爱立信 -如何在移动网络中部署人工智能 How to deploy AI in mobile networks

HowtodeployAIinmobilenetworks

Ericsson|HowtodeployAIinmobilenetworks2

Content

ChapterPage

1Overview 3

2AIinthetelecomlandscape:AIfornetworksandnetworksforAI 4

3StrategyforbusinessgrowthwithAI 5

3.1WiderangeofAI-poweredsolutions

4ThepathtoAI-nativeRAN 6

4.1Evolutionjourneyaheadof6G

4.2ThethreestagesofAIintegrationwithRAN

4.3Datastrategy:High-qualitydatadrivesperformance

4.4SelectingthebestAItechnologydependingontheusecase

5AI-drivenhardwareevolution8

5.1AIRANhardwareprocessingapproachesintheindustry

5.2Ericsson'shardwarearchitectureapproach

5.3EricssonRANComputeevolution

6Deploymentarchitecture:Maximizingvalue10

6.1Balancingperformanceandefficiency

6.2Combiningtheadvantagesofbothcentralizedanddistributedarchitectures

6.3Thepotentialofedgedatacenters

6.4AIexecutedwhereitmakessense

7Futurestrategyandemergingopportunities 13

8RecommendationsandKeytakeaways 15

Authors 18

Ericsson|HowtodeployAIinmobilenetworks3

1Overview

ArtificialIntelligence(AI)istransforminghownetworksarebuilt,operated,andmonetized.ForCommunicationsServiceProviders(CSPs),AIisnolongeroptional;itis

astrategicimperativetomanagegrowingcomplexity,unlockoperationalefficiencyandboostperformancetoscaledifferentiatedservices.

ThisreportoutlinesEricsson'sapproachtodeployingAIinmobilenetworks,groundedinreal-worldexperienceandtechnical

leadership.Itdistinguishesbetween“AIfornetworks”—enhancingnetworkperfor-

manceandautomationusingAI—and

“networksforAI”—deliveringprogramma-blehigh-performinginfrastructureneededtosupportAI-drivenapplications.

Ericsson'sAIRANstrategyspansbothcentralized(rApps)anddistributed(radiosite)deployments,enablingnon-real-timeandreal-timeautomationandnewAIusecases.WithadeepintegrationofAIintotheRANstackandacommonsoftware

strategyfortwohardwarearchitecture

tracks(purpose-builtandCloudRAN),

EricssonensuresthatAIisexecutedwhereitdeliversthemostvalue—whetherattheedgeforultra-lowlatencyorcentrallyfor

network-wideoptimization.TheseAI-

poweredsolutionsimproveuserexperience,coverage,mobility,spectrumefficiency,

andreduceenergyconsumption.

ForCSPsshapingthefutureoftelecom,thisreportprovidesastrategiclensonhowtoscaleAIefficiently,maximizereturnon

investment,andbuildfuture-proofnetworksfor6Gandbeyond.

Ericsson|HowtodeployAIinmobilenetworks4

2

AIinthetelecomlandscape:AIfornetworksand

networksforAI

AItransformsnetworksintwofundamentalways:

first,itenhancesnetworkefficiencyandsecond,

itenablesentirelynewAI-poweredapplicationsthatdemandconsistent,high-performanceconnectivity,suchasAI-poweredvideofeeds,tobeuploadedfromsmartglasses.

Figure1:AIconnectivityshapingsociety

Ericssondistinguishesbetweenthesetwotransformativerolesthroughclear

terminology:“AIfornetworks”refersto

leveragingAItechnologytoenhancenet-workoperationsandperformance,while“networksforAI”describeshowhigh-

performingprogrammablenetworks

enablenewAI-basedapplicationsthrough

differentiatedconnectivity.The“AIfor

networks”approachrepresentsthemost

promisingpathforwardformanaging

today’sexponentiallygrowingcomplexityofmobilenetworks—fromsurgingtraffic

volumesanddeviceproliferationtodiverseusecasesrequiringspecializedconnectivitytodrivebusinesstransformation.

Ericsson|HowtodeployAIinmobilenetworks5

3

StrategyforbusinessgrowthwithAI

AIinnetworksimprovesperformanceandunlocksnewgrowthopportunities.EricssonenvisionsAIasakeytechnologyenablerforbuildinghigh-performingprogrammable

networksthatareservice-aware,AI-poweredandintent-driven.

Intent-drivenmeansthatCSPscan

specifytheirdesirednetworkoutcomeswithoutdetailinghowtoachievethemormentioningthespecificconfigurationsrequiredforimplementation.Thiscouldincludeoptimizingorprioritizingcertaintraffictypes,users,orbalancing

performancewithenergyconsumption,

whileanetwork’sself-adaptive

approachreducesoperationscomplexity.AItechnologiesallownetworkstounder-standtheseintentsorCSP'sbusiness

objectives,processlargedatasets,makereal-timedecisions,handleconflicts

resolution,andoptimizethenetwork

accordingly.Bybuildinghigh-performingprogrammablenetworks,Ericssonis

transformingmobilenetworkswithnewcapabilitiesforbusinessgrowththroughdifferentiatedconnectivityandmore

autonomousoperations.

Figure2:Ericsson'sstrategyforbusinessgrowthwithAI

3.1WiderangeofAI-poweredsolutions

EricssonisapioneerofAIintelecom.InRAN,theAIjourneybeganwith4GandEricsson'sAI-nativeapproach,layingthefoundationwellbeforetheadventof6G.

AItechnologiesusedinEricssonsolu-

tionsforautomation1includegenerativeAI,digitaltwin,neuralnetworks,reinforcementlearningandAIagents.ThegoalistodeployAIinnetworksefficientlyandscalablewithanarchitecturethatmaximizesthereturn

ofinvestmentforeachusecasewhereAIisapplied.Someexamplesare:

GenerativeAIisusedtosupportnetworksoperations,softwaredevelopmentand

developers’enablement3.

DigitaltwinisusedforsimulationandtrainingoftheAImodelsandtodeploynewfunctionalitypreventingany

KPIdegradation2.

Reinforcementlearningoptimizesradioresourceallocationtoimprovespectrumefficiencyanduserthroughputwithreal-timenetworkdata4.

AgenticAIwillenablenetworkstomakeautonomousdecisionsbasedonthe

intents5.

1.IntelligentRANAutomationmanaging5Gcomplexity-Ericsson

2.NetworkSupportServicespoweredbyAIandML-Ericsson

3.EIAPEcosystemforautomationapplications-joinnow-Ericsson

4.AINativeLinkAdaptation

5.UnleashthepowerofAIintent-basedoperations-Ericsson

Ericsson|HowtodeployAIinmobilenetworks6

4

Thepathto

AI-nativeRAN

4.1Evolutionjourneyaheadof6G

TheAIRANjourneythatbeganin4Gis

nowrapidlyevolvingtowardAI-native

architecturesinadvanceof6Gdeployment.Thistransformationrepresentsafunda-

mentalshiftfromAIasanadd-onenhance-menttoAIasanintegraldesignfoundation.

Ericssonpioneeredthisevolutionin4GwithbreakthroughAIfeatures.Notable

examplesincludemobilityoptimizationtoimprovehandoverspeedandreduceddroppedcalls,AIMIMOsleepmodeto

maximizeenergyefficiency,andsleepingcelldetectionforimprovedreliability6.

Figure3:Ericsson'sAIRANnativejourney

4.2ThethreestagesofAIintegrationwithRAN

AIstartedaugmentingrule-based

featureswithdataandMLalgorithms,leadingtohigh-performingAI-poweredfeatures.ThetransformativeAIjourneycontinueswithAI-nativefeatureswhereAIisaninherentpartofdesignand

development7.

AdoptingAIinRANisnowpavingthewayforhigh-performingprogrammablenetworksandintent-drivenRANasanewoperationsparadigm8.Ateverystepofthis

journey,theaddedvalueofAIincreasesasitacquiresnewcapabilitiesandaddressesspecificchallenges,includingrequirementsonhigh-reliability,lowlatency,anddistrib-utedcomputeresources.

Alltheserequirementsdrivetheneedforenhancedstorageandprocessingcapabil-itiesinthehardwareandtheneedfordeeptelcoexpertisetohandleandminimize

complexity,whichbecomescriticaltoreducecostimplications.Enabledby

EricssonRANsoftwareevolution,AI-nativefeaturescanreplacetraditionalalgorithmswithAImodels.Networksoftwareisdevel-opedwithAIbuilt-infromthestart.

AnexampleisAI-nativelinkadaptation,afeatureofEricsson5GAdvancedportfolio,wherethereal-timeprocessofselecting

modulationandcodingschemesforeverytransmissionishandledbyanAI-nativemodelembeddedintheRANsoftware.

Figure4:thethreestagesofAIintegrationinRAN

6.AIMIMOSleep

7.AIjourneyinRANreport

8.Intentdrivennetworkswhitepaper

Ericsson|HowtodeployAIinmobilenetworks7

9.AI-nativelinkadaptationresultsinBellCanada

4.3Datastrategy:High-qualitydatadrivesperformance

DatafuelsAImodels.Morehigh-qualitydatameansbettertrainedalgorithms.

AgooddatastrategyisfundamentalforselectingthebestAItechnologyfor

aspecificpurpose.DataavailabilityandqualitycanlimittheAItechnologiesthatcanbeused.

With5G,inatypicalnetworkdeployment,therewillbetensofterabytesofdatatoprocessfrommorethan1,000distributedsources,openingnewopportunitiesin

AI-nativenetworks.ServingRANdatatoMLalgorithmsinanefficient,secureandsustainablewayrequiresaprofound

understandingofthedataoperationsandtheRANusecases.

Ericsson'sAImodelsofferauniqueadvan-tage:theyaregloballytrainedonrealnet-workdata,sowhendeployed,theyalreadydeliverstrongperformance—further

optimizedwithintheCSP’sownnetworkenvironment.

4.4SelectingthebestAItechnologydependingontheusecase

Figure5:ExamplesofAItechnologiesusedinRAN

Ericsson'sapproachemphasizesthatno

singletechnologydefinesthefutureofAI

fornetworks.Decisionsareguidedbyan

end-to-endperspective,balancinginnova-tionwiththerealitiesofdeploymentand

operation.ThisensuresAIdoesnotbecomejustanenhancementbutisafoundationalelementofRANevolution,empowering

CSPstomeetthedemandsoftodaywhilepreparingfortheopportunitiesoftomorrow.Ericssonevaluates,foreachfunctionality,

thebestAItechnologytoreducecomplexityandmaximizethebenefits.

Differentlearningparadigmscanbeapplicabledependingontheusecase.Ericssonimplementsmultiple

AItechniquesthatincludesupervised

learning,reinforcementlearning,and

neuralnetworkswithAIagents.HandlingthatcomplexityintheRANcontrolloopsrequiresdeepexpertise.

Forinstance,reinforcementlearning-

basedmodelsrequireintegratingexplora-tionintothecontrolloop.Themodelmustactivelytrynewactionstodiscoverbetterstrategiesandoptimalsolutionsindynamicenvironments.Theresultsachievedareupto20%ofimproveddownlinkthroughputand10%ofimprovedspectrumefficiencyinthefirstdeployments.9

Ericsson|HowtodeployAIinmobilenetworks8

10.CloudRANcomputeandaccelerationtechnology

5

AI-drivenhardwareevolution

5.1AIRANhardwareprocessingapproachesintheindustry

DifferentinfrastructurevendorshavedifferentapproachestoexecuteAImodelsinRAN.

Thesearethemainapproaches:

0102

Additionalhardwareboards

onlyforAIworkloads.This

approachincreasesthenumberofprocessingboardsintheradiositerack,increasingtheradio

footprint.

Evolvingexistinghardware

tosupportAI-nativefunctionalitywithoutaddingextraboardsorunitsintheradiositerack.ThisistheEricssonapproach.

5.2Ericsson.shardwarearchitectureapproach

Ericssonsolutionssupporttwohardware

variantsorarchitectures:purpose-builtRANComputewithEricssonSiliconandCloud

RANcomputecurrentlybasedonx86fromEricssonecosystempartners.10

Hardware-softwareco-designforAI

modelsprovidesoptimalperformanceandcost-efficiency.ThisiswhyEricssoninvestsbothinevolvingitsownhardware

platformsaswellasworkingwithhardware

ecosystempartnersonwhatarethecriticalrequirementsforAIinacloud-based

solution.EricssonCloudRANsolutionscanusehardwareaccelerationtechnologiestooptimizetheefficiencyofAIalgorithms.

TheseacceleratorscanoffloadtheCPUsandimproveperformanceandcan

beimplementedeitherinthehardwareorontheSystemonChip(SoC).

Ericsson|HowtodeployAIinradionetworks9

5.3EricssonRANComputeevolution

Ericsson'shardwareplatformhascontinu-ouslyevolvedtosupportagrowingnumberofAI-nativesoftwarefeaturesand

connecteddevices,deliveringenhancedperformancewitheachnewgeneration.

ThelatestEricssonRANCompute

featuresanindustry-standardX86CPU

withadvancedvectorextensionsthat

enhanceAIcapabilities.ThisCPUefficientlyhandlesL3functionalitiessuchastraffic

steering,mobility,andenergyefficiency.RANComputestoragehasincreased20

timestoaccommodateextensiveAImodelmanagementcomparedtotheprevious

generation.

EricssonSiliconusesstate-of-the-art

technologyfromthesiliconindustry.For

time-criticalL1andL2processing,EricssonSiliconutilizesapoolofhundredsofdigitalsignalprocessors(DSPs)intheinnovativeEricssonMany-CoreArchitecture(EMCA).

TheseDSPcoresareoptimizedwithinstruc-tionsspecificallydesignedforAIinference.

ExamplesofAIusecasesonEricsson

SiliconincludeAI-nativelinkadaptation

andAI-nativescheduling,bothrequiringextremelyfastexecution(microseconds

andmilliseconds).Toachievethebest

processingperformanceforAIinthe

industry,EricssonRANComputecombinesboththird-partyCPUsandin-house

ApplicationSpecificIntegratedCircuit

(ASIC)andamassivelyparallelDSPpoolforAIworkloads.

Figure6:LatestRANComputeenhancementsforAIacceleration

FlexibilityandAIacceleration

ThesameboardcanexecutebothAI-nativeandRANworkloadsconcurrentlythankstothemassivelyparallelprocessingcapabilityofEricssonRANComputepoweredby

EricssonSiliconwithEMCAarchitecture,

thatallowsfastAIinferenceandexecutionwithoutrequiringadditionalhardware.

Costefficiencyandsustainability

Byintegratingallprocessingcapabilitiesontothesameboard,withoutrequiring

extrahardware,noadditionalrackspaceisneeded.Thisreducesbothmaintenanceandenergyconsumptioncosts.

HighestROIand

TimetoMarket(TTM)

AIcapabilitiesarealreadyavailablein

Ericsson'sRANCompute.ThisprovidesacompetitiveadvantagetoexecutenewAI-nativefunctionalitythatwillimprovenetworkperformanceandspectrum

efficiencywithlimitedinvestment.

Ericsson|HowtodeployAIinmobilenetworks10

6

Deploymentarchitecture:

Maximizingvalue

AIistransformingthetelecomlandscape,offeringCommunicationsServiceProviders(CSPs)powerfulnewwaystoboost

networkperformance,streamlineopera-tions,andreducecomplexity.Butbeyondtheseinternalgains,AIisalsoopeningthedoortoentirelynewrevenueopportunities.Forinstance,personalizedserviceoffer-

ingswithexclusiveeventexperiencesandAI-powerednetworkslicingforenterprisecustomers.

Tounlockthispotential,CSPsmust

ensuretheirinfrastructureisready.

AImodelsrequiresignificantstorageand

processingpower,makingthehardware

atradiositesakeypartofthedeploymentstrategy.However,withthousandsofsites

inplay,thishardwaremustalsobecosteffectiveandenergyefficient.

Thischapterexploreshowtodesigndeploymentarchitecturesthatmaximizethereturnoninvestment(ROI)fromAI.ItfocusesonhowtointegrateAI-readyinfrastructure,ensuringCSPscanscaleAIcapabilitiesefficientlyandprofitably.

ThefundamentalsofAIRANdeploy-

mentevaluationinvolvevarioustimescales,processingcomplexities,andimplementa-tioncosts.

6.1Balancingperformanceandefficiency

DeployingAIintheRadioAccessNetwork(RAN)involvesnavigatingdifferent

timescales,processingdemands,and

costimplicationsacrosstheRANstack.

Ericssonevaluatesthereturnoninvestment(ROI)anddeploymentchallengesforeachlayer—bothinpurpose-builtorCloudRANenvironments.

LowerRANlayersrequireultra-lowlatencyandhighdatathroughput,demandingmorepowerfulandlocalizedcomputeresources.Incontrast,higherlayersallowformore

relaxedlatencyandcansupportmorecom-plexAImodels.Thisvariationdrivesdistinctcostandinfrastructurerequirements.

Figure7:RANlayersrequirementsandbenefits

Ericsson|HowtodeployAIinmobilenetworks11

AkeyfactoristhelatencyoftheAIcontrolloop:frommicrosecondstomilliseconds.

Tomeetthesedemands,computeresourcesmustbeplacedclosetotheantenna,

makingover-dimensioningcostly.

Thisiswherehardware-software

co-designbecomesessential—delivering

highperformancewhileoptimizingcostandenergyuse.

Tocreatehighlyefficientandcom-

pressedmodels,deepexpertiseinRAN

modelsandtrainingdataisfundamentalforselectingtheappropriatemodelandtooptimizeitfortheunderlyinghardware.

Ericssoncontinuouslyassessesthe

cost-benefitofeachAIdeploymentoption.Investmentsspanbothpurpose-builtandCloudRANsolutions,evolvingEricsson's

ownhardwareplatformsandcollaboratingwithecosystempartnerstodefineAI-readyinfrastructure.

Forexample,Ericsson'sevaluationofAI-nativeLinkAdaptationshowedthatevenwithpowerfulGPUs,uncompressedmodelsfailedtomeetlatencytargets.

CompressedmodelsrunningonDSPsprovedmoreefficientduetolowerdatatransferoverhead.

Currently,L1,L2andL3RANfunc-

tionalityisimplementedintheEricsson

RANCompute,wherehighlyefficientandcompressedAImodelsareexecuted.Itcanalsobedeployedonthird-partypartners

hardwareaspartofEricsson'sCloudRANsolutions.

EricssonisevaluatingusingMassive

MIMOradiostoexecuteAImodels.ThisispossiblebecauseEricssonMassiveMIMOradiosandEricssonRANComputeare

Figure8:AcceleratingAIinwithhigh-efficientandcompressedmodels

builtwithEricssonMany-CoreArchitecture(EMCA),whichisAI-readyhardwarewithparallelprocessingcapabilities.Byexecut-ingAIintheRANstack,Ericssonestimatesverysignificantimprovementsintheareasofspectrumefficiency,uplinkanddownlinkperformance,andtrafficcontrol.

6.2Combiningtheadvantagesofbothcentralizedanddistributedarchitectures

AIprovideshighvalueintegratedinallthelayersoftheRANstack(distributed)for

radioresourcesmanagementoptimizationintheradiosandintherApps(centralized)withakeyroleinoptimizingradionetworkconfiguration,monitoring,andfault

detection.

rApps,hostedintheServiceManagementandOrchestration(SMO)platforminadatacenterlocation,automateoperationsover

secondstoweeks.Ericsson'simplementa-tion,theEricssonIntelligentAutomationPlatform(EIAP)11,isanopen,multi-

vendorSMOthatsupportsrAppsfromEricsson,CSPs,andothervendors12.

AIandautomationoperateondifferenttimescalesintheRANstack(real-time)andSMO(non-real-time),addressingvaryingcomplexitylevels.InrApps,AIisusedfor

predictionandanalyticstoconfigure,optimizeandassureoverallRAN

performanceandreliability.

11.IntelligentAutomationPlatform(EIAP)-EricssonFigure9:CentralizedanddistributedarchitectureforAIandintent-basedautomation

12.rAppDirectory-Ericsson

Ericsson|HowtodeployAIinmobilenetworks12

13.Ericsson'sServiceContinuityAIAppsuite

14.Edgecomputingusecases

Fieldresults

fromselected

implementations:

EricssonNSATrafficOptimizerrApp

improves5GNRfrequenciesutilizationbyNRtrafficpredictionbasedproactiveNSAusersdistribution,achieving18.6%down-link(DL)and9.1%uplink(UL)average

NRcellthroughputgains(NorthAmerica).

Ericsson'sServiceContinuity

AIAppsuite:AIfeaturesworkintandemtomeasure,predictandoptimizeenergyconsumptionacrossthenetwork.Reducedenergyconsumptionby33%(Europe).13

AI-nativelinkadaptation(Ericsson5GAdvancedsolution):Upto20%downlink(DL)throughputand10%spectral

efficiencygains(Canada).

MachineIntelligenceEnabledMobilityradiofeature:60%fasterhandover

decisions,11%fewerinter-frequencyhandoverfailures(NorthAmerica),

1.2%reductionintheoveralldroprate.

6.3Thepotentialofedgedatacenters

Edgecomputingfocusesonbringingcom-putingresourcesclosertowheredatais

generated.Itisbestforsituationswherelowlatencyorreal-timeprocessingare

required,orwherelargevolumesofdataarebeingunnecessarilytransmittedtoacentrallocation.RegionalandenterprisedatacentersenableadvancedAIapplica-tionsofferinglowerlatencythancentral-izedlocationsandwithdatasoverignty.

Inanetworkedge,computingandstorageresourcesaredistributedacrosscommu-

nicationserviceprovider(CSP)premises,

betweennational,regionalandlocalaccesssites.Thesecanbestandaloneorinte-

gratedwiththemobilecloud(runningbothtelecomandthird-partyworkloads).EdgecomputecanbeseenasanextensionoftheCSPsexistingnetworkcapabilities.

Manufacturing,healthcareandgaming

andentertainmentarethreeofthetop

verticalindustrieswithenormouspotentialwhenitcomestoedgecomputingwitha

latencyrequiredbetween50milisecondsandonesecond14.Wehaveclassifiedintothreecategoriesthetypeofnewusecasesthatwouldbenefitfordeployment

inedgedatacenters:

01

0203

Low-latencyAIservices:

Edgeproximityallowsreal-timeprocessingforapplicationslikevideoanalytics,AR/VR,immer-sivegaming,andautonomousvehicles.

Context-awareservices:

Contextawarenessenables

AI-basedsecurity,healthcare,andenterpriseusecases

includingindustrialautomation.

IoTanalytics:Localbreakout

(LBO)mechanismsallowIoTandsensordatatobeprocessednearthesource,enablingimmediateinsightsandreducingcloud

dependency.

6.4AIexecutedwhereitmakessense

AImodeltrainingandinferencerequire

advancedstorageandprocessing.Tomeettheseneeds,AIshouldbedeployedwhereit’smosteffective—centrallyasrAppsor

distributedatradiosites.Fornetwork-leveltaskslikeoptimizationorhealingthat

operateoversecondsorlonger,

centralizeddeploymentindatacentersviarAppsisideal.Forreal-timeradioresourceoptimizationtaskssuchaschannelestima-tion,schedulingandlinkadaptation,

AIshouldbeimplementedintheradiosites.Ericssonadvocatesabalancedapproach,withRANsoftwarefeatures

complementedwithnon-RTRIC(rApps).ThisaimsatenablingAI-driveninnova-

tionwhilemanagingcomplexitywithcostefficiency,ensuringasustainablenetworksevolution.

Ericsson|HowtodeployAIinmobilenetworks13

7

Futurestrategyandemergingopportunities

Ericsson'sAIstrategyisdesignedtounlockvalue

acrossbothdimensionsofTelecomtransformation:AIfornetworksandnetworksforAI.

ByembeddingAInativelyintotheRadioAccessNetwork(RAN),Ericssonenhancesperformance,automation,andenergy

efficiency.Simultaneously,byevolvingtheRANintoahigh-performing,programmableplatform,EricssonenablesnewAI-driven

applicationsandservices.

ThisdualfocusensuresthatAIisnotonlyatoolforoperationalexcellencebutalso

acatalystforinnovationandgrowth.

Newtrafficgrowthinmobilenetworksis

settobedrivenbyhigh-performing5G

networksservingnewdevices,suchasARglasses,togetherwithscalable,multimodalgenerativeAI(GenAI)applications.

Significantnetworkimpactwillstemfromapplicationsthatarebothdata-intensive

andwidelyadopted,includingvideo-basedAIassistantsthatusereal-timevideofeedsforinteraction,requiringconstant

uplink/downlinkflowandsemanticunder-standingwhichcanunlikelybeprovided

byaGenAImodelonthedevice.

DatagrowthpredictionwilldependontheadoptionrateofthenewdevicesforAR/VR(Source:EricssonMobilityReportJune2025).15

Figure10:AIimpactontrafficforecast(source:Omdia,May2025)

InthefigureweshowAItrafficforecast.

Thispredictionanticipatesasignificant

growthondatavolumesinthenextdecade,thisgrowthwouldbedrivenbyAI-enhancedapplications.TheseapplicationsthatexistedpriortoAI,whichupgradetoincludeAI

featuresandenhancements.Overtime,theprojectionshowsthatAIelementsoftheseapplicationwilleclipseconventionaltraffic.Therearetwosubcategories:AI-enhancedapplicationswithAItrafficandAI-enhancedapplicationswithnon-AItraffic.

ExamplesofAIenhancedapplications

include:existingdevelopmenttoolsthat

enablelow-codeenvironments;smart

imagerecognitionandeditingaddedto

existin