人工智能体验网络 The Network for AI Experiences

EricssonWhitePaperGFTL-26:000298UenFebruary2026

ERICSSON

TheNetworkforAI

Experiences

TheNetworkforAIExperiencesContent

February2026

2

Content

Executivesummary3

EmergingconsumerandenterpriseAIusecases5

AIastheuniversalcatalystfortransformation6

Shiftinconsumerbehavior6

AIpowersemergingenterpriseapplications7

KeyenablersforanetworkpoweredAI8

EnhancedUL9

Spectrumallocation:avitalresourceforAIevolution10

Differentiatedconnectivity:solutionsandapproaches10

NetworkexposureAPIs11

Networkcapabilityexpansion:Identityandpositioning13

Industrydigitaltwins:collaborationwithnetworkdigitaltwin13

Conclusionsandcallforaction14

References16

Authors

17

3

TheNetworkforAIExperiencesExecutivesummary

February2026

Executivesummary

Inthefirstthreeyears,generativeartificialintelligence(GenAI)usagegrewatastaggeringpacetonearlyabillionweeklyactiveusers.Asignificantshareoftotalusagewasonmobilephones—initiallytext-basedqueriesbutlatershiftingtomoremedia-basedengagements.Consequently,mobilenetworksmustevolvetoaccommodatethisshift.

Thefutureofthisevolutionreliesontheconvergenceofthreetechnologypillars:AI,cloud,andmobile.AImodelsarebecomingmorepowerful,evolvingintomultimodalsystemsthatuseaudio,images,video,andimmersivemediaasbothinputandoutput.Centralizedcloudinfrastructureplaysacentralroleinmodeltraining,whilemoredistributedcloudcapabilitiesareemergingtosupportinference.Mobileconnectivityiswell-positionedtoconnectexistingAI-enableddevicesandnewAI-nativedeviceswherenetworkavailability,reliability,

andsecurityarevital.

NetworkperformancethusbecomesadefiningfactorintheAIexperience.ThisconvergencecreatesapowerfuleconomicandtechnologicalflywheelwhereAIdrivesnewnetwork

demands,andadvancednetworksunlocknewAIusecasesforconsumersandindustries:

•Consumers:AIenableshyper-personalizedrecommendationsandcontentcreation

forsmartphoneusers.Furthermore,theproduct-marketfitforAI/augmentedreality(AR)glassesisconsolidatingwithon-devicecamerasprovidingimmersiveawareness.Importantly,on-devicepersonalagentsareemergingthatcouldtriggerhighadoptionandusageratesinthefuture.

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TheNetworkforAIExperiencesExecutivesummary

February2026

•Enterprise:Autonomousvehicles(AVs)requirethenetwork,particularlytheuplink(UL).Droidsanddronesarealsoemergingasindustrialapplicationswithgrowingadoption.KnowledgeworkersadoptingAIonenterprise-managedsmartphonesandlaptopswillrequiredependablenetworks.WealsoanticipatearenaissanceinInternetofThings

(IoT)whereon-deviceAIwilldrivenewapplicationsinthefield.

Theconsolidationofexistingandemergingapplicationsispredictedtodrivetrafficgrowth:downlinkat15percentcompoundannualgrowthrate(CAGR)andUL

atasignificant30percentCAGR.Furthermore,usingnetworksasadatasourceisanewdevelopmentwherenetworksnotonlyprovideconnectivitybutalso

generatevaluabledatatodriveenhancedexperience.

Networksarerespondingtorisingdemandthroughthreetechnicalenablers:

enhancedUL,differentiatedconnectivity,andprogrammablenetworkexposure:

•5Gstandalone(SA)and5GAdvancedintroduceULcoverageandcapacityimprovementsviaradiosoftwarefeatures,siteandspectrumupgrades,andULqualityofservice

(ULQoS).Mid-bandandcentimeter-bandspectrum,includingthefuture

7to12GHzrange,ispivotalforAI-enabledtraffic.

•Differentiatedconnectivityusesslicing,userequipmentrouteselectionpolicy(URSP),advancedscheduling,and“Qualityondemand(QoD)”and“Dedicatednetwork

ondemand”applicationprogramminginterfaces(APIs)todeliverservicelevel

agreement(SLA)-gradethroughput,boundedlatency,andreliabilityperAIsession.

•NetworkexposureAPIsprovideagent-friendlyaccesstopositioning,authenticationandnetworkinsights,enablingAIapplicationstodynamicallyrequesttheright

connectivityandtapnetworkdatasources.Examplesincluderadio-basedsensinganddevicepositioning.

Withmoredetailsprovidedinthiswhitepaper,thesecapabilitiesturnthenetworkintoanAIplatformandcreatenewbusinessmodelsbuiltonpremiumULwith

API-baseddifferentiatedconnectivityandpremiumserviceofferings.

TheNetworkforAIExperiences

EmergingconsumerandenterpriseAIusecasesFebruary2026

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Emerging

consumer

andenterpriseAIusecases

Significantnetworkimpactwillstemfromapplicationsthatarebothdata-intensiveandwidelyadopted.Ericsson’sobjectiveistodeliverindustry-leadingnetworks

optimizedforthegrowingdemandofAIapplicationsforconsumersandindustry.Therefore,wewillfirstexaminetheemergingAIusecases,andinsubsequent

sectionsproposesuitablenetworkingsolutionstosupportsuchnewapplications.

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TheNetworkforAIExperiences

EmergingconsumerandenterpriseAIusecasesFebruary2026

AIastheuniversalcatalystfortransformation

AI,specificallyGenAI,isturningintoauniversalcatalystoftransformation,underpinningawidesetofapplicationstoday:fromconsumerapplicationstoindustryusecasessuchasAI-drivendiagnosticsinhealthcare,dynamicpricingalgorithmsinretail,predictive

maintenanceinmanufacturing,andAVsintransportation

[1]

.

(Gen)-AI

enabledhyper-personalized

contentmay

increase

retentionfurther.

Uplink-heavy

computeoffload;usageofAI

GaussianandSplattingetc.

Emergence

ofuplink-heavyvideo/multi-

modalLLMs

andAIAgents/Assistants.

Embedded

edge-(Gen)

AIcapabilities

emerginginIoTsuchassmartcameras.

Autonomous

vehiclesrequireUL&DL

telemetry;effectofULalreadyfelt!

AIAutonomousdroidswith

heavyuplink;industry

andlater

commercialuse.

Figure1.Anillustrationoftheemerginguse-casesunderpinnedbyAIforindustryandconsumers.

Shiftinconsumerbehavior

Ontheconsumerside,AIquietlyrewireshowusersproduceandconsumecontent,andthisshiftdirectlyshowsupintrafficpatterns.

•AI-enabledhyper-personalizedcontent:AIediting,translation,andimageandvideo

generationturnalmosteveryuserintoacontentproducer,drivingamaterialincrease

indownlinktrafficwithvideoalreadydominatingmobiledatatrafficat70to75percent.WhileGenAIcurrentlyrepresentapproximately0.06percentofthenetworktoday,GenAIsessionsdriveasignificantlyhigherULratio—26percentcomparedwithabout10percentinconventionalnetworks.AsignificantchangeimpactingULschedulers,time-division

duplex(TDD)patterns,andmid-bandcelldensityforradioplanning

[2]

.

•AIencouragesadoptionofnewdevices,creatingnewULpressure:AI-native

wearables,suchasRay-BanMetasmartglasses,generatesignificantnewalways-on

ULdemandsastheycaptureaudioandimage.Theyrelyoncloud-basedassistantsforlivetranslationandreal-timevisualunderstanding

[3]

.Thesedevicesstreambriefbuthigh-valuedataupstream,thatisoftenadaptedinresolutiondependingonthecontext.ThisrequiresrobustULcapacity,lowlatency,consistentround-triptimes(RTTs),tightjittercontrol,powerefficientperformanceforthecompaniondeviceforon-demand

oralwaysonMultimodalAIandreliableper-deviceQoS,especiallyduringhandoversorundervariablenetworkconditions.

•Personalagentscoupledtocloud-edgefabrics:Persistentpersonalagentsare

replacingconventionalphone-onlyapplicationsbyseamlesslyintegratingacrossdevicessuchasphones,laptops,glasses,cars,andhomesystems.Theseassistantsprioritizehandlingbasictaskslocallyusingsmallon-devicemodelsandescalate

complexqueriestoadvancedAIcloudinfrastructures.InnovationssuchasAppleIntelligenceandotherprivateAIcomputecloudsexemplifythisarchitecture

[4]

.

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TheNetworkforAIExperiences

EmergingconsumerandenterpriseAIusecasesFebruary2026

ForAI-nativeconsumerusecasesmentionedabove,thenetworkrequiresthefollowingcharacteristics:

•arisingnumberofshort,latency-sensitivecontrolexchangesbetweendevicesandnearbyedgeorregionalAIclusters

•strongpressureforlocalbreakouttoregionaldatacentersclosetolarge

populations,sothatpersonalagentsdonotincurtranscontinentalRTTs

[5]

•additionalfeatures,suchaspositioninginformationorsensing,aswellasauxiliarycapabilities,suchassecurityandtrust.

AIpowersemergingenterpriseapplications

5G,on-premisesedgecomputing,andlow-latencyAIinferenceconvergetocreatemonetizableopportunitiesforindustriesandenterprises.Thecommonframeworkinvolvesdatagenerationfromsensorsandmachinesandreal-timeinterpretationbyedgeAI,followedbyactionviareliable5Gconnections.Belowaresome

ofthekeyusecases:

•AVs:Theserelyon5Gforhigh-rateULsofcameraandlightdetectionandranging(LIDAR)datafortrainingandinsurancepurposes,teleoperationcommandswhenvehiclesarestalled,andoptionalvehicle-to-everything(V2X)signalingforsafetyandcoordination.TheseAVapplicationsrequireULspeedsof5to30Mbps,

end-to-endlatencyunder100msforvideo,andbelow20msforcontroltraffic.

•5G-nativelaptops:SolutionssuchasEricsson’sEnterpriseVirtualCellularNetwork(EVCN)enablesecure,always-oncloudaccessforhybridandAI-poweredworkflowsacrossdevices.TheselaptopsdriveincreasedULtrafficfromvideosharing

andcloud-basedAIinteractions,enhancingoverallworkplacemobilityandproductivity

[6]

.

•IoTrenaissance:Emergingtechnologiessuchassmallandquantizedlarge

languagemodelsrunningonembeddeddevicesallowlocaldatapreprocessing.Thisresultsincompute-heavyburstsforupdatesandinference,while5Gservicecategories—enhancemobilebroadband(eMBB),ultrareliablelow-latency

communications,reducedcapability,andmassivemachinetype

communications—accommodatediverseIoTneedsandbehaviors.

•Networkasadatasource:AsAIapplicationsincreasinglyneedtounderstand

real-worldevents,thenetwork’sroleasanadditionalinformationsourcebecomesmoreimportant,leveragingservicessuchaspositioningortheemerging

6Gcapabilityforradio-basedsensing.

•Edge-cloudinverticalindustries:Sectorssuchasmanufacturing,logistics,andutilitiesincreasinglyleverageprivate5GnetworksintegratedwithedgeAIforreal-timevideoanalytics,autonomousoperations,andpredictivemaintenance.Smartwarehousesandportsexemplifythis,utilizing5G-drivensolutionstooptimizeoperationsefficiently

[6]

.

TheseusecasesrequirelocalbreakouttoedgeAIfortimelyresponse,time-sensitivenetworkingfore.g.sub-20mscontrol-loopdeadlines,andper-sliceobservability

forSLA-gradeoutcomes.

TheNetworkforAIExperiences

KeyenablersforanetworkpoweredAI

February2026

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KeyenablersforanetworkpoweredAI

TomeetthegrowingdemandsofAI-basedapplications,theadoptionof5GSA

technologiesisessential.Thesecapabilitiesenablefeaturessuchassessionbreakouts,

networkslicing,andoptimizedradiodimensioning.Today’s5GnetworksalreadydeliverseamlessperformanceforapplicationssuchassmartglasseswithAIassistantsthatrelyonULanddownlinkmultimodaltraffic.OngoingenhancementstonetworkinfrastructurefurtherstrengthentheperformanceandefficiencyofAI-drivenapplications,including

improvedULcommunication,differentiatedconnectivity,andnetworkexposureAPIs

9

TheNetworkforAIExperiences

KeyenablersforanetworkpoweredAIFebruary2026

EnhancedUL

ULperformanceiscriticalforemergingAIservicessuchasGenAIandagenticAI

applicationsandaugmented,mixed,andvirtualrealityusecases.Thesearekeydriversofdifferentiatedconnectivityand5Gmonetization.ULperformancecanbeimprovedbyadvancingitscoverageandcapacity,aswellastheULQoS.

Aheadof6G,cellularoperatorscanalreadydeploy5Gand5GAdvancedfeaturesandsolutionsthatcanimproveULcapabilities:

•Coverageandcapacity:therearefourwaystoimprovetheULperformance:

•applyingsoftwarefeaturesinradioandbaseband

•improvingthesiteconfiguration

•addingnewsitessuchasmacro,street,orindoortoimprovetheULlinkbudget

•addingsuitablenewspectrumformoreoverallULcoverageandbandwidth

•ULQoS:severaladvancednetworkfeaturesarecrucialtosupporttheresponsivenessneededbytheemergingAIusecasesforULcommunicationsuchaslowlatency,

lowloss,scalablethroughput(ULL4S),ULdelaystatusreporting(DSR),ULrefinedbufferstatusreporting(BSR),andlowlatencymobility.

•AdvancedFDDRadioswithlargerantennas

(8Rx+M-MIMOformid-band)

•AddFDDbandswithcontinuousuplinkonTDDonlysites

•Advancedantennas

withUL-boostingfeatures(highPIMstability

andhighbeamefficiency)

•RFantennaoptimization

•Addmacrostreetand/orIndoorsiteswithbothFDDandTDD

•UL-QualityAwareCarrierSelection

•ULCoordinatedmultipoint

•ULCarrierAggregation

•ULSU-MIMO,ULMU-MIMO

•ULTxSwitching

•DynamicWaveformSwitching

•HighPowerUEforTDDandFDD

Figure2.Uplinkcapacityandcoverageenhancementsolutions.

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TheNetworkforAIExperiences

KeyenablersforanetworkpoweredAIFebruary2026

Spectrumallocation:avitalresourceforAIevolution

Asadditionalspectrumisallocated,networkfeatureswillconsiderhowtobesttake

fullleverageofeachband’scharacteristicsfortheoptimalbalanceofULanddownlinkscheduling,accountingforpropagation,antennadesign,andusecaserequirements

suchasbatteryefficiency,lowlatency,etc.

SpectrumallocationiscriticaltoenableAIfunctionalitiesakintoenergyneedsfordatacenters.Followingsarethekeyspectrumconsiderations:

•Therightmid-/centimeter-bandspectrum:Licensedfull-powermid-andcentimeter-

bandspectrumisessentialtosupportAI-driventrafficandforfutureconnectivitydemands.5Gspectrumadvancements,alongwithemerging6Gdevelopments

in7to12GHzrange,willsignificantlyshapeAInetworkfunctionality.

•Morespectrum:Upto1GHzmid-/centimeter-bandspectrumperoperatorwillberequiredforemergingusecasesmentionedabovetosupport

therighteconomicgrowth.

•Rightregulation:Theframeworkshouldallowflexibleuseofspectrumperband

intermsofnetneutralityandusagepergeneration(G)togetbesteconomicreturns.

Differentiatedconnectivity:solutionsandapproaches

AIapplicationsdependonnetworkinformationtofunctioneffectively,butnetworksfurtherenhancetheirvaluebydynamicallyadjustingthecommunicationservices

tomeetapplication-specificneeds—referredtoasdifferentiatedconnectivity.

Thiscapabilityisparticularlyvaluableforreal-time,multimodalcommunication,suchasvideofeedsfromsmartglassestoAIengines,whereoptimizednetworkperformanceiscritical.Byleveragingdifferentiatedconnectivity,AIapplicationscandynamicallyadjustnetworkconfigurations,ensuringanoptimalbalance

betweenuserexperienceandnetworkefficiency.

Differentiatedconnectivitybringstogetherkey5GSAcapabilities—including

advancedradioscheduling,resourcepartitioning,networkslicing,URSP,NetworkinitiatedQoSandcoreAPIs—todelivertailored,end-to-endperformanceacrossdevices,applications,andthenetwork.

High-performanceprogrammablenetworkscombinecapabilitiessuchasadvanced

massivemultiple-inputmultiple-output(MIMO),beamforming,andintent-driven,

service-awareautomation.Thesecapabilitiesallowcommunicationserviceproviders

(CSPs)provisionandmanageperformancedynamically,deliveringassuredthroughput,boundedlatency,andhighreliabilitytailoredtoAIapplications.Thiscreatesopportunities

fordifferentiatedconnectivityofferings,andenablesAIapplicationstodirectcriticaltraffictotheappropriateperformancelevelforenhanceduserexperience

andefficientnetworkutilization.

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TheNetworkforAIExperiences

KeyenablersforanetworkpoweredAIFebruary2026

NetworkexposureAPIs

NetworkAPIsprovideaccesstocapabilitiessuchasdifferentiatedconnectivity,

positioning,securityandauthentication,andnetworkinsights,effectivelytransforming

thenetworkintoaplatformforinnovation.ByleveragingtheseAPIs,AIapplicationscanbuildnetwork-augmentedserviceswithimprovedresponsiveserviceandmakebetter

decisionsandrecommendationswiththeadditionalinformationprovidedbythenetworks.

AIworkloadscanuseinformationalAPIssuchasposition,connectivitystatus,transactional

APIssuchasslicereservation,ULschedulingrequestormessaging,orchainmultipleAPIstogether.Thiscanaddresstheneedsoftheapplicationbyintegratingthem

directlyintotheworkloadlogic.

DifferentiatedconnectivityAPIs–suchasthe“DedicatedNWondemand”and

“QualityonDemand”APIs–arealsotransactional.TheseAPIssupportawiderangeofAIandnon-AIapplicationsandcanenablecommunicationqualityeveninhigh

loadconditionsandSLAsforspecificconnectivityneedssuchasspeedandlatency.

Today,networkAPIscanbeaccesseddirectlyintheirtraditionalform,orthrough

agent-friendlymechanismssuchasagent-toolinterfacesimplementedusinginterfacessuchasModel-ContextProtocol(MCP)orAgent-to-AgentProtocol(A2A).ApplicationprogrammersvalueAI-basedassistanceinfindingtherightAPIs,anditsinformationaboutdeveloperecosystemsandtoolsmatterinadditiontotheactualinterfaces.

Overtime,suchsupportforagent-friendlyinterfacescanevolvetotelcogradeagenticAIplatforms,builtontopoftheexistingnetworkstointegratenetworkdatasourcesandexposureAPIs,inadditiontoAIservicesandinterfacestonetworkautomation.Thisplatformwouldenablebuildingcollaborativeapplicationsthatbenefitboth

theapplicationandnetworkowners.

WeexpectAIapplications,regardlessofwheretheyare,tobeawareofinformationsourcesandconnectivitytheyneedandbecapableofusingAPIstomeetthese

needs.Theseapplicationshaveconsistentlatencyandqualityrequirementsandwanttooperateonthemostaccurateandlatestinformation.Foroperatorsandcloudproviders,thisopensnewbusinessmodelstoserveAIapplicationssuchastiersofconnectivityorpremiumULservices.

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TheNetworkforAIExperiences

KeyenablersforanetworkpoweredAIFebruary2026

Figure3.NetworkExposureAPIs

Networkasadatasource:usingnetworkdataandinsightsascontextorserviceforvariousapplications

CSPscanexposenetwork-relateddata,suchasmobilitytrends,connectivitystatusinformationorcongestion,byexposingthemthroughAPIsforuseininference,

orevenfine-tuningandoptimizingAImodels.

CSPscanalsoexposereal-worldinformationsuchasdevicepositioninginformation,trajectorydata,thenumberofusersinanarea,orradio-basedsensinginformation.Radio-basedsensingsuchasIntegratedSensingandCommunication(ISAC)

isanemergingmechanismin6Gwherethenetworkcandeterminethecharacteristicsoftheenvironmentorobserveobjectswithinit.Moreinformationaboutsensing

canbefoundin

[7]

.

Additionally,CSPscansupportexposingenterprisesensordatainrealtimeforenterprisecustomers.

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TheNetworkforAIExperiences

KeyenablersforanetworkpoweredAIFebruary2026

Networkcapabilityexpansion:Identityandpositioning

Outdoorpositioningofconnecteddevicesunlocksnewmonetizationopportunitiesinconsumer,enterprise,automotive,airspace,androboticssegmentsbased

onphysicallocationofconnected5Gdevices.Itcomplementsglobalpositioningsystem(GPS)byofferingamorereliable,robust,andcost-efficient5Gtechnologyformissioncriticalapplications.

Thesolutionisbasedondevice-agnosticsoftwareforany5Gdevicetolowertheentrybarrierandincreaseaddressablemarket.Positionaccuracywith5Gcellularadvanced

featureset(inline-of-sight)issuitableforconsumer-basedusecases.Inaddition,higherpositionaccuracycanbeachievedusingreal-timekinematic(RTK)broadcastadvanced

featureset,usingenhancedGPS-assistedprecision

[8]

.Withhigh-precisionandnetwork-verifiedlocationdata,AIapplicationsgetbettercontext,improved

decision-making,andsafer,moreaccuratereal-timeoperation.

Industrydigitaltwins:collaborationwithnetworkdigitaltwin

Today,theindustriesareembracingdigitalizationandthedigitaltwinconcepttoaccelerateproductdesignanddevelopment,production,andassemblyengineeringautomationinthevirtualworld.ThroughAIacceleratedpredictionsandsimulationsinindustrialmetaverse,severalmulti-siteindustryusecasesaremanagedviaXRsimulations.Industrialdigital

twinsandnetworkdigitaltwinscollaboratetocreateacomprehensivedigitalreplicaofaphysicalsystem,mergingindustrialprocesseswiththeirsupportingcommunication

networkandcomputeinfrastructure.Thiscollaborationenablesoptimizedperformance,predictivemaintenance,andcounterfactualanalysisforsimulations,consideringboth

industryscenariosandthecommunicationnetworkwhichisrequiredforsame.

Thisintegrationismorethanoptimizingasingleentitybutratheroptimizingtheend-to-endsystem,includingtheadaptabilitybetweenindustrialIoT,physicalassets,environmental

dependencies,andthenetworkthatconnectsthem

[9]

.

TheNetworkforAIExperiences14

Conclusionsandcallforaction

February2026

Conclusionsandcall

foraction

AIusageisscalingrapidly,becomingricherinmediaandmoreULheavy,andshiftingclosertotheuseracrossdevicesandcloudedges.Thiswhitepaperhasshownhow

theconvergenceofAI,cloud,andmobiletechnologyistransformingfrombesteffortbroadbandpipesintoprogrammableAIplatforms.

ThesefuturenetworksmustdeliveranenhancedUL,differentiatedconnectivity,richnetworkexposure,amongothers,tounderpinthescaleofconsumerandenterpriseAIapplications.TorealizethisvisionandcapturetheemergingAIeconomy,weneedtoactonthesepriorities:

•PrioritizeULcentricnetworkevolution:Fasttrack5GSAand5GAdvanceddeploymentswithafocusonULcoverageandcapacity.Redesignplanning,

optimization,aswellasnetworkingKPIstotreatULperformanceasaprimarybenchmarkthatisthenewcurrency.

•Operationalizedifferentiatedconnectivityasaproduct:Implementslicing,URSP,QoD,andadvancedschedulingtooffersessionlevelSLAsforAIworkloads.CreatecommercialoffersforpremiumULanddifferentiatedconnectivitytargetingkeysegments:GenAI

apps,XR/AIglasses,AVs,drones/droids,andAIenabledindustrialIoT.

•Exposethenetworkasadataandcapabilityplatform:Deployandstandardize

networkexposureAPIsthatmakepositioning,authentication,sensing,andnetwork

insightseasilyconsumablebyAIagentsandapplicationdevelopers.Buildprivacy

preservingdataproductsthatallowCSPstocontributevaluefarbeyondrawconnectivity.

TheNetworkforAIExperiencesConclusionsandcallforactionFebruary2026

15

•Mobilenetworkevolution:AsillustratedinFigure4,mobilenetworksevolvefrom

differentiatedbroadbandtowardanopenplatformthatexposesadvancedcapabilitiesandultimatelyenablesapplicationstorunonthenetworkthroughdistributedcomputeandAIservices,thatistotransformthenetworkfromabareconnectivityprovider

toastrategicecosystempartner.

•AdoptAInativeoperations:AsillustratedinFigure5,introduceAIdrivenautomationtocontinuouslyoptimizefordynamicAItrafficpatternsandservicelevels.Through

programmableandcloudnativenetworkfunctions,alignnetworkevolutiontoenablenetworkforAIexperiences.Bytakingtheseactions,mobileoperatorscanmove

throughthemultisteptransitionoutlinedinthispaper:fromenhancedconnectivitytoaprogrammableAIreadyplatform,andultimatelytoAInativenetworks.

ThosewholeadthisevolutionwillnotonlysustainnetworkperformanceunderthecomingAItrafficsurgebutalsosecureastrategicroleintheglobalAIeconomy.

Figure4.Themulti-steptransitionnetworkswillhavetoundergotounderpinagrowingAIeconomy.

Figure5.ThepillarsofsupportingAIintelecoms,whereanAI-nativenetworksupportstheemergingAIapplications.

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TheNetworkforAIExperiencesReferences

February2026

References

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traffic-today-june-2025

3.AIglassesforsocialconnection

/es/en/ai-glasses/social/

4.PrivateCloudCompute:AnewfrontierforAIprivacyinthecloud

/blog/private-cloud-compute/

5.5GattheEdge

/think/insights/5g-at-the-edge

6.Powerwarehousingandlogisticswithseamless5Gconnectivity

/en/industries/warehousing-and-logistics

7.Sensingin6G:Usecasesandarchitecture

/en/reports-and-papers/ericsson-technology-review/

articles/sensing-in-6g-use-cases-and-archi