科孚（CRIF）：2026年生成式AI趋势报告：行业全景与应用启示（英文版）

n°06

GENAITRENDS2026:

IndustryOverview

andImplicaĦons

⊙Insideaenai

TableOfContents

ExecutiveSummary3

1.Context:InnovationLandscape&DrivingFactors5

2.EmergingTechnologyTrendsinGenAI(2026)8

3.FromTrendstoTangibleImpact:TheCRIFVision11

4.StrategicImplicationsforStakeholders13

Conclusion15

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3

•EXECUTIVESUMMARY

ExecutiveSummary

As2026approaches,generativeAI(GenAI)isatacritical

turningpoint:organizationsaremovingbeyondpilots,

butmoststillstruggletoachievescalable,measurableimpact.

Whilenearly80%ofcompanieshaveexperimented

withGenAI,onlyasmallfractionreporttangiblebusinessvalue.Thishighlightstheurgentneedtobridgethegap

betweeninnovationandexecution.

⊙Insideaenai•EXECUTIVESUMMARY

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Regulatoryframeworks,particularlytheAIAct

andDigitalOperationalResilienceAct(DORA

inEurope,arenowactivelyshapingtheindustry,makingcompliance,riskmanagement,and

transparencynon-negotiableforhigh-risk

AIapplications.Successin2026willnotonly

requiretechnicaladvancement,butalsoprocessharmonization,robustgovernance,andtargetedupskillingofteamstomanageAIresponsibly

andatscale.

Threeprimarytrendsaredefiningthelandscape:

•AgenticAIisevolvingfromconversational

chatbotstoautonomousagentscapable

ofexecutingcomplex,multistep

tasks—transformingworkflowsanddecision-makingacrosssectors.

•Domain-specificandsmalllanguagemodels

(DSLMsandSLMs)aregainingtraction,enablingprivacy-preserving,tailoredsolutionsthatmeet

stringentregulatorydemandsanddeliverscalableintelligence.

•Advancedanti-frauddefenses:

Thereisacriticalneedfordefensesagainst

deepfakesandsyntheticdocumentsamid

theescalationofAI-drivenfraudinonboarding,KnowYourCustomer(KYC),Anti-Money

Laundering(AML),andcollectionprocesses.

CRIFisuniquelypositionedtogovernandguide

thesetrends.It’snotjustaboutadoptingmore

powerfulmodelsbutalsounlockingthevalue

ofexistinginformationassetsandintegratingthemintosecure,auditable,andcompliantdecision

enginespoweredbyAI.

Forthefinancialsector,thismeansturningrisk

intoacompetitiveadvantage.OrganizationsthatsuccessfullyoperationalizeresponsibleAI

andstandardizetheirdecision-makingprocesseswillunlockbothoperationalefficiencyandlastingstrategicadvantages.

Theriseofagenticcommercehasdriven

theemergenceofnewtransactionprotocols

andstandards,presentingbothopportunities

andrisksforfinancialservices,telcos,andutilities.OrganizationsmustadapttoAI-driventransactions,embeddingtrust,identity,andcompliance

attheprotocollevel.

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•1.CONTEXT:INNOVATIONLANDSCAPE&DRIVINGFACTORS

1.Context:

InnovationLandscape&DrivingFactors

⊙Insideaenai•1.CONTEXT:INNOVATIONLANDSCAPE&DRIVINGFACTORS

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TheEndofthe“PilotEra”

2025markstheendofGenAI’spureexperimentationphase.

Companiesarerapidlymovingfromexperimentationtoactualdeployment—buttheresultsaremixed.Accordingto

BCG

,

bylate2025,anestimated78%ofcompanieshadintroducedGenAIinatleastonebusinessarea,yet~80%reported

nomaterialbusinessimpactsofar.Onlyasmallminority

(~5%)offirms—theso-called“future-built”AIleaders—haverealizedsignificantvalueatscalefromAIinitiatives.

Mostarestuckin“proof-of-conceptpurgatory”—testing,iterating,butnotscaling.

Thechallengefor2026istoturnexperimentsintoexecutionandmeasurableresults.

What’sDriving(andBlocking)Progress?

•Technologicalinnovation:Modelsarebecomingmore

powerful,cheaper,andmoreaccessible.Massive

investmentsarefuelingthenextwave,butimplementationcomplexityisgrowing.

•Competitivepressure(fearofmissingout):GenAIisnowaboardroomtopic,notjustanITexperiment.

Theperceivedriskisnolonger“investingtoomuch”,but“beingleftbehind”.

•Thehumanandprocessfactors:Technologyisracingahead,butpeopleandprocessesarelaggingbehind.

There’sarealskillsgap:mostteamslacktheexpertisetomovefrompilottoproduction,ortomanageAIrisksatscale.

⊙Insideaenai•1.CONTEXT:INNOVATIONLANDSCAPE&DRIVINGFACTORS

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RegulationastheBlueprintforResponsibleInnovation

Regulationdeservesaseparatementionasadriverofinnovation—shapingcorporatestrategies

andprofoundlyinfluencingtheEUGenAIlandscape.

AsGenAImaturesandstartsdeliveringrealvalue,

regulatorsaremovingfromawait-and-seeapproachtoactiveenforcement.TheEUAIActandDORA

arepushingfirmstoformalizeAIuse.Theselaws

requireriskmanagement,transparency,andcontrolsforhigh-riskareassuchaslendingandidentity

verification,requiringimmediateinvestments

ingovernanceandcompliance.TheAIActandDORAarereshapingboardroomagendas,forcing

organizationstotreatresponsibleAI

notasacheckboxexercisebutasacorebusinessstrategy.

Here’swhatthatmeansinpractice:

•Operationalizingcompliance:TheAIActand

DORAwillrequirerobust,dynamicframeworksforriskmanagement,transparency,andqualitycontrol—especiallyinhigh-stakesdomains.

•ResponsibleAIasamarketdifferentiator:Firmsthatembedexplainability,fairness,privacy,

andhumanoversightintotheirAIsystemswillwintrustandunlocknewopportunities.

•Privacy-preserving,domain-specificmodels:

SLMsandDSLMswillbecomethebackboneofcompliant,high-valueAI—enablingsecure,tailoredsolutionsthatmeetbothbusiness

andregulatoryrequirements.

•Continuousgovernance:Modeldocumentation,lineagetracking,biastesting,andpost-market

monitoringwillberoutine,supported

bydedicatedteamsandadvancedtooling.

Agilitywillnotbefoundinavoidingcompliance

butinusingregulatoryclaritytomovefaster,turningresponsibleAIintoagrowthengine.

WhyThisMattersforBusinesses

Thewinnersin2026willbethosewhoclosethegapbetweenGenAIhypeandbusinessvalue.Success

won’tbemeasuredbythenumberofPOCsbutbytheabilitytoscalewhatworksandintegrate

AIintocriticaldecisioningworkflows(e.g.,creditorigination,fraudmanagement,etc.).

Regulatoryreadinessisn’toptional.Clientsneed

partnerswhodeliverAIthat’snotjustpowerful,

butprovablysafe,ethicalandcompliant.Upskillingteamsandredesigningprocesses

arejustasimportantasthetechnologyitself.

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•2.EMERGINGTECHNOLOGYTRENDSINGENAI(2026)

2.EmergingTechnologyTrendsinGenAI(2026)

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⊙Insideaenai•2.EMERGINGTECHNOLOGYTRENDSINGENAI(2026)

Trend1:AgenticAI

GenAIchatbotsareevolvingfrompassive

responderstoproactiveagentscapableoftaking

actiontoachievespecificgoals.In2025,wesaw

thefirstagentmodescapableofperforming

autonomous,multisteptasks(e.g.,software

development,reporting,anddeepmarketandwebresearch,etc.).AtCRIFwelaunchedthe

industry’s

firstAIagent

forthebusinessinformationsector,

transformingaccesstoandanalysisofbusinessdata.

Poweredbylargelanguagemodels,theseagents

caninterpretuserrequests,usetoolsforreal-time

information,reasontoenhanceoutput,retain

information,anddelegatetaskstootheragents.

However,theystillhavelimitationssuchasreliability

issuesanddependencyonadvancementsintheunderlyinglanguagemodels.

2026Outlook:AIagentswilldramaticallyimprove

memoryandcontextmanagement.However,their

integrationwithstructuredknowledge(toensure

accuracy)androbustsafeguards(toensurecompliance)willbefundamental.Theirtruevaluewillcomefrom

integrationwithenterprisesystems,notfromoperatingasstandalonetools.

AgenticCommerce–TheNextTransactionStack

2025markedtheriseofagenticcommerce:AIagentsthatautonomouslybrowsewebsites,

compareoptions,andcompletetransactions

withminimalhumaninput.Theseagentsact

onbehalfofindividualsandbusinesses,makingshoppingfaster,personalized,andfrictionless.

ThisrepresentsanewfrontierintheGenAI

landscape,withimplicationsstillunfolding.Unlike

othertrendsanalyzedinthisdocument—whose

impactisbetterunderstoodandmorepredictable—agenticcommerceintroducesdynamicsthatremainlargelyunexplored,warrantingcloserattention.

Earlysignalsarealreadyvisible:

•AIasthefirsttouchpoint:ChatGPT,Google

Gemini,andemergingtoolssuchasOpenAI

AtlasandPerplexityCometaremovingbeyondsearchestowardautonomousshopping

andcheckout.

•Emerginginfrastructure:Standardssuchas

OpenAI’sACP,Google’sAP2,andVisa’sTrustedAgentaregainingtractionamongmajorretail,e-commerce,andpaymentplayers.

Thismattersbecauseagenticcommerceintroducesanewtransactionlayeralongsidetraditional

systems:

•AIagentswillowntheshoppingjourney—

businessesmuststayvisibleandtrustedwhilemanagingnewfraudrisks.

•BanksandPaymentServiceProviders(PSPs)willhavetodealwithAI-to-AIcommerce,requiringtrust,identity,andcomplianceattheprotocol

level—orrisklosingcontrolandrelevance.

•Themainthreatisdisintermediation:

Forexample,auseraskstheirphone’sAIassistanttofindthebestnewphoneplan.TheAIassistantrecommendsaplanandoffersanembedded“Payin3”optionfromtheOSprovider(e.g.,Apple

orGoogle),completelybypassingthetelco’sownfinancingpartnersandthecustomer’sbank.

2026Outlook:Anincreasingrisk

ofdisintermediationmakesprotocol-level

integrationvitalasuser-facingagentsbypass

traditionalchannels.Transactiondataflowing

throughAIplatformswillredefinecreditandfraud

models,whiledelegatedAIactionsintroducenew

liabilitychallenges.IdentitygovernancemustevolvetolinkagentidentitywithKnowYourCustomer(KYC)andAntiMoneyLaundering(AML)rules.

However,useradoptionremainsuncertain—look

outforsignalsofreal-worlduptake.Successwill

dependonbalancinginnovationwithtrust,definingopportunitiesandrisksfor2026andbeyond.

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⊙Insideaenai•2.EMERGINGTECHNOLOGYTRENDSINGENAI(2026)

Trend2:Privacy-Preserving&Domain-SpecificModels

AsGenAIusagegrows,privacyandspecialization

areparamount.Domain-specificlanguagemodels

(DSLMs)offerprecision,privacy,andcompliance.

CRIF’sGenAIFactoryisalreadydeployingsmall

languagemodels(SLMs)trainedonspecificdomainstoensurehighrelevance,lowercost,andnorisk

ofsensitivedataleakage.

2026Outlook:Thetrendistowardmodelsthatkeepdataon-premisesorwithinsecureenclaves.

Wewillseeaproliferationof“smallbutexpert”DSLMstrainedonspecificindustryjargon

and/orprocesses.Thesemodelswon’treplacelargemodelsbutwillaugmentthemforcriticaltasks,

harnessingthepowerofthecompany’sproprietarydataasatruecompetitiveadvantage.

Trend3:Anti-TamperingasaCoreRiskComponent

Defensesagainstdeepfakesandsynthetic

documents(e.g.,payslips,IDdocuments,bank

statements,…)arenolongeroptionalbutrather

arebecominganintegralpartofonboarding,KnowYourCustomer(KYC),Anti-MoneyLaundering(AML),andcollectionprocesses.

Thefraudlandscapeisundergoingamajor

transformation.FinancialinstitutionsarenowfacingAI-poweredfraudsuchasmultilingual

textgeneration,deepfake-as-a-serviceofferings,

syntheticidentities,real-timevoicecloning,

anddocumenttamperingofpayslips,IDdocuments,andbankstatements.

2026Outlook:Deepfake-drivensocialengineering

isexpectedtobecomeroutine,withreal-timeaudioandvideoimpersonationaswellascontentinjectionduringKnowYourCustomer(KYC)processes

andaccountrecoveryemergingasstandardtactics.Contactcentersandvideomeetingworkflows

willturnintonewbattlegrounds.Companieswill

increasinglydeployAItoolstocounterAI-poweredfraud.Documentforensicswillbecomeessential,

pushinginstitutionstointegrateprovenance(chain-of-custody)andanti-tamperingchecksbefore

makinganydecisions.

TheApproachingWaveofAI-DrivenFraud

Thefraudlandscapehasevolvedinto

anindustrialized,AI-poweredecosystem—scalable,automated,andalarminglyeffective.Financial

servicesoperationsarereportingasharprise

incredential-stuffingattacks,hybridsynthetic

identities,anddeepfakevoiceimpersonationduringaccountrecoverycalls.Fakedocumentscombinedwithsyntheticdataareontheincrease,while

Fraud-as-a-Serviceoperatorsareprofessionalizing,sellingverifiedbankaccountspoweredbydeepfaketechnology.

2026Outlook:Combattingincreasinglysophisticatedattackswillrequireacomprehensive,layered

defense—notjustbetterclassifiers.Stronger

digitalidentityframeworks,behavioralanddevice

intelligence,documentverificationandprovenance,andtelecomorconsortium-basedsignalswillbe

essentialtohardendefenses.

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•3.FROMTRENDSTOTANGIBLEIMPACT:THECRIFVISION

3.FromTrendstoTangibleImpact:TheCRIFVision

⊙Insideaenai•3.FROMTRENDSTOTANGIBLEIMPACT:THECRIFVISION

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Emergingtechnologiesmusttranslateintomeasurableimpactsoncoreprocesses:

AgenticAI

→AugmentedDecisionSupport

Increditriskmanagement,anAIagentcanacceleratedecision-makingbyanalyzingcomplexdatasets

(structuredandunstructured)andsimulatingmultiplescenariosinrealtime.Thisisn’taboutreplacing

humanjudgment—it’saboutaugmentingit

withprecisionandspeed,enablinginstitutionstorespondtomarketshiftsfasterthanever.

TrustedData

→TheFoundationforReliableAIAgents

AI-drivendecisionsareonlyasstrongastheintegrityofthedataandthecredibilityoftheproviders

behindthem.Infinancialservices,trustisn’t

negotiable—it’sbuiltintothesystem.Institutions

mustensurethateverydatasetfeedinganAIagentisverifiable,andeveryprovideradherestorigorouscomplianceandsecuritystandards.Thistrustlayerextendstotheagentsthemselves:whenagentsactautonomously,stakeholdersneedconfidencethattheiroutputsareaccurate,unbiased,andauditable.Buildingthischainoftrust—fromdatatoprovider

toagent—iswhattransformsAIfromatoolintoadependablepartnerincriticaldecision-making.

SmallLanguageModels

→ScalableandCompliantIntelligence

LightweightmodelsmakeAIaccessiblebyreducingcostsandlatency.Forfinancialinstitutions,SLMs

unlockconversationalAIatscale.Imagine

amortgageadvisor’sdesktopassistant—runninganSLMlocallyontheirmachineoronthecompany’sself-hostedinfrastructure—providingreal-time

scriptcompliancesuggestions(e.g.,“Youforgot

tomentionthecooling-offperiod”)withoutany

sensitivecustomerdataleavingthebank’ssecureperimeter.ThisdemocratizesAI,makingitpracticalforeverydayworkflows.

DeepfakeDetection&Anti-Tampering→TrustasaDifferentiator

Asfraudtacticsevolve,safeguardingauthenticity

becomescritical.Trustwillbetheprimary

competitivedifferentiator.Institutionsthatinvest

earlyinthesesafeguardswillnotonlyprotect

themselvesbutalsostrengthencustomerconfidence.

Standardization

→ThePrerequisiteforHigh-ValueAI

AIthrivesonstructured,context-richdata.Beyond

harmonizingworkflows,organizationsmustredesigndatatobeadaptiveanddynamicallycontextualized,enablingAIagentstoactwithprecisioninchangingenvironments.Thegoalisn’ttoreplicatehuman

behavior—it’stoempoweragentsthroughflexible,context-awareprocesses.

CustomerExperience

→EfficiencyThatBecomesService

Amoreefficientriskprocessisn’tjustacostsaving;it’sabetterservice.Forexample,atelcocustomer

strugglingtopayabillcaninteractwithaGenAI

agentthatdoesn’tjustreadascript.Itanalyzestheirpaymenthistory,currentusage,andavailableplans,

thenproactivelyoffersapersonalizedone-weekpaymentextensionoratemporaryswitch

toalower-costplan,allwithinthechat.

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•4.STRATEGICIMPLICATIONSFORSTAKEHOLDERS

4.StrategicImplications

forStakeholders

⊙Insideaenai•4.STRATEGICIMPLICATIONSFORSTAKE