2024塑造生成式人工智能的未来：开源在GenAI技术演进与实施中的作用研究报告 Shaping the Future of Generative Al The Impact of Open Source Innovation

ShapingtheFutureofGenerativeAI

TheImpactofOpenSourceInnovation

AdriennLawson,TheLinuxFoundation

StephenHendrick,TheLinuxFoundationNancyRausch,TheLinuxFoundation

JeffreySica,TheLinuxFoundation

MarcoGerosa,Ph.D.,NorthernArizonaUniversity

Forewordby

HilaryCarter,TheLinuxFoundation

November2024

ShapingtheFutureofGenerativeAI

84%oforganizationshavemoderate,high,orveryhighadoptionofGenAI.

41%ofGenAI

infrastructurecodeisopensource.

For92%ofsurveyedcompanies,GenAIisimportant,and51%

consideritextremelyimportant.

For71%oforganizations,theopensourcenatureofa model/toolhasapositiveinfluenceonitsadoption,duetotransparencyandcostefficiency.

82%ofrespondentsagreethatopensourceAIiscriticalforapositive

AIfuture.

78%oforganizations

believeitisimportanttouseopensourcetools

hostedbyaneutralparty,primarilyduetostandards®ulationscomplianceandtrust.

Amongthosewhoserveorself-hostGenAImodels,50%useKubernetesfortheirinference

workloads.

30%oforganizationsuseproprietarydatafortheirproprietarymodels,and22%useitforopen

sourcemodels.

Mostorganizationsadopt multiplestrategiesfor hostingGenAIinference, includingself-hosting inthecloud(49%)andmanagedAPIservices(47%).

65%ofsurveyed

ForthefutureofGenAI,83%ofrespondentsagreethatAIneedstobeincreasinglyopen.

organizationsbuildandtrainGenAImodelsoncloud-based

infrastructure.

GenAIhasimproved

productivityfor79%ofrespondentsandhasallowedthemtolearnnewskillsandimprovecreativityandinnovation.

Copyright©2024

TheLinuxFoundation

|November2024.Thisreportislicensedunderthe

CreativeCommonsAttribution-NoDerivatives4.0InternationalPublicLicense

Contents

Foreword

0

4

Executivesummary

0

5

Introduction

0

7

GenAIadoptionanduseinorganizations

0

8

GenAIadoption

0

8

GenAIactivitybreakdown:Consumptiondominatesas

custommodelbuildinggainstraction

0

9

PrimaryGenAIusecases

12

HowopensourceisexpandingtheroleofGenAI

16

HighadoptersofGenAIaremorelikelytouseopen

sourcetoolsthanlowadopters

16

Thecriticalroleofopensourcetoolsandframeworks

inmodelbuildingandinference

18

Theopensourcenatureofatoolhasapositiveinfluence

onitsadoption

20

GenAIandthecloudnativeapproach

23

Cloudnativeandhybridcloudstrategiesarefoundational

tohoworganizationsdeployandhosttheirGenAImodels

23

Kubernetesasakeyenablerforhostingscalable

GenAIworkloads

25

Cloud-basedinfrastructureleadsthewayin

GenAImodelbuilding,withhybridandon-premises

solutionsremainingkey

27

ChallengesinGenAIadoption

29

PrimaryconcernsofGenAIadoption

30

InvestmentinGenAI

31

Impactonemployment

33

ThefutureofGenAIisopen

34

Topprioritiesforopensourceprojects

34

TheroleofopensourceAIinthefutureofGenAI

36

Conclusionsandrecommendations

38

Methodology

40

Aboutthesurvey

40

Data.Worldaccess

42

Respondentdemographics

42

Abouttheauthors

43

Acknowledgments

44

SHAPINGTHEFUTUREOFGENERATIVEAI3

SHAPINGTHEFUTUREOFGENERATIVEAI4

Foreword

Afewdaysbeforethisreportwaspublished,myson,whoispursuinghisBachelorofMusicdegree,calledmetoaskwhatIthoughttheimpactwouldbeof“opensourceGenerativeAI”onthemusicindustry.“DidIthinkthatopensourceGenerativeAIwouldhelp

creators,orhurtthem?”heasked.Inearlydroppedthephone.OfcourseItookhimthroughmyreasoningforwhyopennesswasthewaytobuildatrustedfuturefordigitalcreationsofallkinds,betheydigitalmusic,ordigitalapplicationsusedinanindustrycontext.

Ievenshowedhimsomedata!

WeknowthatGenAIistransformingindustriesatanunprecedentedpace.Asthistechnologymovesintothemainstream,

organizationsarerallyingaroundtheideathatAI’sfuturemustbeopen.Infact,82%oforganizationsbelieveopensourceAIiscriticalforensuringapositiveAIfuture,and83%agreethatAIneedstobeincreasinglyopentofostertrust,collaboration,andinnovation.Tome,thisistheheadlinetakeawayfromthisreport.

TheLinuxFoundationisproudtochampionthisvisionbynurturinganecosystemwhereopennessdrivesprogress.ProjectslikePyTorchandinitiativessuchastheGenerativeAICommonsexemplifyhowopensourcefuelsinnovation.Meanwhile,theLFAI&DataFoundation’sModelOpennessFrameworkanditscompaniontoolsareempoweringmodelcreatorsanduserswithpractical,transparentguidanceforbuildingandadoptingopenAIsystems.

Cloudnativetechnologiesarealsocentraltothisevolution.NotonlycancloudnativeprovideascalableandreliableplatformforrunningAIworkloadsoncloudinfrastructure,butAIisenhancingcloudnativeofferingsthemselves.Throughsharedstandards,robustframeworks,andsecureinfrastructure,theCloudNativeComputingFoundation(CNCF)isenablingenterprisestoreducecostsandacceleratetheperformanceofAIapplications.Thissymbiosisunderscoresthetransformativepowerofopensourcetomeettoday’sbusinessandtechnicalchallenges.

GenerativeAI’spotentialislimitless,butitssuccessreliesontrust,accessibility,andglobalcollaboration.IamgratefultoLFAI&DataandCNCFforsponsoringhisresearch,andindoingso,creatingdatathatcanhelpdecisionmakingtoscaleandsustainopensourceAIprojects.

Thisreportisatestamenttowhatwecanachievewhentheworldworkstogether,openlyandtransparently.Fornextgenerationcreatorslikemyson,andbusinessdecisionmakers,itprovidesareasontobeoptimistic.

HILARYCARTER

SeniorVicePresident,ResearchTheLinuxFoundation

SHAPINGTHEFUTUREOFGENERATIVEAI5

Executivesummary

Thereport,ShapingtheFutureofGenerativeAI,writtenby

theLinuxFoundation,supportstheimportantroleofopen

sourceintheevolutionandintegrationofgenerativeAI(GenAI)technologieswithinorganizations.Basedonasurveyof316

professionalsacrossdiverseindustries,thereportshowshowopensourceplatformsandtoolsarenotonlyacceleratingGenAIadoptionbutarealsosettingafoundationalframeworkfor

futureAIadvancements.Currently,94%oforganizationsareusingGenAI.Leadingusecasesincludeprocessautomation,contentgeneration,andcodegeneration.

OpensourcesoftwareisalreadyaforceshapingGenAI.On

average,41%ofanorganization’scodeinfrastructurethat

supportsGenAIisopensource.HigheradoptersofGenAIare

morereliantonopensourcecode(47%)comparedtolower

adopters(35%).OrganizationsthatarehigheradoptersofGenAIarenotjustheavyusersofopensourcetechnology;63%arealsosignificantcontributorstoopensource.Consequently,71%of

respondentsreportthatopensourcepositivelyinfluencestheirdecision-making,and73%oforganizationsexpecttoincreasetheiruseofopensourceGenAItoolsoverthenexttwoyears.

CentraltothesuccessoftheGenAIspaceareopensource

frameworkssuchasTensorFlowandPyTorchforbuildingand

trainingGenAImodelsandapplicationframeworksincluding

LangChainandLlamaIndexforinferencing.Theseopensource

frameworksenableorganizationstobuild,train,anddeploy

modelsatafractionofthecostassociatedwithproprietary

tools.Opensourcemodelsempowerorganizationstodevelop

customizedsolutionswhilepreservingtransparencyand

reducingdependencyonclosed-sourceplatforms.Thisflexibilityhasprovedessentialinindustrieswheretrust,transparency,andregulatorycompliancearecritical.

LookingtothefutureofGenAItechnology,opensource’s

influenceintheAIdomainisexpectedtoexpandfurther.This

surveyrevealsthat83%oforganizationsstronglyagreeoragreethatAIneedstobeincreasinglyopen.Additionally,82%reportthatopensourceAIisacriticalcomponentforasustainable

AIfuture,with61%expressingconfidencethatthebenefitsofopensourceoutweightheassociatedrisks.ThegrowthofopensourceGenAItechnologyislikelytobesignificant,with73%oforganizationsexpectingtoincreasetheiruseofopensource

generativetoolsoverthenexttwoyearsand26%anticipatingasubstantialriseinuse.

OrganizationsthatintegrateopensourceGenAItoolsnot

onlybenefitfromreducedcostsbutalsooftencontributetoacollaborativeecosystemthatdrivestechnologicalprogress.Thereportalsodiscussescloudnative’scriticalroleinsupporting

scalableGenAIsolutions.Cloud-basedinfrastructure,

combinedwithopensourceframeworksandtools,allows

organizationstomanageanddeploycomplexAImodelsmoreefficiently.Kubernetes,forinstance,hasemergedasakey

enablerfororchestratingscalableGenAIworkloads,with50%oforganizationsusingKubernetestohostsomeoralloftheirGenAIinferencingworkloads.

“Organizationswithhigherlevelsof

GenAIadoptionarehelpingtoshape

next-generationframeworksand

models,aligningthemmorecloselywithadvanced,real-worldusecases.”

SHAPINGTHEFUTUREOFGENERATIVEAI6

Thisreportrecommendsthatorganizationscontinueto

prioritizeopensourceintheirGenAIstrategiestoremain

competitiveandalignedwithindustrytrends.Italsohighlightstheimportanceofneutralorganizations,suchastheLinux

Foundation,CloudNativeComputingFoundation(CNCF),

andLFAI&DataFoundation,inprovidingopengovernance

structuresthatimprovetrustandcollaboration.AsAIcontinues

toreshapeindustries,opensourcewillremainindispensable,

offeringabalanced,transparent,andcommunity-ledpathway

toinnovationthatwilldefinethefutureofAItechnologies.By

offeringaccessible,adaptable,andcommunity-drivenresources,opensourcehasdemocratizedaccesstoGenAI,allowing

organizationsofallsizestoleveragecutting-edgeAIcapabilitiessecurelyandeffectively.

SHAPINGTHEFUTUREOFGENERATIVEAI7

Introduction

Thisreportexploresthedeployment,use,andchallengesofGenAItechnologiesinorganizationsandtheroleandfutureofopensourceinthisdomain.

LinuxFoundationResearchanditspartnersconductedawebsurveyfromAugustthroughSeptember2024,whichprovidedtheempiricalbasisforthisstudy.Surveyrespondentscreeningensuredthatrespondents:

•Werefamiliar,veryfamiliar,orextremelyfamiliarwiththeadoptionofGenAIintheirorganization

•Workedforanorganization

•Hadprofessionalexperience

Atotalof316respondentscompletedthesurvey.

Therearealsoavarietyofcalloutsthroughoutthisreport.

Thesecalloutsincludeselectedverbatimcommentsin(italicizedbluetextwithnobackgroundcolour)responsetoanopentextquestioninthesurvey,whichasked,“Doyouhaveanyfinal

commentsorthoughtsaboutGenAI?”

SHAPINGTHEFUTUREOFGENERATIVEAI8

GenAIadoptionanduseinorganizations

OrganizationsareadoptingGenAIbecauseof

itsabilitytoaddressabroadarrayofstrategic

andtacticalneeds,includingcontentcreation,

personalizedcustomerexperiences,decision

support,processautomation,employeetraining,andresearchandplanning.Tounderstandthe

developmentanduseofGenAIandhowopen

sourceisimpactingtheevolutionofGenAI,we

needtofirstevaluatehoworganizationsare

involvedwithGenAI,itsleadingusecases,andthematurityofGenAIdeployments.

GenAIadoption

Figure1showstheextentoftheadoptionofGenAI.ThetopchartinFigure1indicatesthat94%of

organizationsareinvolvedwithGenAIandcanbesegmentedintotwocategories:organizationswhohaveveryhighorhighGenAIadoption(42%,higherGenAIadopters)andorganizationswhohaveslightormoderateGenAIadoption(52%,lowerGenAI

adopters).WealsoseeinFigure1that84%of

organizationshaveamoderate,high,orveryhighadoptionofGenAI.

FIGURE1:HOWORGANIZATIONSAREADOPTINGGENAI

TowhatextenthasyourorganizationadoptgenerativeAI?(selectone)

Veryhighadoption:generativeAIiscriticaltowhatourorganizationdoes

Highadoption:generativeAIusedinproductioninselectedareas

Moderateadoption:experimentingwithhowgenerativeAIcanaddvalueinselectedareas

Slightadoption:researchingorevaluatinggenerativeAI

NoadoptionofgenerativeAItoolsandmodels

16%

27%

10%

6%

41%

42%

52%

2024GenAIsurvey,Q7,SampleSize=316

WhatactivitiesdoesyourorganizationundertakewithgenerativeAImodels?(selectallthatapply)segmentedby:

TowhatextenthasyourorganizationadoptedGenAI?(selectone)

WeconsumegenerativeAImodelsforinference

WebuildortraingenerativeAImodels

WeservegenerativeAImodelsinternally

Noneoftheabove

Don'tknowornotsure

65%

61%

69%

38%

33%

44%

43%

36%

52%

8%

10%

5%

7%

8%

6%

TotalSlightormoderateGenAIadoptionHighorveryhighGenAIadoption

2024GenAIsurvey,Q32byQ7,SampleSize=297,ValidCases=297,TotalMentions=479,answeredbyorganizationswhoadoptedGenAIinQ7

SHAPINGTHEFUTUREOFGENERATIVEAI9

GenAIactivitybreakdown:

Consumptiondominatesascustommodelbuildinggainstraction

CoreactivitiesrelatedtoGenAI,includingbuilding(training),

serving,andinferencing(consumingamodel),areshownin

thechartatthebottomofFigure1.Inferencing,at65%overall,isaprimaryGenAIactivity.Inferencingissignificantlyhigher

thaneitherbuildingmodels(38%)orservingthesemodels

(43%).Organizationsarechoosingtotuneand/ortraintheir

ownGenAImodelstomeetspecificbusinessneedsandmake

thesemodelsmoreaccurateandrelevant.Custommodels

alloworganizationstotailorresponses,fine-tunelanguage,

andincorporatedomain-specificknowledgetocreateoutputs

thataligncloselywiththeirbrandandindustryrequirements.

Fine-tuningamodelalsoprovidesenhancedcontrolovertheAI’sevolutionandreducesdependencyonexternalproviders.Figure1(atthebottom)alsoshowsthatorganizationsthathavea

higherlevelofGenAIadoptionalsoaremoreinvolvedinbuilding/trainingmodels(44%),internallyservingthesemodels(52%),

andconsumingthesemodels(69%).

Figure2exploresthevarioustechniquesinusetoimprovetheperformanceofGenAImodels.Theleadingtechnique,promptengineering,isshowingsignificantgainsfornearly80%of

organizationsthathaveadoptedGenAI.Promptengineeringisthepracticeofoptimizinginputs(prompts)todeliverthe

mostaccurate,relevant,orcreativeoutputsfromanAImodel.Bycarefullydesigningprompts,promptengineersimprove

modelperformance.Theeleganceofpromptengineeringisthatthisincreasedperformancedoesnotrequireanychangesto

theunderlyingGenAImodel,althoughitdoesrequireamoredetailedapproachindefininginputs.

Retrievalaugmentationgeneration(RAG)isalsoaleading

techniqueforimprovingperformance.RAGcombinesthe

poweroflargelanguagemodels(LLMs)withreal-time,

relevantinformationretrievaltogeneratehighlyinformedandcontextuallyaccurateresponses.Thisapproachaugments

themodel’soutputsbygroundingthemindomain-specific

information.RAGimprovesmodelperformancebyprovidingabridgebetweenstaticmodelknowledgeanddynamic,up-to-

datecontent,whichisidealforapplicationssuchascustomer

support,research,anddecisionsupportsystems.RAGisdrivingmaterialgainsformorethan70%oforganizationsthathave

adoptedGenAI.

Fine-tuninganLLMisyetanothertechniquethatorganizationscommonlyusetoimprovetheperformanceoftheirGenAI

models.Fine-tuningadjuststheLLM’sinternalparametersbytrainingondomain-specificdata,whichembedsspecialized

knowledgedirectlyintothemodel.Thismakesthemodelmorefluentinspecifictopicsbutlimitsittostaticknowledgepresentduringtraining.Fine-tuningisshowingsignificantgainsfor

nearly70%oforganizationsusingGenAI.

SHAPINGTHEFUTUREOFGENERATIVEAI10

FIGURE2:THETOPTHREETECHNIQUESFORIMPROVINGGENAIMODELPERFORMANCE

HowmuchhavethefollowinggenerativeAItechniquesimprovedtheperformanceofyourbaselineapproach?(oneresponseperrow)filteredfor:Whattechniquesareyouusinginyourorganization?(topthreeshown)

Promptengineering

RAG(RetrievalAugmentedGeneration)

Fine-tuningpre-trainedmodels

Percentorganizational

use

15%42%22%12%2%7%70%

21%36%16%11%4%10%49%

12%32%23%13%2%3%14%46%

0%10%20%30%40%50%60%70%80%90%100%

uExceptionalgainuConsiderablegainaModerategainuMarginalgainuNogainuDiminishedgainaDon'tknowornotsure

2024GenAIsurvey,Q33,SampleSize=297,ValidCases=297,TotalMentions=905,answeredbyorganizationswhoadoptedGenAIinQ7

2024GenAIsurvey,Q34,Samplesize=206to138,sortedbythesumof“Exceptional,considerable,andmoderategain”

SHAPINGTHEFUTUREOFGENERATIVEAI11

Theleft-handchartinFigure3showsthattext(81%),code

(74%),andstructuredortabulardata(48%)aretheleading

GenAImodalities.Text,code,andstructureddataarethemostcommonmodalitiesforGenAIbecausetheyarewidelyavailable,interpretable,andfoundationaltoabroadrangeofapplications.Textdata,whichLLMssupport,coversawidespectrumof

naturallanguageapplications,enablingmodelstogenerate

coherentresponses,summaries,translations,andotherhuman

languageoutputs.Code,asalogicalandrule-basedlanguage,ishighlysuitedforautomatingtasks,generatingscripts,andsupportingsoftwaredevelopment.Structureddata—suchastables,databases,andlabeleddatasets—provideorganizedinformationthatGenAIcanuseforpatternrecognition

anddatasynthesisinareassuchasdecisionsupportandrecommendations.

FIGURE3:COMMONGENAIMODALITIESANDDATAUSE

WhatgenerativeAImodalitiesareyouusingorplanningtouseinyourorganization?(checkallthatapply)

TextCode

StructuredortabulardataMultimodal DevOps Speech VisionAudio

Other(pleasespecify)Don,tknowornotsure

81%

74%

48%

47%

41%

35%

34%

27%

2%

1%

DoesyourorganizationhaveproprietarydatathatcouldbeusedtotrainorimprovetheperformanceofgenerativeAImodels?(selectallthatapply)

22%

oforganizations

usetheirproprietarydatainopensourcemodels

30%

oforganizations

usetheirproprietarydataintheir

proprietarymodels

2024GenAIsurvey,Q31,SampleSize=297,ValidCases=297,Total

Mentions=1,165,answeredbyorganizationswhoadoptedGenAIinQ7

2024GenAIsurvey,Q24,SampleSize=297,ValidCases=297,Total

Mentions=473,answeredbyorganizationswhoadoptedGenAIinQ7

Theright-handchartinFigure3showsthepercentageof

organizationsusingtheirproprietarydatatoimprovethe

performanceoftheirproprietaryGenAImodel(30%)oropensourceGenAImodel(22%).Someorganizationsusetheirowndatatotrainbothproprietaryandopensourcemodels.Whenweredistributethedatabasedonthesethreecategories,we

findthatorganizationsuseproprietarydatatoimprovethe

performancein22%ofproprietarymodels,13%ofopensourcemodels,and9%withbothmodels.Thisyieldsatotalof44%oforganizationsthatareusingproprietarydatatoimprovetheirmodels.

SHAPINGTHEFUTUREOFGENERATIVEAI12

PrimaryGenAIusecases

OrganizationsareusingGenAIinmanyways,althoughtherearefiveprimaryusecases.Figure4showsthattheleadingprimaryusecaseisprocessoptimizationorautomation(25%)followedbycontentgeneration(17%),codegeneration(14%),customer

serviceandsupport(11%),andresearch(6%).

Processautomation/optimizationistheleadingGenAIusecasebecauseitoffersbusinessestransformativeefficiency,reducesmanualtasksanderrors,anddecreasesoperationalcosts.Withitscapacityfornaturallanguageunderstandingandresponse,GenAIcanhandlediversequeriesandtasks,providingamoreadaptableandscalableapproachtoautomation.Byidentifyingpatternsandrecommendingimprovements,GenAInotonly

streamlinesprocessesbutalsocreatesroomforinnovation.

FIGURE4:PRIMARYGENAIUSECASES

What’syourorganization’sprimaryusecaseforgenerativeAI?(selectone)

Top5

ProcessautomationoroptimizationContentgenerationCodegeneration

CustomerserviceandsupportResearch

DataclassificationEducationandtraining

FrauddetectionandpreventionHealthcare

Strategicplanning

None,wedonotusegenerativeAIOther(pleasespecify)Don'tknowornotsure

17%

14%

11%

6%

5%

3%

3%

2%

1%

0%

11%

2%

25%

2024GenAIsurvey,Q13,SampleSize=297,answeredbyorganizationswhoadoptedGenAIinQ7

GenAIisalsousefulforcontent(17%)andcode(14%)generation.Forcontentcreation,GenAIcangeneratearticles,blogs,and

marketingmaterialsinseconds,reducingtheworkloadandensuringconsistencyinstyleandtone.Itenhancesideation,deliveringvariedperspectivesoroutlinesthathelpteams

focusonrefinement.Forcodegeneration,GenAIacceleratesdevelopment,offeringquickprototypes,codesuggestions,anddebuggingsupport.Byautomatingroutinecodingtasks,itreduceserrorsandfreesdeveloperstofocusoncomplexproblemsolving.

SHAPINGTHEFUTUREOFGENERATIVEAI13

GenAIcansupportcustomerservice(11%)byprovidinginstant,constantsupportthroughintelligentchatbotsandvirtual

assistants.Itcanimproveresponsetimes,handlelargevolumesofqueriessimultaneously,andprovideaccurate,context-awareanswers.GenAIcanalsopersonalizeinteractionsbyanalyzingcustomerhistoryandpreferences,deliveringtailoredsolutionsandproactiverecommendations.Additionally,itautomates

repetitiveinquiries,allowinghumanagentstofocusoncomplexcasesthatrequireapersonaltouch.

Figure5showsthefiveleadingusecasesinFigure4segmentedbyhowtheusecaseisintegratedintotheorganization’s

business.Figure5showsthateachofthesefiveprimaryusecasesisuniquelyintegratedintotheorganizationsthat

identifiedit.

FIGURE5:PRIMARYGENAIUSECASESSEGMENTEDBYINTEGRATIONINTOTHEBUSINESS

What’syourorganization’sprimaryusecaseforgenerativeAI?(selectone)segmentedbyHowisyourprimarygenerativeAIusecaseintegratedintoyourbusiness?(selectone)

69%

52%

51%

43%

41%41%

41%

37%

36%

21%

17%

18%

14%

12%

7%

Processautomationoroptimization

Content

generation

Code

generation

Customer

serviceandsupport

Research

GenerativeAIsupportsourinternalprocesses,workflows,ortasks

GenerativeAIisintegratedintoourproductsorservices

WearecreatingsolutionsthatenablethirdpartiestoutilizegenerativeAIintheirproducts

2024GenAIsurvey,Q13top5byQ15,SampleSize=166,DKNSandToosoontotellresponsesexcludedfromtheanalysis

SHAPINGTHEFUTUREOFGENERATIVEAI14

Processautomationoroptimizationshowsarelativelyhighlevelofsupportforinternalprocesses(51%)butalesserdegreeof

integrationwithanorganization’sproductsandservices(37%).Thislesserdegreeofintegrationreflectsthecomplexityof

aligningGenAImodelswithorganizationalworkflows

Theintegrationofcontentgeneration(52%),incontrast,ismorereadilyachievable,becauseanorganization’scontentisalreadyinahighlyconsumableformforGenAI.Codegenerationseesasignificantlyhighlevelofsupportforinternalactivities(69%)inpartbecausecodegenerationprovidesaboundeddomainthatoffersusefulresultswithoutanexcessivedegreeofintegrationorcustomizationtoanorganization’senvironment.However,

thereareseveralreasonswhytheintegrationofcodegenerationisjust17%,whichwe’llexploreinFigure6.

Customerservicehasarelativelyhighlevelofsupportfor

internalprocesses(41%)aswellasintegrationwithproducts

andservices(41%).Theelevatedinterestincreatingsolutionsbythirdparties(18%)reflectsthefactthateveryorganizationhastostaffcustomerserviceandsupportactivities,sothepayoffindevelopinganeffectivesolutionisconsiderable.

“GenAIappliesamathematicalmodel

toaninherentlysubjectiveprocess.It

willneverbegoodenoughinageneric

contextbecausedifferentpeoplewant

differentandincompatiblethingsfromit.However,ithasthepotentialtobecomegoodenoughinsmall,specializeduses,ifitcanstophallucinating.”

Researchisanotherusecasewhereadvancedanalytics

toolprovidersseetremendousopportunityforcreating

solutions.Becausemostdataincludesmetadata,dataisoftenstructured,whichimprovesitsspecificitywhilesimplifying

howorganizationscanuseit.Whiletheinternalusecasesforresearchisintuitivelyclear,thedeploymentofsuchsystemsisstillinitsinfancy.

Figure6showsthefiveleadingusecasesfromFigure4

segmentedbythelevelofadoption.

Contentgenerationisthemosthighlyintegratedusecase(Figure4)isalsoshowninFigure5asthemosthighlyfullydeployed

usecase(22%)andhasthehighestlevelofinitialproductiondeployments(46%).ThereasonforthisislikelyduetoamuchhigherROIthanotherusecasesduetotherelativeeaseof

implementationandthesignificantoperationalvalueadd.

Processautomationisfullydeployedinjust16%oforganizationsusingGenAIandanadditional32%areininitialproduction

deployment.ThisreflectsthechallengesofdefiningthescopenecessaryforprocessautomationwithaGenAImodel.

Bothcodegeneration(codespecificGenAImodels)and

customerserviceandsupport(LLMs)showlowfulldeploymentratesbutaveryhighlevelofinitialdeploymentandexperimentaldeploymentssuggestingthatfulldeploymentratescould

increasesignificantlyiftheseinitialandexperimentaldeplo