2026年塑造未来格局的七大AI趋势研究报告 7 AI trends that will define 2026

EmpoweringVision.DeliveringValue.

7AItrendsthatwilldefine2026

27AItrendsthatwilldefine2026

ByYaroslavMota

HeadofAIandEngineeringExcellenceatN-iX

IsyourbusinessreadyforAIin2026?

ArtificialIntelligence

isnolongerconfinedtothetestingphase;it'srapidlybecomingacornerstoneofbusinessoperationsacrossallindustries.Infact,companiesarenotjustexperimentingwithAIbutareembeddingitdeeplyintotheircorefunctions—

revolutionizingdecision-making,automatingprocesses,anddrivingefficiencies.

AccordingtoGartner,by2026,AIwillnolongerbea"nice-to-have"technologybutwillbecomestandardbusinesspractice,movingbeyondoptionalpilotprogramsandbecomingintegraltoeverydayoperations.

WithAIspendingexpectedtoreach$500billiongloballyby2024,

organizationsthatpreparenowarepositioningthemselvestocapturethebiggestopportunities.

ThosewhodelaywillriskfallingbehindascompetitorsharnessAItogainoperationalefficienciesandstrategicadvantages.Asweapproachthiscriticalinflectionpoint,it’sessentialtounderstandwhichAItrendswilldefinethebusinesslandscape.Here

arethesevenAItrendsthatwillmattermostin2026andbeyond.

Globalartificialintelligencesoftwaremarketrevenue

$150B

Revenue

$100B

$50B

$0B

20182019202020212022202320242024

37AItrendsthatwilldefine2026

Top7AItrendsfor2026

Infrastructurespendingshiftstoinference

CompaniesarerebuildingtheirdatacentersaroundAIinference(whentrained

AImodelsmakepredictionsanddecisionsforrealusers),ratherthantraining,

reflectingoneofthelatestAItrends.ThisshiftfromjusttrainingnewmodelsreflectshowAImovesintoeverydaybusinessoperations.Thenumbersmakethisclear:

GartnerprojectsAIinferenceserverspendingwillgrow42%annuallythrough2028,whiletrainingservergrowthremains24%.

Traininghappensonceorperiodicallywhenbuildingmodels.Inferencehappens

continuouslywhenthosemodelsserveusers,processtransactions,ormake

decisions.Thevolumedifferenceismassive—atrainedmodelmightrunmillionsofinferenceoperationsdaily.

Inferencingandservicing

Source:Gartner

ThediagramaboveillustratesthattheMachineLearningpipelineflowsfrom

initialdatapreparationthroughtrainingtomodeldeployment.However,thereal

businessvalueoccursinthefinal"inferencingandservicing"stage.Thisiswhere

deployedmodelscontinuouslyprocessliveenterprisedatatogeneratepredictions,recommendations,andautomateddecisionsthatdrivebusinessoperations.Whiletheearlierstagesofthepipeline,suchasdatacategorization,training,andmodel

creation,representone-timeorperiodicinvestments,theinferencephaseruns24/7,processingmillionsofrequestsandrequiringrobust,scalableinfrastructure.

Theinfrastructurerequirementsaredifferent,too.Inferenceneedslowlatency

andconsistentavailability.Trainingcanbebatchedanddelayed.Thisdrivesdemandforspecializedinferenceacceleratorsratherthanthemassiveparallelprocessing

systemsusedfortraining.

47AItrendsthatwilldefine2026

Powerconsumptioncreatesimmediateconstraints.AIinferenceworkloadsconsume30-100kilowattsperrackcomparedto7-10kilowattsfortraditionalservers.Most

datacentersweren'tbuiltforthisload.OrganizationsmustupgradepowerandcoolingsystemsorlimittheirAIdeployments.

Companiesaddressingpowerconstraintsnowavoidthesebottlenecks.

By2028,Gartnerestimatesthatover80%ofAIinfrastructurespendingwillsupportinferenceworkloads.

Organizationsthatplanforinference-focusedarchitecturetodaywilldeployAIfasterandatlowercostthanthoseretrofittinglater.

TheAIwindowisclosingfast.MostorganizationswillstrugglewithAIcostsandsecurityiftheygoitalone.Winnersdon'tjustdeploytechnology;theychoosepartnerswho'vealreadynavigatedthefinancialpitfallsandoperationalchaos.ChooseyourAIpartnerbasedontheirexperiencewiththemessyrealities,notjusttheirtechnicalcapabilities.

YaroslavMota

HeadofAIExcellenceatN-iX

57AItrendsthatwilldefine2026

2

FinOpspracticesevolvetohandleAIcomplexity

AIprojectbudgetsconsistentlymisstheirtargets,representingoneofthemostconcerningAIindustrytrendsaffectingorganizationstoday.

GartnerresearchrevealsthatgenerativeAIinitiativescanexperiencebudgetandcostestimateoverrunsofupto1000%.

Thisisn'tanoutlier;it'sbecomingthenormfororganizationsattemptingAIimplementationswithoutpropercostcontrols.

ThecostvariationsstemfromAI'smultifacetednature.Projectsinvolve

infrastructureandcloudresources,modelhostingandusagefees,dataworkloads,andapplicationdevelopment.ThedominantmethodofusingGenAImodelsis

throughcloudproviders.Theseservicesusepricingbasedonparametersthataredifficulttoestimate,suchasinputandoutputtokens.Asmodelsareupdatedandoptimized,unitcostschangefrequently,addinguncertaintytobudgetplanning.

TraditionalITcostmanagementfallsshortbecauseitwasn'tdesignedfor

consumption-basedAIservices.MostorganizationslackvisibilityintoAIspendingpatternsortoolstopredictcostsaccurately.

Thefinancialimpactisforcingchange.By2027,Gartnerpredictsthat60%

oflargeenterpriseswilladoptandapply

FinOps

practicesfortheirAIinitiatives.Thisrepresentsashiftfromreactivecostmanagementtoproactivefinancial

governanceforAIprojects.

The2025GartnerCIOandTechnologyExecutiveSurveyfoundthat

57%ofrespondentsattachhighimportancetohelpingbusinessareasunderstandthefulllifecyclecostsoftheirtechnologyinvestments.

However,the2023GartnerFinancialGovernanceandSustainabilitySurveyrevealedthat69%oforganizationswithfinancialgovernanceprogramsaren'tusingtools

tooptimizecapabilities,and79%aren'tusingtoolsforcostprediction.

OrganizationsimplementingAI-specificFinOpspracticesearlyreportbetter

budgetaccuracyandloweroverallcoststhanthoseusingtraditionalITfinancialmanagementapproaches.

67AItrendsthatwilldefine2026

AgenticAItransformsbusinessoperations

OrganizationsarerapidlyadoptingAI

agentsthatcanmakedecisionsandtakeactionsautonomously,makingthisoneofthetopAItrendstransforming

enterpriseoperations.

Gartnerpredictsthatby2028,33%ofenterprisesoftwarewillinclude

agenticAI

.

AgenticAIreferstogoal-driven

softwareentitiesauthorizedby

organizationstomakedecisionsandactsemiautonomouslyorautonomously

ontheirbehalf.Unlikeroboticprocess

automation,agenticAIdoesn'trequire

explicitinputsorproducepredeterminedoutputs.Theseentitiescanreceivegoalinstructions,iterateontasks,delegate

work,andmakevariableoutputswhileaugmentinghumanwork.

Thebusinesscaseiscompelling.

By2030,AIagentswillautonomouslymake15%ofday-to-daysupplychaindecisions,freeinghumanstofocusoncriticaldecisions.

Incustomerservice,AIagentshandlecomplexworkflowsthatpreviouslyrequiredhumanintervention.Furthermore,AIwillhold67%ofB2Bprocurementby2030,requiringcompaniestostructuretheirofferingsasmachine-readabledatainsteadofrelyingontraditionalmarketingnarratives.

AgenticAIsystemsusememory,planning,sensing,tooling,andguardrailsto

completetasksandachieveobjectives.Theycanworkcollaborativelyinmulti-agentsystemstosolvecomplexproblemsbeyondindividualagentcapabilities,making

themparticularlyvaluableformanufacturing,logistics,andfinancialservices.

OrganizationsimplementingagenticAIreportimprovedautomationinareaslikeprocurement,where40%ofprocurementteamsareexpectedtohaveatleastoneAIagentby2028.

77AItrendsthatwilldefine2026N-i

AIevaluationstandardsareemerging

OrganizationsneedconsistentwaystoevaluateAIsystemsacrossvendorsandusecases,reflectingoneofthelatesttrendsinAItechnologytowardstandardizationandaccountability.

In2026,aMachineIntelligenceQuotient(MIQ)willbecomethestandardcomparisontoolforAIsolutions.

Thiscompositescoringsystemwillcombineaccuracy,efficiency,explainability,speed,andcompliancemetricsintoasinglescore,replacingthecurrentmixofnarrowbenchmarksthatvarybyvendorandmakecomparisonsdifficult.

ThedemandforstandardizedAIevaluationhasgrownasorganizationsadoptAI

technologiesacrossmultiplebusinessfunctions.CurrentevaluationmethodsfocusprimarilyonlanguageunderstandingthroughbenchmarkslikeGLUE,SQuAD,

andRACE,butthesedon'tcapturethefullrangeofcapabilitiesneededforbusinessapplications.TheMIQframeworkwillbemorecomprehensive,incorporatingmetricssuchasreasoningability,ethicalcompliance,andadaptabilityalongsidetraditionalperformancemeasures.

EarlyversionsofMIQ-styleevaluationarealreadyappearinginregulatedindustries.HealthcareorganizationsevaluateAIdiagnostictoolsbasedonaccuracyand

explainabilityrequirementsforregulatorycompliance.FinancialservicesassessAImodelsonprocessingspeedplusadherencetoregulatorystandards.Theseindustry-specificapproachesareevolvingtowardcross-industrystandardsthatenableconsistentcomparisonofAIofferings.

VendorsmustoptimizeAIsolutionstoperformwellonMIQevaluationstoremain

competitive.OrganizationswillprioritizeAIsolutionswithhighMIQscoreswhen

makinginvestmentdecisions,andenterpriseclientswilluseMIQleaderboardrankingsasstartingpointsbeforerunningtheirownevaluationsforspecificusecases.

Thestandardizationextendsbeyondvendorselection.RegulatorsandstandardizationbodiesareexpectedtoadoptMIQaspartofcomplianceframeworksforAI

deployment,makingitakeycriterionforsolutionapproval.CIOsreportthatvendorswithclear,standardizedperformancemetricsareeasiertoevaluateandreceive

approvalfasterthanthoseusingproprietaryorinconsistentevaluationmethods.

87AItrendsthatwilldefine2026

AIenablesultra-leanteamoperations

AIenablessmallerteamstoachieveresultsthatpreviouslyrequiredmuchlargerorganizations,representingoneofthemosttransformativeAItechnologytrendsreshapingbusinesseconomics.

AI-nativecompaniesgenerate$1.35Minannualrevenueperemployee,comparedto$107Kfortraditionalsoftwarecompanies—amorethan10xdifferenceinproductivity.

ThisefficiencygainreflectshowAIcanautomateworkactivitiesthattraditionallyconsume60-70%ofemployees'time.

Thenumbersdemonstrateaclearshiftinbusinesseconomics.

In2020,reaching$30Min

annualrecurringrevenuemeantbuildinga250-personcompany.

In2025,AI-nativebusinessesare

achievingthesamemilestonewith

justthreepeople.Thesecompanies

useAIformarketresearch,customersupport,contentcreation,andproductdevelopment,allowinghumansto

focusonstrategy,oversight,andtasksrequiringcreativityorjudgment.

By2030,somebillion-dollar

companieswilloperatewithteamsofjust3-20people.Thirty-sixout

of84newlyvaluedbillion-dollar

unicornsin2024areAI-native

companies,withthetop30startupsaveraging40xrevenuemultiple

valuations.Theseorganizationsshowcaseextraordinarilyefficientgrowthrates,averaging$27.5MinARRwithinfouryears.

97AItrendsthatwilldefine2026

TheproductivitygainscomefromhybridteamsthatcombinehumanworkerswithagenticAIsystems.

Someteamsreporta2.4xincreaseinproductivitywhenusingAI-augmentedworkflows.

Withover76%ofastartup'soperatingcostsgoingtoheadcount,leanAI-nativeteamscanreducethisexpenseandreallocateresourcestorevenue-generatinginvestments.

Capital-efficientstartupsusingthismodelcuttheirburnrateandachievestrategicmilestonesmorequickly,enablingoperationsthatshortenthepathtopositive

cashflow.Thisreducesinvestorriskandincreasesthelikelihoodofearlier,higher-valuationexits.

AIleadersinvest10%and50%oftheirtechnologyspendingintoAIinitiativesandreinvestsavingsintonewopportunities.

TraditionalcompanieswithlargeworkforceswillneedtoadoptAI-augmentedprocessestoremaincompetitiveagainstthesenimble,efficientteamsthatcaniterateandscalewithoutaddingheadcount.

AIengineersreplacedatascientists

ThejobmarketforAIprofessionalsisshiftingtowardproduction-focusedroles,reflectingbroadertrendsinAIadoptionandimplementationstrategies.

By2027,therewillbethreetimesmore

AIengineer

positionsthandatascientistrolesasorganizationsmovefrombuildingcustomMachine

Learningmodelstodeployingandoptimizingpre-trainedAIsystems.

ThisshiftreflectshoworganizationsactuallyuseAItechnology.Therise

ofgenerativeAIhasmovedthefocusfromdevelopmenttoproductionvalidationofAIapplications.AIengineersensureproductionreadinessandmaintain

continuousfeedbackloopsacrossexperimentation,development,testing,and

deploymentphases.Meanwhile,theextensivepretrainingofgenerativeAImodelsreducestheneedforbuildingcustomMachineLearningapplicationsfromscratch.

LinkedIn's"2025JobsontheRise"listshowsAIengineerasthefastest-growingjobtitlein15countries,rankingnumberoneintheUS,theUK,andtheNetherlands.

107AItrendsthatwilldefine2026

TheGartnerSoftwareEngineeringSurveyfor2025foundthattheAIengineerwasthesecondmostin-demandrole,with57%ofleadersplanningtohireorincreasehiring.

TherolerequiresskillsdifferentfromthoseoftraditionalDataScience.Insteadofstatisticalmodelingandalgorithmdevelopment,AIengineersfocusonmodelselection,rigorousevaluation,buildingpromptlibrariesandretrieval-augmentedgenerationpipelines,ensuringmodelobservability,andmitigatingAIrisks.

Thisrepresentsashiftfromcustommodelcreationtosystemintegrationandoptimization.

TherewillbesubspecialtieswithinAIengineering.Datascientistswithsoftware

engineeringskillsarewell-positionedforspecificAIengineerroleslikeevaluationdesign,modelselection,andfine-tuning.Softwareengineerscantransitioninto

promptdevelopment,applicationorchestration,anduserexperiencedesign

forAIsystems.Dataengineersfitnaturallyintodevelopingcomplexdatapipelinesforunstructureddataprocessing.

Giventhetalentshortageandspecializedskillsrequired,manyorganizationswillneedtopartnerwithreliabletechnologyprovidersthatcansupplyexperiencedAIengineersanddevelopmentteams.ThesepartnershipsbecomeessentialforcompaniesthatlacktheinternalresourcestobuildAIcapabilitiesquicklyenoughtoremaincompetitive.

117AItrendsthatwilldefine2026N-i

MultimodalAIbecomesthe

7

standardinterface

ArtificialIntelligenceismovingbeyondtext-onlyinteractionstoprocessmultiple

datatypessimultaneously,representingoneofthelatesttrendsofAIthat'schanginghuman-computerinteraction.MultimodalAImodelscanunderstandandgeneratecontentacrosstext,images,audio,andvideowithinasinglesystem,representing

asignificantshiftinhowhumansinteractwithAItechnology.Multimodalmodelreleasesincreasedby1,150%overtwoyears.

Thebusinessapplicationsareimmediateandpractical.Fieldengineerscan

photographmalfunctioningequipmentandreceivespokendiagnosticinstructions.ClinicianscanattachX-raystonotesandgetstructuredreportdrafts.Analysts

cancombinecharts,transcripts,andaudioclipsinasinglequery.ThiseliminatesswitchingbetweendifferentAItoolsfordifferentcontenttypes.

Consumeradoptionreflectsthisutility.

By2028,80%ofdigitalworkerswillusemultimodalinterfaceswithAI,significantlyimprovingtaskefficiencyandworkplaceaccessibility.

UsersnolongerneedtodescribevisualproblemsintextwhentheycansimplyshowthemtotheAIsystem.

TheinfrastructuresupportingmultimodalAIisscalingrapidly.Large-scale

multimodalmodelreleasesgrewfrom2in2022to25in2024.Asaresult,major

technologycompaniesareinvestingheavilyinsystemsthatsimultaneouslyprocessdiversedatatypes.

Multimodalcapabilitiesreducefrictioninhuman-AIinteractionbyallowingpeople

tocommunicatenaturallyusingwhatevercombinationoftext,voice,images,orvideobestconveysthei