2026年技术趋势报告 Tech Trends 2026-德勤d

Astechnologyinnovationandadoptionaccelerate,fivetrendsrevealhow

successfulorganizationsaremovingfromexperimentationtoimpact

TechTrends2026

Deloitte.

InsⅢhts

Tableofcontents

02...Executivesummary

04...Innovationcompounds

09...AIgoesphysical:NavigatingtheconvergenceofAIandrobotics

21...Theagenticrealitycheck:Preparingforasilicon-basedworkforce

33...TheAIinfrastructurereckoning:Optimizingcomputestrategyintheage

of

inferenceeconomics

43...Thegreatrebuild:ArchitectinganAI-nativetechorganization

53...TheAIdilemma:SecuringandleveragingAIforcyberdefense

62...Cuttingthroughthenoise:TechsignalsworthtrackingasAIadvances

2

Executivesummary

L

astyear’s

TechTrendsreport

predictedthatartificialintelligencewouldbecomeakintoelectricity,afoundationalelementthat’sseamlesslybakedintoanincrediblybroadrangeofproductsandservices.Thisyear’sreport,the17thannualeditionofTech

Trends,provesthathypothesis.NocornerofenterprisetechnologyisuntouchedbyAIasthedemandforintel-ligentoperationsinformsdecisionsoneverythingfromcomputinghardwaretophysicalrobotics.And,whilelastyear’sfocuswasonbuildingproof-of-conceptprojectsandexploringtheartofthepossible,thisyearisallaboutscaling.Enterprisesacrossindustriesareoperationaliz-ingAI-drivenprocesses.Thereasonissimple:LeadershaverealizedthatthekeytocompetitivedifferentiationwillbeusingAItodriveautomation,innovation,andacceleration.

Innovationcompounds

TechnologyleadersfaceacriticalshiftfromAIexper-imentationtomeasurableimpact.Innovationnowcompoundsexponentially:GenerativeAIreachedapproximately100millionusersinjusttwomonthsversus50yearsfortelephonestoreach50millionusers.Thisiscreatingamultiplyingflywheeleffectwhereimprovementsintechnology,data,investment,andinfrastructuresimultaneouslyaccelerateeachother.Traditionalinfrastructureandsequentialimprovementprocessescan’tkeeppace.Successrequiresmorethansophisticatedtechnology.Organizationsmustredesignratherthanmerelyautomateprocesses,connectinvest-mentstobusinessoutcomes,andexecuterapidly.

AIgoesphysical:Navigatingthe

convergenceofAIandrobotics

PhysicalAIisevolvingrobotsfrompreprogrammedmachinesintoadaptivesystemsthatperceive,learn,andoperateautonomouslyincomplexenvironments.Thesecapabilitiesshowupinindustrialrobots,autonomousvehicles,drones,andothersystems.Currentchallengesincludegapsintraining,safetyconcerns,andcyberse-curityrisks,butfallingcostsareextendingadoptionbeyondsmartwarehousingandsupplychainopera-tionsintothemainstream.Humanoidrobotsarethenextfrontier,withprojectionsof2millionworkplacehumanoidsby2035.Futuredevelopmentsmayincludebio-hybridrobotsandquantumrobotics.

Theagenticrealitycheck:Preparing

forasilicon-basedworkforce

Despiteearlyenthusiasm,manybusinesseshaveyettoseesignificanttransformationfromagenticAIimplementationsbecausemostsimplyautomateexist-ingprocessesratherthanfundamentallyredesigningoperations.Only11%ofsurveyedorganizationshavedeployedagenticsystemsinproduction,withchallengesincludinglegacysystemintegration,dataarchitectureconstraints,andinadequategovernanceframeworks.Leadingorganizationsareadoptingagent-firstprocessredesign,implementingmultiagentorchestrationusingemergingprotocols,andtreatingagentsasasili-con-basedworkforcerequiringspecializedmanage-mentframeworks.Thisincludesagentonboarding,performancetracking,andFinOpscostmanagement.Thefuturepointstowardgraduatedautonomylevels,hybridhuman-digitalworkforces,andleveragingagent-generateddataforcontinuouslearning,transforminghowenterprisesoperateandcompete.

3

TheAIinfrastructurereckoning:

Optimizingcomputestrategyinthe

ageofinferenceeconomics

AsAImovesfromexperimentationtoproduction,enterprisesfaceaninfrastructuredilemma.Whiletokencostshavedroppedsubstantially,overallAIspendingisexplodingduetomassiveusagegrowth.Organizationsarehittingatippingpointwherecloudservicesbecomecost-prohibitiveforhigh-volumeworkloads,withmonthlybillsreachingtensofmillions.Leadingenter-prisesareadoptingstrategichybridarchitectures:cloudforvariableworkloads,on-premisesforconsistentproductioninference,andedgeforlatency-criticalappli-cations.Thiscanrequirepurpose-builtAIdatacentersfeaturinghardwareoptimizedforgraphicsprocessingunits,advancednetworking,andspecializedcooling.Futurechallengesincludeworkforcereskilling,AIagentsmanaginginfrastructure,andsustainablecomputinginnovationslikerenewable-poweredandpotentiallyorbitaldatacenters.

Thegreatrebuild:Architectingan

AI-nativetechorganization

AIisfundamentallyrestructuringtechnologyorganiza-tionsbeyondsimpleautomation.With64%oforgani-zationsincreasingAIinvestmentsandtechbudgetsforAIrising,prioritiesareshiftingfrominfrastructuremainte-nancetostrategicleadership.LeadingorganizationsareanchoringAIinitiativestomeasurablebusinessoutcomes,designingmodulararchitecturesforflexibility,andrede-finingtalentstrategiesaroundhuman-machinecollabo-ration.Newrolesareemerging,suchasAIcollaborationdesigners,edgeAIengineers,andpromptengineers,whilechiefinformationofficersareevolvingfromtechstrat-egiststoAIevangelistsandorchestrators.Futuretechorganizationswillfeatureagenticarchitectures,leanprod-uct-ledteams,blendedhuman-agentworkforces,adaptivegovernance,andecosystem-orientedinnovation.Successrequiresembracingcontinuousevolutionandboldlyreimaginingoperationsratherthanincrementalchange.

TheAIdilemma:Securingandleveraging

AIforcyberdefense

AIcreatesacybersecurityparadox:Thesamecapabil-itiesdrivingbusinessinnovationarealsointroducingnewrisks.OrganizationsfacethreatsfromshadowAIdeployments,adversarialattacks,andintrinsicAIsystemweaknessesacrossfourdomains:data,models,applica-tions,andinfrastructure.ExistingsecuritypracticescanbeadaptedtoaddressAI-specificrisksthroughrobustaccesscontrols,modelisolation,andsecuredeploymentarchitectures.AndAIofferspowerfulnewcapabilitiestocountertheveryvulnerabilitiesitcreates.Leadingorgani-zationsareleveragingAIdefensivelythroughredteamingwithAIagents,adversarialtraining,andautomatedthreatdetectionatmachinespeed.FuturechallengesincludeAI-physicalinfrastructureconvergence,autonomouscyberwarfare,andquantumandspacesecuritythreats.SuccessrequiresembeddingsecurityintoAIinitiativesfrominception,treatingitasanenablerratherthanaconstraintoninnovation.

Cuttingthroughthenoise:Techsignals

worthtrackingasAIadvances

Executivesummary

TechTrendstakesadeepdiveintofivetechnologydevel-opmentsthatarereshapinghowbusinessesoperate,buttherearefarmorethanfivetrendsimpactingorganiza-tionsatanygivenmoment.Eightadjacent“signals”alsowarrantmonitoring.Theyincludewhetherfoun-dationalAImodelsmaybeplateauing,theimpactofsyntheticdataonmodels,developmentsinneuromorphiccomputing,emergingedgeAIusecases,thegrowthinAIwearables,opportunitiesforbiometricauthentication,theprivacyimpactofAIagents,andtheemergenceofgenerativeengineoptimization.Someofthesesignalsmaymatureintodominantforces.Othersmayfade.Butallofthemreflectthesameunderlyingreality:Thepaceoftechnologicalchangehasfundamentallyshifted,andtheorganizationsthatrecognizethesepatternsearlywillhavetimetoadapt.

4

Innovationcompounds

Astechnologyinnovationandadoptionaccelerate,fivetrendsrevealhowsuccessfulorganizationsaremovingfromexperimentationtoimpact

KellyRaskovich

I

spendmostofmyyearinconversationswithtech-nologyleaders,askingwhat’sworking,whatisn’t,andwhatkeepsthemupatnight.Lately,thoseconversationshavetakenonadifferentquality.

Thequestionusedtobe“Whatcanwedowith

AI?”Nowit’s“Howdowemovefromexperimentationtoimpact?”Thefocushasmovedfromendlesspilotstorealbusinessvalue,andthere’sasenseofurgencybehinditall.Notbecausethetechnologyisgettingbetter—thoughitis—butbecausethepaceofchangeitselfhasaccelerated.

Thenumberstellthestory(figure1).Thetelephonetook50yearstoreach50millionusers.Theinternettooksevenyears.AleadinggenerativeAItoolreachedabouttwicethatmanyintwomonths.1Asofthiswriting,thattoolhasover800millionweeklyusers—roughly10%oftheplanet’spopulation.2

Butrapidadoptionisonlythesurface.Innovationiscompounding;forcesaren’tsimplyadditive,butmulti-plicative.Thinkofitasaflywheel:Bettertechnologyenablesmoreapplications.Moreapplicationsgeneratemoredata.Moredataattractsmoreinvestment.Moreinvestmentbuildsbetterinfrastructure.Betterinfrastruc-turereducescosts.Lowercostsenablemoreexperimen-tation.Eachimprovementsimultaneouslyacceleratesalltheothers.

It’swhyAIstartupsscalefromUS$1milliontoUS$30millioninrevenuefivetimesfasterthanSaaScompaniesdid.3It’swhytheknowledgehalf-lifeinAIhasshrunktomonthsfromyears.4Andit’swhyonechiefinfor-mationofficer(CIO)toldme,“Thetimeittakesustostudyanewtechnologynowexceedsthattechnology’srelevancewindow.”

5

Figure1

TheAItransformationinnumbers

800M

weeklyusersofaleadingAItool(10%ofplanet)

havenoagenticstrategy

35%

revenuescaling

5xfaster

(AIvs.SaaSstartups)

Only11%aoadgn

inferencecostreduction,yetbillsintensofmillions

280-fold

Theinvestmentimbalance:

93%ontech

7%onpeople

reportnooperatingmodelchanges

Only1%

Sources:RebeccaBellan,“SamAltmansaysChatGPThashit800Mweeklyactiveusers,”TechCrunch,Oct.6,2025;Deloi廿eEmerging

TechnologyTrendsintheEnterpriseSurvey;WingVentureCapital,“AIgrowingfasterthanSaaS”;StanfordHuman-CenteredAIInstitute,“AIIndexReport2025”;Deloi廿eresearchonAIinvestmentallocationpa廿erns,2025;Deloi廿e2025TechSpendingOutlook.

Everyorganizationwestudiedisdiscoveringthesametruth:Whatgotthemherewon’tgetthemthere.

Theinfrastructurebuiltforcloud-firststrategiescan’thandleAIeconomics.Processesdesignedforhumanworkersdon’tworkforagents.Securitymodelsbuiltforperimeterdefensedon’tprotectagainstthreatsoperatingatmachinespeed.IToperatingmodelsbuiltforservicedeliverydon’tdrivebusinesstransformation.

Thisisn’tonlyaboutenhancement.

It’saboutrebuilding.

For17years,TechTrendshasexploredemergingtech-nologiespoisedtoreshapebusinessinthenext18to24months.OurresearchisbasedontrendsensingfromconversationswithDeloittesubjectmatterexpertsand

externaltechnologyleaders,aswellasDeloitte’spropri-etaryresearchonemergingtechnologies.Thisyear,thedatarevealsfiveinterconnectedforces.

AIgoesphysical:Navigatingthe

convergenceofAIandrobotics

Innovationcompounds

Amazondeployeditsmillionthrobot,anditsDeepFleetAIcoordinatestheentirerobotfleet,improvingtravelefficiencywithinwarehousesby10%.5BMW’sfacto-rieshavecarsdrivingthemselvesthroughkilometer-longproductionroutes.6Intelligenceisn’tconfinedtoscreensanymore;it’sembodied,autonomous,andsolvingrealproblemsinthephysicalworld.

6

Theagenticrealitycheck:Preparing

forasilicon-basedworkforce

Only11%oforganizationshaveagentsinproduction,despite38%pilotingthem.Thegapbetweenpilottoproductiontellsyoueverything.Forty-twopercentarestilldevelopingtheirstrategy,while35%havenostrat-egyatall.7Gartnerpredictsthat40%ofagenticprojectswillfailby20278—notbecausethetechnologydoesn’twork,butbecauseorganizationsareautomatingbrokenprocessesinsteadofredesigningoperations.HPE’schieffinancialofficercapturedwhatworks:“Wewantedtoselectanend-to-endprocesswherewecouldtrulytrans-form,notjustsolveforasinglepainpoint.”9Redesign,don’tautomate.That’sthepatternseparatingsuccessfromfailure.

TheAIinfrastructurereckoning:

Optimizingcomputestrategyinthe

ageofinferenceeconomics

Tokencostshavedropped280-foldintwoyears;10yetsomeenterprisesareseeingmonthlybillsinthetensofmillions.Usageexplodedfasterthancostsdeclined.Organizationsarediscoveringtheirexistinginfrastruc-turestrategiesaren’tdesignedtoscaleAItoproduc-tion-scaledeployment.They’reshiftingfromcloud-firsttostrategichybrid:cloudforelasticity,on-premisesforconsistency,andedgeforimmediacy.

Thegreatrebuild:Architectingan

AI-nativetechorganization

AIisrestructuringtechorganizations,makingthemleaner,faster,andmorestrategic.Only1%ofITleaderssurveyedbyDeloittereportedthatnomajoroperatingmodelchangeswereunderway.11LeadersareshiftingfromincrementalITmanagementtoorchestratinghuman-agentteams,withCIOsbecomingAIevangelists.Successrequiresboldreimagination:modulararchitec-tures,embeddedgovernance,andperpetualevolutionascorecapabilities.

TheAIdilemma:Securingandleveraging

AIforcyberdefense

Thetechnologymeanttogivebusinessesanadvantageisbecomingthetargetusedagainstthem.AT&T’schiefinformationsecurityofficer

capturedthechallenge

:“Whatwe’reexperiencingtodayisnodifferentthanwhatwe’veexperiencedinthepast.TheonlydifferencewithAIisspeedandimpact.”12OrganizationsmustsecureAIacrossfourdomains—data,models,applications,andinfrastructure—buttheyalsohavetheopportunitytouseAI-powereddefensestofightthreatsoperatingatmachinespeed.

Throughoutthisyear’sreport,you’llmeettechnologyleaderssuccessfullynavigatingthisseachange.Theydon’thavealltheanswers,buttherearenoticeablepatternsastheylightthewayforward.

•Theyleadwithproblems,nottechnology.Broadcom’sCIO:“Withoutfocusingonaspecificbusinessproblemandthevalueyouwanttoderive,itcouldbeeasytoinvestinAIandreceivenoreturn.”13

•Specifically,theirbiggestproblems.UiPathCEO:“Ratherthangettingstuckinacycleofperpetualproofsofconcept,considerattackingyourbiggestproblemandgoingforabigoutcome.”14

•Theyprioritizevelocityoverperfection.WesternDigital’sCIO:“We’dratherfailfastonsmallpilotsthanmissthewaveentirely.”15

•Theydesignwithpeople,notjustforthem.Walmartinvolvedstoreassociatesinbuildingitsschedulingapp,whichincludesshiftswapping,schedulevisi-bility,andemployeecontrol.Theresult:Schedulingtimedroppedfrom90minutesto30minutes,andpeopleactuallyusedtheapp.16

•Theytreatchangeascontinuous.Coca-Cola’sCIOdescribedtheirjourneyasmovingfrom“Whatcanwedo?”to“Whatshouldwedo?”17Thatshift—fromcapability-firsttoneed-first—iswhatseparatesproductiveexperimentationfrompilotpurgatory.

7

I’vetrackedtechnologyevolutionlongenoughtorecog-nizethepatterns.Theinternetchangedeverything.Mobilereshapedconsumerbehavior.Cloudcomputingwastransformative.

Butthismomentisdifferent.

It’snotjustthatAIispowerful.It’sthatthe

S-curves

arecompressing.Thedistancebetweenemergingandmainstreamiscollapsing.

Organizationsbuiltforsequentialimprovementcan’tcompetewiththoseoperatingincontinuouslearningloops.Thetraditionalplaybookassumedyouhadtimetogetitright.Thatassumptionnolongerholds.

Theorganizationsthatsucceedwillprobablynotbethosewiththemostsophisticatedtechnology.They’llbethosewiththecouragetoredesignratherthanautomate,

thedisciplinetoconnecteveryinvestmenttobusinessoutcomes,andthevelocitytoexecutebeforethewindowcloses.

Innovationcompounds.Thegapbetweenlaggardsandleadersgrowsexponentially.Howyouresponddeter-mineswhichsideofthatgapyou’reon.

Butyoudon’thavetonavigatethisalone.Wehopethisyear’spublicationremindsyouthateveryone’sfacingthisrapidpaceofchange,andtogether,wecanshapewhatcomesnext.

KellyRaskovich

Executiveeditor,TechTrends

Endnotes

1.JeffDesjardins,“Intheraceto50millionusersthere’soneclear

winner-anditmightsurpriseyou,”WorldEconomicForum,

June26,2018;AlexandraGarfinkle,“ChatGPTontrackto

surpass100millionusersfasterthanTikTokorInstagram:

UBS,”YahooFinance,Feb.2,2023.

2.RebeccaBellan,“SamAltmansaysChatGPThashit800Mweeklyactiveusers,”TechCrunch,Oct.6,2025.

3.ZachDeWitt,“AIgrowingfasterthanSaaS,”WingVentureCapital,November7,2024.

4.BasedonDeloitteanalysisoftechnologyadoptioncyclesandAIcapabilityevolutiontimelines.

5.ScottDresser,“Amazondeploysover1millionrobotsandlaunchesnewAIfoundationmodel,”Amazon,July1,2025.

6.BradAnderson,“WhoneedsfactorydriverswhencarsdrivethemselvesatBMWplants,”CarScoop,Nov.26,2024.

7.Deloitte2025EmergingTechnologyTrendsintheEnterprise

Survey.FromJunetoJuly2025,Deloitteconductedanonlinesurveyof500UStechnologyleaderstoquantifytheprevalence,engagement,andperceptionssurroundingtheadoptionof

emergingtechnologiesacrossindustries.

8.Gartner,“Gartnerpredictsover40%ofagenticAIprojectswillbecanceledbyendof2027,”pressrelease,June25,2025.

9.MarieMyers(executivevicepresidentandchieffinancialofficer,HPE),interviewwithDeloitte,March1,2025.

10.StanfordInstituteforHuman-CenteredArtificialIntelligence,“TheAIIndexreport2025,”accessedNov.12,2025.

11.Deloitte2025TechSpendingOutlook.FromJunetoJuly2025,Deloitteconductedanonlinesurveyof302ITprocurement

leaders,headsofIT,andnon-ITexecutiveswithtechnologyspendingoversighttounderstandhowUSenterprisesinkeyindustriesaremanagingtechnologybudgets.

12.“Ano-nonsenseapproachtosecureAIenablementatAT&T,”DeloitteInsights,Nov.21,2025.

13.KatherineNoyes,“BroadcomCIO:‘Modernizationshouldbedrivenbythebusiness’,”CIOJournal,TheWallStreetJournal,andDeloitte,Sept.10,2025.

14.KatherineNoyes,“UiPathCEO:Agenticautomationwill‘usherinaneweraofwork’,”CIOJournal,TheWallStreetJournal,andDeloitte,Feb.21,2025.

15.KatherineNoyes,“WesternDigitalCIO:IntheAIera,‘Playoffenseorgetleftbehind’,”CIOJournal,TheWallStreet

Journal,andDeloitte,Sept.6,2025.

16.Walmart,“WalmartunveilsnewAI-poweredtoolstoempower1.5millionassociates,”June24,2025.

Innovationcompounds

17.KatherineNoyes,“Coca-ColaCIOonscalingAI:From‘Whatcanwedo?’to‘Whatshouldwedo’,”CIOJournal,TheWallStreetJournal,andDeloitte,Jan.18,2025.

9

AIgoesphysical:NavigatingtheconvergenceofAIandrobotics

Poweredbyartificialintelligence,traditionalrobotsarebecomingadaptivemachinesthatcanoperatein—andlearnfrom—complexenvironments,unlockingsafetyandprecisiongains

JimRowan,TimGaus,FranzGilbert,andCarolineBrown

R

obotspoweredbyphysicalAIarenolongerconfinedtoresearchlabsorfactoryfloors.They’reinspectingpowergrids,assistinginsurgery,navigatingcitystreets,andwork-ingalongsidehumansinwarehouses.Thetransitionfromprototypetoproduction

ishappeningnow.

PhysicalAIreferstoartificialintelligencesystemsthatenablemachinestoautonomouslyperceive,understand,reasonabout,andinteractwiththephysicalworldinrealtime.Thesecapabilitiesshowupinrobots,vehi-cles,simulations,andsensorsystems.Unliketraditionalrobotsthatfollowpreprogrammedinstructions,phys-icalAIsystemsperceivetheirenvironment,learnfromexperience,andadapttheirbehaviorbasedonreal-timedata.Automationalonedoesn’tmakethemrevolution-ary;rather,it’stheircapacitytobridgethegapbetweendigitalintelligenceandthephysicalworld.

Inthenascentbutrapidlyevolvingcategoryofrobots,physicalAIturnsrobotsintoadaptive,learningmachinesthatcanoperateincomplex,unpredictableenviron-ments.ThecombinationofAI,mobility,andphysicalagencyenablesrobotstomovethroughenvironments,performtasks,andinteractwiththeworldinwaysthatfundamentallydifferfromenhancedappliances.Embodiedinroboticsystems,physicalAIisquiteliterallyonthemove.

Today,AI-enableddrones,autonomousvehicles,andotherrobotsarebecomingincreasinglycommon,particu-larlyinsmartwarehousingandsupplychainoperations.Theindustry,regulatorybodies,andpotentialadopters

areworkingtobreakdownbarriersthathinderthedeploymentofsolutionsatscale.Asorganizationsover-comethesechallenges,AI-enabledrobotswilllikelytran-sitionfromnichetomainstreamadoption.Eventually,we’llwitnessphysicalAI’snextevolutionaryleap:thearrivalofhumanoidrobotsthatcannavigatehumanspaceswithunprecedentedcapability.

Fromprototypetoproduction

AIgoesphysical:NavigatingtheconvergenceofAIandrobotics

UnliketraditionalAIsystemsthatoperatesolelyindigi-talenvironments,physicalAIsystemsintegratesensoryinput,spatialunderstanding,anddecision-makingcapa-bilities,enablingmachinestoadaptandrespondtothree-dimensionalenvironmentsandphysicaldynamics.Theyrelyonablendofneuralgraphics,syntheticdatageneration,physics-basedsimulation,andadvancedAIreasoning.Trainingapproachessuchasreinforcementlearningandimitationlearningenablethesesystemstomasterprincipleslikegravityandfrictioninvirtualenvironmentsbeforebeingdeployedintherealworld.

RobotsareonlyoneembodimentofphysicalAI.Italsoencompassessmartspacesthatusefixedcamerasandcomputervisiontooptimizeoperationsinfactoriesandwarehouses,digitaltwinsimulationsthatenablevirtualtestingandoptimizationofphysicalsystems,andsensor-basedAIsystemsthathelphumanteamsmanagecomplexphysicalenvironmentswithoutrequiringroboticmanipulation.

Whereastraditionalrobotsfollowsetinstructions,phys-icalAIsystemsperceivetheirenvironment,learnfrom

10

experience,andadapttheirbehaviorbasedonreal-timedataandchangingconditions.Theymanipulateobjects,navigateunpredictablespaces,andmakesplit-seconddecisionswithreal-worldimplications.Robotdogsprocessacousticsignaturestodetectequipmentfail-uresbeforetheybecomecatastrophic.Factoryrobotsrecalculatetheirrouteswhenproductionschedulesshiftmid-operation.Autonomousvehiclesusesensordatatospotcyclistssoonerthanhumandrivers.Deliverydronesadjusttheirflightpathsaswindconditionschange.Whatmakesthesesystemsrevolutionaryisn’tjusttaskautomationbuttheircapacitytoperceive,reason,andadapt,whichenablesthemtobridgethegapbetweendigitalintelligenceandthephysicalworld.1

TechadvancementsdrivephysicalAI–roboticsintegration

PhysicalAIisreadyformainstreamdeploymentbecauseoftheconvergenceofseveraltechnologiesthatimpacthowrobotsperceivetheirenvironment,processinfor-mation,andexecuteactionsinrealtime.

Vision-language-actionmodels.PhysicalAIadoptstrain-ingmethodsfromlargelanguagemodels(LLMs)whileincorporatingdatathatdescribesthephysicalworld.Multimodalvision-language-action(VLA)modelsintegratecomputervision,naturallanguageprocess-ing,andmotorcontrol.2Likethehumanbrain,VLAmodelshelprobotsinterprettheirsurroundingsandselectappropriateactions(figure1).

Onboardcomputingandprocessing.Neuralprocess-ingunits—specializedprocessorsoptimizedforedgecomputing—enablelow-latency,energy-efficient,real-timeAIprocessingdirectlyonrobots.OnboardcapabilityallowsphysicalAIsystemstorunLLMsandVLAmodels,processhigh-speedsensordata,andmakesplit-second,safety-criticaldecisionswithoutclouddependency—essentialforautonomousvehicles,indus-trialrobotics,andremotesurgery.3Itcanalsotransformrobotsfromisolatedmachinesintoautonomoussystemsthatcanshareknowledgeandcoordinateactionsacrossintelligentnetworks.

Roboticsadvancementshavemaderobotsmoreacces-sibleandcapable:4

•Computervisionfor“seeing”andunderstandingsurroundings

•Sensorsforcapturinginformationsuchassound,light,temperature,andtouch

•Actuatorsformovement,inspiredbyhumanmuscles

•Spatialcomputingfornavigating3Denvironments

•Improvedbatteriesthatenablelongeroperationwithoutfrequentrecharging

Trainingandlearning.Inreinforcementlearning,robotsdevelopsophisticatedbehaviorsthroughtrialanderror

Figure1

Action

Motorcontrolsystemsexecutephysicaltasksbasedonvisualinputandlinguisticinstructions

Howvision-language-actionmodelswork

Vision

Computervisionsystemsinterpretvisual

environmentsandidentifyobjects,obstacles,andspatialrelationships

>

Language

Naturallanguageprocessingunderstandshumancommandsandcancommunicateintentions

Source:Deloi廿eanalysis.

11

byreceivingrewardsorpenalties.Inimitationlearning,robotsmimicexpertdemonstrations.Bothapproachescanbeappliedinsimulatedenvironmentsorinthephysicalworldwithrealhardware.5Ablendofthesetechniques,startingwithsimulation-basedreinforce-menttrainingandthenfine-tuningwithtargetedphys-icaldemonstrations,cancreatecontinuouslearningloops.Thishelpsrobotscontinuetoimprovebyfeedingreal-worlddatabackintotheirtrainingpoliciesandsimulationspaces.6

Compellingeconomicsboostindustrialadoption

Astechnologyadvances,costshavebeencomingdown,andmanyreal-worldapplicationshaveemerged.

AdvancedmanufacturinginfrastructurenowsupportstheproductionofcomplexroboticsandphysicalAIsystemsatenterprisescale.ThismeansthatphysicalAIrobotscannowbeproducedwiththereliabilityandqualitycontrolofsmartphonesorcars,makingthempracticalforeverydayindustrialuse.

Componentcommoditizationandopen-sourcedevelop-mentarereducingentry