2025年数据、人工智能与分析趋势报告 2025

451Research2025Preview

December2024

2025Trendsin

Data,AI&Analytics

ChrisMarsh,ResearchDirector,WorkforceProductivity&CollaborationandData,AI&Analytics

PaigeBartley,SeniorResearchAnalyst,Data,AI&AnalyticsJamesCurtis,SeniorResearchAnalyst,Data,AI&AnalyticsMelissaIncera,ResearchAnalyst,Data,AI&Analytics

AlexanderJohnston,SeniorResearchAnalyst,

Data,AI&Analytics

KrishnaRoy,SeniorResearchAnalyst,Data,AI&Analytics

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Thisreporthighlightsthekeytopicsweexpecttobeprominentin2025inthedata,AIandanalyticsspace.WhilegenerativeAIremainsapredominanttheme,forthetechnologytosucceed,

preexistingissuesmustberesolvedaround

datamanagement,dataoperationalization,

machinelearningoperationalizationandotherinfrastructure-relatedtasks.

Tolearnmoreortorequestademo,visit

/451research

.

Tableofcontents

Executivesummary3

Introduction3

Aboutthisreport3

TheTake4

Trendsweanticipatein20254

Trend1:ROIpressurewillincreaseforGenAIascostsmount4

Trend2:Richmediagenerationwillfinditsniche5

Figure1:PurchaseordevelopmentofGenAIcapabilitiestocreateoutputs5

Trend3:AIgovernancewillbebusiness-critical6

Trend4:AgenticAIwillriseinimportance6

Trend5:GovernancewillberequiredforexpandingagenticAIarchitectures7

Figure2:SlimmajorityoforganizationsusingAIareengaginginAIgovernance

practices/initiatives7

Trend6:Inferencingefficiencywillbecomeanindustrywidepursuit8

Figure3:Inferencingisbecomingmoredemandingfororganizations8

Trend7:Modelsbecomemorespecializedanddomain-specific9

Trend8:SyntheticdatapracticesmatureintheeraofGenAI9

Trend9:Unstructuredcontentwillexperienceareckoningforprivacyandsecurity10

Figure4:Relianceonarchivestorageforsourcingdatatobuildmodels10

Trend10:GenAIOpswilltakeflight11

Trend11:Vectorsupportwillbecomeubiquitous12

Figure5:Vectordatabasemarketrevenueforecast($M)12

Trend12:OptionalitywillbecomingsoontoRAGenvironments13

Figure6:UseofRAGtechniques13

Methodology14

Furtherreading14

Abouttheauthors15

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2025TrendsinData,AI&Analytics|2

Executivesummary

Introduction

Asweapproach2025,thedata,AIandanalyticslandscapeispoisedforsignificant

transformation.Organizationsincreasinglyrecognizethecomplexityinvolvedin

designingandmanagingthedatainfrastructurenecessarytosupportmachinelearning(ML)atscale.Manyarerecalibratingtheirexpectationsandplacinggreateremphasis

onevaluatingtheirinitiatives’performanceandreturnoninvestment(ROI).Thisshift

acknowledgestherisingcostsassociatedwithAIprojects,particularlyasorganizationsmovetowardmoresophisticatedapplications.Further,achievingenterprise-grade

implementationatscalerequiresnotonlyinnovativetechnologybutconsiderableexpertiseandgovernance.

Thetrendswehighlightprovideanuancedunderstandingofthechallenges

organizationsfaceastheyprepareforincreasedsophisticationintheirdataand

applicationstrategies.Ourresearchinsightsserveasaguidefororganizationsstrivingtoleveragethesetechnologieseffectivelyandfortechnologyenablersseekingto

understandtheseevolvingmarketdynamics.

Aboutthisreport

Reportssuchasthisshowcaseinsightsderivedfromavarietyofmarket-levelresearchinputs,includingfinancialdata,M&AinformationandothermarketdatasourcesbothproprietarytoS&PGlobalandpubliclyavailable.Thisinputiscombinedwithongoing

observationofmarketsandregularinteractionwithvendorsandotherkeymarketplayers.

Thisreportspecificallyincludesdatafromthefollowingsources:

–VoiceoftheEnterprise:AI&MachineLearning,UseCases2024—Thisweb-basedsurveywasfieldedNovember14–December14,2024,amongapproximately1,001ITend-userdecision-makersworldwide.

–VoiceoftheEnterprise:AI&MachineLearning,Infrastructure2024—Thisweb-basedsurveywasfieldedMay10toJune4,2024,amongapproximately712ITend-userdecision-makersworldwide.

–VoiceoftheEnterprise:Data&Analytics,DataArchitectureforAI2024—This

web-basedsurveywasfieldedMarch5toApril24,2024,amongapproximately1,112ITend-userdecision-makersworldwide.

–DataPlatformsMarketMonitor&Forecast2024—Thisreportleverages451

Research’sdeepknowledgeofandrelationshipswithinthedataplatformsmarket,resultinginaproprietaryforecastbasedonahybridbottom-upanalysisof174

vendors’currentrevenueandgrowthexpectationsthrough2028.

–GenerativeAIMarketMonitor&Forecast—Thisreportleverages451Research’sdeepknowledgeofandrelationshipswithinthegenerativeAImarket,resultinginaproprietaryforecastbasedonahybridbottom-upanalysisof398vendors’currentrevenueandgrowthexpectationsthrough2028.

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2025TrendsinData,AI&Analytics|3

TheTake

In2025,organizationswillcarefullyconsidertherisk-rewardbalanceassociatedwiththeirAIanddatainitiatives.Whilesignificantopportunitieshavearisenfromrapidtechnologicaladvancementsinrecentyears,numerous

complexchallengesremain.Extractingvaluefromunstructureddatasources,managingtheongoingexplosionofmultimodaldata,andoperationalizingAImodelsintoreal-worldapplicationsallimposesubstantialstrain

onITenvironments.Thisrisk-rewarddynamicisevidentinourdata,whichindicatesthatmorethan40%

oforganizationsexpecttoseeastronglypositiveimpactfromgenerativeAI(GenAI)inthenext12months,

particularlyinareassuchascustomerandemployeesatisfaction,riskmanagementandoperationalefficiencies.However,only29%believetheircurrentITenvironmentcanmeetfutureMLworkloaddemands,accordingtoourVoiceoftheEnterprise(VotE):AI&MachineLearning,InfrastructureandUseCases2024survey.

Asorganizationsnavigatethecomplexitiesofmaximizingoutcomeswhilemitigatingrisks,theywillclosely

examinetheirdatabaseplatforms,datagovernancepractices,AImodelmanagementandanalyticstechnologiestoensuretheyhavetherightblendofessentialinfrastructure,safeguardsandbusinessworkflows.Technologyvendorswillneedtoengagewiththeircustomersthroughoutthisjourney,whichcouldrequirethemtoreevaluatetheirpositionsinthetechnologystackandrethinktheirtargetaudiences,includingdataanalysts,governance

specialists,engineers,datascientistsandproductmanagers.Additionally,asAIdrivesthedevelopmentofnewno-codeapplicationsandagentplatforms,differentbusinesspersonasarelikelytocomeintofocusastargetedusersforthesetechnologies.

Trendsweanticipatein2025

Trend1:ROIpressurewillincreaseforGenAIascostsmount

WhileexperimentationwithGenAIspikedin2024,2025willbetheyearorganizationsstarttomeaningfullyassesstheperformanceoftheirinitialinvestments.Withdemanddrivenfromthetop,organizationshavedemonstratedsignificantappetiteforGenAI,butmanycompanieshavebeenchallengedbyalackofexpertiseandthetechnicalcomplexityofdeployingenterprise-gradeprojectsatscale,whichhashinderedthedeliveryofmany

initiatives.Thefactthatthecapabilitiesneededtoensuresafety,contextualrelevanceandaccuracyofoutputsarestillevolvinghasalsorestrictedthetechnologyssuccessfulapplication.Theinitialdisparitybetweenorganizationalambitionsandthematurityof

availablesolutionswilllikelybecomeevidentintheseearlyROIevaluations,potentiallydampeningenterpriseenthusiasm.

Thismoresobermindset,pairedwiththesizeablecostsassociatedwiththetechnology,meansthatorganizationswilllikelytakeamorefocusedapproachtoGenAI,better

prioritizingwherethetechnologyisapplied.Thatsaid,asimprovingbenchmarksstarttotranslateintoenhancedcommercialofferings,organizationsmayfacealesschallengingyearfordeliveringmeasurablereturns.Thereseemstobeoptimism:Between40%and50%oforganizationsexpectGenAItohaveastrongpositiveimpactonkeyareassuchascustomerandemployeesatisfaction,riskmanagementandoperationalefficienciesoverthenext12months,accordingtoour

VotE:AI&MachineLearning,UseCases2024

survey.

Moretask-orientatedcapabilitiesthatextendbeyondgeneral-purposechatbotswill

likelygaintraction,andcompanieswillbegindeliveringmorebespokeinitiatives.Modelstailoredtoaddressspecificchallenges,integratedwithapplicationsandrelevantdata,

mayextendthereachofGenAIwithinorganizationsandbetterjustifythefinancialoutlay.WeprojectthatrevenuegeneratedbytheGenAImarketwillgrowby68%between2024

and2025,perour

GenerativeAIMarketMonitor&Forecast

.

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2025TrendsinData,AI&Analytics|4

Trend2:Richmediagenerationwillfinditsniche

TheGenAIvendorspaceisexperiencingwavesofinnovation,beginningwithanexplosionoftext-generatingstartupsfollowingthepopularizationofChatGPT,withasurgeinimagegeneratorssoonafter.Videoproductionispoisedformajordisruptionin2025,with

significantimprovementsinvideogenerationmodelscreatinggreateropportunitiesforcommercialization,potentiallysparkinganotherwaveofinnovation.Asclipsizesincreaseandplatformsbecomemoreadeptatdeliveringcoherentscenes,thetechnology’s

potentialextendsbeyondshort-formcontent.Consequently,theusecasesforvideogenerationareexpectedtoexpandsignificantly,withbusinesseslikelyexploring

applicationsbeyondproducingshortclipsforsocialmedia.

Whilemanyenterpriseshaveinitiallyfocusedontext-basedGenAIapplications,thereis

aclearappetitetoembraceunstructuredrichmediadataaspartofAIstrategies.Over

thenext12months,36%oforganizationsthatuseorplantouseGenAIanticipateitwill

supportcontentcreationusecases,while30%arelookingatitforpersonalizedmarketingcontent,30%forlanguagetranslation,28%fordesignandprototyping,and24%formediaenhancement,accordingtoourVotE:AI&MachineLearning,UseCases2024survey.Whilesomeofthiswillstillbetext-based,somewilllikelybemultimodal.

In2025,weexpectinterestinvideogenerationtotranslateintoadditionalcapabilities

becomingavailableinproduction—usecasesincludedatascienceteamssyntheticallygeneratingimagestotrainmachinevisionmodels,marketersdeliveringhyper-

personalizedvideoclipsatscale,orpresentersdeliveringwebinarsinmultiplelanguagesusingaudiogenerationandlip-synchingtechnologies.

Figure1:PurchaseordevelopmentofGenAIcapabilitiestocreateoutputs

mInactiveusewPlantousewithinthenext12monthsNotinuse

Text(n=959) Image(n=960)Structureddata(n=960)

32%

7%

60%

49%

15%

36%

48%

10%

42%

43%

37%

20%

41%

41%

18%

39%

39%

22%

Audio(n=960)Code(n=958)Video(n=960)

Q.HasyourorganizationpurchasedordevelopedagenerativeAIcapabilitythatisbeingusedtocreateanyofthefollowingoutputs?

Base:OrganizationsthatusegenerativeAIorplanto.

Source:451Research’sVoiceoftheEnterprise:AI&MachineLearning,UseCases2024.

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2025TrendsinData,AI&Analytics|5

Trend3:AIgovernancewillbebusiness-critical

AsAIregulationbecomesmorewidespreadandexpansive,organizationswillneedtoensurethatAImodelsaredesigned,developed,implementedandmaintainedinadherencewithrelevantAIlegislationaswellasinternalAIpoliciesandmandates.

ThisshiftisexemplifiedbyenactmentoftheEUAIAct—thefirstcomprehensivelaw

compellingorganizationstoensuretheirAImodelsaretrustworthy,safeanddonot

breachfundamentalhumanrights.Canada,ChinaandcertainUSstateshavealsopassedlegislationgoverningAI,includingGenAI,turningAIgovernanceintoastrategic,high-levelpriorityonaglobalscale.

AIgovernancetools—designedtosupportmodelcompliancewithAIlegalframeworks

andorganizations’internalpolicies—willevolvefromofferingslargelyprovidedby

specialiststoabroaderselectionasenterprisesoftwarevendorsmoveintoaddressthisgrowingneed.Aspartofthismarketdevelopment,datascienceandMLplatformvendorswilllikelyprovideAImodelgovernanceasastandardfeaturesincetheyalreadyaddressmajorpiecesofthemodellifecycle.Moreover,thesevendorswilladdresslargelanguagemodel(LLM)governanceandthegovernanceofothertypesoffoundationmodelsastheyareadoptedforbusinessusecases.Whiledetractorswill,nodoubt,continuetoargue

thatAIgovernanceprocedurescreateadditionalworkandhinderinnovation,legislationislikelytomakeAImodelgovernancemandatory,ratherthanoptional.

Trend4:AgenticAIwillriseinimportance

MuchoftheexperimentationandearlyusecasesforGenAIhaveusedLLMs’most

rudimentarycapacities,suchascontentgeneration,informationsummarizationand

learning,oftenasastand-alonesystem.However,vendorsandorganizationsare

increasinglyintegratingLLMsintomorecomplexscenariosthatexploitthemforhigher-ordercapabilitiesincludingreasoningandautomation.

Inthesescenarios,throughtheuseofsophisticatedprompting,LLMsactasan“agentic”orchestrationlayerthatcanbeassignedtotasks,reasonthroughqueriesandtake

actions.Incontrastwithmonolithicapproachesthatrelyonasinglelargemodelto

handleallaspectsofaquery,withagenticAI,complexAItasksarebrokenintosmaller

componentsthatcanbehandledthroughvarioustools(e.g.,othermodels,retrieversanddatasources),drivingadditionalbenefitsinefficiencyandperformance.

Thishascreatedthepossibilityforhigher-valueAIusecasesrelatedtoproductivityandprocessenhancements.Organizationsarelikelyexpectingtoseebenefitsintaskandprocessautomationfromtheincorporationofagentictechnologies,andmanysaytheyareseekingopportunitiestodeployagentsoropentoexploringthem.Inthecoming

year,weexpectrapidinvestmentandadvancementsnotjustinthecapabilitiesofLLMsthemselves,butalsointechniquestoarchitect,prompt,secureandmanagethese

agenticsystemsatscale.

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2025TrendsinData,AI&Analytics|6

Trend5:GovernancewillberequiredforexpandingagenticAIarchitectures

Manyleadingenterpriseplatformtechnologyprovidersarerollingoutlow-codeor

no-codeoptionsforline-of-businessuserstoeasilydesignanddeployAIagents.

AplausiblefutureexistsinwhichAIagentslargelycommunicateandinteroperate

withotherAIagents(bothwithintheorganizationandoutsideofit)tocompletea

rangeofbusinesstasks,withhumansintheloopforsupport.YetwhileAIagentsandagenticarchitectureholdimmensepromiseforacceleratingbusinessprocesses,

therearepotentialpitfalls.Withnon-technicalpersonasempoweredtodesignand

deploytheseagents,andmanyplatformsprovidinglargelyproprietarystacksfortheiragents,thegovernanceofassociateddata,artifactsandactivityneedstobeamajorconsideration.

TherushandcompetitivepressuretoadoptAIhasattimessidelinedAIgovernance.

Inour

VoiceoftheEnterprise:Data&Analytics,DataArchitectureforAI2024

survey,amongorganizationsthathaveadoptedAIorhaveAI-relatedinitiatives,onlyaslim

majority(52%)reportengaginginAIgovernancepractices.WiththepotentialexplosionofAIagents,governancewillonlybecomemorecritical.Maintainingaccurate

versioningofdatasets,modelsused,promptsprovided,andresponsesorcontent

generatedbyagentswillbeessential,particularlyinbusiness-to-consumerindustriesandhighlyregulatedverticals.LikeanyAI-driventechnology,AIagentsdependonthequalityofdatatheyhaveaccessto;withinplatformsthatsupportagents,massdatacleanupeffortsmayberequiredforoptimalresults.

Figure2:SlimmajorityoforganizationsusingAIareengaginginAIgovernancepractices/initiatives

52%

40%

Yes,currentlyinuse

●No,butplanstousewithinthenext12months

●No,andnoimmediateplanstouse

8%

Q.DoesyourorganizationcurrentlyengageinanyongoingAIgovernancepracticesorinitiatives?

Base:OrganizationsthathaveadoptedAIorhaveAI-relatedinitiatives(n=411).

Source:451Research’sVoiceoftheEnterprise:Data&Analytics,DataArchitectureforAI,2024.

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2025TrendsinData,AI&Analytics|7

Trend6:Inferencingefficiencywillbecomeanindustrywidepursuit

AIworkloadsremaindominatedbytraining—anincrediblyresource-intensive

tasksupportedbysignificantinvestmentsinspecializedhardwareandsoftware.

However,asthemarketshiftstowardgenerativeAIinproductionatscale,inreal-timeapplicationsandresource-constrainedenvironments,includingtheedge,theneedforefficientinferenceisrapidlygainingprominence.

Muchhasbeenhappeningatthehardwarelevel,withspecialistsmakingmeaningfuladvancementsingraphicsprocessingunits,centralprocessingunitsandneural

processingunitsinrecentmonths;however,themostsignificantshiftisthegrowinginvolvementandinvestmentatthesoftwareandmodellevels.Researchersand

businessteamsacrosstheecosystemareexploringoptimizationtechniquesinmodelserving,inferencingframeworksandnewmodularsystemarchitecturesthatenabletheircustomerstooperatemoreefficiently.Modelprovidersaresimilarlyfocused

ondevelopingsmaller,moreefficientmodels,whileothersaredelvingintonovel

architectures.Theincreasedfocusonefficiencyhasalreadyledtoseveralearly

acquisitionsasvendorsseekaccesstoAItalent,productsandprojectsfocusedonefficiency.ManyviewthisasawaytodifferentiateinanoisyAImarket.

TheseacceleratedinvestmentswillbeessentialtomeetenterprisedemandsandambitionsforAI,whilekeepingitsfinancial,socialandenvironmentalcostswithinreasonablelimits.Datafromour

VotE:AI&MachineLearning,Infrastructure2024

surveyshowsthatinferencingisrisingasachallengefororganizationsastheymovemoreGenAI-basedapplicationsintoproductionandlooktodelivermeasurable

businessimpact.

Figure3:Inferencingisbecomingmoredemandingfororganizations

uTrainingMLmodel(s)uDataingestionandpreparationuInferencefrommodel(s)

Current

Innext2years

36%

36%

48%

39%

24%

15%

Q.Whichofthefollowingstagesofthemachinelearningprocessisthemostdemandingonyourorganization’sITinfrastructure?Base:MLisinproductionorproof-of-concept(n=577).

Q.Whichofthefollowingstagesofthemachinelearningprocessdoyouanticipatewillbethemostdemandingonyourorganization’sITinfrastructureoverthenext2years?Base:MLisinproductionorproof-of-concept(n=574).

Source:451Research’sVoiceoftheEnterprise:AI&MachineLearning,Infrastructure2024.

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2025TrendsinData,AI&Analytics|8

Trend7:Modelsbecomemorespecializedanddomain-specificLLMprovidersaredevelopingnewmodelswithagoaltocreatehighlyperformant,

generalistofferingsthatexcelacrossindustries.However,asthesearetrainedmainlyondatainthepublicdomain,theirutilityinmoretargetedenterpriseworkflowsis

limited.AccordingtoourAI&MachineLearning,Infrastructure2024survey,67%of

organizationsusingGenAItechnologiesarefine-tuningapretrainedfoundationmodel,reflectingapressingneedtoimproveoutputaccuracyandrelevancefortheirusers.

Ashifttowardmodelspecializationpresentsopportunitiesforawiderangeof

technologyvendors—beyondthewell-fundedcompanieswithfrontiermodels—toaddvalueanddifferentiatethemselvesinanintenselycrowdedAIlandscape.Throughtechniquesincludingfine-tuning,transferlearninganddataaugmentation,researchteamsareadaptingtheleadingmodelstobesmaller,moreefficientandtailoredforspecifictasksordomains.Thesecanthenenhanceperformanceforusersinspecificapplications,suchaslegaldocumentreview,medicalimagediagnosticsandcustomerfeedbackanalysis.

Additionally,wenoteincreasinginvestmentinindustry-specificmodels.Beyond

continueddevelopmentfromspecialistproviders(suchasHippocraticAI’sworkwith

healthcaremodels),majordevelopersaremakingapushwithsmaller,oftenopen-

sourcemodelstailoredtospecificverticals.Google’sDeepMindrecentlyreleased

anofferingtargetingpharmaceuticalresearch,whileMicrosoftunveiledaseriesof

verticalizedmodelsdevelopedincollaborationwithpartnersincludingBayer,SiemensandRockwellAutomation.Inthemonthstocome,weexpecttoseemoresuchstrategicpartnershipsbetweentechnologycompaniesandnon-techindustryleaders(e.g.,

collaborationbetweenIBMandNASA),inwhichthelatterwillincreasinglyleverage

theirproprietarydatatoestablishthemselvesaskeyparticipantsintheAIlandscape.

Trend8:SyntheticdatapracticesmatureintheeraofGenAI

Syntheticdata,whilenotanewconcept,hasbeengaininggroundinpracticaluses

asorganizationsstrivetobuildandtrainAImodelsin-house,aswellassupplement

off-the-shelfLLMswithdatasetsforpurposessuchasretrieval-augmentedgeneration(RAG).InourVotE:Data&Analytics,DataArchitectureforAI2024survey,46%of

respondentsfromorganizationsthatbuildanddeploytheirownAImodelsindicate

thattheycurrentlyusegenerateddata(includingsyntheticdata)formodeltraining.

Syntheticdatasetscanbeparticularlyvaluableforavoidingexposureofsensitiveor

personaldetails,aswellasforfillingin“gaps”inreal-worlddatasets,suchasthosethatmayexistforuncommonorunlikelyscenarios.Notonlycansyntheticdatabeuseful

forpreservingprivacyandenhancingsecurity,butitcanalsohelpprovidedatafor

situationsthatwouldbeunethicalorimpracticaltocreateinanexperimentalsetting.

Giventhatsyntheticdatahelpssidestepmanycommoncompliancepainpointswhilealsohelpingtoprovidedatathatisotherwisedifficulttoobtainornon-existent,it

willlikelycontinuetogrowinutilization.Importantly,GenAIitselfwillacceleratethe

creationofsyntheticdatasets.OrganizationscanincreasinglyuseGenAItotransformexistingdatasetsintoderivedsynthetictrainingdatasetsbyallowingLLMsandsmallermodelstogeneratenoveldatasetsbyiterativelyrunningqueriesorpromptsonresultsthathavebeenpreviouslygenerated.Whiletheunderlyingqualityof“foundation”

datasetswillbeparamountinthesesyntheticdatascenarios,wecanexpecttoseemoderndatapipelinesincreasinglyincorporatebothnaturallyderivedandsyntheticdatasourcesincombination—whichunderscorestheneedforstronglineage,

provenance,versioningandgovernancecapabilities.

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Trend9:Unstructuredcontentwillexperienceareckoningforprivacyandsecurity

Unstructuredcontenttypicallyrepresentsthebulkofenterprisedatabystorage

volume,butisfrequentlydifficulttohandleinthecontextofAImodeltrainingand

GenAIuseduetoissueswithprivacy,sensitivityandintellectualproperty.Yetas

businessesstrivetobuildtheirownAImodelsin-house,thishasnotpreventedthem

fromtappingexistingcontentrepositoriesforunstructureddatatotrainmodels.BasedonourData&Analytics,DataArchitectureforAI2024survey,55%ofrespondentsfromorganizationsthatbuildanddeploytheirownAImodelshaveindicateda“highreliance”onarchivestorageforsourcingdatatobuildtheirmodels.Thiswasatopresponsethatoutpacedothercommondatasourcesincludingdatalakes/lakehousesormetadata

managementsystems.However,long-standingcontentsystems—suchasarchives—werenotdesignedwithAIdataprocessingormodeltraininginmind,exposingrisk.

Figure4:Relianceonarchivestorageforsourcingdatatobuildmodels

3%

13%

55%

30%

Highreliance

ModeraterelianceLowreliance

Notapplicable

Q.InbuildingitsownAImodels,howfrequentlydoesyourorganizationrelyonthefollowinginternalarchitecturalcomponentsortechnologiesforsourcingdata?-Archivestorage.

Base:Organizationsthatdevelop/buildanddeployownAImodels(n=111).

Source:451Research’sVoiceoftheEnterprise: