mckinsey -打造下一代人工智能体验 Building next-horizon AI experiences

March2026

Mcsey

&company

Buildingnext-horizonAIexperiences

ManyorganizationsarestrugglingtoscalegenAIandagenticAI.But

adoptionchallengesarenottechnical—theyareexperiential.Here’showtodesignAItoolsthatpeoplewillembrace.

byChrisSmithandKentGryskiewicz

withRuthTupeandYueWu

OrganizationsareinvestingbillionsinAI,andemployeesareincreasinglyusingthetechnology.

Yetonlyasmallminorityofcompaniesarereportingmeaningfulormeasurablegainsfromitsuse.It’s

thegenAIparadox

:Thetechnologycanbefoundnearlyeverywhere—exceptonthebottomline.

ThisisnotanAIcapabilityproblem.We’vecreatedsystemsthatcanreason,create,andeven

act.Instead,it’sanexperienceproblem:We’restuckusingsearchbarsandchatboxesbolted

ontointeractionparadigmsdesignedforapre-AIera.IforganizationsaretorealizeAI’spotential,theymustlearntocreatenewkindsofAIexperiencesthatemployeesandcustomerswill

enthusiasticallyembrace.

Doingsowillrequireleaderstorethinkahostoflong-standingassumptions.Fordecades,

softwareoperatedonabasicmodel:usersspecifiedstructuredinputs,andthesystem

respondedwithstructuredoutputs.GenerativeandagenticAIfundamentallybreaksthismodel.

Systemsnowinterpretintent,generatenoveloutputs,andrequireuserinputtointeractwith

andrefinethoseoutputs.Thisisamassiveinteractionshift:theinterfaceisnolongerafixedsetof“commandandexecute”controls;rather,itisa“collaborateanditerate”model.

Yetmostorganizationsarestilldesigningforyesterday’sworklows—layeringAIontolegacy

systemsratherthanreimaginingthosesystems.Theresult:promisingtoolsremaindisconnectedandfailtodeliverontransformation.Thelatest

McKinseyGlobalSurveyonthestateofAI

findsthatmostcompaniesusingAIremaininthepilotorexperimentingphase,withtwo-thirdssayingtheyhavenotyetbegunscalingAIacrosstheorganization.Theproblemisn’tthemodelsthemselves—it’sthatthesetoolsexistoutsidethelowofwork,forcingusersintounfamiliar

interactionpatternswhileoferinglittlevisibilityintohowdecisionsaremade.

Fixingthiswillrequiremorethantrainingpeopletopromptbetter.Itwillmeandesigningsystemsthatembedhumanjudgmentdirectlyintotheinteractionmodel.Inmanyoftoday’sAItools,userstendtooscillatebetweenacceptingoutputsuncriticallyorabandoningtoolswhentheresults

aredisappointing.AI-nativeexperiencesmustmakecollaboration,review,correction,andinterventionfeellikeanaturalpartoftheworklow.

WhatfollowsisaframeworkfordesigningthekindsofintelligentexperiencesthatunlockAI’sfullpotential.

AI-nativeexperiencesmustmakecollaboration,review,correction,andinterventionfeellikea

naturalpartoftheworkflow.

Buildingnext-horizonAIexperiences2

Buildingnext-horizonAIexperiences3

Anewtechnologywithnewdesignchallenges

GenAIandagenticAItoolstendtomovefast,oftenrespondingtoqueriesinsecondswitha

voicethatbrimswithconfidence.Butspeedisnotthesameascomprehension,andconfidentlanguagecanmaskshallowreasoning.Thisquicklybecomesapparentwhenorganizations

attempttobuildAIexperiencesthataretransformative,whetherfortheenterpriseorforconsumers.Ingeneral,leadersencounterfourkeybreakdownsthatmustbesurmounted:

—Intentambiguity:Failuretounderstandwhatuserswant.Evenforthemostskilled

communicators,languagecanbemessy,contextual,andoftenunderspecified.Large

languagemodelscanapproximatemeaning,buttheycannotalwaysinferthefullintent

behindaprompt,resultinginmisunderstandingsandinaccurateoutputs.Inaddition,whilesomeAIsystemsincorporatefollow-upquestions,manyexperiencesstilllackefective

clarificationloops.Asaresult,ambiguityisoftenleftunresolved,andmisinterpretationsgouncorrectedbeforethetaskisexecuted.

—Contextgaps:Failuretoknowwhatinformationisrequired.Systemsarenotdesigned

toidentify,request,orretrievetheinformationrequiredtoperformataskthoroughlyand

accurately.Whileuserstrustthesystemto“knowwhatitneeds,”theAIoftenproceedswithonlyapartialunderstandingofthecontext.Thisshiftstheburdentouserstoanticipate

problems,requiringthemtosupplyexhaustivedetailsthroughlengthyprompts—whichcreatesfriction,inefficiency,andinconsistentresults.

—Genericoutputs:Failuretoapplystandardswithspecificity.Systemsarenotdesignedtolearnandapplyorganizationalstandards.Usersexpectrelevant,in-depth,andhigh-

qualityanswers,butbecausetheAIdoesnotunderstandbusiness-specificpatternsandrequirements,itdeliversgeneric,disappointingresultsthatrequireheavyediting.

—Noncollaborativeiteration:Failuretoevolvetheworkwiththeuser.Systemsaren’tdesignedtoinvitetwo-waycollaborationintotheprocess.AIisbiasedtowarddeliveringoutputsratherthanthinkingalongside—andgenuinelycollaboratingwith—itshumanusers.

Withoutvisibilityintohowdecisionsaremade,whyactionsaretaken,orwhenhumaninputis

requiredtogenerateoptimalresults,usertrustneverreallydevelops.Asaresult,AItoolsfail

toscale,andmeaningful,organization-wideimpactremainselusive.Thismisalignmentisnot

technical—it’sexperiential.WithAItools,theinterfaceisthecollaborationlayerbetweenhumanjudgmentandmachineintelligence—thezoneinwhichintentisexpressed,intelligenceresponds,andtrustisbuilt.Butwe’vebarelybegundesigningforit.

Buildingnext-horizonAIexperiences4

Designingintelligentexperiencesthatscale

GenerativeandagenticAIintroducebehaviors,ambiguity,variability,andprobabilisticreasoningthattraditionaluserexperiencepatternswereneverbuiltfor.Forthesesystemstodeliver,

theywillrequireanewvocabularyofAI-nativedesignpatterns.Thisshiftbuildsonwhatthe

McKinseyreport,

Thebusinessvalueofdesign

,madeclearnearlyadecadeago:Designisa

strategiccapability,notanaestheticlayer.ButintheAIera,thoseprinciplesmustevolve.We

mustcreatewithclaritytoensurethatAIsystemsevolvewithusers,bringdepthtoworklows

sooutputsrelectrealexpertise,andorchestratecocreationsopeopleandAIagentsshapetheworktogether.Designingtherightexperiencebecomestheconnectivetissuebetweenhumanjudgmentandmachineintelligence—aplacewherework,meaning,andconfidenceconverge.

FourdesignprinciplestodriveefectiveAI-nativeexperiences

AcrossourAIworkwithleadingglobalorganizationsinoperations,marketingandsales,and

customerexperience—insectorssuchasbanking,lifesciences,andinsurance—wehave

developedfourdesignprinciplestoguidethisevolution(table).Theseprinciplesaddressthe

everydaybreakdownsthatpreventAIfrombecomingatrustedpartnerandenablesystems

thatareintuitive,collaborative,andtrulyimpactful.Whenworklowsarereimaginedwiththeseprinciplesinmind,adoptionacceleratesandthevalueofAIisunlocked.Below,weexplorethesefourprinciples,illustratingwhattheylooklikeinpracticethroughthestoryofhowwehelpeda

marketingorganizationredefinethewayitcreatescampaigns.

Table

AI-eradesignprincipleDescription

Designsystemsthatmaketheirlogic,assumptions,andoutputsclear,enablinguserstoconfidentlyunderstandtheoutputs

Sustaincontextandmemoryacrossinteractionstocreatecoherent,personalized,andseamlessexperiencesovertime

Enablerich,multistep,domain-specificworklowsthatgobeyondsingleinteractionstosupportmeaningfulend-to-endoutcomes

CreateenvironmentswherehumanexpertiseandAIagentscollaborateluidly—bothinrealtimeandacrossdisciplines—toamplifyimpact

Designforcontinuity

Orchestratecocreation

Leadwithclarity

Buildfordepth

1.Leadwithclarity:Makeintelligenceexplainitself

AIcannotearntrustifitslogicandprocessesremainhidden.Systemsmustrevealhow

conclusionsarereached,whereuncertaintyexists,andwhattrade-ofsshapedtheresult.Whenreasoningbecomeslegible,peoplecanengagewithit,questionit,anddecidewithconfidence.

Example:AmarketerasksanAItooltosuggestdesignandcopytweaksforacampaign.Insteadofprovidinganimmediateanswer(suchasspecificdesignorcopysuggestions),theAIasks

clarifyingquestions,gathersdetailedrequirements,restatesitsunderstanding,andonlythencollaborativelyworkswiththemarketertounpacktherequest.

Buildingnext-horizonAIexperiences5

2.Designforcontinuity:Carrycontextforward

Workrarelyhappensinisolation,yetmanyAIsystemsbehaveasifeveryrequestisafreshstart.AIshouldrecognizeprogressacrossusersandsteps,rememberingwhatcamebeforesoitcananticipatewhatcomesnext.Continuityturnsdisconnectedoutputsintomomentum.

Example:AmarketingcampaignAItoolsupportsanalystsintestingconceptsacrossmultiplesurveyrounds.WhenRound2resultsarrive,theAInotonlysummarizesthenewdatabutalsoautomaticallyconnectsinsightsfromRounds1and2.Thisensuresthenextiterationbuilds

onpriorcontext—highlightingwhatisworking,whatisnot,andnotingwhatshouldchange—ratherthansimplyreactingtothelatestresultsinisolation.TheAIthendeliversholistic

recommendationsgroundedincumulativelearning,notsingle-pointinputs.

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3.Buildfordepth:Automateentireworklowsratherthanjustprovideanswers

TherealopportunityisAI’spotentialtoconnectmultistepprocessesthathumanworkersfollowinstinctively—suchasgatheringdata,applyinglogic,testingalternatives,andrefiningoutputs.DepthtransformsAIfromarapidrespondenttoacapablepartner.

Example:Amarketerinitiatesaresearchplan,andthesystemautomaticallyassemblesateamofspecializedAIagentstoactasacritiquecommittee.Eachagentanalyzesthedraftoftheplanthroughitsownlens—data,audienceinsights,competitivecontext,andcreativequality—and

providesreasoning,recommendations,andrefinementsintheformofadeeplyreasoned,high-qualityresearchplan.

4.Orchestratecocreation:Blendhumanjudgmentwithmachineintelligence

ThefutureofworkwilldependonhowefectivelypeopleandAIsystemsshareresponsibility.Thisgoesbeyondthenotionofincludingahumanintheloop.Thegoalisnotforpeopletocorrectthesystemafterthefact,buttodesignhuman–AIinteractionsthatsimplify,reimagine,andrefine

theworkitself,inawaythatimproveswitheveryinteractiontodriverealoutcomes.AIsystemsmustinviteuserstosteer,revise,anddebate,allowingsolutionstoemergefromcollaborationratherthanone-waygeneration.

Example:RatherthanpositioningAIastheprimaryauthorandthemarketerasadownstream

reviewer,thismodelreframescreationasacollaborativeprocess.AIandhumanmarketers

generateintandem,bringingdistinctstrengths—structuralclarityandstrategicframingfrom

AIandcontextualjudgmentandcreativenuancefromhumans.Thesystemthenmakesthese

strengthsexplicit,comparesalternatives,andempowersthemarketertodeterminewhatworksbest.Thefinaloutputblendsbothperspectives,resultinginhigher-qualitythinking,stronger

outcomes,andamoreconstructivehuman–AIpartnership.

Buildingnext-horizonAIexperiences7

BuildingAItoolsaccordingtothesefourdesignprinciplesenabledtheorganizationtodeliver

higher-qualityoutcomesmoreefficiently.Forexample,whentheclarityprinciplewasapplied

tohelpregionalstoremanagersretrieveinsights,allowingtheAItooltoaskclarifyingfollow-upquestionslednearly75percentofpilotuserstoexpressenthusiasmforthetool,resultingin

greateradoptionandanincrementalmarketsalesupliftofmorethan2percent.Inanothercase,whendesigninganexperiencetopreparesalesrepswithbettertalkingpoints,integratinga

newtoolwiththeirexistingsystemswithoutbreakingcontextwasratedasthetopdesiredAI

featurebymorethan90percentofusers,ensuringcontinuityandpreservingworklow.Whendesigningintelligentexperiencesforhotelmanagers,nearlyall180pilotusersreportedhighertrustandweremorelikelytousethetoolintheirday-to-dayworkassoonasthedesignstartedexposingagenticAI’sreasoninglows.Acrosscases,experiencedesignthatfollowstherightAIdesignprinciplesprovedcriticalfordrivingadoption.

AneweraforAIexperiences

Fordecades,AIhasoperatedquietlyinthebackground,whileusersweretrainedtointeract

withinnarrowinput–outputconstraints.Nowthoseboundariesareshifting.Wearebeginningtounderstandwhatthenewlandscapedemands:designingtheexperiencearchitecturebetweenpeopleandintelligentsystemswillrequireanewmindsetacrosstheorganization.

Forleaders,it’sessentialtosetaclearvisionforhowAIwillreshapethewayyourorganizationcreatesvalue.Thisisnotaboutaddingmoretoolsbutratheraligningtechnology,design,

data,andoperationsaroundsharedworklows.Leadersmustcreatetheconditionsforcross-functionalorchestration,becausecollaborationwilldeterminewhetherAIwillbeastrategic

assetoranotherpilotthatneverscales.

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Fordesigners,thescopeisshiftingfromshapinginterfacestodesigninghowpeopleand

systemsworktogether.Theworkisnolongertomakescreensintuitivebuttounderstand

thelowofjudgment,correction,andcoordinationacrosshumansandAIagents.Designers

mustdevisenewinteractionpatternsthatletteamssharecontext,negotiateintent,andbuildconfidenceasworkunfolds.Theuserisnolongerjustaperson;it’sanetworkofpeople,tools,andintelligentagents.

Forproductmanagers,generativeAIandagenticAIfundamentallyshiftthelogicofproduct

definition.Requirementsbecomeoutcomes,notfeatures,andinteractionmodelsaremore

adaptiveandlessdeterministic.Leadingateamwillbeabalanceofnavigatingambiguitywhilehelpingusersacclimatetonewformsofinteraction.Measuresofsuccesswillchangefrom

featuredeliverytosystemsthatlearn,improve,andcreatevalueacrosstheworklow.Moresothaneverbefore,productmanagersmustunderstandbusinessoutcomesanddriverssotheycancocreateareimaginedexperience.

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