2026科技投资新纪元洞察报告：AI 变革下私募股权重构投资策略

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WithgrowthslowingandAIlooming,privateequity’shottestsectorisgoingtorequireanewplaybook.

ByDavidLipman,ChristopherPerry,andGrantKieffer

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AtaGlance

、Softwaredealmakinglimpedalonginearly2026asslowedrevenuegrowthandthethreatofAIdroveawedgebetweenbuyersandsellers.

、Butamidthetumult,patternsareemergingthatsuggesthowgeneralpartnersandtheirportfoliocompaniescannavigatethroughtheuncertainty.

、ManagingboththeriskandopportunityposedbyAIboilsdowntorethinkingduediligenceandvalue-creationplaybooks,whileretoolingmetricstovalidatesuccess.

Ifsoftwareinvestinghasdeliveredanythingtoprivateequityinvestorssofarin2026,itisthedisquietingcertaintythattheworldhaschanged—probablyforever.

Revenuegrowththatwasrunningaround20%annuallyisnowtrendingathalfthat.Netrevenueretention(NRR)hasdroppedabout8pointssince2021(seeFigure1).Dealmakingisatacrawl.Agingportfolios

aresuddenlyathingintechinvesting.Andloomingovereverythingelseisthespecterofartificial

intelligence,which,atbest,threatenstheonce-unassailableSaaSvaluepropositionand,atworst,raisesfearsthatsomesoftwareusecasesareveeringtowardobsolescence.

Perhapsmostominousisthebuildingevidencethatsoftwareislosingthestructuraladvantagesthat

foradecadeproducedthebuyoutindustry’sshiniestreturns.Thesoftwareassetsthataretransactingintheprivatemarketscontinuetoattracthighprices,largelybecausesponsorshavefocusedtheireffortsonsellingtheirA-pluscompanies.YetdealscompletedaftertheCovid-19pandemic,includingmore

seasonedinvestmentsfrom2020to2022,are,todate,generatingreturnsbelowpre-Covidaverages

(seeFigure2).Whilemanyoftheseinvestmentsarenotyetfullyrealized,andperformancecouldimproveovertime,thatwillbeachallengegiventhehighpricessponsorspaidforassets

andthesector’sslowinggrowth.

Whatcomesnext?

Whilepredictionsaren’tworthalotamidsuchuncertainty,it’ssafetosaythatwe’relivingthroughthe

WildWestatmospherethatattendsanyperiodoftechnologicaldisruption.Whatweknowfromprevioustransformations(thedot-comrevolutionandtheshifttocloudcomputingcometomind)isthatwinningbusinessmodelswilleventuallyemergefromthechaos,andthewinnerswillallhavesomethingin

common:adeepunderstandingofcustomers’needsandaclearvisionforhowtheemergingtechnologiescansolvethosereal-worldproblemswithreal-worldeconomics.

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Figure1:Softwaresectorgrowthhasmoderatedsignificantly,andnetrevenueretentionisindecline

Notes:2025revenuegrowthdataasofFebruary2,2026;last12months(LTM)revenueusedfor2025ifQ4

reportingunreleased;differentcompanysetsperyear;includesfirmswithSoftware(Primary)asindustry

classificationandmorethan$50millioninLTMrevenueinthegivenyear;fornetrevenueretention,n=85;excludesanycompany/quartercombinationswithoutavailabledata

Sources:S&PMarketIntelligence;AlphaSense

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Figure2:Itwillbechallengingforpostpandemictechbuyoutstoachievereturnsinlinewiththepriordecade,givenhighpricespaidandless-certaingrowth

Notes:Includesfullyrealizeddealsanddealsthathaverealizedatleast25%oftheirreturns;includesalldealsizes;allfiguresinUSD

Source:SPIbyStepStone(April2026)

Atthemoment,boldproclamationsaboutAI’sfutureimpacttradeoffalmostdailywithreportsthatfewcompaniesareseeingmeasurablereturnsfromtheirAIinvestments.Bid-askspreadsforsomesoftwarecategoriesmightnarrowsomewhatasAIusecasesandbusinessmodelsbegintoproveout.But

valuationsinmostcategoriesareessentiallyupforgrabsrightnow.Thebiggestissueisthatthe

AIdisruptionismovingfasterthanthedot-comorcloudtransitions.Mostcompaniesarestillindiscoverymode,searchingforwaystomeaningfullydeployAItosatisfycustomerneeds

(orwhattheymightneedsoon).

Yet,againstthisbackdrop,patternsareemergingthatsuggestanumberofpracticalactionsprivateequityinvestorscantakerightnowtokeeppressingforward.WealreadyknowenoughaboutAI’sdisruptive

power,infact,tostartmakingno-regretsshiftsineveryphaseofthePEvalueproposition.Proactivegeneralpartners(GPs)aretakingstepsto:

•pragmaticallyreorientduediligencetomorereliablyunderwriteAIrisksandopportunities;

•refocusvalue-creationplaybookstozeroinonhowAItechnologycantransformofferingsthroughadeeperunderstandingofcustomerworkflows;and

•demonstratebankableprogressbymarshalingdataandmetricsthatcanprovidethenextownerwithcompellingAIproofpoints,notjustrosynarrativesorrandomprototypes.

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Thenewduediligenceimperative

Inthedecadeorsosincethesubscriptionmodeltookoversoftware,boostingvaluehasbeenallabout

maximizingpredictable,recurringrevenue.Growthcamesteadilyasvendorsaddedseats,chargedmorefornewfeatures,andcross-soldnewproducts.Withnophysicalproducttodeliver,eachnewdollarof

revenuefellinlargeparttothebottomline.AndonceaSaaScompanywasestablishedwithacustomer,

therelationshipdeepenedintoacompetitivemoatassoftwarebecameembedded,usersdevelopedhabitsaroundit,anddataaccumulatedwithintheplatform.

AIischangingallthat.Thesenewtechnologiesdon’tjustthreatentoleapfrogsomesoftwaresolutions,allowingAI-nativecompaniestoswimacrossthosemoats(thinkcustomerserviceorsoftware

development).AIproductsalsohavedifferenteconomics.ThemassivecalculationspoweringAIaddsubstantialcosttoeveryquery,whichrequiresrevenuemodelsbasednotonseatsbutonactualusageandresults.Thatshrinksmarginsand,todayatleast,makesgrowthprojectionsuncertain.

TheresultisthattraditionalSaaSsignalsareincreasinglymisleadinginduediligence.Revenuemultiples,annualrecurringrevenue(ARR),andNRRwerereliableproxiesforvaluewhensoftwaremoatswere

moredurableandmarginswerestructurallyhigh.Butthecorrelationbetweengrowthrateandvaluationisflattening,andtraditionalunderwritingisn’tfullycapturingtheelementsthatwillhelppredictwhichassetsareequippedtothriveinalandscapereshapedbyAI.

Diligenceinthisnewenvironmenthastostartwithanassessmentoftwothings:HowmuchcanAI

impacttheuserworkflowsthesoftwaresupports,andhowmuchriskistherethatAIcoulddisplacethesoftwarealtogetherwithinthoseworkflows?Stresstestingrequiresspecificity,sinceproductmoats,

workflowmoats,anddatamoatseachcomeunderdifferentdegreesofpressurefromAI.

Strongdiligencewillalsocaptureupside:HowAI-readyisthecompanyitself?IsitrapidlydeployingAIinternallytoimproveefficiency?IsitgainingtractionwithAIontheproductside,eitherbyadding

featuresorbylaunchingnewproductsthatcustomersvalue?Thekeyhereisevidence:Themarketis

alreadybifurcatingbetweencompaniesthatcandemonstratemeasurableAItractionandthosethatarestillspinninganarrativewithoutnumbersbehindit.Indiligence,thekeyquestionisnolonger

“DotheyhaveanAIstrategy?”but“Cantheyshowmetheproofpoints?”

AItransformationduringownership

UsingAIinternallytoimproveefficiencyandtransformworkflowsisrapidlybecomingtablestakesforanycompanyinyourportfolio,softwareorotherwise.Theopportunitytotrulyinflectperformancestillinvolvessomeexperimentationandfaithinthetechnology,butstandingstillisn’tanoption.

ThemorecomplexquestionishowtoreshapetheproductroadmaptogenerateAI-basedrevenue—and

howtoscalemeaningfulinnovationatspeed.Thatcanbeatallorderforanincumbentsoftwarecompanyusedtooptimizingforatraditionalseat-basedplatform,especiallygivenallthedailyrequirements

ofmanagingthecorebusiness.

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Thecompaniesseeingthemostsuccesstendtohavealight-bulbmomentwhentheyrecognizethat

simplyhelpinghumansdotasksfasterwithincrementalproductenhancementsisincreasinglymissingthepointinanAIworld.Ifanagenticsystem,forinstance,canactuallymanageaworkflowendtoend,thegoalshouldbeenablingastepchangeinmeasurableoutcomes,notjustuserefficiency.

Thatmeansturningthetraditionalproductdevelopmentapproachonitshead.Insteadoffocusingsolelyonhowcustomersusediscreteproductswithinworkflows,themostinnovativecompaniesarelookingtoconnectthedotsacrosstheentireprocess.Theyaregoingdeeponthecustomer’sbroadobjective

andhowtheirsolutioncanberebuilttoachievethatoutcome—sometimesfreeingupworkersformoreproductivepursuits,sometimesreplacingthemaltogether.

ForZendesk,the$2-billion-revenuecustomerserviceplatform,thetippingpointarrivedacoupleofyearsagowhenitbecameobviousthatmostinquiriesbeinghandledbycustomerservicerepscouldbe

resolvedusingAI.AddingAIfeaturestotheexistingplatformmightspeeduphumaninteractiontoa

limiteddegree.ButonceZendeskaskeditselfhowmuchofthecustomerserviceworkcouldbedone

autonomouslyandreliably,thefuturewasclear:ItneededtocompletelyrebuildanAI-basedplatformtosortqueries,surfaceenterprise-wideknowledge,andworkacrossthecompanytoreachresolutions,

allatspeedsandaccuracyneverbeforepossible.

Thekeyquestionisnolonger“DotheyhaveanAIstrategy?”but“Cantheyshowmetheproofpoints?”

AssemblingtheAIcapabilitiesfastenoughposedamajorchallengeforacompanythatstillneededto

maintaingrowthandservicealargeinstalledcustomerbase.Theanswer:astringoftargetedacquisitionsthatwouldimportexpertiseineverythingfromAI-poweredautomationandqualitymanagementto

enterprisesearchandanalytics.

Dialinguptheclockspeedalsorequiredrapidlyrefocusingtheworkforcesothatmorethanhalfthe

companywasworkingonAI,upfromlessthan10%just15monthsearlier.Thatinvolvedanervyprogramtopruneinitiatives,collapseoverlappingwork,killbetsthatwerenolongerstrategic,andotherwise

makethepainfultrade-offsneededtofreeupcapacity.Theresultshavebeenimpressive:Inroughly18months,Zendeskreported$200millioninARRfromAI,with20,000AIcustomersandnearly800

millionAIinteractionsin2025.

ThelessonhereisthatAIisnotagameofincrementalism.Capturingtheopportunitystartswithzero-basingproductassumptionsandreimaginingwhat’spossible.Theanswerwon’talwaysbeacomplete

rebuild.ButyouneedtodecidehowyoucantakecustomeroutcomestoanewlevelbymatchingAI

technologytoadeeperunderstandingoftherelevantworkflows.Andyou’llneedtoassesswhatthatwillrequireintermsofaddingtalentandmakingorganizationalchangestosupportrapidexecution.

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Provingit

It’snosurprisethatGPsandtheirportfoliocompanymanagementteamsareexpendingmassive

bandwidthtodevelopnewAIfeaturesandproducts.Butthey’reprobablynotspendingenoughenergytodevelopthemeanstotrackprogressandmeasureimpactwithconcretedata.Thathelpsexplainthewidegapinvalueexpectationswe’reoftenseeingbetweenbuyersandsellers.

BecausethetraditionalSaaSKPIstack(ARR,NRR,andgrossmargin)wasbuiltforadifferent

valueproposition(aworldofnear-zeromarginalcost,seat-drivenexpansion,andpredictableretention),itfailstocaptureAIimpactreliably.ARR,forinstance,isagreatmeasureofthepredictablerevenue

derivedfromsubscriptionseats.ButAIispricedonactualusageandoutcomes,whichcanbeburstyandunreliable.

SomecompaniestrytoisolateAI’stop-lineimpactbybreakingoutabroadcategorycalled“AI-associatedrevenue.”Butthatdoesn’ttellthestoryanymorethanthelabel“cloud-associatedrevenue”didadozen

yearsago.Understandingthetrueperformanceoftheseproductsrequiresseparatelytrackingatleastthreedistinctrevenuebuckets:traditionalAIandmachinelearning(predictivemodels,riskscores),

AIadd-ons(copilots,assistantfeatures),andagenticproducts(workflowautomation,autonomousexecution).Eachhasadistinctmarginprofile,growthtrajectory,andcompetitivedynamics.

Ifanagenticsystemcanactuallymanageaworkflowendtoend,thegoalshouldbeenablingastepchangein

measurableoutcomes,notjustuserefficiency.

Thecostside,too,isverydifferent.AIproductshavesignificantvariablecostsperusethatdon’texistfortraditionalSaaSsolutions.Afullunderstandingofwhatyou’respendingrequirestrackinghostingandinfrastructurecosts,third-partymodelcosts,andthefullyloadedcostofeachemployeedevotedinfullorinparttoAI-relatedR&D.

Thebottomlineisthatifyoudon’tchangeyourmetrics,youcan’tevaluateAIimpact.Therevenues

andcostsdirectlyattributabletoAIfirstneedtobeseparatedfromthecorebusinessandthenbroken

downintotheirconstituentparts.Withoutthat,it’simpossibletoanswerwithprecisionthethree

essentialquestionsoneveryone’smind:IsAIdrivingincrementalrevenue?IsAIchangingcoststructures?AndareAI-relatedproductsscalingefficiently?

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Armedwithfirmanswers,portfoliocompaniescannotonlydesignthebestproduc