Snowflake：2025年数据与人工智能终极行业指南：组织如何抢占AI风口 驱动业务价值新增长 The Ultimate Guide to Data + AI for Industries

THEULTIMATEGUIDE

TODATA+AIFOR

INDUSTRIES2025

HoworganizationscanleverageAIrightnowtodrivebusinessvalue

ULTIMATEGUIDETODATA+AIFORINDUSTRIES2025TableofContents|2

TABLEOFCONTENTS

INTRODUCTION 3

HOWAICANPOWERSUCCESSINSEVENINDUSTRIES 8

FINANCIALSERVICES 9

MEDIAANDENTERTAINMENT 11

HEALTHCAREANDLIFESCIENCES 13

PUBLICSECTOR 15

RETAILANDCONSUMERGOODS 17

MANUFACTURING 19

TELECOMMUNICATIONS 21

NEXTSTEPS 23

ULTIMATEGUIDETODATA+AIFORINDUSTRIES2025Introduction|3

INTRODUCTION

AIishavingitsmoment.Organizationsfromeverysector—fromfinancialservicesbehemothsandcitygovernmentstobigboxretailersandresearchhospitals—arescramblingtofigureouthowbesttocapitalizeontheenormouspotentialofthistechnology,particularlyadvancementsingenerativeAI.

Accordingtoarecentsurvey,80%of

Fortune500companies

saytheyhavegenAIpoliciesandstrategiesintheworksandplantooperationalizegenAIoverthenextthreeyearstoboost

employeeproductivity,improvecustomerserviceorautomatemanualprocesses.

ButwhileseeminglyeveryoneistalkingaboutAI,noteveryoneisusingit.Infact,onaverage,AIadoptionintheU.S.isstillrelativelylow.AccordingtotheU.S.CensusBureau’sBusinessTrendsandOutlookSurveyAISupplement,anaverageofonly

3.8%

oforganizationsreportcurrently

usingAItoproducegoodsandservices,though

6.5%

saytheyplantoadoptitwithinsixmonths.Adoptionratesrangewidelydependingontheindustry.About18%ofinformationservices

companiesreportedusingAI,comparedto7%forfinancialservicesand5%forhealthcare.Otherindustrieslagevenfurtherbehind,suchasretail(3%)andmanufacturing(3%).

Butthatmaysoonchange.Inthenextfewyears,manyorganizationsnowintheexperimentationphaseplantorolloutnewAIusecases,specificallyforgenerativeAI,citingthepotentialfor

significantreturns,thecompetitivepressuretoinnovateandtheincreasingmaturityofAItechnologies,accordingtothe

HarvardBusinessReview

.

ThepotentialusesforandvalueofAIarevast,spanningvirtuallyeverymajorindustry.Inthefollowingpages,wewillexploreamyriadofwaysorganizationsinarangeofindustriesare

leveragingdataandAItodrivesuccess.

ULTIMATEGUIDETODATA+AIFORINDUSTRIES2025Introduction|4

Herearejustafewexamples:

•Healthcare:Analyzevastamountsofpatientdatatouncoverpatternsandpredictoutcomes,leadingtomoreaccuratediagnosesandpersonalizedtreatments.

•Financialservices:Scanvolumesofmarketdatatoidentifytrendsandgenerateinvestmentdecisions,maximizingreturns.

•Retail:Transformcustomerdataintopersonalizedshoppingexperiences,enhancingcustomersatisfactionanddrivingloyalty.

•Publicsector:Predictdiseaseoutbreaksanddisasterimpactandhelpquicklyandaccuratelydeployemergencyservicestothoseinneed.

•Manufacturing:Detectunusualpatternsanddeviationsinproductionanduse

AI-drivenvisualinspectionsystemstoidentifyqualityissuesandproductdefects,enhancingqualitycontrol.

•Advertising,mediaandentertainment:Extractinsightstoidentifycustomer

behaviors,sentimentsandtrendsandcreatehighlypersonalized,timelyexperiencesforaudiences.

•Telecommunications:Solveandpredictnetworkissuesandservicedisruptionsto

improveservice,reliabilityandoperationalefficiencyandproactivelywarncustomers.

However,despiteitsseeminglyboundlesspotential,theAIadoptionjourneyisnotwithoutits

challenges.Industriesmustnavigatetheconsiderableandlargelyunchartedgovernance,securityandethicalconsiderationsthatcomewithit—nottomentionorganizationalhurdles,dataissuesandthecomplexitiesofthetechnologyitself.

AIADOPTIONVARIESWIDELYBYINDUSTRY

Source:AnalysisofdatafromtheU.S.CensusBureau’s

BusinessTrendsandOutlookSurveyAISupplement

ULTIMATEGUIDETODATA+AIFORINDUSTRIES2025Introduction|5

4KEYCHALLENGESOFGENERATIVEAI

IntherapidlyevolvinglandscapeofgenAI,theinitialaweandskepticismsurroundingits

capabilitieshavegivenwaytoapressingbusinessimperative.Companiesarenowracing

toadoptthetechnologytoenhanceworkforceproductivityandprofitability.However,thepathtoimplementingeffectivegenAIsolutionsisfullofobstacles.Estimatessuggestthat

upto80%

ofAIprojectsfailtoreachproductionandreal-worlduseduetoreasonssuchas:aninabilityto

demonstrateAIvalue,lackoftalentandskills,insufficientdata,misalignmentwithbusinessgoals,andlackoftrustinAIgenerally.

Inarecentinterviewwith

DataCloudNow

,GouthamBelliappa,ManagingDirectorofStrategyandAnalyticsatDeloitte,highlightedfourkeyconsiderationsforbusinessesnavigatingthe

genAIterrain:

Datastrategy:Belliappaemphasizesthecriticalneedforaholisticdatastrategy,assertingthatthe

fragmentednatureofthedatamarketrequiresorganizationstomanagedatainfluxontheirown

terms.AIneedsareliable,continuousstreamofvarieddatatodeliveraccurateinsightsandeffective

solutions.Butorganizationsstrugglewithdataqualityanddatasilosduetolegacysystems.Theyalso

needarobustinfrastructureandstoragesolutiontomanageandprocessthemassiveamountsofdata

required.Awell-defineddatastrategyshouldbethecornerstoneforoverallbusinessstrategies,

prioritiesandinvestments,preventinghastyandpotentiallymisguidedspendingongenAIcapacities.

TaIentandimpIementation:It’salsoimportantfororganizationstofocusontalentacquisitionanddevelopmentinthegenAIeratoguidegenAItowardproducingvaluablecontent.Manyleaders

acrossindustriesfacedifficultiesinhiringandtrainingteamstoimplementAI,andtheymayneedtorestructureteamstoalignwithAIinitiatives.EvenafterovercomingthesechallengestolaunchAI

solutions,organizationsoftenstruggletofullyimplementthem.

Alfluencyandtrust:OrganizationsalsoneedtounderstandAIandbuildtrustinitsapplications.WithgenAI’spotentialtodisruptvariousbusinessfunctions,theneedforfluencyinusage

becomesparamount.

Deloitte

isonecompanythatoffersfluencycoursestoaddressthis

knowledgegap.BuildingtrustinAIisalsoimportant,whilealsoacknowledgingthepotentialbiasesintrainingdataandtheethicalimplicationsofusingAImodels.

Security,governanceandcompIiance:OrganizationsmustsafeguardthedatausedinAI

modelsandapplications,keepingitsecureandincompliancewithprivacyregulations.Doingsorequiresrobustencryption,accesscontrols,continuousmonitoringtoprotectsensitive

information,andastronggovernanceframeworkforcomplianceanddataintegrity.BelliappacallsforethicalgovernanceandregulationtosafeguardagainstthemisuseofgenAIand

underscorestheimportanceofnotcompromisingtrustandpublicsafetyfortheadvantagesofhigherworkforceproductivity.

OvercomingthesechallengesiscrucialforthesuccessfuldeploymentofAIprojects.Leaders

inallsectorsmustprioritizebuildingrobustandsecuredatainfrastructuresanddevelopinga

foundationaldatastrategythatensuresdataquality,accessibilityandcompliance.Byaddressingtheseissues,businessescanunlocksignificantvaluefromAItoincreaserevenue,reduce

operationalcosts,boostproductivityandimprovecustomerexperience.

ULTIMATEGUIDETODATA+AIFORINDUSTRIES2025Introduction|6

SNOWFLAKE:THEPOWEROFDATA+AI

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DeveloperscanbringAImodels,frameworksandapplicationsdirectlytotheirdata,

eliminatingthetimeandriskassociatedwithdatatransfers.UserscanseamlesslyintegrateAIintotheirusecasesusingno-code,SQL,

PythonorRESTAPIinterfaces,enablingabroadrangeofteamstointegrateAI

intotheirworkflows.AndSnowflakehasbuilt-ingovernance,accesscontrolsandsafetyguardrails.

Onceamoderndatafoundationand

unifiedplatformforAIandmachinelearningisinplace,Snowflake’srobustnativeAIandMLcapabilities—alongwithanextensive

partnerecosystem—canhelpcustomers

harnessthepowerofgenerativeAI.

SnowflakeCortexAIoffersLLMfunctions,

universalsearch,DocumentAI,no-code

modeldevelopmentandmore.Together,

thesecapabilitiesensurefasterdeploymentandsimplermaintenanceofAIinfrastructureandLLMs,improvedperformance,cost

savings,and,ultimately,aquickerandgreaterreturnoninvestmentinAI.

AtthecoreofasuccessfulAIstrategyisa

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strongenterprisedatafoundation.With

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acrossindustriesareeliminatingthedatasilosoflegacysystemsandgainingtheabilityto

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seamlesslycollect,shareandapplyadvancedanalytics.CustomerschooseSnowflake

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BuildingandmanaginganAIstackandLLMscanbecomplicated.Theyrequiresubstantialcomputeresourcesandlarge-scalestorage,makingtheset-upandmanagementofAI

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CHATAPIs

OPTIMIZATION

PERFORMANCE

Snowflake’sarchitecturesimplifiesallthat,inseveralways.Itsunifiedplatformisfullymanaged,eliminatingtheneedtoinvestinandmaintainacomplexAIinfrastructure.Snowflakeallowsforseamlessscalingof

computationalresources,accommodatingthedynamicneedsofAIworkflows.

ULTIMATEGUIDETODATA+AIFORINDUSTRIES2025Introduction|7

TRANSFORMINGBUSINESSFUNCTIONS

ACROSSINDUSTRIES

WithAIcapabilitiesatopastrongdatafoundation,organizationsin

everyindustry—whetheraretailstore,hospital,governmentagency,bankorenergycompany—canradicallyoptimize

essentialbusinessandoperationsfunctions.Accordingto

the

HarvardBusinessReview

,mostbusinessfunctionsand

morethan40%ofallU.S.workactivitycanbeaugmented,

automatedorreinventedwithgenAI.HerearejustafewwaysthatAIcantransformcorebusinessfunctionsacrossindustries:

Marketing:GenAIcananalyzecustomerdatatoidentify

patternsandpreferences,somarketerscancreatepersonalizedcampaignsandrecommendations.Itcanalsopowerpredictiveanalytics,helpingmarketersforecastfuturetrendsand

behaviors,andthusoptimizecampaignsandallocateresources.AI-poweredleadscoringcanimprovelead-to-meeting

conversion,improvesalesconversionratesandacceleratenetnewrevenue.Otherusecasesincludepipelineforecasting,

audiencesegmentation,marketingattributionandmore.

Finance:FinancedepartmentscanleverageAIandML

capabilitiestopowercorporateplanningandfinancial

forecastingandautomatefinancialoperations.LLM-poweredfeaturescanautomatethecontractreviewprocessfor

documentslikeorderformsandsalesagreementsreviews,

whichcansaveteamstimeandacceleratesalescyclesaswellashelpensurecontractcompliance.

Humanresources:AI-poweredemployeeassistantscanprovidepersonalizedsupporttoemployees,offeringnear-instant,

relevantinformationbasedonaninternalknowledgebase.

AIhiringassistantscanhelphiringmanagerscreatestandardjobdescriptions,identifyqualifiedcandidatesbasedonjob

descriptionmatch,generateinterviewkitsandspeedup

resumescreening.Overall,AIcanoptimizehiringprocesses,increaseproductivity,andimproveboththecandidatehiringexperienceandtheemployeeexperience.

IT:GenAIandMLcanhelpITteamsoptimizesoftwarelicensesandreduceSaaSspendwhiledrasticallydecreasingmean

timetoresolve(MTTR)forIToperationsandrequesttickets.QAAIassistantscanhelpdevelopersandbusinessanalysts

generatetestcasesforsoftwaredevelopmentquicklyandatscale,savedeveloperstimeandimprovetesting.CloudOpsAIassistantscanofferteamsfast,relevantinformationbasedonaninternalknowledgebasetoimproveoperationalefficiencyandproductivity.

Sales:SalesteamscanuseautomatedBItoperformanalytics

usingnatural-languageprompts.Customersuccessassistants

canusecallnotesandemailstouncovercross-sellingand

upsellingopportunities.Text-processingcapabilitiescanprovidesummarizationandsentimentanalysisofcalltranscripts.

Customerservice:AI-poweredchatbotsandconversational

assistantscanhandlecustomerinquiries,providesupportandresolveservicetickets24/7,leadingtoimprovedcustomer

satisfactionandreducedoperationalcosts.GenAIcancreatepersonalizedresponsesandrecommendations,enhancingthecustomerexperience.

Product/servicedevelopment:AutomatedBIcananalyze

largedatasetstouncoverinsights,trendsandpatternsthat

caninformdecision-makingonfeatureadoption.Product

knowledgeassistantscanusedesignwrite-ups,documentationandinternalresearchtomakerecommendationsfornew

productsandservices.

64%ofbusinesses

believethatAIwillhelpincreasetheiroverallproductivity—ForbesAdvisor

Next,we’llexploretheseandotherusecasesindepthacross

sevenindustries:financialservices;advertising,mediaand

entertainment;healthcareandlifesciences;publicsector;retail;manufacturing;andtelecommunications.We’llalsodiscover

howcustomersandpartnersareusingSnowflaketounlockthepowerofAIfortheirorganizations.

HOWAICANPOWER

SUCCESSIN7INDUSTRIES

ULTIMATEGUIDETODATA+AIFORINDUSTRIES2025FinancialServices|9

FINANCIALSERVICES

FromATMwithdrawalstostockpurchases,nearlyeveryfinancialtransactionconsumersmakecreatesdatathathasthepotentialtoinformfinancialinstitutions—todevelopbetterbusiness-criticalworkflows,enablemoregranularquantitativeresearchandimprovecustomeranalytics.

Butjustasvaluableistheapproximately80%ofdataheldby

financialservicesorganizationsthatisunstructured,intheformofemails,contracts,patentfilingsandtranscriptsthatonly3%ofcompaniesaremakinguseof.Evenweathertrendsanduserreviewscanbeatreasuretroveofinsightsthatgivebanksor

assetmanagersacompetitiveedge.

ThatiswhygenAI’sabilitytoextractvaluefromthiskindof

unstructureddataissopowerfulforthefinancialindustry,

particularlyinbanking,insuranceandcapitalmarkets.By

quicklyidentifyingsubtlesignalsandminimizingmanualerrors,AIcanhelpteamsautomatedataprocessingandfocuson

strategicdecision-makingandcustomerengagement.

HerearethreeofthemanywaysthefinancialservicesindustrycandrivebusinesssuccesswithAI:

ENHANCEDPORTFOLIOANALYTICS

ANDASSETSERVICING

Institutionalinvestorsrequiresophisticatedanalyticson

theirportfoliostoguidecriticaldecisionssuchassecurity

selection,rebalancingandoptimization.WithAI,investors

canusenaturallanguagetoquerydataassetsandgain

actionableinsights.Conversationalassistantscanuseportfoliowarehouses,ordermanagementsystems,riskenginesand

third-partydatatoforecastmarkettrends,optimizeportfolioallocationsandimproverisk-adjustedreturns.Additionally,

machinelearningmodelscanadapttochangingmarket

conditions,providingagilityandresponsivenessinarapidlyevolvinginvestmentlandscape.

ULTIMATEGUIDETODATA+AIFORINDUSTRIES2025FinancialServices|10

MARKETINGCAMPAIGNOPTIMIZATION

Marketersinthefinancialsectormustwalkafineline

betweendeliveringultra-personalizedclientexperiences

whilealsorespectingcustomerprivacy(andcomplying

withfinanciallaw).AIcanhelpbyanalyzingcustomerdata,transactionhistoriesandbehavioralpatternstoprovide

tailoredrecommendationsforspecificfinancialsegments.

ConversationalAIassistantscananalyzetheperformance

ofmarketingcampaignsinrealtime,suggestingadjustmentstomaximizeROI.Theycanalsoanalyzethird-partyfinancialdatatoforecastfuturecustomertrends,helpingmarketingteamsplanandexecutemoreeffectivecampaigns.

EFFICIENTCLAIMSMANAGEMENT

Siftingthroughthemanytypesofdatarequiredtoprocess

insuranceclaims—likewitnessstatements,policydocuments,dashcamfootageoremergencyservicesrecordings—ishighlymanual,time-consuminganderror-prone.InsurancemanagerscansavetimeandexpensebyusingAI-poweredtools,such

astextprocessingandchatbots,toquicklyaccessandquery

data.Thesecapabilitiescanbeappliedfromfirstnoticeofloss(FNOL)throughouttheclaimlifecycletoenhanceoperationalefficiency,lowercostsandleadtofasterclaimsresponses,

ultimatelyimprovingthecustomerexperience.

80%ofdataheldbyfinancialinstitutionsisunstructured,butonly3%ofthemareusingit.—FintechFutures

CUSTOMERSUCCESSSTORIES

StateStreetacceleratesinvestmentinsightsbybuildingAI-enhancedalphadataplatformonSnowflake

WithaSnowflake-powereddatafoundationthatoffers

nativedatasecurityandgovernance,StateStreethas

beenabletoexplorepotentialAIusecasesforitsdata

platform.OneoftheStateStreet-builtdeeplearning

modelsfocusesondataqualityandanomalydetection.

Themodellooksatdifferentaspectsofaportfoliotoseeifthereareanymarketvalueissuesthatananalystmightneedtoverify.Thishappensinararecircumstancewheninvestmentdataiswrong,whichcanhaveabigimpact

onriskmanagementmodelsorportfolioevaluations.

Typically,manualthreshold-basedfiltersarecreatedto

detectanomalies,resultinginalargenumberoffalse

alertsthatrequiremanualinvestigation.StateStreet

appliedAImodels,whichlearnedtobecomemore

efficientatremovingfalsepositivealerts.Theydetected100%oftrueexceptionsandeliminated87%offalse

positives—resultingina25xproductivitygainfordataoperationsteamswhopreviouslyhadtoinvestigateeachexceptionasapotentialerror.

Readthefullstory

TSImagineadoptsgenAIatscaleandsaves4,000hoursofeffortannually

TSImaginedeliversaSaaSplatformforintegrated

electronicfront-officetrading,portfoliomanagementandfinancialriskthathelpsmorethan500globalfinancial

institutionsbettergenerateandprotectassetswithin

today’sfast-evolvingmarkets.Everyyear,membersof

thedatamanagementteamwouldspendthousandsof

hoursreadingmorethan100,000emailstomonitorfor

notificationsfromtheirdataproviders.Thesecritical

emailsdetailedupcomingproductchanges,anyofwhichcouldultimatelyimpactTSImagine’sclients.Missingevenasingleemailcouldleadtoadownstreamproductoutage—andveryunhappycustomers.WithSnowflakeCortexAI,TSImaginehasautomatedemailintakeandsaved

employeesmorethan4,000hoursayearonanerror-

pronesortingprocess.Now,CortexAIdeletesduplicateornonrelevantmessages,andcreates,assigns,prioritizesandschedulesJiraticketsforeachemail.Theteamhasn’tmissedanotificationsinceimplementingthenewprocessinDecember2023.

Readthefullstory

ULTIMATEGUIDETODATA+AIFORINDUSTRIES2025MediaandEntertainment|11

MEDIAANDENTERTAINMENT

Theadvertising,mediaandentertainmentindustriesare

undergoingsignificanttransformationduetoongoing

advancementsintechnologyandevolvingconsumerbehaviors.Asstreamingservicesandsmartdeviceshaveevolvedand

proliferated,audienceshavecometoexpecton-demand,

personalizedcontentanytime,anywhere.Tostaycompetitive,industryleadersmustnavigatealandscapecharacterizedbyrapidinnovation,diversification,regulationsandtheneedfordata-drivenstrategies.

Mediacompaniesareaccustomedtostayingaheadofthe

curve.TheseearlyadoptersofadvancedtechnologyhavebeenusingAIandmachinelearningforyearstopowertargeted

advertising,sentimentanalysisandenhanceduserexperiences.Theycontinuetousehigh-performanceAIandMLcomputingenginestoseamlesslymanagebillionsofonlineinteractions

eachday,analyzecustomerbehaviorpatternsanddetermine

howcontentresonateswithsubscribers.AndindustryleadersareadoptingadvancedgenerativeAIsolutionstogaina

competitiveedge,drivingestimatesthattheglobalAImarketinmediaandentertainmentwillbeworth

$196billionby2033

.ByusingAItoanalyzevastamountsofdataandautomate

processes,companiesareenhancingdecision-making,creatingmorecompellingandpersonalizedcontent,andoptimizingthemediasupplychain.

Herearethreeofthetopwaysadvertising,mediaand

entertainmentcompaniescangainacompetitiveedgewithAI:

PERSONALIZEDEXPERIENCES

Creatingbespokecustomerexperiencesisacrucialcompetitiveedgeintoday’scrowdedmedialandscape.Byusingnatural

languageprocessingalgorithmstoextractinsightsfromtextualcontent,suchassocialmediapostsandemails,companiescanswiftlyidentifycustomerbehaviors,sentimentsandtrends.Thiscomprehensiveanalysisempowerscompaniestocrafthighly

targetedandrelevantcontentmorequickly,aswellasfine-tunecontentrecommendations.Asaresult,eachcustomerenjoysamoreengagingandsatisfyingexperience,leadingtoincreasedcustomerloyalty.

ADVERTISINGREVENUEOPTIMIZATION

Optimizingadspendisaperpetualchallengeforbrand

advertisersandadagencies.GenAIhasthepotentialtoboostadrevenuebyenablingprecisetargetingandpersonalized

campaignsthroughthethoroughanalysisofvastconsumer

data.Thistechnologyallowsadvertiserstocustomize

messagingtoindividualpreferencesanddemographics,

ensuringadsarehighlyrelevantandengaging.Additionally,genAIcanautomateadcreationandstreamlineprocesses,reducingcostsandfacilitatingthedeliveryofeffectiveadsatscale.

ULTIMATEGUIDETODATA+AIFORINDUSTRIES2025MediaandEntertainment|12

IPANDASSETPROTECTION

Safeguardingintellectualproperty(IP)andassetsisvital

forpreservingtheworkandreputationsofartists,creatives,

brandsandcompanies.Usingadvancedalgorithms,genAIandmachinelearningcanmonitordigitalplatformsanddistributionchannelstodetectunauthorizeduseofIPrightsinnearreal

time.Thisallowscompaniestotakeproactivemeasuressuchasissuingtakedownnotices.Additionally,itempowerscompaniestodevelopcomprehensivedigitalrightsmanagementsolutionstosecurecontentdistributionandmonetizationtohelp

combatcostlypiracy.GenAIcanalsoenhancetraditionalassetprotectionmethodsbyanalyzingpatternsindigitalcontent

tohelpidentifycopyrightinfringement,plagiarismandcontentmanipulation.

TheAImediaandentertainmentmarketwillbeworth

$196billionby2033

.

—Market.us

CUSTOMERSUCCESSSTORIES

SYSTEM1

System1improvesteamefficiencywithSnowflakeCortexandSlackbotsolution

System1,aleaderinAI-drivendigitalmarketing,struggledwithsiloedinformationandinefficientcommunication

acro