2024代理型AI：生成式人工智能(GenAI)的新前沿 -Agentic AI – the new frontier in GenAI

AgenticAI

–thenewfrontierin

GenAI

Anexecutiveplaybook

HarnessingAIisn’tjustabout

technology—it’saboutunleashingunprecedentedpotential.

Inanerawherespeed,efficiency,andcustomercentricitydictatemarketleadership,organisationsneedto

harnesseverytoolattheirdisposal.Overthepastcoupleofyears,artificialintelligence(AI)hasexplodedontotheworldstage,withcompaniesandindividualsacrossthegloberapidlyadoptingthetechnology.TheGCCisplayingaleadroleinthespace,withbusinessleadersintheregionexploringwaysofintegratingthisrapidly

developingtechnologyintotheiroperations.

GenerativeAI(GenAI)isbeingrecognisedasagame-changerforinnovationintheregion,empoweringenterprisesbyautomatingroutinetasks,enhancingcustomerexperiencesandassistingincritical

decision-makingprocesses.Insightsfromour27thAnnualCEOSurvey:MiddleEastfindingshaveshownthat73%ofCEOsintheMiddleEastbelieveGenAIwillsignificantlychangethewaytheircompanycreates,deliversandcapturesvalueoverthenextthreeyears

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.GenAIispoisedtomakeasignificanteconomicimpact,with

estimatesindicatingthatitcouldcontributebetween$2.6trillionand$4.4trillionannuallytoglobalGDPacrossvariousindustriesby2030.Inspecificsectors,suchasenergy,investmentsinGenAIareexpectedtotriple,

from$40billionin2023toover$140billionbytheendofthedecade.Thissurgeininvestmentreflectsthe

transformativepotentialofGenAI,particularlyinenhancingproductivity,streamliningbusinessprocesses,andreshapingvaluechainsacrossindustries

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Againstthisbackdrop,multimodalGenAIagenticframeworkshasemergedastransformativecatalysts,

enablingbusinessestoaccelerateprocessautomationatanunprecedentedscale.ThistechnologyinvolvesmultipleAIagentsworkingtogether,eachspecialisingindifferenttasksordatatypes,tosolvecomplex

problemsandautomateprocesses.Bycollaboratingandconstantlylearning,theseagentsenhance

decision-making,optimiseprocesses,anddriveinnovation.ItcombinesrangeofadvancedAItechniquestoprocessdiversedatatypesandautomatecomplextasks.

Thecentralquestionisn’twhethertoadoptthistechnology,buthowswiftlyorganisationscanintegrateittostayaheadofthecompetition.Thisexecutiveplaybookexploreshoworganisationscanleveragethis

technologytoboostoperationalefficiency,enhancecustomerexperience,anddriverevenuegrowth.Itprovidesreal-worldsuccessstoriesspanningindustrysectorsandorganisationalfunctions,strategicinsights,tactical

blueprints,andbestpracticestoguideyourjourneyintothisrevolutionarylandscape.

Keyinsights

●AgenticAI,differentiatedbyitsadvancedhuman-likereasoningandinteractioncapabilities,is

transformingthemanufacturing,healthcare,finance,retail,transportation,andenergysectors,amongothers.

●Organisations’AIstrategiesshouldleveragemultimodalGenAIcapabilitieswhileensuringethicalAI

safeguardstodriveautonomousprocessre-engineeringandenhanceddecision-makingacrossalllinesofbusiness.

●Integratedeffectively,agenticAIcanenhanceefficiency,lowercosts,improvecustomerexperience,and

driverevenuegrowth.

WhatisagenticAI?

AgenticAIgenerallyreferstoAIsystemsthatpossessthecapacitytomakeautonomousdecisionsandtakeactionstoachievespecific

goalswithlimitedornodirecthumanintervention

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KeyaspectsofagenticAI

Goal-orientedbehaviour:TheseAIagentsaredesignedtopursuespecificobjectives,optimising

theiractionstoachievethedesiredoutcomes.

Autonomy:AgenticAIsystemscanoperateindependently,

makingdecisionsbasedontheirprogramming,learning,and

environmentalinputs.

Environmentinteraction:An

agenticAIinteractswithits

surroundings,perceivingchangesandadaptingitsstrategies

accordingly.

Workflowoptimisation:AgenticAIagentsenhanceworkflowsandbusinessprocessesbyintegratinglanguageunderstandingwith

reasoning,planning,and

decision-making.Thisinvolvesoptimisingresourceallocation,improvingcommunicationandcollaboration,andidentifyingautomationopportunities.

Learningcapability:ManyagenticAIsystemsemploymachinelearningor

reinforcementlearningtechniquestoimprovetheirperformanceovertime.

Multi-agentandsystem

conversation:AgenticAI

facilitatescommunication

betweendifferentagentsto

constructcomplexworkflows.Itcanalsointegratewithother

systemsortools,suchasemail,codeexecutors,orsearch

engines,toperformavarietyoftasks.

Learningcapability

Environmentinteraction

Workflow

optimisation

Goal-oriented

behaviour

Autonomy

Multi-agentandsystemconversation

EvolutiontomultimodalGenAIagents

InAI,theonlyconstantischange—embraceacultureofperpetualinnovation.

Thejourneyofagenticframeworksbeganassimple,rule-basedsystemsdesignedtoperformspecifictasks.Overtime,thesesystemshaveevolvedintosophisticated,multimodalagentscapableofprocessingandintegratinginformationfromvarioussources,suchastext,images,andaudio.MultimodalitycapabilitiesallowAIagentstounderstand,employ

reasoning,andinteractlikehumans,enhancingtheireffectivenessandversatilitytosolveawiderangeofbusinessproblems

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Theevolutioncanbebrokendownintothreekeyphases:

(2000s)

IntegrationofMachineLearning(ML)

○Learningfromdata:TheintegrationofMLallowedagentstolearnfromlargedatasets,improvingtheirabilitytomakedecisionsandperformtasks.Thiswasasignificantstepforwardfromrule-basedsystems,asagentscouldnowadapttonewinformationandimproveovertime.

○NaturalLanguageProcessing(NLP)enableduserinteractions:AdvancesinNLPenabledagentstounderstandandgeneratehumanlanguagemoreeffectively,makinginteractionsmorenaturalandintuitive.

(2010s)

Introductionofmultimodality

○Combiningtext,images,andaudio:Multimodalagentsemerged,capableofprocessingandintegrating

informationfromvarioussources.Forinstance,anagentcouldanalyseatextdescription,recogniseobjectsinanimage,andunderstandspokencommands.Thismultimodalitymadeagentsmoreversatileandcapableofhandlingcomplextasks.

○Enhanceduserinteractions:Multimodalagentscouldinteractwithusersinmoredynamicways,suchasprovidingvisualaidsinresponsetotextqueriesorunderstandingcontextfromacombinationofspokenandvisualinputs.

2020s-present

Advancedautonomyandreal-timeinteractions

○Advancedautonomy:Agentscanoperateindependently,rationaliseandsettheirowngoals,developpath(s)toattainthesegoals,andmakeindependentdecisionswithoutconstanthumanintervention,leveragingdatafrommultiplesourcesorsyntheticdatasets.Inamulti-agenticorchestrationsystem,thefirstsetofagents

focusonmimickinghumanbehaviour(e.g.ChatGPT-4o),thatis,thinkingfasttocomeupwithsolution

approach,whilethesecondsetofagentsfocusonslowreasoning(e.g.ChatGPT-1o)tocomeupwithavettedsolution

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.Combiningthinkingfastandslowreasoning,agentscanprocessinformationandmakeoptimal

decisionsinreal-time–crucialforapplicationslikeautonomousvehicles,real-timecustomerservice,and

variousmission-criticalbusinessprocesses.ThisautonomymakesagenticAIparticularlypowerfulindynamicandcomplexreal-worldenvironments.

○UserinteractionswithinanethicalandresponsibleAI-controlledenvironment:Withincreased

capabilities,therehasalsobeenafocusonensuringthatagenticsystemsoperateethicallyandresponsibly,consideringfactorssuchasbias,transparency,andaccountability.

IntegrationofML(2000s)

Learningfromdata

NLPenableduserinteractions

AIagent

Goal-orientedbehaviour

Introductionofmultimodality(2010s)

Combiningtext,images,andaudio

Enhanceduserinteractions

Advancedautonomyandreal-timeinteractions(2020s-present)

UserinteractionswithinanethicalandresponsibleAI-controlledenvironment

Human-likereasoningandadvancedautonomy

Whyorganisationsshouldpayattention

Inthefastlaneoftechnologicalevolution,missingtheAIturntodaymeansbeingoutpacedtomorrow.

AgenticAIofferssignificantadvantagesinefficiency,decision-making,andcustomerinteraction.Byautomatingroutinetasksandprovidingintelligentinsights,agenticAIcanhelporganisationssavetime,reducecost,andimproveoverall

productivity.Moreover,organisationswhoadoptanagenticAIsystemcangainacompetitiveadvantagebyleveragingitscapabilitiestoinnovateandenhancetheirbusinessoperations.Lowercosttoentryandeconomiesofscalemakesit

favourablefororganisationstofullyharnessthecapabilitiesitofferscomparedtoitspredecessorsliketraditionalMLandRoboticProcessAutomation(RPA)-drivenautomations.

AgenticAIsystemscansignificantlyenhanceanorganisation’scompetitiveedgebyautomatingcomplexworkflows,

reducingoperationalcosts,andimprovingdecision-makingprocesses.Thesesystemsaredesignedtoadapttochangingbusinessenvironments,drivinghigherproductivityandenablingorganisationstostaycompetitive.Forexample,agenticAIcanpredictmarkettrendsandcustomerpreferences,allowingbusinessestotailortheirstrategiesproactively.This

adaptabilitynotonlyimprovesefficiencybutalsofostersinnovation,givingcompaniesasignificantedgeovercompetitors

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Moreover,agenticAIsystemscanhandlelargevolumesofdataandextractactionableinsights,whichcanbeusedtooptimiseoperationsandenhancecustomerexperiences.Byautomatingroutinetasks,thesesystemsfreeuphumanresourcestofocusonmorestrategicinitiatives,therebyincreasingoverallorganisationalagilityandresponsiveness

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Enhanceddecision-making

AgenticAIsystemscananalysevastamountsofdataquicklyandaccurately,providingvaluableinsightstoinformbetterdecision-making.Businessescanleveragetheseinsightstooptimiserevenueandoperations,identifymarkettrends,andmakedata-drivendecisions.Forinstance,inthefinancialsector,AIcananalysemarketdatatopredicttrends,inform

investmentstrategies,andboostinvestmentROI.Inretail,itcanstreamlineinventorymanagementbypredictingdemandandoptimisingstocklevels.

Boostedefficiencyandproductivity

AgenticAIcansignificantlyenhancebusinessefficiencyandproductivitybyautomatingroutinetasksandprocesses.Thisallowsemployeestofocusonmorestrategicandcreativeactivities.Forexample,incustomerservice,agenticAIcan

handlecommoninquiries,freeinguphumanagentstotacklemorecomplexissues.Inmanufacturing,AI-drivenrobotscanmanagerepetitivetaskswithprecisionandconsistency,reducingerrorsandincreasingoutput.

Improvedcustomerexperience

ByintegratingagenticAI,businessescanofferpersonalisedandresponsivecustomerexperiences.AI-drivenchatbotsandvirtualassistantscanprovideinstantsupport,answerqueries,andevenrecommendproductsbasedoncustomer

preferencesanddynamicinteractions.Thisimprovescustomersatisfaction,buildsloyalty,anddrivessales.Forexample,e-commerceplatformsuseAItorecommendproductsbasedonbrowsinghistoryandpurchasebehaviour.

HowtoconceptualiseagenticAI

solutionsforfuturebusinessoperations

AgenticAIsystemsareredefiningcustomerservicecentresandaregainingpopularityasagame-changingcapabilityforbothgovernmententitiesandprivatesectororganisations.Whiletraditionalrule-basedchatbots

(software-as-a-service)providedbasic24/7support,andRetrievalAugmentedGenerated(RAG)-basedchatbots

enhancedhuman-likeinteractions(enhancedsoftware-as-a-service),agenticAIsurpassesbothintermsofaccuracy,contextualcoherence,andproblem-solvingability.

Intermsofaccuracy,rule-basedchatbotsarelimitedtoprogrammedresponses,causinginaccuracieswhenqueries

falloutsideofpredefinedrules.RAG-basedchatbotsdependonretrieveddatathatmaynotmatchuserintent.In

contrast,thenovelapproachofagenticAIallowsittounderstandnuancesinlanguage,generatingaccurateresponseseventocomplexorunseenqueries.Itsabilitytolearnfromvastdatasetsenhancesprecisionandadaptability,makingitsuperiorforcustomerinteractions.

Oneofthebiggestlimitationsofchatbotshasbeencontextualcoherence.Rule-basedchatbotsstruggletomaintain

contextinextendedinteractionsduetolinearscripting,leadingtodisjointedresponsesthatharmcustomer

experience.RAG-basedchatbotsmayproduceinconsistentrepliesifretrievalmechanismsdon'tconsiderpreviousinteractions.WhereasagenticAI’sorchestrationcapabilityhelpsitexcelattrackingconversationhistory,

understandingdialogueflow,ensuringresponsesremaincontextuallyappropriateandcoherent,significantlyboostingcustomerengagement.

Thusfar,bothrule-basedandRAG-basedchatbotshavelimitedautonomousproblem-solvingability.Theformercan'thandleproblemsoutsidetheirscriptswhilethelatterprovideinformationbutcan'tsynthesisedataandpreparethe

human-liveproblem-solvinglogictosolvecomplexissuesacrossintegratedsourcessuchasCRMs,ERP,orIVR

systems.TheagenticAIperformsdynamicreasoninganddecision-making,leveragingaseriesofautonomousagents,analysingcustomerissues,consideringmultiplefactors,andapplyinglearnedknowledgetoresolveproblemsmore

efficiently.Theoutcomeisquicker,solution-oriented,andfluidconversationsthatenhancecustomerexperienceandsetnewstandardsforefficiencyandresponsivenessinautomatedcustomerservice.

Micro-agentsOrchestratoragentMasteragent

Customersupportagent

Customersupportagent

User

experience

agent

Issue

resolution

agent

Feedback

collection

agent

FAQagent

Nthagent

Statusupdatesagent

AgenticAIbusinessimperatives

Organisationsmanagingday-to-dayoperationsstandtogainsignificantlyfromagenticAIsystems,embracingthe

emerging"service-as-a-software"model.Thisinnovativeapproachtransformsmanuallabourintoautomated,AI-drivenservices.Ratherthanpurchasingtraditionalsoftwarelicencesorsubscribingtocloud-basedsoftware-as-a-service

(SaaS),businessescannowpayforspecificoutcomesdeliveredbyAIagents.Forexample,acompanymightemployAIcustomersupportagentslikeSierratoresolveissuesontheirwebsites,payingperresolutionratherthanmaintainingacostlyhumansupportteam.Thismodelallowsorganisationstoaccessawiderrangeofservices–whetherit’slegalsupportfromAI-poweredlawyers,continuouscybersecuritytestingbyAIpenetrationtesters,orautomatedCRM

management–atafractionofthecost.Thisnotonlydrivesefficiencybutalsosignificantlyreducesoperationaloverheads.

Byleveragingtheservice-as-a-softwaremodel,businessescanautomatebothroutineandhighlyspecialisedtasksthatwereoncetime-consuming,requiredskilledprofessionals,andtypicallyinvolvedexpensivesoftwarelicencesorcloud

solutions.AIapplicationswithadvancedreasoningcapabilitiescannowhandlecomplextasks,fromsoftware

engineeringtorunningcustomercarecentres,enablingcompaniestoscaletheiroperationswithoutaproportionalincreaseincost.Thistransitionexpandstheservicesavailabletoorganisationsofallsizes,freeingthemtofocusonstrategicprioritieswhileAIsystemsmanagetheoperationalburden.AdoptingtheseAI-drivenservicespositions

businessestostaycompetitiveinanever-evolvingmarketplace

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Transitioningfromcopilottoautopilotmodels

Service-as-a-softwarerepresentsanoutcome-focused,strategicshift,enablingorganisationstotransitionfromtheircurrentstatetooperatingin"copilot"andultimately"autopilot"modes.Sierra,forinstance,offersasafetynetby

escalatingcomplexcustomerissuestohumanagentswhennecessary,ensuringaseamlesscustomerexperience.WhilenotallAIsolutionsofferthisbuilt-infallback,acommonstrategyistoinitiallydeployAIina"copilot"role

alongsidehumanworkers.Thishuman-in-the-loopapproachhelpsorganisationsbuildtrustinAIcapabilitiesovertime.AsAIsystemsdemonstratetheirreliability,businessescanconfidentlytransitiontoan"autopilot"mode,whereAI

operatesautonomously,enhancingefficiencyandreducingtheneedforhumanoversight.GitHubCopilotisaprimeexampleofthis,assistingdevelopersandpotentiallyautomatingmoretasksasitevolves.

OutsourcingworkthroughAIservices

Fororganisationswithhighoperationalcosts,outsourcingspecifictaskstoAIservicesthatguaranteeconcrete

outcomesisanincreasinglyattractiveoption.TakeSierra,forexample:businessesintegrateSierraintotheircustomersupportsystemstoefficientlymanagecustomerqueries.Insteadofpayingforsoftwarelicencesorcloud-based

services,theypaySierrabasedonthenumberofsuccessfulresolutions.Thisoutcome-basedmodelalignscostsdirectlywiththeresultsdelivered,allowingorganisationstoharnessAIforspecifictasksandpaysolelyforthe

outcomesachieved.

ThisshiftfromtraditionalsoftwarelicencesorcloudSaaStoservice-as-a-softwareistransformativeinseveralways:

Targetingserviceprofits:TraditionalSaaSfocusedonsellinguserseats,whereasservice-as-a-softwaretapsintoserviceprofitpools,deliveringsolutionsthatfocusonspecificbusinessoutcomes.

Outcome-basedpricing:Insteadofchargingperuserorseat,service-as-a-softwareadoptsapricingmodelbasedontheactualoutcomesachieved,directlyaligningcostswithresults.

High-touchdeliverymodels:Service-as-a-softwareoffersatop-down,highlypersonalisedapproach,providingtrusted,tailoredsolutionsthatmeetthespecificoperationalneedsofbusinesses.

Whyshouldorganisationsconsiderearlyadoptionandavoidbeinglatemovers?

Latemovers

Earlyadopters

Struggletocatchupandmissoutoncreatingcompetitiveadvantage.

SlowtoinnovatebusinessprocessesandtakefulladvantageofAIsolutionstocreatedifferentiation.

Playcatch-uptomatchthepersonalisedservicesofearlyadopters.

Higherlostopportunitycostduetolateentryandadoptions.

Missoutonearlylearningopportunitiesandindustryinfluence.

Struggletoachievesimilarmarketshare.

Facehigherbarrierstoentryduetoestablishedcompetitors.

Payrelativelylowercostofentryandlowerlearningandexperiments.

Marketposition

Innovation

Customer

relationships

Operationalefficiency

Learningcurve

Marketshare

Barrierstoentry

Costtoentry

Setindustrybenchmarks

andgainfirst-movermarketadvantage.

LeverageAItoinnovatebusiness

processes,deploytheAIsolutionseffectivelyandcreatedifferentiation.

Builddeepercustomerrelationshipsthroughpersonalisedandnewer

experiences.

Streamlineoperationsandreduceoperationalcostearlyon.

Benefitfromtheinitiallearningcurveandshapeindustrystandards.

Increasemarketshareandprofitabilitythroughearlyadoption.

CreatebarriersforcompetitorsthroughdeepAIintegration.

Payrelativelyhighercostofentryanditerativetest-and-learnduetonewAIsolutions.

Real-worldsuccessstories

Catalysingchangeacrossallindustries

Manufacturing:SiemensAG

SiemenstransformeditsmaintenanceoperationsbydeployingAImodelsthatanalysesensordatafrommachinery.Thesystempredictsequipmentfailuresbeforetheyoccur,schedulingmaintenanceproactively.Themultimodalframeworkprocessesdatafromvarioussources–vibration,temperature,andacousticsignals–providingaholisticviewof

equipmenthealthandproactivemaintenanceorchestratedbytheagenticAImodels.

Financialimpact:

●Savings:Reducedmaintenancecostsby20%

●Revenuegrowth:Increasedproductionuptimeby15%

Non-financialbenefits:

●Enhancedequipmentreliability

●Improvedworkersafety

Technologystack:

●AImodels:Regressionanddeeplearningmodels

●Platforms:SiemensMindSphere

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●Tools:Scikit-learn,TensorFlow,Keras,IoTsensors

Healthcare:MayoClinic

ByintegratingAIintoitsradiologyworkflows,MayoClinicallowsforquickerandmoreaccuratediagnoses.ThemultimodalAIprocessesimagingdataalongsidepatienthistoryandlabresults,offeringcomprehensiveinsightsthataidradiologistsindecision-making,automatingdocumentationandprocessautomationacrosstheradiologyvaluechain.

Financialimpact:

●Efficiencygains:Reduceddiagnostictimesby30%

●Costreduction:Lowered

unnecessaryproceduresby15%

Non-financialbenefits:

●Improveddiagnosticaccuracy

●Enhancedpatientoutcomes

Technologystack:

●AIModels:Regressionand

ConvolutionalNeuralNetworks(CNNs)models

●Frameworks:NVIDIAClaraplatform

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●Tools:Scikit-learn,PyTorch,MedicalImagingData

Finance:JPMorganChase

JPMorgan’sContractIntelligence(COiN)platformusesAItoanalyselegaldocuments,extractingkeydatapointsin

seconds.Themultimodalframeworkinterpretscomplexlegallanguage,images,andtables,streamliningaprocessthatoncetookthousandsofhumanhours.

Financialimpact:

●Savings:Saved360,000hoursofmanualreviewannually

●Riskmitigation:Significantlyreducedcompliancerisk

Non-financialbenefits:

●Enhancedaccuracyindocumentanalysis

●Improvedemployeeproductivity

Technologystack:

●AImodels:NLPwithGenerativePre-trainedTransformers(GPT)

●Frameworks:COiNplatform

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●Tools:Python,Hadoop

Retail:Amazon

AmazonleveragesAItoanalysebrowsingbehaviour,purchasehistory,andevenvisualpreferences.MultimodalAImodelsgeneratepersonalisedrecommendations,orchestratetasksacrossorderfulfilmentvaluechains,andenhancethe

shoppingexperiencetodrivesales.

Financialimpact:

●Revenueboost:Increasedsalesby35%throughpersonalised

recommendationsandone-clickorderfulfilment

●Customerretention:Improvedloyaltyratesby20%

Non-financialbenefits:

●Enhancedcustomersatisfaction

●Increasedengagementtimeontheplatform

Technologystack:

●AImodels:RegressionanddeeplearningModels

●Frameworks:Amazon

Personalise

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andAmazonOrderFulfilment

●Tools:AWSSageMaker

Transportationandlogistics:DHL

DHLutilisesAImodelstopredictandorchestrateshippingdemands,optimiseroutes,andmanagewarehouseoperations.Thesystemprocessesdatafromvarioussources,includingtrafficpatterns,weatherconditions,andordervolumes.

Financialimpact:

●Costsavings:Reducedoperationalcostsby15%

●Efficiencygains:Improveddeliverytimesby20%

Non-financialbenefits:

●Enhancedcustomersatisfaction

●Reducedcarbonfootprint

Technologystack:

●AImodels:MLmodelsandrouteoptimisationalgorithms

●Frameworks:DHLResilientsupplychainplatform

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●Tools:IoTdevices,MLmodels

Energy:BP(BritishPetroleum)

BPusesAItoanalyseseismicdata,generating3Dmodelsofsubterraneanstructures.Themultimodalapproachcombinesgeological,geophysical,andhistoricaldatatoidentifyfavourabledrillingsitesandorchestratedrillingequipmentsettings

foroptimaloutcomes.

Financialimpact:

●Savings:Reducedexplorationcostsby20%

●Revenuegrowth:Increased

successfuldrillingoperationsby15%

Non-financialbenefits:

●Reducedenvironmentalimpact

●Improvedsafetymeasures

Technologystack:

●AImodels:RegressionandGenAImodels

●Frameworks:Azurecloudservices

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●Tools:MicrosoftAI

Education:Pearson

Pearson’sAImodelstailoreducationalcontenttoindividuallearnerneeds,adjustingdifficultylevelsandcontenttypesbasedonperformanceandengagementdata.

Financialimpact:

●Revenueincrease:Boostedcourseenrollmentby25%

●Costreduction:Lowered

contentdevelopmentcostsby15%

Non-financialbenefits:

●Improvedstudentoutcomes

●Enhanceduserengagement

Technologystack:

●AImodels:Adaptivelearningalgorithms

●Frameworks:Multimodalcontentdeliverysystems

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●Tools:Python,TensorFlow

Mediaandentertainment:Netflix

NetflixusesAImodelstorecommendandorchestratecontentbyanalysingviewinghabits,ratings,andevenvisual

contentfeatures.Themulti-modalAIensuresthatusersfindcontentthatresonateswiththeirpreferences,keepingthemengaged.

Financialimpact:

●Subscribergrowth:Increasedretentionratesby10%

●Revenueboost:Enhanced

engagementleadingtohighersubscriptionrenewals

Non-financialbenefits:

●Personaliseduserexperiences

●Improvedcontentstrategy

Technologystack:

●AImodels:MLandGenAImodels

●Frameworks:Netflixmultimodaluserinteractionanalysis

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●Tools:AWS,ApacheSpark

Telecommunications:AT&T

AT&T’sAImodelsanalyseandorchestratenetworkperformancedataandcustomerinteractionstooptimisenetworkoperationsandpersonalisecustomerservicethroughchatbots.

Financialimpact:

●Costsavings:Reduced

operationalexpensesby15%

●Revenuegrowth:Improved

upsellingthroughpersonalisedoffers

Non-financialbenefits:

●Enhancednetworkreliability

●Improvedcustomersatisfaction

Technologystack:

●AImodels:MLfornetworkanalytics

●Frameworks:Edgecomputingwithmultimodaldatainputs

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●Tools:AIchatbots,dataanalyticsplatforms

Governmentandpublicsector:SingaporeGovernment

SingaporeutilisesAImodelstoorchestrateandmanagetrafficflow,energyconsumption,andpublicsafety.The

multi-modalsystemprocessesdatafromvarioussensorsandcitizenfeedbackmechanismstomakereal-timedecisions.

Financialimpact:

●Efficiencygains:Reducedadministrativecostsby25%

●Economicgrowth:AttractedUS$12billioninforeign

investment

Non-financialbenefits:

●Improvedpublicservices

●Enhancedqualityoflifeforcitizens

Technologystack:

●AImodels:MLandGenAImodels

●Frameworks:SmartNationplatform

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●Tools:IoTsensors,cloudcomputing

Real-worldsuccessstories

Innovationwithinbusinessfunctions

Humanresources:Unilever

UnileverusesAItoscreencandidatesbyanalysingvideointerviewsandresponses,allowingrecruiterstofocusonthemostpromisingapplicants.

Financialimpact:

●Costreduction:SavedoverUS$1millionannuallyin

recruitmentcosts

●Efficiencygains:Reducedhiringtimeby75%

Non-financialbenefits:

●Enhanceddiversityinhiring

●Improvedcandidateexperience

Technologystack:

●AImodels:NLPandfacialrecognitionalgorithms

●Frameworks:Multimodalcandidateassessmentplatforms

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●Tools:HireVueAIplatform

Customerservice:BankofAmerica

Erica,anAIvirtualagent,handlesoveramillioncustomerqueriesdaily–includingsnapshotsofmonth-to-datespendingandflaggingrecurringc