AI优先操作系统：运营与商业模式创新的蓝图（英文）

The

AI-First

OperatingSystem:ABlueprintfor

Operatingand

Business

Model

InnovationW

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6Images:Getty

ImagesContentsForeword

3Executivesummary

4Introduction

5Block

1:

Intelligenceengine

91.1Thespeed

loop

101.2Thescale

loop

121.3Thescope

loop14

Block2:AdaptiveAItechnologystack172.1Turndata

intofuelforthe

intelligenceengine182.2Ownthe

control

layers192.3Composeamodel-agnostic

portfolio202.4

Makecontextdynamic,

notstatic21

Block3:Operations

redesign223.1Wheretoallocate

intelligence243.2

Howtoredesignwork263.3

Building

intelligence-nativeoperations293.4Operational

leverage:the

new

businesseconomics32

of

intelligenceBlock4:

Human-AIteaming334.1Growing

human-AItalent344.2Organizingfor

human-AIteams364.3

EvolvingtheorganizationforAI-firstexecution38

Block5:

Newvaluecreation

405.1

Designing

product415.2

Prioritizingtrust425.3

Positioning

inthe

market

43DesignyourownAI-first

blueprint

45Conclusion

47Contributors48Endnotes

50DisclaimerThisdocumentispublishedbytheWorld

Economic

Forum

as

a

contributionto

a

project,

insight

area

or

interaction.The

findings,interpretationsandconclusionsexpressedhereinarea

result

of

a

collaborative

process

facilitated

and

endorsed

by

theWorldEconomic

Forumbutwhoseresultsdonot

necessarily

representtheviews

oftheWorld

Economic

Forum,

nor

the

entirety

of

itsMembers,

Partnersorotherstakeholders.©2026World

Economic

Forum.All

rights

reserved.No

part

of

this

publication

may

be

reproduced

or

transmitted

in

any

form

orbyanymeans,includingphotocopying

and

recording,

or

by

any

information

storage

and

retrieval

system.TheAI-FirstOperatingSystem:ABlueprintforOperatingand

BusinessModel

Innovation2Formostofbusiness

history,theenterprisehashadaceiling.Therewere

limits

on

how

fastenterprisescould

learnand

howfast

informationcouldtravelfromtheedgeofan

organization

tothecentreto

informaction.Additionally,therewere

constraintson

how

rapidlydecisionscould

be

made

andexecuted,and

howfarenterprises

couldscale

without

losingcoherenceorexhaustingtalent,capitalortime.Thesewerestructural

limits–

built

intothearchitectureofhow

organizationsthink,

decideand

act.Asmall

butgrowing

numberofenterprises

aredismantlingthose

limits.Theyareachievingthisnot

byadoptingAIasatool,

but

by

designingtheir

entireoperating

logicaroundartificial

intelligence

(AI)–

buildingsystemsthat

learnfromevery

interaction,

improvewitheverycycleand

expand

into

newdomainswithout

proportional

increases

incostor

complexity.Thesearethe“AI-first”enterprises,and

unliketheceilingtheyare

replacing,thisfrontierdoes

not

holdstill.

Itadvanceswith

everybreakthrough,everydeploymentand

everynewcapabilitythatentersthefield.Whattheseenterprisesdemonstrate

is

nota

newfixed

limit,

but

adifferentorderofwhat

an

organization

can

do.TheWorldEconomicForum’sAI-FirstEnterprisesworkstream,partoftheAIGlobalAlliance,brought

together

more

than50of

the

world’smostadvancedenterprisesandthinkers

with

a

shared

mission:

unlock

what

enterprisescandofor

industry,globalecosystems

and

society

byexploringthe

businessandoperating

modelbreakthroughsthatAI

is

making

possibleandshapingthe

nexteraofenterprise.

Overthe

pastyear,theworkstreamwentdeep

intothefrontierof

architectures,decisionsandthe

patternsthatset

genuineAI-firstorganizationsapart.Whatfollows

is

drawndirectlyfromthatcommunity.Whatwefound

is

bothclarifyinganddemanding.It's

"clarifying",

becausewe

havediscoveredacoherentsetoffundamentalsacrosscompaniesthatseemdifferentfromone

another–from

drugdiscoverytofinancial

infrastructure,andfromautonomous

logisticsto

legalAI.

It's

"demanding",

becausethegap

betweenorganizationsthat

have

restructuredaround

intelligenceandthosethat

have

not

iswideningfast.Thefive

building

blocks

inthis

paper–theintelligenceengine,theadaptivetechnologystack,

redesignedoperations,

human-AIteamingand

newvaluecreation–are

nota

roadmap

fortransformation.Theydescribe

howthefrontieralreadyoperates.Theopportunityfor

leaderswilling

toengageseriously

isto

usethem

as

a

lens:to

see

what

is

now

possibleandtodesignaccordingly.Thestructurallimitsaregone.Whatcomes

next,howfarthisfrontiermoves,whoitreaches

andwhat

itmakespossibleforindustries

and

societies

arethe

questionswewillkeeppursuing.TheAI-FirstOperatingSystem:A

BlueprintforOperatingand

Business

Model

InnovationForewordMaria

BassoHead,AI

Applications

andImpact,Centre

for

AI

Excellence,World

Economic

ForumMichael

RömerSenior

Partner&Global

Lead,Activate:

Digital

and

Analytics,KearneyTheAI-FirstOperatingSystem:ABlueprintforOperatingand

BusinessModel

Innovation3Cathy

LiHead,Centre

for

AI

Excellence;

Member,

ExecutiveCommitteeJune2026Anewclassoforganizations

is

emerging,

designing

theiroperationsaround

intelligence,

usingdataas

a

key

resourceandembeddingartificial

intelligence

(AI)

into

processes,decision-makingandexecution.

These“AI-first”enterprisesaredemonstratingenhanced

performance:faster

innovationcycles,

theabilitytoscalewithsmaller

teams

and

thecreationofnew

products

builton

continuously

learningsystems.Thiswhite

paperexamines

howtheseorganizations

are

built,

howtheyoperateandwhatseparatesthem.

Drawingondeepengagementwith

more

than50oftheworld’s

mostadvancedAI-first

enterprisesandthinkers,

it

mapsthe

patterns

and

principlesthatdefine

howthefrontieractuallyoperates.Five

building

blockscapturethefundamentalsof

howAI-firstenterprisesaredesigned

and

run:–Intelligence

engine

at

the

core:

Enterprisesplace

intelligenceatthecentre

oftheiroperationsand

business

models,turningevery

interaction

intoacompoundingsource

ofadvantage.Thisacceleratesspeed,extendsscaleandexpandsscope

inwaysthat

grow

strongerwith

use.–

Adaptive

AI

technology

stack:Organizationsbuild

modular,adaptabletechnologyarchitecturesthat

integratedata,

modelsandworkflowswhile

beingdesignedtoevolveas

the

frontier

moves.–Operationsredesign:Enterprisessystematically

digitizetheirworkflowswith

intelligence–

notoptimizingthematthemarginsbutconnecting

all

oftheiroperationstothe

intelligenceengine.–Human-AIteaming:

Roles,teamsandorganizationalstructuresaredesignedto

enable

continuouscollaboration

between

peopleand

AIsystems,definingwhat

humancontribution

looks

likeatthefrontier.–Newvaluecreation:

EveryAI-firstorganization,

regardlessofsector,

mustdecide

howtoposition

intelligence

integration

inthe

market.

FromAIasa

productfeaturetoAI

as

invisible

infrastructure,

positioningchoicedetermines

whatcustomers

payfor,wherevalueaccrues

andhowthe

businesscompetes.Together,these

building

blocksforma

blueprint

foroperating

inaworld

where

intelligence

isabundantandcontinuously

advancing.Tomakethisactionable,this

paper

extendsthetraditional

business

modelcanvaswith

AI-firstfundamentals,

providing

leaderswitha

practicalframeworkfordesigning

andoperatingasAI-first

enterprises.The

rise

ofAI-first

enterprises

is

in

its

earlystages.

It

remains

unclearwhichapproacheswill

provemosteffectiveacross

industries,

how

broadlytheywillscaleandwhattheir

long-term

implications

willbefor

productivity,employmentand

competition.For

leaders,the

imperative

isto

buildthecapabilities

requiredtotestandscaleAI-firstoperatingsystemsacrosstheenterprise.

Forentrepreneurs,theopportunity

lies

inturningintelligence

intodifferentiated

products,services

andsystemsatscale.

For

policy-makers,thechallengewill

bestayingaheadofthese

rapidly

evolving

modelsandensuringdevelopmenttranslates

into

broad-basedeconomicand

societalvalue.ExecutivesummaryAnewenterpriseoperating

system

is

emerging:

organizationsdesignedaround

intelligenceas

a

corecapabilityforvalue

creation.TheAI-FirstOperatingSystem:ABlueprintforOperatingand

BusinessModel

Innovation4Thespeedand

breadthofadoption

arewhat

make

AIdifferent.

InOctober2025,Wharton

estimatedthat82%ofdecision-makers

useAIweekly,

upfrom37%

in2023.4

Thefactorsdrivingthis

industry

adoptionare:1.Democratizedaccess:Innovationsin

interfaces

andlargelanguagemodels

(LLMs)

have

enabled

non-technicaluserstoadopt,buildandoperate

AI,meetingtheuserattheir

level

of

proficiency.2.

Reasoningandagency:Systemscaninterpret,decideandact

acrossworkflows

with

increasingautonomy.3.

Multimodal

intelligence:AI

can

processorgeneratetext,

images,audioand

more,

expandingthescopeofapplication.4.Parallelexecution:Tasksthatweresequential

can

now

beexecutedsimultaneously,compressingtimetoscale.Fortoday

s

market,

intelligence

ischanging

what

kindsoforganizations

can

be

built.AI-firstorganizationsare

applyingthis

lessonfromtheoutset,

designingoperations,decision-makingand

business

modelsaround

intelligence.Artificial

intelligence

(AI)

isthe

latest

class

ofgeneral-purposetechnology,

likeelectricity,computersandthe

internet.

Historically,thesetechnologies

hada

limited

impactwhen

incumbentorganizationsappliedthemwithin

existingsystems.

Real

breakthroughscamewhen

pioneers

rebuilttheir

businessesaroundthe

technology

itself.Inelectricity,early

manufacturingadopters

replaced

steamengineswithelectric

motorswhile

retaining

thesame

layoutsand

production

processes,resulting

in

no

productivitygains

but

reducing

energycosts

by2060%.1The

breakthroughcamewhen

pioneeringfactories

redesignedtheirentire

blueprintsaroundelectricity;

oneexample

is

Henry

Ford

reworking

hisassembly

linesystem

between

1919and

1926.2

Theseleaders

reconfiguredworkflows,distributed

power

to

individualworkstationsand

rebuilt

production

systems,3

unlockingstep-changes

inoperatingmodelsandsystemsand

layingthefoundationfor

themodern

factory.IntroductionWhat

this

means

for

AITheAI-FirstOperatingSystem:ABlueprintforOperatingand

BusinessModel

Innovation5–Step-change

(re)designof

coreoperatingworkflowsaroundAI–Techstack

(re)structuredto

embedAI

intoexecution,

decision-makingandgovernanceat

scale–Systematicallocation

oftasks

between

humansandAI–Built

for

industry

standards,complianceand

production-

grade

scale–Augmentationandautomation

enhance

humancapabilities,improvingoperational

leverage

beyondwhat

humansorAI

can

do

alone–Unlocks

new

capabilitiesthatcould

not

bedesigned,

delivered

orscaled

withoutAI–AIembedded

in

the

valueproposition,

notjustoperations–Valuescalesthrough

intelligence

compounding

(data

>

model

>

betteroutcomes)–Talent

modelcentred

onAI

researchers,

machinelearning

(ML)

engineersand

modelevaluators–Techstack

intentionally

builtforcontinuous

modeltraining,

deploymentand

rapid

iteration

at

scaleTABLE1Enterprisearchetypes

byAIadoptionArchetype

Definition

Attributes

Litmus

testoperatorsexperimentingand

usingAI

tools–Techstack

integratesthird-party

AItools

intoexisting

systems–Unit

economics

improvethroughcost

reductionor

productivitygains,

not

model-driven

differentiation–AIapplied

at

the

task

level–Performance

improvesthrough

localizedefficiency

andproductivitygains–Talent

modelfocusedon

domainIfAIsystemswere

removed,

couldyour

businessstilloperate

(workflows,decisions,

and

delivery)?Iftheanswer

is

“no,”your

enterpriseisAI-firstIftheAItoolswere

removed,

would

yourworkflowsand

organizationstructurecollapse?Iftheanswer

is

“no,”yourenterpriseisAI-enabledEnterprisesthatapply

AI

toautomateoraugment

discretetaskswithinexistingworkflows,improving

performancewithout

materially

redesigningtheoperating

modelNewenterprisescreated

with

AI

asacore

productioncapability,

withproducts,servicesand

competitive

advantagefundamentallydependentonAI

systemsEnterprisesthatsystematically(re)designworkflows,

rolesanddecision

rightssoAI

becomesthe

strategic

leverforcreating

anddeliveringvalueat

industrial

scaleIfAIsystemswere

removed,

would

yourvalue

propositionstill

exist?Iftheanswer

is

“no,”yourenterpriseisAI-nativeTheAI-FirstOperatingSystem:ABlueprintforOperatingand

BusinessModel

Innovation6AI-enabledAI-nativeAI-firstAI-enabledapproacheslimitthe

fullvaluecreationpotentialofthe

technologyDespiteAI

investmentestimatedatover$250

billion

globally

in2025,5

manyorganizationsare

seeingonly

incrementalgains.Aglobalsurveyfoundthat

only25%sayAI

is

havingatransformative

effect

on

theircompany.

Meanwhile,84%ofcompanies

have

not

redesignedjobsaroundAIcapabilities.6Likeelectricity,the

primary

reason

isstructural.

Inmanycases,AI

isdeployedas

a

layer

on

top

ofexisting

processesandembedded

narrowly

intools.

Thisapproach

improvesefficiencyatthe

marginsbutdoes

not

unlockthetechnology’sfull

potential.Incontrast,AI-first

performancegainsare

early,

butstriking:–Compressed

cycle

times:Commercialinsuranceworkflowswentfrom28daysto2.8

hours.7–Shorterroadstoscale:$100

millionannual

recurring

revenue

(ARR)

in

months,compared

tofourtoeightyears

previously.8,9–Moonshots

within

reach:A2.5-daycontinuousautonomous

inference

loopidentifiedthefirstviabledrugcandidatefor

a

previously

intractable

HIVtarget.10–

Smallteamsachievingtheoutputofentirefunctions:Someteamsareachieving15times

more

productivitywithAItoolsthan

withoutthem.11–Newmarketdynamics:AI

is

reshaping

howcompanies

positionanddelivervalue

tocustomers.12Innovationsinbusiness

and

operating

modellogic

enable

this

impact.To

inform

this

stepchange,theWorld

Economic

Forum

launched

theAI-First

EnterprisesWorkstreamas

partoftheAIGlobalAlliance.

By

bringingtogether

over

50executives

leadingAI-firstcompanies

anddepartmentsanddrawing

on

insightsfrommorethan

150executivesand

experts

frominterviews,workshopsandexpert

consultations,thisgroup

explored:–HowareAI-first

enterprises

changingthe

natureofbusiness

andtheway

it

creates

anddeliversvalue?–Whataretheemerging

patternsand

innovations

behindtheirsuccess?Together,these

patternsand

innovationsareenablinga

newclassofAI-first

enterprises

built

aroundcontinuous

learning,

real-timedecision-

makingandscalable

intelligence.Thefollowing

blueprintoutlinesthecore

building

blocksoftheseorganizations.TheAI-FirstOperatingSystem:ABlueprintforOperatingand

BusinessModel

Innovation7IntelligenceengineIntelligenceatthe

core

as

acompoundingsourceofspeed,scaleandscopePage

9

AdaptiveAItechnologystackArchitectingamodular

stackfor

scale,flexibilityand

controlPage

17

>Page

40

Page33

>OperationsredesignDecidingwheretodistributeand

runthe

intelligenceenginePage

22

>integratedsystem.

Progress

inonearea

reinforces

theothers.Takentogether,theyforman

emerging

blueprintfortheAI-firstenterpriseoperatingsystem.Five

building

blocksdefinethefundamentalsofhowAI-firstenterprisesdelivervalue.Theyshould

be

understood

notasa

linear

roadmap

butas

anFundamentalfivebuildingblocksFundamentalbuildingblocksforanAI-firstoperatingsystemBuildingteamsand

organizationsforcontinuoushuman-AI

executionTheAI-FirstOperatingSystem:ABlueprintforOperatingand

BusinessModel

Innovation8Positioning

newcapabilitiesandsolutions

forthe

marketHuman

and

AIteamingNew

value

creationFIGURE1JDefine/runstrategicoutcomeIntelligenceengineWhen

intelligencesitsatthe

core

of

anenterprise,

it

becomesadurablesource

of

speed,scaleand

scope.AI-firstenterprises

build

intelligenceenginesas

an

operatingsystemaroundwhichthefirmthinks,runsandcreatesvalue.

Bystrategically

embedding

intelligence

intothefoundational

business

logic,theseorganizationsensurethatoperational

data,

proprietary

intellectual

property

(IP)andsourcesof

advantagecompound.Atthecentreisan

intelligence

engine

(Figure2),aself-reinforcinganddata-driven

flywheel

that

embedsalloftheorganizational

knowledge,

learns

fromevery

interactionandtransactionand

grows

smarterwith

use.

Itconnectscustomerdata,operationalcontext,

businessobjectivesand

real-world

performance

intocontinuousfeedbackloops

that

learn,

improveandscalewitheach

cycle:–

Thespeedloop:Acceleratingdiscovery,

experimentationandtime-to-valuethrough

autonomous

inference–Thescaleloop:Operationalizingintelligenceasa

multi-use

platformacross

businessoutcomes–

Thescopeloop:

Expandingcapabilities

into

newdomains,products

and

marketsTheintelligenceengineUnlocksBusinesscase

andperformanceContext

richTheAI-FirstOperatingSystem:ABlueprintforOperatingand

BusinessModel

Innovation9

Dataflywheel

inputsNew

capabilitiesLoop

milestones

Edgecaseand

scope

5

6dataAutonomousinferenceBLOCK1IntelligenceengineEconomicandoperationalalignmentProduction-grade

autonomyValuecreation

and

moonshotexplorationFrontierMultiNewcapability

expansionLearning

accelerationIntelligencefoundationsFIGURE2ScopeScaleSpeedexpansiondatacapabilitiesplatformtraining1324UnlocksUnlocks-use-UnlocksScopeUnlocksScaleUnlocksSpeed1IntelligencefoundationsContext-richtrainingdataDefine/runstrategicoutcomeData

inputs:

Buildsalearningbasefromcontext-richdatatiedtooutcomesand

key

performance

indicators(KPIs),

improvinglearning

performancewithevery

cycleIntelligencefoundations:Anchorsthesystem

indefinedbusinessoutcomesanddomain-specificcontext,calibratingwhat

it

learnsfromand

optimizesforLearningacceleration:Compressesthepathfrom

proof

ofconceptto

proofofvalue

by

usingAIto

rapidlygenerate,simulate,testandvalidate

ideas

beforescalingNew

capabilities–autonomousinference:

Enablessystemstorun

thefulldiscovery-to-deploymentprocessindependently,generating,testing,evaluating,

adaptingand

refiningoutputsagainstdefinedobjectiveswhile

using

persistent

memoryandhumanfeedbacktoimprovewith

every

cycleIn

practice,oneexample

isa

drug

discoverycompany

movingfromcandidatehypothesistovalidatedsimulation

indays,

not

months.Othersincludeasoftwarecompanyshippingfunctionalprototypesfrom

plain-language

briefs

inseconds,

anda

roboticsfirmtestingand

refining

physicalbehaviours

insimulation

beforeasingle

real-world

deployment.Whattheseorganizationsshare

is

notjustspeedbuta

structural

advantage.Objective:AI-firstenterprisesusethespeedloopto

run

moreexperiments,generateandtest

hypotheses,andvalidate

ideas

beforecommitting

resources,

loweringthecostofexperimentationwhile

increasingthe

paceoflearning.

Failures

narrowthesearchspace;successes

become

building

blocksforthe

next

cycle.Overtime,thiscreates

a

systemwhere

proprietary

learningcompounds.1.1FIGURE3TheAI-FirstOperatingSystem:ABlueprintforOperatingand

BusinessModel

Innovation10Thespeedloop

Dataflywheel

inputsNew

capabilitiesLoop

milestones

IntelligenceengineAutonomousinferenceThespeed

loopLearning

acceleration122MechanismsObjective-ledcontextbuildingHow

itworksTranslate

businessgoals

intoclear,

measurableAIobjectives,

linking

outcomes

(e.g.

revenue,

accuracy)to

system-

level

metrics

(e.g.

bindingaffinity,accuracy,cost

peroutput),

bringingtogether

relevant

structured

and

unstructured

datato

buildcontext,

reveal

patternsandgive

meaningto

system-level

signals.Example:WorkeraWorkeradefinesgoodcontextas

personalization.

By

connecting

to

systems

likeWorkday

and

ingesting

role

data,

resumesandstrategic

priorities,

it

buildstwo

layers:

individualcontext

around

a

user’s

skills

and

experience,

andcompanycontextaroundthecapabilitiesthe

organization

needsto

develop.Workera

usesthiscontexttoassessskills,

identify

gaps

and

recommend

targeted

learning

paths.

Context

isconsidered

“good”when

it

improvesthe

precisionoftheseassessments,tailors

recommendationsto

the

individual’s

roleandensuresskilldevelopment

aligns

with

business

objectives.2LearningaccelerationWhatit

enablesSpeeds

upexperimentation

bygenerating,testingand

refiningsolutions

before

real-worlddeploymentHow

itworksApplyautomatedevaluationsystemswithdefined

thresholds,

enabling

safe

and

controlled

autonomous

execution.

Example:ServiceNowServiceNow

builtanAIagentevaluationframeworkwith

quantitative

thresholds

that

agents

must

clear

beforegoing

live.Theframeworkscoresagentsagainst

def