2026年人工智能治理：上下文、控制与企业规模研究报告 AI GOVERNANCE IN 2026 Context,Control,and Enterprise Scale -CollibraTDWI

TOPTRENDS

2026

AIGOVERNANCEIN2026:

Context,Control,andEnterpriseScale

FernHalper,Ph.D.

Contents

AIGovernancein2026:

Context,Control,andEnterpriseScale

MarketContext:AIAccelerationMeetsGovernanceReality 3

KeyTrendsinAIGovernance 6

Trend1:TheAIDataFoundationRemainsa

MovingTarget 6

Trend2:ContextIsKing 7

Trend3:TheShiftTowardAgenticAIRaisesthe

GovernanceStakes 8

Trend4:UnifiedandAutomatedGovernance

BecomesEssential 9

GovernanceastheFoundationforScalableAI 10

ScalingEnterpriseAIwithContext,Control,andUnified

Governance 11

ContextGap,PlatformTrap 11

GovernanceProvidesContextandControl 12

GovernanceEverywhere 12

AbouttheSponsor 14

AbouttheAuthor 15

AboutTDWIResearch 15

AIGovernancein2026:Context,Control,andEnterpriseScale2

AIGovernancein2026:Context,Control,andEnterpriseScale3

MarketContext:AIAccelerationMeetsGovernanceReality

Artificialintelligencehasenteredanewphaseofenterpriseadoption.InTDWIresearch,generativeAInowranksasthetopanalyticspriority,withagenticAIgainingtractionasorganizationslookbeyondcontentgenerationtowardtaskexecutionandautomation.

However,whilemanyorganizationsviewAIasagrowthopportunity,relativelyfewhaveestablishedmaturegovernanceframeworks

tomanageit.Inalate2025TDWIsurvey,onlyabout26percentoforganizationsconsideredtheirAIgovernancemature,comparedto31percentfordatagovernance(Figure1).1Additionally,nearlyhalf

describetheirAIgovernanceasimmature.Atthesametime,just36percentbelievetheirdatafoundationisreadyforAI(notshown).

CurrentratesofmaturityindatagovernanceandAIgovernance.

MaturityLevel MatureSomewhatMatureImmature

Figure1.Basedon135respondents.

31%

33%

36%

DataGovernanceMaturityAIGovernanceMaturity

26%

27%

47%

ThismaturitygapisemergingatatimewhenAIsystemsare

becomingmoreembedded,moreautonomous,andarguablymore

consequential.Regulatoryscrutinyisincreasingglobally.AIcapabilitiesarebeingintroducedthroughthird-partytoolsandenterprisesoftwareupgrades,sometimeswithoutcentralizedoversight.Unstructured

datause,i.e.,documents,contracts,policies,emails,transcripts,andAI-generatedcontent,isexpandingrapidlyandnowplaysacentralroleingenerativeandagenticAIusecases.

Historically,structureddatagovernanceandinformationgovernanceoperatedinparallel.Today,generativeandagenticAIsystemsrely

heavilyonunstructureddata.Thisintroducesnewgovernance

requirements:documentvalidation,versioncontrol,lineagefor

unstructuredassets,accesscontrolatretrievaltime,monitoringof

upstreamchanges,andevaluationoffaithfulnessandrelevanceinAI-generatedresponses.

1See

TDWIResearchBrief:TheImpactofDataandAIGovernanceintheEnterprise.

AIGovernancein2026:Context,Control,andEnterpriseScale4

…agenticAI

introduces

newtechnical,organizational,andgovernancechallenges.”

ThedatainFigure1illustratesthatAIgovernancelagsdata

governance,andevendatagovernancematurityremainsuneven.

AtTDWI,weseemanyorganizationsstillstrugglingwithcoreissues

suchasdatasilos,uncleardataownership,lackofstandardization,

andinaccuratedata.AIgovernanceintroducesadditionallayersof

complexity.Thetopchallengescitedbyrespondentsinarecentsurvey(Figure2)includelackofclearguidelinesandpolicies(51%),lackof

skilledpersonneltomanageAImodels(50%),ethicalconcernssuchasbiasandtransparency(36%),andlackofcontrolsovergenerativeAIleadingtoshadowAI(32%).2

Whatchallengeshaveyouencounteredwith

implementingAIgovernanceinyourorganization?

LackofclearguidelinesandpoliciesLackofskilledpersonnel

Ethicalconcerns(bias,transparency)ComplianceandregulatorychallengesShadowAI/lackofcontrols

Figure2.Basedon135respondents.

36%

36%

32%

51%

50%

Thesefindingshighlightanimportantpoint.AIgovernancerequiresnewcompetenciesthatextendbeyondtraditionaldatagovernance.Whereasdatagovernancefocusesonensuringthatdataisaccurate,consistent,timely,andappropriatelyprotected,AIgovernanceextendsthoseprinciplestosystemsthatlearn,generate,reason,andact.It

addressesquestionssuchas:

•Isthismodelalignedwithbusinessobjectivesandcompanypolicy?

•AreallAIassetsfullyinventoriedanddocumented?

•Ismetadata(ownership,purpose,risklevel,usage)clearlydefinedandmaintained?

•Dowehaveend-to-endtraceabilityofhowAIoutputsareproduced?

•Aretheinputstothesemodelssound?

•Cantheoutputbeexplained?

2Ibid

AIGovernancein2026:Context,Control,andEnterpriseScale5

•Isthemodeldegradingordrifting?

•Isthemodel(ortheagents)hallucinating?

•Whathappensifthesystemactsincorrectly?

•Howshouldagentsinteractwithoneanother?

AIgovernancespanspolicy,technicalcontrols,lifecyclemanagement,andriskmitigation.Itrequirescoordinationacrossbusiness

stakeholders,datateams,engineering,legal,compliance,andsecurity.

Whilethisseemslikealottoconsider,therealityisthatgovernance

canbeanenabler,ifperformedproperly.InTDWIresearch,wesee

thatgovernancematuritycorrelatesstronglywithmeasurableimpact.Organizationswithstrongerdatagovernancereporthigherlevels

ofefficiency,betterdataquality,improvedcompliance,andfaster

insights.Theyaremorelikelytoimplementobservability,lineage,andmonitoringtools,andmorelikelytoreporttop-orbottom-linebenefits.Incontrast,weakgovernanceisassociatedwithinconsistentpractices,complianceexposure,fragmentedecosystems,anderosionoftrust.

Ratherthanslowinginnovation,governanceprovidesguardrailsthatenableit.

ThesamepatternholdsforAIgovernance.Inmoremature

environments,governanceframeworkssupporttransparency,ethicalalignment,riskmitigation,andstructuredoversight.Ratherthan

slowinginnovation,governanceprovidesguardrailsthatenableit.ThegoodnewsisthatorganizationsarebeginningtotakeAIgovernanceseriously.Inarecentsurvey,nearlyhalfoftherespondentssaidthattheywerestartingtoputAIgovernanceinplace.3Thechallengeis

whetherorganizationscanscaleAIresponsibly.

The2026governancelandscapeisuneven.AIadoptionisaccelerating.Regulatoryconcernsaregrowing.Unstructureddataiscentral

tomodernAI.Agenticsystemsintroducemorecomplexity.The

organizationsbestpositionedforenterprise-scaleAIarethosewiththestrongestfoundations:clearownership,semanticalignment,lifecyclediscipline,cross-platformmonitoring,andautomatedcontrols.

ThisreportexaminessomekeytrendsshapingAIgovernancein

2026,includingtheevolvingAIdatafoundation,theriseofcontextengineeringasanarchitecturaldiscipline,theexpandedgovernance

3Ibid

AIGovernancein2026:Context,Control,andEnterpriseScale6

AIgovernanceisamovingtargetshapedbynewdatatypes,newarchitectures,

andnewuse

cases.

requirementsintroducedbygenerativeandagenticAI,andtheshifttowardunified,automatedgovernancearchitecturesdesignedto

supportenterprisescale.

KeyTrendsinAIGovernance

Trend1:TheAIDataFoundationRemainsaMovingTarget

OrganizationsrecognizethatAIisonlyasreliableasthedatait

consumes.Thisistheold“garbagein,garbageout”adage.Yet

buildinganAI-readydatafoundationremainschallenging,andthe

requirementscontinuetoevolve.Datasilosremainatopchallengeforenterprises,yetdatagovernanceneedstospanmultipleenvironments,ratherthanrelyingonpointsolutionsforeachsilo.

Additionally,earliergovernanceprogramsfocusedonstructureddata

usedinreportinganddashboards.Today,AIinitiativesrelyheavilyon

unstructureddataincludingcallcenternotes,contracts,policies,emails,chatlogs,andothertext-basedassets.Theyoftenrelyonimages.

Thisshiftintroducesnewdatagovernancedemands,including

assessingthequalityandcompletenessofunstructureddata.Withdocuments,forinstance,thismayrequirenewmetricssuchas

plausibility.Evenoldmetricssuchasaccuracyorcompleteness

maytakeonnewmeanings.Governingunstructureddatameans

managingprovenanceandlineageacrossdocumentrepositories.Itrequiresapplyingconsistentclassificationandsensitivitycontrols.Itmeansgoverningnewdatasuchasdataembeddedinpromptsandvectordatabases.

Atthesametime,enterprisedataremainsfragmentedacrossERP,

CRM,clouddataplatforms,datalakes,andthird-partysystems.AI

systemsoftenpullfrommultipleenvironmentssimultaneouslyfor

enricheddata.InconsistentdefinitionsandunevencontrolsintroduceriskdirectlyintoAIoutputs.

AIgovernance,then,isamovingtargetshapedbynewdatatypes,newarchitectures,andnewusecases.Althoughorganizationsaretrying

tomovetoaunifieddataplatformforallthisdata,itisunrealistictothinkthatallcompanydatawillbeinonephysicalplatform.Many

organizationsaremovingmoretowardafabricapproachwheretheycanunifydifferentkindsofdatafromdifferentsources.Thatalso

meansatrendtowardadataandAIgovernancelayerthatspansthisfabricthroughoneunifiedview.

AIGovernancein2026:Context,Control,andEnterpriseScale7

Conflicting

interpretationsofkeyentitiesorambiguity

aroundpolicyrulescan

undermine

trustandcreatecompliance

exposure.

Trend2:ContextIsKing

GenerativeAIhashighlightedthatmodelsmayfailduetomissing

orinconsistentcontext.Thinkaboutasimpleexamplesuchasa

chatbot.Ifitisacustomer-focusedchatbot,itneedstobeableto

accessinformationaboutthatcustomertoprovideusefulinsights.Thatmightincludeaccountstatus,servicehistory,contractterms,

andentitlements.Ifitisamaintenanceassistant,itneedstobeabletoutilizeinformationaboutaparticularpieceofequipmenttobe

usefultooperations.Thatmightbeequipmentconfiguration,serviceintervals,partdependencies,andsafetyconstraints.

Contextisveryimportant;withoutittheAIsystemmightgenerate

informationthatisnotusable.Withoutsharedbusinessdefinitions,AIsystemsmayproduceinconsistentorriskyresults.Forinstance,largelanguagemodelscangeneratereasonablesoundingoutput,buttheydonotinherentlyunderstandenterprise-specificbusinessmeaning.

Theydonotknowwhat“customer,”“revenue,”“pipeline,”or“member”meansinsideaspecificorganization.Theycannotdistinguishbetweenrevenueinclusiveoftaxversusnetrevenueunlessthatdistinctionis

encodedsomewhereaccessibleandgoverned.

Conflictinginterpretationsofkeyentitiesorambiguityaroundpolicyrulescanunderminetrustandcreatecomplianceexposure.This

realityisdrivingrenewedemphasisonsemantics,includingbusinessglossaries,metadatamanagement,lineage,andpolicymapping.

Asemanticlayerisalogicallayerthatsitsacrossoneormoredata

systemsandprovidesconsistentbusinessdefinitions,metrics,and

relationshipsfordatausedinanalyticsandAIapplications.Itensures thatagentsandusersinterpretkeyterms(e.g.,“customer,”“revenue,”“pipeline”)consistentlyacrosssystems.

InTDWIresearch,weseethatorganizationsthataresucceedingwithgenerativeandagenticAIareformalizinganenterprisebusiness

vocabularyandexplicitlyencodingbusinessrulesandlogicso

thatmodelsoperatewithindefinedguardrailsratherthaninferred

assumptions.Theyareimplementingasemanticlayerorcurated

semanticviewstieddirectlytogoverneddataproducts,ensuring

thatbusinessdefinitionsareconsistentacrossusecases.SemanticsisbecominganincreasinglyimportantAIgovernancetrend.InarecentTDWIsurvey,forinstance,over65%ofrespondentsfeltasemanticlayerwascriticalforAIsuccess.4

4UnpublishedTDWIData,Analytics,andAISurvey2026.

AIGovernancein2026:Context,Control,andEnterpriseScale8

Trend3:TheShiftTowardAgenticAIRaisestheGovernanceStakes

WhilegenerativeAIremainstheimmediatefocus,organizationsarebeginningtoexperimentwithagenticAI,i.e.,systemscapableof

reasoning,planning,andtakingactionacrossenterpriseprocesses.InTDWIsurveys,weseethatorganizationsareimplementingsingle-agentsystemstostart,althoughthereisgrowinginterestinmulti-agentsystems.

Agentsdiffermateriallyfromassistants.Ratherthangeneratingcontentinresponsetoprompts,theycantriggerworkflows,updaterecordsinoperationalsystems,interactwithAPIs,coordinatetasksacrossmultipletools.OneareathatisbecomingpopularforagenticAIissupplychainmanagement.Thinkaboutanagentthatmightberesponsiblefor

demandforecastingoranotherthatmightfindthebestprice.Thisincreasedautonomyexpandsbothopportunityandrisk.

Agenticsystemsrequireadditionalgovernancecontrols,including:

•Fine-grainedaccessandpermissionmanagementforagents

Thetransition

•Definedbehavioralguardrailsandexecutionboundaries

fromgenerativetoagenticAI

•Human-in-the-loopcheckpointsforhigh-riskdecisions

willsignificantly

•End-to-endtraceabilityofactionsanddependencies

elevate

•Continuousmonitoringforanomalousbehavior

governance

•Versioninganddocumentationoforchestrationlogic

requirements.

Inamulti-agentsystem,thereneedstobeaseriesofcontrolsatthe

handoffsbetweenagents.Somemaybestandardcontrolsappliedtosoftwaresystems,forinstancethosethatpostjournalentries,processpayments,orupdatecustomerrecords.AnagentcallinganAPIneedstohavecontrolsinplacethatmightbesimilartoamicroservicecallinganAPI.Itmustfollowsecureservice-to-servicearchitectureprinciples.

Ofcourse,therewillbenewcontrolsaswell.Organizationswillneed

controlstoensurethatagentscanonlyaccessspecificsystems,data

sources,andtoolsappropriatetotheirrole.Human-in-the-loopcontrolsmayberequiredforhigh-impactactionssuchasfinancialtransactions,policychanges,orcustomercommunications.

Organizationswillalsoneedworkfloworchestrationcontrolstoensurethatagentsfollowapprovedtasksequencesratherthanexecuting

AIGovernancein2026:Context,Control,andEnterpriseScale9

arbitraryactions.Inaddition,agentobservabilityandmonitoring

controlswillbeneededtotrackagentbehavior,toolusage,and

outcomesovertime,alongwithalertswhenbehaviordeviatesfromexpectedpatterns.

Policyenforcementandexplainabilitycontrolswillalsobenecessarysothatorganizationscanunderstandwhyagentstookspecificactionsandensurethoseactionscomplywithinternalpoliciesandregulatoryrequirements.Inotherwords,thetransitionfromgenerativetoagenticAIwillsignificantlyelevategovernancerequirements,andthisisa

trendorganizationsarealreadythinkingabout.

Whengovernanceisunified,itbecomespossibletoapplypoliciesconsistently,tracklineagefromsourcedatatoAIoutcomes,andmonitorhow

modelsandagentsinteractwithenterprisesystems.

Trend4:UnifiedandAutomatedGovernanceBecomesEssential

AIgovernancecannotscalethroughmanualoversightorisolated

platformcontrols.Mostenterprisesoperateacrossheterogeneous

ecosystemsspanningmultipleclouds,on-premisessystems,structuredandunstructureddatasources,anddiverseanalyticstools.Platform-

specificgovernancecapabilitiesareinsufficientwhenAIsystemsdrawfromacrosstheenterprise.Fragmentationleadstoinconsistentpolicyenforcement,unevenvisibility,andgovernanceblindspots.

Inresponse,thetrendisfororganizationstomovetowardmore

unifiedgovernanceframeworksthatprovidecentralizedvisibility

acrossdataandAIassets.Thisincludesonedefinitionforbusiness

termssothereisnoconfusion.Itincludesmonitoringandobservability.ThescaleofAIsystemsmeansthatmanualreviewsbecome

impractical.Therefore,governancemustbeembeddedintopipelines,developmentenvironments,orchestrationlayers,andruntime

monitoringsystems.Forinstance,thatmightmeanautomatedtraceabilityacrossdata,models,andagents.

Organizationsarealsobeginningtotreatgovernanceasacross-

platformsystemofrecordfordataandAIassets,capturing

informationaboutdatasets,businessterms,models,agents,policies,andtheirrelationships.Whengovernanceisunifiedinthisway,it

AIGovernancein2026:Context,Control,andEnterpriseScale10

AgenticAI

systemsmust

bemonitored,audited,and

maintainedovertime.”

becomespossibletoapplypoliciesconsistentlyacrossenvironments,tracklineagefromsourcedatatoAIoutcomes,andmonitorhow

modelsandagentsinteractwithenterprisesystems.GovernancecanhelptoenableAI.

Increasingly,thesecapabilitiesaredeliveredthroughpolicy-

drivenautomation,wheregovernancerulescanbeenforced

programmaticallyratherthanthroughmanualreview.Thisallows

organizationstocontinuouslymonitorcompliance,detectanomalies,andmaintainaccountabilityevenasAIdeploymentsscaleacrosstheenterprise.

Unifiedgovernancedoesnoteliminatetheneedforhumanoversight.However,itenablesgovernanceteamstofocusonhigher-risk

decisionswhileroutinecontrols,suchaspolicyenforcement,lineagecapture,accessvalidation,andmonitoring,arehandledautomatically(andpotentiallywithahumanintheloop,ifneeded).

GovernanceastheFoundationforScalableAI

AIisadvancingrapidlyfromgenerativecopilotstoincreasinglyautonomoussystemscapableofexecutingbusinessprocesses.Organizationsareeagertocapturevalue,andmanyaremovingquickly.However,theabilitytoscaleAIresponsiblydependsongovernancematurity.

Thecurrentstatereflectsbothprogressandrisk.AIadoptionis

widespread,butformalAIgovernanceprogramsarestillemerging.Thiscreatesexposure,particularlyasAIsystemsconsume

unstructureddata,operateacrossfragmentedenvironments,andbegintoactwithgreaterautonomy.

EffectiveAIgovernancerequiresstrengtheningdatafoundations,

investinginsemanticclarity,andembeddinglifecyclecontrols

aroundmodelsandagents.Itdemandsvisibilityacrosssystems,clearaccountabilitystructures,andautomationthatcanscalewithAI

activity.Italsorequiresrecognizingthatgovernanceisnotaone-timeinitiative.AsAIcapabilitiesevolve,governanceframeworksmustadaptaccordingly.

AIGovernancein2026:Context,Control,andEnterpriseScale11

Whileplatform-specific

governance

canbeeffective

withinasingle

environment,

mostenterprisesoperateacross

multiplesystems.

ScalingEnterpriseAIwithContext,Control,andUnifiedGovernance

(BasedonaTDWIinterviewwithaCollibraexecutive.)

EnterpriseAIisrapidlyevolvingfromexperimentationtooperationaldeployment.OrganizationsareembeddinggenerativeAIintobusinessprocessesandbeginningtoexploreagenticsystemscapableof

executingtasksacrossenterpriseenvironments.Atthesametime,theenterprisedatalandscapeisbecomingmorecomplex.Dataresides

acrossERPsystems,clouddataplatforms,analyticsenvironments,SaaSapplications,andemergingAIservices.

Thisfragmentationcreatesnewgovernancechallenges.AImodelsandagentsoftenrelyondatafrommultiplesystemsandenvironments.

Whengovernanceisappliedinconsistentlyorconfinedtoindividualplatforms,organizationslackacompleteviewofhowdataisbeingusedandhowAIsystemsoperateacrosstheenterprise.Fragmentedgovernancecanleadtoblindspotsinlineage,inconsistentpolicy

enforcement,andreducedtrustinAIoutcomes.

ContextGap,PlatformTrap

Collibradescribesthischallengethroughtworelatedconcepts:thecontextgapandtheplatformtrap.

ThecontextgapreferstothelackofenterprisecontextavailabletoAIsystems.LargelanguagemodelsandAIagentsmaygeneratefluentresponses,butwithoutclearbusinessdefinitions,policies,permissions,andlineageinformation,theylackthecontextnecessarytointerpretenterprisedatacorrectly.Inmanycases,AIfailuresarenotcausedbylogicerrorsinthemodelsthemselvesbutbymissingorinconsistentcontextsurroundingthedatausedtotrainoroperatethem.

Theplatformtrapariseswhengovernancecapabilitiesareembeddedonlywithinindividualplatforms.Whileplatform-specificgovernancecanbeeffectivewithinasingleenvironment,mostenterprises

operateacrossmultiplesystems.SAPenvironmentsoftenserveastheoperationalbackboneforcriticalbusinessprocessessuchasfinance,procurement,andsupplychain,butorganizationstypicallycombineSAPdatawithinformationfromclouddataplatforms,externaldata

sources,andanalyticstools.Governancethatremainsconfinedtoasingleplatformcannotprovideconsistentoversightacrossthisbroaderecosystem.

AIGovernancein2026:Context,Control,andEnterpriseScale12

GovernanceProvidesContextandControl

Toaddressthesechallenges,Collibrapositionsgovernanceas

acontextandcontrolengineforenterpriseAI.Inthismodel,

governanceprovidesthesemanticcontextandpolicycontrols

necessaryforAIsystemstooperatereliablywithinenterprise

environments.Datagovernanceestablishesthesemanticlayer,thebusinessdefinitions,metadata,lineage,andpolicies,thatprovidescontextforenterprisedata.AIgovernancefocusesonthecontrollayer,ensuringthatmodelsandagentsbehavewithinregulatory,operational,andorganizationalguardrails.

Thiscombinedapproachcreatesafoundationofgovernedintelligence,whereAIsystemsoperateontrusted,well-understooddatasupportedbyconsistentgovernancepolicies.

AcentralconceptinthisapproachiswhatCollibratermsheadlessgovernance.Traditionalgovernanceprogramsoftenreliedon

centralizedinterfacesormanualreviewprocesses.Whileuseful

fordocumentationandoversight,theseapproachescannotscaleinenvironmentswhereAIsystemsaredevelopedanddeployedcontinuouslyacrossmultipleplatforms.

Unifiedgovernanceenablesorganizationsto

connectoperationalbusinessdatawithbroaderanalyticsandAIenvironments.

Headlessgovernanceembedsgovernancecapabilitiesdirectlyinto

theenvironmentswheredataandAIarecreatedandused.Rather

thanrequiringuserstoaccessgovernancethroughaseparateportal,governanceoperatesasaninvisibleservicelayerintegratedacross

developmenttools,datapipelines,analyticsenvironments,andAI

applications.Policies,metadata,andcontextualinformationare

delivereddirectlytothesystemsandworkflowswheretheyareneeded.

GovernanceEverywhere

ThisapproachenableswhatCollibradescribesasgovernance

everywhere.Regardlessofwheredataresides,withinSAPsystems,

clouddataplatforms,orexternalservices,governancecapabilitiescanprovidevisibility,traceability,andcontrolacrossthefullenterprisedataandAIecosystem.

AIGovernancein2026:Context,Control,andEnterpriseScale13

WhencombinedwithenterpriseplatformssuchasSAP,unified

governanceenablesorganizationstoconnectoperationalbusiness

datawithbroaderanalyticsandAIenvironments.SAPenvironmentsoftenprovidetheauthoritativeoperationaldatathatpowersenterpriseprocesses,whileAIsystemsincreasinglyrelyonthisdatatoautomatedecisionsandactions.MaintainingtraceabilityfromenterprisesystemsthroughdataplatformsintoAImodelsandagentsbecomesessentialasorganizationsscaleAIacrossbusinessoperations.

AsenterprisesmovefromgenerativeAItowardmoreadvanced

agenticsystems,theimportanceofgovernancewillonlyincrease.AIsystemswillrequirebothtrusteddatafoundationsandcontinuousoversightofhowmodelsandagentsbehaveacrossenterprise

processes.

Byclosingthecontextgapandavoidingtheplatformtrap,unified

governanceenablesorganizationstoscaleAIwithconfidence,

ensuringthatAIsystemsoperateontrusteddata,remainalignedwithenterprisepolicies,anddeliverreliableoutcomesacrossincreasinglycomplexenterpriseenvironments.

AIGovernancein2026:Context,Control,andEnterpriseScale14

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AIGovernancein2026:Context,Control,andEnterpriseScale15

AbouttheAuthor

FERNHALPER,PH.D.,isVPofTDWIResearch.HerworkfocusesonAI,generativeAI,agenticAI,AI

governance,cloudcomputing,andothermodernanalyticsapproaches.Shehasmorethan25yearsofexperienceindataandbusinessanalysisandAIandhaspubli