HowEdgeComputing,ArtificialIntelligence,andGenerativeAIarechangingthefutureofrestauranttechnology

?2024Cognizant

Abstract

Frequentchallengesfacedbytherestaurantindustrycanbecategorizedunderthreebroadlevelsegments,includinglowcustomerretention,

inefficientoperationsandinventorymanagementandhighlaborcost.

Lowcustomerretention:Therestaurantindustryishighlycompetitiveandfragmented,with

customershavingawiderangeofchoicesand

preferences.Thepercentageofcustomers

whoareloyaltoaspecificrestaurantbrandis

declining,whereasthosewhoswitchbrands

morethanonceamonthisontherise.Toretain

andattractcustomers,restaurantsneedtoofferpersonalizedandengagingexperiences,suchascustomizedmenus,recommendations,rewards,andfeedback.

Inefficientoperationsandinventorymanagement:

Therestaurantindustryfacesvariousoperationalchallenges,suchasoptimizingfoodquality

andsafety,reducingfoodwasteandspoilage,

managingsupplychainandinventory,and

complyingwithhealthandsafetyregulations.

Accordingtoareport,theaveragefoodwastageinrestaurantsis11%offoodpurchases,which

amountstosignificantlossesannually.To

improveoperationalefficiencyandprofitability,restaurantsneedtoleveragereal-timedataandanalytics,automateprocesses,andoptimize

resources.

2|?2024Cognizant

Highlaborcostandturnoverrates:Therestaurantindustryisoneofthemostlabor-intensivesectors.Accordingtoasurvey,98%ofoperatorssayhigherlaborcostsareanissuefortheirrestaurant.

ThispaperdiscusseshowEdgeComputing,

AI,andGenerativeAIcanhelpaddressthese

challengesbybringingcomputingpowerclosertowherethedataisgenerated,reducinglatency,andenablingfasterdecision-making.ItexplorestheadvantagesofEdgeComputing,theuseof

in-restaurantcloudtechnology,andthebenefitsofusingLargeLanguageModels(LLMs)onEdgedevices.Thepaperalsodiscussesthetechnicalchallengesthatareneededtoberesolved,

leveragingthemethodsformodelquantization.

Moreover,theindustrysuffersfromahighturnoverratewhichimpactsthequalityandconsistencyofserviceandincreasestrainingandhiringcosts.

3|?2024Cognizant

Introduction

Therestaurantindustryisundergoingadigital

transformation,drivenbytheneedtoenhance

customerexperiences,optimizeoperations,

andincreaserevenue.EdgeComputing,AI,and

GenerativeAIaresomeofthekeytechnologies

thatareenablingthistransformation.AccordingtoareportbyGrandViewResearch,theglobal

EdgeComputingmarketsizeisexpectedtoreach$155.90billionby3030,growingatacompound

annualgrowthrate(CAGR)of36.9%.

Thereportalsostatesthat,“ArtificialIntelligence(AI)integrationintotheEdgeenvironmentisprojected

todrivemarketgrowth.AnEdgeAIsystemis

estimatedtohelpbusinessesmakedecisionsinrealtimeinmilliseconds.Theneedtominimizeprivacy

concernsassociatedwhiletransmittinglarge

amountsofdata,aswellaslatencyandbandwidthissuesthatlimitanorganization’sdatatransmissioncapabilities,arefactorsprojectedtofuelmarket

growthinthecomingyears.”

GenerativeAI:GenerativeAIisabranchofAIthat

cangeneratenovelandrealisticcontent,suchas

images,text,music,orvideo,basedonexistingdata.

EdgeComputing:EdgeComputingistheideaof

doingcomputingactivitiesnearwherethedata

comesfrom,toreducethedelaybetweenthedataandthedecisions.Oneofthemaindifferences

betweenEdgeandCloudComputingisthelocationofdataprocessing.WhileCloudComputingrelies

oncentralizedserverstostoreandprocessdata,

EdgeComputingdistributesthedataprocessing

acrosslocaldevicesorserversthatareclosertothe

datasource.Thisreducesthelatency,bandwidth,

andprivacyissuesthatareassociatedwithCloud

Computing.EdgeComputingcanalsoenablemoreefficientandreliableAIapplicationsthatallowreal-timeornearreal-timedecisionmaking.

Restaurantsusein-restaurantCloudtechnologywithEdgeComputingtospeedupandensure

dataprocessingandsystemuptimeforin-storeapplications.

ThecomparisonbetweenEdgeandClouddeploymentsisshowninthepicturebelow.

EdgeQoSCloud

?LowLeveltask

?Memory?HighLeveltask

?LowLatencyGenAI/

?Applications

?LightweightModels

LLM?DataStorage?Asynchronous

?Latencyofflinetasks

?Power?LargeModels

Requirement?MoreComputing

?rputing?ConcurrencyResources

Diagram1:EdgevsCloud

In-restaurantcloudtechnologydrivenbyEdge

Computingcanhelprestaurantsprocessdata

fasterandmorereliablyandenhancesystem

uptime.Dataissynchronizedwiththecentralclouddatastoreandthein-storeapplicationscanswitchbetweenthein-storecloudandthepubliccloudasneeded.ThehybridEdgeinfrastructureblendsthepubliccloudandthein-storecloudandformsthebasisofthenewCloudComputingforrestaurantstoensurebusinesscontinuity.ByanalyzingdataattheEdgeinreal-timeornearreal-time,businessescantrainAImodelsandimprovetheperformanceofAIdrivenapplications.Someofthedecisions

thatcanhappenatthestorelevelare:

?Computervisiontechnology,GenerativeAI,

machinelearninganddeeplearningframeworksforAI-drivenpersonalization,in-restaurant

housekeeping,dynamicpricing,promotion,

inventory,andproductionoptimizationand

variousotherIoTdrivenoperationsthatusehugeamountofdataforpredictiveanalysis.

?Asmallandpowerfulin-storedevice,anexampleofEdgeComputing,bringscomputingpower

tothedatarequiredtoruntherestaurant

operations.Also,theAPIsinthepubliccloudthatareneededforrestaurantoperationsarecopiedatthein-storecloudtoenablefasterorder

managementandpaymentprocessing.TheseAPIswillkeeptherestaurantrunningevenwhenthereisnoconnectivity.

Thiskindofresilientandredundantarchitecturehelpsrestaurantsmaintainbusinesscontinuity,reliabilityinpaymentprocessing,whichreducesfinanciallossesandincreasescustomer

satisfactionduetofasterspeedanduptime.

EdgeComputingisessentialfortherapid

developmentofGenerativeAIasitsolves

theproblemsofreal-timeprocessing,lower

latency,andeffectivedatamanagement.Asthe

companiesdeployGenerativeAIsolutions,theywillhavetodealwiththeissuesoflong-termcost,dataprivacyandsecurity.EdgeComputinghelpsto

overcomethesechallengesandcleanuptheraw

databeforemovingthedatatothepubliccloudformorecostlyAItrainingoperations.

Pre-trainedmodel

Powerful

computation

Cloud

Largescaledataset

Lightweightmodel

Edgeserver

Finetuning

Generatedcontent

Enduserdevices

Userdata

Userdevice

Webbasedapps

Prompt

Diagram2:LogicalrepresentationofLLMModeldeploymentonEdgeandCloud

5|?2024Cognizant

HowEdgeComputingandGenAIcanimproverestaurantoperations

Basedonthisresearch,thefollowingbusinessmapdepictsthemodules(bluehighlighted),inwhichthecombinationofEdge,GenAIandTraditionalAIcanhaveconsiderableimpactonrestaurantoperations.

In-restaurant

FrontofhouseoperationsBackofhouseoperations

Order

management

Payment

Upsell/Crosssell

offer&couponmanagement

Loyalty

KitchenDisplaySystems

Delivery

ManagementSystem

Receipt

Management

Pricing/tax

POSReporting

Drive-Thru

Order

Queueing&Confirmation

Kiosk

Customer

RelationshipManagement

EmployeeClockIn/Out

Channel/ThirdParty

ServiceProviderIntegration

SocialProfileManagement

MultiChannelOrdering&

Mobility

EndofDayProcess

Inventory

Management

TimeKeeping&Payroll

CashandSalesReconciliation

PurchaseOrder

DigitalSignage

LaborScheduling

BOHReporting

StoreAsset

ManagementReconciliation

POS

ConfigurationManagement

EndofDayProcess

FoodSafety

&Waste

Management

Demand&Forecasting

Employee

management

Aboverestaurant

DataPolling&Delivery

DataAggregation&Reporting

ITServiceManagement

IdentityManagement&UserProvisioning

Corporate

MenuandRestaurantDataManagement

Menuengineering

CouponandOfferManagement

RestaurantDesign&Development/VisualMerchandising

LearningManagement

SupplyChainManagement

FranchiseeManagement

VendorManagement/SupplierSpecificationManagementSystem

CustomerRelationshipManagement

CustomercompliantManagement

InsightandAnalytics

Customer360

BrandingandMarketingStrategy

InfoSecurityManagement

BusinessProcessManagement

Finance

HumanResource

PublicRelations

Legal

CorporateProcurement

StoreAssetManagement

FacilitiesManagement

EmployeeServices

Diagram3:RestaurantBusinessMapwithGenAIopportunitieshighlighted.

6|?2024Cognizant

ReferenceArchitecture

Thefollowingarethekeycomponentsofthereferencearchitecture:

AGenAIcloudserverthatrunsthemodelsandapplicationsfortaskslikemenugeneration,

orderprediction,customersegmentation,etc.ThecloudserveralsokeepsandprocessesthedatafromtheEdgedevicesandsendsthemfeedbackandupdates.

AlocalnetworkofEdgedevicesthatoperatetheGenAImodelsandapplicationsatthe

restaurantlevel,suchaskiosks,tablets,cameras,speakers,etc.TheEdgedevicesusecompactAImodelstodotaskslikefacerecognition,voicerecognition,sentimentanalysis,etc.TheEdgedevicesalsotalktoeachotherandtothecloudserverviaWi-Fiorcellularconnection.

Asetofsensorsandactuatorsthatgatherdatafromthephysicalenvironment,suchas

temperature,humidity,noise,motion,etc.Thesensorsandactuatorsalsoregulatethephysicalaspectsoftherestaurant,suchaslighting,heating,ventilation,etc.

Thereferencearchitecturecanenablethefollowingexampleusecases:

?Acustomerwalksuptoakioskandisidentifiedbythefacerecognitionmodel.ThekioskshowsacustomizedmenucreatedbytheGenerativeAImodelbasedonthecustomer’spreferences,history,andcontext.Thecustomerordersusingvoicerecognitionandpaysusingbiometric

authentication.

?Atabletonatablesensesacustomerandturns

onthespeaker.Thespeakerwelcomesthe

customerandoffersasuggestioncreatedbytheGenerativeAImodelbasedonthecustomer’s

profile,mood,andtimeofday.Thecustomercantalkwiththespeakerusingnaturallanguage

andorder.

?Acameratracksthecrowdsizeandbehavior

intherestaurantandsendsthedatatothe

GenerativeAImodel.TheGenerativeAImodel

estimatesthedemandandsupplyoffooditemsandchangestheinventoryandproduction

accordingly.TheGenerativeAImodelalsoimprovesthestaffingandschedulingoftherestaurantbasedonthedata.

?Asensorrecordsthetemperatureandhumidityinthekitchenandsendsthedatatothe

GenerativeAImodel.TheGenerativeAImodel

managestheheatingandventilationsystemtokeeptheoptimalconditionsforfoodpreparationandsafety.TheGenerativeAImodelalsowarnsthestaffifanyabnormalityorhazard

isdetected.

7|?2024Cognizant

TechnicalArchitecture

LargeLanguageModels(LLMs)onEdgeDevices:

LLMsonEdgeDevicescanprovidemorespeed,

betterprivacyandsecurity,andonlineandofflinefunctionalityHowever,therearesomechallengesthatneedtoberesolved,includinghardwarelimits,energyuse,maintenance,andethicsissues

Quantization:QuantizationisamethodtoshrinkthemodelsizeandmakeitmoreefficientforuseonEdgedevicesItusesatechniquethatlowerstheprecisionofnumericalvaluestolowerthecomputationaland

memoryrequirementsofAImodelsQuantization

canbeappliedatdifferentlevels,suchasweights,activations,oroutputsQuantizationcanalso

beperformedatdifferentstages,suchasduring

training,aftertraining,orduringinference

Quantizationcanimpacttheaccuracy,speed,andsizeofAImodels

BusinessDrivers

Providesrealtime

insightsfromedgetocentralizedsites

SecureDevOps

managementacrossrestaurantsites

Reducing

maintenancecost

Restaurant

Local

Dashboard

EdgeAl

Application

SensorData

Edge

Management

Abovestore

感

SensorDataStream

Edge

Management

ContainerImages

Q

Secrets

DevOps

Management

AlOps

Management

DataLake

MLModelTraining

Hybirdcloudmanagement

Diagram4:TechnicalReferenceView

8|?2024Cognizant

TheGenerativeAIcloudserveristhecentralcomponentofthearchitecture,asithoststhemainmodelsandapplicationsforrestaurantmanagementandoptimization.ThecloudserverusesavarietyofAItechniques,suchasnaturallanguageprocessing,computervision,machinelearning,andGenerativeAI,tocreateandimprovethesolutionsfortherestaurant.ThecloudserveralsocommunicateswiththeEdgedevicesviaAPIsorMQTTmessages,sendingthemfeedback,updates,andcommands.

TheEdgedevicesaretheperipheralcomponentsofthearchitecture,astheyruntheGenerativeAImodels

andapplicationsattherestaurantlevel.TheEdgedevicesusequantizedAImodelstoperformtasksthat

requirelowlatency,highprivacy,orofflineavailability,suchasfacerecognition,voicerecognition,sentimentanalysis,etc.TheEdgedevicesalsocommunicatewitheachotherandwiththesensorsandactuatorsvia

Bluetooth,Zigbee,orWi-Fi,exchangingdataandinformation.

Thesensorsandactuatorsarethephysicalcomponentsofthearchitecture,astheycollectandcontrol

datafromtheenvironment.Thesensorsandactuatorsusesimpleprotocols,suchasGPIO,I2C,orSPI,to

connectwiththeEdgedevices,sendingthemsignalsandreceivinginstructions.ThesensorsandactuatorsalsoenabletheGenAImodelsandapplicationstointeractwiththephysicalaspectsoftherestaurant,suchaslighting,heating,ventilation,etc.

9|?2024Cognizant

Technologyoptions

GoogleCoral:Thisisaplatformthatoffersarangeofproducts,suchasadevelopmentboard,aUSBaccelerator,andasystem-on-module,whichcan

runTensorFlowLitemodelsattheEdge.Itcan

beusedasanEdgedevicetoenableGenerative

AIcapabilitiessuchasfacedetection,object

recognition,andsentimentanalysisforQSRkiosksandotherrestaurantdevices.SomeadvantagesofGoogleCoralareitseaseofuse,scalability,

andintegrationwithGoogleCloudservices.Somedisadvantagesareitslimitedsupportforother

frameworksandlanguages,itsdependencyonGoogle’secosystem,anditsnewand

evolvingstatus.

NVIDIAJetsonNano:Thisisapotentandenergy-

efficientplatformthatcanrunmultipleneural

networksinparallelandprocesshigh-resolution

datafrommultiplesensors.Itcanbeusedasan

EdgedevicetoboostGenerativeAItaskssuchas

computervision,naturallanguageprocessing,

andspeechrecognitionforQSRkiosksandother

restaurantdevices.SomeadvantagesofNVIDIA

JetsonNanoareitshighperformance,lowpower

consumption,andcompatibilitywithpopular

frameworksandtools.Somedisadvantagesareitshighercost,complexity,andlearningcurve,aswellasitspotentialoverheatingandinstabilityissues.

RaspberryPi:Thisisalow-cost,small,and

adaptablesingle-boardcomputerthatcanrun

Linux-basedoperatingsystemsandsupport

variousprogramminglanguages.ItcanbeusedasanEdgedevicetohostGenAImodelsand

applicationsforQSRkiosksandotherrestaurantdevices.SomeadvantagesofRaspberryPiareitscost-effectiveness,mobility,versatility,andlargecommunitysupport.Somedisadvantagesareitslimitedprocessingpower,memory,andstorage,aswellasitsrelianceonexternalperipheralsandpowersources.

GoogleAnthos:Thisisaplatformthatenablesthedeploymentandmanagementofcloud-native

applicationsacrossdifferentenvironments,suchason-premises,publiccloud,orEdgedevices.It

canbeusedasanEdgedevicetorunGenerativeAImodelsandapplicationsforQSRkiosksand

otherrestaurantdeviceswithconsistentpolicies

andsecurity.SomeadvantagesofGoogleAnthosareitsportability,scalability,andintegrationwithGoogleCloudservices.Somedisadvantages

areitshighcost,complexity,anddependency

onGoogle’secosystem.GoogleAnthossupportsKubernetes,whichisaframeworkthatoffers

variousoptionsforquantization,suchasoperator-levelquantization,model-levelquantization,and

graph-levelquantization.

Thetechnologychoicesdescribedabovehavedifferentimplicationsforquantization:

GoogleCoral:ThisdevicehasadedicatedTPU,

whichmeansthatitcanrunTensorFlowLite

modelsattheEdgewithhighspeedandlow

latency.Therefore,quantizationisrequiredfor

GoogleCoral,asitcanenablethedevicetorun

themodelsontheTPU.However,quantization

canalsolimittheflexibilityandcompatibility

ofGoogleCoral,asitcanrestrictthechoiceof

frameworksandlanguages.GoogleCoralsupportsTensorFlowLite,whichisaframeworkthatoffers

variousoptionsforquantization,suchasfullintegerquantization,floatfallbackquantization,and

hybridquantization.

NVIDIAJetsonNano:ThisdevicehasapowerfulGPU,whichmeansthatitcanrunhigh-resolutionandparallelAImodelseffectively.Therefore,

quantizationmaynotbeneededforNVIDIA

JetsonNano,asitcanhandlethecomputationalandmemorydemandsoflargemodels.

However,quantizationcanstillbebeneficialforNVIDIAJetsonNano,asitcanreducethepowerconsumptionandincreasethebatterylifeofthedevice.NVIDIAJetsonNanosupportsTensorFlow,PyTorch,andONNX,whichareframeworks

thatoffervariousoptionsforquantization,suchasquantization-awaretraining,quantization

emulation,andquantizationexport.

RaspberryPi:ThisdevicehasalimitedCPUand

GPU,whichmeansthatitcannotruncomplicatedorlargeAImodelsefficiently.Therefore,

quantizationcanbehelpfulforRaspberryPi,as

itcanreducethemodelsizeandimprovethe

inferencespeed.However,quantizationcan

alsocauseaccuracyloss,whichcanaffectthe

performanceofGenerativeAItasks.Raspberry

PisupportsTensorFlowLite,whichisaframeworkthatoffersvariousoptionsforquantization,suchaspost-trainingquantization,dynamicrange

quantization,andinteger-onlyquantization.

GoogleAnthos:Thisisaplatformthatallowsthe

deploymentandmanagementofAIapplications

acrossdifferentcloudprovidersandon-premise

environments.Therefore,quantizationcanbe

usefulforGoogleAnthos,asitcanenablethe

portabilityandscalabilityofAImodelsacross

heterogeneoushardwareandsoftwareplatforms.However,quantizationcanalsointroducesome

challengesforGoogleAnthos,suchasensuringtheconsistencyandcompatibilityofquantizedmodelsacrossdifferentframeworksandlanguages.

GoogleAnthossupportsTensorFlow,PyTorch,

andScikit-learn,whichareframeworksthatoffervariousoptionsforquantization,suchasmixed

precisiontraining,quantization-awarefine-tuning,andmodeloptimizationtools.

ApplicationofEdgeandGenerativeAIusecasesintypesofrestaurants

Finedining

?GenerativeAIcanmakenewanddifferentrecipes,menus,andpairingswiththeingredients,cuisines,seasons,andoccasions.Forexample,aGenerativeAImodelcancreateadishwithunusualtastesandtextures,orawinethat

matchesadessert.

?GenerativeAIcanalsomakethefoodlookbetterbymakingartisticand

attractivedesigns,colors,andarrangements.Forexample,aGenerativeAImodelcanuseedibleflowers,sauces,andgarnishestomakeamorevisuallyappealingeffect.

?EdgeComputingcanspeedupandimprovethedataprocessingandcommunicationbetweentherestaurant’sfront-endandbackend,andthecloud.Forexample,anEdgedevicecanhandlethecustomer’sreservation,order,feedback,andpayment,andsendthemtothekitchen,themanagement,andtheloyaltyprogramrightaway.

?EdgeComputingcanalsoprotectthecustomer’sdataandfollowdatarules.Forexample,anEdgedevicecanhideandchangethecustomer’spersonalinformation,liketheirname,email,phonenumber,andpaymentdetails,beforesendingthemtothecloud.

?EdgeComputingcanalsolettherestaurantworkofflineorwithlowconnection,whichcan

maketheservicemoreavailableandreliable.Forexample,anEdgedevicecankeepthe

importantdataandfunctionslocallyandsynchronizethemwiththecloudwhentheconnectionisback.

WithGenerativeAIandEdgeComputing,finediningrestaurantscangivemorenew,fine,andcustomexperiencesforthecustomers,andfaster,correct,andlower-costoperationsfortherestaurants.

Quickserverestaurant

?Usingcomputervisionandnaturallanguageprocessingtoidentifythecustomer’sface,voice,andorder,andsuggestcustomizedrecommendationsandoffers.

?Usingmachinelearningandreinforcementlearningtochange

themenu,theprices,andthepromotionsbasedonthedemand,theseason,andthecompetition.

?Usingsensorsandactuatorstocheckandmanagethe

temperature,thehumidity,andthehygieneofthefoodandtheequipment,andtonotifythestaffofanyissuesoranomalies.

?Usingchatbotsandvirtualassistantstohelpthecustomersandtheemployeeswiththeirquestions,complaints,andsuggestions,andprovidefeedbackandguidance.

?Usingdataanalyticsanddashboardingtomeasureand

showtheperformance,thetrends,andtheoutcomesoftherestaurant,andspotareasforimprovementandinnovation.

11|?2024Cognizant

12|?2024Cognizant

ApplicationofEdgeandGenAIUseCasesinkeyrestaurantfunctions

QSRkiosks

?GenerativeAIcancreatetailor-mademenus,deals,andsuggestionsbasedonthe

customer’sprefrences,behavior,location,andtimeoftheday.Forinstance,aGenerativeAImodelcanrecommendalow-caloriesaladforacustomerwhocaresabouttheirhealthoracombomealforafamilywithchildren.

?GenerativeAIcanalsoimprovetheuserinterfaceandinteractionofthekiosksbycreatingnaturallanguageresponses,voicesynthesis,facialexpressions,andgestures.For

example,aGenerativeAImodelcanwelcomethecustomer,taketheirorder,verifytheirpayment,andexpresstheirgratitudefortheirvisit.

?EdgeComputingcanenablequickerandmoredependabledataprocessingand

communicationbetweenthekiosksandthekitchen,aswellasthecloud.Forinstance,anEdgedevicecanprocessthecustomer’sorder,transmitittothekitchen,andupdatetheinventoryandsalesdatainreal-time.

?EdgeComputingcanalsoprovidemoreprivacyandsecurityforthecustomer’sdata,as

wellascompliancewithdataregulations.Forinstance,anEdgedevicecanencryptand

anonymizethecustomer’spersonalinformation,suchastheirname,email,phonenumber,andpaymentdetails,beforesendingittothecloud.

?EdgeComputingcanalsoallowthekioskstoworkofflineorinlowconnectivityscenarios,whichcanenhancetheavailabilityandresilienceoftheservice.Forinstance,anEdge

devicecanstoretheessentialdataandfunctionslocallyandsynchronizethemwiththecloudwhentheconnectionisrestored.

ByusingGenerativeAIandEdgeComputing,QSRkioskscanoffermorepersonalized,

interactive,andconvenientexperiencesforthecustomers,aswellasmoreefficient,precise,andcost-effectiveoperationsfortherestaurants.

13|?2024Cognizant

RestaurantPOS

Thepoint-of-sale(POS)systemisakeyelementof

anyrestaurant,asithandlesthepayments,orders,

inventory,andcustomerdata.However,traditional

POSsystemsareoftenold-fashioned,slow,andpronetomistakesandbreaches.ByusingEdgeComputingandGenerativeAI,restaurantscanupgradetheir

POSsystemsintosmart,fast,andsecureplatformsthatcanimprovethecustomerexperienceand

businessefficiency.

Quickerandmoredependabledataprocessingandcommunication:EdgeComputingcansolvethe

latencyandbandwidthproblemsthatoftenimpact

cloud-basedPOSsystems,especiallyduringbusy

timesornetworkoutages.Byprocessingthedata

locallyontheEdgedevices,thePOSsystemcan

workquickerandmoredependably,ensuringsmoothpaymentsandorders.

Betterprivacyandsecurityofcustomerdata:EdgeComputingcanalsosafeguardcustomerdatafromunauthorizedaccessorleakage,asitreducesthe

exposureofsensitiveinformationtothecloudortheinternet.Byencryptingandanonymizingthedata

ontheEdgedevices,thePOSsystemcanfollow

dataregulationsandavoididentitytheft,fraud,orcyberattacks.

Improvedcustomizationandinteractionofcustomerservice:GenerativeAIcanenablethePOSsystem

toprovidemorepersonalizedandengaging

serviceforcustomers,byusingnaturallanguage

processing,computervision,andspeechrecognitiontounderstandandrespondtocustomerneedsand

preferences.Forexample,aGenerativeAImodelcanwelcomethecustomerbytheirname,offerthem

relevantdiscountsorloyaltyrewards,recommenditemsbasedontheirorderhistoryordietary

limitations,andgeneratenaturalandhuman-likeconversations.

Enhancedefficiencyandaccuracyofrestaurant

operations:GenerativeAIcanalsohelpthePOS

systemoptimizerestaurantoperations,byusing

dataanalytics,machinelearning,andreinforcementlearningtomonitorandimprovetheperformance,

trends,andoutcomes.Forexample,aGenerativeAImodelcantrackandmanagetheinventory,supplychain,andwaste,predictthedemand,adjustthe

pricesandpromotions,andprovidefeedbackandsuggestionsforthestaff.

14|?2024Cognizant

OneexampleofhowGenerativeAI,AIandEdgeComputingcanhelpimproverestaurantPOSisasfollows:

?Acustomerwalksintoafast-foodrestaurant

andscansaQRcodeonthetablewiththeir

smartphone.TheQRcodedirectsthemtoa

webappthatservesasaPOSsystemforthe

restaurant.ThewebapprunsontheEdge

device,whichisasmallserverlocatedinthe

restaurant.TheEdgedeviceprocessesthedatalocallyandcommunicateswiththecloudonly

whennecessary,ensuringfastandreliableservice.

?Thewebappgreetsthecustomerbytheirnameandshowsthemamenuthatiscustomized

basedontheirpreviousorders,preferences,andallergies.Thecustomercanusevoiceortexttoplacetheirorder,andthewebappusesnaturallanguageprocessingandspeechrecognition

tounderstandandconfirmtheir