世界经济论坛-工业运营中人机协同落地行动手册+Human-Machine+Collaboration+in+Industrial+Operations

IncollaborationwithWJRLD

AccentureECCNOMIC

FORUM

Human-MachineCollaboration

inIndustrialOperations:

ActivationPlaybook

JUNE2026

Images:GettyImages

Contents

Foreword3

ExecutiveSummary4

1FromVisiontoReality:WhyHuman-MachineCollaboration5

1.1Thecomplementarityimperative7

1.2Theadoptiongap8

1.3Thetrustchallenge8

2Human-MachineCollaborationFrameworkinIndustrialOperations10

2.1Howworkischanging11

2.2Implicationsfororganizations12

2.3Applyingtheframeworkinpractice12

3FrameworkAdoption17

3.1Sitelevel:culture,learningandfrontlineownership17

3.2Enterpriselevel:theintegratedreinventionagenda19

3.3Ecosystemlevel:prioritizingsystem-widealignment21

Conclusion23

Acknowledgements24

Endnotes26

Disclaimer

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Thefindings,interpretationsand

conclusionsexpressedhereinarearesultofacollaborativeprocessfacilitatedand

endorsedbytheWorldEconomicForumbutwhoseresultsdonotnecessarily

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Partnersorotherstakeholders.

©2026WorldEconomicForum.Allrightsreserved.Nopartofthispublicationmaybereproducedortransmittedinanyformorbyanymeans,includingphotocopyingandrecording,orbyanyinformation

storageandretrievalsystem.

Human-MachineCollaborationinIndustrialOperations:ActivationPlaybook2

Human-MachineCollaborationinIndustrialOperations:ActivationPlaybook3

June2026

Human-MachineCollaborationinIndustrialOperations:ActivationPlaybook

Foreword

KivaAllgood

ManagingDirector,WorldEconomicForum

Theintelligentindustrialtransitionunderwayisnotincremental.Autonomousandroboticsystemsinautomotive,electronicsandsemiconductorsectorsareabsorbingentireworkflowfunctionsthat

oncedependedoncontinuoushumanexecution,atascaleandvelocitythatoutpacesmost

organizations'capacitytoredesignworkaround

them.Theproductivitygainsaremeasurableandthetrajectoryisclear.Whatremainsunresolved,

inmostorganizations,istheharderarchitectural

question:notwhatthetechnologywilldo,butwhatinstitutionalconditionsmustbebuilttoensurethathumanjudgmentisintegratedintothedecisions

thatmattermost.

Thisisnotasofterversionofautomation.Itisa

sharperone.Asexecutionbecomescommoditized,judgmentbecomesthedifferentiator.Organizationsthatinvestinpeoplealongsidetechnologyrealize

MaryKateMorleyRyan

ExecutivePrincipal,Accenture

productivitygainsabove11%;thosethatsidelinethehumanfactorseegainsof4%.1Thegapisnottechnical,itisadesignchoice.

BoththisActivationPlaybook,andtheResource

Hubitispairedwith,arebuilttohelporganizationsmakethatchoicedeliberately,atthesite,enterpriseandecosystemlevels.Itsintendedaudienceis

theplantmanagerdeployingnewtechnology

nextquarter,thegovernmentofficialdesigning

workforcepolicyforanentireregion,andthe

executivewhounderstandswhatthetechnology

candobuthasnotyetbuiltthehumanarchitecturetomatchit.

Thegainsfromintelligentindustrialoperations

willbesharedwidelyorconcentratednarrowly.Whichoutcomeprevailsdependsonwhetherthetransitionisdesignedwiththehumanintheloop.

Human-MachineCollaborationinIndustrialOperations:ActivationPlaybook4

ExecutiveSummary

Technologyistransformingindustrialoperationsfasterthanmostorganizationshaveredesignedworkto

absorbit.Thegapbetweentechnologydeploymentandworkforcereadinessisthedefiningchallengeformanufacturingandsupplychaincompetitiveness.

Frontiertechnologiesaremovingmanufacturing

andsupplychainsfromstatic,efficiency-oriented

systemstowardsintelligentoperationsthatsense

conditions,anticipatedisruptionsandadapt

workflowsinnearrealtime.Thisshiftredistributes

workbetweenhumansandmachines.Machines

areabsorbingtasksthatdemandspeed,scaleandpatternrecognition,fromsemi-autonomousprocesscontroltoreal-timeworkflowreconfiguration.

Humancontributionconcentrateswhere

judgment,accountabilityandexceptionhandling

isneeded:settingthestrategicintentthatshapeswhatsystemsoptimizefor,arbitratingbetween

competingpriorities,andgoverningtherulesunderwhichautonomoussystemsarepermittedtoact.Acrossfunctions,workismovingfromexecutiontogovernance.Criticalthinking,creativity,and

consciencewillremaincorehumanstrengthsdrivingintelligentoperations.

Thisredistributiondoesnotdiminishtherole

ofpeopleinindustrialoperationsbutrather

reshapesthejobstheyperform.Acrossthesevenfunctions,jobsareexpectedtochangealong

threedimensions.Somejobsareexpectedto

expand,focusingonmachineorchestrationandoversight.Forexample,futureManufacturing

Engineersmayspendlesstimerunningtasks

andmoretimedirecting,validatingandgoverningthesystemsthatperformthosetasksontheir

behalf.Somejobswillbeelevated,shifting

fromprecisionexecutiontowardshigher-order

judgmentandstrategicaccountabilitythatdidnotpreviouslydefinethem,oftenunderanewtitle.

Today'sProductionOperatormayevolveintoan

Early-CareerProductionTechnicianwhohandles

exceptionsandsupervisesautonomousequipment.Finally,somejobsmayconsolidateintootherjobsasnetnewjobsemergetomeetnewneeds.For

instance,theProductionOperatorjobmaybecomelessprevalentasaRoboEngineer/Orchestrator

emergestodirectrobotfleetsonthefactoryfloor.

Staffingthetechnology-infusedhumanjobsthatmayemergerequiresupdatedskillsets.Some

63%ofemployersciteskillsgapsasthesingle

largestbarriertotransformationand,by2030,

44%ofmanufacturingworkersand49%ofsupply

chainworkerswillneedupskillingandreskilling.2

Inaddition,only30%ofC-suiteleadersare

confidentintheirchangecapabilities,eventhoughallanticipatechangestotheirworkforce.3Closingthegapstartswithadefensiblepictureofwhatthefuturelookslike.

TheWorldEconomicForum’s

Human-Machine

Collaboration(HMC)initiative

,launchedinJune2025incollaborationwithAccenture,offerstwointegratedoutputstosupportthiswork.Itoffersavisionoftheworkandworkflowsoftomorrowsotheworkers,enterprisesandcommunitiesatthecentreofindustrialproductionunderstand

it.TheResourceHub(the“what”ofHMC),

availablehere

,providesfuture-stateworkflow

scenarios,jobprofilesandskillsmatrixacross

sevenmanufacturingandsupplychainfunctions,offeringashared,evidence-basedstartingpointfortechnologydeploymentforfutureindustrial

operationsandworkforceplanningandskilling.

ThisActivationPlaybook(the“why”and“how”

ofHMC)showshowtoapplythoseinputsand

whatactionscanbetakenbyorganizations,

governmentsandtrainingproviders.Atthe

enterpriselevel,theC-suite–includingchief

operatingofficers(COOs),chieftechnologyofficers(CTOs)andchiefpeopleofficers(CPOs)–must

operateasco-ownersofasingleprogramme,withworkflowredesign,governance,skillsarchitectureandincentivealignmentsequencedbefore

technologygoeslive.

Atthesitelevel,technologydeploymentsenable

performancewhenfrontlineworkersareinvolved

inredesigninghowworkisdone,whenlearning

isbuiltintodailyoperationsandwhenfailuresare

treatedasskillingopportunities.Alignmenton

ManufacturingandSupplyChainonaskillsmatrix

givesgovernments,educatorsandemployersa

basisforcoordinatinginvestmentsothatcredentialsearnedinonecompanyortrainingprogramme

counttowardsadvancementinanother.Together,

theResourceHubandtheActivationPlaybook

giveleadersawaytoclosethegapbetweenwhat

technologymakespossibleandwhattheirworkforcemustbepreparedtodotoensureasmooth

transitiontoanintelligentoperationsofthefuture.

Thisworkprovidesimpulsestowardsavisionaryoutlookinthenextdecadeonhowfuturestateworkflows,jobprofilesandskillsmightlook

basedontoday’sinsightsfromindustrialexpertsandtechnologicaldevelopments.

Human-MachineCollaborationinIndustrialOperations:ActivationPlaybook5

1

FromVisiontoReality:WhyHuman-MachineCollaboration

Logistics

Managesthemovement,storagegoodsacrossandfromtheprodutheyarriveattherighttimeandp

anddeliveryof

ctionnetworksolace.

TheWorldEconomicForum’sIntelligentIndustrialOperationsOutlook2026reportdescribeshow

artificialintelligenceandotherfrontiertechnologieswillshiftmanufacturingandsupplychainsfunctions(Figure1)overthenextdecadefromstatic,

efficiency-orientedsystemstointelligentoperationsthatsenseconditions,anticipatedisruptionsand

adaptworkflowsinnearrealtime.Realizingthis

visionwillrequirehumansandmachinestoworktogetherinwaysthatmostorganizationshave

notdeliberatelydesignedfor.Theorganizationsrealizingpositiveandsustainablegainsfrom

frontiertechnologydeploymentarethosethatareredesigningworkflowswhilebuildingtheforward-facingworkforcecapabilitiesandskillsthose

workflowsrequire.

FIGURE1Thesevenindustrialoperationsfunctions(coveredinthisreport)

ProductDevelopment

Designsandengineersnewproducts–fromconceptthroughproduction-

readyspecifications.

Planning

Forecastsdemandandaligns

productionschedules,inventoryandresourcestomeetproductionandlogisticalrequirements.

Maintenance

Ensuresthatplantequipmentand

machineryremainoperationalandsafe.

Production

Overseesthephysicaltransformationofrawmaterialsandcomponentsinto

finishedgoods.

Quality

Establishesandenforcesstandardstoensure

productsmeetend-useandregulatoryspecifications.

SupplyChainManagement

Coordinatestheend-to-endflowofmaterials,

informationandvaluefromsupplierstocostumers.

parametersinclosedloopsandhumanengineers

concentrateonexceptionarbitrationandgovernance.

Productionschedulingwillshiftfromrigidweekly

cyclestoself-optimizingsystemsthatbalancelabour,energyandmaterialflowsacrosssitesdynamically.Fewerthan1in10manufacturersand1in4supplychaincompanieshavemeaningfullybegunthe

journeytofullautonomy,suggestingthatclosingthe

Inpractice,thismeansfactorieswillmovefrom

reactivemaintenancetopredictivemaintenance-

leveragingdatafromsensors,cameras,vibration

analysisandequipmentalerts,enabledbymachine-learning(ML)modelsthatcanpredictwhen

equipmentcouldfailinnearfuturebyproposing

mitigationsolutions.Qualitymanagementwillevolvefromperiodicinspectiongatesintozero-defect

operationswhereautonomousagentsfine-tune

gapbetweenwheremostorganizationsaretodayandwhereoperationsareheadingwillgeneratesignificantcompetitivevalue.4

Insupplychains,theshiftisfromratherstatic,

reactivenetworkstowardsnetworksthatoperateandadaptcontinuously.Anticipationofdisruptionswillbecoordinatedacrossenterpriseboundariesinnearrealtime.Demandplanningwillmove

frommanualforecastgenerationtoAI-driven

systemsthatsensesignalsandsimulatescenarioscontinuously,withhumanssettingstrategic

prioritiesandvalidatingoutcomesratherthan

buildingtheforecaststhemselves.Inventoryand

networkdecisionsthatcurrentlyrequirehuman

initiationateachstepwillbegovernedbyself-

adjustingsystemsoperatingwithinpolicyguardrails,withhumansinterveningatconfidencethresholdsthesystemcannotcrossalone.

Thisvisionisnotsciencefiction.Aspectsofhuman-machinecollaborationarealreadybeingdeployed

byleadingenterprisesintheGlobalLighthouse

Network.ACGGroupandKunlene,forinstance,havedevelopedreplicableapproachestoenablinghuman-machinecollaborationtodrivebusinessvalue.

Empoweringoperatorsthroughmachinelearning

ACG'ssiteoperatesequipmentmorethan60yearsold,producingover5,000SKUswithtighttolerances.Machineset-uphadalwaysdependedonoperatorexperience,andincorrectsettingsmeantyieldandqualitylossesandextendedleadtimes.ThesitedeployedMLmodelstrainedonovertwoyearsofdatatoprescribeoptimal

first-time-rightsettings,whichoperatorsintegratedintoeachmachine’sprogramming.Operatorsthenrefinethemodelscontinuouslybasedonobservedoutcomes;the

operatorswhorunthemachinesarealsotrainingtheintelligencebehindthem.

ACGGroup

+37%

–43%

–13%

FirstpassyieldQualityset-uptimeProductionleadtime

Shirwal,IndiaCasesource:GlobalLighthouseNetwork

越KUNLENE

–50%

–57%

–37%

KunleneFilmIndustr

iesCo.

AI-assistedadvancedparametersetting

Kunlene'sfilmproductionfacilitymustachieveuniformthicknesswhilemanaging

frequentchangeoversandlimitedaccesstomachineparameterinterfaces.Thesite

integratedanIIoTplatformwithAImodelsthatproposeadjustmentsinrealtime.

Operatorsreviewandconfirmeachadjustmentbeforeittakeseffect,theymaintain

decisionauthorityoverproduction-criticalchangesandobservetheactionsdirectly,

givingthemaconcretebasisfortrusting,verifyingandrefiningthesystem.

Changeoverinramp-upPtime

Suzhou,China

erformanceloss

Defectratefromspeedissues

Casesource:GlobalLighthouseNetwork

Human-MachineCollaborationinIndustrialOperations:ActivationPlaybook6

Human-MachineCollaborationinIndustrialOperations:ActivationPlaybook7

Thecomplementarityimperative

1.1

exceptionsandgoverntherulesunderwhichautonomoussystemsarepermittedtoact.

Designingworksothatmachineandhumans

operatesattheirpointofcomparativeadvantage

requiresdeliberatedesign,lestautomationadd

complexitywithoutperformancegains.5Accentureresearchindicatesthatcompaniesthatprioritize

investmentinpeoplealongsidetechnologycanleadtoproductivitygainsofover11%,whilethosethatsidelinethehumanfactorexperiencehavegainsofjust4%.6

Whatleadingorganizationsshareisanintentional

designinwhichhumansandmachinescontribute

theircomplementarystrengthstodriveoutcomes

(Figure2).Machineshandletasksrequiringspeed,

scaleandconsistency;machinessenseproductionconditionscontinuously,recognizepatternsinlargedatasets,predictdriftandexecutecorrectionswith

repeatableprecision.Humansoverseecontext,

stakesandownership.Humanssetthestrategic

intentthatshapeswhatthesystemoptimizesfor,

exercisejudgmentwhereconsequencesextend

beyondalgorithmicboundaries,resolveunanticipated

Complementarystrengthsofhumansandmachines

FIGURE2

HUMANS

CriticalthinkingandjudgmentDefineboundaries;intervenewhen

systemsreachtheirlimits

Conscience

Valuejudgmentsrequiringhumanmoralreasoning

Creativity

Novelsolutionsforsituationsoutside

5

Speedandscale

VolumesnohumanworkforcecanmatchConsistency

Uniformexecutionofcomplextaskswithoutfatigueordrift

Patternrecognition

Anomaliesacrossdatasetstoolargeforhumananalysis

HUMAN+MACHINE

MACHINES

existingpatterns

thresholdforcomplextasks

Communication

Human-machineinteractionvariesbetweenfullyautonomousand

manualaction

combinedwithphysicalmanipulationofartefacts

Continuousmonitoring

Real-timedetectionnohumanteamcansustain

Routinecoordination

Repeatableworkflowsexecutedautonomously

HUMANSHUMAN+MACHINEMACHINES

Augmenteddecision-makingMachinesexpandoptions;humansnarrowtowhatisright

KnowledgeencodingHumanexpertisebecomesmachinecapability

Dexteroustasks

Highlycomplexmovements

Judgmentatscale

Machineshandlevolume;humanshandlevariance

Trustcalibration

Learningwhentorelyonthesystemandwhentooverride

Capabilityextension

Technologylowerstheskill

Theadoptiongap

1.2

Theadoptiongapistypicallyrootedinworkforce

readiness.Acrossmanufacturingandsupplychains,86%ofemployersexpectAIandinformation-

processingtechnologiestotransformtheirbusinessby2030and85%ofsupplychainexecutivesplantoincreaseAIspendingin2026.7YettheWorld

EconomicForum’sTheFutureofJobs2025Reportsuggeststhat44%ofmanufacturingworkers

and49%ofsupplychainworkerswillneedtobe

reskilledby2030and63%ofemployersidentify

skillsgapsasthesinglelargestbarriertobusinesstransformation.8Investmentinworkforcecapabilityhasnotkeptpacewithinvestmentinthetechnologyitself.Gartnerpredictsthatby2028,60%ofsupply

chaindigitaladoptioneffortswillfailtodeliverpromisedvalueduetoinsufficientinvestmentinlearninganddevelopment.9

Thegapisnotoneofawareness.InAccenture’s

surveyoffactorymanagers,70%rateworkforce

transformationasthesinglemostcriticalfactor

formanufacturingsuccessyetnearlyhalfcitethecostoftrainingasamajorbarrier,notbecause

trainingisunaffordableinabsolutetermsbut

becausetechnologybudgetsareprioritizedrelativetoseparateworkforceinvestments.10Leaders

recognizethegap,nameitastheirtoppriorityandstillunderinvestinthecapability.

Thetrustchallenge

1.3

Beneaththeadoptiongapisamorefundamentalchallengeoftrust,whichisultimatelyunderpinnedbytrustinthetechnologyusedbytheenterprise,

expectationsforjobsecurityandleadershipcredibility(Figure3).

Threedimensionsoftrust

FIGURE3

Human-MachineCollaborationinIndustrialOperations:ActivationPlaybook8

Systemtrust

Confidencemustbearned

Experiencedworkersareaskedtoacton

recommendationsfromsystemsthatarrivedrecently—inareaswheretheirownjudgmenthasbeenthestandardforyears

Onlyathirdbelievetheyhavethe

technologicalexpertise—orcanarticulateacompellingtransformationnarrative.

Whenconfidenceexceedscompetence,credibilityerodes.

THETRUSTEQUATION

Workforcetrust

Howworkersreadwhetherchangeisbeingdonewiththem—ortothem

Inmanufacturing,46%offactorymanagerssayworkersfearautomationwillmaketheirjobsobsolete.

ofworkersreportAIis

increasingtheirjobinsecurity

executivesfeelequippedtoleadAI-drivenchange

Leadershipcredibility

~60%

1in3

Systemtrust

Thefirstandmostimmediatedimensionisjob

security.Nearly60%ofworkersreportthatAIis

increasingtheirjobinsecurityand,inmanufacturingspecifically,46%offactorymanagerssayworkersfearautomationwillmaketheircurrentroles

obsolete.11Workerswhoholdthisconcernwill

interpreteverysubsequentsignal,fromdata

collectiontojobredesigntoreskillingprogrammes,throughthelensofwhethertheorganizationis

preparingthemforthefutureorpreparingtoreplacethem.Thatconcerndeepenswhenworkersare

notinvolvedearlyonintechnologyselectionanddeploymentdecisionsandwhensystemscollectdataonindividualperformanceusingautomation-assistedsurveillance.

Aseconddimensionistrustinthesystems

themselves.Experiencedworkersarebeingaskedtoactonrecommendationsfromsystemsthat

arrivedrecently,inareaswheretheirownjudgmenthasbeenthestandardforyears.Confidencemustbeearnedthroughstructuredopportunitiestotesttechnologicalrecommendationsagainstexperience.

Athirddimensionisleadershipcredibility.Only

oneinthreeexecutivesbelievetheyhavethe

technologicalexpertiseortheabilitytoarticulateacompellingtransformationnarrativeforAI-drivenchange.Yetthesesameleadersareaskingtheirworkforcetocommittoavisiontheycannotfullydescribeinoperationalterms.12Whenleadershipconfidenceexceedsleadershipcompetence,

thedisconnecterodescredibilityfasterthananycommunicationscampaigncanrestoreit.

Skills-centricarchitecturefortalentdevelopment

Tohelpnewoperatorsmanageagrowingportfolioofautomationequipmentandcollectfeedback,SchneiderElectric(SE)introducedgen-AIenabledaugmentedreality(AR)

glasses.SEuploadedequipmentdata,recommendationsandmanualssooperatorscouldrelyonthesystem'soutputsfromdayone.TheresultingARglassesdisplay

potentialcausesandcorrespondingsolutions,reducingtimefornewtechniciansto

masterequipmentfrom18toninemonths.Thekeyenablerwasacalibratedtrustloopinwhichoperatorsinterrogateandrefinemodelrecommendations,transferringyearsoftacitknowledgefromexperiencedoperatorswhilebuildingconfidence.

Fastertimetomasterequipment

Wuhan,ChinaCasesource:GlobalLighthouseNetwork

SchneiderElectric

50%

Human-MachineCollaborationinIndustrialOperations:ActivationPlaybook9

Human-MachineCollaborationinIndustrialOperations:ActivationPlaybook10

2

Human-Machine

CollaborationFrameworkinIndustrialOperations

Tohelporganizationsnavigatethistransition,thisinitiativehasdevelopedatwo-partHuman-MachineCollaborationFramework(Figure4):

1.TheResourcesHub,availablehere,providesfuture-stateworkflowscenarios,jobprofiles

andskillstaxonomiesinsevenmanufacturingandsupplychainfunctions,offeringashared,evidence-basedstartingpointforworkflow

evolutionandworkforceplanningandtraining.

2.TheActivationPlaybookprovidesaction

strategiesonhowenterprises,site-levelteamsandecosystemstakeholderscanimplementhuman-machinecollaborationusingthe

ResourcesHubtofittheircontext.Together,theyreplacefragmented,organization-specificapproacheswithacommonarchitecturethatscalesindustries,geographiesandlevelsof

technologicalmaturity.

BoththeResourcesHubandthePlaybookare

informedbystructuredconsultationswithvariousstakeholdersandseniorexecutivesfromindustry,academiaandgovernment.Theseconsultations

surfacedaconsistentfinding:organizationsthataremakingprogressonhuman-machinecollaborationarenotwaitingforperfecttechnologyorcompleteworkforcereadiness.Theyaremakingdeliberate

designchoicesonhowtodistributeworkbetweenpeopleandmachines,investinginthecapabilitiesthosechoicesrequireandbuildingthegovernancestructurestosustainthem.

4

FIGURE

TheHuman-MachineCollaborationframework:Thedualcomponents

2.1

Fromcyclestocontinuousorchestration

AIsystemsmonitoroperationsaroundtheclockandadjustinrealtime,

replacingscheduledreviewcycleswithalways-onmanagement.

Fromdoingtasksto

governingsystems

Workersmoveintohigher-judgmentjobs:settingtherulesthesystemfollows,

checkingitsoutputsandmakingthecallsitisnotauthorizedtomakeonitsown.

FIGURE5

Howworkischanging

Workismovingfromperiodic,human-executedprocessestocontinuous,human-orchestrated

operations.Systemsthatpreviouslyrequired

peopletocollect,reconcileandactoninformationarebecomingcapableofdoingthosethings

Human-MachineCollaborationinIndustrialOperations:ActivationPlaybook11

THESHIFTUNDERWAY

Howworkis

changing

Fourreinforcingshiftsinwherehumaneffortgoes

Observablethemes–howworkischanging

autonomously.Humansarenowpredominantlyinvolvedinhighervalue-addedtaskssuchas

Fromdetectionto

prevention

Intelligentsystemspredictwhensomethingisabouttogowrongandintervenebeforeitdoes,shiftinghumanworkfromreactiontoforesight.

Fromclosedorganisationsto

openecosystems

Futurejobsrequiremanagingshareddata,shareddecisionsandsharedaccountabilityacrossnetworksofsuppliersandpartnersthatnosingleorganizationowns.

decisionsonwhatthesystemshouldoptimizefor,governanceofsystemsandarbitrationofconsequence-heavyoutcomes.

SkillsMatrix

Astructuredviewofthefunctional,technical,behaviouralandmindsetcapabilitiesrequiredinfuturejobs.

JobProfiles

Detaileddescriptionsofemergingjobs,includingresponsibilities,keytasksandskills,groundedinworkflowexpectations.

2ActivationPlaybook

WhyHuman-MachineCollaboration

Thecaseforredesigningworkaroundhuman-machinecollaboration.

CompaniontotheResourceHub

Guidancefororganizations,siteleadersandecosystempartnerstousetheworkflows,jobprofilesandtheskillsmatrix.

StrategiesforAcceleratingAdoption

StrategiesforacceleratingadoptionofHuman-MachineCollaborationandrecommendationsfordesigningworkforcetransitionprogrammesthat

aligntechnicaldeploymentandworkforcereadiness.

1ResourceHub

Future-StateWorkflows

Mapsofhowworkisorganizedtodayandhowitisexpectedtochange,showingwhatstops,evolvesandpersistsacrosseachfunction.

Human-MachineCollaborationinIndustrialOperations:ActivationPlaybook12

2.2Implications