofcloudservicesexpand–includingmorecomputingpower,betterdatamanagementtoolsandmoresophisticatedAIservices–duetocompetitionbetweenAmazon(AWS),Google(GCP)andMicrosoft(Azure).Businessusersbenefitfromthisandcanessentiallyachievemorewithless.Atthesametime,flexiblepricingmodelsletthemtailorexpenditurestoactualusage.

8|RolandBerger

MODELFEES

Model(orlicense)costsareanothersignificantexpenseitem.Open-sourcemodelssuchas(manyof)theFrenchMistralmodelsdonotdemandalicensefee,buttheydorequireself-hosting,whichincursinfrastructurecosts.Incontrast,closed-source(fee-paying)models,likeOpenAI'sGPTmodels,Google'sGeminiandAnthropic'sClaude,areusedheavilytopowerGenAIapplicationssuchascustomersupportchatbots,codingassistants,translationtoolsandeventhe3DdesigngeneratorsusedinR&D.

Eitherway,fiercecompetitionbetweenthesecompaniesandmoreefficientmodel-buildingtechniquesaredrivingdownthecostofusinglargelanguagemodels(LLMs).ThefigurebelowshowshowfarthepricesofsuccessiveGPT-4andClaudemodelshavealreadydropped.Similarly,thenewestGeminimodel(Gemini1.5Pro)isalsopricedverycompetitively,startingatUSD3.5permillioninputtokens.

Toputthesedecliningcostsintoperspective,considerthecaseofacorporateChatGPT-likechatbotpoweredbyOpenAI'sGPTmodels:Ifanenterpriseconsumes10,000,000tokensperday–theequivalentof7,000,000words–thedailycosttotheenterprisewouldbeUSD600withtheolderGPT-432kmodel,butonlyUSD50usingnewermodels(suchasGPT-4o).Sincetokencostsareexpectedtobecomemorecost-efficientwitheveryiteration,suchmassivecutsincostswillenablecompaniestobetterscaletheirGenAIapplications.D

HIDDENCOSTS

ItmustbesaidthatthereisanothercostinherentinGenAI'santicipatedtriumphalmarch:acosttotheenvironmentandpotentiallytotheclimate.Weestimatethatby2040,theenergyneededtorunGenerativeAIhardwareindatacenterscouldsoartosomethinglike4,800terawatt-hoursperyear,surpassingtheelectricityconsumptionoftheentireUnitedStatestodayifsignificantadvancesinenergyefficiencyarenotmade.Moreover,poweringGenAIsystemsconsumeshugeamountsofwater

DDecreasingmodelfees

Usagefeesforpopularclosed-sourcemodelsPrice1per1Mtokens[USD]

60

8

-92%

5

GPT-432k

GPT-4Turbo

GPT-4o

Claude2Claude3.5

Sonnet

1PriceofinputtokensasofJuly28,2024Source:OpenAI,Anthropic

tocooltheirprocessors.Someexpertsthuspredictthatweareheadingtowardan"AIenergycrisis",asfutureGenAImodelscouldconsumeasmuchpoweraswholecountriesaredoingin2024.Thatsaid,technology(andenergy)providersareworkingveryhardtomakedatacentersmoreenergy-efficient,butalsotoswitchtomoreandmorerenewableenergyandsubstantiallyreducetheircarbonfootprintinotherways.GiventheimportanceandscaleoftheanticipatedadvancesinGenAI,itistobehopedthattheseeffortsbearfruitsoonerratherthanlater.

-63%

3

10

GenAI-driventransformation|9

Regulation

LAISSEZ-FAIRE,HEAVY-HANDEDOVERSIGHTORSOMETHINGINBETWEEN?

Emergingtechnologiesoftentransitionfromaphaseofminimalorvirtuallynoregulationtoincreasedoversightastimegoeson.ThiswastrueoftheInternet,andartificialintelligenceisnoexceptiontotherule.TheEuropeanUnionisattheforefrontoflegislativemoves,withtheEUAIActslatedtobecomelawin2026.Someothercountries,suchastheUSAandJapan,havebeguntointroducebasicregulationsand/orvoluntaryguidelines.Mostregions,

however,havenoregulationsatallatthepresenttime.ForatechnologyascomprehensivelytransformativeasAI,though,standardsandregulationswilldoubtlessbeneeded–asineveryotherindustry–andwilltakeshapeovertime.E

Futureoutlook

WHATTHEPOST-HYPEFUTUREHOLDS……ANDHOWTOPREPAREFORIT!

GenAIhashadawildrideoverthepast24months.Predictionsin2022rangedfromprofoundlyskepticalto

ETheEUleadstheworldinGenAIregulation…

…whilemostregionshavesofardonenothingatall

-

-

Source:RolandBergerdeskresearch

10|RolandBerger

massivelyoverhyped.AndwithtoolssuchasChatGPTnotyetonthemarket,awarenessofandinterestinthistechnologywereminimal.Sincetheirlaunch,however,theinitialhypehasgivenwaytothoughtful,oftenexperimentalimplementation–andGenAIadoptionrateshavesoared.ThereleaseoftheGPT-4omodelaloneexemplifieshowswiftlytechnologicalbreakthroughssuchasmultimodalitycanimpactonthemarket.

Modelcapabilitiesarestillimprovingatpaceascomputingpowerincreasesandmarketcompetitionispushingpricesdown.Productivitygains–intermsofmodelqualityandusability–arelikewiseprojectedtorisesubstantially.Inallprobability,GenAIwillthereforebecomeincreasinglyaffordable,betteratwhatitdoesandubiquitousinallwalksofbusinesslife.Moreover,astheresidualhypediesdown,userswillincreasinglydiscoverscalingasakeysolutiontomanyofthechallengestheyface.Thekeyfactorsoutlinedinthischapter–capabilities,costsandregulation–willlargelydeterminehowquicklyGenAIisadoptedgoingforward.Butsotoowillanotherfactorwehavenotyetlookedat:thereadinessoforganizationstoharnessGenAIinawaythatgenuinelytransformstheirbusiness.

Companiesthatunderstandhowtoleveragethis technologyandcanadapttheirorganizationaccordingly–regardlessofhowfasttechnologicaldevelopmentsandadoptionratesunfold–willenjoyasignificantcompetitiveadvantage.Yetpreciselythisissue,more sothanfearsofflatteningcurves,regulatoryowngoals,orevendisappointingperformance,posesaveryreal threattothesuccessofGenAIimplementationatmanycompanies.Why?Becausemostorganizationsarequite simplynotyetpreparedto"adoptandadapt".Toget themselvesready,however,theymustfirstunderstandexactlywhattheimpactofGenAIwillbeintheirchosen lineofbusiness.

"Aswemovetowarda

GenAI-enabledfuture,every

companyfacesabrutalchoice:

AdoptAIorbeunabletocompete.AIisheretostay,andthosewho

integrateiteffectivelywillgetbetterproductstomarketmorequickly

andatlowercost.Thistechnologywillquitesimplyimpactallindustriesandallregions,reshapinghow

businessesoperateglobally."

MariaMikhaylenko

SeniorPartner,GlobalManagingDirector

"Whenthehypestartstodie

down,that'swhenyoubegin

toseewhetheratechnologyis

genuinelythedisruptivegame

changeritwasmadeouttobe.Andifanything,peoplearegettingevenmoreexcitednowasitbecomes

apparentthatGenAIreallyisgoingtochangeeverything."

Dr.JochenDitsche

SeniorPartner,HeadofDigital

GenAI-driventransformation|11

ExpectedimpactofGenAI–Insightsfromindustryexperts

ThischapteroutlineswhatbenefitspractitionersindifferentindustriesexpectfromGenAI.Italsoaskswhethertheirorganizationsaresufficientlymaturetoreapthefullrewardsofthistechnology.

O

urexpertsurveyfindingsareunequivocal:GenAIwillhaveapowerfulimpactonwholeorganizationsandonallbusinessfunctions,albeittovaryingdegrees.GenAIoperatesintwomainways:first,byenablingawiderangeoftaskstobeautomated,andsecond,byenhancingtheaccuracy,valueandprofessionalism–i.e.,thequality–ofoutputs.Wewillnowaddresseachareaofpotentialinturn.

POTENTIALFORAUTOMATION

Inoursurvey,expertassessmentssingledout"customerrelations,sales&marketing"and"service&support"asthebusinessfunctionswiththehighestpotentialforGenAI-basedautomation,astheseunitscommonlyrequire

creativecapabilities,anuancedunderstandingoflanguageandgoodwritingskills.Thatsaid,ratingsof"moderatetohighpotential"predominateinvirtuallyeverynamedbusinessfunction.Althoughsomefunctionsareclearlyseentohavelesspotentialthanothers,morethan50%ofrespondentsexpectGenAItohaveatleastamoderateimpactonautomationevenin"businessdevelopment&strategy",whichbringsuptherear.F

POTENTIALFORQUALITYIMPROVEMENTS

TheperceivedpotentialforGenAItoimprovethequalityofoutputs(seechartGonthenextpage)sharessomesimilaritieswithchartF(below),butalsorevealscertainstrikingdifferences."Customerrelations,sales&marketing"areagainseenasthebusinessfunctionswhereGenAIcandeliverthegreatestimpact.Butinterestingly,secondplacethistimegoesto"R&Dandqualitycontrol"

–twodisciplinesexposedtoavastarrayofinputsand

FThepotentialofGenAItoautomatetasksindifferentbusinessfunctions

Evenwherepotentialisseenasweakest,morethanhalfofexpertsstillexpectamoderatetohighimpact

Deepdive-Potentialforautomation1

Customerrelations,sales&marketingService&supportfunctions(F&C,Legal,HR,IT)

R&DandqualitycontrolProcurement,SCMandlogistics

ProductionBusinessdevelopment&strategy

LowModerateHigh

7%29%64%

10%27%63%

17%28%55%

17%31%52%

24%36%40%

40%40%20%

1Question:PleaseratethepotentialofGenAItoautomatetasksinthefollowingbusinessfunctionsinyourindustry.

Source:RolandBergerGenAIexpertsurvey(N=100+)

GenAI-driventransformation|13

variables.Researchanddevelopmentinparticularalsopresupposeiterativecyclesofprototyping,testingandchanging,andtheemergenceofmultimodalityandAIvisionmodelshascausedpotentialforGenAI-drivenqualityimprovementstoskyrocketintheseareas.InindustrialdesigninthecontextofR&D,forexample,GenAIisnowchurningoutdeliverableswithremarkableaccuracy.

Thenexttwosetsofbusinessfunctions–"Production"and"Procurement,supplychainmanagement(SCM)andlogistics"–arealltightlyinterwovenwitheachother.Theyarealsogenerallycapex-drivenandfollowcertaininvestmentcycles.Here,embeddingGenAIinthesoftwaresystemsusedisagainclearlybelievedtoyieldsubstantialbenefits,butwillalsorequirecoordinatedadoptionssimplyduetothenumberofpartiesinvolved(insupplychainsandprocurement,forinstance).

Shared"service&supportfunctions"(suchasfinanceandcontrolling,legaldepartments,HRandIT)ranklowerintermsofexpectedqualityimprovementsfromGenAIthanisthecaseforautomationpotential.Thisisperhapsduetotherepetitiveandlargelydata-drivennatureofmanyoftheircoreactivities,whichlendthemselvesmoretoautomationandstreamliningthantootheraspectsofimprovedquality.Evenhere,however,morethan80%oftheexpertssurveyedareconvincedthattheimpactofGenAIonqualitywillbemoderatetohigh.G

Aswithautomationpotential,thespreadofassessmentsherelargelyreflectsthe"strengthsandweaknesses"ofGenAIwithregardtodifferenttasks.Thoseareas–suchas"customerrelations,sales&marketing"wherenuancedlinguisticcapabilitiesareatapremium–areunderstoodtobetheoneswherethepotentialforqualityimprovementsisgreatest.Ontheotherhand,itis(still)difficulttoteachanAImodel

GThepotentialofGenAItoimprovethequalityofoutputsindifferentbusinessfunctions

Hereagain,eventhe"lowest"scoresseeamajorityanticipatingmajorimprovementsduetothedeploymentofGenAI

Deepdive-Potentialforqualityimprovements1

Customerrelations,sales&marketingR&DandqualitycontrolProduction

Procurement,SCMandlogisticsService&supportfunctions(F&C,Legal,HR,IT)

Businessdevelopment&strategy

LowModerateHigh

13%27%60%

16%