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StateofAIData

ConnectivityReport:

2026Outlook

Over200dataandAIleaderssaydatainfrastructureisthebiggestbarriertoAIsuccess

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TheTopLine

TheAugust2025MITreport,TheGenAIDivide:StateofAIinBusiness2025,1madewavesamong

businessleadersandAIproductownerslargelyduetoitsheadlinestatistic:95%ofgenerativeAIpilotsatcompaniesarefailing.Withtheunprecedentedscaleofinvestmentandthehighexpectationsfor

enterpriseapplicationsoflargelanguagemodels(LLMs),bothGenAIevangelistsandskepticswerequicktoweighinonthedisappointingoutcomesoftheseearlyexperiments.

Whiletheaccuracyofthatspecificstatisticcontinuestobedebated,thecoreissueitsurfacesisnot:alargeshareofcompaniesarefailingtorealizemeaningfulROIfromtheirAIefforts.Themoreimportantquestionis,why?

Wesurveyed200+dataandAIleaders,bothfromenterpriseswithinternalAIadoptioninitiativesaswellassoftwarecompaniesembeddingAIcopilotsandagentsintotheirproducts.Andhere’swhatwelearned:enterpriseAIisnolongerlimitedbymodels.It’sconstrainedbydatainfrastructureandenterprisecontext.

ThestrongestpredictorofAIsuccessin2026isthematurityoftheunderlyingdatainfrastructurethatdeliversenterprisecontexttothesemodels.

Infact,60%ofcompaniesatthehighestlevelofAImaturityalsohavethemostmaturedata

infrastructure.Andtheinverseisalsotrue:53%ofcompanieswithimmatureAIhaveimmaturedatasystems.

Inthisreport,AImaturityreferstotheextenttowhichanorganizationhasoperationalizedAI,movingbeyondexperimentationtomeasurablebusinessimpact.Ourframeworkconsidersdimensions

suchasmodeldeployment,dataintegrationmaturity,governance,andROItracking.Wecategorizematurityinafive-stageprogressiveframeworkthatdrawsfromEY-Parthenon’sAImaturitymodel:

experimenting,implementing,scaling,optimizing,andleading.2

“TheparadoxofAIreadinessisthatourdatainfrastructure

becomesmorepowerfulnotthroughendlessadaptability,

butthroughintentionalsemanticboundariesthatgiveLLMs

thepredictablecontractstheyneedtoorchestratecomplex

workflows.Withoutthisdeliberatearchitectureofconstraints,

we’releftwithsystemsthatburntokensonambiguityratherthandeliveringvalue.”

—CarlisiaCampos,AISoftwareEngineer,GrokkingTech

1“TheGenAIDivide:StateofAIinBusiness2025”,MITNANDA,Aug.18,2025

2“HowaTop-DownHolisticStrategyCanMaximizeGenAIROI”,EY-Parthenon,June18,2024

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AsanyoneusingenterpriseAItoolslikeChatGPT,LangChain,orAgentforcecanattest,it’snosurprisethatcontextplaysadefiningroleinAImaturity.Largelanguagemodelsdependheavilyonitfor

accurate,reliable,usefuloutputs.Whatissurprisingishowfeworganizationsareactuallysetuptodeliverthatcontext.

Otherfindingsfromtheresearchhighlightthespecificchallengesstandingbetweenintentionandexecution.Acrossbothenterprisesandsoftwareproviders,wefound:

Finding

Implication

71%ofAIteamsspendmorethanaquarteroftheir

Whensignificantresourcesaretiedupindata

implementationtimeondataintegration—including

integration,attentionispulledawayfromstrategic

modelingdata,implementingETLpipelines,configuringconnectors.

productdevelopmentandinnovation.

46%oforganizationsrequirereal-timeaccesstosix

EachAIusecaserequiresconnectingtomultiple

ormoredatasourcesforanaverageAIusecase.

systems,whichaddsarchitecturalcomplexityandincreasestheburdenondatateams.

AI-nativesoftwareprovidersare3xmorelikelyto

Modernsoftwarecompaniesarearchitectingfor

requiremorethan26externaldataintegrationsin

scalefromdayone,exposingintegrationgapsin

product,ascomparedtotraditionalproviders(46%vs.15%).

moretraditionalproviders.

100%oforganizationssayreal-timedatais

necessaryforAIagentsandcustomerservice

automation.While80%ofenterpriseshavebegunimplementingreal-timeintegration,mostarestillintheearlystagesofscalingiteffectively.

Thereisasignificantreal-timeintegrationcapabilitygapthatcouldlimittheadoptionofAIagentsandautomationatscale.

Allhigh-AI-maturity(“l(fā)eading”)enterpriseshave

Semanticallyconsistentdataaccessisnotjust

builtcentralized,semanticallyconsistentdata

abestpractice?it’sbecominganAIimperative.

access:80%oflow-maturity(“experimenting”)

Softwareprovidersandenterprisesthatlackitwill

enterpriseshaven’tevenstarted.

struggletokeepup.

58%ofrespondentsprioritizestructureddata

sources(organized,schema-basedformatslikedatabasesandAPIs)forAIfeatures,whileonly

11%primarilyrelyonunstructureddata(free-formcontentsuchasdocuments,chatlogs,and

mediafiles).

There’slotsofdiscussionaboutunstructureddata,butstructureddataremainsthecorebuildingblockformostAIapplications.

Only9%ofrespondentsrankAImodelacquisitionordevelopmentastheirtopinvestmentpriority,but83%areimplementingorplanningacentralized,

semanticallyconsistentdataaccesslayer.

Themarketisprioritizingdatainfrastructureovermodelbuilding,signalingthatdataaccessistherealbottleneckinAIprogress.

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Thesurveyresultspointtoasoberingtruth:generativeandagenticAIaren’tbottleneckedbythe

capabilitiesoffoundationalAImodels,butbyaccesstoconnected,contextualized,controlleddata.AndtheAIlandscapeisrifewithdataintegrationissues,fromfragmentedsystemstoalackof

connectorsandreal-timeinfrastructure.

That’sthebadnews.Thegoodnews?Thereareenterprisesandsoftwareprovidersthataregettingitright,andthesurveysurfacedthekeyinitiatives,priorities,andinvestmentsbehindtheirsuccess.Ifyou’reanenterpriselookingtoself-assessyourAImaturityorthecurrentstateofout-of-the-

boxagenticAIsolutions,thisreportoffersvaluableinsight.Ifyou’reasoftwareprovideraimingtobenchmarkyourselfagainstindustryleadersandbetterunderstandenterpriseinvestmentpriorities,you’llalsofindpracticalguidancehere.

Thereportismadeupoftwomajorparts:

1.EnterpriseAIadoptionanddatachallenges:AdeepdiveintohowenterpriseorganizationsaredeployingAIandwhatinfrastructuralblockersareslowingprogress.

2.ProductAIstrategyamongsoftwareproviders:AnexplorationofhowproductleadersareembeddingAIintotheirplatformsandwhydataintegrationremainsacriticaldependency.

Together,thesesectionsformacomprehensivepictureofhowdataconnectivity,infrastructurematurity,andintegrationstrategydictateAIsuccessinbothenterpriseandproductcontexts.

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TableofContents

SurveyMethodologyandRespondentDemographics 6

PartI:EnterpriseAIAdoptionandtheDataInfrastructureGap 9

EnterpriseAIisn’tonthehorizon:it’sinproduction 9

Stuckinthemiddle:mostenterprisesareimplementingandscalingAI,butveryfewareleading 10

KnowledgeassistanceandcustomerserviceautomationarethemostprevalentapplicationsofenterpriseAI 11

AmajorityoforganizationshavealreadydeployedagenticAIsystems 12

AItoolsprawlisfragmentingcontext,atatimewhencontextmattersmost 13

ThecurrentstateofdatainfrastructurepoweringAI 14

EnterpriseAIleadersarelargelyunsatisfiedwithcurrentintegrationarchitecture 14

WhendataconnectivitybecomestheAIbottleneck 16

Real-timeintegrationisamaturitymarker 20

Beyondmodels:thearchitectureandcapabilitiesofAIreadiness 21

AImaturitycorrelateswithintegrationmaturity 21

Centralized,semanticallyenricheddataaccessisaprerequisiteforscalableAI 21

TopinvestmentareasforAIsuccess 24

PartII:TheSoftwareProviderLensandAIProductStrategy 25

AIfeaturesarebecomingtablestakesforproductleaders 26

DatafragmentationandintegrationisthebiggestlimitingfactorforAIfeaturedevelopment 28

MostAIusecasesneedmultipleintegrationstocustomerdata 31

Semanticstandardizationandreal-timeintegrationdemandsfromenterprisesareshapingproductroadmaps 33

TheFinalSay:TheAIConnectivityImperative 36

GlossaryofTerms 37

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SurveyMethodologyand

RespondentDemographics

Theinsightsinthisreportdrawfromtwocomplementarysurveysconductedin2025;onecapturingtheperspectiveofenterpriseAIimplementationleaders,andtheotherfromproductleaders

atsoftwareproviders.Together,theyofferadualviewofhoworganizationsareadoptingand

operationalizingAI:fromenterprisesembeddingAIintotheiroperations,tosoftwareprovidersbuildingAIdirectlyintotheirproducts.EachsurveyaimedtouncoverthecurrentstateofAIadoption,the

infrastructurechallengesshapingprogress,andtheinvestmentprioritiesdefiningthenextphaseofAImaturity.Accordingly,PartIofthereportfocusesonenterpriseAIadoptionandthedatainfrastructuregap,whilePartIIexaminesthesoftwareproviderperspectiveandtheevolvingstrategiesbehindAI-

poweredproductdevelopment.

PartImethodology:Weusedanindependentresearchfirmtoblindsurvey100enterprisedataandAIleaders,acrossindustriesandsizesrangingfromstartuptoover$10Binannualrecurringrevenue.

Nearlyhalf(49%)oftherespondentswereC-levelexecutivesresponsiblefortechnology,IT,data,

andAIfunctions.Includingthe22%VPsanddirectorswhorespondedtothesurvey,thedatasetisstronglyrepresentativeofenterpriseleaderswithdecision-makingauthorityandamandatetodriveorganization-wideimpactthroughtheadoptionofAI.

ChiefTechnologyOfficer(CTO)

ChiefDataorAIOfficer(CDO/CAIO)

HeadofData/HeadofAI

30%

17%

14%

2%

6%

9%

9%

13%

ChiefInformationOfficer(CIO)

EnterpriseDataArchitect

VPorDirectorofAI/ML

AIProductorPlatformOwner

VPorDirectorofData

Seventy-fourpercentofrespondentswerefromcompanieswithmorethan$500Minannualrevenue,whiletheremaining26%belongedtomid-sizedcompaniesandstartups.ThedatasetisthusskewedtowardorganizationsthathavebiggerITbudgetsandexposuretoawideswathofAIanddata

infrastructureapproachesinthemarket.

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Under$50M

$50M-$249M

$250M-500M

$500M-$2B

$2B-10B

Over$10B

5%

12%

9%

30%

23%

21%

Thisrespondentmixreflectsafront-rowviewofhowAIisbeingbuiltanddeployedtodayintheenterprise.

PartIImethodology:Thishalfofthereportrepresentsresultsfromablindsurveyconductedbyan

independentresearchfirmof100productandengineeringleadersfromamixofsoftwarecompanies,rangingfromAI-nativestartupstosomeofthemostestablishedplayersinSaaSandenterprise

platforms.ThisoffersusauniquelycomprehensiveviewintohowdifferentproductstrategiesintersectwithAIreadinessandintegrationapproaches.

13%

6%

AInativecompany

Cloudprovider/hyperscaler

31%

Horizontalenterpriseapplication

50%VerticalSaaS

Inthiscohort,58%ofrespondentsaresoftwareprovidersreporting$500MormoreinARR.Titles

includeproductleadersacrossfunctions:fortypercentareVPsordirectorsofproduct,withsignificantrepresentationfromengineering,architecture,andAIleadershiproles.Twenty-ninepercentareC-leveldecision-makers(CTOsandCPOs),settingorganization-wideprioritiesregardingAIimplementationinproduct.

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$50M-$249M

$250M-500M

Over$500M

19%

23%

58%

ChiefProductOfficer

(CPO)

ChiefTechnologyOfficer(CTO)

HeadofAI/MLorAIProduct

TechnicalProductManagerorAI

9%

20%

8%

5%

40%

18%

VPorDirector

ofEngineering/Architecture

VPorDirectorofProductManagement

Definitionsusedinthisreport(seeglossaryoftermsonpage37foradditionaldefinitions):

GenerativeAI(GenAI)—AI-poweredfeaturesbuiltintoproductsthathelpcustomerscompletetasksbygeneratingcontent,surfacinginsights,orinteractingwithdata,oftenusingLLMs.Thisreport

focusesontwocommonGenAIapplications:

?AICo-pilot—AnAI-poweredassistantembeddedinyourproductthathelpsuserscompletetasksbygeneratingcontent,retrievingdata,coding,orrecommendingnextsteps,butalwaysrequireshumaninputtoinitiateorapproveactions.Example:Acopilotthatsummarizesrecentcustomeractivityandsuggestsfollow-upactions,whichtheuserreviewsandapproves.

?AutonomousAIAgent—Actswithminimalornohumanpromptingtocompletetasksorachievegoals.Theseagentscanreason,makedecisions,andtakeactionacrosssystemsorworkflowsonbehalfoftheuser.Example:anAIagentthatmonitorspipelineactivity,flagsat-riskdeals,andsendsproactivealertsormessages.

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PartI:EnterpriseAIAdoptionandtheDataInfrastructureGap

ThefindingsbelowhighlightkeythemesthatemergedfromoursurveyofenterpriseleadersresponsibleforadvancingAIadoptionandmaturity.

Keytakeaways:

?AIisalreadyinproduction,notinpilot.78%ofenterpriseshavemovedbeyondexperimentation,embeddingAIintooperations,butonly17%areinadvancedstageswhereROIismeasurable.

?AIcapabilitiesandmodelsizearenotthetopblockerstoadoption.

Dataandcontextare.73%oforganizationscitedataqualityand

integrationastopblockers,and71%spendoveraquarterofAIprojecttimejustondataconnectivity.

?Scaleandmaturitygohand?in?hand.Largeenterpriseswithmaturedatainfrastructurearepullingahead,while80%offirmsunder$50MinARRremainstuckinearlyimplementation.

?Real?time,governeddataisthenewdifferentiator.60%rank

governanceand42%rankreal?timeconnectivityastopinvestmentpriorities,farsurpassinginvestmentintheAImodelsthemselves(9%).

?Fragmentedtoolsdemandunifiedintegration.44%oforganizationslisted“l(fā)ackofunifiedmetadataandsemanticcontext”amongtheirtopfivecurrentblockerstoenterpriseAIadoption,and83%oforganizationshavebuiltorareplanningtobuildcentralized,semanticallyconsistentdataaccess.

Whatfollowsisadeepdiveintothepriorities,roadblocks,andemergingtrendsshapingenterpriseAIadoption,basedonthesurveyresults.

EnterpriseAIisn’tonthehorizon:it’sinproduction

Finding:

Beyondexperimentation:66%ofcompaniesaredeployingGenAIandautonomousagentstoaugmenthumanworkflows.

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Stuckinthemiddle:mostenterprisesareimplementingandscalingAI,butveryfewareleading

AIisnotafutureaspirationformostenterprises.It’shere.Infact,78%ofenterprisesarepastthepilotphase,withAIuse-casesalreadyembeddedinoperations.

Amajorityofenterprises(68%)fallintothemiddlestagesofAImaturity,betweenthe“implementing”and“scaling”stages.However,only17%areinadvancedstages(“optimizing”or“l(fā)eading”)whereROIismeasurableandAIiscoretostrategy.

WherewouldyouplaceyourorganizationontheAImaturitycurve?

Stage%oforganizations

Experimenting(earlypilots,proofsofconcept,learningphase)

Implementing(deployinginitialproductionusecases,establishinggovernance)

Scaling(expandingAIacrossmultipledepartmentsandusecases)

Optimizing(AIintegratedintocoreoperations,measuringROIandefficiency)

Leading(AIdrivescompetitiveadvantageandinnovationstrategy)

15%

37%

31%

7%

10%

Thedataalsoshowsbiggercompaniesarepullingahead.Only4.8%ofenterprisesover$10Bin

annualrevenuearestillintheearlystageofexperimentingwithAI,while80%ofthoseunder$50Minannualrevenueremainstuckinearlyimplementation.

100%

80%

60%

40%

20%

0%

Under$50M

$50M-$249M

$250M-$500M

$500M-$2B

$2B-$10B

Over$10B

ExperimentingImplementingLeadingOptimizingScaling

Implication:Scalematters.Largeenterpriseshavethedatainfrastructureandin-housetalenttooperationalizeAI,whilesmallerfirmsarestilllayingthepipestogetpilotsofftheground.

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“Ayearago,weimplementedAIassistantswithinallourcall

centers,fullyinproduction.Itisfullyintegratedwithourbackenddata,sowhenacustomercalls,itautomaticallyrecognizestheirnumber,looksuptheorder,thedeliverystatus,andanswersthecall,allbeforeahumanagentcanevenpickupthecall,inreal-time.Theresultsweredramatic.”

—SVPofTechnologyPortfolio,globalretailbrand

KnowledgeassistanceandcustomerserviceautomationarethemostprevalentapplicationsofenterpriseAI

Earlysuccessstoriesfocusoninternalknowledgeassistantsandcustomersupportautomation.Codegenerationisclosebehind.Theseusecasesthriveonaccesstobothstructured(databases,APIs,spreadsheets)andunstructured(images,emails,documents)data,butevenmoreadvancedcapabilities(e.g.,AIagents,decisionsupport)aregainingtraction.

WhichusecasesisyourorganizationtargetingwithGenAIoragenticAItodayorinthenearfuture?

Usecase%ofOrgs

Employeeoragentco-pilot(e.g.,internalknowledgeassistants,agentaugmentation)

Customersupportandserviceautomation(e.g.,virtualagents,chatbots,ticketdeflection)

AI-poweredsearchorknowledgeretrieval(e.g.,RAGsystems,semanticsearch)Codegenerationoraugmentation(e.g.,internaldevtools,LLM-driven

refactoring)

Intelligentdocumentprocessing(e.g.,summarization,extraction,classification)

Marketingorcontentgeneration(e.g.,campaigncopy,imagegeneration,personalization)

Processorworkflowautomation(e.g.,agent-triggeredactions,RPAaugmentation)

Decisionsupportorscenarioanalysis(e.g.,contextualinsights,what-ifmodeling)

PredictiveanalyticsforGTM,revenue,orcustomerretention

Internalbusinessintelligenceenhancement(e.g.,naturallanguagedashboards)

Predictiveanalyticsforsupplychain,logistics,oroperations

AIagentorchestrationacrosssystems(e.g.,updatingrecords,syncingworkflows)

79%

70%

61%

60%

58%

55%

54%

52%

49%

47%

37%

33%

ipsum

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AIchatassistantsandagentsareprimarilydeployedtoaugmenthumanworkflows,notreplacethem.Thetopusecases,employee/agentcopilots(79%)andcustomersupportautomation(70%),signal

thatenterprisesarefocusingonhuman-in-the-loopaugmentation.TheseusecaseshelpknowledgeworkersoperatemoreefficientlywithouthandingoverfullcontroltoAI.

Theimpressiveadoptionofcodegeneration(60%)andmarketing/contentcreation(55%)showsthatAIisnowembeddedintechnicalandcreativeworkflowsalike.Theseareproductivitymultipliersthatarelowriskbuthighimpact,andareoftenearlywinsforAIadoption.

Implication:EnterprisesarebettingonAItoboostproductivity,notreplacepeople.Theearlyfocusoncopilots,supportautomation,andcodegenerationshowsthatadoptioniscenteredonpractical,human-in-the-loopusecasesthatdeliverfastvaluewithlowerrisk.

AmajorityoforganizationshavealreadydeployedagenticAIsystems

GenerativeandagenticAIadoptionisprevalent,withaclearshiftfromexperimentationtodeployment,especiallyaroundagent-basedusecases.

Whatbestdescribesyourorganization’scurrentinternalengagementwithgenerativeandagenticAI?

WeareinearlyexplorationorPoCstagesforenterpriseAIsolutions

7%

7%

66%

We'vedeployedoraredeployingbothgenerativeAIusecasesandAIagents

We'vedeployedorare deployinggenerativeAIusecases,butnotAIagents

20%

We'vedeployedoraredeployingAIagents

Implication:AIagentadoptionisn’ttheoretical.It’salreadyhappeningatscale,signalingafast-movingshifttowardmoreautonomous,workflow-integratedAI.

“Dataisabsolutelythelifebloodofagentsactuallybeinghelpful

foryourenterprise.Andso,havingtherightconnections,therightfidelity,therightsecurity,therightcompliancearoundyourdataisallcritical.”

—PhilipStephens,SeniorStaffSoftwareEngineer,Google

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AItoolsprawlisfragmentingcontextatatimewhencontext

mattersmost

While76%oforganizationsleveragefoundationalmodelsinenterpriseLLMplatforms,theAI

technologystackisnotcentralized.EnterprisesalsoreportsignificantuseofBI-nativecopilots,agentplatforms,andcustomerserviceAI.

WhichAIapplicationsorplatformsaremostimportanttoyourorganization’susecases?

Platformcategory

EnterpriseLLMplatforms(OpenAI,Claude,Gemini)

Enterprisedataplatforms

(Snowflake,Databricks,etc.)

BusinessintelligenceAI

(MicrosoftCopilot,TableauAI,etc.)

CodegenerationAI

(GitHubCopilot,Cursor,etc.)

EnterpriseAIagents(SalesforceAgentforce,CopilotStudio)

CloudAIservices

(Vertex,Bedrock,AzureAI,etc.)

CustomerserviceAI

(ZendeskAI,ServiceNowAI)

CustomAIapplications(BuiltIn-House)

AIdevelopmentframeworks(LangChain,LlamaIndex,etc.)

OpensourceAImodels(Llama,Mistral,etc.)

Industry-specificAIsolutions

76%

65%

54%

48%

43%

34%

31%

29%

28%

20%

14%

Implication:Thissprawlcreatesintegrationcomplexityandcontextfragmentationthatmustbeaddressedthroughcentralized,tool-agnosticsemanticsandintegration.

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“Mostenterprises,especiallyoldercompanieswithlotsofhistory,havedisparatesystemsthatarecobbledtogether.Yourabilitytogetvalueoutofthesedataassetsislargelyafunctionofyourdataintegrationcapability.”

—ChiefDataandAnalyticsOfficer,Fortune100manufacturer

ThecurrentstateofdatainfrastructurepoweringAI

Finding:

Only6%ofenterprisesaresatisfiedwiththeircurrentdatainfrastructureforAI.

EnterpriseAIleadersarelargelyunsatisfiedwithcurrentintegrationarchitecture

Mostenterprisesstillrelyonamixoffragileormanualapproaches,with53%relyingoncustom-builtAPIs,connectors,anddatapipelinestodeliverenterprisedatacontexttoAImodels.

HowdoesyourorganizationcurrentlyconnectAIsystemstoenterprisedatasources?

Custom-builtAPIs,connectors,anddatapipelines

Out-of-the-boxconnectorsfrom

dataintegration/ETL/ELTplatformsDirectdatabaseconnections

Clouddataplatformintegrations (Snowflake,Databricks,etc.)Manualdataexportsandimports Third-partyiPaaSsolutions(MuleSoft,SnapLogic,etc.)

53%

31%

23%

13%

3%

3%

Overall,organizationsreportahighdegreeofpainanddissatisfactionwiththeircurrentintegrationstrategyandinfrastructure.Only6%reportedtheywere“verysatisfied”withtheirintegrationstrategy.FourteenpercentreportedtheirintegrationstrategycreatessignificantchallengesforAIinitiatives.

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HowsatisfiedareyouwithyourcurrentdataconnectivityapproachforAIinitiatives,

includingingestionofdatafromsourcesystems,contextinjectionforGenAImodels,real-timedataintegration,etc.?

60%

50%

40%

30%

20%

10%

0

55%

Somewhatsatisfied:

Worksbuthaslimitations

14%

Somewhatdissatisfied:Createssignificant

challengesforAIinitiatives

25%

Neutral:Adequateforcurrentneeds

6%

Verysatisfied:

Meetsallour

needsefficiently

“AItechnologyhasadvancedfasterthanorganizationaldata

capabilities,creatingacriticalbottleneckforAIadoption.WhilesophisticatedAImodelsarereadilyavailable,mostcompaniesstrugglewithpoordataquality,fragmentedsystems,and

inadequatedatapreparationprocesses.Ultimately,AIsuccessdependsmoreonhavinghigh-quality,well-prepareddatathanonhavingthemostadvancedmodels.”

—HarshitKohli,Sr.TechnicalAccountManager,AWS

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Implication:Thecostofbespokeintegrationishigh;notjustindollars,butindelaysandfragility.The

MITReportonEnterpriseAIadoption

indicatesthatcustom-builtsolutionsresultinasignificantly

higherrateoffailureforenterpriseAIinitiatives.Theoveralldissatisfactionexpressedbyenterpriseda