深度挖掘人工智能商业化 Deep Diving on AI Commercialization

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CONFIDENTIALJULY2023

DeepDivingonAICommercialization

TableofContents

I.KeyTakeaways

1.Currentmarketleadershavecost-effective,diversifiedmonetizationschemes

2.Large-scalepartnerships,oftenbackedbyequityinvestments,provideconceptvalidation

3.AnewtrendamongSaaSleaderstobuyandpackagehasemerged

4.Growthequityinvestorsstandtobenefitfromthebarbell-shapedmarket

5.HorizontalAItoolsfavoruser-basedpricing(fornow)

6.AIdataandcompute"picksandshovels"criticaltobroaderindustrycommercialization

7.Currentsector-orientedinvestmentslessfocusedonmonetizationtimelines

8.GTMapproachcriticalwhencommercializingAI-poweredhardware/physicalassets

9.WithgenerativeAI,valuedeterminedbymetrics,notrevenue

10.Open-sourcecouldexpediteAIcommercialization

II.Methodology/SampleDetails

III.SelectAICompanyGTMProfiles

TylerHolly

tyler.holly@

NickJames

nick.james@

AliBirkby

Alastair.birkby@

MatthewSykes

matthew.sykes@

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SectionI:KeyTakeaways

Weevaluatedpricingandgo-to-market(GTM)modelsfor150ofthemostpromisingVC-backedAIcompanies,rangingfrompre-revenuetounicorn-stageinnovators.Hereareourtop10learnings:

1.Currentmarketleadershavecost-effective,diversifiedmonetizationschemes–basedonthesubsetoflate-stagegrowth($40-100Mestimatedrevenue)andscaled($100M+estimatedrevenue)AIcompaniesweexamined,today’smarketleadershavemaximizedtheircommercializationcapabilitiesthroughlayeredpricingmechanismsandGTMstrategies.Thesecompaniesaveragedalmost3xthenumberofcombinedpricingmodelsandthird-partysaleschannelsleveragedcomparedtotheirearly-andmid-stage(<$40Mestimatedrevenue)peers.WealsofoundthathybridGTMmodelsencompassingbothuser-andusage-based/pay-as-you-gopricing,oftencoupledwithfeatureadd-onsoradditionalproductsofferedatflatortieredrates,weremostcorrelatedwithhigherrevenuescaleandmarketvalue(mostrecentpost-moneyvaluation).IntraditionalSaaS,usage-basedmodelshavebeenassociatedwithsomeofthehighest-valuemarketleaders,particularlyintheinfrastructurecategory(i.e.Snowflake,Datadog,Zscaler).

Becausegrossmarginsofhigh-performingAIsoftwarecompanies(~50-60%)oftentrailtraditionalSaaSbenchmarks(~75-85%)–primarilyduetohigherinputcostsandtheneedforcustomer-specificservices–hybridsubscription/consumption-basedmodelscanimproveoverallmarginprofilesbyencouragingupsellswhilealsoprovidingflexibilityinonboardingnewcustomerswhoarestillinanAIdiscovery/experimentationphase.AIsoftwarecangeneratesubstantialupsidevaluethroughmonetizationmodelsbasedondataconsumption–especiallywhenlayeredontoflat-feeaccesssubscriptions–asdatavolumeandqualityaredirectdeterminantsofAIsystemsuccessandROI.WethinkAIcompaniesthatadopthybridpricingstrategiesaspartoftheirinitialGTMstrategieswillhaveacompetitiveadvantageincapturingmaximumvaluefromearlyflagshipcustomers.

Figure1:Benefitsofhybridmonetizationmodelstomaximizecustomervalue–SaaSsample

Usage-basedcompanieshavebetterNDR……butalsolowergrossmargins

110%109%

100%101%

Usage-basedsubscriptionNousage-basedpricingtiers

MedianTopQuartile

78%

75%

72%

67%64%

51%

122%

105%

Largelyusage-basedpricing

Usage-basedsubscriptionNousage-basedpricingtiers

Largelyusage-basedpricing

MedianTopQuartile

NDR=Netdollarretention

Sources:LazardVGBInsights,a16z,Bain&Company,OpenViewPartners

2.Large-scalepartnerships,oftenbackedbyequityinvestments,provideconceptvalidation–thenaturalextensibilityofAI’scorevaluepropositions–automatingmanualprocesses,personalizing

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customerexperiences,andmakingpredictiveinterpretationsofthedataflowingthroughahardware/softwareplatform–enableAIproviderstoreachamuchbroaderaudiencebyworkingwithchannelpartnersandthird-partymarketplaces.Thistrendheldtrueinouranalysis,asthemonetizedpartnershipscategorywasthegreatestdifferentiatorbetweenthe“growth-scaled”and“early-mid”stagedcompaniesweexamined,asshowninFigure2.Additionally,wefoundthatgrowth-stagecompaniesmorefrequentlyofferedadd-onfeaturessoldindependently,activatingexpansionsintotheirsalesmotions.Early-andmid-stagecounterparts,incontrast,reliedheavilyonfreemium,tieredmonthlyrates–mostlybasedonteamsizeandusecase–withcomparablysimplerproduct/featurepackaging.

Figure2:Greatestmonetizationcategorydisparities–early-midvs.growth-scaledAIcompanies

30%

14%

19%

35%

31%

11%

42%

15%

92%

35%

0%10%20%30%40%50%60%70%80%90%100%

Growth-ScaledEarly-Mid

Non-RecurringServices

Freemium

User-BasedSubscriptions

FeatureAdd-Ons

Partnerships/Integrations

AsuccessstoryleveragingthismodelinitsearlygrowthphasewasDatabricks,whichusedpartnershipsandintegrations,alongwithnewfeaturedevelopment,toscalefromanopen-sourceprojecttoanat-scaleindustryleaderwithover$100MinARRinjustthreeyears.ThecompanypartneredearlywithMicrosoft—whoalsobecameastrategicinvestor—tocollaborativelydeveloptheAzureDatabricksofferingandenable700M+AzurecustomerstoconsumetheirproductswithoutfrictionthroughtheAzureMarketplace.Databricksalsodeftlytookacloud-agnosticapproachinitsGTMstrategybyenablingcustomerstopaycomputeanddatastoragecostsdirectlytothecloudprovidersratherthancollectingthisasdirectrevenue,whichestablishedfriendlydynamicswiththehyperscalers.Additionally,initsquesttofurtherdifferentiatefromSnowflakeasitscaledtoover$1BinARR,Databrickscontinuouslylayerednewfeaturesintoitsenterpriseplatformtobecometheleading“datalakehouse”—convergingmanyofthecapabilitiesofadatalakeandwarehouseintoone.

We’vealsoseenthisapproachvalidatedattheinfrastructurelayerthroughequitycommitmentsandstrategicpartnershipsexecutedbyleadingcloud/SaaSvendors.Thesetechgiantsareoperatingunderthethesisthattheycancreatean“economyofscaleeffect”tocommercializeAIbyleveragingtheirexistingGTMengines,deepbalancesheets,andcomputingresources.Ratherthanbuildin-house,

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currentmarkettrendssuggestpartnershipsandintegrationsarewidelyviewedbylegacyvendorsasthebestentrypointforupscalingAIresearchanddevelopment,creatingpurpose-builtmodels,andincorporatingAIcapabilitiesintoexistingcommercial-readyconsumerandenterpriseproducts.

Cloud/SaaSLeader

PrimaryPartner(s)

Details

NALaunched$500MfunddedicatedtoAIstrategicinvestmentsandled

$100MroundforTypeface

Figure3:Selectcloud/SaaSleaders’AI/MLinfrastructureinvestmentandpartnershipactivity

EquityinvestmentandexclusivepartnershiptoembedCohereinexistingservices

Multi-year,$10B+partnership;MSFTdeploysOpenAImodelsacross

consumerandenterpriseproductswhileprovidingsupercomputingsupportforOpenAI’sresearch

JointGTMstrategyenablingproductintegrationsacrossAI,lowcode/nocodeappdevelopment,anddatagovernance

EquityinvestmentandstrategicpartnershiptobuildthelargestGPUclusterinexistencetodeveloplarge-scaleAImodels

Partneredtoprovidebusinessesaplatformtocreatecustomized

generativeAIappswithintheSnowflakeDataCloudusingabusiness’sproprietarydata

AWSagreementtobecomeStability.AI’spreferredcloudprovider

EnablesHuggingFaceandAWStoaccelerateMLadoptionusingthelatestmodelshostedonHuggingFacewiththecapabilitiesofAmazon

SageMaker

zoom

EquityinvestmentandstrategicpartnershiptointegrateAnthropic’smodelintoZoom’sContactCenterportfolio

Google

$750M+investmentandpartnershiptointegrateAnthropic’sadvancedAIassistantintosoftwareproductsandprovidefurtherR&Dsupport

ReplitdevelopersgetaccesstoGoogleCloudinfrastructure,services,andfoundationmodelsviaGhostwriter,thestart-up’ssoftwaredevelopmentAI

NA

Added$250MtoexistingfundtoinvestspecificallyintoAI/ML

NA

New$50MfunddedicatedtoAIandnewintegratedAIfeatures

Sources:VentureBeat,FinancialTimes,TechCrunch,CompanyWebsites,PressReleases,TomaszTunguz,ContraryCapital

3.AnewtrendamongSaaSleaderstobuyandpackagehasemerged–despitetheproliferationofpartnershipsdominatingthemarketleaders’growthstrategiesthusfar,wehaverecentlyseenenterprisesoftwarecompaniespayupfront–oftenatapremiumorflatvaluetoarecenthighly-pricedequityraise–toacquireandintegrateAI/MLsolutionsintotheirexistingplatformswiththestatedintentofquicklycommercializingabundledoffering(seeFigure4).Thedynamicnatureoftoday’sAIfoundationalmodelsanddatainfrastructure–whichserveusecasesacrossnearlyeveryindustryandattractbothtechandnon-techusers–presentsachallengeforB2BsoftwarecompaniesseekingdefensibleAI/MLstrategies.ThisfighttoestablishacompetitivemoathasacceleratedM&Atimelinesandsetafoundationforvaluationmultiplesthatishighlyinconsistentwithcurrentmarketprecedents.Buyersincreasinglyseektoownpowerfulmodel/infrastructureassetsandindustry-orientedapplicationsbeforetheyhavegainedprovencommercialtraction,believingtheirexistingbreadthofGTMcapabilitieswillcreatean“economiesofscale”effectandenablethemtobefirst-to-marketwithpre-packagedsolutionsspecifictotheircustomerbase.

Sources:RichardWaters(FT),LazardVGBInsights

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Figure4:SelectAI/MLinfrastructureM&Aactivity–1H2023

Acquiror

Target

Amount($M)

Step-UpfromLastRound

Rationale

$1,300

~6x

MosaicMLwillbecomepartoftheDatabricksLakehousePlatform,providinggenerativeAItooling

alongsideDatabricks’existingmulti-cloudofferings

$650

~5x

ThomsonReuters’thesiswasthatCasetextwillaccelerateand

expandthecompany’smarket

potentialforgenerativeAIofferings

$200

~1.2x

Thecombinationenablesdata

teamstotransformtheirbusinessintelligencetobeAI-first,while

reducingbottlenecksand

increasingaccesstoinsightsthatdrivetangiblebusinessresults

$150

NA

Snowflakeplanstoinfuseand

leverageNeeva’sgenerativeAI-

enabledsearchexperienceacrossitsDataCloudplatform

Undisclosed

NA

ByintegratingOmniML’stechintoitsedgeofferings,NVIDIAcan

optimizemodelsforefficient

deploymentonlower-end

hardware.Additionally,NVIDIAcancreatecustomprofiles,

maximizingtheutilizationofitsedgehardwaresuite

Sources:TransactionPressReleases,PitchbookData,Inc.

4.Growthequityinvestorsstandtobenefitfromthebarbell-shapedmarket–disproportionatelysignificantvolumesofearly-stageAIcompaniesarefloodingthemarket,largelyenabledbythemodelandinfrastructureproviderswhohavebornethehighcostsofmodel-buildinganddata-tuningtofacilitaterapid,low-costshipmentofnewAI/MLapplications.Thisnascent,yetfast-growingproductdevelopmentactivity–coupledwithahighlyconcentratedfundingenvironment–hascreatedabarbelldynamicintheAI/MLmarket(seeFigure5).WebelievethisislikelytocreateawaveofinvestmentopportunitiesattheSeriesBandCstagesoverthenext6–12+monthsasmoresector-specificAIenterprisetoolswillattractmoregeneralistB2BSaaSgrowthinvestorsintotheAIfundingrace.Themostactiveearly-stageAIinvestorstodate–includingbrandnameslikeSequoia,IndexVentures,a16z,TigerGlobal,andKhosla–willprovideavalidatedpipelinefortop-tiergrowthinvestorstomine,whilethebulkofmid-quartilegrowthinvestorswillneedtofollowSoftbank’sleadincraftingseparateAImandatesenablingthemtoinvestearlierthaninotherverticals.

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Capitalhighlyconcentratedamongselectwinners,SeriesB/Cfinancingsyettotakeoff

Figure5:BarbelldynamicsoftheAImarket–historicaldealcountsandcapitalinvested

Dealcountsreflectflurryofnewcompanyformation

$24,028$22,510

$14,125

$8,239

$5,821

20182019202020212022YTD2023

Angel/SeedSeriesASeriesBSeriesCSeriesD+

$inmillions

$42,793

913

20182019202020212022YTD2023

Angel/SeedSeriesASeriesBSeriesCSeriesD+

3,054

2,296

2,144

2,230

2,538

Source:PitchbookData,Inc.

>50%oftotalAIfundingthrough1H’23

sourcedfrom7late-stageinfrastructuredeals

$11.3B

$250M$100M$270M$200M

$1.3B$450M

Source:Crunchbase

FundingdatadisplayedinFigure6suggeststhatthetier1VCsinvestingattheearlystagesarealsolookingbeyondcurrentmarketconditionswhenunderwritingnewAIinvestments,adoptingalong-termviewthatbackingpotentialcategoryleadersearlyamidanewplatformshiftjustifiespremiumpricing,evenattheriskofincurringhigher-than-averagelossratios.Incontrast,medianlate-stagedealsizeshavefollowedalumpytrajectoryasinvestorshaverecentlyfocusedonthe20–40top-fundedAI/MLinfrastructureproviders.MediandealsizescontractedsignificantlyinQ1’23tosub-pandemiclevelsbeforedoublingbacktopre-COVIDnormlevelsinQ2.

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…andthesecompaniesareattractinghigher

pre-moneyvaluations

$-$10.0$20.0$30.0$40.0$50.0$60.0

Q12023Q42022

Other

AI

Other

AI

$13.0

$13.5

$16.0$13.5

-4%

QoQ

+19%

QoQ

$37.5$35.0

$inmillions

$52.0$50.4

+7.2%

QoQ

+3.1%

QoQ

Figure6:Early-stageAIfundingtrendssuggestinvestorsaretakingalong-termview

Investmentintoearly-stageAIiscountering

broadermarkettrends

SeedSeriesA

$inmillions

-53.9%QoQ

$4,515

AIOtherAIOther

SeedSeriesA

Q42022Q12023

-34.1%

QoQ

$1,638

$1,080

+58.4%

QoQ

$754$476

+2.5%

QoQ

$194$199

$2,080

Late-stageAImediandealsizefluctuationsreinforceanascent,concentratedmarketforgrowthinvestors

$inmillions$95

$62

$50$50

$25

$50

2019202020212022Q1'23Q2'23Sources:CartaInsights,PitchbookData,Inc.

5.HorizontalAItoolsfavoruser-basedpricing(fornow)–ouranalysisfoundthatindustry-agnosticAIsoftware–encompassinggeneralenterpriseworkflowtoolsandgenerativecapabilitiestodeliverandenhanceformcontent(text,image,andvideo)–skewedheavilytowardsseat-basedpricingmethodologiesbyafactorof3xoverothersincludedinoursample.Nearly80%ofallGTMstrategiescenteredarounduser-basedpricingthatwestudiedcamefromthishorizontalsubset.Thereareseveralpotentialexplanationsforthistrend:1)usage-basedmodelsaremorechallengingtoimplementascompaniesneedtorewardhigh-volumeconsumerswhilealsofindingwaystodrivehigherengagementamonglow-averageusers;2)generativetoolshavemyriadusecasesforcontentdevelopmentwithunclearROIdistributiontoinformwhichusagemetricstoincentivizethroughpricingschemes;and3)generalback-office/ERPsolutionsoftenhavestandardizeduserprofileswithlimitedupsellpotentialonaper-userbasis.ThisisconsistentwithBain&Company’srecentfindingsonhorizontalapplications

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beingarelativelypoorfitforusage-basedmodelsrelativetoinfrastructureplatformsthatleveragedataasthecoreasset.

Sources:VentureBeat,LazardVGBInsights

Figure7:Shareofcustomersusingconsumptionmodelsvs.thosewhowanttousethem(2022)

PlatformasaServiceOSandvirtualizationITSM

Storage

Endpointsecurity

IncidentresponseandanalyticsAppdevtools

Databaseanddatawarehouse

Dataintegration/analyticsFinancialERP

HCM

OtherERP

SupplychainmgmtSales/serviceCRMMarketing

EngineeringandcontentCollaboration

18%19%

23%

24%

PaaS

InfrastructureSoftware

Dev/DeploymentSoftware

Applications

14%9%

11%

13%

Whereseat-based

pricingworksbestforenterprisecustomers

UsingTodayPreference

Source:Bain&CompanyTechnologyReport2022

6.AIdataandcompute“picksandshovels”criticaltobroaderindustrycommercialization–enterprisesarerushingtoidentifydifferentiatedwaystoincorporateLLMsandvectordatabasesintotheirtechstacks,andasrecentlynotedbyNVIDIACEOJensenHuang,areincreasinglyfocusedoncloud-firstAIstrategiesthatenablefastdevelopmentandscalabledeployment.ThequickestscalersthatwestudiedintheAI/MLinfrastructurecategoryallhadacommontraitofbeingtheearlyfacilitators—"thepicksandshovels”—enablingenterprisestoleveragemodelswiththeirownproprietary,unstructureddataandaccessnecessarycomputepowertobuildscalableapplications.Whilemuchoftheindustry’sfocushasbeencenteredaroundthemodelprovidersthemselves,thedataandresourceoptimizersaretheonesmostimpactingcommercializationacrossthebroadermarket.

ScaleAIisoneexampleofacompanythathasmaximizeditsmarketvalue(>$7B)bybeingthego-toplatformthatsitsbetweentherawdataandtheAImodelsthemselves,actingasanenablerforcompaniesseekingtoleveragesmarterAIcapabilitiesbutwithoutthetechnicalresourcestoimplementthem.TheScaleAIplatformautomatesthemanually-intensiveprocessofannotatingandlabelingenterprisedatabeforeitcanbefedintoAImodels,andthroughitsownrigorousback-endMLmodeltraining,isabletodosoinsmarterwaysthanifhumanscontrolledtheprocess.Havinggrownfromanimageandvideo-taggingbusinessinitsearlydays,thecompanyhasexpandeditsGTMstrategyovertimebyfocusingonvolume-basedpricingthatscaleswiththedatalabelledforcustomers(stickyexpansionopportunity)andaddingofferingssuchasdatadebuggingtoolsandsyntheticdatagenerationtofillingapsfromcustomers’existingdatasets.BeingthefoundationalaccessvectoranddemocratizertoAImodelshasenabledthecompanytoamasssignificantmarketshareandfendoffcompetitionfromearlier-stageplayerssuchasLabelboxandDataLoop.

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Onthecomputeside,Coreweavehasdifferentiateditselfbybeingthefirstat-scaleaccessprovidertoNVIDIAGPUs(highestqualityforAImodels),andclaimstodosoat80%lesscostthanexistingcloudproviders.Thishasledtolarge-scale,monetizedstrategicpartnerships,includingarecentdealwithMicrosoftthatisreportedtobeworthbillionsofdollarsovermultipleyears.Thecompanyhasgrownits1,000+customerbaseacrossfourverticals:generativeandopen-sourceAI/ML,batchprocessing,pixelstreamingandvisualeffects,andrendering.Evenwhilecompetinghead-to-headwiththemajorcloudproviders–AWS,GoogleCloudandAzure–Coreweavehassuccessfullymarketeditselfastheleadinghardwareproviderspecificallyforinference.Ultimately,Coreweave’searlyGTMstrategyofofferingaccesstobest-in-classGPUsatcustomerfriendly,usage-basedrateshasbeenthedifferentiatingfactorenablingitsscale.Whetherthecompanycanmaintainthatpricingadvantage,ordiversifyitsgenerativeAI-ledcustomerbase,willultimatelydetermineitsgrowthpotentialinacrowdedmarket.

Sources:WallStreetJournal,ContraryCapital,CompanyWebsites,LazardVGBInsights,CNBC,TechCrunch

Figure8:SelectAIdataandinfrastructureproviders

CompanyTotalCapitalRaised($M)Description

Coreweave$

482SpecializedcloudproviderpoweringGPU-acceleratedworkloads(AI,VFX,andHPC)atscale.

Lightmatter

$

266

Changeschiparchitecture,poweringfaster,energy-efficientcomputingwithphotonicprocessorsforsustainableAIadvancement.

Anyscale

$

260

AcceleratesthedevelopmentandproductionizationofanyAIapp,onanycloud,atanyscale.

Weights&Biases

$

200

Providesadeveloper-firstMLOpsplatformthatoffersperformancevisualizationtoolsformachinelearning.

LangChain

$

10

LLMapplicationdevelopmentlibrary.

OctoML

$

133

Offersanaccelerationplatformthathelpsengineeringteamsdeploymachinelearningmodelsonanyhardware.

Weaviate

$

68

Builds,maintains,andcommercializestheopen-sourcevectordatabaseWeaviate

InstaDeep

$

109

DeliversAI-powereddecision-makingsystemsfortheEnterprise,tosolvecomplexindustrialproblems.

CelestialAI

$

164

MLacceleratorcompanythatdevelopsdatacenterandedgeAIcomputingsolutions.

Comet

$

69

Allowsdatascientiststoautomaticallytracktheirdatasets,codechanges,experimentationhistory,andproductionmodels.

RelationalAI

$

122

Creatorofabreakthroughrelationalknowledgegraphsystem.

ResistantAI

$

43

HelpstoprotectAIsystemsfromtargetedmanipulation,adversarialmachinelearningattacksandadvancedfraud.

ScaleAI

$

603

ThedataplatformforAI,providingtrainingdataforleadingmachinelearningteams.

Pinecone

$

138

DevelopsavectordatabasethatmakesiteasytoconnectcompanydatawithgenerativeAImodels.

Replit

$

208

Browser-basedintegrateddevelopmentenvironmentforcross-platformcollaborativecoding.

LightOn

$

5

Develops"extreme-scale"AI(LLMs,FoundationModels)fortheenterprise

Synthesis

$

25

On-demandsyntheticdataforcomputervision

MostlyAI

$

32

BuildinganAI-poweredglobalB2Bhealthcaremarketplace

BeeKeeperAI

$

24

ZerotrustcollaborationplatformprotectingbothalgorithmIPandregulateddata.·

Sources:Crunchbase,PitchbookData,Inc.

7.Currentsector-orientedinvestmentslessfocusedonmonetizationtimelines–oursamplefoundthatvertical-focusedAIdealstodatehavelookedmorelikeDeepTechinvestmentsratherthantraditionalverticalB2Bsoftwareplays,judgingfromtheriskprofiles,longlead-times,anduncertaintyaroundcustomeradoptioninherenttothesecompanies.Webelievethisdynamicwillevolveastheapplicationlayercontinuestobebuiltoutandasindustry-specificmodelsenablemorewidespreadintegrationofAIfunctionalityontoenterprisesdatasets.Healthcare,mobility,InfraTech(industrial+logistics/supplychain),andcleanenergytechnologieswerethepredominantvertical-focusedsolutionscoveredinouranalysis.Thecommontraitsofthesebusinessesinclude:

Highbarriersformarketentrywithrequiredtrials/proof-of-concepts

Proprietary,self-generatingdatasets–oftenwithahardwarecomponent

HighCAPEXrequirementsforproductdevelopmentandtoreachoperationalscale

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Industry-specificregulatoryandcustomeradoptionhurdles

Longerrelativesalescycles,thoughoftenbringinglong-term,high-upsidecontractsFigure9:Industrydistributionofvertical-focusedAIcompaniesinoursample

20.4%

14.3%

12.2%

10.2%

8.2%

6.1%

6.1%

6.1%

6.1%

4.1%

2.0%

2.0%

2.0%

HealthcareAutonomousVehiclesInfraTech

CleanEnergyTechMediaLegal

Retail/Ecommerce AgTech FinTech Defense HRTech SpaceConsumer

Source:LazardVGBInsights

Despitethis,wethinkanewwaveofverticalizedAIapplicationsthatlookmore“SaaS-like”–automatingmoretraditionalB2Bworkflowsspecifictoanindustry–islikelytofloodthemarketinthenext12months.Ratherthanbeingdevelopedtosolvecomplextechnicalproblemsorenablenovelproductcreation(i.e.newmedicaltherapeutics,innovativeinfrastructureprojects),entrepreneursarelikelytofocusondeliveringsolutionstrainedonhighly-specifi