布鲁盖尔研究所-公共部门采用人工智能的案例研究（英）-2023.3

WORKINGPAPER|ISSUE03/2023|13MARCH2023

AIADOPTIONINTHEPUBLIC

SECTOR:ACASESTUDY

LAURANURSKI

Thiscasestudyillustratesthedriversofandbarrierstoartificialintelligenceadoptionbyorganisations,andacceptanceofAIbyworkersinthepublicsector.Severalfactorswerecrucialinthesuccessfuladoptionofahuman-centredapproachtoAI,includingafastdiscoveryphasethatinvolvedworkers(orendusers)inthedevelopmentearlyon,andaligninghumanresources,informationtechnologyandbusinessprocesses.Subsidysupportmechanismswerealsospecificallytargetedandacquiredtosupporttheadoption.

However,makingAIsupportavailabletoworkersprovedinsufficienttoensureitswidespreadusagethroughouttheorganisation.TheslowadaptationofexistingworkprocessesandlegacyITsystemswasabarriertotheoptimalusageofthetechnology.Moreover,theusefulnessofthetechnologydependedonboththetaskroutinenessandworkerexperience,therebynecessitatingarethinkingoftheworkdivisionbetweentechnologyandworkers,andbetweenjuniorandseniorworkers.

Successfulhuman-centredroll-outofAIinEuropewillthereforedependontheavailabilityof,orinvestmentsin,complementaryintangibleorganisationalcapital.Verylittleiscurrentlyknownabouttheseinvestments.

TheauthorisgratefultoTomSchraepen(Bruegel)forresearchassistance,toMiaHoffmann(Georgetown’sCenterforSecurityandEmergingTechnology)forcommentsonearlierversions,andtothecontactsatthecaseorganisations,whoprovidedtheircooperationandinputtothestudy.

LauraNurski(laura.nurski@)isaResearchFellowatBruegel

Recommendedcitation:

Nurski,L.(2023)‘AIadoptioninthepublicsector:acasestudy’,WorkingPaper03/2023,Bruegel

1

Tableofcontents

1Introduction 2

1.1Productivityandtechnologyacceptance 2

1.2Theorganisationsinthiscasestudy 3

1.3Selectionofthecase 3

1.4Methodology 4

2AIadoptionbytheorganisation 5

2.1Adoptionprocess 5

2.2Driversandbarrierstoadoption 9

3AIacceptancebystaffmembers 13

3.1Studiedalgorithm:AI-assistedquestionanswering 13

3.2Frameworkforuseracceptanceandactualuse 14

3.3Barrierstotheuseofthealgorithm 15

4Impactandsupport 21

4.1Impactonworkdivisions,learningandsocialrelationships 21

4.2PathtowardsincreasingAIacceptance 22

5Conclusionandrecommendations 26

References 27

Annex:Listofcasestudymaterials 30

S

1Introduction

1.1Productivityandtechnologyacceptance

VL〔!：!c!9l!u〔Gll!8GucG(Vl)!29uGM8GuGL9lbnLbo2G〔Gcpuolo8l(ebl)GxbGc〔Gq〔opL!u8bLoqnc〔!^!〔l89!u2,pn〔9l2o〔o!山b9c〔〔pGu9〔nLG9uqdn9l!〔lo：MoLk.lpG!u〔Loqnc〔!ouo：bLG^!on2ebl2,!uclnq!u8GlGc〔L!c!〔l9uqco山bn〔GL2,p922poMu9lou8l98pG〔MGGu〔pG9qob〔!ouo：uGM〔Gcpuolo8!G2!u〔pGbLoqnc〔!ouo：8ooq29uq2GL^!cG2,9uqM!qG2c9lGop2GL^9plG!ucLG92G2!u〔o〔9l：9c〔oLbLoqnc〔!^!〔l.lp!2bnSSl!u8op2GL^9〔!ou!2o：〔GuqnppGq〔pG“productivityparadox”(「9uq9nGL,Taa2),!G〔pGbpGuo山Guou〔p9〔l9L8G!u^G2〔山Gu〔2!uuGMll〔Gcpuolo8!G2pl：!L山2p9^Guo〔pGGu9cco山b9u!Gqpl2np2GdnGu〔 !ucLG92G2!uu9〔!ou9lbLoqnc〔!^!〔l2〔9〔!2〔!c2.lMocLnc!9l：9c〔oL2!uGxbl9!u!u8〔p!2b9L9qox9LG〔pG2loM9q9b〔9〔!ouo：pn2!uG22bLocG22G29uq〔pGnuqGLn〔!l!29〔!ouo：〔Gcpuolo8lplMoLkGL2(DG^9L9)9uqKopl!,S003).

MpGuoL89u!29〔!ou9lbLocG22G29uq!ucGu〔!^G29LGuo〔9l!8uGqM!〔p〔Gcpuolo8ln2G,MoLkGL2M!lluo〔：nlll9qob〔uGM〔Gcpuolo8!G2!u〔pG!LMoLk(V〔k!uetal,S0TS).Vl9bbl!c9〔!ou2Gdn9lll2n：：GL：Lo山2ncpnuqGLn〔!l!29〔!ou,922〔nq!G2o：〔pGp9uk!u8(Xn9uqSpn,S0ST),LG〔9!l(K9M98ncp!,S0S0)9uqpG9l〔pc9LG(19nketal,S0ST)2Gc〔oL2qG山ou2〔L9〔G.EnL〔pGL山oLG,!〔〔9kG2〔!山G,山ouGl9uqM!ll!u8uG22：oLpn2!uG22山oqGl29uqoL89u!29〔!ou9l山oqGl2〔o9q9b〔〔o〔pGn2Go：uGM〔Gcpuolo8l.2ncp9q9b〔9〔!ouLGdn!LG2!u^G2〔山Gu〔!ubGoblGbL9c〔!cG2oLpn山9uLG2onLcG2(H匕)bL9c〔!cG2,!uclnq!u8

〔L9!u!u8,bGL：oL山9ucGG^9ln9〔!ou29uqp!L!u8(Bloo山etal,S0TS).lu^G2〔山Gu〔!29l2ouGGqGq!uoL89u!29〔!ou9lbL9c〔!cG2,2ncp92pn2!uG22bLocG22LGGu8!uGGL!u8,qGcGu〔L9l!29〔!ouoLoL89u!29〔!ouLGqG2!8u(BLG2u9p9uetal,S00S).

lo9u9ll2G〔pGqL!^GL2o：9uqp9LL!GL2〔oVl9qob〔!ouploL89u!29〔!ou2,9uq9ccGb〔9ucGo：VlplMoLkGL2,MG!u^G2〔!89〔G92bGc!：!cc92G!u〔p!22〔nql.lpG2〔nqlqoG2uo〔2GL^G929‘2〔9Lc92G,(Mp!cp

qG山ou2〔L9〔G2LGbLoqnc!plGpG2〔bL9c〔!cG2)oL929‘LG2G9Lcpc92G,(Mp!cp9!山2〔o!山bLo^GGcouo山!c〔pGoL!G2o：c9n29l!〔l)(B9kGL9uqe!l,S0T3).lu2〔G9q,〔p!2!29‘〔G9cp!u8c92G,〔p9〔!lln2〔L9〔G22c!Gu〔!：!c〔pGoL!G2!u9bL9c〔!c9lGx9山blG9uqpL!q8G22G^GL9lq!2c!bl!uG2(!uclnq!u8ll,山9u98G山Gu〔,oL89u!29〔!ou9lpGp9^!onL,b2lcpolo8l9uqGcouo山!c2)pll!uk!u8〔pGoL!G2：Lo山LG2bGc〔!^Gqo山9!u2!uouGc92G.lpG8o9l!2〔o!qGu〔!：lb!〔：9ll2!u〔pGbLocG22o：〔Gcpuolo8l9qob〔!ou9uq〔obLo^!qG2o山G lG22ou2：oLpo〔pbol!cl9uqpn2!uG22.lp!2c92G2〔nql!2b9L〔o：〔pGEn〔nLGo：MoLk子lucln2!^GeLoM〔p

3

project

1

atBruegel,whichaimstoidentifytheimpactoftechnologyonthenature,quantity,andqualityofwork.

1.2Theorganisationsinthiscasestudy

WeanalyseAIadoptionbyFlandersInvestmentandTrade,apublicorganisation,whichwasassistedbyRadix,aprivatefirm

2

.Notethatthroughoutthepaperwerefertoalistofcase-studymaterialsthroughnumeralsshowninsquarebrackets.Theannexliststhecase-studymaterials.

FlandersInvestmentandTrade(FIT)isthetradepromotionorganisation(TPO)ofFlanders,aregionofBelgium.TPOsarefacilitativeagenciesthatpromoteandstimulatetradebyprovidinginformation,linkages,technicaladvice,marketingandpolicyadvocacy(Giovannucci,2004).Theiractivitiescanbegroupedintofourbroadcategories:productandmarketidentificationanddevelopment;tradeinformationservices;specialisedsupportservices;andpromotionalactivitiesabroad(Jaramillo,1992).FIT’smissionistointernationalisetheeconomyofFlandersbyassistingFlanders-basedcompaniesintheirexporteffort(‘trade’)andbyattractingforeigncompaniesandinvestmenttotheregion(‘invest’).Alongsidedeliveringtradeandinvestmentservices,FITengagesinpromotionalanddevelopmentactivitiesincludingthehostingofeventsandpublicationofmarketinsights.FIThassixregionalofficesinFlandersandBrussels(employingabout150people)and100localofficesabroad(employingabout180people).

RadixisaBelgianAIsolutionprovider,foundedin2018.Ithasateamof40engineersandsolutionleadsacrosstwoofficesinFlandersandBrussels.RadixprovidesaportfolioofAIsolutionstoimproveoperationsinarangeofindustries,includingmanufacturing,transportation,financialservicesandthepublicsector.

1.3Selectionofthecase

ThecasewasfoundthroughthewebsiteoftheAIdeveloper(Radix),whichshowcasesclientstories.SeveralRadixclientstorieswererelevantfortheFutureofWorkandwerethereforeconsidered.Amongthemweretwoclientsinthehumanresourcesandpublicemploymentsectors:anAI-supportedorientationtestdevelopedfortheFlemishpublicemploymentagency,andanAI-poweredjob-matchingalgorithmdevelopedforaprivateHRservicescompany.AIwilllikelyplayamajorroleinmatchingjobseekerstojobvacanciesinthelabourmarketsofthefuture.Boththeopportunitiesand

1See

/future-work/future-work-and-inclusive-growth-europe

.

2See

/

and

https://radix.ai/

.

4

potentialdangersofthisapplicationarecurrentlybeingstudiedanddebatedwidely,withspecificfocusontheriskofincreasingdiscriminationinthelabourmarket.However,inthisparticularcasestudy,thegoalistostudyAInotinthejob-matchingprocess,butintheproductionprocessitself.

FITwashighlightedasaRadixclientthatadoptedAIinoneoftheircorebusinessactivities:answeringtrade-relatedquestionsfromFlemishcompanieslookingtodotradeabroad.OtherclientcasesofthisAIdeveloperwithapplicationsintheproductionprocessincluded:aproductionplanningalgorithmthatimproveson-time-deliveryofproductionorders,takinglesstimethanahumanplanner;analgorithmthatimprovesvaccinedevelopmentbycountingandreportingcolonyformingunits;andanalgorithmthatautomaticallytagsnewarticlesofanewssupplierwithtopicalhashtags.Weselectedthequestion-answeringalgorithmforFITovertheseotherexamplesbecauseitfittedthecurrentnarrativeofAIreplacingroutinecognitivetasksofknowledgeworkers.AnotherreasonwasthatthedevelopernotedintheirFITclientprofilebothproductivityincreases(27percenttimesavings,36percentmorequestionsanswered)andjobsatisfactionimprovements(focusonmorecomplexcasesandotherpartsoftheirjobs)[8–seetheannex],whichfittedourgoalofstudyingbothproductivityandjob-qualityeffects.

1.4Methodology

Thecasewasstudiedthroughthecollectionandanalysisofseveraldatasources.First,deskresearchwasperformedontheexistingscientifictheoriesandevidenceonAIadoptionandacceptance.Thisdeskresearchresultedinthepublicationofseveralblogpostsandpapersonthesetopics(seeforexampleHoffmanandNurski,2021a,2021b).Second,deskresearchwascarriedoutonpubliclyavailableinformationonthecases,mostnotablytherespectivewebsitesofFITandRadix.Inathirdstep,interviewguidesweredevelopedonthetopicsontechnologyadoptionandacceptanceforseveralintervieweetargets.InterviewswereconductedwithFIT’sAIleadandHRlead(see[4],[10],[13])andwithfour‘endusers’ofonespecificAIapplicationatFIT,alsoknownas‘casehandlers’(see[12]).Thefourendusers(twomenandtwowomen)werestationedinfourdifferentoffices:France,Germany,ItalyandtheUSA.Dependingontheinternalorganisationoftheoffice,someoftheintervieweesspecialisedincertainregionsoftheircountry,whileothersspecialisedincertainindustriesinthatcountry.Afinaldatasourceconsistedofcollecteddocuments,includingslidedecks,screenshotsandtrainingmaterials.Thefulllistofcasestudymaterialscanbefoundintheannex.

5

2AIadoptionbytheorganisation

2.1Adoptionprocess

2.1.1Timeline

Aspartofitsdigitalinnovationstrategy(seesection2.2.1),FITisadoptingAIacrossarangeofactivitiesinitsprimaryservices,namelythetradeandinvestservices.Overfouryears(2017to2021),FITwentthroughthreeAIprojectcyclesto:(1)experimentwithproof-of-concepts(POCs),(2)buildanAIstrategy,and(3)set-upthenecessarydatainfrastructure.

Table1:SummaryofphasesintheAIadoptionprocess

Year

Phase

Goal

2017-2019

AIproof-of-concepts

QuickPOCstoexperiment,learnanddiscover

opportunities

2020

AIstrategy

Assessingcurrentas-isAImaturityanddevelopingaroadmaptowardsthedesiredto-bestateofAIadoption

2020-2021

Datainfrastructure

Installrequiredinfrastructureforcentralisingandprocessingallinternalandexternaldatasources.

Source:Bruegelbasedon[4].

2.1.2Phase1:DevelopingAIproof-of-concepts(2017-2019)

Inthefirstphase,FITfamiliariseditselfwithAItechnologytodiscoveropportunitiesandinvestigatewhetheritwouldbeusefultoexplorefurther.AnexternalAIagency(Radix)setupa‘fastdiscoveryworkshop’forFIT’sAIleadtoscreenFIT’sbusinessprocessesforpotentialAIopportunities[7].ThisworkshopconsistedofaseriesofbrainstormingexercisesbetweentheAIdeveloperandtheorganisationlookingtoadoptAI.Firstalonglistofideaswasassembledbygatheringideasfromdifferentstakeholders;nexttheideaswereanalysedandprioritisedinlightoftheirtechnicalfeasibilityandbusinessvalue;finally,effortandvalueestimationsweremadefortheselectedopportunities[14].

Thisprocessgeneratedfiveproof-of-concepts(POCs)forusingAItosupportFIT’scorebusinessservices,namelythetradeandinvestservices.Theyrangedfrominformationgatheringonforeigncompaniesthroughwebscraping,leaddetectionofpotentialclientsthroughsociallistening,andpredictivemodellingformarketingbasedonlikelihoodstoinvestandtrade[4].Thislistofopportunitieswasprioritisedaccordingtotheirbusinessvalueandtechnicalfeasibility(effortandcomplexityofimplementation)(seeFigure1).ThePOCthatcameoutasa‘quickwin’(highvalue,low

6

Value

complexity)wasaquestion-answeringalgorithmforFIT’s‘tradecases’

3

,aimedatpartlyautomatingtheprocessofansweringtradequestionsfromFlemishcompaniesaboutforeignmarkets.Usingnaturallanguageprocessing,trainedonalargedatasetofpasttradequestionsandanswers,thealgorithmwasdesignedtoretrievepastanswerstofrequentlyaskedroutinequestions.The‘highvalue’wasestimatedbecauseofthelargesharethistasktakesupintheworkloadofcasehandlers(namely,60percentto70percentoftheirworkload).The‘lowcomplexity’wasestimatedduetotheavailabilityofhighquality‘offtheshelf’naturallanguageprocessing(NLP)modelsthatcouldbetrainedonFIT’slargehistoryoffiveyearsofpreviouslyansweredquestions(about10,000peryear).Finally,analgorithmwasdesignedtoretrievepastanswerstoroutinequestions,sothatFITadvisorscouldspendmoretimeonthecomplexquestions.TheapplicationactsasanAI-poweredsearchengine,notjustcomparingindividualwords,butinterpretingtheentirebodyofthequestionandfindingthemostrelevantpastanswer.

Figure1:Value-complexitymatrixforprioritisingAIopportunities

Highvalue,lowcomplexity

Quickwins

Highvalue,

highcomplexity

Strategic

initiatives

Lowvalue,lowcomplexity

Lowvalue,

highcomplexity

Complexity

Source:[7].

The‘tradecases’question-answeringPOCwasfurtherdevelopedintoacompleteAIproductbyintegratingthealgorithms’recommendationsintoFIT’sexistingCustomerRelationshipManagementsoftware(CRM),MicrosoftDynamics.Toevaluateandimprovethequalityofthisfirstminimumviableproduct(MVP),thedeveloperconducted10interviewsacrossseveralofFIT’sinternationalofficesandassessedtheresultsfor175newtradequestionsthatwerehandedtotheAI.Ineachcase,thealgorithmsuggestedfivepreviousanswers,meaningabout875AI-suggestedanswerswereevaluated.

3A‘tradecase’isaquestionfromaFlemishcompanyaboutaforeignmarket,thatconcernsservicesofFIT,forexampleinquiriesaboutthesizeorcustomsofalocalmarket,potentialforeignbusinesspartners,traderegulationsorbarriers,subsidies,ormarketopportunities.Seesection

3.1

belowformoredetailonthebusinessprocessandAIsupport.

7

Thedeveloperusedstaffmembers’personalmemoriesofpastcasesbyaskingthemifabetteranswerfromthepastexisted,andthenanalysedwhythealgorithmdidnotretrievethemostrelevantanswer.Justasworkerslearnhowtoimprovetheiranswersovertime,thealgorithmwasretrainedbasedonthecorrectionsofFITstaff.Reasonsformissingbetteranswersfromthepastincluded:unrecognisedsynonyms(sametopicbutdifferentwords),wronglanguage(sametopicbutdifferentlanguage,egEnglish,Dutchorotherlanguage),unclearlink(sametopicbutnotexplicitlymentioned),wrongfocus(AIdidn’tfocusonrightwords),andout-of-vocabulary(AIdidn’tknowcertainwords).Bytakingintoaccountstaffmemberfeedback,thehitrate(casesinwhichtheAIfoundarelevantanswertoaquestion)increasedfrom51percentto62percent[7].Involvingusersinthedesignofthealgorithmthusimproveditsquality(andthereforeuseability,see3.3.2)substantially.

2.1.3Phase2:BuildinganAIstrategy(2020)

ThefirstphaseshowedthatitwaspossibleandopportunetoexpandtheadoptionofAIinawiderrangeofFIT’sprocesses.Inthesecondphase,theytookastepbackfromtheoriginalfivePOCsandtookamorestructuralapproachtoAIbybuildinganAIvisionandstrategy(orAI‘blueprint’)fortheorganisation.Withthehelpofthreeexternalexperts,anAImaturityassessmentwasdone,followedbythedesignofafuturevisionandaroadmaptomovefromtheas-issituationtothedesiredto-bestate[4].

ThemethodologyforbuildingtheAIstrategyconsistedofthreebuildingblocks.First,anenterprisearchitecturewasdrawnup,mappingthecurrentbusinessprocessesonapplications,datalayersandtechnicalsystems.Second,anAImaturityassessmentwasconductedtoassessthe‘as-is’stateofAImaturityandtodevelopanAIroadmapofpotential‘to-be’statesofAIadoption.ThethirdpartoftheAIstrategyrelatedtotrainingandhumanresources.ItincludedsettingupanAIunitresponsibleforAIimpactanddisseminationatFIT,trainingeveryoneatFITonbasicAIliteracy,andspecifictrainingforthedigitalmarketingteamondata-drivenmarketingstrategiesandtools.

Theexternalexpertsclassifiedtheas-isstateofFIT’sAImaturityat‘AIready’,whichisthesecondlevelofmaturityintheirassessmentframework:

•AINovice:AInoviceshavenottakenproactivestepsontheAIjourneyand,atbest,areinassessmentmode.

•AIReady:SufficientlypreparedtoimplementAIintermsofstrategy,organisationalset-upanddataavailability.

8

•AIProficient:AreasonabledegreeofpracticalexperienceandunderstandingofhowtomoveforwardwithAI.Therearestillgapsandlimitations.

•AIAdvanced:AgoodlevelofAIexpertiseandexperience,withaproventrackrecordacrossarangeofusecases.Goodoperationalproceduresinplace.

TheAIroadmaptowardstheto-bestatewasdrawnuptomovethroughthreestates.Inafirststage,FITwoulduseself-servicebusinessanalytics

4

anddashboardingapps(suchasPowerBIandAzuredataservices)andready-madeAIsupportedinsights(forexampleOffice365workplaceanalytics)tobuildadatafoundationandsupportadata-drivendecision-makingculture.Inasecondstage,FITcouldusesolution-specificAIservicesandAI-basedcontentunderstanding(forexamplechatbotsandApplicationProgrammingInterfaces(APIs)toNaturalLanguageProcessing(NLP)models)tobuildanFITconversationalknowledgeplatform.Finally,inthethirdstage,FITcouldadoptadvancedcloudinfrastructuresandopenmachine-learningframeworks,aswellasdeveloptheirowncustomdatascienceanddeepAIcapabilitiestosupportthedigitalmarketingpipeline(forexampleontargetedads,leadsanddirectmarketing).

2.1.4Phase3:Settingupthenecessarydatainfrastructure(2020-2021)

Fromtheassessmentinphase2,itbecameclearthatFITlackedtherequiredinfrastructureforlarge-scaleAIprojectsthat,forexample,requiretheprocessingofunstructureddatainrealtime.Thefirststepintheroadmapthereforeconsistedofbuildingadatahub(ordatavault)forabsorbingdatafromdifferentinternaldatasources[4].TheseinternalsourcesincludedFIT’saccountingsystem,EnterpriseResourcePlanning(ERP)system,website,CRMsystemandtwooldlegacysystemsthatstillfedintotheCRM.Thedatahubwouldalsocentraliseandingestallpurchasesofexternaldata,likecompanydatabases.Ontopofthephysicalinfrastructureforstoringdata,anoperationaldatabaselayerwouldbebuiltaroundcustomers,products,accountsandtransactions.ThisdatalayerwouldfeedintoanAPIaccesslayerthatwouldgrantdifferentbusinessapplicationsaccesstoandmonitortheiruseofthedata.Thisset-upwouldserveasthebasisforallfuturedataconsumption(bothstructuredandunstructured),datasharingandexchange,datamonitoringandaccessmanagement.Bysupportingnearreal-timedataprocessingandreporting,itwouldserveasthefoundationforallfutureAIdevelopment.

4Self-serviceanalyticsisaformofbusinessintelligence(BI)inwhichline-of-businessprofessionalsareenabledandencouragedtoperformqueriesandgeneratereportsontheirown,withnominalITsupport.

(/en/information-technology/glossary/self-service-analytics

).

9

2.2Driversandbarrierstoadoption

Anorganisation’sdecisiontoadoptanewtechnologyisinfluencedbythetechnological,organisationalandenvironmentalcontext(Baker,2012;HoffmannandNurski,2021).AccordingtoaEurope-widecompanysurvey(EuropeanCommission,2020),themainreasonsforfirmstonotadoptAIarealackoffinancialmeans,humancapitalanddataavailability,bothwithinthefirmandfromtheexternalenvironment(HoffmanandNurski2021).Table2listsdriversandbarriersthatwereidentifiedinthiscasestudyineachofthethreecontexts,whilethefollowingparagraphsdivedeeperintoeachofthefactors.

Table2:IdentifieddriversandbarrierstoAIadoptionatFITinthetechnological,organisationalandenvironmentalcontext

Identifieddrivers&facilitators

Identified(overcome)barriers

Technological

context

Expectedproductivitygains

Dataavailability

Hightrialability

LackofcompatibleITinfrastructure

Organisational

context

Leadershipandmanagementsupport

Environmental

context

Competitivepressures

Accesstoskilledlabourand

externalfunding

Source:BruegelbasedonBaker(2012),interviews,documentsandwebsites(seetheannex).

2.2.1Maindriverofadoption:competitiveenvironment

Asasmall,openeconomy,internationalbusinessisakeyfactorintheeconomicdevelopmentofFlanders.In2021,Flandersimported€378.8billionworthofgoodsandservicesandexported€380.5billion,puttingtheFlandersregioninthetop20ofglobalexportercountries(WTOStatsdashboard).Topexportedproductsincludepharmaceutical,chemical,andmineralproducts,andmachinery,electronicandtransportequipment.ThemaintradingpartnersareneighbouringcountriesGermany,FranceandtheNetherlands,andintra-EUtraderepresentstwo-thirdsoftotalexportsfromFlanders[2].WhileseparatenumbersareunavailableforFlanders,exportfromBelgiumasawholesupports843,900jobsinBelgiumoutoffivemilliontotalemployment(Rueda-Cantucheetal,2021).

TPOsaroundtheworldcompeteforlocalinvestmentsbymultinationalcompaniesandneedsophisticatedapproachestoattract,andkeepforeigninvestors(Zanattaetal,2006).FITconsidersdigitalisationakeyfactorinitsstrategytostaycompetitiveinthisinternationallandscape[3].FIT

10

thereforeaimstobean‘earlyadopter’(Rogers,1983)indigitalisation.Theachievementofthisgoalisrecognisedbyitsenvironment,asFITisconsideredoneofthebestpracticesfordigitalisationandAIadoptionbytheEuropeanCommission[4and9].

ThedigitisationofFITreflectsthewiderdigitaltransformationoftheFlemishgovernmentandtheFlemishDigitalStrategy,buildingontheFlemishDataStrategythatwasapprovedon18March2022[5].Whilethedigitalstrategyisstillbeingbuilt,theFlemishgovernmentaimstoreachatop-fivespotintheEuropeanrankingofdigitalpublicservices,asmeasuredbytheDigitalEconomyandSocietyIndex(DESI)[6].

2.2.2Overcomingfinancialbarriers:externalfinancing

Foreachofthethreephases,externalprojectsubsidieswereacquiredforthespecificgoalofdigitalisationandAIadoption,eitherdirectlyorindirectlyfinancedbypublicfunds.Thefirststage(AIPOCs)andthirdstage(datainfrastructure)tookplacewithintheframeworkofFlandersAccelerates,whichisFIT’sinternationalisationstrategyfortheFlemisheconomy.TheexecutionofthisstrategyissupportedbyacombinationofEuropeanandregional(Flemish)funds.Fortheperiod2017-2022,FITreceived€1.8millionfromtheEuropeanRegionalDevelopmentFund(ERDF)and€1.6millionfromtheFundforAccompanyingEconomicandInnovationPolicy(HermesFund),managedbytheFlemishInnovationandEntrepreneurshipagency(VLAIO).BothfundswereawardedspecificallyforFIT’sdigitalisationstrategy.

Thesecondphase(buildingtheAIstrategy)wasspecificallyanddirectlysupportedbytheStructuralReformSupportProgramme(SRSP),managedbytheEuropeanCommission’sDirectorate-GeneralforStructuralReformSupport(DGReform),theEUbodythathelpscountriesdesignandimplementreformsaspartoftheireffortstosupportjobcreationandsustainablegrowth.TheCommissionprovidedsupportovera12-monthperiodintheformoftechnicaladvisoryservicesbyentitieswithsubstantialexperienceinthedevelopmentofblueprintsforAIforpublicadministrations[9].TheadvisoryservicessupportedthethreeelementsoftheAIstrategydiscussedabove,namely:(1)developinganAImaturityassessment;(2)recommendingafuturearchitectureandroadmapforAIdeployment;(3)proposingcurriculaforAI-relatedtrainingofFITstaff.DGreformfeaturestheprojectonitswebsiteasinspirationforotherEUcountries[9].

11

2.2.3Overcominghumanandorganisationalbarriers:hiringandtraining

FollowingFIT’sdigitalisationandinnovationstrategy(see2.2.1)themanagementteamdecidedthat“FITwantedtojointheAItrain”[10].Abusinessandinformationsystemsengineerwithsevenyears’experiencein