2026年AI智能体时代基础设施现状报告

Google

Stateof

infrastructure

AIera

intheagentic

1,402globalleaders.2026andinnovationfrom

Keypivotsforefficiency

Executivesummary

Wereinthemiddleofaninfrastructure

renaissance.Fordecades,organizations

builtfoundationsthatexcelledinaworld

whereapplicationsmovedatthespeedof

humansasfastasausercouldtypeorclick.ButagenticAIhasfundamentallychanged

thespeedofthegame.

Multi-agentsystemsarerapidlycollaborating,reasoning,andacting.Today,inferencetheactualdoingofAIaccountsforthemajorityofallAIworkloads,officiallyeclipsingtraining.

Traditionalarchitecturesarecrackingunder

thispressure.Accordingtoourdata,62%of

leadersareseeingacostlyinferencetaxduetotheirlegacystacks—specificallydataegress,storagebloat,andidlespecializedhardware.

TheshifttoagenticAIrequiresadeparturefromtraditionalfoundationsandamove

towarddynamic,AI-optimizedarchitecture.

Tonavigatethistechnicalshift,oursecond

annualreportdrawsonthedirectexperiencesof1,402globalITleaders.Ourfindings

showthatinnovativeorganizationsaredoing

morethanjustupgradinghardware—they’re

reimaginingtheirentirearchitecturalapproach.

Byshiftingtodistributedenvironments,securingworkloadsatthesource,andembedding

governancedirectlyintotheorchestrationlayer,leadersareensuringtheirinfrastructureisas

ambitiousastheagentsitsupports.

Thisreportisn’tjustasurveyofthelandscape;itsaroadmapforestablishingthenewstandardforproduction-gradeautonomoussystems.

NiravMehta

VP,ProductManagementGoogleCloud

Googlecloud2

Googlecd3

findings

01AgenticAIrequires83%oforganizations

anewinfrastructurestandard

requireinfrastructureupgradestosupportproduction-grade

agenticAIsystems.

02

Governanceistheprerequisitefor

scalinginference

4outof5leaders

citesecurity,governance,andMLOpsasthemostsignificantchallengestoscalinginference.

03

HybridmulticloudisthenewstandardforAIdeployment

52%oforganizations

nowusehybridmulticloud

architecture,with90%rankingedgedeploymentasimportant.

04Efficiencyisnowamission-critical

requirement

91%ofleaders

nowfactorpowerconsumptionintohardwareselection.

Googlecloud4

The

infrastructuremandateforagenticAI

Acrosstheboard,agenticAIisagrowingglobalpriority.

Accordingtoourresearch,93%of

technologyleadersrecognizethevalueofAItoolsandsolutions,likeagenticAI,andusethemwithintheirorganizations.

However,thereisanincreasinggap

betweentheambitionofagenticAIandtheinfrastructuralcapabilitytoachieveit.

Astaggering83%oforganizationsrequire

infrastructureupgradestosupport

production-gradeagenticAIsystems.

Agenticworkloadsintroduceanewset

ofinfrastructuraldemands.Asuccessful

foundationfortheagenticeraneedsto

maintaincontextacrosscomplexworkflowsthatspandisparatedatasources.

Ithastobesecure,providinggranulardataresidencyandsecurityacrosson-premises,edge,andhybridmulticloudenvironmentstomeettherequirementsofafragmentedglobalregulatorylandscape.

Anditshouldoptimizeforinference—

deliveringhigh-concurrencyperformanceforalways-onworkloadswhilemaintainingthe

cost-efficiencyrequiredforsustainableROI.

Successintheagenticerarequiresmorethanjustacloudmigration.Tomovefrompilottoproduction,enterprisesmust

optimizetheirinfrastructureholistically,balancingthecorrectstrategyacross

hardware,deploymentmodels,security,andgovernance.

Thisreportrevealshowleading

organizationsareusingtheirinfrastructureasacatalystforgrowthandinnovation.

Fromthepracticalchallengestostrategicopportunities,weshowhowyoucanbuildanAIfoundationasambitiousastheagentsrunningonthem—optimizedforefficiency,security,andglobalscale.

2

4

3

Finding1

AgenticAI

requiresanew

infrastructure

standard

Autonomousagentsaretransforming

entireindustriesanddemandanew

approach.Successrequiresafoundation

builtforcontinuousintelligence—

withcost-efficiencyattheforefront.

2

3

4

Finding1

Theagenticreadinessgap

AIhasevolvedfromatoolthatsummarizesthe

worldintoanenginethatactsuponit.Inretail,

itcuratespersonalizedstylesandsources

productsinstantly.Inmarketing,itbuildsand

scalesthousandsoftargetedcampaignsatspeed.

93%ofseniorITdecisionmakersrecognizethevalueofAIanduseitacrosstheirorganization.

Yet,asAIworkloadsmovefrompilotsto

production,theyarecollidingwithafunctionalceiling—legacyinfrastructurewasnotbuiltforthedemandsofautonomousagenticsystems.

Forthe83%oforganizationswhosearchitecturerequiresupgrades,agentshitaspeedceiling—

theycanonlymoveatthepaceofthelegacysystemstheyareconfinedwithin.

Howpreparedisyourcurrent

infrastructureforagenticAIsystems?

upgradestosupportagenticAI

83%believetheirinfrastructurerequires

12%29%27%16%17%

Requires

significantfundamentalupgrades

Requiresmajor

upgradestospecificcoresystems

Requiresminorintegrationworkandtuning

Cansupportinitialpilotagentswithminimaleffort

Fullconfidencein

supportingmission-critical,production-gradeagents

Surveyquestion:HowpreparedisyourcurrentinfrastructuretosupporttheuniquedemandsofagenticAIsystems(e.g.,persistent,stateful,long-runningprocesses)?

2

3

4

Finding1

AgenticAIdemands

anewscaleofexecution

TheagenticerahasfundamentallychangedAI

Percentageofbudgetconsumedbytype1

workloads.Asingleagenticpromptcantrigger

hundredsofdownstreamactions—independentlybrowsing,querying,andexecutingacross

multiplesystems.

47%

Inference

28%

Training

Asupplychainagent,forexample,canidentifylowlevelsofstockaheadofapeaksalesseason.Itproactivelychecksyourreal-timesalesagainsthistoricpatternsandliveinventory,coordinateswithprocurementagentstosourcestock,

16%

Modeloptimization

andnegotiateswithcarrieragentstomanageshipping—allbeforepreparingapurchase

orderforapproval.

9%

Innovationworkbench

ThisshifttowardautonomyhasdramaticallychangedAIworkloads.Inferencenow

accountsfor47%ofallworkloads1—

surpassingtraining(28%)—asorganizationsmovetowardproduction-gradeagentic

systemsthatrequirecontinuousreasoningandautonomousdecision-makingatscale.

PUMAhasenabledover90,000fanstoco-createmorethan

350,000customfootballkitsthroughitsAICreatorplatform—

turningproductdesignintoafan-drivenexperience.Acrosstwo

activationswithmajorEuropeanfootballclubs,PUMApartneredwithGoogleCloudtoimplementdynamicworkloadscheduling—scalingcomputeinrealtimetodeliverfast,seamlesscreationatglobalscalewhileoptimizingcostandperformance.

1IDC,2026,TheAIEfficiencyGap:FromTotalCostofOwnershipCrisistoOptimizedCostandPerformanceGooglecloud7

2

3

4

Finding1

Buildingafoundationforcontinuous

intelligence

Agenticsystemsoperatethrough

continuous,high-speed,secureaccesstomultipledatasources.

AgentsneedtoconnecttoERPsandCRMsthatwerenotdesignedformachine-to-

machineconversations.Theyneedlong-termmemorytorememberauser’spreference

fromthreeweeksagowhileexecutingatasktoday.Andtheymustsustainthischainof

thoughtatscale.

Withoutafoundationbuiltforcontinuousintelligence,agentsremainforgetfulandlimited.Thesehurdlesarewherethe

innovationgapiswidest.

Whatareyour

biggestagenticAIinfrastructuregaps?

43%

36%

35%

DifficultyintegratingwithlegacyAPIsanddatasources

Lackofspecialized,high-throughput

vectordatabases

Insufficient

securityformulti-systemaccess

Surveyquestion:Whatarethemostsignificantgapsinyour

currentinfrastructurewhenitcomestosupportingagenticAI?

Googlecloud8

Googlecloud9

2

3

4

Finding1

Centralizingcontrol

andobservability

Tosolvethefragmentationproblem,

organizationsarechoosingcloud

platformswithorchestrationand

observabilitytools.Theseplatforms

provideasinglepointofcontrolto

managetheflowofdatabetweenagentsandlegacysystems,coordinatecomplex,multi-stepworkflows,andallowhumanstowatchanagent’sthoughtprocessandinterveneifitveersoffcourse.

Reignitingcloud

migrations

Enterprises’fragmenteddatalandscapes

andlegacyinfrastructurecreatesignificant

barrierstosuccessfulAIadoption.These

challengesstarveAIofpropercontext

andslowresponsetimes,whichproduces

Sabreshifteditsinfrastructurefrom19expensivedatacenterstoGoogleCloud,leveragingCloudSpanner

fortransactionalconsistencyand

BigQuerytomanage50petabytes

ofdata.Beyondthecostsavings,thepartnershipenabledSabretomaintain99.999%SLAforitsglobalmarketplace.

Drivenprimarilythroughthis

migrationtothecloud,theglobal

traveltechnologyleaderhassaved

$150millionannually—whichdoesn’t

includeanyothersavingsresulting

fromdevelopmentefficiency(AI,

standards,tools,etc.)—andsignificantlyaccelerateditspaceofinnovation.

Sabrenowhasmorethan70AIsolutionsinproduction,includingagenticAPIs

builtusingGemini’sADK.

incompleteresultsandpooruserexperiences.

TosucceedwithAI,enterprisesneedto

migratecriticalworkloadstoAI-readycloud

platformsthatprovideunifieddatalakes,

fluidcompute,millisecondlatency,and

integratedAIdevelopmenttools.ThisclosesthefragmentationgapsandbuildsthefoundationthatorganizationsneedtosucceedwithAI.

AdvancedAIadopters,thosewithAI

embeddedinmostworkflowsthatactivelyseekoutandadoptnewAIsolutions,arenow50%morelikelytousefullyorprimarily

managedservicesthanorganizationsthatarestillintheearlierstagesofAImaturity.

Byoffloadinglower-levelinfrastructuremanagementtasks,suchascluster

provisioning,scaling,andhardwareabstractiontocloudproviders,

organizationsreduceoverheadcostsandfreeupvaluabletimeforinternalteamstofocusoninnovation.

2

3

4

Finding1

Costefficiencyisatoppriority

Withagentsdrivinganinferenceboom,

enterprisesareshiftingtheirfocusfromthepossibilityofAItoitslong-termprofitability.96%ofrespondentssaythatcostefficiencyisimportanttotheirAIinfrastructure

decision-makingprocess.

Whilecomputeisthemostvisibleexpense,otherfactorsbeneaththesurfacearedrivingupthetotalcostofownership(TCO).

Howimportantiscost-efficiencywhenmakingAIinfrastructuredecisions?

32%49%15%

i

EQ\*jc3\*hps26\o\al(\s\up7(Extrem),mport)

EQ\*jc3\*hps26\o\al(\s\up7(e),a)

nl

EQ\*jc3\*hps26\o\al(\s\up7(y),t)

Vi

EQ\*jc3\*hps26\o\al(\s\up7(er),mp)

EQ\*jc3\*hps26\o\al(\s\up7(y),o)

rtantImportantwhat

EQ\*jc3\*hps40\o\al(\s\up2(%),t)

importantimportant

Surveyquestion:Howimportantiscost-efficiencyinyourdecision-makingprocessforgenerativeAIinfrastructuresolutions?

WhatarethetophiddencostswhenscalingAI?

81%

Operational

complexityand

engineeringoverhead

62%

Infrastructureand

directusagecosts

57%

Workforceandtalent

Thisincludesthecountless

hoursengineersspendon

manual“gluework,”patchingtogetherAIagentsacross

disparatesystemsand

databases,aswellasthe

operationalcostofmaintainingsecurityandcompliance.

Theseexpensesstemfroma

combinationofmountingdataegressfees,ballooningstorageneeds,andbudgetwastedonover-provisioningspecializedhardwarethatoftensitsidle.

ThepremiumforspecializedAItalentishigh,andupskillingexistingteamscandraintimeandbudgetwithouttherightapproach.

Googlecloud10

Surveyquestion:Whatarethemostsignificanthiddencostsyou’veencounteredwhenscalinggenerativeAIworkloads?

Googlecloud11

2

3

4

Finding1

Keytakeaways

Themostsuccessfulorganizationsare

movingtowardamanagedfoundation

thatcutsdownonoperationalcomplexity.

Modernizeyourfoundation

AdvancedAIadoptersarepivotingto

moderncloudplatforms,with24%now

usingfullyorprimarilymanagedservices.Theycanbuild,deploy,andscaleagents

withpurpose-builtdatabasesand

integratedAIdevelopmenttools,bypassingthebottlenecksofapatchworksystem.

Buildforscale

Orchestrationplatformsprovidetherepeatablescaffoldingto

connectagentsandsystems,

automateworkflows,and

monitoreverythinginoneplace.

LiveX.AIreducedoperationalcostsby66%andfreedupcrucial

timefortheirengineers.ByoffloadingclustermaintenancetoGoogleKubernetesEngine(GKE)Autopilot,thisstartupreducedoverheadsandgaveitsengineersmorehourstofocusonagentbehavior.

Toefficientlysupportthemassivescaleanddailytrafficof

KakaoTalk,Kakaooptimizedinfrastructurecostsandperformance

TALK

byadoptingGoogleCloudTPUsandtheJAXframework.Throughthistransition,theysuccessfullydevelopedtheKananamodelfamily—

specializedfortheKoreanlanguage—andachieveda2.7xincreaseinmodelthroughput,particularlywhenutilizingthelatestTrilliumTPUs.Furthermore,byapplyingaMixture-of-Experts(MoE)architecture

toenhanceinferenceefficiency,KakaohasbuiltafoundationtoflexiblydeployadvancedAItechnologiesacrossvariousservicesusedbymostSouthKoreanpopulations.

1

4

3

Finding2

Governanceis

theprerequisite

forscaling

inference

AIagentshavefundamentally

redefinedthesecuritylandscape.

Leadingenterprisesareplacingsecurityandgovernanceatthe

heartoftheirinfrastructurestrategy.

1

3

4

Finding2

AutonomousAIhaschangedhowwe

thinkaboutsecurity

Intheagenticera,thechallengeisnolongeraboutstoppingthreatsattheperimeter—

it’saboutgettingsecurityandgovernance

rightsothatscalingAItoproductionbecomesaseamless,repeatableprocess.

79%oftechleaderscitesecurity,governance,andoperationsas

themostsignificantchallenges

toscalinginference.

Maturegovernancehasbecomeastrategic

prerequisite.Organizationswithmature

governanceareacceleratingAIadoptionwithmoreconfidence,2whileorganizationswithoutthesecrucialstructuresfallbehind,creating

aninnovationgapasthemarketmovestowardautonomousexecution.

Robustgovernanceprovidesthefreedomto

innovateatspeed.Insteadofactingasahurdle,asolidsecurityandgovernancefoundation

actsasalaunchpad,determiningwhetheran

organizationcanconfidentlyleadthemarket

orifitwillslowdowntoaddressemergingrisks.

WhatareyourtopchallengeswhenscalingAIinference?

64%

64%

Businessandsystemalignment

FocusonbudgetconstraintsandhowthemodelfitsintothewiderITecosystem.

Modelperformanceandefficiency

Focusontherawspeed,volume,andtuningof

themodelsthemselves.

79%

Security,governance,

andoperations(MLOps)

Focusonthesecurity,safety,reliability,andmanagementofthemodelsinproduction.

39%

46%

saycostmanagementisatopchallenge

saysecurityisatopchallenge

Surveyquestion:WhatareyourtopchallengeswhenitcomestoscalinggenerativeAIinference?

Googlecd13

2CloudSecurityAllianceandGoogleCloudSecurity,2025,TheStateofAISecurityandGovernance

1

3

4

Finding2

Theagenticsecurity

paradox

Agentsaredesignedtoact—toreademails,querydatabases,andexecuteAPIcalls.

Thequestionis,howdoyougiveagents

enoughfreedomtobeusefulwithenoughguardrailstobesafeandsecure?Thisisthedefininggovernancechallengeof2026.

35%ofseniorITdecisionmakers

cite“insufficientsecurityformulti-systemaccess”asaprimarygap

preventingagenticdeployment.

Legacysecurityisbuiltforhumans,not

autonomousagents.Agentsrequiremore

accesstofulfilluserqueries,butarevulnerabletonewvectors.Examplesincludeindirect

promptinjectionandtoolpoisoning,whereattackershijackanagent’slogicthroughthedataitprocessesortheAPIsitcalls—turninganagent’sabilitytoactintoaliability.

1

3

4

Finding2

Securingthe

chainofthoughtisparamount

Traditionalcybersecurityfocused

ontheperimeter.AIsecuritymustfocus

onthemodelanditsprovenance—itsorigin,datasources,andtraininghistory.

SecuringtheAI/MLsupplychain(48%)isthetopsecurityconcernforleaders.

Enterprisesarefocusedonprotectingtheirmodelsfromcompromisedtrainingdataorvulnerabilitiesintroducedviaopen-sourceweights.Theriskisthatacompromised

componentdeepinthedependencytreecouldintroducelatentbehaviorsthatonlytriggerunderspecificconditions.

Asmodelsrunonsharedinfrastructure,thefearofdataleakageinmulti-tenantenvironments(41%)isalsoacute.ThisisparticularlycriticalforGSIsandSaaS

providerswhomustguaranteethatonecustomer’spromptsdonotinfluence

theweightsorcontextofanothercustomer’smodel.

TopsecurityconcernsaroundAIinfrastructure

48%

SecuringtheAI/MLsupplychain

41%

Protectingdatainmulti-tenantenvironments

39%

Unauthorizedmodelaccessandextraction

Surveyquestion:Fromaninfrastructureperspective,

whatareyourtopsecurityconcernsrelatedtoAI?

Googlecloud15

1

3

4

Finding2

Governanceisdrivingamovetofull-stack

cloudplatforms

AsAImaturesfrompilottoproduction,organizationsarestrugglingtomanagegovernanceonpatchworkstacks.

Increasingly,leadingenterprisesarepivotingtointegratedcloudplatformsforsimplicityandcentralizedcontrol.

Bymovingfromdisconnectedtoolstoa

centralcontrolplane,organizationsgaina

singlesystemofrecordforagentpermissionsandworkflows.Thishelpsthemachieve

somethinglegacysetupscan’tdeliver—straightforwardgovernanceatscale.

69%ofsurveyedexecutivesnowrateafull-stackplatformasacriticalrequirement.

Governanceistheprimarydriverofthisshift,

with80%ofleadersidentifyingdatacomplianceasthenumberonefactordictatingtheir

infrastructurechoice.

Forthemodernenterprise,thecloudproviderisnolongerjustasourceofcompute—itistheprimaryanchorforasecureAIstrategy.

Thisrelianceonmajorcloudprovidershas

surgednearly30pointsinasingleyear,

with78%oforganizationsnowsourcingtheirgenAIsolutionsdirectlyfromtheirprimary

cloudpartner—upfromjust48%in2025.3

3Google,2025,StateofAIinfrastructure

Googlecloud17

1

3

4

Finding2

Keytakeaways

EffectivegovernanceandsecurityintheagenticerarequiresclearframeworksattheheartofdevelopmentandunifiedarchitecturebehindyourAI.

MakeAI“secure-by-default”

withSAIF

Insteadofreactingtoproblemsaftertheyhappen,usetheSecureAIFramework(SAIF)tomakesecurityanatural,embeddedpartofbuildingAI.ThisframeworkhelpsmitigateAI-specificriskslikepromptinjection

orcompromiseddata,deployAIfaster,andscalewithgreaterconfidence.

Centralizeoversightonaunifiedplatform

Managingagentsacrossdisparatesystems

createsrisk.Useanintegratedplatformto

actasacentralcontrolplane.Theseplatformsunifypermissionmanagementandworkflow

logic,allowingforhuman-in-the-loopoversighttoflagwhenanagentrequiresapprovalbeforemovingforward.

WeareusingModelArmornotonlybecauseitprovides

robustprotectionagainstpromptinjections,jailbreaks,

andsensitivedataleaks,butalsobecausewe’regetting

aunifiedsecurityposturefromSecurityCommandCenter.Wecanquicklyidentify,prioritize,andrespondtopotentialvulnerabilities—withoutimpactingtheexperienceofour

developmentteamsortheappsthemselves.Weview

ModelArmorascriticaltosafeguardingourAIapplications,andbeingabletocentralizethemonitoringofAIsecuritythreatsalongsideourothersecurityfindingswithinSCC

isagame-changer.”

JayDePaul,

ChiefCybersecurityandTechnologyRiskOfficer,Dun&Bradstreet

1

4

2

Finding3

Hybrid

isthenew

multicloud

standardfor

AIdeployment

Agenticreadinessmeansmovingtowardahybridarchitecturethatintegratesthepowerofthe

publiccloud,edgeperformance,andsovereigngovernanceinto

asingle,cohesivestandard.

1

2

4

Finding3

Theshiftto

hybridmulticloudarchitecture

Intheagenticera,thedebatebetweenpublic

cloudandlocalcomputinghassettledinto

aclearconsensus:hybridisthedestination.

Hybridmulticloudadoption

surged26.8%YoY,movingfrom41%inour2025report4to52%oforganizationsthisyear.

Cloudinfrastructureapproachesfor

generativeAI

52%

Hybridmulticloud

(on-premisesandmulticloud)

48%

Hybridcloud

(on-premisesandsinglepubliccloud)

23%

Multicloud

15%

Singlepubliccloud

14%

On-premises

Surveyquestion:WhichcloudinfrastructureapproachdoesyourorganizationprimarilyuseforgenerativeAIworkloads?

4Google,2025,StateofAIinfrastructure

This11-pointjumpsignalsafundamental

pivot.Enterprisesnolongerviewhybridasatransitionalstate—it’sthestrategicchoicethatenablesthemtobalancetherawpowerofthecloudformodeltrainingwiththe

speedoflocalenvironmentsforinference.

Googlecloud19

Recursion’shybridarchitecturehelpedreducecostsby50%.Usingbothitson-premisessupercomputerandGoogle

Cloudinfrastructure,Recursioncan

manageover50petabytesofbiologicaldataacrossadistributedstack,utilizeTPUstoreducecostsby50%,and

maintainthemassivedatagravityrequiredforitsmodels.

1

2

4

Finding3

Googlecloud20

Distributing

AItotheedge

Howimportantis

deployingAIattheedgeforyourorganization?

OrganizationsarerethinkingwhereAIphysicallyprocessesdatatodeliverunparalleledspeed

andperformance.Inadditiontocentralized

cloudenvironments,organizationsarerunningmodelsclosertothesourceofdatageneration,includingdirectlyonmobileandIoTdevices.

90%

Important

72%

Extremely/

Veryimportant

Foryears,thepubliccloudwasthedefault

laboratoryforAI,providingthepowerneededtotrainmassivemodels.However,asAI

Surveyquestion:HowimportantisdeployinggenerativeAImodels

attheedge(e.g.,onIoTdevices,mobiledevices)foryourorganization?

workloadsexpandfromperiodictrainingtoalways-oninference,relyingsolelyoncentralizedinfrastructurecanintroducephysicalandfinanciallimits.

Tooptimizeperformance,organizationsare

embracingahybridAIarchitecture,usingthecloudwherenecessarywhilehandlingspecific,high-volumeinferenceattheedgeforthree

WithGoogleDistributedCloud,

wehavebeenabletoreimaginethe

possibilitiesofbuildinganddeployinginnovativetechnologysecurelyacrossNAPAsdistributedlocations.GDC

iscentraltoourstrategytosimplify

infrastructureandacceleratesoftwaredelivery.Itprovidestheagilitytobringnewfeaturestoourstoreswithgreaterspeedandsecuritywhileoptimizing

ourlong-termcosts.”

NavinAnandaraj

TechnologyFellow,GenuinePartsCompany

corereasons:

Latency

Foragenticworkflows,theround-triptimetoacentraldatacenterisamassivespeedlimiter,particularlyforthoseinvolvingvoiceorvideo.

Low-latency,real-timeinferencemusthappenlocally.

Theinferencetax

Byrunninginferenceattheedge,organizationscanhandlethemajorityoftheircomputelocally,reservingcentralcloudinfrastructurefor

trainingandcomplex,cross-functionaltasks.

Thisdramaticallyreducesvariableper-tokencosts.

Operationalresilience

BPC

®

EdgedeploymentsallowAItocontinuefunctioningevenwhentheprimary

networkconnectionisinterrupted.

1

2

4

Finding3

Digitalsovereigntyandgovernanceinagloballandscape

ThedefiningboundaryforagenticAIisno

longerjustthenetwork,butthephysical

jurisdictionofthedataitself.Intheagenticera,security,privacy,andinternationallawshave

reshapedtheapproachtocloudcomputing.

Thepushfordigitalsovereigntyisdrivenbythreeconcerns:

•Thejurisdictionalriskposedbyforeigndatarequestsandshiftingregulations.

48%ofleadersareprioritizing

infrastructurewithdataresidencycontrols,supportingcompliancewithlocaldatasecuritylaws.

75%ofallenterprisesoutsideoftheUSwillhaveadigital

sovereigntystrategyby2030.5

•Theeconomicdependencyrisk

createdbyalackofdomesticcloudinfrastructureproviders.

•Theneedtosafeguardcriticallocalservicesagainstglobaldisruptions.

Together,thesepressuresaredriving

enterprisesandgovernmentstoreclaimcontrolovertheirdata,infrastructure,anddigitalfuture.

5Gartner,2025,GartnerCIOSurvey

Googlecloud22

1

2

4

Finding3

Key

takeaways

BuildingforAIimpactin2026requiresa

fundamentalarchitecturalpivot:movingintelligenceclosertothedatasourceandgovernanceclosertothelocaljurisdiction.

Adoptahybrid

architecturestrategy

Balancecloudhardwarewithedgecomputing.Bymovinghigh-volumeinferencetotheedge,enterprisescanreducetheirhardwarecosts

andincreasespeed,whilereservingthecentralcloudforheavytrainingandcomplexreasoning.

Prioritize

sovereignty-firstdesign

Architectforlocaljurisdictionfromdayone

toavoidlegalbottlenecksandcostlyinnovationgaps.Usemanagedcloudplatformsthatoffergranulardataresidencyandaccesscontrols

tosatisfylocallaws,whilemaintainingasingle,centralizedcontrolplaneforglobaloperations.

DeutscheTelekomprovedthatyoucanmovemassiveamounts

ofdatatothecloudwhilebalancingstrictlocallaws.BypartneringwithT-SystemsandGoogleCloud,theybuiltaplatformthatkeepstheirdatastrictlywithinEuropeanbordersandundertheirown

digitallockandkey.Thissetupdidntjustsatisfythesovereign

regulations;ithelpedthecompanyturbochargetheidea-to-insightjourneyonsensitivedatamuchfasterprovingthathighsecuritycanbeaspeedboosterratherthanabottleneck.

1

2

3

Finding4

Efficiency

isnowa

mission-critical

requirement

Effectiveinfrastructure

optimizationbalancesspeed

withenergyefficiency,using

therighthardwaretoimprove

thesustainabilityandproductivity

ofcomputeworkloads.