人工智能时代的组织变革：企业如何最大化发挥AI潜力

OrganizationalTransformationintheAgeofAI:

HowOrganizations

MaximizeAI’s

PotentialW

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6Industriesinthe

IntelligentAgeImages:AdobeStock,Getty

ImagesContentsReadingguide

3Foreword

4Executivesummary

5Introduction

6Focus

1Real-time,

individualizedCX

7Focus2Efficientand

resilientoperationsthatadapt

and

evolve13Focus3Accelerated

R&Dand

breakthrough

innovation19Focus4Predictive,AI-poweredstrategic

planning25Focus5Data-driven,

personalizedtalentexperienceandworkforce

planning30Key

principlesenablingadoptionatscalewithinorganizations36Conclusion38Contributors39Endnotes

40DisclaimerThisdocumentispublished

bytheWorld

Economic

Forumas

a

contribution

to

a

project,insight

area

or

interaction.Thefindings,interpretationsandconclusionsexpressedherein

are

a

result

of

a

collaborative

process

facilitatedandendorsedbytheWorld

Economic

Forumbutwhoseresultsdonotnecessarily

representtheviews

ofthe

WorldEconomic

Forum,nor

the

entirety

of

its

Members,

Partnersorotherstakeholders.©2026World

Economic

Forum.All

rights

reserved.

Nopartofthispublicationmay

be

reproduced

ortransmittedinanyformorbyany

means,

including

photocopying

andrecording,or

by

any

information

storage

and

retrieval

system.OrganizationalTransformationintheAgeofAI:HowOrganizationsMaximizeAI’s

Potential2Each

paperoffersa

practical,executive-levelviewofwhattransformation

looks

likeontheground,drawingon

real-worldcasestudies,

leadingpractices,emergingdatafromacross

industriesand

figuresofimpactachievable

in

selected

contexts.Whileeach

paper

isstandalone,commonthemes

emerge:

newoperating

models,evolving

rolesofleadership,

human-AIcollaborationandthegrowing

importanceofAIgovernanceandorchestration.AsAIadoptionaccelerates,thisseriesaimsto

equip

leaderswiththe

insight,capabilitiesanddecisionframeworksrequiredtobuildcompetitive,responsibleandfuture-readyAI-enabledorganizations.TheWorld

Economic

Forum’sAITransformationof

Industries

initiativeseekstocatalyse

responsible

industrytransformationacross

industriesandsociety.

Itadvances

understandingofartificial

intelligence’s

(AI)

impacton

businessandsocietywhileactivelyaccelerating

practicalimplementationthrough

leadershipconvening,

ecosystemcollaborationandthescaling

of

real-

worldsolutions.Thiswhite

paperseriesexaminesthetransformative

roleofAIacross

industries,

combiningcross-industryanalysiswith

in-

depthsectoraland

regional

perspectives.OrganizationalAIin

Action:BeyondLeveragingFromParadoxArtificialIntelligenceTransformationExperimentationtoGenerative

AIfor

JobtoProgress:andCybersecurity:intheAgeofAI:TransformIndustryAugmentationandANet-Positive

AIBalancingRisksHowOrganizations

WorkforceProductivityEnergy

FrameworkandRewards

Maximize

AI’sPotentialMedia,entertainmentandsportArtificialIntelligencein

Media,Entertainment

and

SportTransportIntelligentTransport,

Greener

Future:AIas

aCatalysttoDecarbonizeGlobalLogisticsReading

guideAdvancedmanufacturingandsupplychainsUpcomingHealthcareTheFutureofAI-EnabledHealth:Leadingthe

WayFinancialservicesUpcomingImpactonindustries,sectorsandfunctionsImpactonindustrialecosystemsOrganizationalTransformationintheAgeofAI:HowOrganizationsMaximizeAI’s

Potential3TheStrategicRoleofTelecomProviders

Acrossthe

AIValue

ChainIndustryorfunctionspecificImpacton

regionsRegionalspecificCross

industryUpcoming:ChinaConsumergoodsTelecommunicationsUpcomingfromsupportinganalysisto

participatingdirectly

inexecution.

Inthisenvironment,organizations

must

redefinethe

relationship

between

people

and

intelligentsystems,ensuringthat

humanjudgement,

responsibilityandoversight

remain

firmlyatthe

centre.TheWorld

Economic

Forum

has

longchampioned

collaborativeapproachestocomplexsystemicchallenges.

Inthisspirit,thefindings

presentedhereare

rooted

incross-industry

practiceandreflectthecollectiveexperienceof

organizations

attheforefrontofAItransformation.Theyalsoreaffirmacentral

insight:success

withAI

isnot

merelyatechnologicalachievement,

butanorganizationalone.

Itdepends

on

strategicleadership,clearaccountability,trust

inAI-supporteddecisionsandoperating

modelsthatbalance

humanagencywith

machine

intelligence.Thiswhite

paper

is

partoftheWorld

EconomicForum’sIndustriesintheIntelligent

Ageseriesand

buildsonthe

insightsfromtheAITransformation

of

Industriescommunity.Weofferthis

paperasa

resourceanda

callto

action.

IntegratingAI

into

howvalue

iscreatedanddecisions

are

made

isamongthe

mostconsequential

leadershiptasksofthisdecade.Organizationsthat

actwithclarity,coherenceandcommitmentwill

unlock

transformative

productivity,

resilienceandgrowth.Thosethatdonotriskfalling

behind–

not

becauseAI

failsthem,butbecauseorganizationalchange

does.Artificial

intelligence

(AI)

isenteringadecisivephase.Across

industriesand

regions,organizations

are

moving

beyondexperimentationanddemonstratingtangible

resultsfromAIadoption.Yetasorganizations

unlockvaluefrom

isolatedAIusecases,adeepertransformation

remains

elusive:embeddingAI

intothecore

processesthatdefine

howworkgetsdoneand

how

decisionsare

made

acrossenterprises.This

paper

reframesthechallengefacing

leaderstoday:

notwhetherAIworks,

buthoworganizations

must

re-architecttheirworkflows,operatingmodels

anddecision

rightsto

harnessAIas

a

sourceofsustainedenterpriseadvantage.Todate,

muchofAI’s

impact

has

beendelivered

throughtargetedapplicationsandfunctionalpilots.Theseefforts

have

proventhatAIworks–

buttheycaptureonlya

fraction

of

itspotential.Thegreatestgainsarise

whenAI

isembeddedintocoreworkflows,decision-making

processesandoperating

models,

reshapinghoworganizationscompeteandgrow.Achieving

thisshift

is

nota

primarilytechnologicalchallenge,

butanorganizational

one.ScalingAI

requireschanges

in

howwork

is

designed,

howdecisionsare

madeand

how

accountability

isexercised.

Itcallsfor

newapproachestogovernance,

leadership,skills

andtrust–

particularlyasAIsystems

moveOrganizationalTransformationintheAgeofAI:How

Organizations

Maximize

AI’s

PotentialForewordMaria

BassoHead,AI

Applications

andImpact,Centre

for

AI

Excellence,World

Economic

ForumStephanMergenthalerManaging

Director,Chief

TechnologyOfficer,World

Economic

ForumKathleenO’ReillyGlobal

Lead,

Deal

Structuring&Pricing,AccentureOrganizationalTransformationintheAgeofAI:HowOrganizationsMaximizeAI’s

Potential4March2026Acrossthefocusareas,threestructural

shifts

areemerging:–From

isolated

usecasesto

connectedsystems,wherecustomerexperience

(CX),operations,

researchanddevelopment(R&D),

strategyandtalent

reinforceoneanother–Fromepisodic

initiativesto

continuousprocessesthatsensesignals,

makedecisions

and

learn

in

realtime–Fromtaskautomationto

human

valuecreation,withpeoplefocusingonjudgement,orchestrationandaccountabilitywhileAIaccelerates

insightandexecutionWhilethefocusareas

illustratewhereAI

istransformingvaluecreation,sustainingtheseshifts

atscaledependson

howorganizations

redesignthemselves.ScalingAI

requiresa

rethinkingofdecisionownership,operatingstructuresandgovernance

mechanismssothat

intelligentsystems

areembedded

intoexecution

ratherthan

layeredontoexisting

processes.Organizationsthatsucceed

keep

humansfirmly

inthe

lead,

redesignoperatingmodelsaroundend-to-endoutcomes,treattrustandtransparencyasexecution

enablers,

institutionalizedisciplinedexperimentationand

invest

inscalabletalentsystems.

Intheseenvironments,AIenhancesspeedand

intelligence

inexecutionwhile

humans

remain

responsiblefordirection,trade-offsandoutcomes.Takentogether,thefindings

highlighta

broaderorganizationaltransition.AsAI

becomesembedded

inexecution,sustainedvaluedepends

less

ontechnicalsophisticationand

moreon

leadership’s

abilitytoaligngovernance,

incentivesandways

ofworkingwith

intelligentsystems.OrganizationsthatsucceedactonAI-supportedevidence,continuously

reallocate

resourcesandadapt

howwork

isdone.Artificial

intelligence(AI)

has

moved

beyondcuriosity

andearlyexperimentation.Acrossindustries,organizationscannowpointto

measurable

gainsfromAIadoption.Yetformost,thesegains

remainfragmented–capturedthroughisolateduse

casesratherthanembeddedintohow

the

enterpriseoperates.Asaresult,thecentral

challenge

hasshifted:notwhetherAIworks,buthow

organizations

mustchangeto

realize

itsfull,sustainedvalue.This

paperexamines

how

leadingorganizationsare

makingthattransition.

Drawingonconsultations

anddiscussionswiththeWorld

Economic

Forum’s

AITransformationof

Industriescommunity–comprising

morethan450executivesacrosssectors–

itexplores

howAI

is

being

integrated

into

coreenterpriseworkflowsand

reshapingoperating

models,decision-makingandthenature

ofworkitself.Thefindings

buildon

AIin

Action:Beyond

Experimentationto

TransformIndustries,

moving

from

proofofconcepttoorganizational

redesign.Theanalysisfocusesonfive

criticalfocus

areas

wherecommunity

membersareactively

re-architecting

howworkis

performed,andwhere

AI

isalreadydrivingenterprise-level

impact:ExecutivesummaryFrom

pilotstooperation

models,

leading

firmsembedartificial

intelligence

intocore

workflowstodeliverenterprisevalue.Focus

2Efficientandresilientoperations:Shiftingfromforecast-basedexecutiontoadaptive,

AI-orchestratedsystemsFocus

3Accelerated

research

and

development(R&D)and

breakthrough

innovation:Shifting

from

lineardevelopmentintocontinuous,evidence-drivenlearningData-driven,personalizedtalentexperienceand

workforce

planning:Shifting

from

role-basedmanagementtodynamic,capability-basedsystemsReal-time,individualizedcustomerexperiences:

Shiftingfromstaticjourneystocontinuous,intent-drivenengagementPredictive,AI-poweredstrategicplanning:Shiftingfrom

periodic

planningcycleswithongoingstrategicsteeringOrganizationalTransformationintheAgeofAI:HowOrganizationsMaximizeAI’s

Potential5Focus

1Focus

5Focus

4Aswithearliertransitionsfromanalogueto

digital,scalingAIrequiresmorethantechnologyadoption.

It

demandschangestooperatingmodels,governancestructures,skillsand

leadership

practices.Whilepathwaysdifferacross

industriesand

regions,organizationsadvancing

beyondexperimentationareconvergingona

set

ofshared

principles:

clearbusinessownershipofAI,workflow

redesign

rather

than

pilotexpansion,sustained

investment

inworkforce

leadershipcapabilitydevelopment,andtrustandexperimentationasfoundationalcapabilities.Buildingon

AIinAction:BeyondExperimentationto

TransformIndustries,this

paperexamineshoworganizationsaretranslatingAIambition

intomeasurableoutcomes.

DrawingonconsultationsandobservationsfromtheAITransformationofIndustriesCommunityattheWorld

EconomicForum,comprising

morethan450

leadingadoptersadvancingAIatscaleacross

industries,itsynthesizestheorganizationalchanges

observed

amongsuccessfulenterprises.The

paper

reflectspatternsemerging

in

practiceand

is

not

intendedas

a

prescriptivesetofrecommendations.

It

highlights

fivecorefocusareaswhere

leaders

are

alreadyembeddingAItodriveenterprise-wide

impact:Focus1:

Real-time,

individualizedcustomer

experience

(CX)Focus2:

Efficientand

resilientoperationsthat

adaptand

evolveFocus3:Accelerated

researchanddevelopment

(R&D)and

breakthrough

innovationFocus4:

Predictive,AI-poweredstrategicplanningFocus5:

Data-driven,

personalizedtalentexperienceandworkforce

planningAcrosseachfocusarea,the

paper

highlightsvalue

opportunities,theorganizationalshifts

requiredand

examplesofprogresstowardsenterprise-wideimpact.The

pastdecade

markedan

important

inflection

point

intheadoptionofartificial

intelligence

(AI).Organizations

moved

rapidlyfromexperimentation

tocapability,advancingthrough

pilots,

proofsofconceptandearlydeployments.Asexplored

inAIinAction:BeyondExperimentationto

Transform

Industries,

many

leaders

have

nowdemonstrated

thatAI

usecaseswork.AsagenticAIstartsbeing

integratedandcostof

learning

collapses,

the

next

phaseofAIadoption

requiresstructural

organizationalchange.Increasingly,organizations

recognizethatthe

greatestvaluefromAI

is

not

realizedthrough

standalone

usecases

butfromembeddingAIdeeply

intocoreworkflowsand

operatingmodels.Atthisstage,AI

becomesacatalystfortransformation–

reshaping

howworkisdone,

howvalue

iscreatedand

how

productivity

andgrowthare

achieved.MuchofAI’searlyvalue

has

comefromnarrowlydefinedapplicationsthatdeliveredlearning,

localizedefficiencygainsand

proofofreturn.Applied

indiscrete

use

cases,AIoftenaugmentsexistingworkflows

but

rarelytransformsthem,constrainingthescaleanddurabilityofimpact.Greater

impact

emergeswhenorganizations

redesign

processesend-to-

end,creatingcompoundingeffectsacross

the

enterprise.Yettoday,onlyasmall

proportionoforganizations–approximately

15%–areusingAItofundamentally

redesign

howwork

is

performed.1

As

moreorganizations

progressbeyondsegregated

pilots,thevaluegeneratedbyAIshiftsfrom

incremental

improvementtowards

moretransformativeoutcomes.Whilestudiesshowdouble-digit

productivitygainsatthetasklevel,these

have

not

consistently

translated

intoenterpriseor

macroeconomicimpact.Withoutredesigningend-to-endworkflowsanddecision

rights,

individualgainsdo

not

convert

intostructuralvalue.IntroductionAI’s

next

phasedemandsa

rethinkingofcoreworkflowstounlockenterprise-wide

impact,

ratherthananexpansionof

pilots.OrganizationalTransformationintheAgeofAI:HowOrganizationsMaximizeAI’s

Potential62Fromstaticjourneystodynamic,

real-timeorchestrationtailoredtoeverycustomer:

Replacestaticjourney

mapswithcontinuous,

moment-level

decisionsacross

channels.3Fromhuman-onlyexecutionto

agentic

actionon

behalf:

Move

routineCXexecutiontoAI

agentsoperatingwithinguardrails,with

humanfocus

reservedforjudgement,empathyand

exceptions.4

Fromreactiveoutcomesto

continuous

experiencelearningandtrustoptimization:Shiftsfrom

post-hocchurn

responsetocontinuousoptimizationof

value,

trustandautomationthresholds.–

Increaseconversionandreduce

churnthroughtimely,

predictiveand

personalized

interventionsatthe

momentof

riskor

opportunity.Upto25%

higherconsumerconversationrates

and21%

reduction

in

churn2

leadingto5-8%

revenue

uplift3–

Reducecost-to-servewhileimprovingexperience4

by

preventing

issues,automating

resolutionandaccelerating

human-led

interactions.20–30%

lower

cost-to-serve,515–30%

productivity

gains6–

Strengthentrustandbrandconsistency

at

scalebyconsolidatingconsumer

profilesand

deliveringonecoherent

relationshipacross

channels.Up

to15–20%

higher

customersatisfaction

score

(CSAT)7Real-time,individualizedCXAIturnscustomerjourneys

into

real-time,

adaptivesystemsthat

predict

intent,actautonomouslyand

learncontinuously.1

Fromperiodiccampaigntargetingtoone-to-one,

predictivediscovery:Shiftfromcampaign-led

reachandcustomer-initiatedcontacttoAI-driven

inference

of

latent

intent,valueand

risk.AIenablesorganizationstosensecustomer

intent

in

realtime,steerexperiencesdynamically

and

act

on

customers’

behalfwithinclearlydefinedguardrails.

Asa

result,CXshiftsfrom

a

series

of

discreteinteractionstocontinuous,adaptive

relationships,

anticipating

needs,

resolving

issuesearlierandlearningfromeveryengagement.

AtaglanceCXspansend-to-endprocessesthroughwhich

customersdiscover,evaluate,

purchase,

useand

receivesupportfor

productsandservices.Traditionally,thesejourneys

have

beendesigned

as

linearflowsand

managedthroughfragmented

channel

interactions.TABLE1AI-enabled

transformation

of

CXAwareness

Consideration

Purchase

Service

RetentionFocus

1OrganizationalTransformationintheAgeofAI:HowOrganizationsMaximizeAI’s

Potential7

Action:howorganizationsare

changing Ambition:opportunities

to

capture

toagenticactionon

behalfAIautonomouslyexecutesroutineCXactions–suchasresolvingissues,adjustingterms,

routingwork

and

initiatingfollow-ups–under

clearly

defined

guardrails:e.g.refund

limits

below

a

definedfinancial

threshold,

escalation

triggersforrepeatedcomplaintsorhigh-valuetransactionsorhumanreviewwhen

modelconfidencefalls

below

a

set

level.Theseparametersensurethatautonomyexpandswhereriskiscontained,whileaccountabilityforcustomer

outcomesremainsexplicitlyassigned.tocontinuousexperiencelearning

andtrustoptimizationAIcontinuouslybuildscustomerprofilesandupdateswhoreceives

retentionactions,which

offers

are

allowed

andwhenautomationispermittedbasedonobservedlifetimevalues,

experience

outcomes

andtrust

signals.toreal-timeevaluationsteeringtodynamic,real-

timeorchestrationtailoredtoeverycustomerAIreplacespre-builtjourneyflowswithreal-timedecisionsonwhatcontent,offerorhuman

intervention

istriggered

nextforeachindividualinteraction.

toone-to-one,predictivediscoveryDiscoveryshiftsfrombroadcastingthesameofferstomanycustomerstosensingpersonal

intentand

context

inrealtimeandsurfacingwhat’smostrelevant

inthe

moment.Takentogether,theseshiftstransformCX

into

areal-timevalueallocationsystemthatcontinuously

optimizesoutcomesacross

keydimensions–enhancingexperiencequality,

reducingstress,

accelerating

responsivenessand

improvingaccuracy.

Insuchsystems,AIdynamicallyprioritizesattention,autonomyand

incentiveswhile

guiding

human

interventionwhere

it

matters

most,

balancinggrowth,cost,

riskandtrust.OrganizationalTransformationintheAgeofAI:HowOrganizationsMaximizeAI’s

Potential8Fouroperatingmodelshifts

inCXFromperiodiccampaigntargeting...Fromhuman-onlyexecution...Fromreactiveoutcomes...Fromstaticjourneys...FIGURE34211CASESTUDY1Interactive“nextbestaction”tosteercustomerengagementFord

usedAI-drivendecisioningtodynamically

movecustomers

inandoutofaudiences

during

multi-wavecampaigns

basedon

real-time

responses,

ratherthanfixed

journeys.Thisenabled

rapidadjustmentofwhotoengage,whenandwithwhat

message.

Inthreeweeks,the

FordPass

MobileApp

reportedover300,000customersengagedand

a26%

increase

inconversion.8Organizationalchangesobserved:–Establishcross-functionaldiscovery

teamscombiningdifferentexpertise–e.g.

marketing,

CX,datascienceand

product

ownership.–Shiftaccountabilityfromcampaign

planning

to

real-timedecisionenginesthatdetermine

engagementandsuppression.–Expand

human

rolestowardssignal

curation,

policydefinitionandacceptable

actions.–Introducesharedstandards

for

customer

signals,consent

rulesandconfidencethresholdsacrosschannels.Earlyvsadvancedadopters:–Early:

UseAIto

refinetargeting

andengagementwithinexistingcampaignstructures

usinga

limitedsetof

clear

intent

signals.–

Advanced:Operatediscoveryasa

real-time,

predictivedecisionsystemcoordinatedacross

marketing,salesand

service.Shiftsin

how

CXoperates:–Complementor

replacefixedcampaigntargetingwithcontinuous,AI-drivenselectionofcustomerstoengage,suppressor

defer

based

on

predicted

intent,valueand

risk.–Combinesignalsacrossthejourney

(browsing,

comparison,

pauses,

retries,device,

location,

service

history)to

inferwhatcustomersaretryingtodo

now.–Shiftcustomerdiscoveryfrom

staticsegmentationtoanadaptive

processthat

infersandactively

inquires

intoevolvingcustomer

behavioursand

preferences,continuously

learningas

individuals

move

throughthejourney.–EvolveCX

and

marketing

rolesfrom

audience

and

messagedesigntosignal

definition,guardrailsanddecisionthresholds.Fromperiodiccampaigntargetingto

one-to-one,predictivediscoveryOrganizationalTransformationintheAgeofAI:HowOrganizationsMaximizeAI’s

Potential91.1CASESTUDY2ContinuouscustomerprofilingandadaptiveengagementRabobankuses

itsCustomer

Decision

Hubto

unifycustomer

profilesandcontinuouslyadaptengagementacross

app,web,online

bankingandcall-centrechannels.TheAI

engine

aggregates

behaviouraland

interactiondata

in

realtimetodelivernext-bestactionstailoredtoevolving

customerneeds,enablingover

1.5

billion

personalized

interactions

per

year,afourfold

increase

inclick-through

rates,a208%

lift

in

conversion,a4.7%

increase

incustomer

lifetimevalueand

a

2.4%

reduction

incosttoserve.9Shiftsin

how

CXoperates:–Shiftfrom

predefined,

linear

pathsto

real-timesteeringthatadaptsdynamicallyas

individuals

evaluate,

hesitate,compareorchange

direction.–AIcontinuously

interpretsdecisionsignals(stalling,

backtracking,comparisondepth,optionoverload,

repeatederrorsand

individual

context)todesignhowthejourneyadapts

next.–Dynamically

reordersteps,content,

choice

sets

andassistance

in

realtimeto

match

individual

decision-making.–Shiftjourneydesignfromstatic

mapstoadaptive

rules,

pathwaysand

intervention

logic.Organizationalchangesobserved:–Transitionfromchannel-orcampaign-specific

ownershiptoend-to-endjourneygovernance.–Establishsharedjourney

logicacross

digital,

assistedandhumantouchpoints.–RedefineCX

rolesto

focus

on

adaptive

rules,

thresholdsanddecision

patterns.–Deploy

real-timeorchestration

layersthat

can

modifyjourneys

mid-stream.–Align

incentivesaroundjourneycompletion

and

momentum

ratherthan

performance.Earlyvsadvancedadopters:–Early:Apply

real-time

nudgesor

assistance

atselectedhigh-friction

moments.–

Advanced:

Runjourneysascontinuously

adaptive

processes,dynamicallysteering

each

individual’s

path

basedon

real-time

decisionsignals.Fromsta