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）

Bridging

the

LanguageGapTheRoleofMobile

NetworkOperatorsinAlEcosystemsAuthors:Eugénie

Humeau,GSMA

Mobilefor

Development

ZarahUdwadia,GSMA

Mobilefor

DevelopmentContributors:KimberlyBrown,GSMA

MobileforDevelopment

IbrahimSajid,GSMAMobilefor

DevelopmentMaureenImiegha,GSMA

MobileforDevelopment

(Marketing)Acknowledgements:Wewouldliketothankthemanyindividualsand

organisationsthatcontributedtothisresearch.Thisincludes

Digital

Umuganda,gheero,GIZ

FAIR

Forward

–ArtificialIntelligenceforAll,IndosatOoredoo

Hutchison,

Karya,

Mozilla

Foundation,

Pindo,

Reverie

LanguageTechnologiesandRAIght.ai.Published:March

2026TheGSMAisaglobalorganisation

unifyingthe

mobileecosystemtodiscover,developanddeliverinnovationfoundationaltopositivebusinessenvironmentsandsocietalchange.Ourvisionistounlockthefullpower

of

connectivitysothatpeople,industry,andsocietythrive.

Representingmobileoperatorsandorganisationsacross

themobileecosystemandadjacentindustries,the

GSMA

deliversforitsmembersacrossthreebroad

pillars:ConnectivityforGood,IndustryServicesandSolutions,

andOutreach.Thisactivityincludesadvancingpolicy,tacklingtoday’sbiggestsocietalchallenges,underpinningthetechnologyandinteroperabilitythat

makemobilework,andprovidingtheworld’s

largestplatformtoconvenethemobileecosystemattheMWC

andM360

series

of

events.WeinviteyoutofindoutmoreatTheGSMAEmergingTechprogramme

acceleratesimpactandclimateactionbyfosteringtheadoptionofAIandemergingtechnologiesinlow-andmiddle-incomecountries

(LMICs)byworkingwithpublic,privateandthird

sectorinnovatorstodevelopscalableandsustainablesolutions

thathaveinclusiveandresponsibleAI

atthe

core.

TheEmergingTechprogrammeworkscloselywiththeGSMA

AIforImpactinitiativetodrivereal-world,impact-focused

implementationwithtelcosinLMICs.Togetintouchwiththe

EmergingTechteam,

pleaseemail:emergingtech@Thismaterialhas

beenfundedby

UK

InternationalDevelopmentfromtheUKgovernmentandissupported

by

theGSMAanditsmembers.

Theviews

expressed

do

notnecessarilyreflecttheUKGovernment’sofficial

policies.Bridging

theLanguage

Gap

2GSMAEmergingTechProgrammeUkInternational

Development

progressprosperitypartnership3.MobilenetworkoperatorsandlocallanguageAI

253.1.

AI

adoption

trends

263.2.Case

studies

281.Orange:Supporting

Senegal’s

customers

in

local

languages282.Dialog

Axiata:Creating

inclusive

digital

services

in

Sri

Lanka

303.Beeline

(VEON

Group):Bridging

the

AI

language

gap

in

Kazakhstan334.Indosat:Building

sovereign

AI

for

Indonesia37ContentsDefinitions

41.Introduction

91.1.

The

languagedivide111.2.

The

importance

of

cultural

and

linguistic

diversity

151.3.

The

opportunity:models

in

local

languages

161.4.

Research

objectives

172.Insights

from

theecosystem

182.1.Existing

initiatives

and

approaches

192.2.Challenges

faced

by

local

language

initiatives

222.3.Implications

for

digital

sovereignty

244.Lessonsandimplications

414.1.Key

lessons

from

the

case

studies

424.2.Pathways

for

MNOs

to

contribute

to

local

language

AI464.3.Conclusion

49Listof

figures,spotlightsand

tables

6Acronymsandabbreviations

5Executivesummary

7Bridging

theLanguage

Gap

3ArtificialIntelligence(AI)Artificialintelligence

(AI)iscomprisedofwidelydifferenttechnologiesthatcanbebroadly

definedas“self-learning,adaptivesystems.”1

AIhasthecapabilitytoprocess

language,solveproblems,recognisepicturesandlearn

byanalysing

patterns

in

large

sets

of

data.AIsovereigntyThecontrolandautonomyasovereignstatehasoverthe

development,

deploymentandgovernanceofallaspectsoftheAIecosystemwithinitsborders.Sometimes

referredto

as

“AInationalism”.BenchmarkInthecontextofAI,abenchmarkisastandardised

datasetand

evaluationtask

usedto

measureandcomparetheperformanceoflanguagemodelson

specific

languages

or

tasks.Benchmarksareessentialforassessingprogress,identifyinggapsand

guiding

modeldevelopment,particularlyforunderrepresentedlanguages.ComputeComputereferstotheprocessofperformingcalculationsorcomputations

requiredforaspecifictask,suchastraininganAImodel.It

also

encompassesthe

hardwarecomponents,likechips,thatcarryoutthesecalculations,aswellastheintegratedsystems

ofhardwareandsoftwareusedtoperformcomputingtasks.2CrowdsourcingCrowdsourcingreferstothelarge-scalecollectionorannotationofdatathroughopenorsemi-openparticipation,ofteninvolvingmanycontributorsperforming

small,

discrete

taskssuchasrecordingspeech,transcribingaudioorvalidatingtranslations.DigitalortechnologysovereigntyAsovereignstate’sabilitytoshapethedigitaltransformationinaself-determinedmanner

withregardtohardware,software,servicesandcompetencies.For

digitaltechnologiesandapplications,thismeansbeingabletodecideindependentlytowhatextentone

enters

intooravoidsdependenceonprovidersand

partners.Fine-tuningFine-tuningreferstotheprocessofcontinuingthetrainingofapre-existingAImodel

on

aspecificdatasettoadaptittoanarrowerdomain,taskor

language.This

process

adjusts

themodel’sinternalweights,allowingittospecialiseandimprove

performance

inthatspecificcontext.FoundationmodelAfoundationmodelisalarge,general-purposeAImodeltrained

on

broad

datasetsand

designedtobeadaptedformultipledownstreamtasksorlanguagesthroughfine-tuning

orothertechniques.Examplesincludelargemultilingual

language

modelsthatserve

as

a

baseformorespecialisedapplications.GenerativeAI(GenAI)AtypeofAIthatinvolvesgeneratingnewdataorcontent,includingtext,

images

orvideos,

basedonuserpromptsandby

learningfrom

existing

data

patterns.LanguagemodelAlanguagemodelisanAIsystemtrainedto

understandand

generate

human

language

by

learningpatternsfromlargeamountsoftextand/orspeechdata.

Language

modelscanperformtaskssuchastextgeneration,translation,summarising,speechrecognitionand

answeringquestions.Theyrangefromsmall,task-specificmodels–oftenreferredtoas

smalllanguagemodels

(SLMs)–tolarge,general-purposemodelstrainedon

multilingual

data,referredtoaslarge

language

models

(LLMs).LocallanguageAlocallanguagereferstoalanguagethatis

spoken

within

a

specific

community,

region

orcountry,oftendistinctfromthedominantornationallanguage.

It

may

or

may

not

be

officiallyrecognisedandistypicallycentraltoculturalandsocialidentity.LocallanguageAIInthisreport,locallanguageAIreferstoAIsystemsthatare

designed,trained

or

adapted

toworkinlocallanguages.Thisincludestoolsand

modelsthat

understand,generate

ortranslatelocallanguages,makingAImoreaccessible

and

relevantto

speakers

ofthose

languages.Bridging

theLanguage

Gap

41.Definition

by

the

International

Telecommunication

Union(ITU).2.AI

Now

Institute.(2023).

Computational

Power

and

AI.DefinitionsLow-resourcelanguageAlow-resourcelanguageisonethathas

limitedor

no

representation

in

AI

research,datasetsanddigitalproducts,incontrastwith“high-resource”languages,

whicharewellrepresentedinAIsystems.Theselanguagesoften

lacksufficienttext

or

speechdata,evaluationbenchmarksandothercomputationalresources.

Insome

cases,

existing

materialsmaybefragmented,inaccessibleorinunusableformats.

Inthis

report,we

use

theterms“low-resource”and“underrepresented”interchangeably.Machinelearning(ML)AsubfieldofAIbroadlydefinedasthecapabilityofa

machinetoimitate

intelligent

human

behaviourandlearnfromdatawithoutbeingexplicitlyprogrammed.3Naturallanguage

processing(NLP)AfieldofMLinwhichmachineslearnto

understand

natural

language

as

spoken

andwrittenbyhumans,insteadofthedataandnumbers

normally

usedtoprogram

computers.Retrievalaugmentedgeneration(RAG)RAGisanAItechniquethatcombinesalanguagemodelwith

an

external

knowledge

source.Themodelretrievesrelevantinformationwhen

it

is

queried

and

uses

ittogeneratemoreaccurateandinformedresponses.

RAGcan

serveas

a

lightweight

and

moreflexiblealternativetofine-tuning,especiallywhenworkingwithlimiteddataor

changingknowledgesources.AIArtificialIntelligenceMLMachine

LearningAPIApplicationProgrammingInterfaceMNOMobileNetwork

OperatorASRAutomaticSpeechRecognitionMTMachineTranslationGPUGraphicProcessing

UnitNLPNaturalLanguage

ProcessingHITLHuman-in-the-loopRAGRetrievalAugmentedGenerationIVRInteractiveVoiceResponseSLMSmall

Language

ModelLLMLargeLanguage

ModelTTSText-to-SpeechLMICLow-andMiddle-IncomeCountryAcronymsandabbreviationsBridging

theLanguage

Gap

53.Definition

by

the

MIT

Sloan

School

of

Management,based

on

the

definition

by

AI

pioneer

Arthur

Samuel.Figure1:TheAIecosystemframeworkFigure2:PredominanceofEnglishin

online

contentFigure3:NumberoflivinglanguagesacrossAfricancountriesFigure4:Mapoflocal

languageAI

initiativesFigure5:PercentageoftelcoAIdeploymentsFigure6:DevelopmentprocessforDialog’sLLM

integrationFigure7:KazLLMpartnershipecosystemFigure8:KazLLMtechstackFigure9:SahabatAItechstackTable1:DimensionsofAIsovereigntyTable2:TechnicaladaptationapproachesusedinlocallanguageAI

deploymentsTable3:MNOcontributionpathwaysforlocal

languageAI

Spotlight1:

Understandinglow-resourceandunderrepresentedlanguages

Spotlight2:TheGSMAAfricanAILanguageModelsinitiativeBridging

theLanguage

Gap

6Listof

spotlightsListof

figuresListof

tablesExecutivesummaryBridging

theLanguage

Gap

7Modelstrainedondatathatdoesnotrepresenttheworld’svastlinguisticandculturaldiversityarenotaccessible,relevant,reliableorimpactfulforpeoplewholivetheirlivesinlow-resourcelanguages.Thisrisks

wideningexistingdigitaldivideswhilealsothreatening

thepreservationoflanguagesacrosstheworld.Agrowingnumberofeffortsareaddressing

thislinguistic

imbalance.Startups,innovators,researchersand

communities

in

LMICs

are

building

and

applyinglocally

relevant

AI

models,curating

and

crowdsourcinglinguistically

diverse

datasets

and

creating

enablingenvironments

for

greater

AI

language

inclusion.However,

theseeffortsareoperationallydemanding,resourceintensive

and

introduce

several

ethical

considerations,particularlywhen

theyinvolvecommunitycrowdsourcing.They

also

lack

the

capacity

to

reach

last-mile

users

atscale,creating

datasets

and

models

without

distribution.These

challenges

are

compounded

by

limitations

incomputeinfrastructureandsustainable

fundinginLMICs.WithinthedigitalandAIecosystem,MNOs

play

astrategicallysignificantyetnotwellunderstood

roleinbridgingthelanguagedivide.Throughfourcasestudiesin

LMICs–OrangeinSenegal,

DialogAxiatainSri

Lanka,

Beeline

(VEONGroup)in

KazakhstanandIndosatin

Indonesia–thisresearch

exploreshowMNOsareadvancingmore

inclusiveAIthroughmodelsinlocallanguages.

The

case

studies

rangefromMNOsusinglanguageAIfor

customer

support(themostcommonentrypoint)to

building

large-scale

nationalAIinfrastructure.Ineachofthecase

studies,

theMNOsrecognisetheimportanceof

languageinclusioninthedigitalworldandtheopportunityto

enableitthroughAI.ThecasestudiesshowthreeclearpathwaysforMNOs

tosupportlanguageinclusionand,increasingly,enable

sovereignAIambitions.First,asserviceprovidersandlast-miledistributors,MNOsintegratelanguagetechnologiesintoexistingcustomerservices,delivering

supportinlocallanguageswhilecreatingreal-worldenvironmentsfortesting,iterationandoperationalimprovement.Second,asecosystemconvenersandbridges,MNOsleveragetheirinstitutionalpositiontobringtogethergovernments,academiaandtechnology

providersinmutuallybeneficialpartnerships,aligningLanguageremainsoneofthebiggestbarrierstotheequitabledevelopmentofartificialintelligence(AI)inlow-andmiddle-income

countries(LMICs).Thedigitalworldisdominatedbyasmallnumber

of“high-resource”languages,particularlyEnglish,withabundantdigitaldataresourcesavailable.Thevastmajorityoftheworld’slanguages,bycontrast,are“lowresource”andlackthemachine-usabledatathatcanbeusedfortrainingnaturallanguageprocessing

(NLP)models,particularlylargelanguagemodels

(LLMs),whichrequiremassiveamountsofdata.–Indosat(Indonesia)investsin

sovereign

compute

andopenlanguagemodelstosupport

nationalAI

capacityacrosspublicservicesandindustry.Takentogether,thefindingsshowthatinclusivelocal

languageAIwillnotemergefromasingle

actor

ortechnicalapproach.Instead,progressdepends

oncomplementaryrolesacrosstheecosystem.MNOsaremosteffectivewhentheyfocusontheirstructural

strengths–deployinglanguagetechnologiesatscale,conveningpartnersand,in

some

cases,providingshareddigitalandAIinfrastructure–whilecommunity-ledinitiativescontinuetodrivelinguisticdepth,culturalgroundinganddatacreation.Ultimately,closingtheAIlanguagegap

in

LMICs

willdependonhoweffectivelyinstitutions

alignincentives,shareriskandbuild

partnershipsthat

translatelinguisticinnovationintosustainableand

large-scaleimpact.incentivesaroundsharedobjectivesforlanguageinclusion,nationalprioritiesandscale.Third,someMNOsareemergingassovereignAIenablers,investingincompute,cloudplatformsandmodel-hostingenvironmentsthatpositionlocallanguageAIaspartof

broadernationaldigitalinfrastructure.Inthispathway,

MNOsdonotjustdeployAIintheirownservicesbutprovidetheinfrastructurelayerthatenablesboth

private-sectorinnovationandpublic-sectordigital

transformation.Thefourcasestudiesillustratethesepathways

inpractice:–Orange(Senegal)focusesonhybrid

language

systemstodelivercustomersupportinWolof

throughconversationalinterfaces,includingspeech-enabledchannels.–Dialog(SriLanka)uses

prompt-basedand

hybrid

languagetechniquestolowerbarrierstodigitalcreationforwomenentrepreneurs,withno-code

approaches.–Beeline(Kazakhstan)leadsa

multi-stakeholderefforttobuildKazakhlanguage

models

anchored

in

openaccessandpublic-sector

use.Bridging

the

Language

Gap

Executive

summary81.IntroductionBridging

theLanguage

Gap

9In

less

than

three

years,more

than1.2billion

peopleonbroaderfoundationssuchasdigitalinfrastructure,haveusedAI-enabledtools,outpacingtheearlyhumancapitalandenablingpolicyenvironments,adoptionofboththeinternetandsmartphones.4aswellascross-cuttingenablersincludingfinance,However,AIadoptionremainsdeeplyunequal.Usagepartnershipsandresearchanddevelopment.6ratesinhigh-incomeeconomiesareroughlytwiceWeaknesses

in

any

of

these

layers

can

limit

thethoseobservedinLMICs,withthegapwideningsharplyadoptionanddiminishtheimpactofAI.Limitedin

countries

with

GDP

per

capita

below

USD

20,000.5availabilityofdatainlocallanguagesremainsoneThesedisparitiesreflectnotonlydifferencesinaccessofthemostpersistentbarriers,affectingboththetotechnology,butalsostructuralimbalancesinhowAIdevelopmentofAIsystemsandtheirrelevance,systemsaredevelopedanddeployed.usabilityandtrustworthinessamongendusers.AddressinglanguageinclusionisthereforeessentialThedevelopmentofAIdependsonthreebuildingtoensurethatAIdeliversinclusiveandlocallyblocks–data,compute

and

skills–which

in

turn

relyFigure1:TheAIecosystem

frameworkrelevantoutcomes.7Source:GSMAMobilefor

Development8Thepotentialofartificialintelligence(AI)tosupportsocialandeconomicdevelopmentiswellestablished.AIapplicationsareincreasinglyseenascriticaltoolsforimprovingservicedelivery,expandingaccesstoinformationandsupportinginclusivegrowth,

particularlyinlow-andmiddle-incomecountries(LMICs)wheredevelopmentneedsaremostacute.AIiswidelyregardedasa

general-purposetechnology,anditsadoptionhasbeenfaster

thananypreviousdigitalinnovation.Financing

mechanismsResearchand

development4.Microsoft.(2025).

AI

Diffusion

Report:Where

AI

is

most

used,developed,and

built.5.

Ibid.6.GSMA.(2024).

AI

for

Africa:Use

cases

delivering

impact.7.World

Bank

Group.

(2025).

Strengthening

Foundations:Digital

Progress

and

Trends

Report2025.8.GSMA.(2024).

AI

for

Africa:Use

cases

delivering

impact.Bridging

theLanguage

Gap

Introduction

10Humancapital

PolicyandandskillsregulationCross-cuttingenablersPartnershipsDigitalinfrastructureCompute

AIskillsDigitaleconomy

foundationsAI

fundamentalsDataLLMsaremostlytrainedondatasetsfromhigh-income

countries

(HICs),indominantlanguages

like

English,FrenchorSpanish.Theinternet,where

a

large

part

of

theworld’sknowledgeisstored,serves

asthe

single

mostimportantdatasetfortrainingAI.Yet,morethan

halfofthiscontentisin

English,despite

English

being

spokennativelybyjust5%oftheglobal

population.11,12Theoverwhelmingmajorityoftheworld’s7,000

languageslackthedata,toolsortechniquesfor

naturallanguageprocessing

(NLP),makingthem

“low-resource”incontrasttoahandfulof

“high-

resource”languages,includingEnglish,

French,

Spanish,Germanand

MandarinChinese.13Countrieswherelow-resourcelanguagesdominateconsistentlyshowlowerlevelsofAIadoption,reinforcingexistingdigitaldivides.9

Thischallengebecomesmostvisibleinthedesignanddeploymentoflargelanguagemodels

(LLMs).

ModelssuchasChatGPT,LlamaandClaudearerapidlytransforminghowpeopleaccessinformation,communicateandbuilddigitaltools.However,despitetheirtransformative

potential,LLMsremainlargelyinaccessibleand

notfitforpurposeincountrieswherenon-dominantlanguagesarespoken,largelyinLMICs.State-of-the-artLLMsstillshowalargeandsystematic

gap

in

performancebetweenEnglishandlow-resourceand

non-Latinscriptlanguages.109.Microsoft.(2025).

AI

Diffusion

Report:Where

AI

is

most

used,developed,and

built.10.Ahuja,S.et

al.(2024).

MEGAVERSE:Benchmarking

Large

Language

Models

Across

Languages,Modalities,Models

and

Tasks.Microsoft

Corporation.11.Britannica.

“Languages

by

number

of

native

speakers

–List,Top,&Most

Spoken”.Accessed10September2025.12.Common

Crawl.

“Statistics

of

Common

Crawl

Monthly

Archives

by

commoncrawl”.

Accessed11September2025.13.Ravindran,S.(20July2023).

“AI

often

mangles

African

languages.Local

scientists

and

volunteers

are

taking

it

back

to

school”.Science.1.1.

The

language

divideBridging

theLanguage

Gap

Introduction

115141224

36

1Nativespeakers

OtherspeakersGerman

Japanese

French45

665

5

443

21English

Russian

Chinese

Spanish

Unknown

Other

languagesFrench

Russian57

5

3

3

230I

I

I

English

Chinese

Spanish

Other

languages218

7

5

5

352Figure2:PredominanceofEnglishinonlinecontenta.Distributionofleadinglanguagesspoken,2025(percentageofglobalpopulation)I

IEnglishHindi

SpanishRussian

Other

languagesBridging

theLanguage

Gap

Introduction

12c.Open-sourcedatasets

fromHuggingFacebylanguage,2024(percentage)d.YouTube

videosbylanguage,2022(percentage)14.World

Bank

Group.(2025).

Strengthening

Foundations:Digital

Progress

and

Trends

Report2025.b.GlobalURLsbylanguage,2025(percentage)EnglishMandarinChinesePortuguese

ArabicSource:WorldBank14SpanishHindiLanguageisfoundationaltoAIsystems,andterms

suchas“local