世界经济论坛、BCG：物理人工智能：推动工业运营的新时代

Physical

AI:

Powering

the

NewAge

ofIndustrial

OperationsW

HI

T

E P

A

P

E

RS

E

P

T

E

M

B

E

R 20

2

5In

collaboration

withBoston

Consulting

GroupImages:

Getty

Images,

MidjourneyPhysicalAI:Powering

theNew

Ageof

Industrial

Operations

2DisclaimerThis

document

ispublishedby

theWorld

Economic

Forum

as

a

contributionto

a

project,

insightareaor

interaction.Thefindings,

interpretationsandconclusionsexpressedherein

are

a

resultof

a

collaborative

process

facilitated

andendorsedby

the

WorldEconomic

Forumbut

whoseresultsdo

notnecessarilyrepresentthe

views

of

the

World

EconomicForum,

nor

theentiretyof

its

Members,Partners

or

otherstakeholders.©

2025World

Economic

Forum.

All

rightsreserved.

Nopart

ofthis

publication

maybe

reproduced

or

transmitted

in

any

formor

by

any

means,

including

photocopyingand

recording,

or

by

any

informationstorage

andretrieval

system.ContentsForeword3Executivesummary4Introduction5What’s

new:

Breakthroughs

in

intelligent

roboticsTechnological

breakthroughs

redefining

robotic

capabilitiesEnhancedcapabilities

enablingend-to-end

automationLimitations

yet

to

be

resolvedWhere

it

isworking:

Frontierapplications66710112.1

Revolutionizing

the

manufacturing

value

chain122.2

Spotlight

on

the

pioneers

–

transformation

journeys

of

earlyadopters3

How

it

scales:

Technology

platforms

andpartnerships13163.1

The

new

physical

AItechnology

stack163.2

Strategic

partnerships

are

essential17Who

leads

it:

Empowering

the

new

industrial

workforceA

target

picture

for

robotics

and

workforce

developmentA

shift

in

skills

and

rolesThe

new

workforce

imperatives

Conclusion:

Time

for

action1818182021Contributors22Endnotes25ForewordAmid

mounting

global

pressures

–

from

economicvolatility

and

geopolitical

disruption

to

growingsupply-chain

complexity,

and

labour

and

talentshortages

–industrialoperations

areenteringatransformativenew

phase.

While

these

challengesare

notnew,heightened

uncertaintyhassignificantlyintensified

their

impact,

forcing

a

fundamentalrethink

of

how

work

is

organized,

executedand

scaled.At

this

inflection

point,

a

new

eraof

industrialautomation

is

emerging–

powered

by

physical

AI.

These

intelligent

robotic

systemscombineperception,

reasoningand

action,

enabling

alevelof

autonomy

andadaptabilitythat

marks

a

criticaljuncturein

industrialautomation.

By

bridging

thedigital

and

physical

realms,

physical

AI

promises

toreimagine

how

industrial

systems

function

–

fromfactory

floorstosupplychains.As

physical

AI

becomesincreasingly

viable

andstrategically

essential,industry

leaders

are

seekinga

deeper

understanding

of

how

to

makeuseoftheseinnovations

for

sustainable,

long-termcompetitiveness.

At

such

a

pivotal

moment,

this

white

paper

–

developed

through

the

WorldEconomicForum’s

NextFrontier

of

Operationsinitiativein

collaboration

with

Boston

ConsultingGroup

–

builds

on

a

tradition

of

strategic

foresightandmultistakeholder

engagement

to

chart

a

boldpath

forward.Theinsightspresentedhere

draw

on

thecollectiveexperience

of

global

manufacturers,

roboticsinnovators

and

leadingacademic

experts.Grounded

in

real-world

use

cases

and,

moreimportantly,

the

transformation

journeys

theyrepresent,

thepaperexplores

how

physical

AIis

reshaping

operations,enabling

new

forms

ofhuman–machine

collaboration

andunlockingproductivity

at

scale.But

this

transformation

is

notsolelyabouttechnology.

Italsorequires

theindustrial

workforceto

beequippedwith

new

skills

tocollaboratewithintelligent

systems

and

take

on

emerging

roles.

Weinvite

allstakeholders

–

including

manufacturers,policy-makers,

researchers

and

technologists

–

toengagewith

this

agenda.Together,

throughbold

and

coordinated

action,

we

can

shape

a

future

inwhich

intelligent

automation

drives

inclusive,

resilientand

sustainable

industrial

growth.Kiva

Allgood

Managing

Director,WorldEconomic

ForumDaniel

KuepperManaging

Director

and

Senior

Partner,

BostonConsulting

Group

(BCG)PhysicalAI:Powering

theNew

Ageof

Industrial

OperationsSeptember

2025PhysicalAI:Powering

theNew

Ageof

Industrial

Operations

3Executive

summaryTechnological

breakthroughs

are

pushingthe

boundaries

of

automation

–

tasks

thatwere

once

too

variable

or

cost-prohibitiveto

automate

are

now

both

technicallyfeasible

and

economically

viable.Although

traditional

industrial

robots

arefoundational

toautomation,theyhave

long

beenconstrainedbylimitedadaptability

and

highintegration

costs.

Today,

the

world

is

entering

a

new

age

of

robotics

defined

by

intelligence

andflexibility

powered

by

the

convergence

of

advancedhardware,

artificial

intelligence

(AI)and

visionsystems.

Together,

theseadvancesareunlockingthe

nextfrontierof

robotics.Approaches

such

astrainingmethods(reinforcement

learning,

imitation

learning)

andmultimodal

foundation

models1

for

robotics,

aswell

as

dexterous

hardware

components

(e.g.

softgrippers,

tactile

sensors)are

enablingrobots

tohandlevariability,reasonin

context

and

adapt

inreal

time.

Simplified

deployment,

such

as

throughvirtual

training

and

intuitiveinterfaces,

is

significantlyreducing

time-to-value

and

expanding

accessibilitytosmall-and

mid-sized

manufacturers

andlogistics

providers.

Throughout

this

paper,

the

term“manufacturers”

is

used

as

a

shorthand

to

refer

toboth

manufacturers

andlogistics

providers.Such

advancesleadtothree

foundationalroboticssystems

that

will

coexist

in

the

future

of

industrialoperations,

together

forming

alayered

automationstrategy.Thesesystemsarecomplementary,eachsuitedto

specific

combinations

of

task

complexity,variability

andvolume.Rule-based

robotics,

delivering

unmatchedspeed

and

precision

in

structured,repetitivetasks

(e.g.

automotive

welding)Training-basedrobotics,

mastering

variabletasks

viareinforcementlearningor

imitationlearning

(e.g.

adaptive

kitting)Context-based

robotics,

capable

of

zero-

shot

learning2

and

execution

in

unpredictableprocesses

and

newenvironments

(e.g.

robotreceives,

reasons

and

acts

oninstructions

vianatural

language)Automation

is

expanding

opportunities

acrosstheentire

industrial

valuechain.Early

adoptersarealreadyachieving

significantresults.

For

example,Amazon,

operatingtheworld’s

largestrobotics

fleet,has

demonstratedhow

the

integration

ofmobile

robots,AI-based

sortation

and

generativeAI-guidedmanipulators

canimprovefulfilmentcentre

performance.

By

orchestrating

theseautonomous

systems,

next-generation

facilitieshaverealized

25%

faster

delivery,

30%

more

skilledrolesand

a

25%

boost

in

efficiency.3

Similarly,Foxconn

applied

AI-powered

robotics

and

digitaltwin

simulationtoautomate

high-precision

taskssuch

as

screw

tightening

and

cable

insertion,previouslyconsidered

too

complex

for

automation.Throughreal-time

adaptive

force

controlandsimulation-based

deployment,

it

cut

deploymenttime

by

40%

andreducedoperationalcostsby

15%.However,realizing

such

outcomes

at

scaledemands

morethan

cutting-edgetechnology.It

requires

a

future-ready

automationstrategy

that

incorporates

both

technicaland

organizational

foundations:Embedding

the

emerging

AI

technology

stackinto

the

existing

industrial

toolchain

and

forgingecosystem

partnershipsacrossrobotics,

AI

and

manufacturing

to

ensure

interoperability,scalability

and

continuous

innovationWorkforce

transformation

through

reskillingand

upskillingto

enable

human–machinecollaboration,

and

prepare

workers

for

emergingroles

such

as

robot

supervisors,

AI

trainers

andsystem

optimizersManufacturers

who

act

now

and

embed

roboticsas

a

strategic

asset

will

lead

the

next

phase

ofindustrial

competitiveness

–

shaping

a

futurein

which

intelligent

automation

becomes

acornerstone

of

sustainable

growth,

workforceempowerment

and

systemic

resilience.PhysicalAI:Powering

theNew

Ageof

Industrial

Operations

4IntroductionManufacturers

must

embrace

intelligentrobotics

now.Manufacturers

today

find

themselves

at

acrossroads.

Persistent

labour

shortages,

escalatingcost

pressures

and

fragile

global

supply

chains

–amplifiedby

geopoliticaland

market

uncertainty

–are

convergingto

threaten

productivity,profitabilityand

resilience.

At

the

same

time,

growingconsumer

expectations

for

speed,

customizationand

sustainabilitydemand

a

step-changeinoperational

flexibility.These

intensifying

pressures

are

accelerating

thesearch

for

transformative

innovations

throughfrontiertechnologies.

At

theforefront

is

oneundergoing

profound

transformation:

robotics.

Nolonger

confinedtoisolatedefficiency

gains,roboticsis

emerging

as

a

strategic

enabler

of

resilienceand

competitiveness.

Robotics

is

entering

a

newera

–

in

which

intelligence

allows

for

autonomy,and

physical

AI

redefines

what

machines,

and

byextensionhumans,

are

capable

of.Then:

Robotics

for

the

few.

Inflexible.

StaticSince

their

initial

deployment

in

the

1960s,

industrialrobotshavereshapedmanufacturing.

They

played

a

pivotal

role

in

sectors

such

as

automotive

andelectronics,

where

high-volume,

standardizedproduction

justified

the

investment.

However,adoptionremained

limited

to

large

enterprises

withhighly

standardized

production

processes.

Smallandmid-sizedmanufacturers,

aswell

as

thosewith

variableoperations,

were

left

behind

due

toprohibitive

cost,complexityandinflexibility.Now

and

next:

Intelligent

robotics

for

theIntelligent

AgeBut

this

is

changing.

Robotics

isevolvingintointelligent

systems

–capable

of

learning,

adaptingand

acting

autonomously.

This

shift

marks

a

pivotalmoment

in

the

history

of

automation,

driven

bythe

convergence

of

robotics

hardware,AIandvisionsystems.Today,

robotics

is

scaling

–

and

fast.

By

2023,more

than

4

million

industrial

robots

had

beeninstalled

globally.4

At

the

same

time,

advancesin

robotics

software

and

hardware

are

enablingbroader

capabilities

–

ranging

from

dexterousmanipulation

toautonomous

navigation–andsignificantlyreducingthe

engineering

effortrequiredfor

deployment.

Innovations

are

accelerating

inresponse.

Start-up

activities

and

investmentsare

surging,

driven

by

the

promise

of

physicalAI.From

foundation

models

for

robotics

(e.g.

SKILD

AI,

Covariant,

DeepMind,

TRI)to

general-purposerobots

(such

as

the

humanoid

robots

from

Figure,Neura,

Boston

Dynamics

and

Apptronik),

delivery

inthe

innovationpipeline

is

accelerating.As

the

pace

of

changeaccelerates,leadersfacea

set

of

critical

questions:

What

technologicalbreakthroughsare

driving

this

shift?

How

is

roboticsalready

reshaping

manufacturing

operations,workforce

roles

andindustrial

competitiveness?And

how

should

the

technological

and

peoplefoundations

be

laid

toprepare

for

what’s

next?Thiswhite

paper

provides

atimely,in-depth

look

at

how

the

robotics

landscape

in

industrialoperations

is

rapidlyevolving.It

goes

beyondsurface-level

trends

to

provide

real-world

usecases,

and

presents

a

forward-looking

visionofhow

physical

AI

can

enable

flexible,

resilient

andscalableautomation.

With

achievable

insights

formanufacturers,technology

leaders

and

policy-makers

alike,the

paperaims

to

serveas

a

strategicguide

to

lead

–

not

follow

–

in

the

IntelligentAge.5No

longerconfined

toisolated

efficiencygains,

roboticsis

emerging

as

astrategic

enablerof

resilienceandcompetitiveness.PhysicalAI:Powering

theNew

Ageof

Industrial

Operations

51What’s

new:

Breakthroughsin

intelligent

roboticsTechnological

breakthroughs

expand

the

scopeof

automation

to

encompass

what

has

beentechnologically

unfeasible

or

economicallyunviable,

and

simplify

implementation

todeliver

scalable,end-to-end

automation.1.1 Technological

breakthroughsredefining

robotic

capabilitiesRecentinnovations

in

software

and

hardware

haveushered

in

a

step-change

in

robotic

capability,enabling

robots

toperformcomplextasks

indynamic

environments

with

simpler

deployment.Advances

in

AI

andcomplexsimulations,

enabledbyaccelerated

computing

using

graphicsprocessing

units

(GPUs),havemade

it

feasible

torun

AImodels

and

algorithms

in

realtime,

unlockingnew

applications.

ThisAI-based

approach

focusesonenabling

robots

to

perceive,

plan

and

act

incomplex,

real-world

scenarios,

effectively

achievinga

level

ofphysicalintelligence.The

roboticslandscapeisundergoinga

profoundtransformation

drivenby

recentadvances.

This

section

outlines

the

primary

dimensions

of

this

transformation,which

collectively

mark

a

turning

point

for

industrial

automation.PhysicalAI:Powering

theNew

Ageof

Industrial

Operations

6EnhancedperceptionAdvances

insensorsandAI

havedramaticallyimprovedrobots’

abilitytoperceivetheir

surroundings.Affordablehigh-resolution

cameras,

light

detection

and

ranging

(LiDAR)

and

next-generation

tactilesensors,

among

othersensors,

giverobotsricher

raw

inputs,whileadvanced

computervisionalgorithms(powered

by

deeplearning)enablevisual

perception

approaching

human-level

capabilities.

Robots

can

now

recognize

and

interpret

complexenvironments

in

real

time

–

identifying

objects,recognizing

their

3D

orientation

and

assessing

theirphysicalproperties

–

essential

prerequisitesfor

developinganunderstanding

of

how

to

interact

with

objects.Theseadvancesallow

robots

to

“see”

and

comprehendan

object

and

its

environment

withunprecedented

clarity.Autonomousdecision-makingand

planningInnovations

in

AI

and

software

haveenabled

robots

to

make

intelligent

decisionsin

real

time.

Instead

of

rigid

pre-programming,

robots

now

exploit

reinforcementlearning

and

simulationtolearn

behavioursthroughtrial

anderrorinvirtual

environments.

Advanced

simulators

(e.g.

high-fidelity

physics

simulators)and

domainrandomization

techniques

(e.g.

randomization

of

parameters

such

as

lighting

or

friction)

are

closing

thesimulation-to-reality

gap,sothatbehaviours

learnedinsimulation

transfer

seamlessly

toreal

machines.

Robotsalso

increasingly

benefitfrom

powerful

foundationmodels

that

integratevision,

language

and

action.

Thesemodels,

such

as

Google

DeepMind’sGeminiRobotics6

andNvidia’s

IsaacGR00T,7

ingest

multimodal

inputs

and

generate

task-appropriateoutputs

–

allowingfor

intuitive

human–robot

interactions

and

superior

contextualunderstanding.

Thisenables

robust

workflow

planning:given

a

goal

(e.g.

unloading

a

shipment),

the

systemdetermines

a

sequencedset

of

actions

(use

the

forkliftto

unload,cut

thebanderole,

open

the

packages,

etc.).This

progression

enables

robots

to

evolve

from

executing

isolatedmotions

to

performing

coherent,

multisteptasks,

approaching

human-level

task

intuition

and

planning

capabilities.

In

essence,

robots

are

enabled

to

“think”and

plan

tasks

with

a

level

of

flexibility

and

context-awareness

previouslyunattainable.Dexterousmanipulationand

mobilityAdvances

inmaterials,actuators

and

roboticdesignshavegreatly

expandedwhat

robots

can

physically

do.Hardware

breakthroughs

–

from

high-precisionforce-controlled

motors

to

soft

robotic

grippers

–give

machinesmuch

more

dexterityin

handlingobjects.

Robots

can

now

grasp

irregular

or

delicateitems

reliably,

ratherthanbeing

limited

to

rigid,

predefined

motions.

This

is

complemented

by

AI-driven

control

software

that

adjusts

gripand

force

in

real

time.Notably,

the

incorporation

of

asense

of

touch

through

moderntactile

sensors

is

a

primaryenabler

of

human-level

dexterity,

allowing

robots

to

finelymanipulate

objects

through

feedback

of

pressure

andslip.

Longer

battery

life

is

significantly

increasingthe

uptime

of

mobile

robots,

supporting

more

autonomousdeploymentsandleadingtoextended

mobility.

Moreover,roboticsisnolonger

confinedtotraditional

form

factors.

Innovations

have

introduced

quadrupeds,

humanoids,mobile

manipulators

and

hybrid

forms,

broadeningthe

range

of

industrial

applications

and

increasing

the

scope

of

feasible

automation.

These

physicalinnovationsenablerobots

to

“act”

on

the

world

with

fargreaterskill

andautonomy.1.2 Enhanced

capabilities

enablingend-to-end

automationTheseenhancedcapabilitiesled

to

the

evolutionof

robotics

from

(1)

rule-based

robotics

that

areexplicitly

programmed

to

(2)

training-based

roboticsthat

acquire

their

skill

in

the

realworld

and

throughsimulation

training

to

(3)

context-based

roboticsperforming

tasks

autonomously

without

explicittraining

through

zero-shot

learning.

Advances

inallthreeroboticsystemstransform

operationsand

expand

the

automation

scope

to

tasks

thatpreviously

could

not

beautomatable.8At

the

heart

of

this

transformation,

however,lies

the

coexistence

of

all

three

foundationalrobotics

systems,

each

expanding

in

automationscope

and

sophistication.

Together,

they

form

acomplementaryecosystem.

Rather

than

replacingone

another,

theyenable

a

layered

automationstrategy,

aligned

with

operational

needs

(e.g.degrees

of

task

variability)

andeconomicconsiderations.

Furthermore,

as

factories

andwarehouses

move

towards

greater

automation,manufacturersandwarehouse

operators

will

deploya

mix

of

robotic

systems

and

embodiments

–

fromautonomous

mobile

robots

(AMRs)

to

humanoids

–guided

by

task

requirements,

economic

viability

andprocess

characteristics.PhysicalAI:Powering

theNew

Ageof

Industrial

Operations

7Expanding

the

boundaries

of

automation

(illustrative)FIGURE

1Process

characteristicsPredictable

processesand

known

environmentUnpredictable

processesand

known

environmentUnpredictable

processesand

new

environmentAutomation

potential(Physical)

AI

considerablyincreasesthe

automation

scope

in

industrial

operationsRule-based

robotics(e.g.

AI-supported

coding)Training-basedrobotics(e.g.

simulation-basedAI

training)Context-basedrobotics(e.g.

zero-shotlearning)Source:BCG,

World

Economic

Forum,

expert

interviews.How

to

interpret

this

chartThe

area

of

the

chart

maps

outphysical

tasks(e.g.assemblysteps,materialhandling,packaging)within

a

factory

or

warehouse.These

tasksare

categorized

along

twodimensions:Automation

potential

(y-axis)

is

indicatedthrough

colourshading:Process

characteristics

(x-axis)

are

defined

byparameters

such

as

objectposition,

orientation

andsize,

and

if

the

system

operates

in

a

known

or

new

environment.

Illustrative

target

state

along

differentprocess

characteristics:

Predictable

processes:

Parameters

are

either

constant

or

varyonlywithin

atightly

controlledrange–

enablingdeterministic,repeatable

execution

without

the

need

for

adaptive

behaviour.

Unpredictable

processes:

Parameters

vary

significantlyor

cannot

be

anticipated.

New

environments:

Scenarios,

layouts,

objects

or

tasksoutside

the

robot’s

training

distribution

(e.g.

a

different

factoryline,unfamiliarparts

or

altered

warehouse

layout).Grey:

Tasks

already

automatablewith

today’srule-based

roboticsBlue:

Additional

scope

unlockedby

physical

AINavy:

Illustrative

share

expected

to

remain

manualin

the

near

termPhysicalAI:Powering

theNew

Ageof

Industrial

Operations

8The

process

characteristics

determine

whichrobotic

system

to

use:Rule-based

robotics

continuesto

deliverunmatched

precision

and

cycle-timeperformance

in

structuredenvironmentswithrepetitive

tasks

and

predictable

processes.These

systems,

ubiquitous

in

automotivebody

shops

andsimilar

settings,

remainindispensable

foroperationswhereconsistencyand

low

variability

are

paramount.

Ongoingadvances

in

programming

interfacesandAI-supported

coding

(such

as

SiemensIndustrial

Copilot

for

generative

AI-assistedprogrammable

logic

controller

[PLC]programming)9

are

extending

their

applicabilityand

easing

deployment

challenges.Training-based

robotics

is

rising

to

prominencein

morevariableenvironments.Enabledbyadvanced

reinforcement-learning

algorithms

andsimulations,

theserobots

learnthrough

virtualand

real-world

experiences.

The

virtualizationof

training

significantly

reduces

deploymenteffort,

as

robots

can

be

trained

and

validated

insimulated

environments

before

real-world

rollout,thereby

expanding

the

scope

of

economicallyviableautomation.Theydemonstrateresiliencein

tasks

involving

controlled

variation

–

such

asflexible

parts

kitting

oradaptive

logistics

–

andare

increasingly

viable

for

mid-volumeor

non-repetitive

production

where

rule-based

roboticslacks

flexibility.–

Context-based

robotics,

the

newest

frontier,makes

use

of

robotics

foundation

models

andzero-shot

learningtoautonomously

perceive,reason

and

act

in

unfamiliar

scenarios.Thesesystems

interpret

high-level

instructions

andrespondto

real-world

complexitywithoutpriortask-specific

training,

making

them

particularlyvaluable

in

unpredictableenvironmentswithunknown

parts

or

new

environments.Roboticsfoundation

models

form

the

cognitive

core

thatenables

context-basedgeneral-purpose

robots

–such

as

humanoids

–

to

flexiblyexecutediverse

tasks

across

different

environmentswithout

reprogramming.Whilethethreesystem

types

–rule-based,training-based

and

context-based

–

form

a

layeredautomation

strategy,

their

boundaries

often

overlap,and

a

single

robot

can

use

a

hybrid

approach

thatcombines

allthree.Forexample,in

a

collaborativeassemblycell,

a

robot

might

followrule-basedlogic

to

perform

tasks

with

high

precision.Simultaneously,it

monitors

itsenvironment

usingperception

systems.

When

deviations

from

theexpected

workflow

occur

–

such

as

a

missing

part

orhumanintervention–

the

robot

switches

tocontext-based

reasoning

to

interpret

the

situationand

resolve

it

autonomously,before

returning

to

itsrule-based

execution.Comparison

of

traditional

and

physical

AI-enabled

roboticsFIGURE

2Vision

of

the

differences

today

vs.

the

futureField

ofautomationImplementationprocessTime

toindustrializationScalabilityHuman-machineinteractionTodayEffective

in

predictabletasks

or

in

controlledscenarios

withknown

partsHigh

and

complexmanualeffort

forcoding

and

trainingMid/longindustrialization

time(se