2026 AI就绪度：构建标准化就绪度框架分析报告V2.0（英文）

ITUPublications

International

Telecommunication

UnionTelecommunicationStandardizationSectorAIReady–

Analysis

Towards

a

StandardizedReadinessFrameworkVersion2.0InterimReportJanuary2026ITUDisclaimerTheviews

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membership.ISBN978-92-61-41911-0(electronicversion)978-92-61-41921-9(EPUBversion)AIReady–

AnalysisTowardsaStandardized

ReadinessFrameworkVersion2.0Interim

ReportJanuary2026ITUArtificialIntelligence(AI)isreshaping

the

way

weaddresscomplexsocietal

challenges,offeringnewpossibilitiesinareassuchashealthcare,climateresilience,education,anddigital

inclusion.The

ITUAI

Readiness

projectwas

launched

in

2024to

measurethe

ease/difficulties

andtheability

to

reap

the

benefits

of

AI

integration.Lastyear,tofurtheradvancethediscussions,

ITU

launchedthe

ITUAI

Readiness

pilot

Plugfesttocollateandstudyprojectsonapplying

AI

tosolverealworldproblems.

TheITU

AIReadinessproject

also

called

for

engagement

of

experts

to

provide

strategic

feedback

and

guidance.88experts

from38

countries

were

carefully

selected.

Mentoring

and

comments

on

the

Plugfestprojects

were

provided

by

the

experts

in

addition

to

valuable

regional

perspectives

to

shape

theITU

AI

Readiness

Framework.This

project

brings

together

contributions

from

multiple

sectors

–

industry,

academia,

government,

and

civil

society

–

creating

a

collaborative

environmentwhereideas,knowledge,andexperiencesareshared

todevelop

thestandardized

AIReadinessFramework.Bringing

theexperience

fromanalysingusecases,in2025,ananalytical

approach

was

followed

in

combinationwith

a

bottom-up

approach.This

approach

derives

dimensions

and

metricesfor

readiness

analysis

from

the

Plugfest

project

reports.A

way

forward

for

integrating

regionalcustomizations

is

provided

in

the

form

of

Indices.

In

addition

to

the

analysis,

a

practical,

livingtoolkit

is

designed

and

presented

which

can

be

used

by

countries,

enterprises,

Non-Governmental

Organizations(NGOs),and

other3rd

parties.We

acknowledgethe

support

and

arevery

grateful

forthe

encouragement

provided

bythe

Kingdom

of

SaudiArabia

andthe

Ministry

of

Industry

and

InformationTechnology

of

Chinaduring

this

project.

We

acknowledge

also

the

work

done

by

ITU

Members

in

ITU

Study

Groupsand

for

their

contribution

to

AI

Readiness

standards.As

we

continue

developing

the

AI

Readiness

Project,

we

look

forward

to

deepening

our

collaboration

with

partners

worldwide,

developing

AI

Readiness

standards,

building

AIReadiness

capacity,and

contributing

to

multi-level

AI

Governance.ForewordiiForeword

.......................................................................................................................................iiListofcontributors......................................................................................................................ivAcronyms

......................................................................................................................................vi1.Introduction

..........................................................................................................................

7Background..........................................................................................................................7InsightsfromAI

ReadinessStudy.......................................................................................8ReportStructure

.................................................................................................................112.ITUAIReadiness

Basic

Framework.................................................................................12Data

.....................................................................................................................................13Digital

Infrastructure

..........................................................................................................15DigitalSkills.........................................................................................................................15Innovation

Ecosystem........................................................................................................15AI

Policy

...............................................................................................................................163.StructuralApproach

..........................................................................................................

17Factors

.................................................................................................................................17Dimensions.........................................................................................................................184.AIReadinessGapAnalysis

...............................................................................................355.AIReadinessFrameworkEngagement..........................................................................37AI

ReadinessToolkit..........................................................................................................

376.Futurework........................................................................................................................43Appendix:AdditionalInformation..........................................................................................44Appendix:FAQ...........................................................................................................................46References...................................................................................................................................51Table

of

contentsiiiListofcontributorsNameAhmedSaidAlirezaYariAmenyKhachloufAmit

KumarSrivastavaAmjad

Maawia

ElnayalAmmarSalehAliMuthannaAnnaAbramovaAntoniaMorenoÁlvaroSotoAsratMulatu

BeyeneAyshaAhmedAl

kohejiChenxiQIUFahadAl

balawiHabibMohammed

HussienHalimaMohamed

IsmaeelIan

Nyasha

MutamiriInnocentNzimenyeraKatarzynaWacKiran

Raj

PandeyLilibethAcostaMarceloGabriel

MendozaRochaMaxwell

AbabioMohammedAl

awadAffiliationMinistryofCommunicationandInformationTechnol

-

ogy,

EgyptICT

Research

Institute,

IranTunisieTelecomDepartmentofTelecommunications,IndiaTelecommunicationsRegulatoryAuthorityofBahrain,

BahrainSaintPetersburgStateUniversityofTelecommunica-

tionsMoscowStateInstituteofInternational

Relations

(MGIMO)TheNational

CenterofArtificial

IntelligenceinChile

(CENIA)PontificiaUniversidadCatólicadeChile,TheNational

CenterofArtificial

IntelligenceinChile(CENIA)AddisAbabaScienceandTechnologyUniversityTelecommunicationsRegulatoryAuthorityofBahrain,

BahrainChinaAcademyofInformationCommunications

Technology,MIITofChinaSaudiData&AIAuthority,KingdomofSaudiArabiaAddisAbabaScienceandTechnologyUniversityMinistryofTransportationandTelecommunications,

BahrainPostal

andTelecommunicationsRegulatoryAuthority

ofZimbabwe,ZimbabweGGGIUniversityofGenevaHealthAIforAll

Network(HAINet)GGGIPontificiaUniversidadCatólicadeChile,TheNational

CenterofArtificial

IntelligenceinChile(CENIA)ShieldTechHubSaudiData&AIAuthority,KingdomofSaudiArabiaiv●NameAffiliationMunezeroMihigo

RibeusGGGIOsmar

BambiniumgrauemeioPrashaSoofulNTHealth,AustraliaRim

Bel

hassineCherifTunisieTelecomShanXUChinaAcademyofInformationCommunications

Technology,MIITofChinaShweta

KhushuVector

InstituteTsafakDjoumessiPauline

GnimpiebaMinistèredesPostesetTélécommunicationsde

la

RépubliqueduCameroun,CameroonXingzhi

MAChinaAcademyofInformationCommunications

Technology,MIITofChinaYue

QINChinaAcademyofInformationCommunications

Technology,MIITofChina(continued)vAIArtificial

IntelligenceAI-REToolkitAIReadiness

EnablementToolkitAPIApplicationProgrammingInterfaceCPUCentral

ProcessingUnitEGExpertGroupGPUGraphicsProcessing

UnitIAPIncidentActionPlanIoTInternetofThingsIPIntellectual

PropertyKBKnowledge

BaseKPIKeyPerformance

IndicatorMLMachineLearningNGONon-Governmental

OrganizationSDKSoftwareDevelopment

KitTACTechnicalAdvisoryCommitteeAcronymsviAI

Ready–AnalysisTowardsaStandardized

Readiness

Framework1.

IntroductionBackgroundThis

report

provides

an

analysis

ofthe

Artificial

Intelligence

(AI)

Readiness

study

aimed

atdeveloping

aframeworkfor

assessingAI

Readiness,

which

indicatesthe

ability

to

reap

thebenefits

ofAI

integration.

By

studyingthe

actors

and

characteristics

in

different

domains,

a

bottom-up

approach

isfollowed,

which

allows

ustofind

common

patterns,

metrics,

andevaluation

mechanisms

for

the

integration

of

AI

in

these

domains.The

ITU

AI

Readiness

framework

aims

to

engage

with

multiple

stakeholders

around

the

world,assessandimprove

thelevel

ofintegrationof

AIin

variousdomains,studyusecases

to

validate

theweightageofthe

keyfactors

inthosedomains,

improveglobal

AIcapacity

building,

and

fosteropportunitiesforinternational

collaboration.In

September

2024,

ITU

published

its

first

version

ofthe

AI

Readiness

report,

where

6

keyfundamental

factorswereidentified:•Open

Data:Accessibility

and

quality

of

datasets

for

analysis

of

AI

applications.•Research:Collaboration

between

domain-specific

and

AI

research

communities.•Deployment:Infrastructure

and

ecosystem

readiness

for

AI

deployment.•Standards:Ensuring

trust,interoperability,and

compliance.•Open

source:

Enabling

rapid

adoption

through

an

open

developer

ecosystem.•Sandbox:Platforms

for

AI

experimentation

and

validation.To

furtherstudy

theroleplayedby

thesecomponentsin

thereal

practice,ITUand

theKingdomof

Saudi

Arabiacalled

forengagement

from

the

fieldandlaunchedapilot

AIReadinessPlugfestduring

the

2024

GAIN

Summit

in

Riyadh.

The

ITU

AI

Readiness

Plugfest

is

an

initiative

toexplain

the

AI

Readiness

factors

to

various

stakeholders

and

allow

stakeholders

to

“plug

in”

their

regional

AI

readiness

factors,

such

as

data

accessibility,

AI

models,

compliance

withstandards,toolsets,

andtraining

programs.Additionally,theTechnical

Advisory

Committee

(TAC)

and

Expert

Group

(EG),

composed

of

experts

invited

through

AI

for

Good

initiatives,provide

strategic

guidance

and

feedback

on

AI

readiness

projects.Expert

Groups

are

composed

of

global

experts

with

different

backgrounds

coming

from

38

countries.

Experts

are

mainly

from

Academia

(33%),

government

ministries/regulatoryauthorities

(32%),telecommunication

companies,

research

institutes/Think

Tanks,

regional/international

organizations,andprivatecompanies.

Thereare88expertsinEGs,among

whom

62.5%come

from

developing

countries.32experts

are

women

leading

figures

in

the

countriesand

the

domain,representing36%of

all

experts.To

study

the

sandbox

environments

and

their

inluence

on

AI

readiness,cloud

credit

support

is

providedto

selected

projects,furtherfacilitatingthe

development

and

deployment

ofAIsolutions

in

real-world

applications.InJuly2025,thethird

ITUAI

Readinessworkshopatthe

ITUAIforGoodGlobal

Summit

washosted.The

workshop

invited

global

stakeholders,industry

leaders,and

researchers

to

fostercollaborationonITUAIReadiness.TheworkshopservedasacompilationofprojectstowardsChapter

17ITU

AI

Readiness2.0,

featuring

the

sharing

of

plugfest

project

learnings

along

with

the

partnerpresentations

centering

on

their

understandings

of

AI

Readiness.

During

the

workshop,

ITUannounceditsfurtherstepstowards

ITUAI

Readiness3.0activities.Oneofthe

maincontributionsofthis

reportisthefurtherdevelopmentoftheframeworkforassessing

AIReadiness,

whichindicates

theability

toreap

thebenefitsof

AIadoption.

After

theAI

for

Good

Global

Summit

in

July2025,wecontinuedouranalysis,summarized

thelearningsfrom

theplugfestprojectreports.Bycontinuing

AIusecasestudies,initiatingconsultations

withexperts

from

industry,

research

institute,

academia

and

government,we

derived

13

genericdimensionsfromthe

expert

guidance

duringthe

plugfest.

Metrices

quantify

and

measuredetailed

domain-specific

values

under

each

dimension.Indices

serve

as

filters

or

weightages,

which

capture

the

granular

priorities

of

the

user.Indices

could

be

applied

to

both

dimensionsand

metrics

to

allow

users

to

adjust

the

relevant

importance

when

self-evaluating.The

basic

framework

and

the

details

are

complementary

to

each

other,making

the

frameworkavailablefor

both

policymakerswith

guidance

onAI

and

domain

expertswithtechnical

andactionablerecommendations.For

better

stakeholder

engagement

around

the

ITU

AI

Readiness

Framework,ITUdesignedapilotAI

Readiness

EnablementToolkit(AI-REToolkit),which

isadynamic

model

and

a

livingtool

that

enables

self-evaluation

for

the

users.The

toolkit

uses

theprincipleofafoundationalmodel

builtfromthe

ITUAI

Readiness

Knowledge

Base(KB)inthe

ITUAIforGoodSandbox

and

a

finetuned

model

integrating

regional

customizations

for

users

to

self-assess

the

AI

performancein

theircontext.

TheITU

AIReadinessKnowledgeBase

functionsas

thebrainof

thetoolkit.It

is

built

with

AI

techniques

and

gathers

input

mapped

to6fundamental

factors

in

the

framework.Output

from

the

framework

contains

the

evaluation

of

the

status

quo,gap

analysis,andcustomizedactionablerecommendations.Each

timeusersinputnewmaterials,suchas

the

latestversionofthe

report,

unstructureddata,anddeployment

stories,the

knowledge

basecan

iteratively

learn

from

the

new

input.Toincreaseadoption

fromgeneral

users,

theITU

AIReadinessChallenge,

withaspecific

focuson6factors,was

launched

by

the

end

of

October2025

during

the

AI

for

Good

Impact

Africaevent

in

Johannesburg,South

Africa.Participants

were

requested

to

build

the

basic

frameworkof

the

knowledge

base.To

review

the

framework,dimensions,the

pilot

toolkit

design,and

the

standards

gap

on

theground,several

rounds

of

review

meetings

with

experts

from

EGs

were

held,with

a

specificfocus

on

collecting

feedback

and

potential

inputs.From

the

feedback

with

experts,potentialusers

ofthetoolkitwere

identified,

pain

points

ofthe

users

onthe

groundwere

noted,

andcontributions

from

the

member

states

were

discussed.InsightsfromAI

ReadinessStudy1.Strengthening

ICT-related

higher

education,

leveraging

open-source

ecosystems,

andengagingwithinternational

educationand

trainingplatforms,andenablingleapfroggingopportunities

can

accelerate

AI

skills

development.2.A

strong

positive

correlation

exists

between

national

income

levels

and

general

digitalliteracy,

measured

through

ICT

skill

penetration.

However,

substantial

variation

existswithinincomegroups.Middle-incomecountriesoftenexhibithigheroptimismandtrust

towardAItechnologiesthan

high-incomeeconomies,creatingfavorableconditionsforlarge-scale

AI

adoption

if

skills

gaps

are

addressed.AI

Ready–AnalysisTowardsaStandardized

Readiness

Framework本报告来源于三个皮匠报告站（）,由用户Id:349461下载,文档Id:1068233,下载日期:2026-01-268Digital

skills

development

accelerates

most

rapidly

at

the

middle-income

stage.

ICT

skillpenetration

typicallyremainslowinlow-incomeeconomies.Policychoicesandeducation

investment

duringthis

phase

play

a

decisive

role

inwidening

or

narrowing

national

AIreadinessgaps.3.Datareadinessisacritical

determinantofeffective,

trustworthy,andinclusive

AIadoption.Beyonddatascaleandaccessibility,

thequality,diversity,representativeness,andlabelingofdatasetsdirectlyshapeAIsystem

performance,aswell

astheirfairness,transparency,

andadaptability.4.Insufficient

data

quality

and

biased

datasets

risk

reinforcing

discrimination

and

limitingreal-world

impact,

particularly

in

localized

deployment

contexts.

Strengthening

publicdataopennessanddataservicecapabilities–includingdatacollection,datacleaning,anddata

labeling–is

therefore

essential

to

enable

scalable

and

localized

AI

adoption

acrosspriority

sectors

such

as

education,agriculture,and

transportation.5.AIreadinessgloballyisconstrainedbylimiteddatascaleandunevenInternetpenetration.GlobalInternetusagestandsat55.56%,indicating

thatnearlyhalf

of

the

world’spopulationremains

outside

the

digital

ecosystem

required

for

large-scale

AI

data

generation.While57%

of

countries

have

Internet

penetration

above

60%,

nearly

half

remain

below

50%,and

only

18%

of

countries

exceed

90%

penetration,

highlighting

persistent

constraintson

global

data

scale

for

AI

development.6.Data

readinessgapsare

driven

by

service

capability

and

governance,

not

access

alone.On

average,developed

economies

have

more

than

three

times

as

many

Internet

service

providers

per

million

inhabitants

as

developing

economies,with

median

values

showing

an

even

larger

gap.

In

addition,

lack

of

data

governance

frameworks

limits

effective

andtrustworthydata

use.7.Basic

network

coverage

supports

entry-level

AI

use,

but

advanced

network

readinessremains

uneven.While96%of

the

global

population

is

covered

by

mobile

broadband,accesstoadvancednetworksremainshighlyuneven.

Global4G

coverage

reaches

93%,but

only56%

in

low-income

economies.Global

5G

coverage

stands

at

55%,

comparedto84%in

high-income

economies

and

just4%in

low-income

economies,with

significantregional

and

urban–rural

disparities.8.Shortfalls

in

computing

infrastructure,

energy

supply,

and

edge

devices

constrain

AI

deployment.

Availability

of

data

centers,

per

capita

electricity

supply

in

developedeconomiesismore

than

twice

thatofdevelopingeconomies.IoTmarketsizeindeveloped

economies

is

on

averagefourtimes

largerthan

in

developing

economies,

limitingtheavailability

of

edge

devices

for

AI-enabled

industrial

applications.9.Open-source

technologies

lower

entry

barriers

for

AI

adoption

worldwide.Contributionsto

major

open-source

AI

and

LLM-related

repositories

extend

beyond

application-

layer

developmentto

include

core

model

architectures,training

pipelines,

evaluation

benchmarks,

and

governance

mechanisms.

Measurable

upstream

contributions

to

top-tier

open-source

LLM

initiatives

and

participation

in

the

opensource

technology

development,

especially

development

of

foundational

and

large

language

models(LLMs),isanimportant

metricofAI

readiness.10.Overall,the

level

of

open-source

engagement

correlates

strongly

with

other

readinessdimensions,

including

R&D,computing

capacity,and

the

overall

innovation

ecosystem.

R&D

capacity

is

an

important

dimension

of

AI

readiness,

leading

to

metrics

such

asstrongerAI

research

output,

higher

publication

impact,

and

greater

resilience

intalentdevelopment.Attheenterprise

level,company

investment

inemergingtechnologies

–including

AI,data

platforms,and

advanced

computing–plays

a

critical

rolein

translatingresearch

into

scalable

systems.

Corporate

AIR&D

expenditurebrings

cumulative

advantages

along

with

robust

public

research

institutions

and

innovation

support

mechanisms.Chapter

1AI

Ready–AnalysisTowardsaStandardized

Readiness

Framework911.Investment

patterns

inluence

AI

readiness

levels.

Public

investment

in

AI,

supportedby

effective

national

AI

strategies,

will

help

establish

research

and

innovation

systems.Dedicated,multi-year

public

funding

mechanisms

for

AI

research,experimentation,and

standards

engagement

with

supportive

private

investment,

including

venture

capitalinvestmentinAIstartups,areimportant.Theseecosystemsbenefitfrommaturefinancialmarkets,strongexitpathways,anddensenetworkslinkingstartups,researchinstitutions,and

large

technology

firms.Investment

patterns

influencestartupformation,

scale-uppotential,domestic

commercialization,

and

enable

AI

ecosystems

to

focus

on

not

only

deployment

and

adoptionbutalsoendogenousinnovation.12.Regional

evaluation

of

AI

Readiness

could

be

linked

to

strong

performance

across

alldimensions

of

AI

readiness.In

some

cases,

tight

linkages

between

academia,

industry,

government,

and

activeparticipation

in

international

AI

standardization

processes

play

a

decisive

role

in

shapingglobal

technical

specifications,reference

architectures,and

evaluation

methodologies.Incontrast,insomeothercases,expandingAIadoption,selectiveresearchstrengths,butlimited

inluenceoverfoundational

technologies,with

moderateengagement

inopen-source

AI

projects(primarily

at

the

application

and

integration

layers),growing

public

AIfunding,

butfragmented

governance

and

coordination,

leadto

limited

participation

incore

open-source

LLM

development

and

international

AI

standardization.Lastly,

ifthere

are

structuralconstraints

across

all

dimensions,

it

willlead

to

minimalupstream

engagement

in

open-source

AI

and

LLM

projects

with

limited

access

to

privatecapital

and

global

AI

investment

networks.AI

deployment

in

such

cases

is

frequentlydriven

by

imported

technologies,

increasing

dependency

risks

and

limiting

nationalinluence

over

interoperability,security,and

long-term

system

evolution.Participation

in

international

AI

standardization

processes

remains

low,further

reducing

visibility

of

localneeds

in

global

technical

frameworks.13.Policy