【璞跃】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

TheUltimateAIPlaybook:

10EnterpriseDeploymentsBoostingROI

Ebook

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TheUltimateAIPlaybook:10EnterpriseDeploymentsBoostingROI

TableofContents

Introduction

•FromExperimentationtoEnterpriseImpact:TheAIInflectionPoint

•TheShifttoRealDeployment

•PartnerPerspectives

•OvercomingAI’sRealityCheck

3

4

5

6

TheTop10AIUseCases

•AIUseCaseinSupplyChain

•#1.MitsubishiElectricxKametAI

•AIUseCaseinHealth

•#2.IvoclarxbitHuman

•AIUseCaseinRealEstate&Construction

•#3.KajimaxArchetypeAI

•AIUseCasesinEnterprise&AI

•#4.UnisysxParloa

•#5.UnisysxFreshworks&EasyVista

•AIUseCasesinInsurtech

•#6.AflacxLazarus

•#7.UnumxSkanAI

•AIUseCasesinTravel&Hospitality

•#8.TUIxParloa

•#9.iGAİstanbulAirportxHummingdrone

•#10.iGAİstanbulAirportxDELIVERS.AI

8–9

10–11

12–13

14–15

16–17

18–1920–21

22

23–24

25–26

AIHighlights

•OurGlobalAIPresence

•AICentersofExcellence

28–2930

•AIInvestmentHighlights31

•AIFocusAreaHighlights32

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TheUltimateAIPlaybook:10EnterpriseDeploymentsBoostingROI

Introduction

2026ispoisedtobethepivotalyearforenterpriseAI,markingthe

transitionfromexperimentalphasestofull-scaleoperational

deployment.Thisshiftdemandsaclear-eyedapproachthatmoves

beyondthehypetotackletherealworkofintegration.AsScaleAI’sCFO,DennisCinelli,notes,AIisn’ta“magicwand,”itrequiressignificant

efforttodelivervalue.Thisebookservesasaproof-basedguide,

leveragingreal-worldcasestudiesfromourpartnerstoillustratea

use-case-drivenmethodology.Wewillexploretheprimarychallengesenterprisesface,fromdatareadinesstotalentgaps,andprovideaclearpathtoachievingmeasurableROIthroughstrategicAIimplementation.

Whatmakesthisplaybookdifferentisthatthesecasestudiescome

directlyfromPlugandPlay’spartnerecosystemandstartupnetwork.Eachcollaborationfeaturedherewasenabledthroughourplatform,

connectingenterpriseswithstartupsfromourglobaldatabaseacrossindustries,includingmanufacturing,insurance,aviation,construction,andrenewableenergy.WehaveseenfirsthandhowAImovesfrompilottoreal-worldimpact,andthepatternsareclear.Successful

organizationsfocusonsolvingdefinedoperationalproblems,aligningcross-functionalteamsearly,andscalingwhatprovesmeasurable

value.TheexamplesinthisplaybookreflectAIimplementationinactionacrossmultiplesectors,offeringpracticalinsightgroundedin

experienceratherthanspeculation.

TheorganizationsfeaturedinthisplaybookareusingAItosolve

measurablebusinessproblems,reducerisk,improveefficiency,andbuildlong-termcompetitiveadvantage.

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TheUltimateAIPlaybook:10EnterpriseDeploymentsBoostingROI

TheShifttoRealDeployment

Foryears,organizationshavebeeninastateofexploration,runningpilotprogramsandproofofconceptstounderstandwhatAIcando.Now,theexpectationhasshifted.

Stakeholdersaredemandingtangiblereturns,andthefocusismovingfrom“CanweuseAI?”to“HowdowedeployAIeffectivelyandatscale?”Thistransitionislessabout

technologicalnoveltyandmoreaboutstrategicintegrationintocorebusinessprocesses.It’saboutmakingAIareliable,always-oncomponentoftheoperationalfabric,capableofdrivingefficiency,innovation,andmeasurablecompetitiveadvantage.

Theindustry’sgrowingpragmatismhighlightsthismovetowardoperationalization.ThissentimentcutsthroughtheprevailinghypeandhighlightstheintensiveeffortrequiredforsuccessfulAIadoption.It’sajourneythatinvolvescleaningandpreparingdata,re-

engineeringworkflows,andfosteringaculturethatembracesdata-drivendecision-

making.Ourebookisdesignedtobeyourguidethroughthisjourney.Insteadofabstracttheories,weprovideaproof-basedmanualfilledwithrealcasestudiesfromour

partnerswhohavenavigatedthistransition,offeringpracticallessonsandprovenstrategiesforturningAIinitiativesintooperationalsuccesses.

ThisapproachiscentraltohowPlugandPlayhelpslargeenterprisesadoptartificial

intelligencebysimplifyingtheprocessandspeedingupintegration.Weachievethis

throughtwokeyinitiatives:developing

AICentersofExcellence

(CoE)andrunningour

dedicatedEnterprise&AIvertical.BycreatingAICoEs,wehelpcompaniesbuild

centralizedhubsforinnovationandstrategy.Thesecentersbringtogethercross-

functionalteams,facilitateknowledgesharingthroughworkshops,anddesignroadmapstosmoothlyintegrateAIintoexistingsystemswithscalablegovernance.

Our

Enterprise&AIvertical

connectsleadingcorporations,governments,universities,andfoundationswithinnovativeAIstartupsofferingready-to-deploysolutions.Weworkwithenterprisestoidentifyhigh-impactAIusecases,buildpilotprojectpipelines,and

scalesuccessfulpilotsintofulldeployments.PlugandPlayalsohelpsbridgethegap

betweenlegacysystemsandAIstartupsbyofferingguidanceonmodernizing

infrastructureanddesigningtailoredimplementationstrategies.Thisensuresastartup'ssolutioncomplementstheenterprise’sexistingsystems,minimizingdisruptionand

maximizingvalue.

650+PlugandPlayInvestmentsinAICompanies

23

$75M-100MValuation

56

$100M–1BValuation

15

$1B+Valuation

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TheUltimateAIPlaybook:10EnterpriseDeploymentsBoostingROI

PartnerPerspectives

Oneareawherethisintegrationisbecomingincreasinglycriticalisartificialintelligence,astheroleofAIin

businesshasevolvedrapidlyoverthepastfewyears.Whatwasonceapproachedwithcautiouscuriosityisnowbecomingacorecomponentofstrategicdecision-making.Acrossindustries,enterpriseleadersareembracingAInotjustasanexperiment,butasacrucialtoolfordrivinginnovation,efficiency,andgrowth.Below,heardirectlyfromtheseleadersastheysharehowtheirperspectivesonAIhaveshiftedandthe

tangibleimpactsit’shavingontheirorganizations.

AIwillnotonlyhelpsolvebigchallengesinenergy,butit’salsohelpingfuela

demandforpowerthatcanoptimizetheuseofthegridandreduceenergycostsforeveryone.Thefutureisbright!

PattiPoppe

CEO,PG&E

ThemostexcitingthingaboutAIisitspowerasatool.Ifit’sfocusedontherightproblem,itcancreateanenormousimpact,whetherthat’shelpingacustomer,buildingafeature,orsolvingchallengesforsociety.Thepossibilitiesareendless.

JoelHoneyman

VPofGlobalInnovation,DoosanBobcat

There’ssomuchhappeningwithAIrightnow.Youhavetheopportunityto

automateatremendousamountofworkandfreepeopleuptodootherthings.ButwhatexcitesmemostisthatthedeeperwegetwithAI,themorewe’llvaluewhatmakesushuman—therelationshipsandtheemotionsAIcan’treplicate.

BrianBeasley

SeniorManagerofInnovationStrategy,ArcBest

ArcBes

Themosttimeconsumingprocessforventurecapitalisduediligence.Ifwefindagreatcompany,thenextthingwehavetoprepareistheICmemo.ThenpreparingtheICmemo,itdependsonhowmuchyouhavetowriteout,butit'stypically20

pages.ByusinganykindofAIitissoquick.Thisisaprettyhugeimpactforus.

ShoheiYamada

CEO,SBIHoldingsUSA

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TheUltimateAIPlaybook:10EnterpriseDeploymentsBoostingROI

OvercomingAI’sRealityCheck

Asenterprisescommittofull-scaleAIdeployment,theyinevitablyencountera“realitycheck,”aseriesofsignificanthurdlesthatcanstallprogressand

diminishreturns.Oneofthemostcommon

challengesisdatareadiness.Manyorganizationsdiscovertheirdataissiloed,inconsistent,or

insufficientfortrainingeffectiveAImodels.Over

80%ofnewdataisunstructured,addingfurther

complexitytothechallengeofmakingdatausableforAI.Additionally,legacysystemsoftenlackthe

flexibilityandAPI-firstarchitectureneededto

integrateseamlesslywithmodernAIplatforms,

creatingatechnicalbottleneckthatrequirescostlyandtime-consumingmodernizationefforts.ThesefoundationalissuesmustbeaddressedbeforeanymeaningfulAIimplementationcansucceed,

requiringastrategicapproachtodatagovernanceandinfrastructureoverhaul.

Beyondtechnicalbarriers,acriticaltalentgap

oftenemerges.Thereisapronouncedshortageofprofessionalswiththehybridskillsneededto

bridgethegapbetweendatascienceandbusinessoperations.Findingindividualswhocannotonly

buildanAImodelbutalsounderstanditsbusinessapplicationanddriveitsadoptionisamajor

challenge.Tonavigatetheseobstacles,weadvocateforause-case-drivenapproach.Bystartingwitha

specific,high-valuebusinessproblem,

organizationscanfocustheirefforts,defineclearsuccessmetrics,anddemonstratetangibleROI

morequickly.Thismethodprovidesaclear,iterativepathforward,allowingteamstobuild

momentum,learnfromeachdeployment,and

systematicallyscaletheirAIcapabilitiesacrosstheenterprise.

TheadventofAIagentsoffersapromisingwayto

overcomesomeofthesechallenges.Unlike

traditionaltools,thesesophisticatedsystems

operatesemi-independently,leveraginglarge

languagemodelstoplan,reflect,learn,andexecutetaskswithminimalhumaninput.Bybreaking

complexproblemsintomanageablecomponents

andadaptingasnewinformationemerges,AI

agentsmimichowhumanstackletasks.This

capabilityhasthepotentialtoaddressthetalent

gapbyautomatingsomeofthemoretechnical

aspectsofAIimplementation,enabling

organizationstofocusonhigh-valueusecasesanddriveadoptionmoreeffectively.Forinstance,

GooglereportedinaQ32024earningscall

that25%ofallnewcodeatGoogleisnowbeingwrittenbyAI.Astheseagentsevolve,theyarepoisedtobecomethenexttransformativeforceinthefutureofwork,unlockingnewlevelsofproductivityandinnovation.

TheTop10AIUseCases

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TheUltimateAIPlaybook:10EnterpriseDeploymentsBoostingROI

1.CASESTUDY//MITSUBISHIELECTRICXKAMETAI

SupplyChain

PredictiveAIRoboticsforManufacturing

Asmanufacturingoperationsscale,maintainingconsistentroboticperformancebecomes

increasinglycomplex.MitsubishiElectric

partneredwithKametAItobringpredictive

intelligenceintoroboticsystems,improving

reliability,efficiency,andlong-termperformanceacrossmanufacturingenvironments.

failuresorslowdownsoccur.MitsubishiElectricidentifiedgrowingchallengesrelatedtoroboticarmconfiguration,trajectorycomplexity,and

inconsistentperformanceacrosssystems.

Theseissuesmadeoptimizationtime-

consumingandincreasedtheriskofproductioninterruptions.

1.1TheProblem

Industrialrobotsplayacriticalroleonthe

factoryfloor,butsmallinefficienciesin

configuration,motionpaths,orpowerusagecanquicklyleadtoperformancedegradationand

downtime.Traditionalmaintenancemodelsarelargelyreactive,addressingissuesonlyafter

1.2WhyItMatters

Unplanneddowntimeandinefficientrobotic

setupshaveadirectimpactonproduction

output,operationalcosts,andprofitabilityas

manufacturingsystemsbecomemore

advanced.Relyingonmanualadjustmentslimitsscalabilityandslowscontinuousimprovement.

TheUltimateAIPlaybook:10EnterpriseDeploymentsBoostingROI

1.CASESTUDY//MITSUBISHIELECTRICXKAMETAI

SupplyChain

ForMitsubishiElectric,improvingreliability

meantmovingtowardsmartermanufacturingsystemsthatcouldidentifyissuesearly,

optimizeperformancecontinuously,andsupportlong-termefficiency.

1.3TheAISolution

MitsubishiElectriccollaboratedwithKametAItoapplymachinelearningtorobotic

performancedata.Kamet’splatformanalyzesfactorssuchasspeed,acceleration,torque,

andpowerconsumptionacrossroboticaxestoidentifyinefficienciesandperformancetrade-offs.

Byprocessinglargevolumesofsensordata,

thesystemhighlightswhereroboticprogramscanbeadjustedtoimproveefficiencywhile

maintainingaccuracyandsafetystandards.Insightsaredeliveredthroughareal-timedashboard,enablingteamstorefine

configurationsquicklyandconfidently.

Thesolutionalsosignificantlyacceleratednewassemblylinesetup,reducingsetuptimelinesfromsixmonthstolessthanonemonthin

certaincases.

1.4Impact&Results

Thepartnershipdeliveredstrong,measurableresults.MitsubishiElectricachieveda600%

ROIbyextendingtheusefullifeofrobotic

.

Predictiveinsightsreducedtheriskof

downtime,improvedoperationalefficiency,

systemsandimprovingoverallperformance

andenabledfasterscalingofmanufacturing

operations.Theabilitytooptimizesystems

continuouslyhelpedMitsubishiElectricunlockgreatervaluefromexistinginfrastructure

whilesupportingsmarter,moreresilientmanufacturingworkflows.

“Kamethasalreadydeliveredpromisingvalueto

MitsubishiElectricinthefirstphaseofour

collaboration.Weconfirmthattheinitialanalysisofrobotoperationaldataprovidedinsightsfor

optimizingthesetupconfigurationsofthearmrobots.Thistranslatesintohigheryieldsathighquality.”

TakehiroIshiguro

Sr.ManagerofOpenInnovation,MitsubishiElectric

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TheUltimateAIPlaybook:10EnterpriseDeploymentsBoostingROI

2.CASESTUDY//IvoclarxbitHuman

bintHuma

Health

AI-PoweredDigitalHumansforCustomerInteraction

Ivoclarisagloballeaderindentalmanufacturing,supportingcustomersacrossregions,

languages,andtimezones.Asitsproduct

portfolioandglobalreachexpanded,the

demandsonitscustomercareteamsincreased.Tokeepupwithrisingexpectationswhile

maintainingahigh-qualityexperience,Ivoclar

partneredwithbitHumantoexplorehowAI-

powereddigitalhumanscouldsupportcustomerinteractioninamorescalableandengagingway.

2.1TheProblem

Ivoclar’scustomercareorganizationhandlesa

highvolumeofcustomercallsandchatseveryday.Theteamincludesroughly100customer-facingemployeessupportingusersacross

multipleregionsandlanguages.Many

incomingrequestsarerelativelysimpleinnature,butstillrequiretimeandattentionfromtrainedstaff.

Atthesametime,customereducationand

technicalguidancearedifficultandcostlyto

scalethroughhuman-onlysupport.Providingconsistentanswersacrossmarkets,especiallyacrossmultiplelanguages,waschallenging

andstrainedoperations.

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TheUltimateAIPlaybook:10EnterpriseDeploymentsBoostingROI

2.CASESTUDY//IvoclarxbitHuman

bintHuma

2.2WhyItMatters

Customerexpectationsareincreasinglyshapedbydigital-firstexperiences.Dental

professionalswantquickanswers,clear

guidance,andsupportthatfeelsaccessible

whenissuesarise.ForaglobalorganizationlikeIvoclar,thismeansdeliveringconsistent

engagementacrossmarketswhilemaintainingtrustandquality.

Withoutascalablewaytomanageroutine

inquiries,customercareteamsriskbeing

overwhelmed,responsetimescanslow,and

operationalcostsrise.Ivoclarneededan

approachthatcouldsupportcustomers

efficientlywhileallowinghumanteamstofocusonmorecomplex,high-impactinteractions.

2.3TheAISolution

IvoclarpartneredwithbitHuman,astartupfocusedonAI-baseddigitalhumans,to

introduceinteractiveavatarsintocustomer-facingexperiences.TheseAIavatarsengagecustomersconversationally,answercommonquestions,andprovideguidanceinmultiple

languages.

Duringtesting,Ivoclarfocusedonpractical

performanceindicatorssuchasresponse

speed,conversationalquality,andlanguage

coverage.Fromthebeginning,theavatars

wereevaluatedacrosseighttotenlanguages,reflectingIvoclar’sglobalcustomerbase.

Tocreateamoreengagingexperience,Ivoclarcustomizedtheavatarwithbrandedelements,includingadentalprofessional’scoatand

Ivoclar’slogo.Ratherthandeliveringpurely

technicalresponses,theavatarinteractedinamorehumanway,addingcontextand

Health

storytellingthatencouragedcustomerstostayengagedandabsorbinformationmore

effectively.

Thesolutionwasshowcasedatindustry

exhibitionsandtestedliveatalargeeventin

Mexico,wheretheAIavatarintroduced

Ivoclar’smanagementteamonstageinmultiplelanguages,demonstratingitsabilitytointeractnaturallywithaglobalaudience.

2.4Impact&Results

Earlyresultsshowedstrongcustomer

engagement,withusersrespondingpositivelytotheconversational,human-likeinteraction.Customerswereoftenwillingtospendmoretimeengagingwiththeavatar,whichhelpedimproveunderstandingandoverall

satisfaction.

Fromanoperationalperspective,theAI

solutionhelpedreducepressureoncustomercareteamsbyhandlingameaningfulshareofroutineinquiries.Thisallowedhumansupportstafftospendmoretimeoncomplexcases

“Besmart,behumble,taketherisk...Ifyoudon’ttakearisk,that’sthebiggestrisk.Soyouhavetomove.Ifyouwaitforotherstodoit,thenmaybeit’stoolate.”

MichaelKrug

GlobalHeadCustomerCare,Ivoclar

whereexpertisematteredmost.Ivoclar’sfast-movingapproachtoinnovationalsosupportedprogress,withthecompanyrunningnearly10pilotswithinjustafewmonths,enablingrapidlearninganditeration.

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TheUltimateAIPlaybook:10EnterpriseDeploymentsBoostingROI

3.CASESTUDY//KAJIMAXARCHETYPEAI

RealEstate&Construction

AIforWorkplaceSafetyandRiskDetection

Kajimaisaglobalconstructioncompanywithalonghistoryofoperatingcomplex,high-riskprojects.Asjobsitesgrewlargerandmoredifficulttooversee,

especiallyinremoteenvironments,Kajimasoughtawaytoimprovevisibility,safety,anddecision-makingwithoutrelyingsolelyonconstanton-sitepresence.Toaddressthesechallenges,KajimapartneredwithArchetypeAItoapplycomputervisionandpredictiveanalyticstoreal-worldconstructionenvironments.

3.1TheProblem

Constructionsitesareinherentlyrisky,withhazardsthatcanemergequicklyandchangethroughouttheday.Traditionalsafetymanagementreliesheavilyonmanualchecksandreactivemeasures,often

identifyingissuesonlyafterincidentsoccur.

Kajimafacedchallengesinmaintainingcontinuousvisibilityacrossjobsites,particularlyforlarge-scaleorremoteprojects.Reviewingvideofootage

manuallywastime-consuming,andcriticalsafety

signalscouldeasilybemissed.Thislimitedtheabilitytoproactivelyidentifyrisksandrespondquicklytoevolvingconditions.

3.2WhyItMatters

Workplacesafetyisbotharegulatoryrequirementandahumanresponsibility.Accidentsresultin

serioushumanconsequences,projectdelays,andfinancialloeses.

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TheUltimateAIPlaybook:10EnterpriseDeploymentsBoostingROI

3.CASESTUDY//KAJIMAXARCHETYPEAI

RealEstate&Construction

Asprojectsgrowmorecomplexandlabor

shortagesincrease,relyingonconstantphysicaloversightbecomeslessfeasible.

ForKajima,improvingsafetymeantfindingawaytomonitorconditionscontinuously,identifyrisksearlier,andsupportsaferoperationswithout

increasingon-siteburden.

3.3TheAISolution

KajimapartneredwithArchetypeAItoapply

Archetype’sPhysicalAIplatform,Newton,tojob-sitemonitoring.Newtonusescomputervision

andmultimodaldataanalysistointerpretvideoandsensordatafromconstruction

environments,turningrawfootageintoactionableinsights.

Duringalarge-scalecanalreconstruction

projectinNiigata,Japan,Newtonanalyzed11,957job-sitevideos,extracting662targetedclipsthatcapturedkeyactivitiesandsafety-relatedevents.Specializedanalyticalviewsallowedproject

managerstounderstandworktimelines,

equipmentusage,andenvironmentalconditionswithoutmanuallyreviewingfootage.

Theplatformalsoreducedthetimerequiredtoretrievecriticalinformationtounder60

seconds,enablingfasterresponsesandbetter-informeddecisions.Bycombiningobject

detection,videoverification,andcontextualdatasuchasweatherconditions,Kajimagained

clearer,morereliableoversightofjobsiteactivity.

3.4Impact&Results

Thecollaborationdeliveredmeasurable

improvementsinvisibility,efficiency,andsafetyoversight.Bytransformingthousandsofhoursofunstructuredvideointosearchableinsights,

Kajimasignificantlyreducedtheburdenofmanualreviewandimprovedtransparencyacrossoperations.

Newtonsupportedsaferdecision-makingby

enablingremoteoversight,helpingKajimamovetowarditsgoalofmanaging50%ofjobsites

remotely.Theabilitytoidentifyrisksearlierandmonitorconditionscontinuouslystrengthenedcomplianceeffortsandreducedexposureto

safetyincidents,whileallowingteamstostayinformedwithoutbeingphysicallypresentonsite.

“ArchetypeAI’sabilitytounderstandreal-world

conditionsandprovide24/7visibilityonthejobsitehasbeentransformative.Itallowsustobeon-sitewhennecessary,whilestillmaintainingfull

situationalawarenessfromtheoffice.Thisisnotaboutmonitoringorcontrol;itisabout

safeguardingworkersandensuringthatprojectprogressstaysontrack.”

AkiManda

InnovationManager,Kajima

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TheUltimateAIPlaybook:10EnterpriseDeploymentsBoostingROI

4.CASESTUDY//UNISYSXPARLOA

Enterprise&AI

AIAgentsforEnterpriseCustomerSupport

Unisyssupportsenterpriseandmid-market

clientsacrosscomplexenvironmentswhere

customerservicequalitydirectlyimpacts

retentionandgrowth.Ascustomerexpectationsincreasedandserviceoperationsscaled,Unisysrecognizedtheneedtomodernizehowsupportagentsaccessedinformationandhandled

customerrequests.Toaddressthesechallenges,UnisyspartneredwithParloatoexplorehowAI-poweredagentscouldimproveknowledge

management,streamlineworkflows,and

supportscalablecustomersupportoperations.

4.1TheProblem

Unisysfacedagrowingvolumeofcustomer

requestsacrossmultipleclientsand

environments.Supportagentswererequiredtonavigatecomplexenterprisesystemswhilemanagingfragmentedknowledgesources,

whichslowedresponsetimesandincreasedoperationalstrain.

AsUnisysexpandeditsclientbase,additional

challengesemergedaroundmulti-tenancy.

Supportingmultiplecustomerswithdifferentneedsusingtraditionalagent-assisttoolsmadeitdifficulttoscaleefficientlywithoutadding

costorcomplexity.Thesefrictionpoints

limitedproductivityandmadeitharderforagentstodeliverconsistent,high-qualitysupport.

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TheUltimateAIPlaybook:10EnterpriseDeploymentsBoostingROI

4.CASESTUDY//UNISYSXPARLOA

4.2WhyItMatters

Customerexperiencehasbecomeakey

competitivedifferentiator.Slowor

inconsistentsupportdirectlyaffects

satisfaction,renewalrates,andlong-term

clientrelationships.Atthesametime,supportcostscontinuetoriseasserviceoperations

growmorecomplex.

ForUnisys,improvingagentefficiencywasnotjustaboutspeed.Itwasaboutenablingagentstoresolveissuesmoreeffectivelywhile

supportingbusinessgrowthacrossmid-marketandenterpriseclientswithout

increasingoperationaloverhead.

4.3TheAISolution

UnisyspartneredwithParloa,astartup

specializinginAI-poweredcustomerserviceagents,tointroduceintelligentagentassistcapabilitiesintoitssupportworkflows.

Parloa’ssolutionusesAIagentstoautomaterouting,surfacerelevantknowledgeinrealtime,andsupportagentswithcontextual

guidanceduringcustomerinteractions.

Thefocuswasonimprovinghowinformationisaccessedandapplied,reducingmanual

effortwhileallowingagentstoconcentrateonhigher-valueconversations.Byaddressing

knowledgemanagementandworkflow

challengestogether,Unisyswasableto

supportmorecomplexclientenvironmentswhilemaintainingconsistencyacross

customers.

Enterprise&AI

4.4Impact&Results

Thecollaborationdeliveredmeasurable

improvementsacrossproductivityand

customersupportperformance.Unisys

reducedproductivitypainpointsby50%,

helpingagentsworkmoreefficientlyand

confidently.Casehandlingtimedecreasedby20%,enablingfasterresponsesanda

smoothercustomerexperience.

Beyondoperationalgains,theAIagentassistsolutionalsosupportedbusinessgrowth.

Unisyssawa2xincreaseinitscustomer

pipeline,showinghowstrongersupport

capabilitiescancontributedirectlyto

commercialoutcomes.TheresultshighlighthowAI-poweredagentassistcanimprovebothservicequalityandscalabilitywithoutaddingunnecessarycomplexity.

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TheUltimateAIPlaybook:10EnterpriseDeploymentsBoostingROI

5.CASESTUDY//UNISYSXFRESHWORKS&EASYVISTA

Enterprise&AI

AI-PoweredServiceManagement

Asenterprisesscale,ITservicemanagement(ITSM)becomesincreasinglycomplex.Unisys,aglobalIT

solutionscompany,facedsignificantchallengesin

optimizingtheirITSMprocessesformid-market

clients.Toaddresstheseissues,Unisyspartnered

withFreshworksandEasyVista,leveragingAI-

poweredtoolstoinnovateanddeliverscalable,

cost-effectiveITSMsolutions.ThiscollaborationnotonlystreamlinedoperationsbutalsoshowcasedthetransformativepotentialofAIinservice

management.

5.1TheProblem

Unisysanditsclientsstruggledwithfragmented

ITSMsystems,leadingtoinefficienciesinhandlingahighvolumeofinternalservicerequests.Manual

tickethandlingsloweddownresolutiontimes,

creatingbottlenecksthatimpactedemployee

productivity.Theselegacysystemslacked

scalability,makingitdifficulttoadapttogrowingbusinessneeds.Additionally,operationalcosts

soaredasITteamsspentexcessivetimemanagingrepetitivetasksratherthanfocusingonstrategicinitiatives.

5.2WhyItMatters

IToperationsarethebackboneofemployee

productivity.Whenservicesystemsfailtomeet

expectations,delaysinresolvingissuescandisruptworkflows,increasecosts,andhindergrowth.Formid-marketenterprises,

TheUltimateAIPlaybook:10EnterpriseDeploymentsBoostingROI

5.CASESTUDY//UNISYSXFRESHWORKS&EASYVISTA

Enterprise&AI

inparticular,scalableandintelligentITSM

solutionscanmakeorbreakoperationalefficiency.Slowsupportandmanualprocessesnotonly

burdenITteamsbutalsoerodeusersatisfaction,ultimatelyaffectingtheorganization’sbottomline.Theneedforasystemthatcombinesautomation,scalability,andintelligencewasclear.

5.3TheAISolution

Toaddressthesechallenges,UnisysimplementedAI-poweredITSMsolutionsthroughits

partnershipswithFreshworksandEasyVista.

Theseplatfor