2026 AI原生劳动力：工程与产品价值链中的工作与技能未来研究报告-

AI-nativeworkforce:Futureof

workandskillsinengineering

andproduct

valuechainFebruary2026TableofcontentsExecutive

summary

4New

rules

of

technology

talent

5Evolution

of

software

engineering

roles

8Evolution

of

product

management

roles

17Emerging

skills

of

the

future

20Organisations

must

be

READY

to

embrace

this

change

23AI-native

workforce:Future

of

work

and

skills

in

engineering

and

product

value

chain3CurrentdataindicatethatGenAItoolscanimprovenewproduct

developmenttimesby50percentandsignificantlyacceleratesoftwaredevelopmenttasks,withproductivitygainsof

30–40

percentwitnessedinengineeringteams.¹However,realising

this

valueestimatedat~7percentof

globalGDP7

requiresorganisationstolookbeyondautomatingindividualtasksand

redesignentireworkflows,developskillsandreimaginetalent

andleadershiptoaccommodatehuman-agentcollaboration.Thetechnologylandscapeisundergoingastructuraltransformationdrivenbytwoconvergingforces:

Thematurity

ofGenerative

AI(GenAI)into“agentic”workflowsandthestabilisationofhybridworkmodels.Softwareengineeringistransitioningfromadisciplineof

creationtooneof

orchestration,

andproductmanagementevolvesfromcoordinationtostrategicacceleration.ExecutivesummaryTheremotework“productivityparadox”Asignificantdisconnectexistsbetweenengineeringdataandleadershipsentiment.theavailabletechnology;thegoalisaugmentationratherthan

displacement.1,6

AIagentsarebecoming“virtualcoworkers”capableofplanningandexecutingmultistepworkflows,suchas

migratinglegacycodeorautonomouslymanagingsalesleads.About64percentofdevelopersreporthigherproductivityworkingremotely.2

Datashowa4percentincrease

in“focused

work”(keystrokesperminute)anda5percent

increase

incodingduringcorebusinesshoursamongremoteworkers.2Wehavemovedpastsimplecodecompletion.Thefutureworkforcewillbeacollaborationofpeople,agentsandrobots.

Itissuggestedthatwhile~52percentof

workertasksintheUS

couldbecompletedfasterwiththesamelevelofqualitywithWecanseethesetrendsplayingoutacrossthetalentlandscapeandinhowtherolesofsoftwareengineersandproduct

managersareevolvingThemacroenvironment:Drivers

ofchangeOrganisationsmustimplementobservabilitytoolstobridgethis

trustgapratherthanenforcingmandatesthatriskattrition.Only12percentofleadersexpresscompleteconfidencein

remoteproductivity.3

LeadershipscepticismThe

era

of

agentic

AI●

ImplicationAI-native

workforce:Future

of

work

and

skills

in

engineering

and

product

value

chainDeveloperreality4New

rulesoftechnologytalentAI-native

workforce:Future

of

work

and

skills

in

engineering

and

product

value

chainTheidealcandidate:“Thearchitectofintelligence”ValuedfordesigningsystemswhereAIagents

performtaskssecurelyandreliablyRolesnowindemandAIresearchMLengineersNewfocusonhighereducation40–45percentofrolesintheAmericasandEuropenowdemandaMasters

or

PhD

Global

talent

divergence1Fromcodertoconductor:Thenewrulesof

techhiringAIiscausingaseismicshiftintechhiring,decouplingproductivityfromheadcountandfavouringspecialised,seniortalent.Goal:IncreaseheadcountforproductivityMoreengineerswerehiredtowritemoreraw

codeandmanually

build

featuresTheidealcandidate:“Thecoder”Valuedforproficiencyinspecificlanguages

andabilitytowritecodequicklyRolesnowin

declineDataanalysts

SoftwaretestersThenewAI-drivenplaybook:FocusonvalueTheoldhiringplaybook:FocusonvolumeGoal:AI-ledsupervisionHiring

fewerseniorspecialiststo

guide,

validateandorchestrate

AIoutputs.AI-native

Workforce:Future

of

work

and

skills

in

Engineering

and

Product

value

chainC?rLO《6lookingforevidenceof

“AIfluency”“ethicaljudgment”and“adaptability”ratherthanjustyearsofexperienceinaspecificlanguage.7Volumevs.valueAsAIautomatesroutinetaskssuchascodegeneration,bugfixesandUIscaffolding,thedemand

forentry-level“coders”issoftening.Thefocushasshiftedtoengineerscapableof

“AI-ledSupervision”

guiding,validatingandintegratingAIoutputsrather

thanwritingrawcode.The“Orchestrator”profileHiringisprioritising“Cross-disciplinaryskills.”Candidatesmustgraspadjacentdomains,blending

coreengineeringwithdatapipelines,modelbehaviourandgovernancerisks.Netnew

rolesRecruitmentisopeningforentirelynew

jobtitles,

includingAIenablementengineers,InternalDeveloperPlatform(IDP)product

managers,agentorchestrationengineersandcontext(RAG)

engineers,whicharemulti-skilledhybrids/variants

fromthe“existing”jobs.Fromrolesto

skillsInternalbuildThereisastrongpreferencefor“reskillingoverreplacing.”Insteadofmasslayoffs,

companiesare

reskillingexistingengineersforAI-drivenroles.EcosystemsourcingExternalhiringisincreasinglyfocusingon

non-traditionalpools,suchasopen-sourcecommunities,

hackathonsandAIresearchcollaborations,rather

thanrelyingsolelyon

jobportalssuchas

LinkedIn.GraduateexpectationsFornewgraduates(next24–36months),companies

arespecificallylookingforcapstoneprojectswithexternalsponsors,courseworkthatembedsAIinto

non-AI

subjects(e.g.,AI

in

OS

or

DB

courses)

andevidenceof

“disciplinedAIusage”(e.g.,maintaining

AIlogsand

modelcritiques).Hiringtrendsrevealageographicalsplitinengineeringvaluechains.TheAmericasandEuropeareincreasinglyfocusingon

highly

specialisedtalent(PhDs,masters)forAIresearchandmodelarchitecture.Conversely,SouthandSoutheastAsiaarewitnessing

high

demandforapplication-basedandoperationalengineeringroles.Talentacquisitionisshiftingfrom“role-basedhiring”

to“capability/skill-basedhiring.”RecruitersareAI-native

workforce:Future

of

work

and

skills

in

engineering

and

product

value

chainEvolutionofsoftware

engineeringrolesAI-native

workforce:Future

of

work

and

skills

in

engineering

and

product

value

chainSnapshotof

theemergingchangesinthesoftwareengineeringroleSoftwareengineeringisundergoingamajorevolution,

knownas

“SE3.0.”This

new

paradigm,driven

byAI,

redefinesthe

roleofan

engineerfromasimple“coder”toastrategic“orchestrator”whomanagesandcollaborateswithAIagentstobuildcomplexsystems.Thegreatreallocation:FromcreationtoorchestrationNewfocus:AIorchestrationandoversightEngineerswillnowdefinetasks

forco&-genetationagents

andmanagetheiroutput.SystemdesignoverimplementationArchitectural

planninganddefiningriskarecriticalthan

hands-oncodingManualcodingandreviewsThetimespentonmanualcodingandisprojectedtodropsignificantly.30-35%OverallProductivity

LiftAl

integration

is

expected

to

boost

productivity

across

the

entire

lifecycleRiseof

theAI-nativespecialistThetraditionalengineerroleisfracturingintonew

specialisationsProductivity

gains

and

emerging

rolesAI-native

workforce:Future

of

work

and

skills

in

engineering

and

product

value

chainProductivityimpactacrossdevelopmentstagesAgentorchestration

engineerAlenablement

engineerCodingand

implementationArchitectureanddesign30–35%40%70%Testing30–50%20–35%10–20%A9Businessneedsand

productdiscoveryOpportunityProblemStakeholderHypothesisEarlyexperimentssizingframinginterviewsAcceptancevalidationNon-functionalRequirementsand

analysisArchitectureand

systemdesignCodingandimplementationDORA

metricmappingArchitecturecriteriarequirementsInterfaceBuild-vs-buydecisionsanalysis

contractsAPILogic

imple-CodesDocumentationcaffolding

developmentmentationReviewerCode

reviewandmergeIntegrationchecksassignment

Deepdive:ThefutureofSoftware

Engineering(SE)Deloitteresearchdefinesthefuturestateofengineeringas

“SE3.0”,characterisedbyatransitionfrommanual“codefactories”towardshigh-valueoversightandhuman-AIcollaboration.Thesoftwaredevelopmentlifecyclewillwitnessproductivitygainsof30-35percentduetotheevolutionof

technologythatincludes

internaldeveloperplatforms,re-usablecodeandGenAI-ledtoolingofcoders.Impactonsoftwaredevelopementlifecycleintheageof

technologyandtoolingThemajorityof

theimpactwillbeseenintheareasofcoding,implementation,

reviewsandtesting,wheretherolesofasoftware

developmentengineerandanengineeringmanagerareexpectedtoundergofundamentalshifts.Wehavespecificallystudiedthe

impactof

technologyandtoolingonfourroles-Softwaredevelopmentengineer,Machinelearningengineer,

Productionservice

engineerandEngineering

manager.10Deploymentandrelease

ReleaseRolloutBlue-greendeploymentsPost-mortemsplanningstrategyLog/traceOperations,monitoringandincidentresponse

SLO/SLAmanagementanalysis10-20%15-25%10-20%30-60%20-40%30-50%15-30%20-35%20-30%15-30%15-25%integration/Unit/CoverageE2EtestsreportingSecurityandSAST/DASTLicenceThreatcompliancetuningchecksmodelling

Build,CI

andPipelineEnvironmentautomationprovisioningmanagement

FeedbackandTelemetryTech-debtDeveloperimprovement

analysistrackinganalysis

SDLC

stage

L2

activities

AI-native

workforce:Future

of

work

and

skills

in

engineering

and

product

value

chainImpactonproductivityMostimpactedStaticanalysisdiagramsandData

mockingenvironmentFeatureflagsUserstoriesAlerttuningArtifactstoragecontinuousexperienceTeststrategyRefactoringmodelsIaCsetupreviewTestingfixesStyleSBOMADRsPRDsDataPRAI/AgentOrchestration/Promptengineering40%Planning,stand-ups,retrospectives20%80%Codedevelopmentand

reviewsCurrent

state

To-be

stateSDE(Mid–Senior)AI/agentorchestration15%10%Thoughtleadership10%Mentoring30%Code

reviewsPlanning,stand-ups,retrospectives40%40%Codedevelopment

CurrentstateTo-bestateSoftwaredevelopmentengineer:From"Doer"to"Reviewer"and"Orchestrator"Thefundamentalnatureofcodingischanging.Traditionally,SoftwareDevelopmentEngineers(SDEs)spentapproximately70percentof

theirtimeoncodedevelopmentandreviews.1

Inthefuturestate,thisisprojectedtodropto40percent,withthe

remainingcapacityshiftingtowards

AI/AgentOrchestration.1Phase-specificimpactProductivitygainsareunevenacross

thelifecycle:30–50percent

in

coding/

implementationversusonly10–20percentinarchitectureanddesign,whichremainshighly

human-centric.1Thereview

burden

Mid-seniorlevelengineersmust

developadvancedforensicsskills

to

validate

AI-generatedcode.Developersmustshiftfrom

writing

codetoreviewingcode

forsecurity

flawsandsubtlehallucinations.GenAItoolsallowdevelopers

toinsert1.3x

morecharacters

perkeystroke,

indicatingamassivereductionintypingandanincrease

ineditingand

orchestration.AI-native

workforce:Future

of

work

and

skills

in

engineering

and

product

value

chain

SoftwareDevelopmentEngineer(SDE)ProductivitygainsSDE(Early–

Mid)3%5%40%20%27%15%5%11DocumentationInfrastructureandplatformtoolingMonitoringandreliabilityDeploymentandintegrationModeldevelopment,evaluationand

testingDataprepandfeatureengineeringCurrent

state

To-be

state12Machine

learning

engineers:Increased

focus

on"reliability"Duetothewideavailabilityof

AI-ledtooling,thenatureof

workformachinelearningengineersinthefutureisexpectedtoshiftfrom

datapreparationtodatamodelevaluation,monitoringandreliability.In

the

future-state,

while

“systems”and

“agents”

willfocusonselectivefine-tuning

ofmodels,about33percentof

the

time

available

willbe

focusedonmodeltesting

throughrigorousevaluationsofmodels

focusedontasks,safety,robustness,hallucinations,etc.,alignedtoglobal

safetyrequirements.Today,

where

themajorityof

timeisspentoncleaningandlabellingdataaswellasmaintainingdataquality,isexpectedtomovetowards

curatingaknowledgecorpusanddatacontracts

withsyntheticdatageneration

governed

throughagent-

leddatapipelines5%14%14%14%34%10%Timespentonmonitoringand

reliabilitywillgrowby3Xdue

toincreasingvisibilitythrough

telemetry,tracingandprompt

lineagetoensureoutputsare

inlinewithexpectations.Monitoring

and

reliability

5%10%5%10%30%30%AI-native

workforce:Future

of

work

and

skills

in

engineering

and

product

value

chainModeldevelopment

andtestingMachinelearningengineersDatapreparationCurrent

state

To-be

stateProduction

service

engineer:Declining

manual

opsDevOpsissolidifyingintoacloud-firstreliabilitydiscipline,whereinfrastructureistreatedentirelyascodeandmanualmonitoringisreplacedby

AI-drivenobservability.Theroleofproductionengineersisshiftingfromincidentresponseandstakeholdermanagement(enabledbyself-serviceportalsandinternaldeveloperplatforms)todevelopingautomationsforscriptsandrunbooks,

whilemaintainingreliability,ensuringcomplianceandensuringsecurity.ProductionserviceengineerAI-/LLM-OpsandproactiveautomationPlatformenablement(IDP,tooling)SecurityandcomplianceopsReliability/SLOanderrorbudgetsCapacityandperformancemanagement

Automation(scripts,IaC,

runbooks)DeploymentandchangesupportMonitoring/observabilityandalerttuning

Incidentresponseandstakeholdersupport3%10%7%10%10%12%18%30%9%6%15%13%7%17%7%11%15%AI-native

workforce:Future

of

work

and

skills

in

engineering

and

product

value

chain13DevExandplatformenablement3%10%9%6%Securityandcompliance/governance7%15%15%Reportingandadmin10%7%HiringandtalentacquisitionTechnicaloversightandreliability12%17%PlanningandstakeholdermanagementDeliverycoordination18%7%Peoplecoachingandperformance30%15%CurrentstateTo-bestateEngineering

Manager:Re-balancing

managerial

responsibilitiesTechnicaloversightandreliabilitywillbecometheprimaryfocus,whiletaskssuchasdeliverycoordinationandreportingwillbecome

lessprominent.Adoptionofinternaldeveloperplatforms,

standardisedmetrics,error-budgetpolicies,clarifiedteamstructuresandAIagentswithguardrailswillstreamline

processestoimprovereliability,reduce

firefightingandenhancecommunication.Managerialrolesareshifting

fromsupervision

tostrategic

focus,supportedby

tools

for

analytics,codereviewsand

dependencymanagement.AIandinternaldeveloperplatforms

willreducecognitiveload,automate

routinetasksandenableself-serviceenvironments.ReductionintacticalworkAI-native

workforce:Future

of

work

and

skills

in

engineering

and

product

value

chainTransformationenablersAI-drivenchangesEngineeringmanager14SyntheticdataengineersGeneratesyntheticdatasetstoincreasedatadiversity,addressprivacyandenablemodeltrainingundercompliance,especiallyinhighlyregulatedindustriessuchashealthcare,defenceandfinance.AIgovernanceandcomplianceengineerFocusesonAImodelinventory,

riskclassification,audittrailsandpolicyadherence(per

NIST

AI

RMF/

EU

AI

Act,oranyapplicable).Context/RetrievalengineersSpecialistswhobuild

RAG(Retrieval-AugmentedGeneration)layers,sodevelopmentagentsunderstandinternalAPIsanddocumentation.AIenablementleadsNewrolesdedicatedtomeasuringandimprovingdevelopervelocitythroughAItoolchainsbycurating“goldenpaths”forAI-assistedcodingandreviews.AgentorchestrationengineersProfessionalswhocomposeAIagents,defineguardrailsandmanagemodelselection.Theseengineersbuild“logic”todriveagentbehaviourandensuresafety,reliabilityandobservability.PlatformengineersAshiftfrommanualinfrastructuremanagementtobuildingInternalDeveloper

Platforms(IDPs).Thisrolefocuseson“pavedroads”andautomation-firstorchestration.Emergingrolesand“seismic”shiftsLegacyrolesarefragmentingintospecialised,high-valuefunctions:AI-nativeworkforce:Futureof

workandskillsinengineeringandproductvaluechain15AIAI-augmentedengineeringandproductivityEmergingroles

in

theecosystem•

UseGitHub/Copilot-like

tools

to

scaffold

functions,auto-generate

testsandacceleratedebugging•

Internal

toolsauto-summarisedocumentationsandrecommend

reusablecomponents

1Embedded

AI/MLroleswithinProductSquadsEmergingroles

in

theecosystem•

EmbeddedGenAI

tools

for

textandimage

generation,

integrateddirectlyintoproductPODs•

MLmodelspoweringcontent

rankingandpersonalisation3PlatformengineeringasaforcemultiplierEmergingroles

in

theecosystem•

Platformexposing

APIsandruntimeenvironments

forinternal/externaldevelopers

to

buildextensions•

Data-as-a-productenabled

with

servicealigneddataplatform

for

scalinganalyticsCResponsibleAIascoreEmergingroles

in

theecosystem•

Campaignsonethicaluseand

biasreductionincreative

AI•

Contentmoderationwith

safeguardsagainstunsafe/copyrightcontentinAIoutputsCasestudy1Emergingtrendsintheengineeringfunction–Globaldesignplatform•

Trustandsafety

engineers•

Compliance

engineers•

MLengineers

4•

MLengineers•

Contextengineers•

AIproduct

managers•

LLMengineers•

AIproduct

managersAI-native

workforce:Future

of

work

and

skills

in

engineering

and

product

value

chain•

Platform

engineers•DevEXengineers2Use

caseUse

caseUse

caseUse

case16Evolutionofproduct

managementrolesAI-native

workforce:Future

of

work

and

skills

in

engineering

and

product

value

chainPMsuse

AItosynthesisedata,enablingthemtoleadproductstrategy.40%increaseinproductivityGenAItoolsreduce“hightoil”tasksandacceleratedecision-making.Newskills:“Agentic”fluencyandriskmanagementPMsmustmastercomplex

AIsystemsandsafelyintegrateethicalsafeguards.Strategy,

visionandempathyFocusAI-synthesisedinsightsDiscoveryBlendinginto“productdeveloper”RoleResponsibilitiesincludedbackloggrooming,meetingsummariesandroutinedocumentation.RoleasacommunicationhubActedas

thecentralpointof

contact

betweenengineering,designandsales.Coreskills:ProcessmanagementExpertisewascentredonagilemethodologiesandstakeholdermanagement.AdminandcoordinationManualuserresearchDistinctfromengineeringSnapshotof

thefutureofProductManagement(PM)

Currentstate

Futurestate(AI-augmented)

Primaryfocus:

Administrativetoil

Focusshiftstostrategic

visionEngineeringDesignSalesThe

futurePM:

The

AI-augmented

“mini-CEO”The

traditionalPM:

ThecoordinatorAI-native

workforce:Future

of

work

and

skills

in

engineering

and

product

value

chain18Shifts

in

responsibilities

and

skillsAsadministrativetoildecreases,thePMroleispivoting:

Strategicalignment

Agentic

frameworks

Riskstewardship

PMs

can

focus

on

product

PMs

must

now

understand

how

to

deploy

LLMs

PMs

are

increasingly

responsiblevision

and

stakeholder

that

work

together

to

complete

tasks(e.g.,for

working

with

risk

experts

tomanagement,

whichagentic

frameworks”),requiringproficiencyinintegratesafeguardsintotheproductremainlargelyhuman-low-code

toolsanditerativeprompting,leadingdefinitionphase,addressingliability,centric

tasks.to

a

decrease

in

MVP

cycles

and

documentation.data

privacy

and

trust

deficits.ProductmanagementDeep-diveintotheemergingchangesinthe

PMroleThe

AI-Augmented“Mini-CEO”ProductmanagersareusingGenAItocompresstheProductDevelopmentLifeCycle(PDLC),shiftingfromcoordinationtostrategicacceleration.High-impactareas:Themostsignificantgainsarein“content-heavy”tasks.GenAIhas

nearlytwicethepositiveimpactontaskssuchassynthesisinguserresearch,drafting

pressreleasesandcreatingProductRequirements

Documents(PRDs)

comparedwith

“content-light”datavisualisationtasks,leadingtoefficiencygainsof~10percentinthe

roleofproductmanagers.18GTMand

lifecycleDataandexperimentationStakeholdermanagementDeliveryexecutionandoversight

WritingandartifactsStrategyand

roadmapDiscoveryRoleconvergence:

ThePManddeveloperrolescould

eventuallymergeintoasingular

“ProductDeveloper”persona.

Thisindividual

woulduse

AI

tools

todefinerequirementsandimmediately

generate

thecode,prepareanMVP,rather

thanbuildingouta

traditionallylongPRD/BRD.Theexperiencegap:SeniorPMsderivehigherquality

outputsfromGenAIbecausetheypossessthe“product

sense”requiredtoeffectivelycritique

AIoutput.

Junior

PMsgainspeedbutoftenattheexpenseofquality,necessitatingnewmentorshipmodels.CurrentstateTo-bestateAcceleration:UseofGenAI

inthe

PDLCacceleratesnewproductdevelopment

byupto50

percent.5

45%10%15%20%15%20%15%5%12%15%15%8%25%20%AI-native

workforce:Future

of

work

and

skills

in

engineering

and

product

value

chain19Emergingskillsof

thefutureAI-native

workforce:Future

of

work

and

skills

in

engineering

and

product

value

chainEmerging/HighdemandAIfluency:

Promptengineering,

RAG,

agentorchestration,assistedcodedevelopment(7xgrowthindemand).Cloud-nativesecurity:“Shift-left”security,identitygovernance.Systemthinking:Designingdistributed,

agenticarchitectures.DevExproductskills:

Internaldeveloperplatformsimplementations,

including“goldenpaths”andpolicyimplementation.AILiteracy:Understanding

model

capabilities/costs.Userempathy:Interpretingunarticulatdneeds.Dataanalytics:Real-timedatasynthesis.Problem-solving:Topsoftskill(21%focus).Adaptability/Resilience:Psychologicalsafetyin

rapid-change

environments.Decisionquality:Supplementtechnicalproficiencywith

judgment-

led

reasoning.Declining/LegacyLegacyscripting:

Node.js(insomecontexts),

UnixShell.ManualQA:

Replaced

by

AI-augmentedautomated

testing.Rudimentaryanalysis:Businessanalysis,

BI,statisticsBasicadmin:Routinedocumentation,meeting

notes,basic

backloggrooming.Roteexecution:Following

rigid,pre-defined

process

steps.Stable/CoreCorelanguages:Python,

Java,SQL.DevOps:CI/CD,

SRE,

Kubernetes.Systemdesign:Microservicesarchitecture.Privacy/Datagovernance:

Data

qualityanddrift.Communication:Stakeholdermanagement.Lifecyclemanagement:Planningandexecution.Collaboration:Workingin

hybrid/

remoteteams.Theskillsmatrix:2025-2030Theshelf-lifeof

technicalskillsisshrinking.Organisationsmustpivotfromrole-basedplanningtoskill-basedplanning.->>AI-native

workforce:Future

of

work

and

skills

in

engineering

and

product

value

chainCategoryHumanskillsEngineeringProduct21Real-timeandstreaminganalyticsUsecase

ContributingskillsReal-timepersonalisation,frauddetection,inventoryandpricingdecisions,andlogisticsoptimisation

5CloudnativeandcompostablearchitecturesUse

case

C