2024GlobalAITrendsReport

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DiscoverhowAIistransformingindustriesandwhatittakestostayahead.Readthefull2024GlobalAITrendsReport,basedonasurveyof1,500AIpractitioners,tounlockkeyinsightsandstrategiesfornavigatingtherapidlyevolvingAIlandscape.

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Tableofcontents

Introduction

Keyfindings

DefiningAIleaders

AImaturityandadoption

AIprojectscalingchallenges

RiseofgenerativeAI

GPUavailability

Environmentalimpact

Conclusions

Methodology

Abouttheauthors

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Introduction

The2024GlobalTrendsinAIreportdelvesintotheunderlyingtrendssurroundingAIadoption.Inlastyear’sGlobalTrendsinAIreport,we

exploredthedividebetweenorganizations

thatweresuccessfullyrunningAIinproductionandthosethatwerenot.Inthisyear’sstudy,

werevisitthisAIleadershiptheme,drawingonsomekeypracticesthatleadingorganizationsaredoingdifferently—whiledeep-divingintothevaluedrivers,infrastructuredecisionsandenvironmentalpracticesthatareshapingAI

strategies.Todevelopthisstudy,S&PGlobal

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MarketIntelligencesurveyedmorethan1,500globalAIdecision-makersandengagedin1:1interviewswithseniorITexecutivesabouttheirAIprojectsandinitiatives.

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Keyfindings

1.AIapplicationsarenow

pervasiveintheenterprise;

investmentsinproductquality

andITefficiencyaretoppriorities.

AIadoptioncontinuesatbreakneckspeed,withthetechnologyincreasinglyviewedasan

embeddedandstrategiccapability.

–AIinitiativesarematuringrapidly:Inthelastyear,reportedlevelsofAImaturity

haveundergonearadicalshift.In2023,surveyrespondentswerestilllargely

experimentingwithAIorhadisolated

deploymentsinsmallpocketsoftheir

organizations.Thisyear,themajorityofrespondentsreportthatAIis“currently

widelyimplemented”and“drivingcriticalvalue”intheirorganization.

–Productimprovementandoperational

effectivenessarekeyinvestmentdrivers:OrganizationsareincreasinglyapplyingAItoenhancetop-linerevenueand

competitivedifferentiation,withimprovingproductorservicequality(42%)themostpopularobjective,andwithmany

targetingincreasedrevenuegrowth(39%).Simultaneously,

organizationsrecognizethepotentialtoboosttheiroperationaleffectivenessbyimprovingworkforceproductivity(40%)andITefficiencies(41%),alongwith

acceleratingtheiroverallpaceofinnovation(39%).

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2.ManyAIprojectsfailtoscale—legacydataarchitecturesarethe

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culprit.

AIprojectsarechallengedbyweakdatafoundations.Legacydataarchitecturesareimpedingbroaderdeployment.

–Achievingscaleremainsachallenge:

Organizationsarefacingsignificant

challengesinachievingthedesiredreachoftheirAIprojects.Theaverage

organizationhas10projectsinthepilotphaseand16inlimiteddeployment,butonlysixdeployedatscale.

–Availabilityofqualitydataisamajorobstacle:DataqualityisthegreatestchallengetomovingAIprojectsintoproduction.Thechallengeforprojectteamsisnotsomuch

aboutidentifyingrelevantdata,butits

availability;organizationsarestrugglingtobuildaconsistent,integrateddata

foundationforprojects.

–Modernizingdataarchitecturesiscriticaltosuccess:Giventhis,itisunsurprising

thatthegreatestproportionof

respondents(35%)citestorageanddatamanagementastheprimary

infrastructureissueshinderingAI

deployments—significantlygreaterthancompute(26%),security(23%)and

networking(15%).

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GenerativeAItrailblazers

arethe24%oforganizationsthatsuggestthat

generativeAIisan

integratedcapabilityacrossworkflowswithintheir

business.

3.GenerativeAIhasrapidly

eclipsedotherAIapplications.

GenerativeAIhasgainedsignificanttractioninashorttime.AItrailblazersarerealizing

concretebenefitsandarepoisedto

compoundtheircompetitiveadvantage.

–GenerativeAIisthefocus:Anastonishing88%oforganizationsarenowactively

investigatinggenerativeAI,far

outstrippingotherAIapplicationssuchaspredictionmodels(61%),classification

(51%),expertsystems(39%)androbotics

(30%).

DedicatedbudgetsforgenerativeAIasaproportionofoverallAIinvestmentsare

growingasorganizationsbeginto

recognizethepotentialbenefitsof

integratedgenerativeAIcapabilities.

–GenerativeAIadoptionisexploding:

Despiteonlybeinginthemarketfora

relativelyshorttime,24%oforganizationssaytheyalreadyseegenerativeAIasanintegratedcapabilitydeployedacross

theirorganization.Just11%ofrespondentsarenotinvestingingenerativeAIatall,

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andthemajorityoforganizationsareactivelyintheprocessofturningthisinvestmentintoscaled-up,integratedcapabilities.

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–GenerativeAItrailblazersareexpectedtocompoundtheircompetitiveadvantage:Organizationsthathavealready

integratedgenerativeAIacrosstheir

organizationplantocontinueincreasingtheirinvestments:TheyexpectgenerativeAIbudgetstoreach47%oftheirtotalAI

budgetinthenext12months,far

outpacingless“AI-mature”organizations.ThemajorityofthesegenerativeAI

trailblazersareseeingasignificant

positiveimpactfromthetechnology

acrossthefullrangeoftargetedbenefits.Thosebenefitsarelikelytocompound

theircompetitiveadvantagegiventhatthosestillintheexperimentationphasesoftheirgenerativeAIprojectsarenot

seeingthesameincreasesin

organizationalinnovation,newproductdevelopmentandtimetomarket.

4.GPUavailabilitycontinuestobeconstrained,shaping

infrastructuredecision-making.

AccesstoGPUsisamajorconcernfor

organizations;GPUcloudsmayofferascalablesolution.

–AccessingGPUscontinuestobea

challenge:Fourin10organizations

surveyedsuggestaccesstoAI

acceleratorsisaleadingconsiderationintheirinfrastructuredecision-making,and30%citeGPUavailabilityamongtheirtopthreemostseriouschallengesinmovingAImodelsintoproduction.

–Regionalpressurespersist:Insome

geographies,particularlyinAsia-Pacific,lackofaccesstoGPUsisrestricting

organizationsfromdeployingAI;38%of

organizationsinIndiaseeaccelerator

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accessamongtheirtopthreechallengestomovingAIprojectsintoproduction.

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–HyperscalerandGPUcloudsserveaskeychannelsforcompaniestoaccessGPUs:Theneedforacceleratorshasdriven46%ofsurveyedorganizationstoleverage

hyperscalepubliccloudsformodeltraining,aswellas—increasingly—specialistGPUcloudproviders(32%).

5.ConcernsaboutAI’s

environmentalimpactpersistbutarenotslowingAIadoption;

sustainableAIpracticesofferopportunitytomitigate

emissions.

AI’senvironmentalandenergyimpactisstillaprominentconcernformanyorganizations,butitisnotslowingthedecisiontoinvestinAI

projects.Withmanyorganizationsseeing

sustainabilitypracticesdelivermeaningful

impacts,thereisaclearopportunitytoaddresstheemissionschallenge.

–ConcernsaboutAI’senergyandcarbonimpactremainprominent:Nearlytwo-

thirds(64%)oforganizationssaythattheyareconcernedabouttheimpactof

AI/machinelearning(ML)projectsontheirenergyuseandcarbonfootprint;25%of

organizationsindicatetheyareveryconcerned.

–Adoptionofsustainabledata

infrastructuretechnologiesisanareaoffocus:Clearly,sustainabilitycredentials

fromtechnologyprovidersarebecomingessential,with42%oforganizations

indicatingthattheyhaveinvestedin

energy-efficientIThardware/systemstoaddressthepotentialenvironmental

impactsoftheirAIinitiativesover

thepast12months.Ofthose,56%believethishashada“high”or“veryhigh”impact.Othershavefoundthatmakingchangesindatainfrastructurevendors(59%)and

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AIprojectscope(57%)havehada“high”or“veryhigh”impact.

–Sustainabilityisanimportant,butnot

theprimary,factorinAIdecision-

making:Morethanaquarter(30%)of

organizationsreportthatsustainability

initiativesareadriverofAIadoptionas

theylooktoapplyAItoimproveenergy

efficiencyandmitigateemissions.Whilethisisnotable,sustainabilityis,infact,theleast-mentioneddriveroverall.Even

whereenergy-reductioninitiativesarethegoal,meetingsustainabilitytargetscan

takeabackseattocostsavingsand

improvingoperationalefficiencies

astheprincipalobjective.InthecontextofalltheissuesthatmoststronglyinformAIinfrastructuredecision-making,

sustainabilityismid-table:37%of

organizationsareprioritizingit,butitis

outrankedbymoreprominentissuessuchassecurity(47%)andaccesstoAI

accelerators(44%).

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DefiningAIleaders

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Inthepastyear,thesentimenttowardAIhas

rapidlychanged,ashasAI’sstrategicrole.AI

maturityhasgrowntothepointthatthevast

majorityoforganizationshavesomeformofAIinproduction,soacomparisonofthe“haves”andthe“havenots”offerslittleanalyticalvalue.Instead,theemergingdivideappearstobe

thosethatareabletoharnessthelatest

technologybreakthroughsanddeliverAIatscale,andthosethatcannot.

WiththemostsignificantAIbreakthroughandarguablymostimportanttechnological

innovationinthepastdecade,generativeAIasastrategicimperativeisinescapable.Those

thathavequicklyandefficientlytappedinto

thistechnologicalbreakthrougharedistancingthemselvesfromthechasingpack.

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Basedontheseassumptions,wehavedefinedAIleaderstobethosethathavereportedthefollowingaccomplishments:

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–AI/MLprojectsinproductionenvironmentsdrivingreal-worldimpactincritical

operations.

–ImplementedAI/MLwidelyacrosstheirorganization,achievingfargreaterscalethanlimitedandsiloedAIprojects.

–Capitalizedonthemostsignificant

technologicalbreakthroughofthis

decade(generativeAI)andpositioneditasanintegratedcapabilityacrosstheir

businessandworkflows.

–Thetop10%ofthemarketengagein

severaldistinguishingpracticesthatsetthemapartfromthebulkoforganizationsacrossthereport’sfivekeythemes.

Figure2:WhoareAIleadersin2024?

10%oftotal(n=153)

Source:S&PGlobalMarketIntelligence451ResearchGlobalTrendsinAIcustomsurvey,2024.

ThereareseveralnoticeablevariancesinthemakeupandcharacteristicsofAIleadersin2024.

–Industry:Healthcarerespondents(18%)

haveagreaterproportionofAIleadersthanotherindustries.

–Companysize:Enterprises(16%)with

greateraccesstocapital,resources,AIskillsetsandtypicallymorematuredigital

transformationprojectsleadother

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companysizesproportionallyasAIleaders.

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–Region:NorthAmerica(16%)hasa

significantlygreaterproportionofAIleadersthanAsia-Pacific(8%)andEMEA(6%).

ContributingfactorsmaybegreateraccesstoAItalentfromindustryandeducational

institutions,andregionalavailabilityofventurefundingandcapital.

–Businessmodels:AIproviders(15%)are

morelikelytobeconsideredAIleadersthanotherorganizations.However,thedifferenceislesspronouncedthanonemightassume;insomeinstances,AIproviders,despite

buildingAIsolutionsforcustomers,havenotnecessarilyfullyimplementedcapabilities

withintheirowncompany.

AIapplications

arenowpervasiveintheenterprise;investmentsin

productqualityandITefficiencyaretoppriorities

Inthelastyear,AI’srolehaschangedwithinmanyorganizations,shiftingfromaminor

componentofanoverallstrategytoacriticalembeddedcapability.

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Keyinsights:

–Themostcommonadoptionstatusfor

AIhasshiftedfromaminor

componentofabroaderstrategyin

2023tocurrentlywidelyimplementedanddrivingcriticalvaluein2024.

–ImprovingproductorservicequalityandgeneratingcostsavingsfromITefficienciesaretheleadingdriversfordevelopingAIapplications.

–Manystrategicobjectivessuchas

improvingtimetomarketandgainingproductorservicedifferentiationareseeinggreaterfocus.

AIisbecomingafundamentalaspectofmanyorganizations’strategies,increasinglyseenasbothwidelyimplementedandcritical.The

proportionofrespondentswhoindicatethatAIisa“minorcomponentofabroaderstrategy”

intheirorganizationhalvedfromlastyear’s

survey,whiletheproportionofrespondents

whoseeAIas“widelyimplemented,driving

criticalvalue”increasedfrom28%to33%,

becomingthemostcommonanswer.ForNorthAmericarespondents,itisevenhigherat48%,incomparisontoAsia-Pacific(26%)andEMEA(25%).

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AIseeingwiderdeploymentin2024

AIorganizationalscale

2024

2023

Widelyimplemented

33%

28%

Fewusecases

33%

26%

Singleusecase

16%

16%

Minorcomponent

17%

30%

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AI’sreachisnotlimitedtothebreadthof

implementationbutincludesthetechnology’sstrategicimpact.Historically,AI’svalue

propositionhasbeencloselyassociatedwith

reducingcosts.PreviousAIadvancementsin

roboticprocessautomation,forexample,werecloselyalignedwithobjectivessuchas

headcountreductionorreducingoutsourcingcosts.Itisnotsomuchthatthecost-reductionopportunitypresentedbyAIisbeingcrowdedout—indeed,generatingcostsavingsfromITefficienciesisthesecondmostpopular

objectiveforAI—rather,costdriversarebeingpairedwithmorestrategicobjectives.For

example,morethanathird(39%)of

respondentsinoursurveyseerevenuegrowthasakeydriveroftheirAIinitiatives.AsFigure3illustrates,companiesarenotjusttryingto

achievemorewithAIthantheywerelastyear,buttheyalsoseeacleareralignmentwith

revenuedrivers.TheyaresignificantlymoreawareoftheopportunityforAItobeusedtogainproductdifferentiationanddrive

improvedtimetomarketthantheywerelastyear.

Figure3:Theyear-over-yearchangeindriversforAI

applicationdevelopment

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WhatareAIleadersdoing

differently?

LeadersperceiveawiderrangeofobjectivesasdrivingtheirAI

strategies.Thisspreadof

objectiveshelpsbetterinform

whereAIcouldbemostimpactful.ItalsosetsthebasisforastrongerbusinesscaseforinvestinginAI,

helpingbuildanarrativethatcanappealtoawiderspreadof

stakeholders.

“Istartedwiththeseexperimentsayearandahalfago.Andthenittookusayeartobuild…wenowhaveabout5-10use

casesinproduction.”

CIO,transportation/logistics/warehousing,

1,000-5,000employees,US

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ManyAIprojectsfailtoscale;

legacydata

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architecturesaretheculprit

AI’sgrowingstrategicimportanceisdrivingasignificantincreaseininitiativesacross

businesses.Broadexperimentationand

educationareandorganizationswouldbe

remissnottoencourageit.However,the

opportunityisbeingthrottledbyprojectsthatlackaclearpathwaytorecognizingvalue,

hamperedbydatachallenges.AIprojectsriskstallinginalimiteddeploymentpurgatory,

costingacompanymoney,timeand

resources,whilenotseeingdesiredlevelsof

use.Initiativesarebecomingsnaggedondatasiloes,poordataqualityandineffectivedata

andmodelpipelines.

Keyinsights:

–Intheaverageorganization,51%ofAIprojectsareinproductionbutnot

beingdeliveredatscale.

–DataqualityisthegreatestinhibitorwhenmovingAIprojectsinto

productionenvironments.

–Storageanddatamanagementarethemostcommoninfrastructural

inhibitorstoAIinitiatives,identifiedby35%oforganizations;however,thosethathaveAIwidelyimplementedfeelthesechallengeslesskeenly.

AsorganizationsinvesttoapplyAItoanever-growingsetofobjectives,akinkisemerginginorganizations’projectpipelines.Whilemore

initiativesarefunneledtowardAIproject

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teams,thereisabuildupofinitiativesthathave

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onlybeenpartiallydeployed.AsFigure4

illustrates,respondentorganizations,on

average,havemoreprojectsclassifiedas

beinginproductionwithalimiteddeploymentthanscaled-upcapabilities.Inchasingnew

initiatives,manyorganizationsmayfailto

maximizethevalueoftheirexisting

investments.Thecruxoftheproblemappearstobedataqualityandavailability,withlegacydataarchitecturescausingthispipeline

stoppageinmanyorganizations.

Dataqualityisthemostfrequentlyidentified

challengeasorganizationsmovetheirprojectsfrompilotstoproduction.AsFigure5illustrates,dataqualityconcerns—identifiedby42%of

organizationsasamongtheirtopthreebarriers—areevenmoresignificantthanskills

shortages(32%)andbudgetlimitations(31%).Organizationsinmediaandentertainment

(59%),highereducation(53%),andaerospaceanddefense(48%)feelthedataquality

challengeparticularlykeenly.

Figure4:Manyprojectsfailto

graduatefromlimited

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deploymenttodeliveringatscale

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Q.HowmanyAI/MLprojectsdoyoucurrentlyhave:In

pilot/proofofconcept;inproduction,limiteddeployment;inproduction,atscale?

Base:Allrespondents(n=1,519).

Source:S&PGlobalMarketIntelligence451ResearchGlobalTrendsinAIcustomsurvey,2024.

Thedataqualitychallengeisnotalackofdatatobuildperformantmodelsbut,rather,thatthedataisnotsetupinsuchaswaythatprojectteamscantakefulladvantageofit.When

askedspecificallytoranktheprimarydatachallengestomoveprojectstoproductionenvironments,respondentsindicatedthat

availabilityofqualitydataisamorenotableimpedimentthanidentifyingrelevantdata.

With34%oforganizationsperceiving

availabilityofqualitydataasatopthreedatachallenge,outrankedonlybydataprivacy

concerns(35%),itisclearthatmany

organizationsarepoorlysetupforeffectivedatamanagement.

Legacydatatechnologiesseemtobea

leadingcauseofthesedatamanagement

shortcomings.DatamanagementandstoragearemostcommonlyseenastheinfrastructurecomponentsthatinhibitAIapplication

development.Morethanathird(35%)of

respondentsseethemasamoreseriousissuethansecurity(23%),compute(26%)and

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networkingresources(15%).

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Tellingly,organizationsthataremosteffectivelyscalingAIinitiativesarelessconstrainedby

thesedatamanagementandstorage

components.Just28%ofrespondentswho

reportedthatAIiswidelyimplementedwithin

theirorganizationperceivestorageanddata

managementchallengesastheirgreatest

inhibitor;instead,theyfeelgreaterpressure

fromnetworkingorcomputeresources.This

comparesto42%ofrespondentswhoperceiveAIasbeinglimitedtoafewusecasesor

projectswithintheirorganization.OrganizationsthataredeliveringAIatscaleappeartohave

focusedoninvestinginupgradingthesystemsandtechnologiesusedtostoreormanage

data.

“Westillhavechallengeswithmasterdata.BrancheshaddifferentSKUsforinventory;ifItakethatsiloeddataandputitintoamodel,we’llgetthewrongresults.Cleaningupthisdataisour

focus.”

CIO,transportation/logistics/warehousing

1,000-5,000employees,US

Figure5:TopthreeimpedimentstoorganizationsmovinganAI/MLapplicationfrompilotto

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Q.Whataretheprimarychallengesorimpedimentsto

movinganAI/MLapplicationfromproof-of

concept/pilotingstagestoproductionenvironments?Base:Allrespondents(n=1,519).

Source:S&PGlobalMarketIntelligence451ResearchGlobalTrendsinAIcustomsurvey,2024.

Thisinvestmentappearstobekeybecause

datamanagementandstorageshortcomingsarefilteringthroughintoAIprojectlifecycles,withorganizationsstrugglingtoeffectively

preparedataformodelbuildingand

deployment.Manyorganizationsreportthat

themostchallengingaspectsofAIinitiatives

arethedatapreprocessingstages(seeFigure6).Despitethegrowingnumberof

organizationsindicatingthatAIhasbeen

widelyimplementedwithintheirorganization

overthepast12months,therehasbeenno

meaningfulimprovementyearoveryearin

termsofperformanceagainstthesedata

preprocessingsteps.BringingAIprojectslive

butlimitingtheirvalueorextensibilitywithweakdatafoundationssetsapoorprecedentforthenextwaveofinitiativesintheearlystagesof

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“ThefirstthingI’vedoneisdoubleddownondatastrategy,effectivelybuildinga

dataplatformandgovernanceand

capabilitiesaroundthat.Thisgivesus

morecontroloverourdata.You’ll

probablyfindinalotofcompaniesthattheyboltedonmanyofthese

acquisitionsandhavealotofdisparatesystems,whichmeansdisparatedata–that’sachallenge.”

CIO,manufacturing/foodandbeverage

1,000-5,000employees,UK

Figure6:OrganizationsfindtheearlydatastepsoftheAIlife

cycleaschallengingasmodelbuilding

Proportionofrespondentsthat

identifyAIlifecyclestageas“mostchallenging”

Q.WhatstagesoftheAI/MLapplicationlifecyclearecurrentlythemostchallenging(Rank1)?

Base:Allrespondents(n=1,519).

Source:S&PGlobalMarketIntelligence451ResearchGlobalTrendsinAIcustomsurvey,2024.

Immaturedatamanagementtoolsetsareaworryingbackdropfortheincreasinglydata-hungryAIstrategiesmanyorganizationsareembarkingupon.Morethanthree-quarters

(80%)ofrespondentsforecastanincrease

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overthenext12monthsinthevolumeofdata

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theyusetodeveloptheirAImodels,andjust

lessthanhalf(49%)areforecastinggrowthindatavolumesofmorethan25%.Perhapsmorefundamentally,though,isthatthechallengefororganizationsthatunderinvestindata

managementmaycomewithnewdata-

relatedpressures—inparticular,themixof

datatypesorganizationsareemployingfor

modeltraining.Theproportionoforganizationsusingunstructuredrichmediaandtextdata

forAIinitiativeshasincreasedsince2023,andoutdateddatamanagementtechnologies

maypreventorganizationsfromdeliveringtheseprojectsmeaningfully.

WhatareAIleadersdoing

differently?

Leadersaresignificantlylesslikelytoseestorageanddata

managementastheirprimary

inhibitors,presumablybecausethesecompanieshavealready

prioritizedmodernizingtheirdataarchitectures.Bybuildingasoliddatafoundationattheoutset,AI

leadershaveensuredthat

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valuablepilotshaveaclearpathtodeliveratscale.

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GenerativeAIhasrapidlyeclipsedotherAI

applications

Organizationshaverushedtoinvestin

generativeAI,withinterestoutstrippinglonger-standingformsofAI.Asthedustsettlesonthisexplosionofinvestment,asmallcohortof

generativeAItrailblazershasemerged.Theseorganizationshavemorewidelyintegrated

capabilitiesandareseeingsignificant

competitivebenefitsfromthetechnology

aroundnewproductdevelopment,enhancedinnovationandfastertime-to-market.These

competitiveadvantagesarelikelytogrowasgenerativeAItrailblazerssetouttoestablishasignificantgapbetweenthemselvesand

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others,shapedbytheirinvestmentandinfrastructuraladvantages.

Keyinsights:

–88%oforganizationsareactivelyinvestigatinggenerativeAI.

–24%alreadyseegenerativeAIas

havinggraduatedtoanintegratedcapabilityacrosstheirorganization.

–ThemajorityofthesegenerativeAI

trailblazersseea“high”or