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:

Futureofworkandskillsinengineeringandproductvaluechain

February2026

AI-nativeworkforce:Futureofworkandskillsinengineeringandproductvaluechain

3

Tableofcontents

Executivesummary4

Newrulesoftechnologytalent5

Evolutionofsoftwareengineeringroles8

Evolutionofproductmanagementroles17

Emergingskillsofthefuture20

OrganisationsmustbeREADYtoembracethischange23

AI-nativeworkforce:Futureofworkandskillsinengineeringandproductvaluechain

Executivesummary

Thetechnologylandscapeisundergoingastructural

transformationdrivenbytwoconvergingforces:ThematurityofGenerativeAI(GenAI)into“agentic”workflowsandthe

stabilisationofhybridworkmodels.Softwareengineeringis

transitioningfromadisciplineofcreationtooneoforchestration,andproductmanagementevolvesfromcoordinationto

strategicacceleration.

CurrentdataindicatethatGenAItoolscanimprovenewproductdevelopmenttimesby50percentandsignificantlyaccelerate

softwaredevelopmenttasks,withproductivitygainsof30–40percentwitnessedinengineeringteams.¹However,realisingthisvalueestimatedat~7percentofglobalGDP7requires

organisationstolookbeyondautomatingindividualtasksandredesignentireworkflows,developskillsandreimaginetalentandleadershiptoaccommodatehuman-agentcollaboration.

Themacroenvironment:Driversofchange

TheeraofagenticAI

theavailabletechnology;thegoalisaugmentationratherthandisplacement.1,6AIagentsarebecoming“virtualcoworkers”

capableofplanningandexecutingmultistepworkflows,suchasmigratinglegacycodeorautonomouslymanagingsalesleads.

Wehavemovedpastsimplecodecompletion.Thefuture

workforcewillbeacollaborationofpeople,agentsandrobots.Itissuggestedthatwhile~52percentofworkertasksintheUScouldbecompletedfasterwiththesamelevelofqualitywith

Theremotework“productivityparadox”

Asignificantdisconnectexistsbetweenengineeringdataandleadershipsentiment.

Developerreality

About64percentofdevelopersreporthigherproductivity

workingremotely.2Datashowa4percentincreasein“focusedwork”(keystrokesperminute)anda5percentincreasein

codingduringcorebusinesshoursamongremoteworkers.2

Leadershipscepticism

Only12percentofleadersexpresscompleteconfidenceinremoteproductivity.3

●Implication

Organisationsmustimplementobservabilitytoolstobridgethistrustgapratherthanenforcingmandatesthatriskattrition.

Wecanseethesetrendsplayingoutacrossthetalentlandscapeandinhowtherolesofsoftwareengineersandproductmanagersareevolving

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AI-nativeworkforce:Futureofworkandskillsinengineeringandproductvaluechain

Newrulesof

technologytalent

AI-nativeWorkforce:FutureofworkandskillsinEngineeringandProductvaluechain

Globaltalentdivergence1

Fromcodertoconductor:Thenewrulesoftechhiring

AIiscausingaseismicshiftintechhiring,decouplingproductivityfromheadcountandfavouringspecialised,seniortalent.

Theoldhiringplaybook:Focusonvolume

Goal:Increaseheadcountforproductivity

Moreengineerswerehiredtowritemorerawcodeandmanuallybuildfeatures

《

Theidealcandidate:“Thecoder”

Valuedforproficiencyinspecificlanguagesandabilitytowritecodequickly

Rolesnowindecline

DataanalystsSoftwaretesters

Goal:AI-ledsupervision

Hiringfewerseniorspecialiststoguide,validateandorchestrateAIoutputs.

Theidealcandidate:“Thearchitectofintelligence”

ValuedfordesigningsystemswhereAIagentsperformtaskssecurelyandreliably

Rolesnowindemand

AIresearchMLengineers

Newfocusonhighereducation

40–45percentofrolesintheAmericasand

?r

LO

C

EuropenowdemandaMastersorPhD

ThenewAI-drivenplaybook:Focusonvalue

6

AI-nativeworkforce:Futureofworkandskillsinengineeringandproductvaluechain

Hiringtrendsrevealageographicalsplitinengineeringvaluechains.TheAmericasandEuropeareincreasinglyfocusingonhighlyspecialisedtalent(PhDs,masters)forAIresearchandmodelarchitecture.Conversely,SouthandSoutheastAsiaarewitnessinghighdemandforapplication-basedandoperationalengineeringroles.

Volumevs.value

AsAIautomatesroutinetaskssuchascode

generation,bugfixesandUIscaffolding,thedemandforentry-level“coders”issoftening.Thefocushas

shiftedtoengineerscapableof“AI-ledSupervision”guiding,validatingandintegratingAIoutputsratherthanwritingrawcode.

The“Orchestrator”profile

Hiringisprioritising“Cross-disciplinaryskills.”

Candidatesmustgraspadjacentdomains,blendingcoreengineeringwithdatapipelines,model

behaviourandgovernancerisks.

Netnewroles

Recruitmentisopeningforentirelynewjobtitles,includingAIenablementengineers,Internal

DeveloperPlatform(IDP)productmanagers,

agentorchestrationengineersandcontext(RAG)engineers,whicharemulti-skilledhybrids/variantsfromthe“existing”jobs.

Fromrolestoskills

Internalbuild

Thereisastrongpreferencefor“reskillingover

replacing.”Insteadofmasslayoffs,companiesarereskillingexistingengineersforAI-drivenroles.

Ecosystemsourcing

Externalhiringisincreasinglyfocusingonnon-

traditionalpools,suchasopen-sourcecommunities,hackathonsandAIresearchcollaborations,ratherthanrelyingsolelyonjobportalssuchasLinkedIn.

Graduateexpectations

Fornewgraduates(next24–36months),companiesarespecificallylookingforcapstoneprojectswith

externalsponsors,courseworkthatembedsAIintonon-AIsubjects(e.g.,AIinOSorDBcourses)andevidenceof“disciplinedAIusage”(e.g.,maintainingAIlogsandmodelcritiques).

Talentacquisitionisshiftingfrom“role-basedhiring”to“capability/skill-basedhiring.”Recruitersare

lookingforevidenceof“AIfluency”“ethicaljudgment”and“adaptability”ratherthanjustyearsofexperienceinaspecific

language.

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AI-nativeworkforce:Futureofworkandskillsinengineeringandproductvaluechain

Evolutionofsoftwareengineeringroles

AI-nativeworkforce:Futureofworkandskillsinengineeringandproductvaluechain

9

Snapshotoftheemergingchangesinthesoftwareengineeringrole

Softwareengineeringisundergoingamajorevolution,knownas“SE3.0.”Thisnewparadigm,drivenbyAI,redefinestheroleofanengineerfromasimple“coder”toastrategic“orchestrator”whomanagesandcollaborateswithAIagentstobuildcomplexsystems.

Thegreatreallocation:Fromcreationtoorchestration

70%

40%

Manualcodingandreviews

Thetimespentonmanualcodingandisprojectedtodropsignificantly.

Newfocus:AIorchestrationandoversight

Engineerswillnowdefinetasksforco&-genetationagentsandmanagetheiroutput.

Systemdesignoverimplementation

Architecturalplanninganddefiningriskarecriticalthanhands-oncoding

Productivitygainsandemergingroles

30–35%

30-35%OverallProductivityLift

Alintegrationisexpectedtoboostproductivityacrosstheentirelifecycle

Productivityimpactacrossdevelopmentstages

20–35%

10–20%

30–50%

Architectureanddesign

Testing

Codingandimplementation

RiseoftheAI-nativespecialist

Thetraditionalengineerroleis

fracturingintonewspecialisations

Agentorchestrationengineer

Alenablementengineer

A

AI-nativeworkforce:Futureofworkandskillsinengineeringandproductvaluechain

Deepdive:ThefutureofSoftwareEngineering(SE)

Deloitteresearchdefinesthefuturestateofengineeringas“SE3.0”,characterisedbyatransitionfrommanual“codefactories”

towardshigh-valueoversightandhuman-AIcollaboration.

Thesoftwaredevelopmentlifecyclewillwitnessproductivitygainsof30-35percentduetotheevolutionoftechnologythatincludesinternaldeveloperplatforms,re-usablecodeandGenAI-ledtoolingofcoders.

Impactonsoftwaredevelopementlifecycleintheageoftechnologyandtooling

Impacton

productivity

Businessneedsandproductdiscovery

Opportunity

Problem

Stakeholder

Hypothesis

Early

experiments

sizing

framing

interviews

Acceptance

validation

Non-functional

Architecture

criteria

requirements

Interface

Build-vs-buy

decisions

analysis

contracts

API

Logicimple-

Codes

Documentation

caffolding

development

mentation

Reviewer

Integration

checks

assignment

10-20%

15-25%

10-20%

30-60%

20-40%

Unit/

Coverage

E2Etests

reporting

30-50%

Securityand

SAST/DAST

Licence

Threat

compliance

tuning

checks

modelling

15-30%

Build,CIand

Pipeline

Environment

automation

provisioning

management

20-35%

Deploymentandrelease

Release

Rollout

planning

strategy

Log/trace

Operations,

monitoringand

incidentresponse

SLO/SLA

management

analysis

20-30%

15-30%

Feedbackand

Telemetry

Tech-debt

Developer

improvement

analysis

tracking

analysis

15-25%

Requirementsandanalysis

DORAmetric

mapping

PRDs

Userstories

Architectureandsystemdesign

diagramsand

models

ADRs

Data

Codingand

implementation

Refactoring

Codereviewand

merge

review

fixes

Staticanalysis

PR

Style

Testing

Teststrategy

Datamocking

integration/

SBOM

environment

Artifactstorage

IaCsetup

Blue-green

deployments

Featureflags

Post-mortems

Alerttuning

continuous

experience

Most

impacted

SDLCstageL2activities

Themajorityoftheimpactwillbeseenintheareasofcoding,implementation,reviewsandtesting,wheretherolesofasoftwaredevelopmentengineerandanengineeringmanagerareexpectedtoundergofundamentalshifts.Wehavespecificallystudiedtheimpactoftechnologyandtoolingonfourroles-Softwaredevelopmentengineer,Machinelearningengineer,ProductionserviceengineerandEngineeringmanager.

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AI-nativeworkforce:Futureofworkandskillsinengineeringandproductvaluechain

Softwaredevelopmentengineer:From"Doer"to"Reviewer"and"Orchestrator"

Thefundamentalnatureofcodingischanging.Traditionally,SoftwareDevelopmentEngineers(SDEs)spentapproximately

70percentoftheirtimeoncodedevelopmentandreviews.1Inthefuturestate,thisisprojectedtodropto40percent,withtheremainingcapacityshiftingtowardsAI/AgentOrchestration.1

Phase-specificimpact

Productivitygains

Mid-seniorlevelengineersmustdevelopadvancedforensicsskillstovalidateAI-generatedcode.

Thereviewburden

GenAItoolsallowdeveloperstoinsert1.3xmorecharactersperkeystroke,indicatinga

massivereductionintyping

andanincreaseineditingandorchestration.

Productivitygainsareunevenacrossthelifecycle:30–50percentincoding/implementationversusonly10–20

percentinarchitectureanddesign,

whichremainshighlyhuman-centric.1

Developersmustshiftfromwritingcodetoreviewingcodeforsecurityflawsandsubtlehallucinations.

SoftwareDevelopmentEngineer(SDE)

SDE(Early–Mid)

20%

AI/AgentOrchestration/Promptengineering

40%

Planning,stand-ups,retrospectives

20%

80%

40%

Codedevelopmentandreviews

CurrentstateTo-bestate

SDE(Mid–Senior)

AI/agentorchestration

15%

10%

Thoughtleadership

10%

Mentoring

30%

Codereviews

Planning,stand-ups,retrospectives

5%

3%

5%

15%

27%

40%40%

Codedevelopment

Currentstate

To-bestate

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AI-nativeworkforce:Futureofworkandskillsinengineeringandproductvaluechain

Machinelearningengineers:Increasedfocuson"reliability"

DuetothewideavailabilityofAI-ledtooling,thenatureofworkformachinelearningengineersinthefutureisexpectedtoshiftfromdatapreparationtodatamodelevaluation,monitoringandreliability.

Modeldevelopmentandtesting

Datapreparation

Monitoringandreliability

Today,wherethemajorityoftime

isspentoncleaningandlabelling

dataaswellasmaintainingdata

quality,isexpectedtomovetowardscuratingaknowledgecorpusand

datacontractswithsyntheticdata

generationgovernedthroughagent-leddatapipelines

Timespentonmonitoringandreliabilitywillgrowby3Xduetoincreasingvisibilitythroughtelemetry,tracingandpromptlineagetoensureoutputsareinlinewithexpectations.

Inthefuture-state,while“systems”and“agents”willfocusonselectivefine-tuningofmodels,about33percentofthetimeavailablewillbefocusedonmodeltestingthroughrigorousevaluationsofmodelsfocusedontasks,safety,robustness,

hallucinations,etc.,alignedtoglobalsafetyrequirements.

Machinelearningengineers

5%

10%

5%

10%

Documentation

Infrastructureandplatformtooling

Monitoringandreliability

Deploymentandintegration

Modeldevelopment,evaluationandtesting

Dataprepandfeatureengineering

30%

30%

5%

14%

14%

14%

34%

10%

CurrentstateTo-bestate

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AI-nativeworkforce:Futureofworkandskillsinengineeringandproductvaluechain

Productionserviceengineer:Decliningmanualops

DevOpsissolidifyingintoacloud-firstreliabilitydiscipline,whereinfrastructureistreatedentirelyascodeandmanualmonitoring

isreplacedbyAI-drivenobservability.Theroleofproductionengineersisshiftingfromincidentresponseandstakeholder

management(enabledbyself-serviceportalsandinternaldeveloperplatforms)todevelopingautomationsforscriptsandrunbooks,whilemaintainingreliability,ensuringcomplianceandensuringsecurity.

Productionserviceengineer

3%

10%

9%

6%

AI-/LLM-OpsandproactiveautomationPlatformenablement(IDP,tooling)

7%

15%

10%

Securityandcomplianceops

10%

13%

Reliability/SLOanderrorbudgets

7%

12%

17%

18%

CapacityandperformancemanagementAutomation(scripts,IaC,runbooks)

7%

11%

Deploymentandchangesupport

30%

15%

Monitoring/observabilityandalerttuningIncidentresponseandstakeholdersupport

CurrentstateTo-bestate

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AI-nativeworkforce:Futureofworkandskillsinengineeringandproductvaluechain

EngineeringManager:Re-balancingmanagerialresponsibilities

Technicaloversightandreliabilitywillbecometheprimaryfocus,whiletaskssuchasdeliverycoordinationandreportingwillbecomelessprominent.

AI-drivenchanges

Managerialrolesareshiftingfromsupervisiontostrategicfocus,supportedbytoolsforanalytics,codereviewsanddependencymanagement.

Reductionintacticalwork

AIandinternaldeveloperplatformswillreducecognitiveload,automateroutinetasksandenableself-

serviceenvironments.

Transformationenablers

Adoptionofinternaldeveloperplatforms,standardisedmetrics,error-budget

policies,clarifiedteamstructuresand

AIagentswithguardrailswillstreamlineprocessestoimprovereliability,reducefirefightingandenhancecommunication.

Engineeringmanager

DevExandplatformenablement

3%

10%

9%

6%

Securityandcompliance/governance

7%

15%

15%

Reportingandadmin

10%

7%

Hiringandtalentacquisition

Technicaloversightandreliability

12%

17%

Planningandstakeholdermanagement

Deliverycoordination

18%

7%

Peoplecoachingandperformance

30%

15%

Currentstate

To-bestate

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AI-nativeworkforce:Futureofworkandskillsinengineeringandproductvaluechain

15

Emergingrolesand“seismic”shifts

Legacyrolesarefragmentingintospecialised,high-valuefunctions:

Platformengineers

Ashiftfrommanualinfrastructure

managementtobuildingInternal

DeveloperPlatforms(IDPs).This

rolefocuseson“pavedroads”and

automation-firstorchestration.

AIenablementleads

Newrolesdedicatedtomeasuringand

improvingdevelopervelocitythrough

AItoolchainsbycurating“golden

paths”forAI-assistedcodingand

reviews.

Agentorchestrationengineers

ProfessionalswhocomposeAIagents,

defineguardrailsandmanagemodel

selection.Theseengineersbuild“logic”

todriveagentbehaviourandensure

safety,reliabilityandobservability.

Syntheticdataengineers

Generatesyntheticdatasetsto

increasedatadiversity,address

privacyandenablemodeltraining

undercompliance,especiallyin

highlyregulatedindustriessuchas

healthcare,defenceandfinance.

AIgovernanceandcompliance

engineer

FocusesonAImodelinventory,risk

classification,audittrailsandpolicy

adherence(perNISTAIRMF/EUAIAct,

oranyapplicable).

Context/Retrievalengineers

SpecialistswhobuildRAG(Retrieval-

AugmentedGeneration)layers,so

developmentagentsunderstand

internalAPIsanddocumentation.

AI-nativeworkforce:Futureofworkandskillsinengineeringandproductvaluechain

16

Casestudy1

Emergingtrendsintheengineeringfunction–Globaldesignplatform

EmbeddedAI/MLroleswithinProductSquads

Usecase

•EmbeddedGenAItoolsfortextandimagegeneration,integrateddirectlyinto

productPODs

•MLmodelspoweringcontentrankingandpersonalisation

3

•MLengineers

•Context

engineers

•AIproductmanagers

Emergingrolesintheecosystem

AI-augmentedengineeringandproductivity

Usecase

•UseGitHub/Copilot-liketoolstoscaffoldfunctions,auto-generatetestsandacceleratedebugging

•Internaltoolsauto-summarise

documentationsandrecommendreusablecomponents

1

•LLMengineers

•AIproductmanagers

Emergingrolesintheecosystem

AI

C

ResponsibleAIascore

Emergingrolesintheecosystem

•Trustandsafetyengineers

•Complianceengineers

•MLengineers4

•CampaignsonethicaluseandbiasreductionincreativeAI

•Contentmoderationwithsafeguardsagainst

unsafe/copyrightcontentinAIoutputs

Usecase

Platformengineeringasaforcemultiplier

Usecase

•PlatformexposingAPIsand

runtimeenvironmentsfor

internal/externaldeveloperstobuildextensions

•Data-as-a-productenabledwithservicealigneddataplatformforscalinganalytics

•Platformengineers

•DevEX

engineers

Emergingrolesintheecosystem

2

AI-nativeworkforce:Futureofworkandskillsinengineeringandproductvaluechain

Evolutionofproductmanagementroles

AI-nativeworkforce:Futureofworkandskillsinengineeringandproductvaluechain

18

SnapshotofthefutureofProductManagement(PM)

ThetraditionalPM:Thecoordinator

ThefuturePM:TheAI-augmented“mini-CEO”

Primaryfocus:AdministrativetoilFocusshiftstostrategicvision

Responsibilitiesincludedbacklog

grooming,meetingsummariesand

routinedocumentation.

Roleasacommunicationhub

Actedasthecentralpointofcontactbetweenengineering,designandsales.

Coreskills:Processmanagement

Expertisewascentredonagile

methodologiesandstakeholder

PMsuseAItosynthesisedata,enablingthemto

leadproductstrategy.

Engineering

40%increaseinproductivity

GenAItoolsreduce“hightoil”tasksand

acceleratedecision-making.

Design

Newskills:“Agentic”fluencyandriskmanagement

PMsmustmastercomplexAIsystemsandsafely

integrateethicalsafeguards.

Sales

management.

CurrentstateFuturestate(AI-augmented)

Adminandcoordination

Strategy,visionandempathy

Focus

Manualuserresearch

AI-synthesisedinsights

Discovery

Distinctfromengineering

Blendinginto“productdeveloper”

Role

AI-nativeworkforce:Futureofworkandskillsinengineeringandproductvaluechain

19

Deep-diveintotheemergingchangesinthePMrole

TheAI-Augmented“Mini-CEO”

ProductmanagersareusingGenAItocompresstheProductDevelopmentLifeCycle(PDLC),shiftingfromcoordinationtostrategicacceleration.

High-impactareas:Themostsignificantgainsarein“content-heavy”tasks.GenAIhasnearlytwicethepositiveimpactontaskssuchassynthesisinguserresearch,draftingpressreleasesandcreatingProductRequirementsDocuments(PRDs)comparedwith“content-light”datavisualisationtasks,leadingtoefficiencygainsof~10percentintheroleofproductmanagers.18

Acceleration:UseofGenAIinthePDLCaccelerates

newproductdevelopmentbyupto50percent.54

Shiftsinresponsibilitiesandskills

Asadministrativetoildecreases,thePMroleispivoting:

StrategicalignmentAgenticframeworksRiskstewardship

PMscanfocusonproductPMsmustnowunderstandhowtodeployLLMsPMsareincreasinglyresponsible

visionandstakeholderthatworktogethertocompletetasks(e.g.,forworkingwithriskexpertsto

management,whichagenticframeworks”),requiringproficiencyinintegratesafeguardsintotheproduct

remainlargelyhuman-low-codetoolsanditerativeprompting,leadingdefinitionphase,addressingliability,

centrictasks.toadecreaseinMVPcyclesanddocumentation.dataprivacyandtrustdeficits.

Productmanagement

GTMandlifecycle

Dataandexperimentation

Stakeholdermanagement

DeliveryexecutionandoversightWritingandartifacts

Strategyandroadmap

Discovery

5%

10%

15%

20%

15%

20%

15%

5%

12%

15%

15%

8%

25%

20%

Currentstate

To-bestate

Roleconvergence:ThePManddeveloperrolescouldeventuallymergeintoasingular“ProductDeveloper”

persona.ThisindividualwoulduseAItoolstodefine

requirementsandimmediatelygeneratethecode,prepare

anMVP,ratherthanbuildingoutatraditionallylongPRD/BRD.

Theexperiencegap:SeniorPMsderivehigherqualityoutputsfromGenAIbecausetheypossessthe“productsense”requiredtoeffectivelycritiqueAIoutput.JuniorPMsgainspeedbutoftenattheexpenseofquality,

necessitatingnewmentorshipmodels.

AI-nativeworkforce:Futureofworkandskillsinengineeringandproductvaluechain

Emergingskillsofthefuture

AI-nativeworkforce:Futureofworkandskillsinengineeringandproductvaluechain

Theskillsmatrix:2025-2030

Category

Stable/Core

Corelanguages:Python,Java,SQL.

DevOps:CI/CD,SRE,Kubernetes.

Systemdesign:Microservices

architecture.

Privacy/Data

governance:Dataqualityanddrift.

Communication:Stakeholder

management.

Lifecycle

management:Planningandexecution.

Collaboration:

Workinginhybrid/remoteteams.

Declining/Legacy

Legacyscripting:Node.js

(insomecontexts),UnixShell.

ManualQA:ReplacedbyAI-augmentedautomatedtesting.

Rudimentaryanalysis:Businessanalysis,BI,

statistics

Basicadmin:Routine

documentation,meetingnotes,basicbacklog

grooming.

Roteexecution:Followingrigid,pre-definedprocesssteps.

Engineering

-

>

Product

>

Human

skills

Theshelf-lifeoftechnicalskillsisshrinking.Organisationsmustpivotfromrole-basedplanningtoskill-basedplanning.

Emerging/Highdemand

AIfluency:Promptengineering,RAG,agentorchestration,assistedcode

development(7xgrowthindemand).

Cloud-nativesecurity:“Shift-left”

security,identitygovernance.

Systemthinking:Designingdistributed,agenticarchitectures.

DevExproductskills:Internal

developerplatformsimplementations,including“goldenpaths”andpolicy

implementation.

AILiteracy:Understandingmodelcapabilities/costs.

Userempathy:Interpretingunarticulatdneeds.

Dataanalytics:Real-timedatasynthesis.

Problem-solving:Topsoftskill(21%focus).

Adaptability/Resilience:

Psychologicalsafetyinrapid-changeenvironments.

Decisionquality:Supplement

technicalproficiencywithjudgment-ledreasoning.

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AI-nativeworkforce:Futureofworkandskillsinengineeringandproductvaluechain

Casestudy2

Emergingtrendsintheengineeringfunction|Largeglobale-commerceandcloudservicesplayer

Observability,SREandselfhealing

UsecaseContributingskills

Comprehensive

monitoring,chaos

engineeringand

automatedrecoveryforcriticalevents

•SREpractices

•Monitoring

•Anomalydetection

•Automatedremediation 4

AIandLLMsasproductfabric

UsecaseContributingskills

GenAItosupport

search,product

recommendations,sellertoolsandchat-based

personalisation

1

•LLMengineers

•Appliedscientists

•AIproductmanagers

Real-timeandstreaminganalytics

UsecaseContributingskills

Real-time

personalisation,frauddetection,inventoryandpricingdecisions,and

logisticsoptimisation

5

•Streamingdatapipelines

•OnlineML

•Reinforcementlearning

Cloudnativeandcompostablearchitectures

UsecaseContributingskills

•Kubernetes

•Servicemesh

•APIdesign

•DevExplatform

development

2

Scalabilityduring

peakevents,faster

deploymentcyclesandrobustmulti-cloud

resilience

AdvancedAdTechandmeasurement

UsecaseContributingskills

Integrationofcommerceandadvertisingwith

advancedattributionandRTBcapabilities

6

•AdTechengineering

•Growthanalytics

•Causalinferenceforadincrementality

Edgecomputingandlow-latency

UsecaseContributingskills

Last-milelogistics

IoTdevicesuseedgecomputing

3

•Edgesystemengineering

•Offline-firstdesign

22

AI-nativeworkforce:Futureofworkandskillsinengineeringandproductvaluechain

Organisations

mustbeREADYtoembracethischange

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AI-nativeworkforce:Futureofworkandskillsinengineeringandproductvaluechain

TheR.E.A.D.YFramework

Tocapitaliseonthe~US$7trillionopportunity7supportedbyadvancesinGenAI,naturallanguageprocessingandautomation,organisationsmustmovebeyondpiecemealtooladoption.ThefollowingrecommendationsutilisetheR.E.A.D.Y.frameworktoguidethetransformation.

Shifttrainingfrom“doing”to

“reviewing.”Buildskillsinarchitectural

thinkingandforensicreviewof

AI-generatedoutput.

Measureoutcomes,notinputs,toprove

AIvalue.Trackmetricssuchasdelivery

velocityandcycletimereduction,notj