《企业+AI+实战手册》

TheEnterpriseAIPlaybook

Lessonsfrom51SuccessfulDeployments

TheEnterpriseAIPlaybook

Foreword

Thereisnoshortageofpredictionsandsentimentsurveysaboutartificialintelligencetoday.

EveryweekbringsnewforecastsanddebatesaboutwhetherAIisuseful,whichjobswilldisappear,whichindustrieswilltransform,whichcompanieswilldominate.Butwhenwespeakwith

executivesactuallydeployingAIinsidetheirorganizations,wehearadifferentsetofquestions.Notwhatmighthappeninfiveyears,butwhatishappeningrightnow.Practicalrealities,notabstractframeworks.

Thisreportwasbornfromasimpleconviction:themostvaluableinsightsaboutAIadoptionarenotinhypotheticalsorpredictions.Theyareinthepatternsofthosewhohavealreadywalkedthepath.

Wesetouttobuildsomethingempirical.Todocumentreal-worldusecasesthathaveactually

deliveredbusinessvalue.TomapthepracticesoforganizationsthatarenotjustexperimentingwithAIbutsuccessfullydeployingitatscale.Wewanteddepth.Tounderstandthepitfallsthatdonot

makeitintopressreleases,thenuancesthatseparateasuccessfulpilotfromafailedone,andtheorganizationalrealitiesthatnovendorwhitepaperwilltellyou.

Across51enterprisecasesover5months,wefoundstoriesoftransformationmeasuredinweeksandothersmeasuredinyears.Sametechnology,sameusecases,vastlydifferentoutcomes.ThedifferencewasnevertheAImodel.Itwasalwaystheorganization.Itsreadiness,itsprocesses,itsleadership,itswillingnesstochangeandfail.

Ourambitionwiththisresearchissimple:toofferapracticalwindowintowhatisactually

happeninginsidecompaniesastheycreatevaluewithAI,includingdetailedcompanycasestudies.Thefutureofworkonlymakessensewhenonefirstunderstandsthepresentofwork.

Intheconclusion,weoffersomeforward-lookinginsightsbasedonupcomingtrendsintheAI

space.Wehopethesefindingsserveasbothamirrorandamap.Reflectingwhereyour

organizationmightbeandilluminatingthepathsonhowyoucanmoveforwardwithconfidence.

ElisaPereira,AlvinWangGraylin&ErikBrynjolfsson

TheResearchTeam

StanfordDigitalEconomyLab

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Contributors

ElisaPereira

Researcher,StanfordDigitalEconomyLab·MSxCandidate,StanfordGraduateSchoolofBusiness

ElisaPereiraisaresearcheratStanford'sDigitalEconomyLabandMSxcandidateattheStanfordGraduateSchoolofBusiness,withabackgroundinventurecapitalandhands-onexperiencebuildingdozensofenterpriseAIsolutionsacrossLatinAmerica.Hercurrent

researchfocusesonmeasuringthereal-worldimpactofthesedeployments,identifyingpatternsbehindsuccessfulimplementations,andexploringhowLatinAmericacan

establishtechnologicalsovereignty.

AlvinWangGraylin

DigitalFellow,StanfordDigitalEconomyLab·StanfordUniversity

AlvinWangGraylinisDigitalFellowattheStanfordDigitalEconomyLab,andanauthor,

serialentrepreneurandtechnologyexecutivewithover35yearsofexperienceinAI,XR,cybersecurityandsemiconductorindustries.He’scurrentlythechairmanoftheVirtual

WorldSociety,SeniorFellowattheAsiaSocietyPolicyInstituteCCA,lectureratMITandadvisesgovernments,organizationsandcorporationsontechnologytransitions.Hisbook,OurNextReality,discusseshowAIandimmersivetechnologywillreshapeourworldinthecomingdecade.HiscurrentresearchisfocusedontheeconomicsofAIandtheassociatedgovernmentalpoliciesneededtoensureasmoothtransitiontoapost-laboreconomic

model.

ErikBrynjolfsson

Director,StanfordDigitalEconomyLab·Professor,StanfordUniversity

ErikBrynjolfssonistheDirectoroftheStanfordDigitalEconomyLabandtheJerryYangandAkikoYamazakiProfessorandSeniorFellowattheStanfordInstituteforHuman-

CenteredAI(HAI).HeisalsotheRalphLandauSeniorFellowatSIEPR,professorby

courtesyattheStanfordGraduateSchoolofBusinessandDepartmentofEconomics,andaresearchassociateattheNationalBureauofEconomicResearch(NBER).Oneofthe

most-citedauthorsontheeconomicsofinformation,hehasco-authoredhundredsof

articlesandbooks,includingTheSecondMachineAgeandMachine,Platform,Crowd.HeputshisacademicinsightstopracticaluseviaWorkhelix,acompanyheco-foundedto

identifyandmeasurethebenefitsofAI

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Contents

Foreword

Contributors

TheMacroContext

Methodology

Keyfindingsbriefly

Chapter1

WhydoAIbusinesscasesunderestimaterealinvestment?

Chapter2

HowtocrossthevalleyofdeathbetweendeploymentandROI?

Chapter3

Howmuchhumanoversightisoptimal?

Chapter4

Whatseparatessponsorswhodriveresultsfromthosewhojustapprovebudgets?

Chapter5

Wheredoesfatalresistancecomefrom?

Chapter6

Whenproductivitygainsarehigh,whathappenstoheadcount?

Chapter7

WhereisAIopeningdoorsthatwerepreviouslyclosed?

Chapter8

IsagenticAIgeneratingrealvalue?

Chapter9

Howcleandoesenterprisedataactuallyneedtobe?

Chapter10

Doesrigoroussecurityprotecttheprojectorkillit?

Chapter11

Whenisfoundationmodelchoicenotacommodity?

Conclusion

Appendix

Measurementsandwhattoavoid

ResearchSample

Endnotes

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TheMacroContext

WhyenterpriseAIimplementationmattersnow

GeneralpurposetechnologieslikeAIenableandrequiresignificantcomplementaryinvestmentsinprocessredesign,workforcedevelopment,andorganizationalrestructuring.Theseinvestmentsarelargelyintangibleandpoorlymeasuredinnationalaccounts,whichmeansproductivitygrowthissystematicallyunderestimatedintheearlyyearsofanewtechnologyandoverestimatedlater,

whenthebenefitsareharvested.Brynjolfsson,Rock,andSyverson(2021)formalizedtheseobservationsinamodelcalledthe"ProductivityJ-Curve".[1]

Themacroeconomicoutcomehingesnotonthetechnologyitselfbutonhoworganizationsdeployit.Wefacea“productivityfork”:AIcaneitheraugmentworkersandcreatenewcapabilitiesor

primarilyautomateexistingtasksandcutheadcount.Thepathchosenwillshapeeconomicgrowthfordecades.[2]Inparticular,automationdisplacesworkersfromexistingtasks,butthecreationofnewtasksinwhichhumanshaveacomparativeadvantagecanreinstatelabordemand.WhetherAIleadstobroadprosperityorconcentratedgainsdependsonwhetherorganizationsgenerate

enoughnewopportunitiestooffsetlabordisplacement.[3]

Someemploymenteffectsarealreadysurfacing.Analysisofhigh-frequencypayrolldatacovering

millionsofU.S.workersfindsthatearly-careerworkersinAI-exposedoccupationshaveexperienceda16%relativedeclineinemployment,withsoftwaredevelopersaged22to25seeinganearly20%drop.[5]These"canariesinthecoalmine"suggestthatsomeofthelabormarketdisruptionsmanyanticipatedarenolongerhypothetical.

Thesemeasurementchallengesarenotmerelyacademic.StandardmetricslikeGDPsystematicallyfailtocapturethewelfarecontributionsofnewandfreedigitalgoods.TheirGDP-Bframework,

whichmeasuresconsumerbenefitsratherthanproductioncosts,revealssubstantialunmeasured

valuecreationinthedigitaleconomy.Ifaggregatestatisticsundercountthegainsfromrelatively

simpledigitalservices,theyarelikelytomissevenmoreofthevaluethatAIcreatesinside

organizations—preciselythekindofvaluethisreportattemptstodocument.[6]Onenewnon-

monetarybenefitthatAIagentsystemsaredeliveringtosoftwaredevelopersis“freetime”tothink.WhileAIagentsautonomouslybuildincreasinglylargerportionsofthecode,humancodersare

allottedmorecoffeebreakstoponderbiggerpictureissues.Thiswon’tshowupinstandard

productivitymeasurements,butitisarealbenefitthatchangestheirdailyworkforthebetter.

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Thegapbetweenthesemacrofindingsandwhathappensinsideorganizationsissignificant.Theeconomicmodelsdescribeaggregateeffects.Thefirm-levelexperimentsmeasuremorecontrolledsettings.NeithercapturesthemessyoperationalrealityofdeployingAIacrossdepartments,

overcomingresistance,andbuildingthecomplementaryinfrastructurethattheJ-curveframeworkidentifiesasessential.Thisisthegapourresearchaddresses.

Whythisresearch

DespitebillionsinenterpriseAIspending,a2025studyfromMIT’sNANDAinitiativeconcludedthat95%ofgenerativeAIpilotprogramsfailtoproducemeasurablefinancialimpact.[7]Theyarguedthatthefailuresstemnotfrommodelqualitybutfrompoorworkflowintegrationandmisaligned

organizationalincentives.Thisisthegapbetweenwhattechnologycandoandwhatorganizationsmanagetodowithit.

Incontrast,ourobjectivewastounderstandthecaseswhereAIwasdeployedsuccessfully.We

dovedeepintocompaniesandanalyzed51caseswhereenterpriseAIdeliveredmeasurablevalue.Whatdidtheseorganizationsdodifferently?Whatdidintegrationactuallycost?Wheredid

resistancecomefrom,andmostimportantly,howwasitovercome?

Howweincorporatedfailure

Whilethisreportfocusesonimplementationsthatdeliveredmeasurablevalue,wedidnotstudy

successinisolation.Ineveryinterview,weexplicitlyaskedparticipantstodescribethefailures,falsestarts,andabandonedpilotsthatprecededtheircurrentresults.Weaskedwhattheytriedfirst,

whyitdidnotwork,andwhattheychanged.

Whatemergedisnotastoryoforganizationsthatavoideddifficulty.Itisastoryoforganizations

thatfailediterativelyandbuiltsystematicapproachestoovercomeinitialsetback.Twothirdsofthecompaniesweinvestigatedhadsignificantfailedattemptspriortoachievingvaluecreation.The

patternsinthisreportreflectwhattheseorganizationslearnedfromtheprocessasmuchaswhattheyachievedthroughsuccess.

Wewanttobetransparentthatthisdoescarryaknownlimitation:selectionbiastowardpositiveoutcomes.Ourfindingsdescribewhatsuccesslookslikeandwhatittooktogetthere;wedon’tclaimtoproviderepresentativedataonhowcommonsuccessisacrossthebroadereconomy.

“Allhappyfamiliesarealike;eachunhappyfamilyisunhappyinitsownway.”—LeoTolstoy

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Methodology

Thisresearchisbasedonin-depthinterviewswithexecutivesandprojectleaderswhohave

deployedAIsolutionsatscale.Wefocusedexclusivelyoninitiativesthathavemovedbeyondpilotstageandaredeliveringmeasurablebusinessvalue.

ResearchSampleProfile

Our51casestudiesdrawfrom41organizations,7countries,5regions,representingoveramillionemployees.(fulllistofanonymizedcompaniesintheappendix).

Figure1.Researchinterviewandanalysisworkflow

SelectionCriteria

FourdimensionsdefinethematureAIprojectsweselectedforanalysis:

●OperationalStability

Systemislive,integratedintorealworkflows,andconsistentlyusedinproduction.

●SustainedBusinessAdoption

TeamsacrossfunctionsactivelyrelyontheAIsystemfordecision-makingovermonths(3+months).

●QuantifiedValueCreation

Clearbusinessoutcomessuchasproductivitygains,revenuegrowth,orcustomersatisfaction.

●Scalability&Replicability

Canbeextendedorreplicatedacrossteams,geographies,orbusinessunits.

Technologiesrangefromdatasciencemodels(machinelearning,deeplearning)toagenticworkflows.

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InterviewApproach

Eachcasestudywasdevelopedthroughatleastonestructured60-minuteinterviewpercompanyfollowingaconsistentdiscussionframework.Wesupplementedinterviewdatawithwrittendocumentationprovidedbyparticipantcompanies,includinginternalmetrics/reports,projectplans/reviews,andfinancialupdates.InterviewswereconductedbetweenAug.‘25andFeb.‘26.

Figure2.Interviewapproachandsupplementarydatasources

ScoringCriteria

Eachdimensionwasscoredbasedondocumentedevidence:

3=Strong(allcriteriamet),2=Moderate(mostcriteriamet),1=Weak(fewcriteriamet).Werequiredevidencefromsystems,documentation,ornamedowners.

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SampleComposition

Intermsofbusinessfunctions,ourcasescoverawiderangeofapplications.Thisdiversityallowsustoidentifypatternsthattranscendspecificusecases.

Figure3.Samplecompositionbybusinessfunction

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Oursamplespans9industries,withparticulardepthinmanufacturing,financialservices,andtechnology.ThedistributionreflectsthecurrentlandscapeofenterpriseAIadoption.

Figure4.Samplecompositionbyindustry

Limitations

Thisresearchreliesprimarilyonself-reporteddatafrominterviewparticipants.Whilewe

triangulatedinformationwherepossibleandfocusedonmatureinitiativeswithdocumentedoutcomes,readersshouldconsiderpotentialselectionbiastowardsuccessfuldeployments.

Oursample,whilediverse,isnotrepresentativeofallenterpriseAIinitiatives.TheconcentrationintechnologyandfinancialservicesreflectsearlyadoptionpatternsratherthanthefulluniverseofAIdeployment.

Alldatawasanonymizedandaggregatedtoprotectproprietaryinformationandfollowsubject

companydisclosurepolicies.Specificcompanynamesandidentifyingdetailshavebeenremovedorgeneralized.

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KeyFindingsSummary

1.Technologyisnotthehardestpart.77%ofthehardestchallengeswereinvisibleandintangiblecosts:changemanagement,dataquality,andprocessredesign.61%ofsuccessfulprojectsincludedatleastonepriorfailure,whosecostsneverappearinthefinalROI.

2.Timelinevarianceisorganizational,nottechnical.Similarusecasestookweeksatonecompanyandyearsatanother.Thedifferencewasexecutivesponsorship,existingorganizationalprocesses,andenduserwillingness.

3.Escalation-basedmodelswereassociatedwithbetterresults.Escalation-basedmodels(AI

handles80%+autonomously,humansreviewexceptions)delivered71%medianproductivitygainsversus30%forapprovalmodels.Thismay,inpart,reflectdifferenttypesoftasksaddressed.

4.Executivesponsorshipisaboutactions,notapproval.Effectivesponsorsclearblockersweekly,bridgebusinessandtechnicalteams,andtieAIadoptiontocorporateOKRs.Mostcritically,theycreateaculturethatgivespermissiontofail.

5.Stafffunctionsarethemostfrequentsourceofresistance,butsomepartsmaybecome

enablersafterbuy-in.Legal,HR,Risk,andCompliancewerethemostfrequentsourceofresistanceat35%,aheadofinternalend-usersat23%.

6.Headcountreductioniscommonbutnotinevitable.Headcountreductionwasthelargest

outcomein45%ofthedeployments,butalternatives(hiringavoided,redeployment,noreduction)accountedfor55%.BroaderlabormarketdatasuggestsentrylevelrolesinAIexposedoccupationsarealreadydeclining.

7.RevenuefromAIisreal,butstillrare,andfollowsthreepatterns.Personalizationthatconverts,speedthatwinsdeals,andinternaltoolsrepackagedasproducts.AsmallsubsetofcasesalsoshowsAIenablingworkthatwaspreviouslyimpossible.

8.AgenticAIworks,butmostfirmshavenotusedit,yet.Agenticimplementationsshowed71%medianproductivitygainsversus40%forhigh-automationbutrepresentedonly20%ofcases.

AgenticAIisn’tanewUI;it’saredefinitionoftheroleofhumansandmachinesintheworkflow.

9.Messydataisnotablockerifyoudesignaroundit.LLMsfixedmanyofthedataproblemstheyweresupposedtostrugglewith.Storeeverything,connectit,andletthemodelsdothecleaning.

10.Securityenablesmorethanitblocks.Securitywasnotaprojectkillerinanyofthecaseswestudied.Requirementsthatwereinitiallybarrierslaterenabledprojectstohandlesensitivedata.

11.Modelchoiceisacommodityformanyusecases.For42%ofimplementations,modelchoicewasfullyinterchangeable.Companiesdon’talwaysneedthebestavailableAImodels.Thedurableadvantageisintheorchestrationlayer,notthefoundationmodel.

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Chapter1

WhydoAIbusinesscasesunderestimaterealinvestment?

Thehiddencoststhatdeterminesuccessorfailure

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PublishedFindings

Scalingrequiresheavynon-modelinvestment.McKinsey'sresearchidentifiesthathigh-performingAIorganizations(thoseattributing>5%ofearningsbeforeinterestandtaxes[EBIT]toAI)are

significantlymorelikelytoinvestin"rewiring"businessprocessesanddataproductsratherthanjustmodeldeployment.[8]

"ProofofConceptFactories"representsunkcosts.Accentureestimatesthat80-85%ofcompaniesarestuckina"ProofofConceptFactory"stage,wheretheyconductexperimentsbutachievelowreturnsandlowscalingsuccessrates.[9]

Datafoundationsareamajorlineitem.Strategicscalersarefarmorelikelytopossessalarge,

accuratedataset(61%vs38%fornon-scalers)andinvestheavilyindataquality,management,andgovernanceframeworks.

TheProductivityJ-Curveimplieshiddeninvestment.Earlierresearchfoundthatforevery$1oftangibletechinvestment,companiesspendupto$10onintangibles(processredesign,reskilling,organizationaltransformation),initiallydepressingproductivitybeforegainsarerealized.[1]

WhatWeFound

77%ofthehardestchallengespractitionersfacedwereinvisiblecosts:changemanagement,dataquality,andprocessredesign,nottechnicalissues.Technologywasconsistentlydescribedastheeasiestpart.Thetruecostofasuccessfuldeploymentusuallyincludesatleastonefailedattempt(seeFinding2),andthebulkofinvestmentgoestoeverythingexceptthemodel.

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Finding1

77%ofthehardestchallengesare"invisiblecosts"

Whenweaskedpractitioners,"whatwasthehardestthingtofix?",theanswersrevealwhereAIbudgetsactuallygo.

Figure5.HardestchallengesinAIimplementation

"Allthehardworkisinprocessdocumentationanddataarchitecture.Ifyoucandothosetwothings,everythingelseisquitesimple."

-Executive,TelecomCompany

"Technologywasn'tthebottleneck-organizationaladoptionwasthefailurepoint."

-Executive,ProfessionalServicesCompany

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Finding2

61%hadafailedAIprojectbeforetheircurrentsuccess

Thesefailedexperimentsrepresentsunkcoststhatneverappearinthe"successful"project'sROI:

HadpreviousAIfailure(s)

61%

Nopreviousfailures

39%

Thesefailedexperimentsaresunkcoststhatmayneverappearinthesuccessfulproject'sROIbutwereoftenessentialtoit.Thefailuresshareapattern:teamstreatedAIasatechnologyproject

insteadofaprocessandchangemanagementproject.Firstattemptsfailedwhenappliedtobrokenworkflows,whenledbytechnicalteamswithoutbusinessownership,orwhenorganizations

assumedthemodelwouldfixproblemsthatrequiredredesigningtheworkitself.

"ThiswasactuallythesecondtimetheylookedtoAIfortherecruitingprocess.Itfailedinitiallybecausetheydidn'taccountforbias,andtheythoughtAIwouldjustfixprocessesinsteadofrequiringprocessredesign."

-AIProjectLead,ProfessionalServicesCompany

Thetechnologywasconsistentlydescribedastheeasiestpart.

"Themoreyouinvestinyourdata,thebetteryoucangetoutoftheseAIsolutions."

-Manager,TechnologyCompany

"Theproblemisn'tthemodels."

-Executive,ProfessionalServicesCompany

Theimplicationforbudgeting:thetruecostofasuccessfulAIdeploymentusuallyincludesatleastonefailedattempt,andthebulkoftheinvestmentgoestoeverythingexceptthemodel.

CASESTUDY

InvoiceProcessingataLogisticsCompany

Howtheyovercameinvisiblecosts

TheCompany

A$1B+US-basedlogisticscompanymanagingalargefleetofrefrigeratedtrailers.Thecompanyreceives+100kinvoicesannuallyfromvendorsacrossthecountryperformingmaintenanceontrailers-everythingfromtirechangestosensorreplacements.

TheChallenge

Thevolumeandvariationofinvoicescreatedasignificantoperationalburden.Sevenfull-time

employeeswerededicatedexclusivelytothistask:consolidatinginvoices,matchingthemto

internaltemplates,validatingthework,enteringdataintotheenterpriseresourceplanning(ERP)system,andgeneratingclientinvoices.

"Theygetalltheseinvoicesindifferentchannels,includingfax.Theymightgetphonecalls.Alotoftheserepairshops,middleofnowhere,theyjustdialinandsay,hey,wedidthisrepair.Sothey

mightbephonecalls,theymightbeemails,theymightbealltypesofwaysthattheygetthisinformation."

-SeniorExecutive,TechnologyServicesCompany

TheInvisibleWork

ProcessSimplification:thousandsoftemplatesreducedtohundreds.Yearsofaccumulatedinvoicetemplateswereredundantandinconsistent.ThiscleanupwasrequiredbeforeanyAIcouldwork.

"Weveryquicklyrealizedthatthe750templatesdon'tmakeanysenseandmostofthemarerepetitive.Nobodyreallydidareviewonthis."

-SeniorExecutive,TechnologyServicesCompany

DataAnnotation:Subjectmatterexperts(SMEs)reviewedthousandsofAIoutputs.They

validatedAI-generatedinvoicesontopoftheirdailywork,explainingeverymistaketoimprovethemodel.

ExecutiveSponsorship:Presidentinvolvedinweeklycheck-ins.Thisremovedbottlenecksandensuredbuy-infromtheoperationsteam.

"Thepresidentwascheckingineveryweek-whatistheprogress,wherearewe,whatarethebottlenecks?Thentherestoftheteamalsoengaged."

-SeniorExecutive,TechnologyServicesCompany

KnowledgeTransfer:TwojuniorITstaffembeddedfromdayone.Dailystand-ups,weeklyandmonthlyreviews.Noblackbox-thecompanycouldoperatethesystemindependently.

TheSolution

Insimpleterms,thecompanybuiltasystemthatautomaticallyreadsinvoicesregardlessofhowtheyarrive,understandstheircontent,andentersthedatadirectlyintothecompany'sfinancialsystem,eliminatingtheneedformanualprocessing.

ThetechnicalimplementationusedAzureDocumentIntelligencewithAzureOpenAIService,

combiningopticalcharacterrecognition(OCR)parsingwithlargelanguagemodel(LLM)-based

semanticmapping.Thesystemingestsinvoicesfrommultiplechannels(email,fax,phone

transcriptions),parsesandextractsdatausingOCR,mapsinvoicecontenttothesimplifiedtemplatetaxonomy,andwritesvalidateddatadirectlytoMSDynamicsD365.

TheResults

Headcount

Accuracy

7→2full-timeequivalents(FTEs)

85%

Processingtime

Timetoproduction

<24hours

8weeks

Valuecreated

>$1M

KeyLessons

"Italwaysstartswiththepeople.Therearepeople,process,andtechnology-andIknowit'sinthatordereventhoughI'mrepresentingatechnologycompany.Thetechnologywastheeasiestpart.

Webasicallyusedalotofopen-sourceandofftheshelfstuff."

-SeniorExecutive,TechnologyServicesCompany

"Lookguys,80%isperfectforus.Wecantakethesefolks,wecanjustputthemintheother

bottleneck.Iunderstandthatyoucankeepimprovingandatonepointthemodelisgoingtobe

95%,butwedon'tcare.Whatwecareisimmediatecostsavingandgettingridofthesebacklogs."

-President,LogisticsCompany

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Chapter2

HowtocrossthevalleyofdeathbetweendeploymentandROI?

Whatseparatesweeksfromyearsinsimilarusecases

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PublishedFindings

Intentionaltimelinesbeat"movefast."AccentureconcludesthatsuccessfulAIscalersare65%

morelikelytoset1–2-yeartimelinestomovefrompilottoscale.Contrarytothe"movefast"ethos,theyaremoreintentionalaboutthetimerequiredtoscaleresponsibly.[9]

Highperformersredesignworkflows,notjustdeploytools.McKinseyreportsthattopperformersarenearlythreetimesmorelikelytofundamentallyredesignworkflowsaspartoftheirAIefforts.55%ofhighperformersredesignedworkflowsaroundAIversusonly20%ofothercompanies.[11]

Mostcompaniesarestuckinpilotmode.While88%oforganizationsuseAIinatleastone

function,onlyone-thirdhavebeguntoscaletheirAIprogramsattheenterpriselevel.Two-thirdsremainintestingorproofofconceptphase.

WhatWeFound

Similarusecasescantakeweeksoryearsdependingontheorganization.Weidentifiedthree

factorsthatconsistentlyaccelerateprojects-executivesponsorship,existingfoundations,andend-userwillingness-andfourthatslowthemdown.Everysuccessfulprojectinoursampleusedan

iterativeapproach.

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Finding1

Therangeisdramatic:fromweekstoyearsforsimilarusecases

AlargefintechusedanAIcodingagenttomigratemillionsoflinesoflegacyextract,transform,load(ETL)codetoamodernarchitecture.Theprojecttookweeks.Atechnologycompanyredesigned

theircustomersupportsystemwithAIandlaunchedinsixmonths.Amajorbankattemptingthesamecustomersupportusecasereportsthatprojectstakemultipleyears.

"WithinweeksoftheAIagent'slaunch,weidentifiedaclearopportunitytoacceleratethemigrationatafractionoftheengineeringhours."

-Executive,Fintech

"Ittakesusmultipleyearsjusttoevenstandoneofthesethingsup."

-Executive,FinancialServices

Thesameusecase,thesameAImodels,vastlydifferenttimelines.Theinsighthereisnota

medianoraverage.Itisthatorganizationalcontextmattersmorethanthetechnologyitself.

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Finding2

Threefactorsconsistentlyacceleratetimetovalue

AccelerationFactor

Frequency

ExecutiveSponsorship

43%

BuildingonExistingFoundation

32%

EndUserWillingness

25%

BuildingonExistingFoundation.Projectsthatleveragedexistinginfrastructureorplatformsmovedsignificantlyfaster.OnetechnologycompanybuilttheirsalescopilotinmonthsbecausetheyhadalreadydevelopedanAIplatformforcustomersupport.

"WelaunchedthefirstMVP[minimumviableproduct]inApril.Becausewef