IBM -物理AI 当AI开始行动时 构建生活运营体系 Physical AI When AI starts to act

IBMInstituteforBusinessValue|ResearchInsights

PhysicalAI:

WhenAIstartstoact

Buildingthelivingoperationalsystem

Contents

Introduction 3

Partone

TurningphysicalAIintoaliving

operationalsystem 5

Parttwo

PhysicalAIredefinesmanufacturing 11

Partthree

Turningthevisionofphysical

AIintoreality 17

Actionguide 23

1

IntroductionThelivingoperational

system

PhysicalAIredefinesmanufacturing

PhysicalAIvisiontoreality

Actionguide

Key

takeaways

Organizationsthat

purposefullyconnectdigitalsystems,

physicaloperationsandhumanexpertiseexpect55%enterpriseproductivitygains

and114%operationalROIby2030.

ThemachineenablesphysicalAI.Thelearningloopcreatestheadvantage.Organizationsthatpurposefullyconnectdigital

systems,physicaloperationsandhumanexpertiseexpect55%enterpriseproductivitygainsand

114%operationalROIby2030.Theadvantagecomesfromturningeveryphysicaloutcomeintolearningthatimprovesthenextaction.

PhysicalAIreshapesworkmorethanitreshapestheworkforce.

Executivesexpectnearly50%productivitygains

by2030,alongsideworkforcereductions

ofonly9%.AsphysicalAIredefinesmanufacturing,thebiggershiftisinroles,workflowsandhow

operationalexpertiseiscapturedandscaled.

PhysicalAIchangestheeconomicsonlywhenpilotsbecomeoperatingmodels.By2030,expectedoperationalROIrisesfrom43%amongorganizationsstillexploringphysicalAI

toanindicative161%amongthosewhereitiscoretooperations.Thestep-changecomeswhen

physicalAImovesfromisolatedusecasesintothewaythebusinessactuallyruns.

2

Abouttheauthors

PraveenVelichety

Partner&GlobalLeader,PhysicalAIandDigitalTwin,IBM

/in/velichety/

ForoveradecadeatIBM,Praveenhasworkedwheredatameetsthephysicalworld,leadingofferingsasthefieldhasgrownfromIoTintodigitaltwinsandnowphysicalAI.

Dr.RamiAhola

GlobalIndustryLeader,IndustrialManufacturingIBMConsulting

/in/ramiahola/

Ramihasover30yearsofexperience

withsemiconductor,consumerelectronics,automotiveandtelecommunications

industries.Hecombinesdeepindustryknowledgewithfirst-handexperience

inR&D,manufacturing,supplychain,and

marketingprocessestohelpclientswith

businessstrategies,processes,andexecution.

Prof.Dr.DavidRomero

ScientificVice-Chairman

WorldManufacturingFoundation

/in/david-romero-43077bb/

Prof.Dr.DavidRomeroisaProfessor

ofAdvancedManufacturingattheTecnolgicodeMonterreyUniversityinMexicoand

theScientificVice-ChairmanfortheWorldManufacturingFoundation(WMF).

Hehaspublishedmorethan250journal

andinternationalconferencearticles

(Top2%Scientists:Stanford/Elsevier,andCareer-LongImpactRanking).

Dr.PushkarP.Apte

StrategicTechnologyAdvisorandGlobalLeadforSmartData-AIInitiative

SEMI

/in/pushkar-apte-5363/

PushkarisfocusedonacceleratingthesynergybetweensemiconductorsandAI-specifically

onreducingtheenergyconsumptionandcostofAI,andonapplyingdigitaltwinstodriverapidinnovationacrosstheentireAIstack.

NorikoSuzuki

GlobalResearchLead,IndustrialManufacturingIBMInstituteforBusinessValue

/in/norikosuzuki/

Norikoleadsthoughtleadershipfortheautomotive,electronics,andindustrial

manufacturingattheIBMInstituteforBusiness

Value.Drawingon30yearsofexperience

advisingglobalmanufacturers,sheexploreshowemergingtechnologiesarereshapingproducts,operations,andbusinessmodels.HerrecentresearchfocusesonAIfor

industrialapplications,software-definedvehicles,AIsemiconductor,andhybridcloudformanufacturing.

3

IntroductionThelivingoperationalPhysicalAIredefinesPhysicalAIvisionActionguide

systemmanufacturingtoreality

Introduction

AIleavesthescreen

ThepromiseofAIhasneverfeltmoretangible.Robotscandance.Carscan

drivethemselves.Dronescandeliverpackages.We'renowsofamiliarwithusingAIforeverythingonourcomputersandphones-buthowAIworksonascreen

isfundamentallydifferentfromhowitworkswhenitentersthephysicalworld.

DigitalAIhelpsusunderstandtheworld.PhysicalAImustactinit.OnceAI

startsmovingmachinesandvehicles,anddrivingphysicalprocesses,theruleschange.Successisnolongermeasuredbytheoutputofanon-screentask,

butbytheconsequencesofareal-worldoutcome.Didthefactoryrobotcompleteitsassemblycorrectlyandsafely?Didtheself-drivingcarnavigateitsroute

withoutaccident?Didthedronedeliveritspackagetotherightplace?Inthisenvironment,whatmattersisnotonlyhowintelligentlysystemsanalyze

andreturninformation,buthoweffectivelydigitalsystems,physicaloperations,andhumanexpertiseworktogetherandlearntoactinrealtime.

Thestakesaresubstantial.PhysicalAIismovingrapidlyfromexperimentationtowardbroaderdeployment.TheglobalphysicalAImarket,estimatedatUS$5billionin2025,isprojectedtoexceedUS$82billionby2035,growingatnearly33%annually.Andorganizationsaretakingnotice:morethannineintenbelievephysicalAIwillcreateacompetitiveadvantage.

Toachievemeaningfulreturns,organizationsmustsuccessfullyoperate

physicalAIatscale.Buttoday,limitedscalecoincideswithlowmaturityacrossthephysicalAIlifecycle:onlyone-quarteroforganizationsarescalingbeyondselectedusecases,whilefewerthanfourintenreportmaturecapabilityinanyindividuallifecyclearea.

“Thephysicalworldisunforgiving:errorsarecostlyandcanhaveseriousconsequences.”

AndreaCassoni

GlobalHeadofCollaborativeRobotics,

ABBRobotics

IntroductionThelivingoperationalsystem

PhysicalAIredefinesmanufacturing

PhysicalAIvisiontoreality

Actionguide

Thepathtovaluedependsonstrengtheningmultiplecapabilitiesacrossthe

physicalAIlifecycle,fromintegrationandgovernancetolearningandexecution.

Inaddition,connectingdata,training,inference,actuation,andfeedbackintoascalableoperationalarchitectureremainsamajorhurdle.

ThisIBMInstituteforBusinessValue(IBMIBV)reportwasproducedin

collaborationwithWorldManufacturingFoundationandSEMI.ItdrawsonIBMIBVresearchinvolving800executivesandseniorleadersacrossthephysicalAIecosystem,inadditionto12in-depthinterviews.

WewillexaminewherephysicalAIistoday,whatthefutureofmanufacturing

couldlooklike,andwhatitwillaketogetthere.Specifically,weexplore

whyphysicalAIbehavesdifferentlyfromdigitalAI,howorganizationsconnectdigitalsystems,physicaloperations,andhumanexpertiseintolivingoperationalsystems,andhowtheycanovercomethebarrierspreventingphysicalAIfrom

scaling.ThereportconcludeswithanactionguidethatidentifiesthecapabilitiesleadersshouldstrengthennextontheirphysicalAIjourney.

4

IntroductionThelivingoperationalPhysicalAIredefinesPhysicalAIvisionActionguide

systemmanufacturingtoreality

5

Partone

TurningphysicalAIinto

alivingoperationalsystem

OrganizationsarenowcomfortableusingdigitalAItosenseconditionsandanalyzeinformation:camerasdetect

defects;AImodelspredictequipmentfailures;digitaltwinstestproductionchangesbeforetheyreachthefactoryfloor.

PhysicalAI-definedinthisreportasmachinesthatautonomouslyperformtasks-addsanotherstep:actingintheworld.Inphysicalsystems,that

stepofinitiatingoradjustingmovementisknownasactuation.ItmarkstheshiftfromAIthatanalyzesandrecommendstoAIthatcandirectlyinfluencewhathappensnext.

OnceAIstartsmovingmachines,guidingvehicles,orchangingphysical

processes,decisionsmeetequipment,materials,people,andenvironmentsthatmaynot-andprobablywon't-behaveexactlyasexpected.Physical

AIsystemsmustthereforecontendwithchangingconditions,unexpectedinputs,andoutputsthatarehardertopredict.ThatiswhyphysicalAI

breaksthedigitalAIplaybook:therealworldisfarmoreunpredictablethanthecontrolledenvironmentsthatdigitalintelligencetypicallyoperatesin.

"Thechallengeisnotonlytrainingthesystemtomakedecisions.Itisalsodetermininghow

thosedecisionsareactuatedintherealworld.”

HiroshiOkuyama

ManagingDirector,CorporateStrategy,

Innovation&Technology,Digital,Yanmar

PhysicalAIredefinesmanufacturing

PhysicalAIvisionActionguide

toreality

IntroductionThelivingoperationalsystem

6

Figure1

PhysicalAIturnsdataintoaction

ThebiggestchangecomeswhenAImovesfromunderstanding

aconditiontoactingonitreliablyintherealworld.

AIcapability:Sense→

Whatitdoes

Examples

Whereorganizationsaretoday

Primarychallenge

Detectchangingconditions

Sensors,cameras,operatorinput

AIuseisexpanding

Capturingreliable,relevantdata

Analyze→

Interpretinformation

andpredictoutcomes

AImodels,simulations,digitaltwins

Matureinselectedapplications

Accountingforphysical

andenvironmental

variability

Act

Initiateoradjust

physicalmovement

Machines,robots,vehicles,controlsystems

Earlyadoptioninreal-worldoperations

Performingreliablyandsafelyunder

real-worldconditions

IntroductionThelivingoperationalPhysicalAIredefinesPhysicalAIvisionActionguide

systemmanufacturingtoreality

7

PhysicalAIsimulationmeetsanunpredictableworld

PhysicalAIimplementationrequiresadvancedsimulationcapabilities.

Digitaltwinsinvirtualenvironmentsacceleratetrainingandalloworganizationstotestrareorunsafeconditionswithoutdisruptingoperations.

Butnosimulationcaptureseveryvariationinmaterials,equipment,weather,

workermovement,oroperatingconditions.Realmachinesandenvironments

frequentlybehavedifferentlyfromtheirdigitalrepresentations,andthatmeanssimulationsarethestart,nottheendofaphysicalAIprogram.

AmongorganizationsthatbothbuildandoperatephysicalAIsystems,71%

identifythegapbetweensimulatedandreal-worldperformanceasachallenge.Butindustrialvaluedependsonreliability.PhysicalAIsystemsmustcontinueperformingconsistentlyasconditionschange,notsimplysucceedonce

inacontrolledenvironment.Thatmeanssimulationsmustcontinuallyintegratelearningfromreal-worldexperiencetoimprov=efutureperformance.

Whiledigitaltwinshelporganizationsprepareforreality,humanexpertise

isstillneededtobridgethegapwhenrealitydivergesfromthemodel-

providingjudgment,context,andoperationalknowledge.AphysicalAIsystemmaylearnaworker'smovements,butitdoesnotinherentlyunderstandwhy

anexperiencedoperatorchoseaparticularactionunderspecificconditions.

Reliableactionthereforedependsonmorethanthemachineitself.Itdependsondigitalintelligence,physicaloperations,andhumanexpertiseworkingtogether.

“Gettingtherobottodowhat'sneeded

isthetipoftheiceberg.Therealchallenge

isindustrializingit-makingitworkreliably

onashopfloororinawarehouse.”

RahulGarg

VicePresident,IndustrialMachinery,

SiemensDigitalIndustriesSoftware

“Withahumanintheloop,thedata

canbeincompleteoreveninconsistent-thehumanfindsandfixesit.Butyou

can’thavethatinafullyautonomousworld.Ithastobecompleteandconsistent."

MichaelMunsey

VicePresidentofSemiconductorIndustry,

SiemensDigitalIndustriesSoftware

Actionguide

PhysicalAIredefinesmanufacturing

PhysicalAIvisiontoreality

IntroductionThelivingoperationalsystem

8

Connectingdata,machines,andpeoplemultipliesphysicalAIvalue

ThechallengeofbringingphysicalAItolifeisnotsimplyconnectingdigitalsystems

tophysicaloperations.Muchoftoday'seffortisfocusedonintegratingAImodelsand

physicalsystems.Butouranalysissuggeststhatthemostoverlookedelementis

humanintelligence.

Asystemthatpurposefullyconnectsdigitalintelligence,physicaloperations,andhumanexpertiseformsathree-worldintelligenceloop.Digitalsystemsanalyzeconditionsandgenerateinsight;machinesandphysical

operationsturnthatinsightintoactionandgenerateoutcomes;andhumanexpertiseprovidesthecontextandjudgmentneededtointerpretthoseoutcomesandimprovefuturedecisions.

Therealvalueemergeswhenallthreeworldscontinuallyinformoneanother.Datadrivesaction.Actionproducesoutcomes.Humanexpertiseprovidescontext.Thoseinsights

improvethenextdecision,closingtheloopofoperationalintelligence.

Whenthiswayofworkingbecomesembeddedacrosstheenterprise,theorganizationbecomesalivingoperationalsystem:onethatlearns

fromexperience,adaptstochangingconditions,andimprovesovertime.

Whatisalivingoperationalsystem?

AlivingoperationalsystemisthedestinationstateforphysicalAI:anoperatingenvironmentwheredigitalsystems,physicaloperations,

andhumanexpertisecontinuouslylearnfromoneanother.It:

-Connectsintelligenceacrosspeople,machines,andoperations.

-Learnsfromreal-worldoutcomesandoperationalexperience.

-Orchestratesdecision-making,workflows,andactionsacrossthephysicalAIlifecycle.

-Improvesperformanceovertimebyturningeveryoutcomeintolearningthatcanbe

repeatedandscaled.

PhysicalAIcreatesvalueonlywhen

learningbecomesrepeatableandscalable.

Organizationsthatcanconnectreal-world

learning,lifecycleorchestration,andcoordinatedworkflowsarebetterabletoturnisolated

deploymentsintoreliableperformance

improvementsacrossoperations.Theresultishigherproductivity,strongeroperational

ROI,andagrowingadvantagethatcompoundswitheverylearningcycle.

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IntroductionThelivingoperationalPhysicalAIredefinesPhysicalAIvisionActionguide

systemmanufacturingtoreality

Figure2

Thethree-worldintelligenceloop

Connectingdata,machines,andpeoplerequiresanintelligenceloopthatturnsdataintoaction,actionintooutcomes,andoutcomesintolearningandimprovement.

Analyze

Sensors

Digitaltwins

Assets

SimulationComputeMachines

Digital

systems

Physical

Models

Facilities

operations

DataValuecl-

multiplies

Sense

ujea7

Human

Judgment

Operational

knowledge

expertise

ContextIntervention

Improve

IntroductionThelivingoperationalPhysicalAIredefinesPhysicalAIvisionActionguide

systemmanufacturingtoreality

10

Thedatasuggeststhatvaluedoesnotcomefromexcellinginanysingle

area-digitalsystems,physicaloperations,orhumanexpertisealone.Infact,strengthinonlyoneworldcreatesnomeasurableproductivityadvantage

overstrengthinnoneatall.Thegainsarelargestamongorganizationsstrongacrossallthreeworlds.Themultipliercomesfromtheinteraction,not

theindividualparts.

Figure3

Thethree-worldadvantage

PhysicalAIvaluepeakswhenallthreeareas-digitalsystems,

physicaloperations,andhumanexpertise-reinforceoneanother.

114%

□Expectedenterpriseproductivitygainby2030

ExpectedoperationalROIby2030

94%

71%

55%55%

44%

38%*36%*

Strength

innoarea

OOO

StrengthStrength

inoneareaintwoareas

●○O●O

Areasworkingtogether(digital,physical,human)

Strength

inallthreeareas

*38%and36%areroundednumbers.Thedifferenceisinsignificant.

IntroductionThelivingoperationalPhysicalAIredefinesPhysicalAIvisionActionguide

systemmanufacturingtoreality

11

Parttwo

PhysicalAIredefinesmanufacturing

Livingoperationalsystemsdomorethanimproveindividualdecisions.Theychangehowworkisperformed,how

expertiseisshared,andhowmanufacturingsystemsoperate.

While78%ofexecutivesexpectmanufacturingandfactoryoperations

toseethebiggestchangesamongphysicaloperationtasks,organizationsanticipatenearly50%productivitygainsalongsideworkforcereductions

ofonly9%.Theimplicationisclear:physicalAIisexpectedtotransformworkfarmorethanworkforcesize.

PhysicalAIreshapesrolesandworkflows

PhysicalAIismorelikelytoreshaperolesthanreplacethem.

Asroutineactivitiesshifttowardintelligentmachines,peoplecanfocusmoreonjudgment,exceptions,maintenance,andimprovement.

“Theaverageageofourteammembersis

increasing,andthatknowledgeisconcentratedinapopulationthatwillshrinksignificantlyaspeopleretire.Whentheyretire,thatknowledgeandcapabilitygoeswiththem.”

MarkusBernhardt

GlobalManufacturingEngineeringITLeader,

GeneralMotors

IntroductionThelivingoperationalPhysicalAIredefinesPhysicalAIvisionActionguide

systemmanufacturingtoreality

12

Theexpertisegapmakesthisespeciallyimportant.Asexperiencedworkersretire,valuableoperationalknowledgeisatriskofleavingwiththem.

PhysicalAIcreatesanopportunitytocapturethatexpertiseandmake

itavailableacrosssites,shifts,andfuturedecisions.Unliketraditional

automation,physicalAIcanlearnfromhowskilledworkersrespond

tounexpectedsituations,resolveexceptions,andadapttochangingconditions.Overtime,organizationscanembedthatexpertiseintodigitalsystems.

“Thefactorywillbehighlyautomated.

Peopletalkaboutthedarkfactorywithout

anyhumanbeingworkingthere."

YongYang

HeadofIndustryResearch,

Seres,China

Perspective

WillphysicalAIcreatedarkfactories?

By2030,only15%ofleadersexpectphysicalAItooperatewithnear-full

autonomy,while55%expecthuman-machinecollaborationorco-creationtobecomethedominantoperatingmodel.Thepeopleclosesttophysical

operationsarethemostcautiousaboutremovinghumans.Endusersand

operatorsarehalfaslikelyassystemintegratorstoexpectnear-fullautonomy,reflectingamoregroundedviewofwhatphysicaloperationsrequire.

Theevidencesuggeststhat,fortheforeseeablefuture,human-machine

collaborationwillbeamorecommonoperatingmodelthanfully

autonomousfactories.

“There'salotofhypethathumanoidswilltake

overeverything.Idon’'tseeithappening,

becauseyouhavetoaskwheretheaddedvalueis.”

Dr.BobStruijk

VicePresidentandDirector,EmergingMarkets,

FANUC

13

IntroductionThelivingoperationalsystem

PhysicalAIredefinesmanufacturing

PhysicalAIvisiontoreality

Actionguide

CaseStudy

BritishSugarturnsexpertiseintooperationaladvantage²

BritishSugar,theUK'ssoleprocessorofsugarbeet,facedagrowingrisk:decadesofoperationalknowledgewereconcentratedamong

experiencedemployeesapproachingretirement.TheorganizationcombinedAI,digitaltwins,operationaldata,andpredictive

maintenancecapabilitiestomakethatexpertisemoreaccessibleacrossmanufacturingoperations.

Theresultwasa25%reductionindecision-makingtime,a20%

reductioninproductionissue-resolutiontime,anda10%reduction

inraw-materialcosts.Operationalknowledgebecameeasiertoshare,helpingteamsmakefaster,moreconsistentdecisionsacrossshiftsandsites.

BritishSugardemonstrateshowhumanexpertisecanbecome

ascalablesourceofvaluewhenitisconnectedtodigitalintelligenceandoperationaldata.Inturn,digitalsystems,physicaloperations,andhumanexpertisereinforceoneanother,creatingtheproductivitygainsassociatedwiththelivingoperationalsystem.

Actionguide

PhysicalAIvisiontoreality

PhysicalAIredefinesmanufacturing

IntroductionThelivingoperationalsystem

PhysicalAIchangestheeconomicsofmanufacturing

ThebusinesscaseforphysicalAIoftenbeginswithfamiliarimprovements:lessdowntime,betterquality,fasterdecisions,higherassetutilization,saferoperations,andgreaterflexibility.

OrganizationsalreadyembracingphysicalAIexpect109%operationalROIby2030,comparedwith67%amongtheirpeers.Thedifferenceisnotsimplyadoption.ItistheabilitytointegratephysicalAIintohowtheorganization

operatesandlearns.

ExpectedROIrisesasorganizationsmovefromexplorationtoselected

deployment,remainsbroadlylevelatthescalingstage,andishighestwhenphysicalAIbecomescoretotheoperatingmodel.Organizationsatthis

finalstageexpectanindicative161%operationalROIby2030,nearlyfourtimeswhatthosewhoremaininexplorationmodeexpect.

Closingthelearningloopcanbecomeoneofthelargestvalueopportunities

availabletomanufacturersscalingphysicalAI.Organizationsscaling

physicalAIacrossmultipleproducts,sitesoroperationsreportashare

ofrevenueattributabletophysicalAIthatis1.24percentagepointshigherthanorganizationswithselecteddeployments.Foranillustrativeindustrial

productsmanufacturerwithUS$20billioninannualrevenue,thistranslatesintoUS$248millioninadditionalannualrevenue.Theopportunitygrows

asorganizationsturnphysicalAIfromacollectionofdeploymentsintoasystemthatcontinuouslylearnsandimproves.

14

PhysicalAIvisionActionguide

PhysicalAIredefinesmanufacturing

toreality

IntroductionThelivingoperationalsystem

15

Figure4

TheeconomicsofphysicalAI

ThelargestpayoffemergeswhenphysicalAIbecomespartoftheoperatingmodel.

ExpectedROIatdifferentstagesby2030

105%

102%

80%

43%

161%

Exploring

opportunities

Runningpilots

Deployedinselectedusecases

Scaling

acrosssitesorproducts

→

Coretothe

operatingmodel

Note:Percentagesareself-reportedexpectations,notprojections.Becausethegroupatthefinalstageissmall,theresultsignalsthepotentialvalueofdeeperoperationalintegrationratherthanaguaranteedreturn.

Actionguide

PhysicalAIvisiontoreality

PhysicalAIredefinesmanufacturing

IntroductionThelivingoperationalsystem

16

“Beyondsupportingorautomatingindividual

tasks,AIisalsobeingexploredforapplicationsrelatedtoproductionprocessesandshop

flooroperations."

MihoIto

ExecutiveOfficer/HeadofAdvancedResearch&InnovationCenter,DENSOCorporation,Japan

PhysicalAIexpandswhatmanufacturingcanachieve

PhysicalAIcouldtransformmorethanindividualtasks,productionlines,

orfactories.Ithasthepotentialtochangehowentiremanufacturingnetworksaredesignedandoperated.

NineintenexecutivesexpectphysicalAItoincreaseproductionflexibility,while87%expectittostrengthensupplychainresilience.Moreadaptableoperationscouldsupportregionalizedproduction,reshoring,and

manufacturingnetworksthatrespondfastertochangesindemand,productmix,andoperatingconditions.

Realizingthatvisionwillrequiremorethandeployingadditionalsystems.

ItdependsontheabilitytoscalephysicalAIsafely,reliably,andcontinuouslyacrossoperations.

Autonomycannotsimplybeswitchedon.Itmustbeearnedthroughstrongfoundationsinintegration,governance,safety,andoperationalexecution.

Thelastingadvantagewillnotcomefromdeployingthemostmachines.ItwillcomefrombuildingthecapabilitiesthatallowphysicalAItolearnfromeveryactionandoperatereliablyatscale.

17

IntroductionThelivingoperationalPhysicalAIredefinesPhysicalAIvisionActionguide

systemmanufacturingtoreality

Partthree

Turningthevision

ofphysicalAIintoreality

MostorganizationsunderstandwherephysicalAIisheaded.

Farfewerhavebuiltthecapabilitiesneededtogetthere.

88%oforganizationsrecognizewhatphysicalAIarchitecturerequires,butonly36%haveachievedclosed-loopcapabilities,combiningreal-

worldlearning,end-to-endlifecycleorchestration,andagenticworkflowcoordination.TheadvantageliesnotsimplyindeployingAI,butin

turningexperienceintobetterdecisionsandactions.

Ouranalysisreinforcesthispoint.Learningfromoperationaloutcomesis

thecapabilitymoststronglylinkedtoenterpriseproductivity,whilelifecycleorchestrationisthecapabilitymoststronglylinkedtomanufacturing

andlogisticsproductivity.Theserelationshipsholdacrossorganizationsofdifferentsizes.

Knowingwhattobuildisnotenough.Simplyunderstandingthearchitectureisnotassociatedwithhigherexpectedproductivity.Tocapturevalue,

organizationsmustconnectdata,training,deployment,action,andfeedbackintoacontinuouslyimprovingsystemthatcanoperatereliablyatscale.

Thecapabilitiesdescribedinthissectionstrengthenthelearningloopthatturnsoperationaloutcomesbacktolearningtoimprovefutureoutcomes.

Anymissinglinkintheloopwilldragproductivitygains.

guide

PhysicalAIredefinesmanufacturing

PhyscalAIvision

toreality

Action

IntroductionThelivingoperationalsystem

18

ArobustoperatingbackboneisessentialtoscalephysicalAI

MakingphysicalAIworkatscalerequiresanoperatingbackbonethat

connectsexistingmanufacturingsystemswithnewAIcapabilities.

Theoperatingbackboneprovidesthefoundationforclosed-looplearningbymakingreal-timeoperationdataaccessiblebyphysicalAIsystems.

Yetturningadiversecollectionofsystemsintoonecoordinatedoperationremainsmanufacturers'biggestobstacletoscalingphysicalAI.

AmongtheorganizationsthatbothprovideandusephysicalAI,one-thirdidentifylegacysystemintegrationasthetopoperationalchallenge.

Considerafactory.AnERPsystemgeneratesaproductionplan,directing

theplanttoproduce1,000unitsduringaparticularweek.Factoryoperationaltechnology(OT)systemsturnthatplanintoactionbycoordinatingtasks

acrossshopfloors.

BusinessITsystemsdeterminewhatneedstobeproduced.OperationalOTsystemsexecutethosedecisionsinthephysicalworldandreportbackwhatactuallyhappened.

ManymanufacturingprocessesarealreadyhighlyautomatedonlegacyOT

systems,butautomationisnotthesameasautonomy.Theoperationallayersclosertophysicalsystemsoftenremainfragmentedbecausemanufacturingexecutionsystems,plant-monitoringenvironments,ind