英文【科技棱镜】2025聚焦科技引领的商业变革

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LookingGlass

Bringingtech-ledbusinesschangesintofocus

/thoughtworks

strategyDesign.Engineering·2025

Introduction3

OperationalizingAIforbusinessimpact4

Strengtheningthedatavaluechain11

ReimaginingresponsibletechfortheeraofgenerativeAI18

Enablingricherexperiencesthroughmultimodalinteractions26

Unlockinggreatervaluefromphysical-digitalconvergence34

Glossary42

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Introduction

WelcometotheLookingGlass2025.Unlikemanytechtrendreports,

Thoughtworks’LookingGlassisnotintendedtoshinealightonthe

latestbuzzwords.Instead,wetakealongtermlookatthetechnology

horizonsandexplorewhatthatmeansforbusinesses.Whatarethethingsyouneedtoknowaboutnow?Andwhat’slikelytobeimportantinthe

longerterm?TheLookingGlassenablesyoutounderstandandinterpretemergingtechnologiessoyoucanmakesound,strategicchoicesfor

yourorganization.

Therelentlessspeedoftechnologicaladvancementmakesithardertopredictwhat’scomingandwhereyourinvestmentswillpayoffthemost.BreakthroughsinareassuchasagenticAIpromisetoupendhowwe

thinkabouttechnology.Buthowquicklyshouldyoupreparetoadapt?Here’swhereThoughtworks’LookingGlasscomesin.

Inthisedition,weexploremorethan90trendsthroughfivedistinct

perspectivesthatdefinetheevolvingtechlandscapeinbusiness.

Someofthesetrendsarealreadytransformingoperations,whileothersremainjustoverthehorizon,sparkinginterestanddebatebutstill

unfolding.Forbusinessleaders,keepingabroad,strategicperspectiveonthesedevelopments—bothcurrentandfuture—isessential.

LookingGlassoffersexactlythat:aframeworktogainacomprehensiveunderstandingofkeytrends.

Thefivelensesprovideclarityandfocus,helpingensureyourorganizationremainsadaptable,resilientandreadytoharnessorrespondtothe

inevitableshiftsintechnologythatshapeourmodernworld.

RachelLaycock

ChiefTechnologyOfficer,Thoughtworks

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OperationalizingAIforbusinessimpact

ThemainstreamingofAI—andgenerativeAIinparticular—iscontinuingapace.ButasAIproliferates,it’smoreevidentthatsuccessfullyoperationalizingAImodelsandbringingthemtoproductionremainsachallenge.Fromquestionableoutputtounintendedconsequences,thereareahostofrealand

projectedscenariosthatpreventorganizationsfromleveragingAItoitsfullpotential.

Enterprisescontinuetostrugglewithdataquality,dataaccessibilityandthechallengesofdataat

scale,allofwhichremainfoundationaltorobust,effectiveAI.Asourdataplatformlensexplores,

carefuldatacuration,andeffectivedataengineeringandarchitectureareessential.Theimportanceofsyntheticdata,particularlyinresearchcontexts,asatooltoavoidprivacyanddataintegrityissuesisalsobecomingmoreandmoreapparent.

OrganizationsalsoneedtodevelopbetterapproachestotheevaluationandcontrolofAIsystems.

Forward-lookingenterprisesareadopting‘evals’—testsofAIoutputtodeterminereliability,accuracyandrelevance—andguardrails,programmedpolicylayersthatmitigatetheinherentunpredictabilityofgenerativesystems.

Asadoptionincreases,

improvingthemechanisms

throughwhichAIsystems

areconnectedwithenterpriseapplicationsgrowsmore

important.ProxyservicesareemergingtohelpdeveloperslinkAImodelswiththe

applicationstheybuild.

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OperationalizingAIforbusinessimpact

AIagentsaresometimespositionedasthenextstepintheevolutionofAI,duetotheircapacitytomimichumanreasoning.However,thetechnologyremainsrelativelynew,andfindingapplicationsforagentsrequiresdomainexpertise,aswellastheabilitytopreciselymapandmodelcomplex

processesandinteractions.TobuildasustainableandproductiveAIpractice,it’svitalthatthe

organizationdoesn’tresorttoshortcuts,acquirestherequisiteskillsandkeepsinnovationrootedinbusinessrealities.

“Thelessonsfromautomationendeavorsinthe

‘80scouldhelptobuildtherightlevelofhuman-AIagenthandovers.Wemustfocusonaugmentinghumansratherthantryingtosubstitutetheir

currenttaskscompletely.”

SrinivasanRaguraman

TechnicalPrincipal,Thoughtworks

Signals

Theemergenceofsmalllanguagemodels,suchasMicrosoft’sphi-3,andAMD’sAMD135.

ThesemakeitpossibletorunAImodelsattheedgeofnetworksondeviceslikemobilephones,andbecausetheyarerelativelylightweight,focusedandefficient,havearangeofpositive

business,securityandsustainabilityimplications.LLMsalsocontinuetoevolve,withAnthropic’sClaude3.5SonnetLLM,whichhassetindustrybenchmarksintermsofperformance,recentlyupgradedtoincludecomputerusecapabilities.

Researchshowingthatformanyorganizations,AIinvestmentsandadoptionarentnecessarilytranslatingintodeploymentorbusinessimpact.Whileinterestin(andspendingon)AIsolutionsremainshigh,businessesarebeginningtopaymoreattentiontothecostofAIprojects,and

steppingupeffortstoensuretheydelivervalue.

ThecomingintoforceoftheEuropeanUnionsAIAct,whichsetsaninternationalbenchmarkbylayingoutobligationsarounddatagovernance,documentation,humanoversightandsecurityforbusinessesadoptingAIsystems.

Sustained,massiveinvestmentindatacenters,withGoogleeventurningtonuclearpower

togeneratethevastamountsofpoweritsAIofferingsarelikelytorequire.ThisindicatesAIisa

long-termbetthatwillcontinuetogainmomentuminthebusinesscontext,andinsocietyasawhole.

ThegrowthoftoolssimplifyinghowengineersandothersinterfacewithAImodels,suchasLiteLLMandLangchain.

RenewedfocusontacklingLLMhallucinationsandfabrications,withnoveltechniqueslike

‘semanticentropy’beingappliedtorootouterrors,andLLMspolicingtheoutputofotherLLMs.

RisingawarenessofshadowAI,ortheuseofunsanctionedAItoolsintheenterprisecontext,

whichcouldposesignificantproblemsforcompaniesifsensitiveinformationisleakedtoLLMsbyemployees.InonerecentsurveyathirdoforganizationsadmittedtofindingithardtomonitortheillicituseofAIamongtheirteams.

©Thoughtworks,Inc.AllRightsReserved.6

OperationalizingAIforbusinessimpact

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AnticipateAnalyzeAdopt

Strategicrecommendation

Seeingnow

Adopt

1.Accessibilityinmultimodalexperiences

2.Agent-basedsimulation

3.AIagents

4.AIasaservice

5.AIinsecurity

6.AI-assistedsoftwaredevelopment

7.Automatedcompliance

8.Collaborationecosystems

9.Datamesh

10.Edgecomputing

11.Ethicalframeworks

12.EvaluatingandmanagingAIoutputs

13.Evolutionaryarchitectures

14.ExplainableAI

15.GenerativeAI

16.Integrateddata

andAIplatforms

17.InterfacingwithAI

18.LLMOps

19.MLOps

20.Modeltrainingoptimization

21.Onlinemachinelearning

22.Platformsasproducts

23.Privacyfirst

24.Software-definedvehicles

25.Vectordatabases

Analyze

26.AImarketplaces

27.AIsafetyandregulation

28.AI-generatedmedia

29.Automatedworkforce

30.Autonomousrobots

31.ChangingperceptionsofAI

32.Easingaccessto

generativeAI

33.Federatedlearning

34.MultimodalAI

35.Personalizedhealthcare

36.Syntheticdata

Anticipate

37.Understandableconsent

Beginningtosee

Adopt

38.AI-readydata

39.Finegraineddata

accesscontrols

Analyze

40.AIObservability

41.Datalineage

42.GenAIcomputercontrol

43.Intelligentmachineto

machinecollaboration

44.Productionimmunesystems

45.Smalllanguagemodels

46.Talktodata

Anticipate

47.Adversarialmachinelearning

48.Affective(emotional)computing

49.AIinrobotics

Onthehorizon

Adopt

Analyze

50.AIavatars

Anticipate

51.AGIresearch

OperationalizingAIforbusinessimpact

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Theopportunities

Bygettingaheadofthecurveonthislens,organizationscan:

EnhanceknowledgemanagementandtransferbyadoptingGenAItohelpemployeessiftthrough,summarizeandanalyzestoresofenterprisedata,whetherstructured

orunstructured.Awiderangeofproductsareemergingtofacilitatetheretrievalanddisseminationofimportantinformationinindustrieslikeproperty.

HarnessAItoaccelerateprocesseslikelegacymodernizationandcoding.ThoughtworksisalreadysuccessfullyapplyingGenAItoassistteamswithoneofthemostdifficultaspectsofmodernization:understandingandunpackingtheintricatewebofconnectionsthat

typicallyunderpinlegacysystemsandcodebases.AIassistantscanalsosignificantly

boosttheproductivityofsoftwaredevelopmentandotherteamsbytakingoverfrequent,repetitivetasks.

ExploreAIagentstoelevateautomation,potentiallytransforminghowemployeesperformtaskslikeschedulingandcustomersupport,andraisingthebarforengagementand

personalizationincustomerinteractions.

BoostthespeedatwhichLLMsarebroughtintoproduction,andtheireffectiveness

whendeployedthroughemergingpracticesandtoolslikeLLMOps,whichacceleratemodeldevelopment;retrieval-augmentedgeneration(RAG),whichcanenhancemodels’reliability;andAIgatewaysorsmartendpointstoconnectAIsystemstoapplications.

Developandcommunicateajoined-upAIstrategythatempowersemployeestoexperimentwithAIinastructuredway,whilepreventingtheemergenceof‘shadowAI’thatcouldposeathreattotheorganization’sintellectualpropertyorreputation.

LeveragesmalllanguagemodelstobringAIinnovationstoedgedevices,offering

opportunitiesforeverythingfromoperationalanalyticstopersonalization—without

compromisingprivacy,sincedatadoesn’thavetobemovedtothecenterofanetwork.

LeadthewayintermsofcomplianceandethicalAIpractices.WeurgeourclientsnotjusttofollowbutembraceregulationsliketheEUAIAct,assuchlegislationoftenreflectswidersocietalsentimentandconcerns—andpotentialcustomerstakenoticeofbusinessesthatareresponding.

©Thoughtworks,Inc.AllRightsReserved.8

OperationalizingAIforbusinessimpact

Whatwe’vedone

PEXA

ThoughtworkspartneredwithdigitalpropertytechnologycompanyPEXA,AWSandRedactiveto

developaninnovativeandversatileAIassistantthathasboostedtheproductivityofPEXA’semployeesbyprovidingpersonalizedanswerstoqueriesandaugmentingtaskslikeinformationdiscovery.

SeamlesslyintegratedwithPEXA’sinternalsystems,thesolutionalsometrobustrequirementsfordatasecurityandprivacybyequippingtheassistantwithpermissionsawareness,ensuringemployeesareonlyabletoaccessinformationclearedforsharing.

OperationalizingAIforbusinessimpact

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Actionableadvice

Thingstodo(Adopt)

•IdentifyAIchampionswhocanhelpguideandteachyourorganizationaboutthepotentialuse

casesforemergingsolutions—butunderstandthatAIcanandwillbeappliedindifferentways

inalmosteverypartoftheenterprise,whichmeansthesechampionsneedtokeepanopenmind.Havingpeoplewithaclearideaofwhat‘good’lookslikecanreducerisksandensureAIinitiativesfocusonmeaningfulbusinessresults.

•ImplementaholisticandcomprehensiveAIstrategyforyourorganizationthatincludesguidelinesonpermittedtoolsandthecontextsinwhichAIcanbeused,tominimizetherisksofshadowAI.

•Adoptretrieval-augmentedgeneration(RAG)whendevelopingAIsystems,togivereliabilityanupliftandpositionmodelstocreatemorespecificoutputs.Integratingevalsandobservabilitycanfurtherenhancetheresilienceofsystemsoverthelongterm.

•EmbedAIthroughoutthesoftwaredevelopmentlifecycle.Maximumresultsareachievedwhen

theroleofAIisn’tjustlimitedtocoding,butassistswithprocessesliketestinganddocumentation.

•ApplydatameshanddataproductthinkingtoensureAIapplicationsarebuiltontherobustdatafoundationneededtoensuretheydeliverbusinessorcustomervalue.Disciplineslike

datacuration,whichcreates,organizesandmanagesdatasetssothey’retransparentandeasilyaccessible,alsocontributetothesuccessofAI.

•UseproxiestosimplifythewayteamsinteractandleverageAImodels,pavingthewayfortheenhancementofapplicationstheydevelopwithAIfeaturesandcapabilities.

OperationalizingAIforbusinessimpact

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Thingstoconsider(Analyze)

•Avoidwhat’sknownasthe‘substitutionmyth’—theideathatAIcansimplydirectlyreplacea

human.Instead,buildandimplementsystemsthataugmentrolestomaketeamsmoreproductiveandengaged,whileacknowledgingthecontinuedimportanceofhumanjudgementandoversight.

•BecognizantofvariedexpectationsaroundAI.ResearchsuggestspeoplemayapproachAIdifferentlydependingonculturalbackground,withsomewantingahighdegreeofcontrolandothersprioritizingasenseofconnection.Thesedifferences,aswellasvariancesincontextorsituation,needtobeunderstoodandacknowledgedwhenplanningandimplementingAI.

•Paycloseattentiontocosts,andtrytoidentifytheapproachesmostlikelytomeetyourneeds

whilegeneratingreturnoninvestment.RunningAImodelscanbeexpensive,especiallyifexpenseslikeemployeecompensationarefactoredin.Keepingspendingincheckrequiresactivefinancial

monitoring(i.e.FinOps)andconsiderationofthingslikesmalllanguagemodels.

•MonitorAIregulationandfuturepolicydevelopments,particularlyhowtheseintersectwith

privacylaws,whichcouldhaveamassiveimpactonthedataresourcesavailableforAIprojects.MultipleUSstates,andcountriesfromCanadatoIndiaandJapan,areplanningtoenhanceorrolloutlegislationthatwillsetguardrailsaroundAIuseanddevelopment.

Thingstowatchfor(Anticipate)

•QuestionsaroundlegalliabilityandaccountabilityforthenegativeconsequencesofAIuse.AsissuessuchasAImisleadingcustomersandtheassociatedlegalchallengesemerge,authoritiesliketheEUaremovingtomakeorganizationsmoreculpable.

•ThepotentialgrowthofAIcompanions,designedtoprovideemotionalsupport,friendshiporevenintimacy.Whilethesecouldhelpcombatlonelinessandisolation,theymayalsohavetroublingimplicationsforhumaninteraction,requiringbusinessestothinkcarefullyabouttheintroductionofAIwithcompanion-likefeatures.

©Thoughtworks,Inc.AllRightsReserved.11

Strengtheningthedatavaluechain

LeveragingdataplatformsandAI

AsenterpriseadoptionofAIgainspace,there’srisingawarenessofdata’sroleasadifferentiator,andasourceofcompetitiveedge.Developingthecapabilitiestoleveragedataatspeedandscale,and

becometrulydata-driven,hasbecomeanemergingpriority.Treatingdataasaproductrepresentsoneofthemosteffectivemeanstoachievethisgoal,andthebestwaytobuildanddistributedataproductsisthroughdataplatforms.

Theprinciplesthatunderpinhigh-performancedataplatformsremainthesame—decentralization

andfederateddataownership—butnewtrendsandopportunitiesinthespacearepresenting

challengesthatorganizationsneedtobepreparedfor.Inparticular,theriseofgenerativeAI(GenAI),andtheimportanceofunstructureddatainit,requiresteamstothinkdifferentlyabouthowdatais

managedandprocessed.It’sbecomingcriticaltotreatunstructureddataasafirstclasscitizen,notasstructureddata’spoorercousin.

It’salsoimportanttonotetherisingneedforbetter—andideallyautomated—governanceofdataproducts.

Dataproducts—reusabledata

assetsengineeredtodeliver

trusteddatasetsforspecific

purposes—existindynamic

environmentswheretheneedsofteamsandthewiderorganizationareconstantlyevolving,andit’s

importantthattheyalsodevelopinawaythatdeliversvalue.

Maintainingthecapacityforcompetitiveandsustainablechangerequiresintentionaldesign

ofcohesivecentralizedanddecentralizedcapabilities.Someorganizationsarenavigatingawayfromcreatingconsensus-based‘singlesourcesoftruth’toformingintegrated‘contextualtruths’.

©Thoughtworks,Inc.AllRightsReserved.12

Strengtheningthedatavaluechain

Equallyessentialisensuringdataproductsarebuiltwithaclearlinetobusinessadoption.Platformandproductthinkingcanhelp,butthere’saneedtomovebeyondexistingparadigmsandtooling,andconsiderapplyinghuman-centereddesignformoreeffectivewaysfordatatobeconsumed

andleveragedbybusinessusers.GenAIandtrendslike‘talktodata’andgraph-baseddiscoveryarecreatingpromisingopportunitiesinthisspace,transformingthewayteamsinteractwith

andconsumedata.

“AnopenandevolvingdataandAIplatformallowsorganizationstoembraceuncertaintyinrhythmwithchangingdemands,fosteringacultureof

continuouslearning.”

NimishaAsthagiri

TechnicalPrincipalandDataMeshLeader,Thoughtworks

Signals

•Unstructureddatamovingfromasupportingtoastarringrole.

There’sgrowingfocus

onthe

useofunstructureddata(suchastext,video,imagesandaudio)tobuildbetterAItrainingmodels,whichrequiresintegratingandworkingacrossdifferenttypesofdatainasfrictionlessawayas

possible.

Startupsinthisspacearegainingsignificantinvestment

andthelikesofIBMareunveiling

newproducts

specificallydesignedtohelpenterprisesunleashthepotentialofunstructureddatainanalyticsandAI.

•EnterprisesapplyingGenAItobetterleverageunstructureddata.GenAI’sabilitytoparseandsummarizevastquantitiesoftheinformationcontainedineverythingfrommeetingrecordingstoPowerPointpresentations,andtosupportnaturallanguageinteractions,is

transformingthe

wayteamsaccessandusedata

andenhancingknowledgemanagement.However,thistrendisalsoraisingquestionsastowhetherAIandGenAIplatformsshouldbeintegratedwithotherdataplatformsorkeptdistinct,which,insomecases,isleadingtoplatformproliferation.

•Moreorganizationsgrapplingwiththechallengesoftreatingdataasaproduct,asitbecomesabusinessimperative.

Researchshowsthevastmajorityofbusinessesseeclearbenefits

fromsuchanapproach,includingimproveddatasharingandstrengtheningtheconnectionbetweendataandbusinessgoals.However,theyareconfrontingmultiplebarriersalongtheway,fromfragmented

systemstouncertaintyaboutdataprovenance.

•Therisingimportanceofdatadiscoverability.Byempoweringuserstobetterdiscover,

understandandusedataassets,datacatalogscanplayanimportantroleindataplatforms

andadataproductapproach.Buttheycanalsocausemoreissuesthantheysolveiftheiruserexperiencesorcapabilitiesarelimited,impedingthediscoveryprocess.Therecent

introduction

ofknowledgegraphs

todataplatformsisaddressingtheserisks,makingitpossibletodrawoutrelationshipsandnuancesindatathataretypicallylostintheprocessofabstraction.

•Morepressurebeingputondatateamsto

demonstrateROIandmanagecostsmoreeffectively

.

Theincreasinglyestablishedlinkbetweendatastrategyandenterpriseperformance

alsomeanstheseteamscannolongerworkinisolation;insteadstrategiesshouldbeco-developedwith,

andcreateplatformsthatdeliverresultsfor,thebusiness.

©Thoughtworks,Inc.AllRightsReserved.13

Strengtheningthedatavaluechain

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AnticipateAnalyzeAdopt

Strategicrecommendation

Seeingnow

Adopt

1.AIasaservice

2.Automatedcompliance

3.Collaborationecosystems

4.Datacatalog

5.Datafitnessfunctions

6.Datamesh

7.Dataproductspecification

8.Developerexperienceplatforms

9.Digitaltwin

10.Edgecomputing

11.Ethicalframeworks

12.ExplainableAI

13.FinOps

14.Greencomputing

15.IntegrateddataandAIplatforms

16.Knowledgegraphs

17.MLOps

18.Modeltrainingoptimization

19.Onlinemachinelearning

20.Platformsasproducts

21.Privacyfirst

22.Privacy-enhancingtechnologies(PETs)

23.Securesoftwaredelivery

24.Smartsystemsandecosystems

25.Vectordatabases

Analyze

26.Autonomousrobots

27.Autonomousvehicles

28.Datacleanroom

29.Datamarketplaces

30.Decentralizeddataarchitectures

31.Federatedlearning

32.Semanticrepresentationaltechnologies

33.Syntheticdata

Anticipate

34.Understandableconsent

Beginningtosee

Adopt

35.AI-readydata

36.Datacontract

37.Finegraineddataaccesscontrols

Analyze

38.Datalineage

39.Integrating

unstructureddata

40.Intelligentmachinetomachinecollaboration

41.Talktodata

Anticipate

42.Decentralizedpersonaldatastores

Onthehorizon

Adopt

Analyze

Anticipate—

Strengtheningthedatavaluechain

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Theopportunities

Bygettingaheadofthecurveonthislens,organizationscan:

ConsolidatedataandAIplatformcapabilities,enablingAIasaservicetoembedthis

newtechnologyandempoweruserstoleverageitsuccessfullythroughouttheorganization.SurveyshaveshownthatdespiteconcernsaboutthewiderimpactsofAI,adoptionhas

positiveimplications

forteams’collaboration,efficiencyandperformance.

UseAI(andGenAI)tobuildandmaintaindataproductsmoreeffectively.EmergingAItoolshavethepotentialtocontributetodataproductsina

numberofways

,fromsynthesizingandanalyzinginformationgarneredinend-userresearchortesting,toacceleratingcodingand

creatingdocumentationthatcansmooththepathtoeffectiveadoption.

Enhancecontrolovercosts.Withdatamanagement

oftendominatingenterprisetechnology

spending

,introducingnewtoolingtotrackdatalineageandanalyzetheimpactofcomplex

datainitiativescanhelpteamsdetermineanddemonstrateROIwithgreaterprecision.

FinOps

thinking

cancontributesignificantlytothisprocessbystrengtheningthelinksbetweentechandbusinessteamsandensuringinvestmentscomewithfinancialaccountability.

Strengthendatagovernancebyintroducingemergingbestpracticesandstructures.Theseinclude

datacleanrooms

,secure,self-containedenvironmentswhereenterprisescanblendproprietaryandthird-partydatatoimproveanalyticsandpersonalizationwhileprotecting

customerprivacy;and

datacontracts

,whichbysettinggroundrulesfordatausersand

consumers,canimprovetransparencyandtrustwhensharingdataacrossanorganization.

CombineknowledgegraphsandGenAI,whichcanenhanceunderstandingoflarge,

complexdatasetsbymappingtherelationshipsamongentitieswithinthem.Thisopens

thepossibilityofmoresemanticapproachestointegration,whichinturncanhelpcreate

abetteruserexperiencefordataconsumers.Inaddition,combiningknowledgegraphsandGenAIcanalsodeliverbetterLLMresponsesbecausewe’retakingexplicitknowledgefromknowledgegraphsandcombiningitwithimplicitstatisticalknowledgefromLLMs.

©Thoughtworks,Inc.AllRightsReserved.15

Strengtheningthedatavaluechain

Whatwe’vedone

Pfizer

Thoughtworksisworkingactivelywiththeseleadingpharmaceuticalcompaniestocreatedatameshplatformsthatenhancetheirabilitytocreateanddelivertransformativedataproducts.WithPfizer,

wehelpeddevelopcutting-edgelayeredplatformsservingAI-powereddataproducts,graph-basedsemanticinteroperability,andLLM-basedagentsthatdrivethefirm’soncologyresearch,supportingearlydrugdiscovery.

Gilead

ForGilead,wesupportedthedesignandimplementationofGileadDnA,ascalableenterprise-widedataplatformthatprovidesdataengineersandresearcherswithasecureself-serviceenvironmentfordataprocessing,completewith‘talktodata’functionality.

Strengtheningthedatavaluechain

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Actionableadvice

Thingstodo(Adopt)

•Laytherightfoundationsforcreatingeffectivedataproductsbyimplementinga

datamesh

,

whichplacesdatawithinthereachofteamsthatneeditmostandreducesfrictionbetweendataproducersandconsumers.

•Automatedatagovernanceasmuchaspossibletoensurepoliciesareimplementedconsistentlyandwithminimalimpactondatausageandconsumerexperience.

Fitnessfunctions

andmore

rigorousmonitoringofservicelevelindicators(SLIs)canbegoodplacestostart.

•Starttreatingunstructureddataasafirstclasscitizenthatisgiventhesameattentionand

prominenceasstructureddatainyourdataplatform,anddrawonitspotentialtoimproveanalyticsandAImodels.

•Investinasuperiordataproductdevelopmentexperiencetoaccelerateadoption.Mapping

decisionjourneyscanhelptheorganizationbetterunderstandandtracehowtomovefromusecases