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APACAIOutlook2025
THENEXTFRONTIEROFAIRACE:
SCALINGAIFORIMPACT
NOVEMBER2024
AUTHOREDBY
SASHMUKHERJEE
VPIndustryInsights,Ecosystm
SPONSOREDBY
2
CASESTUDIES
INTRODUCTION
THENEXTWAVEOFAI
SCALINGFORSUCCESS
Introduction
ImagineafuturewhereAIdrivesrealbusinessimpact,notjustbuzz.Whileitspotentialisevident,manyorganisationsstillstruggletoharnessiteffectively.
In2024,experimentationandtrend-chasingdefinedtheAIlandscape.In2025,thefocuswillshifttodeliveringtangible
valuethroughrobustinfrastructure,efficientoperations,andskilledtalent.Successwillhingeonastrategicapproach:clearoutcomes,strongdatamanagement,andgovernance.
ThiswhitepaperexamineskeyAItrendsandthechallengesorganisationsmustaddresstounlockAI'stransformativepotential.Insightsfrom17APACorganisationsprovideablueprintforacceleratingAIinitiativeswhilemanagingriskseffectively.
ECOSYSTMAPACAIOUTLOOK2025|IBM
TheNextWave
ofAI:Whatto
Expectin2025?
3
INTRODUCTION
THENEXTWAVEOFAI
SCALINGFORSUCCESS
CASESTUDIES
LargeenterpriseshavebeenactivelyexperimentingwithAIin2024,particularlyinthewakeofGenAI’srapidadvancement.Initialenthusiasm,fuelledbybusinessmandatesandreadilyavailabletechnology,ledtoaflurryofAIprojects.
However,astheyearprogressed,amorenuancedunderstandingofAI'spotentialandchallenges
emerged.Whilethefocusremainsonidentifyingusecasestoenhanceemployeeproductivityand
customerexperience,organisationsarenowprioritisingfoundationalelementslikedatagovernance,
dataquality,andskilledtalent.Moreimportantly,theemphasishasshiftedtomaximisingROIfromAI
investments,giventhesignificantresourcerequirements.Theneedforopen-sourceAIandtheabilitytointegrateAIplatformsfromanytechproviderhasalsobecomeincreasinglyimportant,mostlytoavoid
vendorlock-ins.
AIWake-upCall:RethinkYourStrategy
Wehaveenoughusecases-weneedtoprioritisethembasedondataavailability,securityandprivacyconsiderations,andROI.
BANKINGCDO,SINGAPORE
OurAlinitiativeshavesloweddown.Weareextremelyconcernedaboutdatagovernancestartingfirstwithprivacyandsecurity.
INSURANCECIO,AUSTRALIA
AlhastakenusbacktotheearlyClouddays-wearestrugglingwithprotectingourorganisationfromShadowITandBYOAI.
RETAILCTO,INDIA
ECOSYSTMAPACAIOUTLOOK2025|IBM
THENEXTWAVEOFAI
SCALINGFORSUCCESS
Whileorganisationshavestruggledtorealisetheirexpectedbenefits,technologyvendorscontinuetoinnovate,makingAImoreaccessibletoenterprises.
Examiningthekeytrends–bothorganisationalandtechnological–expectedtoimpacttheAIlandscapein2025canguideenterprisesonhowtocalibrateorstarttheirAIjourneys,andmoreimportantly,wheretobegin.
Herearethe5keytrendsthatwillimpacttheAIlandscapein2025:
StrategicAI
MaximisingImpact
RightsizingAI
Targeted,Open-
SourceAIModels
forEfficiency
UnifiedAI
Ensuring
Managementand
Governance
AgenticAI
Empowering
IntelligentSystems
Beyond
Productivity
TheHuman-Centric
FutureofAI
4
INTRODUCTION
CASESTUDIES
ECOSYSTMAPACAIOUTLOOK2025|IBM
THENEXTWAVEOFAI
SCALINGFORSUCCESS
#1StrategicAI:MaximisingImpact
OrganisationswilladoptamorestrategicapproachtoAI,prioritisingprojectsbasedonfeasibilityandbusinessimpact.
AI'slong-termbenefitsandhighupfrontcostschallengetraditionalROImetrics,oftenleading
businessleaderstopushforearlyresultswithoutgraspingthecomplexities.Toaddressthis,
techleadershavetraditionallyfocusedonquick-winusecasestobuildtrustandinternalbuy-in.Asorganisations’AIjourneysmature,theywillaimtobalanceshort-termwinswithlong-termAIstrategies.ThefocusofAIinvestmentsisshiftingbeyondemployeeproductivityandcustomer
experience,towardsbroaderstrategicgoalssuchasinnovationandimpactoncompanyfinancials.
Nearly60%ofAsiaPacific
organisationsanticipaterealising
thebenefitsoftheirAIinvestmentswithin2-5years.Only11%expect
immediatereturnswithinthenexttwoyears.
SOURCE:ECOSYSTM,2024
BEYONDTHEIMMEDIATE:LONG-TERMBENEFITSOFAI
36%
32%
26%
Innovationofservice/product/businessmodel
21%21%
18%
Increasedrevenue
12%11%12%
Costsavings
12%
7%
9%
Increasedemployeeproductivity
3%3%3%
Improvedcustomerexperience
5
INTRODUCTION
CASESTUDIES
ANZASEANIndia
ECOSYSTMAPACAIOUTLOOK2025|IBM
Q:WhatbenefitsdoesyourorganisationexpectfromAIinthenext2years?N=518;Source:Ecosystm,2024
Wewillseeashiftfromlow-risk,non-coreusecasesto
deployingGenAIincorebusinessfunctionsforcompetitiveadvantageandimprovedROI.
TechanddataleaderswilladoptcomprehensiveAIevaluationframeworks,assessingfinancialmetricsalongsidebroaderimpactslikejobroles
anddatagovernance.Selectingtherightusecaseinvolvesatwo-step
process:prioritisingwithstructuredassessmentsandevaluatingtechnicalfeasibility.Thiswillincludeexaminingdatausability,infrastructure,digitalinvestments,processreadiness,andresourceneeds.
TraditionalROImetricsstrugglewithAI’slong-term,intangiblebenefitsandhighupfrontcosts.WhilePoCsvalidatefeasibility,theyoftenmissscaling
complexitiesandtruecosts.Toaddressthis,organisationswillembrace
morenuancedevaluationapproaches,balancingtangibleandintangiblebenefits.Aholisticcostingstrategyinvolvingbusiness,technology,data,andfinanceteamswillbeacriticalaspecttoaccountforinfrastructure,
hardware,software,andpersonnelexpenses,acrosstheprojectlifecycle.
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INTRODUCTION
THENEXTWAVEOFAI
SCALINGFORSUCCESS
CASESTUDIES
CASESTUDIES
StrategicAdoptionofAI
StarUnionDai-ichiLifeInsurance(SUDLife)focusedon
tacklingaspecific,highimpactusecase–thechallenge
ofoutperforminglarge-capportfoliosinIndia’scompetitive
capitalmarkets.ByleveragingGenAI,thecollaborationaimstodelivercriticaldata-driveninsightsforanewinvestment
product.Outcomesincludeenhanceddataanalysisfor
extractingvaluableinsights,improveddecision-making
throughdata-driventoolsforfundmanagers,andadherence
toresponsibleAIpractices.AIhasbecomeanessentialtool
forfundmanagerstonavigatevastdatavolumes,transformingitfroma"nice-to-have"toa"must-have."
StarHubhasstrategicallyintegratedAItoenhancecustomer
experience,streamlineprocesses,anddriveinnovation.TheirCloudInfinity,theworld’sfirstmetropolitanhybridmulti-cloudarchitecture,usesAIforautomatedresourcemanagement,
allowingenterprisestoefficientlyscaleresourcesandoptimiseapplicationsanddata.TheyarealsocollaboratingwithamajorretailoperatortodeployaSmartRetailsolutionthatcombines
GenAIandbusinessintelligence,generatingactionableinsightsfromcustomerdata.
ECOSYSTMAPACAIOUTLOOK2025|IBM
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INTRODUCTION
CASESTUDIES
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#2RightsizingAI:Targeted,Open-sourceAImodelsforEfficiency
Smaller,open-source,specialisedmodelswillgaintraction,offeringabalancebetweenperformance,resourceefficiency,andflexibility.
OrganisationsinAsiaPacificwillincreasinglyleverageopen-sourceAImodelstodriveinnovationandefficiency.Thiswillbeagame-changer,offeringcost-effectiveness,seamlessintegrations,andtheflexibilitytousecustommodelsorleveragevendor-specificcapabilities.
Whilelargelanguagemodels(LLMs)havecapturedtheimagination,smaller,specialisedmodelstailoredtospecifictasksordomainsofferacompellingalternative.Thesemodelsoftendelivercomparableperformancewhilerequiringlesscomputationalpower,makingthemidealfororganisationsaimingtotrainmodelsusingproprietarydata.Additionally,theyaremoreenergy-efficient,aligningwithgrowingsustainabilityconcerns.
AswemonitorthecarbonfootprintofourAImodels,werealisetheefficiencyofusingSLMstrainedonmorelimiteddatasetsforspecific,restrictedusecases,ratherthandeployingLLMsforeveryapplication.
CDOOFAMANUFACTURINGCOMPANY,NZ
Purpose-builtmodels,includingthosedesignedforlocallanguagesandnuancedregionalcontexts,willbeparticularlyindemand.Thesemodelsnotonlyaddressdiverselinguisticneedsbutalsoenhanceexplainabilityandarewell-suitedfordeploymentonsmalleroredgedevices.
ECOSYSTMAPACAIOUTLOOK2025|IBM
AsorganisationsrefinetheirAIstrategies,techanddataleaderswillevaluatemodelsbasedon:
GovernanceConstraints
Industrieswithstrictprivacyandsecurityrequirementsmayprefermodelsdeployableonisolatednetworksorcompliantwithspecificregulations.
TaskComplexity
Simplertasksmayonlyrequiresmallermodels,whereascomplex,
data-intensivetasksmightrequirelarger,moresophisticatedmodels.
DataAvailabilityandQuality
Smallermodelscanperformwellwithlimiteddata,whilelargermodelsoftendependonextensive,high-qualitydatasets.
ComputationalResources
TheavailabilityofGPUs,TPUs,andotherresourceswillguidemodelselectionandtrainingstrategies.
PerformanceMetrics
Latency,accuracy,cost-efficiency,andproximitytodatasourceswill
influencemodeldeployment,withmanyorganisationsoptingforedgecomputingtooptimiseinferences.
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INTRODUCTION
THENEXTWAVEOFAI
SCALINGFORSUCCESS
CASESTUDIES
CASESTUDIES
AIModeltoSuitObjectives
KasikornBusiness-TechnologyGroup(KBTG)hasdevelopeditsownfoundationalLLMmodelcalled"THaLLE”(Text
HyperlocallyAugmentedLargeLanguageExtension),tailoredforfinanceandtheThailanguage.THaLLEhasachieved
CSALevel2certification,ensuringcompliancewithindustrystandardsforaccuracy,reliability,andfinancialanalysis.Byopen-sourcingthemodel,KBTGiscontributingtotheAI
communitywhileadvancingnext-generationAIgovernanceframeworks.
BangkokBank.ThefinancialindustryisrapidlyadoptingAItoenhancecustomerexperiencesandoptimiseoperations.BangkokBankchampionsthesynergyofhumansandAI
workingtogether,embracingacollaborativeintelligence
approachrootedinhuman-centredAIprinciples.ByleveragingAItoamplifyhumanpotential,thebankaimstoachieve
significantperformanceimprovements.Tounlockthefull
potentialofAI,thebankhighlightstheneedforindustry-
widecollaboration,responsibleinnovation,andasteadfastcommitmenttousingAIforthebettermentofsociety.
ECOSYSTMAPACAIOUTLOOK2025|IBM
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INTRODUCTION
CASESTUDIES
THENEXTWAVEOFAI
SCALINGFORSUCCESS
#3UnifiedAI:EnsuringManagementandGovernance
In2025,evolvingregulations,diverseneeds,andresponsibleAIwilldriveorganisationstoinvestintoolsforvisibility,governance,andseamlessAIintegration.
AsAIevolves,dataandtechnologyleadersincreasinglyrelyonmulti-modal,multi-vendorenvironmentsthatintegratediversedatasourceslike
text,images,andaudiotopowerintelligentapplications.Organisationsmustnavigatecomplexregulations,suchastheEUAIAct,whilemanagingtheseintricateAIecosystems.Internaltechteamsfacethechallengeofensuringcompliance,fosteringresponsibility,andmaintainingtransparencyacrossmultipleAIsolutions.
Organisationswillconsider:
ModelOrchestration
HarnessingmultipleAImodelsrequiresrobustorchestrationtoolsto
manageandcoordinateworkflows,ensuringseamlessintegrationandpeakperformance.
VendorManagement
WithAIsolutionsfrommultiplevendors,organisationswilladopt
unifiedgovernanceframeworkstomaintainconsistency,security,andcompliance.Thiswillallowavendor-agnosticstrategytoenhance
flexibilityandadaptabilitytonewtechnologies.
DeveloperToolkits
Streamlinedtoolkitssimplifydevelopment,automatetasks,andenhancereliability.Featureslikeautomatedtesting,explainability,andintegration
withdiversetechnologiesenableorganisationstoaccelerateAIinnovation.
AutomatedAILifecycleManagement
Centralisedmodelinventorieswilltrackperformance,usage,andlineage,providingreal-timeoversight.Automatedmonitoringsystemswilldetectandaddressissueslikemodeldriftandperformancedegradation.
“AImodelsarepronetobiasanddrift,whichcanleadtounintendedconsequences.Tomitigatetheserisks,wecarefullycurateandmonitortrainingdataandregularlyevaluateandretrainmodelsforaccuracyandfairness.Automationisessentialtoachievethis.”
CTOOFABANK,SINGAPORE
ECOSYSTMAPACAIOUTLOOK2025|IBM
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INTRODUCTION
CASESTUDIES
THENEXTWAVEOFAI
SCALINGFORSUCCESS
CASESTUDIES
PrioritisingSafeandSeamlessAIManagement
GSLab|GAVShasdevelopedZIF.AIthatexemplifies
governance-driveninnovationbyembeddingresponsible
AIpracticesintoitsGenAIsolutions.ZIF.AIhasenhanceditspredictiveandproactivecapabilitiestopreventapplicationandinfrastructuredowntimes.Keygovernancemeasures
includetransparencyinidentifyingLLMsources,robust
privacyfeatures,andastrongfocusonethicalAIpractices,
ensuringreliabilityandcompliance.ThesesafeguardsprovideguardrailsagainstissueslikeAIhallucinationwhilesupporting
proactiveissuedetectionandimproveddataintegrityassessments.
FeedloopAI,aleadingIndonesianGenAIprovider,has
partneredwithIBMtointegrateitsFL1AILargeLanguageModel(LLM)withthewatsonxplatform.AsoneofthefirstIndonesian-languageLLMs,FL1enableslocalgovernmentsandbusinessestomanageAIwithrobustgovernance,
risk,andcompliancetools.Throughwatsonx,Feedloop
customerscanautomateregulatoryobligationtracking,
ensuringcompliancewithcurrentandfuturestandardswhilemaintainingadherencetobusinessrequirements.
ECOSYSTMAPACAIOUTLOOK2025|IBM
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INTRODUCTION
CASESTUDIES
THENEXTWAVEOFAI
SCALINGFORSUCCESS
#4AgenticAI:EmpoweringIntelligentSystems
AItoautonomouslyexecutetasksanddrivebusinessvalueasworkfloworchestrationbecomesincreasinglyessential.
Traditionalautomationtools,likeRPA,haveproveneffectiveinstreamliningrepetitivetasks.However,theyoftenstrugglewiththecomplexityanddynamismofreal-worldworkflows.Agenticworkflows,poweredbyAIagents,offeramoreadvancedandflexibleapproach.
"WeknowthatincorporatingAIrequiresredefiningourworkflows.However,traditionalworkflowsarecomplexandconsumevaluableresourcesaswenavigatesystems,copy-pastesequences,andhandleauthentication
hoops."
CDOOFATELECOMPROVIDER,INDIA
CombiningAIwithautomationdrivessignificantgainsinoperationalefficiency,customerexperience,anddecision-making.AsAIadvances,agenticworkflowswillbepivotalinredefiningthefutureofwork.
ECOSYSTMAPACAIOUTLOOK2025|IBM
WhyorganisationswillinvestinsolutionswithAgenticAIcapabilities:
Autonomy
AIagentscanindependentlyexecutetasks,makedecisions,andadapttochangingcircumstances.
Intelligence
LeveragingGenAI,theycanunderstandcomplexinstructions,reason,andlearnfromexperience.
Collaboration
AIagentscancollaboratewithhumanworkers,augmentingtheircapabilitiesandimprovingefficiency.
Adaptability
Agenticworkflowscanadapttochangingbusinessneedsandunexpecteddisruptions.
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INTRODUCTION
THENEXTWAVEOFAI
SCALINGFORSUCCESS
CASESTUDIES
CASESTUDIES
AutomatingWorkflowOrchestrationforAIEfficiency
SirirajPiyamaharajkarunHospital(SiPH)hasrevolutioniseditspathologydiagnosticsthroughworkflowautomation.Byintegratinglaboratorysystems,imagescanning,andcentraldataprocessing,SiPHhassignificantlyimprovedefficiencyandaccuracyincancerdiagnosis.Thesystem’sautomatedworkflowsandAI-drivenslideimageanalysis—currently
pilotedforprostatecancer—streamlinestheidentification
ofpotentialcanceroustissues,allowingdoctorstofocuson
high-riskcases.ThistransformationlaysafoundationforfutureadvancementsincomputationalpathologyandAIdiagnosticsinThailandandbeyond.
AglobalupstreamoilandgascompanyisleveragingAItoautomateworkfloworchestrationandenhanceefficiency,
particularlyinseismicloganalysis.Byimplementingmachinelearningmodels,thecompanyautomatesdatacleaningandgap-fillingprocesses,significantlyreducingthemanualeffort
required.Thisenablesengineerstoprocess10-20logsin30
minutes–ataskthatpreviouslytookafulldayperlog-freeingthemtofocusonhigher-valuetasks.Theseadvancementsnotonlyimproveefficiencybutalsoenhancedecision-makingandproductivitybyprovidingfaster,moreaccuratedatainsights.
AsthecompanytransitionstooperationalisingAI,itcontinuestorefineworkflowstomaximisethetechnology'spotential
whilefosteringcross-functionalcollaboration.
ECOSYSTMAPACAIOUTLOOK2025|IBM
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INTRODUCTION
CASESTUDIES
THENEXTWAVEOFAI
SCALINGFORSUCCESS
#5BeyondProductivity:TheHuman-CentricFutureofAI
In2025,organisationswillshifttheirfocusfrommerelyadoptingAItoolstoharnessingtheirpotentialforhuman-centredinnovation.WhileproductivitytoolshavebeenamajorfocusofAIadoption,thefutureliesinleveragingAItoenhance
humanexperiencesandcapabilities.
Foremployees,AIwillbecomeapowerfultooltoaugmenttheirroles,automateroutinetasks,andunlocknewopportunitiesforcreativityandinnovation.OrganisationswillprioritiseemployeeeducationandtrainingtoensureasmoothtransitiontoanAI-poweredworkplace.
Customershavebecomeaccustomedtochatbotinteractionsoverthepastfewyears.In2024,therewasapromisetoenhancecustomer
experiencesbyintegratingGenAIandadvancedcustomerintelligenceintotheseinteractions.Movingforward,human-centredAIdesignwillbeparamount.Byprioritisingempathyandengagement,organisationscanfosterstrongercustomerrelationshipsandbrandloyalty.AIsolutionswillbetailoredtomeetspecificcustomerneedsandpreferences,deliveringpersonalisedexperiencesthatdrivesatisfaction.
Byprioritisinguserneedsandpreferences,organisationscancreatemorepersonalisedandintuitiveinteractions,forbothemployeesandcustomers.Thisinvolves:
PersonalisedExperiences
TailoringAI-poweredexperiencesto
individualusers,
deliveringhighly
relevantcontentandrecommendations.
EmpatheticDesign
Understandinguser
emotions,motivations,andpainpointsto
designAIsolutionsthatresonate.
TransparentandExplainableAI
Providingclear
explanationsforAI-generatedoutputstobuildtrustand
confidence.
EthicalAI
EnsuringthatAIsystemsarefair,
unbiased,andalignedwithhumanvalues.
Continuous
Improvement
IterativelyrefiningAIsystemsbasedon
userfeedbackand
performancemetrics.
ECOSYSTMAPACAIOUTLOOK2025|IBM
INTRODUCTION
CASESTUDIES14
THENEXTWAVEOFAI
SCALINGFORSUCCESS
CASESTUDIES
Ahuman-centricapproachtoAIsuccess
AleadingASEANautomotivecompanyfacedsignificant
organisationalreluctancetoadoptAI,despitemanagement'sstrongbeliefinitspotentialasabusinessdifferentiator.To
tacklethis,thecompanydelayedimplementation,focusing
onfosteringacceptanceandaligningculturalshiftswithAI
integration.Keyinitiativesincludedupskillingandreskilling
employeestomeettheevolvingdemandsofthetechnology.
TheSouthWaikatoDistrictCouncil(SWDC)adopted
ahuman-centredAIapproachtoimproveinformation
accessibilityforitscitizens.Thecouncilimplementeda
virtualassistantenablingquickandaccurateresponsestouserqueries.With91.5%accuracyacrosstestquestions,thesolutionenhanceduserexperiencethroughnatural
languageprocessing,advanceddatafiltering,andtransparentaccesstodocumentsources.Thisinitiativenotonly
addressedinformationsilosbutalsoempoweredcitizens
withstreamlined,conversationalaccesstovitalinformation,exemplifyingAI'sroleinfosteringbetterpublicservices.
ECOSYSTMAPACAIOUTLOOK2025|IBM
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INTRODUCTION
CASESTUDIES
THENEXTWAVEOFAI
SCALINGFORSUCCESS
AIacrossAsiaPacific:AComparison
ANZ
KeyDriversofAIAdoption
53%49%34%
Needtoreducecostsandautomatekeyprocesses
Pressurefromcustomers
Legalandregulatorycompliancepressures
KeyChallengesofAIAdoption
55%Costofimplementation/solution47%Ethicalconcerns
38%Limitedusecasesdefined
BiggestFocusofAIInvestmentsin2025
20%19%18%
Employeeexperienceandproductivity
Back-officebusinessprocessautomation
Salesautomationandcustomerlifecyclemanagement
N=202;Source:Ecosystm,2024
KeyDriversofAIAdoption
62%60%47%
Needtoreducecostsandautomatekeyprocesses
AdvancesinAIthatmakeitmoreaccessible
AIembeddedintostandardoff-the-shelfbusinessapplications
KeyChallengesofAIAdoption
46%42%38%
Dataaccessibilityissues
LimitedAIskills,expertise,orknowledge
Difficultyinintegrationandscaling
INDIA
BiggestFocusofAIInvestmentsin202527%Customerexperience
16%Planningandstrategy
16%OptimisationofITfunctions
N=216;Source:Ecosystm,2024
ECOSYSTMAPACAIOUTLOOK2025|IBM
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INTRODUCTION
CASESTUDIES
THENEXTWAVEOFAI
SCALINGFORSUCCESS
SINGAPORE
KeyDriversofAIAdoption
48%31%31%
Competitivepressure
Environmentalpressures
Needtoreducecostsandautomatekeyprocesses
KeyChallengesofAIAdoption
45%39%33%
Limitedusecasesdefined
LimitedAIskills,expertise,orknowledge
LackoftheabilitytoproperlygovernAImodels
BiggestFocusofAIInvestmentsin2025
32%17%15%
Back-officebusinessprocessautomation
Planningandstrategy
Employeeexperienceandproductivity
N=84;Source:Ecosystm,2024
KeyDriversofAIAdoption
49%41%39%
Labourorskillsshortages
Needtoreducecostsandautomatekeyprocesses
Competitivepressure
KeyChallengesofAIAdoption
51%45%39%
Dataaccessibilityissues
LackofAIstrategy
LimitedAIskills,expertise,orknowledge
MALAYSIA
BiggestFocusofAIInvestmentsin2025
44%17%14%
Customerexperience
Salesautomationandcustomerlifecyclemanagement
Back-officebusinessprocessautomation
N=71;Source:Ecosystm,2024
ECOSYSTMAPACAIOUTLOOK2025|IBM
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INTRODUCTION
CASESTUDIES
THENEXTWAVEOFAI
SCALINGFORSUCCESS
INDONESIA
KeyDriversofAIAdoption
56%37%33%
Needtoreducecostsandautomatekeyprocesses
AdvancesinAIthatmakeitmoreaccessible
Competitivepressure
KeyChallengesofAIAdoption
48%47%40%
LackofAIstrategy
Difficultyinintegrationandscaling
Limitedusecasesdefined
BiggestFocusofAIInvestmentsin2025
21%20%20%
OptimisationofITfunctions
Salesautomationandcustomerlifecyclemanagement
Planningandstrategy
N=81;Source:Ecosystm,2024
KeyDriversofAIAdoption
42%41%39%
AdvancesinAIthatmakeitmoreaccessible
Environmentalpressures
Pressurefromcustomers
KeyChallengesofAIAdoption
41%38%34%
Vendorlock-ins
Lackoftools/platformsfordevelopingAImodels
Costofimplementation/solution
THAILAND
BiggestFocusofAIInvestmentsin2025
29%18%16%
Back-officebusinessprocessautomation
OptimisationofITfunctions
Salesautomationandcustomerlifecyclemanagement
N=76;Source:Ecosystm,2024
ECOSYSTMAPACAIOUTLOOK2025|IBM
THENEXTWAVEOFAI
SCALINGFORSUCCESS
PHILIPPINES
KeyDriversofAIAdoption
47%47%45%
Needtoreducecostsandautomatekeyprocesses
Competitivepressure
Labourorskillsshortages
BiggestFocusofAIInvestmentsin2025
23%18%17%
Customerexperience
Back-officebusinessprocessautomation
Employeeexperienceandproductivity
KeyChallengesofAIAdoption
43%Limitedusecasesdefined
40%
Difficultyinintegrationandscaling
37%LackofAIstrategy
18
INTRODUCTION
CASESTUDIES
N=60;Source:Ecosystm,2024
ECOSYSTMAPACAIOUTLOOK2025|IBM
THENEXTWAVEOFAI
SCALINGFORSUCCESS
ScalingforSuccess:OvercomingBarriers
Despitepromisingearly-stageprojects,regulatoryhurdles,businessreadinessissues,andtechnologicallimitationscontinuetohinderwidespreadAIadoption.
NavigatingtheComplexitiesofAIImplementation
LeadingtheAICharge:CEOImperatives
DefineAI’sValueProposition.
Focusonhigh-impactAIusecases
alignedwithbusinessgoals,setclearfinancialobjectives,and
measureROIintime,cost,andoutcomes.Developascalable,sustainableroadmapforAI
initiatives.
AddresstheHumanFactor.
BridgetheskillsgapwithAI/
MLtalentdevelopment,manage
changetoovercomeresistance,
andempoweremployeeswith
clearcommunication,training,andsupport.
PromoteCross-Functional
Collaboration.AlignbusinessandITteamsunderaunifiedAIvision,governancemodel,andcentralisedAICentreofExcellence(CoE).
Secureexecutivesponsorshiptodriveprioritiesandresourceallocation.
StrengthenGovernanceandTransparency.Embedrobust
centralgovernanceand
transparencyintotheculturewhileempoweringbusinessunitswith
projectownershipforethicalandsecureAIpractices.
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INTRODUCTION
CASESTUDIES
ECOSYSTMAPACAIOUTLOOK2025|IBM
THENEXTWAVEOFAI
SCALINGFORSUCCESS
BuildingaData-CentricOrganisation:GuidanceforDataLeaders
ConductaThorough
DataAudit.Address
qualityissuesand
leverageautomation
toolsforfastercleaningandannotation.Ensureaccesstosufficient,
high-qualitydata,
includingsyntheticdatawhenneeded.
PrioritiseDataPrivacyandSecurity.Preparetomeetamorecomplex
regulatoryenvironment,
beyondexisting
frameworkssuchas
theGDPR,DPDP,
PDPA,andthePrivacyActs.Establishinternalgovernancepolicies
andregularlyupdatesecurityprotocolstomitigaterisks.
AddressDataAccessandIntegration
Challenges.Break
downsilostoenable
seamlessdatasharinganduseintegration
toolstoharmonise
diversedatasources.
Strengthengovernancepracticesfor
consistencyandquality.
ClarifyDataOwnership
andIntellectualPropertyRights.
Defineclearpolicies
fordataownershipandintellectualproperty,
consultinglegalexpertstoensurecompliance
withregulations.
BuildaFuture-Proof
DataFoundation.WorkwiththeCIOtoupgradelegacysystems,adoptcloud-basedsolutions,andredefinethedata
architecturethatalignswiththeorganisation’sAIroadmap.
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INTRODUCTION
CASESTUDIES
ECOSYSTMAPACAIOUTLOOK2025|IBM
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CASESTUDIES
THENEXTWAVEOFAI
SCALINGFORSUCCESS
TechLeadership
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