2026企业AI终极实操手册十大提升投资回报率的人工智能落地方案

10EnterpriseDeploymentsBoostingROI2Introduction•FromExperimentationtoEnterpriseImpact:TheAIInflectionPoint•TheShifttoRealDeployment•PartnerPerspectives•OvercomingAI’sRealityCheck3456•#1.MitsubishiElectricxKametAI•#2.IvoclarxbitHuman•AIUseCaseinRealEst•#3.KajimaxArchetypeAI•AIUseCasesinEnte•#4.UnisysxParloa•#5.UnisysxFreshworks&EasyVista•AIUseCasesinInsurtech•#6.AflacxLazarus•AIUseCasesinTravel&Hos•#8.TUIxParloa•#9.iGAİstanbulAirportxHummingdrone•#10.iGAİstanbulAirportxDELIVERS.AI•OurGlobalAIPresence•AICentersofExcellence30•AIInvestmentHighlights•AIFocusAreaHighlights3Introduction2026ispoisedtobethepivotalyearforenterpriseAI,markingthetransitionfromexperimentalphasestofull-scaleoperationaldeployment.Thisshiftdemandsaclear-eyedapproachthatmovesbeyondthehypetotackletherealworkofintegration.AsScaleAI’sCFO,DennisCinelli,notes,AIisn’ta“magicwand,”itrequiressignificantefforttodelivervalue.Thisebookservesasaproof-basedguide,leveragingreal-worldcasestudiesfromourpartnerstoillustrateause-case-drivenmethodology.Wewillexploretheprimarychallengesenterprisesface,fromdatareadinesstotalentgaps,andprovideaclearpathtoachievingmeasurableROIthroughstrategicAIimplementation.WhatmakesthisplaybookdifferentisthatthesecasestudiescomedirectlyfromPlugandPlay’spartnerecosystemandstartupnetwork.Eachcollaborationfeaturedherewasenabledthroughourplatform,connectingenterpriseswithstartupsfromourglobaldatabaseacrossindustries,includingmanufacturing,insurance,aviation,construction,andrenewableenergy.WehaveseenfirsthandhowAImovesfrompilottoreal-worldimpact,andthepatternsareclear.Successfulorganizationsfocusonsolvingdefinedoperationalproblems,aligningcross-functionalteamsearly,andscalingwhatprovesmeasurablevalue.TheexamplesinthisplaybookreflectAIimplementationinactionacrossmultiplesectors,offeringpracticalinsightgroundedinexperienceratherthanspeculation.TheorganizationsfeaturedinthisplaybookareusingAItosolvemeasurablebusinessproblems,reducerisk,improveefficiency,andbuildlong-termcompetitiveadvantage.4Foryears,organizationshavebeeninastateofexploration,runningpilotprogramsandproofofconceptstounderstandwhatAIcando.Now,theexpectationhasshifted.Stakeholdersaredemandingtangiblereturns,andthefocusismovingfrom“CanweuseAI?”to“HowdowedeployAIeffectivelyandatscale?”Thistransitionislessabouttechnologicalnoveltyandmoreaboutstrategicintegrationintocorebusinessprocesses.It’saboutmakingAIareliable,always-oncomponentoftheoperationalfabric,capableofdrivingefficiency,innovation,andmeasurablecompetitiveadvantage.Theindustry’sgrowingpragmatismhighlightsthismovetowardoperationalization.ThissentimentcutsthroughtheprevailinghypeandhighlightstheintensiveeffortrequiredforsuccessfulAIadoption.It’sajourneythatinvolvescleaningandpreparingdata,re-engineeringworkflows,andfosteringaculturethatembracesdata-drivendecision-making.Ourebookisdesignedtobeyourguidethroughthisjourney.Insteadofabstracttheories,weprovideaproof-basedmanualfilledwithrealcasestudiesfromourpartnerswhohavenavigatedthistransition,offeringpracticallessonsandprovenstrategiesforturningAIinitiativesintooperationalsuccesses.ThisapproachiscentraltohowPlugandPlayhelpslargeenterprisesadoptartificialintelligencebysimplifyingtheprocessandspeedingupintegration.Weachievethisthroughtwokeyinitiatives:developingAICentersofExcellence(CoE)andrunningourdedicatedEnterprise&AIvertical.BycreatingAICoEs,wehelpcompaniesbuildcentralizedhubsforinnovationandstrategy.Thesecentersbringtogethercross-functionalteams,facilitateknowledgesharingthroughworkshops,anddesignroadmapstosmoothlyintegrateAIintoexistingsystemswithscalablegovernance.OurEnterprise&AIverticalconnectsleadingcorporations,governments,universities,andfoundationswithinnovativeAIstartupsofferingready-to-deploysolutions.Weworkwithenterprisestoidentifyhigh-impactAIusecases,buildpilotprojectpipelines,andscalesuccessfulpilotsintofulldeployments.PlugandPlayalsohelpsbridgethegapbetweenlegacysystemsandAIstartupsbyofferingguidanceonmodernizinginfrastructureanddesigningtailoredimplementationstrategies.Thisensuresastartup'ssolutioncomplementstheenterprise’sexistingsystems,minimizingdisruptionandmaximizingvalue.650+PlugandPlayInvestmentsinAICompanies56565PartnerPerspectivesOneareawherethisintegrationisbecomingincreasinglycriticalisartificialintelligence,astheroleofAIinbusinesshasevolvedrapidlyoverthepastfewyears.Whatwasonceapproachedwithcautiouscuriosityisnowbecomingacorecomponentofstrategicdecision-making.Acrossindustries,enterpriseleadersareembracingAInotjustasanexperiment,butasacrucialtoolfordrivinginnovation,efficiency,andgrowth.Below,heardirectlyfromtheseleadersastheysharehowtheirperspectivesonAIhaveshiftedandthetangibleimpactsit’shavingontheirorganizations.AIwillnotonlyhelpsolvebigchallengesinenergy,butit’salsohelpingfuelademandforpowerthatcanoptimizetheuseofthegridandreduceenergycostsforeveryone.Thefutureisbright!ThemostexcitingthingaboutAIisitspowerasatool.Ifit’sfocusedontherightproblem,itcancreateanenormousimpact,whetherthat’shelpingacustomer,buildingafeature,orsolvingchallengesforsociety.Thepossibilitiesareendless.VPofGlobalInnovation,DoosanBobcatThere’ssomuchhappeningwithAIrightnow.Youhavetheopportunitytoautomateatremendousamountofworkandfreepeopleuptodootherthings.ButwhatexcitesmemostisthatthedeeperwegetwithAI,themorewe’llvaluewhatmakesushuman—therelationshipsandtheemotionsAIcan’treplicate.ArcBesSeniorManagerofInnovationStrategy,ArcBesThemosttimeconsumingprocessforventurecapitalisduediligence.Ifwefindagreatcompany,thenextthingwehavetoprepareistheICmemo.ThenpreparingtheICmemo,itdependsonhowmuchyouhavetowriteout,butit'stypically20pages.ByusinganykindofAIitissoquick.Thisisaprettyhugeimpactforus.6Asenterprisescommittofull-scaleAIdeployment,theyinevitablyencountera“realitycheck,”aseriesofsignificanthurdlesthatcanstallprogressanddiminishreturns.Oneofthemostcommonchallengesisdatareadiness.Manyorganizationsdiscovertheirdataissiloed,inconsistent,orinsufficientfortrainingeffectiveAImodels.Over80%ofnewdataisunstructured,addingfurthercomplexitytothechallengeofmakingdatausableforAI.Additionally,legacysystemsoftenlacktheflexibilityandAPI-firstarchitectureneededtointegrateseamlesslywithmodernAIplatforms,creatingatechnicalbottleneckthatrequirescostlyandtime-consumingmodernizationefforts.ThesefoundationalissuesmustbeaddressedbeforeanymeaningfulAIimplementationcansucceed,requiringastrategicapproachtodatagovernanceandinfrastructureoverhaul.Beyondtechnicalbarriers,acriticaltalentgapoftenemerges.Thereisapronouncedshortageofprofessionalswiththehybridskillsneededtobridgethegapbetweendatascienceandbusinessoperations.FindingindividualswhocannotonlybuildanAImodelbutalsounderstanditsbusinessapplicationanddriveitsadoptionisamajorchallenge.Tonavigatetheseobstacles,weadvocateforause-case-drivenapproach.Bystartingwithaspecific,high-valuebusinessproblem,organizationscanfocustheirefforts,defineclearsuccessmetrics,anddemonstratetangibleROI morequickly.Thismethodprovidesaclear,iterativepathforward,allowingteamstobuildmomentum,learnfromeachdeployment,andsystematicallyscaletheirAIcapabilitiesacrosstheenterprise.TheadventofAIagentsoffersapromisingwaytoovercomesomeofthesechallenges.Unliketraditionaltools,thesesophisticatedsystemsoperatesemi-independently,leveraginglargelanguagemodelstoplan,reflect,learn,andexecutetaskswithminimalhumaninput.Bybreakingcomplexproblemsintomanageablecomponentsandadaptingasnewinformationemerges,AIagentsmimichowhumanstackletasks.ThiscapabilityhasthepotentialtoaddressthetalentgapbyautomatingsomeofthemoretechnicalaspectsofAIimplementation,enablingorganizationstofocusonhigh-valueusecasesanddriveadoptionmoreeffectively.Forinstance,GooglereportedinaQ32024earningscallthat25%ofallnewcodeatGoogleisnowbeingwrittenbyAI.Astheseagentsevolve,theyarepoisedtobecomethenexttransformativeforceinthefutureofwork,unlockingnewlevelsofproductivityandinnovation.8PredictiveAIRoboticsforManufacturingAsmanufacturingoperationsscale,maintainingconsistentroboticperformancebecomesincreasinglycomplex.MitsubishiElectricpartneredwithKametAItobringpredictiveintelligenceintoroboticsystems,improvingreliability,efficiency,andlong-termperformanceacrossmanufacturingenvironments.Industrialrobotsplayacriticalroleonthefactoryfloor,butsmallinefficienciesinconfiguration,motionpaths,orpowerusagecanquicklyleadtoperformancedegradationanddowntime.Traditionalmaintenancemodelsarelargelyreactive,addressingissuesonlyafterfailuresorslowdownsoccur.MitsubishiElectricidentifiedgrowingchallengesrelatedtoroboticarmconfiguration,trajectorycomplexity,andinconsistentperformanceacrosssystems.Theseissuesmadeoptimizationtime-consumingandincreasedtheriskofproductioninterruptions.1.2WhyItMattersUnplanneddowntimeandinefficientroboticsetupshaveadirectimpactonproductionoutput,operationalcosts,andprofitabilityasmanufacturingsystemsbecomemoreadvanced.Relyingonmanualadjustmentslimitsscalabilityandslowscontinuousimprovement.9ForMitsubishiElectric,improvingreliabilitymeantmovingtowardsmartermanufacturingsystemsthatcouldidentifyissuesearly,optimizeperformancecontinuously,andsupportlong-termefficiency.MitsubishiElectriccollaboratedwithKametAItoapplymachinelearningtoroboticperformancedata.Kamet’splatformanalyzesfactorssuchasspeed,acceleration,torque,andpowerconsumptionacrossroboticaxestoidentifyinefficienciesandperformancetrade-offs.Byprocessinglargevolumesofsensordata,thesystemhighlightswhereroboticprogramscanbeadjustedtoimproveefficiencywhilemaintainingaccuracyandsafetystandards.Insightsaredeliveredthroughareal-timedashboard,enablingteamstorefineconfigurationsquicklyandconfidently.Thesolutionalsosignificantlyacceleratednewassemblylinesetup,reducingsetuptimelinesfromsixmonthstolessthanonemonthincertaincases.Thepartnershipdeliveredstrong,measurableresults.MitsubishiElectricachieveda600%ROIbyextendingtheusefullifeofroboticsystemsandimprovingoverallperformance.Predictiveinsightsreducedtheriskofdowntime,improvedoperationalefficiency,andenabledfasterscalingofmanufacturingoperations.TheabilitytooptimizesystemscontinuouslyhelpedMitsubishiElectricunlockgreatervaluefromexistinginfrastructurewhilesupportingsmarter,moreresilientmanufacturingworkflows.“KamethasalreadydeliveredpromisingvaluetoMitsubishiElectricinthefirstphaseofourcollaboration.“KamethasalreadydeliveredpromisingvaluetoMitsubishiElectricinthefirstphaseofourcollaboration.Weconfirmthattheinitialanalysisofrobotoperationaldataprovidedinsightsforoptimizingthesetupconfigurationsofthearmrobots.Thistranslatesintohigheryieldsathighquality.”TakehiroIshiguroSr.ManagerofOpenInnovation,MitsubishiElectricbitHumanHealthbitHumanAI-PoweredDigitalHumansforCustomerInteractionIvoclarisagloballeaderindentalmanufacturing,supportingcustomersacrossregions,languages,andtimezones.Asitsproductportfolioandglobalreachexpanded,thedemandsonitscustomercareteamsincreased.Tokeepupwithrisingexpectationswhilemaintainingahigh-qualityexperience,IvoclarpartneredwithbitHumantoexplorehowAI-powereddigitalhumanscouldsupportcustomerinteractioninamorescalableandengagingway.Ivoclar’scustomercareorganizationhandlesahighvolumeofcustomercallsandchatseveryday.Theteamincludesroughly100customer-facingemployeessupportingusersacrossmultipleregionsandlanguages.Manyincomingrequestsarerelativelysimpleinnature,butstillrequiretimeandattentionfromtrainedstaff.Atthesametime,customereducationandtechnicalguidancearedifficultandcostlytoscalethroughhuman-onlysupport.Providingconsistentanswersacrossmarkets,especiallyacrossmultiplelanguages,waschallengingandstrainedoperations.bitHumanbitHuman2.2WhyItMattersCustomerexpectationsareincreasinglyshapedbydigital-firstexperiences.Dentalprofessionalswantquickanswers,clearguidance,andsupportthatfeelsaccessiblewhenissuesarise.ForaglobalorganizationlikeIvoclar,thismeansdeliveringconsistentengagementacrossmarketswhilemaintainingtrustandquality.Withoutascalablewaytomanageroutineinquiries,customercareteamsriskbeingoverwhelmed,responsetimescanslow,andoperationalcostsrise.Ivoclarneededanapproachthatcouldsupportcustomersefficientlywhileallowinghumanteamstofocusonmorecomplex,high-impactinteractions.IvoclarpartneredwithbitHuman,astartupfocusedonAI-baseddigitalhumans,tointroduceinteractiveavatarsintocustomer-facingexperiences.TheseAIavatarsengagecustomersconversationally,answercommonquestions,andprovideguidanceinmultiplelanguages.Duringtesting,Ivoclarfocusedonpracticalperformanceindicatorssuchasresponsespeed,conversationalquality,andlanguagecoverage.Fromthebeginning,theavatarswereevaluatedacrosseighttotenlanguages,reflectingIvoclar’sglobalcustomerbase.Tocreateamoreengagingexperience,Ivoclarcustomizedtheavatarwithbrandedelements,includingadentalprofessional’scoatandIvoclar’slogo.Ratherthandeliveringpurelytechnicalresponses,theavatarinteractedinamorehumanway,addingcontextandHealthHealthstorytellingthatencouragedcustomerstostayengagedandabsorbinformationmoreeffectively.ThesolutionwasshowcasedatindustryexhibitionsandtestedliveatalargeeventinMexico,wheretheAIavatarintroducedIvoclar’smanagementteamonstageinmultiplelanguages,demonstratingitsabilitytointeractnaturallywithaglobalaudience.2.4Impact&ResultsEarlyresultsshowedstrongcustomerengagement,withusersrespondingpositivelytotheconversational,human-likeinteraction.Customerswereoftenwillingtospendmoretimeengagingwiththeavatar,whichhelpedimproveunderstandingandoverallsatisfaction.Fromanoperationalperspective,theAIsolutionhelpedreducepressureoncustomercareteamsbyhandlingameaningfulshareofroutineinquiries.Thisallowedhumansupportstafftospendmoretimeoncomplexcases“Besmart,behumble,taketherisk...Ifyoudon’ttakearisk,that’sthebiggestrisk.Soyouhavetomove.Ifyouwait“Besmart,behumble,taketherisk...Ifyoudon’ttakearisk,that’sthebiggestrisk.Soyouhavetomove.Ifyouwaitforotherstodoit,thenmaybeit’stoolate.”MichaelKrugGlobalHeadCustomerCare,IvoclarAIforWorkplaceSafetyandRiskDetectionKajimaisaglobalconstructioncompanywithalonghistoryofoperatingcomplex,high-riskprojects.Asjobsitesgrewlargerandmoredifficulttooversee,especiallyinremoteenvironments,Kajimasoughtawaytoimprovevisibility,safety,anddecision-makingwithoutrelyingsolelyonconstanton-sitepresence.Toaddressthesechallenges,KajimapartneredwithArchetypeAItoapplycomputervisionandpredictiveanalyticstoreal-worldconstructionenvironments.Constructionsitesareinherentlyrisky,withhazardsthatcanemergequicklyandchangethroughouttheday.Traditionalsafetymanagementreliesheavilyonmanualchecksandreactivemeasures,oftenidentifyingissuesonlyafterincidentsoccur.Kajimafacedchallengesinmaintainingcontinuousvisibilityacrossjobsites,particularlyforlarge-scaleorremoteprojects.Reviewingvideofootagemanuallywastime-consuming,andcriticalsafetysignalscouldeasilybemissed.Thislimitedtheabilitytoproactivelyidentifyrisksandrespondquicklytoevolvingconditions.3.2WhyItMattersWorkplacesafetyisbotharegulatoryrequirementandahumanresponsibility.Accidentsresultinserioushumanconsequences,projectdelays,andfinancialloeses.Asprojectsgrowmorecomplexandlaborshortagesincrease,relyingonconstantphysicaloversightbecomeslessfeasible.ForKajima,improvingsafetymeantfindingawaytomonitorconditionscontinuously,identifyrisksearlier,andsupportsaferoperationswithoutincreasingon-siteburden.KajimapartneredwithArchetypeAItoapplyArchetype’sPhysicalAIplatform,Newton,tojob-sitemonitoring.Newtonusescomputervisionandmultimodaldataanalysistointerpretvideoandsensordatafromconstructionenvironments,turningrawfootageintoactionableinsights.Duringalarge-scalecanalreconstructionprojectinNiigata,Japan,Newtonanalyzed11,957job-sitevideos,extracting662targetedclipsthatcapturedkeyactivitiesandsafety-relatedevents.Specializedanalyticalviewsallowedprojectmanagerstounderstandworktimelines,equipmentusage,andenvironmentalconditionswithoutmanuallyreviewingfootage.Theplatformalsoreducedthetimerequiredtoretrievecriticalinformationtounder60seconds,enablingfasterresponsesandbetter-informeddecisions.Bycombiningobjectdetection,videoverification,andcontextualdatasuchasweatherconditions,Kajimagainedclearer,morereliableoversightofjobsiteactivity.3.4Impact&ResultsThecollaborationdeliveredmeasurableimprovementsinvisibility,efficiency,andsafetyoversight.Bytransformingthousandsofhoursofunstructuredvideointosearchableinsights,Kajimasignificantlyreducedtheburdenofmanualreviewandimprovedtransparencyacrossoperations.Newtonsupportedsaferdecision-makingbyenablingremoteoversight,helpingKajimamovetowarditsgoalofmanaging50%ofjobsitesremotely.Theabilitytoidentifyrisksearlierandmonitorconditionscontinuouslystrengthenedcomplianceeffortsandreducedexposureto“ArchetypeAI’sabilitytounderstandreal-worldconditionsandprovide24/7visibilityonthejobsitehasbeentransformative.Itallowsustobeon-site“ArchetypeAI’sabilitytounderstandreal-worldconditionsandprovide24/7visibilityonthejobsitehasbeentransformative.Itallowsustobeon-sitewhennecessary,whilestillmaintainingfullsituationalawarenessfromtheoffice.Thisisnotaboutmonitoringorcontrol;itisaboutsafeguardingworkersandensuringthatprojectprogressstaysontrack.”InnovationManager,KajimaEnterprise&AIAIAgentsforEnterpriseCustomerSupportUnisyssupportsenterpriseandmid-marketclientsacrosscomplexenvironmentswherecustomerservicequalitydirectlyimpactsretentionandgrowth.Ascustomerexpectationsincreasedandserviceoperationsscaled,Unisysrecognizedtheneedtomodernizehowsupportagentsaccessedinformationandhandledcustomerrequests.Toaddressthesechallenges,UnisyspartneredwithParloatoexplorehowAI-poweredagentscouldimproveknowledgemanagement,streamlineworkflows,andsupportscalablecustomersupportoperations.Unisysfacedagrowingvolumeofcustomerrequestsacrossmultipleclientsandenvironments.Supportagentswererequiredtonavigatecomplexenterprisesystemswhilemanagingfragmentedknowledgesources,whichslowedresponsetimesandincreasedoperationalstrain.AsUnisysexpandeditsclientbase,additionalchallengesemergedaroundmulti-tenancy.Supportingmultiplecustomerswithdifferentneedsusingtraditionalagent-assisttoolsmadeitdifficulttoscaleefficientlywithoutaddingcostorcomplexity.Thesefrictionpointslimitedproductivityandmadeitharderforagentstodeliverconsistent,high-qualitysupport.4.2WhyItMattersCustomerexperiencehasbecomeakeycompetitivedifferentiator.Sloworinconsistentsupportdirectlyaffectssatisfaction,renewalrates,andlong-termclientrelationships.Atthesametime,supportcostscontinuetoriseasserviceoperationsgrowmorecomplex.ForUnisys,improvingagentefficiencywasnotjustaboutspeed.Itwasaboutenablingagentstoresolveissuesmoreeffectivelywhilesupportingbusinessgrowthacrossmid-marketandenterpriseclientswithoutincreasingoperationaloverhead.4.3TheAISolutionUnisyspartneredwithParloa,astartupspecializinginAI-poweredcustomerserviceagents,tointroduceintelligentagentassistcapabilitiesintoitssupportworkflows.Parloa’ssolutionusesAIagentstoautomaterouting,surfacerelevantknowledgeinrealtime,andsupportagentswithcontextualguidanceduringcustomerinteractions.Thefocuswasonimprovinghowinformationisaccessedandapplied,reducingmanualeffortwhileallowingagentstoconcentrateonhigher-valueconversations.Byaddressingknowledgemanagementandworkflowchallengestogether,Unisyswasabletosupportmorecomplexclientenvironmentswhilemaintainingconsistencyacrosscustomers.EnterpriseEnterprise&AI4.4Impact&ResultsThecollaborationdeliveredmeasurableimprovementsacrossproductivityandcustomersupportperformance.Unisysreducedproductivityhelpingagentsworkmoreefficientlyandconfidently.Casehandlingtimedecreasedby20%,enablingfasterresponsesandasmoothercustomerexperience.Beyondoperationalgains,theAIagentassistsolutionalsosupportedbusinessgrowth.Unisyssawa2xincreaseinitscustomerpipeline,showinghowstrongersupportcapabilitiescancontributedirectlytocommercialoutcomes.TheresultshighlighthowAI-poweredagentassistcanimprovebothservicequalityandscalabilitywithoutaddingunnecessarycomplexity.Enterprise&AIAI-PoweredServiceManagementAsenterprisesscale,ITservicemanagement(ITSM)becomesincreasinglycomplex.Unisys,aglobalITsolutionscompany,facedsignificantchallengesinoptimizingtheirITSMprocessesformid-marketclients.Toaddresstheseissues,UnisyspartneredwithFreshworksandEasyVista,leveragingAI-poweredtoolstoinnovateanddeliverscalable,cost-effectiveITSMsolutions.ThiscollaborationnotonlystreamlinedoperationsbutalsoshowcasedthetransformativepotentialofAIinservicemanagement.UnisysanditsclientsstruggledwithfragmentedITSMsystems,leadingtoinefficienciesinhandlingahighvolumeofinternalservicerequests.Manualtickethandlingsloweddownresolutiontimes,creatingbottlenecksthatimpactedemployeeproductivity.Theselegacysystemslackedscalability,makingitdifficulttoadapttogrowingbusinessneeds.Additionally,operationalcostssoaredasITteamsspentexcessivetimemanagingrepetitivetasksratherthanfocusingonstrategicinitiatives.5.2WhyItMattersIToperationsarethebackboneofemployeeproductivity.Whenservicesystemsfailtomeetexpectations,delaysinresolvingissuescandisruptworkflows,increasecosts,andhindergrowth.Formid-marketenterprises,Enterprise&AIinparticular,scalableandintelligentITSMsolutionscanmakeorbreakoperationalefficiency.SlowsupportandmanualprocessesnotonlyburdenITteamsbutalsoerodeusersatisfaction,ultimatelyaffectingtheorganization’sbottomline.Theneedforasystemthatcombinesautomation,scalability,andintelligencewasclear.Toaddressthesechallenges,UnisysimplementedAI-poweredITSMsolutionsthroughitspartnershipswithFreshworksandEasyVista.Theseplatformsintroducedintelligentticketrouting,automatingrepetitivetasksandensuringthatissuesweredirectedtotherightteams.AI-poweredinsightsenabledprioritizationofcriticalissues,helpingITteamsresolveurgentmattersfaster.Additionally,thesolutionsdeliveredpredictiveanalytics,allowingUnisystoproactivelyaddresspotentialproblemsbeforetheyescalated.Bytailoringthesetoolstomid-marketclientneeds,Unisysensuredabalancebetweenaffordabilityandhighperformance.5.4Impact&ResultsTheresultsofthecollaborationweresignificant.Issueresolutiontimesimprovedby40%,dramaticallyenhancingtheinternaluserexperience.Byautomatingroutinetasks,UnisysreducedtheoperationalburdenonITteams,freeingthemtofocusonstrategicprojects.Additionally,theseAI-drivensystemshelpedcutoperationalcostsby25%,makingthembothpracticalandfinanciallysustainableformid-marketclients.TheUnisysjourneyisaclearexampleofhowstrategicpartnershipsandadvancedtechnologycancreatetailored,impactfulsolutions,pavingthewayforinnovationandefficiencyinITservicemanagement.InsurtechAIforInsuranceOperations&ClaimsAutomationInsuranceoperationsareinherentlydocument-heavy,withclaimsprocessingrelyingonlargevolumesofforms,medicalrecords,andsupportingdocumentation.Asclaimvolumesincreasedandcustomerexpectationsrose,Aflacsoughtamoreefficientwaytomanagedocumentintakeandclaimsworkflows.Toaddressthesechallenges,AflacpartneredwithLazarustoapplyAI-drivenautomationacrossitsinsuranceoperations.Aflac’sclaimsworkflowsreliedheavilyonmanualdocumentprocessing.Extracting,validating,androutinginformationfromcomplexinsuranceformswastime-consumingandpronetoerror.TraditionalOCRtoolsrequiredextensivecustomizationandstruggledwithaccuracyacrossdifferentdocumentformats,limitingscalabilityasvolumesgrew.Thesemanualprocessesslowedclaimshandlingandplacedsignificantoperationalstrainoninternalteams,makingitdifficulttomaintainspeedandconsistencyacrossworkflows.6.2WhyItMattersCustomerexpectationsforfasterclaimsprocessingcontinuetorise,withspeedandaccuracydirectlyinfluencingsatisfactionandtrust.Atthesametime,insurersfaceincreasingpressuretocontrolcostswhilemeetingstrictcomplianceandsecurityrequirements.Withoutsmarterautomation,manualclaimsprocessingcreatesbottlenecksthatincreaseoverhead,delayoutcomes,andlimitanorganization’sabilitytoscaleefficiently.InsurtechAflacpartneredwithLazarustointroduceAI-poweredworkflowautomationandintelligentdocumentprocessingintoitsclaimsoperations.LazaruscombinedOCRwithaspecializedlanguagemodeltoextract,classify,andvalidateinformationfromcomplexinsurancedocumentswithhighaccuracy.ThesolutionintegrateswithAflac’sexistingsystems,enablingteams