2026年弥合语言鸿沟：移动网络运营商在AI生态系统中的角色研究报告（英文版）

BridgingtheLanguageGap

TheRoleofMobileNetworkOperatorsinAlEcosystems

TheGSMAisaglobalorganisationunifyingthemobile

ecosystemtodiscover,developanddeliverinnovation

foundationaltopositivebusinessenvironmentsand

societalchange.Ourvisionistounlockthefullpowerofconnectivitysothatpeople,industry,andsocietythrive.Representingmobileoperatorsandorganisationsacrossthemobileecosystemandadjacentindustries,theGSMAdeliversforitsmembersacrossthreebroadpillars:

ConnectivityforGood,IndustryServicesandSolutions,andOutreach.Thisactivityincludesadvancing

policy,tacklingtoday’sbiggestsocietalchallenges,

underpinningthetechnologyandinteroperabilitythatmakemobilework,andprovidingtheworld’slargest

platformtoconvenethemobileecosystemattheMWCandM360seriesofevents.

Weinviteyoutofindoutmoreat

UkInternationalDevelopment

partnership

progressprosperity

ThismaterialhasbeenfundedbyUKInternational

DevelopmentfromtheUKgovernmentandissupportedbytheGSMAanditsmembers.Theviewsexpresseddonot

necessarilyreflecttheUKGovernment’sofficialpolicies.

GSMAEmergingTechProgramme

TheGSMA

EmergingTechprogramme

acceleratesimpact

andclimateactionbyfosteringtheadoptionofAIand

emergingtechnologiesinlow-andmiddle-incomecountries(LMICs)byworkingwithpublic,privateandthirdsector

innovatorstodevelopscalableandsustainablesolutionsthathaveinclusiveandresponsibleAIatthecore.The

EmergingTechprogrammeworkscloselywiththeGSMA

AIforImpact

initiativetodrivereal-world,impact-focusedimplementationwithtelcosinLMICs.

TogetintouchwiththeEmergingTechteam,pleaseemail:

emergingtech@

Authors:

EugénieHumeau,GSMAMobileforDevelopmentZarahUdwadia,GSMAMobileforDevelopment

Contributors:

KimberlyBrown,GSMAMobileforDevelopmentIbrahimSajid,GSMAMobileforDevelopment

MaureenImiegha,GSMAMobileforDevelopment(Marketing)

Acknowledgements:

Wewouldliketothankthemanyindividualsandorganisationsthatcontributedtothisresearch.

ThisincludesDigitalUmuganda,gheero,GIZFAIRForward–ArtificialIntelligenceforAll,IndosatOoredooHutchison,Karya,MozillaFoundation,Pindo,ReverieLanguage

TechnologiesandRAIght.ai.

Published:March2026

BridgingtheLanguageGap2

BridgingtheLanguageGap3

Contents

Definitions4

Acronymsandabbreviations5

Listoffigures,spotlightsandtables6

Executivesummary7

1.Introduction9

1.1.Thelanguagedivide11

1.2.Theimportanceofculturalandlinguisticdiversity15

1.3.Theopportunity:modelsinlocallanguages16

1.4.Researchobjectives17

2.Insightsfromtheecosystem18

2.1.Existinginitiativesandapproaches19

2.2.Challengesfacedbylocallanguageinitiatives22

2.3.Implicationsfordigitalsovereignty24

3.MobilenetworkoperatorsandlocallanguageAI25

3.1.AIadoptiontrends26

3.2.Casestudies28

1.Orange:

SupportingSenegal’scustomersinlocallanguages28

2.DialogAxiata:

CreatinginclusivedigitalservicesinSriLanka30

3.Beeline(VEONGroup):

BridgingtheAIlanguagegapinKazakhstan33

4.Indosat:

BuildingsovereignAIforIndonesia37

4.Lessonsandimplications41

4.1.Keylessonsfromthecasestudies42

4.2.PathwaysforMNOstocontributetolocallanguageAI46

4.3.Conclusion49

BridgingtheLanguageGap4

Definitions

Artificial

Intelligence(AI)

Artificialintelligence(AI)iscomprisedofwidelydifferenttechnologiesthatcanbebroadlydefinedas“self-learning,adaptivesystems.”1AIhasthecapabilitytoprocesslanguage,

solveproblems,recognisepicturesandlearnbyanalysingpatternsinlargesetsofdata.

AIsovereignty

Thecontrolandautonomyasovereignstatehasoverthedevelopment,deploymentand

governanceofallaspectsoftheAIecosystemwithinitsborders.Sometimesreferredtoas“AInationalism”.

Benchmark

InthecontextofAI,abenchmarkisastandardiseddatasetandevaluationtaskusedtomeasureandcomparetheperformanceoflanguagemodelsonspecificlanguagesortasks.Benchmarksareessentialforassessingprogress,identifyinggapsandguidingmodeldevelopment,particularlyforunderrepresentedlanguages.

Compute

Computereferstotheprocessofperformingcalculationsorcomputationsrequired

foraspecifictask,suchastraininganAImodel.Italsoencompassesthehardware

components,likechips,thatcarryoutthesecalculations,aswellastheintegratedsystemsofhardwareandsoftwareusedtoperformcomputingtasks.2

Crowdsourcing

Crowdsourcingreferstothelarge-scalecollectionorannotationofdatathroughopen

orsemi-openparticipation,ofteninvolvingmanycontributorsperformingsmall,discretetaskssuchasrecordingspeech,transcribingaudioorvalidatingtranslations.

Digitalor

technology

sovereignty

Asovereignstate’sabilitytoshapethedigitaltransformationinaself-determinedmannerwithregardtohardware,software,servicesandcompetencies.Fordigitaltechnologies

andapplications,thismeansbeingabletodecideindependentlytowhatextentoneentersintooravoidsdependenceonprovidersandpartners.

Fine-tuning

Fine-tuningreferstotheprocessofcontinuingthetrainingofapre-existingAImodelonaspecificdatasettoadaptittoanarrowerdomain,taskorlanguage.Thisprocessadjuststhemodel’sinternalweights,allowingittospecialiseandimproveperformanceinthat

specificcontext.

Foundationmodel

Afoundationmodelisalarge,general-purposeAImodeltrainedonbroaddatasetsanddesignedtobeadaptedformultipledownstreamtasksorlanguagesthroughfine-tuningorothertechniques.Examplesincludelargemultilinguallanguagemodelsthatserveasabaseformorespecialisedapplications.

GenerativeAI(GenAI)

AtypeofAIthatinvolvesgeneratingnewdataorcontent,includingtext,imagesorvideos,basedonuserpromptsandbylearningfromexistingdatapatterns.

Languagemodel

AlanguagemodelisanAIsystemtrainedtounderstandandgeneratehumanlanguagebylearningpatternsfromlargeamountsoftextand/orspeechdata.Languagemodelscan

performtaskssuchastextgeneration,translation,summarising,speechrecognitionandansweringquestions.Theyrangefromsmall,task-specificmodels–oftenreferredtoassmalllanguagemodels(SLMs)–tolarge,general-purposemodelstrainedonmultilingualdata,referredtoaslargelanguagemodels(LLMs).

Local

language

Alocallanguagereferstoalanguagethatisspokenwithinaspecificcommunity,regionorcountry,oftendistinctfromthedominantornationallanguage.Itmayormaynotbeofficiallyrecognisedandistypicallycentraltoculturalandsocialidentity.

Local

languageAI

Inthisreport,locallanguageAIreferstoAIsystemsthataredesigned,trainedoradaptedtoworkinlocallanguages.Thisincludestoolsandmodelsthatunderstand,generateor

translatelocallanguages,makingAImoreaccessibleandrelevanttospeakersofthoselanguages.

1.DefinitionbytheInternationalTelecommunicationUnion(ITU).

2.AINowInstitute.(2023).

ComputationalPowerandAI

.

BridgingtheLanguageGap5

Low-resourcelanguage

Alow-resourcelanguageisonethathaslimitedornorepresentationinAIresearch,

datasetsanddigitalproducts,incontrastwith“high-resource”languages,whichare

wellrepresentedinAIsystems.Theselanguagesoftenlacksufficienttextorspeech

data,evaluationbenchmarksandothercomputationalresources.Insomecases,existingmaterialsmaybefragmented,inaccessibleorinunusableformats.Inthisreport,weusetheterms“low-resource”and“underrepresented”interchangeably.

Machine

learning(ML)

AsubfieldofAIbroadlydefinedasthecapabilityofamachinetoimitateintelligenthumanbehaviourandlearnfromdatawithoutbeingexplicitlyprogrammed.3

Naturallanguageprocessing

(NLP)

AfieldofMLinwhichmachineslearntounderstandnaturallanguageasspokenand

writtenbyhumans,insteadofthedataandnumbersnormallyusedtoprogramcomputers.

Retrieval

augmented

generation(RAG)

RAGisanAItechniquethatcombinesalanguagemodelwithanexternalknowledgesource.Themodelretrievesrelevantinformationwhenitisqueriedandusesitto

generatemoreaccurateandinformedresponses.RAGcanserveasalightweightandmoreflexiblealternativetofine-tuning,especiallywhenworkingwithlimiteddataorchangingknowledgesources.

Acronymsandabbreviations

AI

ArtificialIntelligence

ML

MachineLearning

API

ApplicationProgrammingInterface

MNO

MobileNetworkOperator

ASR

AutomaticSpeechRecognition

MT

MachineTranslation

GPU

GraphicProcessingUnit

NLP

NaturalLanguageProcessing

HITL

Human-in-the-loop

RAG

RetrievalAugmentedGeneration

IVR

InteractiveVoiceResponse

SLM

SmallLanguageModel

LLM

LargeLanguageModel

TTS

Text-to-Speech

LMIC

Low-andMiddle-IncomeCountry

3.DefinitionbytheMITSloanSchoolofManagement,basedonthedefinitionbyAIpioneerArthurSamuel.

BridgingtheLanguageGap6

Listoffigures

Figure1:

TheAIecosystemframework

Figure2:

PredominanceofEnglishinonlinecontent

Figure3:

NumberoflivinglanguagesacrossAfricancountries

Figure4:

MapoflocallanguageAIinitiatives

Figure5:

PercentageoftelcoAIdeployments

Figure6:

DevelopmentprocessforDialog’sLLMintegration

Figure7:

KazLLMpartnershipecosystem

Figure8:

KazLLMtechstack

Figure9:

SahabatAItechstack

Listofspotlights

Spotlight1:Understandinglow-resourceandunderrepresentedlanguages

Spotlight2:TheGSMAAfricanAILanguageModelsinitiative

Listoftables

Table1:

DimensionsofAIsovereignty

Table2:

TechnicaladaptationapproachesusedinlocallanguageAIdeployments

Table3:

MNOcontributionpathwaysforlocallanguageAI

Executivesummary

BridgingtheLanguageGap7

BridgingtheLanguageGapExecutivesummary8

Languageremainsoneofthebiggestbarrierstotheequitable

developmentofartificialintelligence(AI)inlow-andmiddle-incomecountries(LMICs).Thedigitalworldisdominatedbyasmallnumberof“high-resource”languages,particularlyEnglish,withabundant

digitaldataresourcesavailable.Thevastmajorityoftheworld’s

languages,bycontrast,are“lowresource”andlackthemachine-

usabledatathatcanbeusedfortrainingnaturallanguageprocessing(NLP)models,particularlylargelanguagemodels(LLMs),which

requiremassiveamountsofdata.

Modelstrainedondatathatdoesnotrepresentthe

world’svastlinguisticandculturaldiversityarenot

accessible,relevant,reliableorimpactfulforpeople

wholivetheirlivesinlow-resourcelanguages.Thisriskswideningexistingdigitaldivideswhilealsothreateningthepreservationoflanguagesacrosstheworld.

Agrowingnumberofeffortsareaddressingthis

linguisticimbalance.Startups,innovators,researchers

andcommunitiesinLMICsarebuildingandapplying

locallyrelevantAImodels,curatingandcrowdsourcing

linguisticallydiversedatasetsandcreatingenabling

environmentsforgreaterAIlanguageinclusion.However,theseeffortsareoperationallydemanding,resource

intensiveandintroduceseveralethicalconsiderations,

particularlywhentheyinvolvecommunitycrowdsourcing.Theyalsolackthecapacitytoreachlast-mileusersat

scale,creatingdatasetsandmodelswithoutdistribution.Thesechallengesarecompoundedbylimitationsin

computeinfrastructureandsustainablefundinginLMICs.

WithinthedigitalandAIecosystem,MNOsplaya

strategicallysignificantyetnotwellunderstoodrole

inbridgingthelanguagedivide.Throughfourcase

studiesinLMICs–OrangeinSenegal,DialogAxiata

inSriLanka,Beeline(VEONGroup)inKazakhstan

andIndosatinIndonesia–thisresearchexplores

howMNOsareadvancingmoreinclusiveAIthrough

modelsinlocallanguages.Thecasestudiesrange

fromMNOsusinglanguageAIforcustomersupport

(themostcommonentrypoint)tobuildinglarge-scalenationalAIinfrastructure.Ineachofthecasestudies,theMNOsrecognisetheimportanceoflanguage

inclusioninthedigitalworldandtheopportunitytoenableitthroughAI.

ThecasestudiesshowthreeclearpathwaysforMNOstosupportlanguageinclusionand,increasingly,enablesovereignAIambitions.First,asserviceproviders

andlast-miledistributors,MNOsintegratelanguage

technologiesintoexistingcustomerservices,deliveringsupportinlocallanguageswhilecreatingreal-world

environmentsfortesting,iterationandoperational

improvement.Second,asecosystemconvenersand

bridges,MNOsleveragetheirinstitutionalpositionto

bringtogethergovernments,academiaandtechnologyprovidersinmutuallybeneficialpartnerships,aligning

incentivesaroundsharedobjectivesforlanguage

inclusion,nationalprioritiesandscale.Third,some

MNOsareemergingassovereignAIenablers,investingincompute,cloudplatformsandmodel-hosting

environmentsthatpositionlocallanguageAIaspartofbroadernationaldigitalinfrastructure.Inthispathway,MNOsdonotjustdeployAIintheirownservicesbut

providetheinfrastructurelayerthatenablesbothprivate-sectorinnovationandpublic-sectordigitaltransformation.

Thefourcasestudiesillustratethesepathwaysinpractice:

–Orange(Senegal)focusesonhybridlanguagesystemstodelivercustomersupportinWolofthroughconversationalinterfaces,including

speech-enabledchannels.

–Dialog(SriLanka)usesprompt-basedandhybridlanguagetechniquestolowerbarrierstodigital

creationforwomenentrepreneurs,withno-codeapproaches.

–Beeline(Kazakhstan)leadsamulti-stakeholder

efforttobuildKazakhlanguagemodelsanchoredinopenaccessandpublic-sectoruse.

–Indosat(Indonesia)investsinsovereigncomputeandopenlanguagemodelstosupportnationalAIcapacityacrosspublicservicesandindustry.

Takentogether,thefindingsshowthatinclusivelocallanguageAIwillnotemergefromasingleactoror

technicalapproach.Instead,progressdependson

complementaryrolesacrosstheecosystem.MNOs

aremosteffectivewhentheyfocusontheirstructuralstrengths–deployinglanguagetechnologiesat

scale,conveningpartnersand,insomecases,

providingshareddigitalandAIinfrastructure–

whilecommunity-ledinitiativescontinuetodrive

linguisticdepth,culturalgroundinganddatacreation.

Ultimately,closingtheAIlanguagegapinLMICswilldependonhoweffectivelyinstitutionsalign

incentives,shareriskandbuildpartnershipsthattranslatelinguisticinnovationintosustainableandlarge-scaleimpact.

1.Introduction

BridgingtheLanguageGap9

BridgingtheLanguageGapIntroduction10

Thepotentialofartificialintelligence(AI)tosupportsocialand

economicdevelopmentiswellestablished.AIapplicationsare

increasinglyseenascriticaltoolsforimprovingservicedelivery,

expandingaccesstoinformationandsupportinginclusivegrowth,particularlyinlow-andmiddle-incomecountries(LMICs)where

developmentneedsaremostacute.AIiswidelyregardedasageneral-purposetechnology,anditsadoptionhasbeenfasterthananypreviousdigitalinnovation.

Inlessthanthreeyears,morethan1.2billionpeople

onbroaderfoundationssuchasdigitalinfrastructure,

haveusedAI-enabledtools,outpacingtheearly

humancapitalandenablingpolicyenvironments,

adoptionofboththeinternetandsmartphones.4

aswellascross-cuttingenablersincludingfinance,

However,AIadoptionremainsdeeplyunequal.Usage

partnershipsandresearchanddevelopment.6

ratesinhigh-incomeeconomiesareroughlytwice

Weaknessesinanyoftheselayerscanlimitthe

thoseobservedinLMICs,withthegapwideningsharply

adoptionanddiminishtheimpactofAI.Limited

incountrieswithGDPpercapitabelowUSD20,000.5

availabilityofdatainlocallanguagesremainsone

Thesedisparitiesreflectnotonlydifferencesinaccess

ofthemostpersistentbarriers,affectingboththe

totechnology,butalsostructuralimbalancesinhowAI

developmentofAIsystemsandtheirrelevance,

systemsaredevelopedanddeployed.

usabilityandtrustworthinessamongendusers.

Addressinglanguageinclusionisthereforeessential

ThedevelopmentofAIdependsonthreebuilding

toensurethatAIdeliversinclusiveandlocally

blocks–data,computeandskills–whichinturnrely

Figure1:TheAIecosystemframework

relevantoutcomes.7

Cross-cuttingenablersPartnerships

Researchanddevelopment

Digitaleconomyfoundations

HumancapitalPolicyand

andskillsregulation

Financingmechanisms

Digital

infrastructure

ComputeAIskills

AIfundamentals

Data

Source:GSMAMobileforDevelopment8

4.Microsoft.(2025).

AIDiffusionReport:WhereAIismostused,developed,andbuilt

.

5.Ibid.

6.GSMA.(2024).

AIforAfrica:Usecasesdeliveringimpact

.

7.WorldBankGroup.(2025).

StrengtheningFoundations:DigitalProgressandTrendsReport2025

.

8.GSMA.(2024).

AIforAfrica:Usecasesdeliveringimpact

.

BridgingtheLanguageGapIntroduction11

1.1.Thelanguagedivide

Countrieswherelow-resourcelanguagesdominate

consistentlyshowlowerlevelsofAIadoption,

reinforcingexistingdigitaldivides.9Thischallenge

becomesmostvisibleinthedesignanddeployment

oflargelanguagemodels(LLMs).Modelssuchas

ChatGPT,LlamaandClaudearerapidlytransforming

howpeopleaccessinformation,communicateand

builddigitaltools.However,despitetheirtransformativepotential,LLMsremainlargelyinaccessibleandnot

fitforpurposeincountrieswherenon-dominant

languagesarespoken,largelyinLMICs.State-of-

the-artLLMsstillshowalargeandsystematicgapinperformancebetweenEnglishandlow-resourceandnon-Latinscriptlanguages.10

LLMsaremostlytrainedondatasetsfromhigh-incomecountries(HICs),indominantlanguageslikeEnglish,

FrenchorSpanish.Theinternet,wherealargepartoftheworld’sknowledgeisstored,servesasthesinglemostimportantdatasetfortrainingAI.Yet,morethanhalfofthiscontentisinEnglish,despiteEnglishbeingspokennativelybyjust5%oftheglobalpopulation.11,12

Theoverwhelmingmajorityoftheworld’s7,000languageslackthedata,toolsortechniquesfornaturallanguageprocessing(NLP),makingthem“low-resource”incontrasttoahandfulof“high-resource”languages,includingEnglish,French,Spanish,GermanandMandarinChinese.13

9.Microsoft.(2025).

AIDiffusionReport:WhereAIismostused,developed,andbuilt

.

10.Ahuja,S.etal.(2024).

MEGAVERSE:BenchmarkingLargeLanguageModelsAcrossLanguages,Modalities,ModelsandTasks

.MicrosoftCorporation.

11.Britannica.

“Languagesbynumberofnativespeakers–List,Top,&MostSpoken”

.Accessed10September2025.

12.CommonCrawl.

“StatisticsofCommonCrawlMonthlyArchivesbycommoncrawl”.

Accessed11September2025.

13.Ravindran,S.(20July2023).

“AIoftenmanglesAfricanlanguages.Localscientistsandvolunteersaretakingitbacktoschool”

.Science.

BridgingtheLanguageGapIntroduction12

Figure2:PredominanceofEnglishinonlinecontent

a.Distributionofleadinglanguagesspoken,2025(percentageofglobalpopulation)

514

122

43

61

NativespeakersOtherspeakers

English

MandarinChinese

Spanish

Hindi

b.GlobalURLsbylanguage,2025(percentage)

GermanJapaneseFrench

45665544321

EnglishRussianChineseSpanishUnknownOtherlanguages

c.Open-sourcedatasetsfromHuggingFacebylanguage,2024(percentage)

FrenchRussian

57533230

III

EnglishChineseSpanishOtherlanguages

d.YouTubevideosbylanguage,2022(percentage)

PortugueseArabic

218755352

II

English

HindiSpanish

RussianOtherlanguages

Source:WorldBank14

14.WorldBankGroup.(2025).

StrengtheningFoundations:DigitalProgressandTrendsReport2025

.

BridgingtheLanguageGapIntroduction13

ArecentanalysisbyMicrosoftshowedthatlow-

resourcelanguagecountriesadoptAIatrates20%lowerthanhigh-resourcelanguagecountries,eventhosewithsimilarGDPandconnectivityconditions.15Thisindicatesthatloweradoptionisnotdrivenby

incomeorinfrastructuregaps,butbylinguistic

barriers–specifically,weakermodelperformance

andhigheradaptationcostsinlanguageswithlimitedtrainingdata.16Thesedisparitiesarereflectedin

benchmarkresults.Whilestate-of-the-artLLMs

achievearound80%accuracyinEnglish,theydrop

below55%forsomelow-resourcelanguagessuchasYoruba,oneofNigeria’sthreemajorlanguagesspokenbymorethan50millionpeopleacrossAfrica.17

Spotlight1:

Understandinglow-resource

andunderrepresentedlanguages

LanguageisfoundationaltoAIsystems,andtermssuchas“local”,“indigenous”,“underrepresented”

and“lowresource”areoftenusedtodescribe

distinctbutoverlappingrealities.Locallanguages

arethoseusedineverydaycommunicationwithin

acountryorregion.Theymaybeofficialornon-

officialandcanbespokenbymillionsofpeopleorbymuchsmallercommunities.Somelanguagesarealsoindigenouslanguages,meaningthattheyare

closelytiedtoIndigenousPeoples,culturesandknowledgesystems.

Manyoftheselanguagesaredescribedas“low

resource”,“underrepresented”or“underserved”in

AIandNLPecosystems.Thesetermsdonotrefertothenumberofspeakers,buttolimitedrepresentationinAIresearch,datasetsandcommercialproducts.Alanguagecanhavetensofmillionsofspeakersandstillbelow-resourceifitlacksdigitaltext,speech

dataorevaluationbenchmarks.Bycontrast,somelanguagesspokenbyrelativelysmallpopulati