Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Ericsson Technology Review: BSS and artificial intelligence – time to go native

42 views

Published on

5G and the Internet of Things (IoT) represent major growth opportunities for communication service providers (CSPs) in the near future. However, the ability to support emerging use cases in these areas requires business support systems (BSS) that can handle complex business situations and optimize outcomes with minimal manual intervention. Artificial intelligence (AI) is the obvious answer, but adding it on to existing BSS here and there is not sufficient.

The latest Ericsson Technology Review article argues in favor of architectural changes to traditional BSS that would allow it to become ‘AI native’. One of the key differences between traditional BSS and AI-native BSS is the fact that the latter enables the various applications within the BSS to share business information with each other in an efficient and secure manner – a critical capability in the emerging 5G-IoT world.

Published in: Technology
  • Be the first to comment

  • Be the first to like this

Ericsson Technology Review: BSS and artificial intelligence – time to go native

  1. 1. Enterprise Strategic level Tactical level Operational level Business model #1 Business model #2 Common enterprise resources Resources common to all business models, for example: ∙ Party model ∙ Channels ∙ Credit check service Credit rules Channel serviceCustomer Contract Sales Onboarding Billing Settlement Rating Customer support Intent Intent Intent Intent Intent Intent Intent Inten Intent Intent Inte Intent Intent ERICSSON TECHNOLOGY C H A R T I N G T H E F U T U R E O F I N N O V A T I O N | # 0 1 ∙ 2 0 1 9 AI-NATIVE BUSINESSSUPPORT SYSTEMS
  2. 2. ✱ AI-NATIVE BUSINESS SUPPORT SYSTEMS 2 ERICSSON TECHNOLOGY REVIEW ✱ JANUARY 30, 2019 The growing need to support disruptive services emerging from the Internet of Things (IoT) and 5G requires a fundamental transformation of business support systems (BSS). At Ericsson, we believe that the best way to achieve this is by forging BSS and artificial intelligence (AI) together to create truly AI-native BSS. LARS ANGELIN, JOHAN SILVANDER Although AI is of obvious benefit in terms of business optimization, and has been used in all sorts of businesses for decades, AI and BSS have neverbeenintegratedintooneefficient system. ■ ExamplesofareasinwhichAIisalreadyusedin conjunctionwithBSSsoftwareincludecustomer retention,chatbots,revenueandcostpredictions, customeranalysis,customerexperiencemanagement, customeryieldoptimization,automation,process reengineering,simulations,qualityimprovements, andfraudandanomalydetection. AIcapabilitiesenableimprovedbusiness decisiondynamicsandbetterdecisionprecision, resultinginbetterbusinessperformanceandagility. Virtuallyallbusinessactivitiescanandwillbenefit fromAI,andas5GandtheIoTcontinuetoexpand, thenumberofusecaseswillonlycontinuetogrow. Thechallengewefaceatpresentisthatthelearning, insight-buildingandreasoningcapabilitiesofAIin today’stelcoBSSarenotasstrongastheyneedtobe tocopewithemergingusecases. Forthemostpart,AIcapabilitiestodayare simplyboltedontotelcoBSSonebyone.Butthis isinefficientintermsoflife-cyclecosts,because theBSSmustberepeatedlyupgradedtobenefit fromtheAIalgorithms.Further,astheyareseparate systems,theBSSinformationmustbetransformedto fitAIsystems,andviceversa.Amuchmore efficient alternativeisAI-nativeBSS–thatis,BSSwith intrinsicAIcapabilitieswheretheAIlogic BSSandartificial intelligence–TIME TO GO NATIVE
  3. 3. AI-NATIVE BUSINESS SUPPORT SYSTEMS ✱ JANUARY 30, 2019 ✱ ERICSSON TECHNOLOGY REVIEW 3 isanaturalpartofBSSlogicintermsofbothdesign andoperation.Thisapproachresultsinasystemthat canhandlemorecomplexbusinesssituations, generatingmoreoptimizedbusinessoutcomes. BSSevolutiondrivers Themaingrowthopportunitiesforcommunication serviceproviders(CSPs)withinthenextdecadeare 5GandtheIoT,withanestimatedannualvalueof approximatelyUSD600billion[1,2]. Tocapitalize onthisopportunity,CSPsmustbeabletosupporta marketplacewithanecosystemofmanyactorsthat havetheirownbusinessmodels,whereeachactor maybebothsupplierandcustomertootheractors. Businesssupportcomplexityincreasesdramatically inthisenvironment.CurrenttelcoBSS,whichcan onlysupportasingleenterpriseshopwithafew businessmodels,are simplynotuptothetask. Surveysshowthataclearmajorityofthe telecommunicationsindustryactorsexpect AItohavesignificantbusinessimpactinthe comingfiveyears,affectingboththetopand bottomlines.TheyalsoexpectAItobring asignificantcompetitiveadvantagetothe enterprisesusingthem,growingproportionally withAIusage.Analystspredictthatenterprises willinvestinAIcompetence,AImaturityandin organizationalAIcapabilities[3,4],despite thecosts[5]. Thereareessentiallythreemainforcesdriving thecombinedAI-BSSevolution[1].Firstly,business agilityisahighlyvaluedBSSpropertysincethe businessitselfevolvesandnewbusiness opportunitiesemerge.AIplaysakeyroleinboth identifyingopportunitiesandinshapingthenew businessmodelstopursuethem.Secondly,the maturityofthecommunicationsindustryisdriving everlowerbusinesstransactioncosts,as demonstratedbyexistingplatformplayerslike AmazonandAlibaba.Inlightofthis,AI-supported processautomationandreengineeringarethetools Artificial intelligence (AI) depends on software algorithms. At Ericsson, we use the term AI in its widest sense, including several subfields such as machine learning, representation learning and deep learning. AI-related areas such as natural-language processing, automated reasoning, multiagent systems, symbolic learning, knowledge representation, intelligent tutoring systems and high-level computer vision are also included [8]. ARTIFICIAL INTELLIGENCE Terms and abbreviations AI – Artificial Intelligence | BSS – Business Support Systems | CRISP-DM – Cross-Industry Process for Data Mining | CSP – Communication Service Provider | IoT – Internet of Things
  4. 4. ✱ AI-NATIVE BUSINESS SUPPORT SYSTEMS 4 ERICSSON TECHNOLOGY REVIEW ✱ JANUARY 30, 2019 ofchoice.Finally,thecloudprovidesanideal foundationforcontinuousintroductionofAIand BSSmarketplacecapabilities.Thisisbecausethe cloudoffersdeploymentflexibility,elasticscaling andamicro-servicearchitecturethatenablesamore fine-grainedseparationofconcernsand componentizationwithloosecoupling. KeychallengestoaddingAItoBSS IntroducingAIintoexistingBSSisnot straightforward.Somechallenges,suchasdata acquisition,datapipingandtrainingalgorithms,are obviousandwell known.Others,however,areless so.Oneexampleofalessobviouschallengeisthe factthattheinterpretationoftheAIresultsrequires businesscompetence;anotheristhatmonetization requiresbothretrainingwithintheorganizationand redesigningoftheexistingsetofbusinessrulesand processesandsystemreengineering[5]. Traditional,non-AI-nativeBSSaredividedinto componentsilos–suchascustomerrelationship management,catalogs,billingandorder management–eachwiththeirowninformation. ThisarrangementcontradictsAIefficiencyand dynamicsenablement,whichrequireanopen,pan- BSSinformationandrulesview.WhenanAI capabilityisaddedtothisenvironment,itistreated asanadd-on,requiringbothAIandBSSsystem competence,informationtransformationandin manycasespartialsystemre-implementationor reconfiguration.BSSperformanceissuessuchas latencyandscalingmayarise. Manybusinesssituationsaremultifaceted,have manyrootcausesandmayincludebothgainsand risks.Inmanycases,amainbusinessintent(also knownasaKPI)mustbebrokendownintoa combinationofsubintents.Thesewillbebasedon manydatasetsandalgorithms,andthenbestitched togetherbyasuper-algorithmtodeliverthemain businessintent.Thereisalsoariskoflostbusiness control,asahigh-levelintentmayaffectmanyofthe lower-levelbusinessrulesandprocesses.Thiseffect isconsiderablysmallerwhenanintentisintroduced atlowerlevels,butinthosecasestherewillbeless businessgain.Theuniquenessofanintentandits contextmeansthereislittleopportunityforreuseor experiencebuilding. EricssonbelievesthatanewBSSarchitecture stylethatincorporatesAI-nativeproperties– includingdata-centric,learningloop,intentand event-drivenlogic,businessrulehierarchy,and supportforstrategic,tacticalandoperationallevels– isamuchmoreefficientwaytointegrateAIwith BSS.WefullyagreewiththeviewthatfutureBSS andAIwillbeinseparablylinkedandmustmature together[4,5]. IntroducingintentstoBSS Anenterpriseisahierarchicalorline-of-command structureinwhichbusinessrulesatthetopsteer, alignandcontroltheactivitiesandbehaviorsfurther downinthestructure.Businessrulesalsosteerall behaviorinBSS,inpursuitofthegoalofcreatingand maintainingasuccessfulbusiness.Thestepbetween abusinessruleandabusinessintentisverysmall; justasmallshiftofperspective. Abusinessruleisstaticandstateswhattodoina givensituation,whileabusinessintentstatesthe desiredoroptimaloutcomeofagivensituation– thatis,interpretingwhatthestakeholder’sinterest isandtryingtodeliverasclosetoitaspossible. ABUSINESSINTENT STATESTHEDESIREDOR OPTIMALOUTCOMEOF AGIVENSITUATION
  5. 5. AI-NATIVE BUSINESS SUPPORT SYSTEMS ✱ JANUARY 30, 2019 ✱ ERICSSON TECHNOLOGY REVIEW 5 Businessintentscanhandlecomplexanddynamic situationsandallowforfeedbackandcomparison ofactualanddesiredoutcomes,enablinglearning andknowledgebuilding[1]. Higher-levelintentsinBSSareoftenexpressed asbusinessrulesorKPIs.Intentsarefoundatall levelsofthebusinesshierarchy,supportingboth top-andbottom-lineoutcomes.Anintentcan rangeincomplexityfroman‘atomicintent’ toan‘algorithmofintents’thatcombinesaset ofsubintents.Theterm‘atomicintent’refers tothesimplestpossibleintentstructure,suchas “ourcompanywillrunaprepaidbusinessmodel.” Notethatanatomicintentonastrategiclevel islikelytofanoutintoseveralintentsona lowerlevel.Whileintentscanbeformulated forbothhumanandmachineconsumption, theymustbestatedinadeclarativeformat tofacilitateautomationinBSS. Figure1illustratesthestructureofthe businessintenthierarchy,whichisdesignedto mirrorthebusinessrulehierarchy.Anenterprise musthaveatleastthreedifferentintentlevels –strategic,tacticalandoperational[5]–allof whicharetheresponsibilityoftheBSS. Insoftwareterms,theselevelsareequivalent torequirements,designandimplementation, andexecution. Figure 1 The business intent hierarchy mirrors the business rule hierarchy Enterprise Strategic level Tactical level Operational level Business model #1 Business model #2 Common enterprise resources Resources common to all business models, for example: ∙ Party model ∙ Channels ∙ Credit check service Credit rules Channel serviceCustomer Contract Sales Onboarding Billing Settlement Rating Customer support Intent Intent Intent Intent Intent Intent Intent Intent Intent Intent Intent Intent Intent Intent Intent
  6. 6. ✱ AI-NATIVE BUSINESS SUPPORT SYSTEMS 6 ERICSSON TECHNOLOGY REVIEW ✱ JANUARY 30, 2019 Thebusinessstrategylevelownsandformulates theenterprise’stopintents.Forthesakeofsimplicity, Figure1breaksdownonlyonebusinessmodeltoall threelevels,butitisimportanttonotethatan average-sizeoperatorrunsseveraldifferentbusiness models.Thebusinesstacticlevelisresponsiblefor designingandimplementingtheintentsofeachof thesebusinessmodelsattheoperationallevel.This isnormallyachievedbybreakingdownthestrategic intentintosmaller,digestibleparts,suchas subintentswithassociatedrules,informationand processes.Thebusinessoperationlevel–thecoreof traditionalBSS–isresponsibleforexecutingto delivertheintents.Thislevelrequiresfurther automationtomeetownershipandbusiness transactioncostrequirements.Allthreelevels benefitfromAIsupportbuthavedifferentusage patternsandcharacteristics,asshowninFigure2. Thedifferencesmostworthnotingareintermsof repetitions,context,explorationanddata characteristics. TheOODAloop TheOODAloop[6]wasinitiallydevelopedinthe 1970sasanin-combatdecisiontooloftheU.S.Air Force.OODAstandsforobserve,orient,decideand act.ManyoftheOODAloop’sbasicconceptsare foundintoday’ssoftwareagentsystems.Theversion Figure 2 AI usage patterns are different at strategic, tactical and operational levels Strategic level Tactical level Operational level One-off or few Human interaction Learning/reasoning Yes Yes No Reasoning Learning/reasoning Execution Feedback Limited Yes, key element Large volumes Main constraint Quality Quality Time Clarity of data use Undetermined Limited to own data Deterministic and limited BSS set Many, external and large volumes Context, sources and data volumes Data types and time series BSS BSS and inference thereof LargeData size Large but limited Optimized for the intent All types Many but limited Few and limited to BSS origin Recurrence Enough to learn Very many
  7. 7. AI-NATIVE BUSINESS SUPPORT SYSTEMS ✱ JANUARY 30, 2019 ✱ ERICSSON TECHNOLOGY REVIEW 7 showninFigure3iscomplementedwithexplicit intentandlearningcapability. TheOODAloopideaisquitesimple.First, observeordetectchanges,events,stimuliorother thingsthathappeninthecontextofinterest, includinginternalstates.Observationscanbesingle orunfoldingevents,andtheycanbesimpleor complexinstructure.Dependingonthedata,AIis oftenneededtointerpretobservations.Typical observationsinBSScouldbetheavailabilityofa customer’susagerecordorthearrivalofapotential customertoawebshop. Theorientationstepconsistsofaggregatingand analyzingtheobservationsthatformthebasisforthe decisions.Analyzingtheindividualobservations andaggregatingthemintoacompletesituation descriptionrequiresmultilevelAIsupport.The orientationstepinBSSshouldenrichthe observationwithcustomerdataasmuchaspossible. ThisdataenablestheBSStoselectthecorrectrating andchargingparametersforaparticularcustomer whentheirusagerecordbecomesavailable,for example,ortoconcludethatavisitortoawebshopis lookingforanewphonebutseemstobeprice sensitive. Toclearlyseparatetheintentfromthedecisionof actionthatfulfillstheintent,wehaveaddedintentto ourmodifiedOODAloop.Weachievedthisby Figure 3 The OODA loop, modified to enable learning and intents Orientation Feed back Feed forward BSS internal actions BSS external/interaction actions Unfolding external circumstances Unfolding interaction with environment External events Feed back Feed back Feed back Feed back Feed forward Feed forward Feed forward Observations Action execution Decision of actions Evaluation and learning Intent
  8. 8. ✱ AI-NATIVE BUSINESS SUPPORT SYSTEMS 8 ERICSSON TECHNOLOGY REVIEW ✱ JANUARY 30, 2019 dividingthetraditionalOODAdecisionstepinto twodistinctprocesssteps:intentanddecision. IntentsinBSSarestatementsofthedesiredbusiness outcomeinagivensituation.Inthecaseofinvoicing, thiswouldmeanensuringthattherates/chargeson theinvoiceareinaccordancewiththecustomer’s contract.Inthecaseofaprice-sensitivepotential customervisitingawebshoptolookforalow-priced phone,theintentwouldbetoconvincethat individualtobuyaphoneinthemedium-pricerange ratherthanchoosingtheleast-expensiveoption. Thedecisionislimitedbytheinventoryof availablepossibleactions.Aselectionismadefrom theavailablearrayofactionsthatbestmatchesthe intent.Itisalsopossibletoenrichthedecisionwith simulationstopredicttheactionoutcome.The actionissimplytheexecutionofthedecision.InBSS, actionsarecarriedoutbybusinessprocessesand theyresultinbusinessoutcomes. ExamplesofBSSdecisions(andresultingactions) wouldincludethedecisiontoapplythestandard rate/chargeprocessinthecaseofanewcustomer usagerecord,orthedecisiontoshowaprice- sensitivewebshopvisitornotonlylow-priced phonesbutalsomedium-pricedmodelsthathave receivedexcellentcustomerratings. Evaluation,learningandfeedbackareessentialto buildasystemwithoptimalperformancethatcan adapttobothbusinessandenterprise-external changes.Anoptimalsystemusestheprocessof orientation,intentsanddecisionstocontinuously compareandevaluatebothbusinessoutcomesand itsowncapabilities.Adaptionmayrequirenewor additionalhigher-levelanalysis,algorithmredesign andalgorithmretraining.Sometimes,itisenoughto havegoodin-operationslearning,suchasa continuousalgorithmretrainingcapability.An exampleoflearninginBSScouldbereachingthe conclusionthatwhendealingwithprice-conscious webshopvisitors,betteroutcomescanbeachieved byshowingamixoflow-priceandmedium-price phoneswithgoodratings,asopposedtoincluding theexpensivephonesaswell. TheOODAloopcanhavevariousdepthsof reasoning,fromdeterministictodeeplearning–that is,thesameOODA-loopenginecanbeusedto observe,decideandselecttheproperactionsforall eventtypes,regardlessofcomplexity.Itcanalsobe usedrecursivelytobuildlayeredstructureswith arbitrarydepth–thatis,itcansupportmultilayered businessprocessesandinteractions. ItiscriticalthatthepeopleworkinginAI-enabled processesareabletounderstandthereasoning behindAI-generatedresults.Allofthestepsinour modifiedOODAloopcanbeunderstoodand executedbybothhumansandmachines–which makesitpossibletoworktogetherinthemost efficientwaypossiblebasedontheparticular circumstancesoftheorganization. Usecase:reducingmanualhandlingofinvoices Aninvoicing-relatedusecaseprovidesagood illustrationofhowAIaddsvaluetoBSS.Inthis scenario,atelecomoperatornoticesanincreasein thenumberofinvoicesthatrequiremanual handling,whichiscostlyforthecompany.The executiveteaminitiatesastrategicprojecttoaddress theissue. Theobjectiveatstrategiclevelistwofold:to establishfactsontheinvoicesituationandtostatea futureinvoicestrategy–thatis,tosetanintent– regardinginvoicehandlingandcost.Thestrategy teampoolsinformationaboutknownchallengesin invoicingandgathersexternaldatafor benchmarking.Withthehelpofclassificationand statisticalanalysisalgorithms,thefollowing strategic-levelintentsforinvoicesareestablishedby thestrategicproject: ❭❭ manual handling of less than 1 percent of all invoices ❭❭ average handling cost per invoice of less than USD 1. EVALUATION,LEARNING ANDFEEDBACKAREESSENTIAL TOBUILDASYSTEMWITH OPTIMALPERFORMANCE
  9. 9. AI-NATIVE BUSINESS SUPPORT SYSTEMS ✱ JANUARY 30, 2019 ✱ ERICSSON TECHNOLOGY REVIEW 9 Thisuseoftwodimensionsofintentensuresasound businessbalance,helpingtoavoidpotentialpitfalls, suchasthepossibilityofreaching0.0001percentof manuallyhandledinvoicesatanaveragecostofUSD 100perinvoice. Oncethestrategicintenthasbeenset,workonthe tacticallevelcanbegin.Theprimarychallengeatthis stageistounderstandandclassifyallthereasons whysomeinvoicesrequiremanualhandlingwhile othersdon’t,andtoselecttheoptimalAIalgorithms thatcandeliverresultsinlinewiththestrategic intents.Thetacticallevelbeginsbydefiningan efficientsubintentstructureandestimatingeach subintent’syieldtothestrategicintent,inorderto selectthemostvaluableones.Then,foreach subintent,itclassifiespossibleAIalgorithmsto identifythebestones.Importantconsiderations include: ❭❭ the information requirement and the complexity ❭❭ the volatility of the constituent knowledge components in the problem, which in turn determines whether machine learning is enough or if it must be combined with machine reasoning or deep learning to create deep enough or adaptable algorithms ❭❭ feasibility, effort and automation level in business operations ❭❭ cost estimations, implementation, operation and support. Inthistypeofinvoicingusecase,itmakessenseto introducecustomizedcommunicationpatternsthat varyaccordingtocustomercharactertype. Therefore,partoftheworkatthetacticallevel involvesdefiningthreedistinctcustomerpersonas –angry,regularanddocilecomplainers,forexample– andcustomizeanomalyinvoicemessagesforeachof them.Thenextstepistotestthesedifferent messagesonasmallportion(1-3percent)ofthe customerpopulationtofindtherightmessagefor eachcustomerpersonatoensurethattheir complaintsareresolvedwithoutescalationto manualhandling.Selectingonlyasmallfraction ofthetotalcustomerpopulationreducesthe businessrisk. Findingthenecessaryknowledgecomponentsis aniterativetaskthatrequiresAIsupportandaccess torelevantinformation.Thetacticallevelisalso responsiblefortheAIalgorithmlifecycleincluding design,implementationandoperationallaunch. Further,itisresponsibleforstatingthenecessary changestoBSS,soitcanbothcalculateaccordingto theAIalgorithmsandautomaticallyexecutethenew behaviorintheinvoicefunctionality. Thetactical-levelsubintentsfortheinvoicing usecaseare: ❭❭ invoice input correctness: higher than 99.999 percent ❭❭ invoice anomaly statistics and predictions at both group and individual level: anomaly type, costs, volumes, services and dates ❭❭ customer persona classification into three levels (angry, regular and docile complainers) with less than 1 percent error ❭❭ customized message success rate above 90 percent. Thetactical-levelchangestoBSSinterms ofnewrules,informationandprocessesare: ❭❭ calculate invoice anomalies and recheck invoice input if anomaly probability is higher than 15 percent ❭❭ determine customer persona complaint classification with continuous learning capability ❭❭ customize the message success rate to achieve continuous learning capability ❭❭ instruct customer support team to “cut it short” in cases with no anomaly and with angry ❭❭ calculate and expose subintent and intent outcome, along with their projections and variance. Themethodologyofthetacticallevelissimilarto thatofAI-supportedCRISP-DM[7].Oncethe tacticallevelstepshavebeencompleted,therole oftheoperationallevelissimplytoexecutethe algorithmsandthenewBSSlogicwithasmuch automationaspossible.Afterashorttrainingperiod, theCSPcansortouttheinvoicesthatrequire
  10. 10. ✱ AI-NATIVE BUSINESS SUPPORT SYSTEMS 10 ERICSSON TECHNOLOGY REVIEW ✱ JANUARY 30, 2019 manualhandling,identifytherootcauses,classify customersthatsystematicallycomplain,andselect themostefficientcustomizedmessage.Theerror rateandthecostperinvoiceareinitiallyquitehigh butdecreaserapidlybelowthestrategicallystated intentastheinvoicecommunicationistuned. Implications TheinvoicingusecasemakesitclearthatBSSmust haveanomnipresentAIabilitytobeabletosupport strategicandtacticalinvestigations,aswellashaving theagilityinoperationstochangebehaviorto accommodatenewalgorithms,rules,information andprocesses.Theinclusionofbusinessmodels– thatis,thegroupingofrules,informationand processestunedtoworktogethertodeliverbusiness outcomesforspecificbusinesssituations–isalso criticalintheevolutionofBSS.Theintentstructures mustmirrorthebusinessmodelstructuresandtheir lifecycles. AI-nativeBSSrequireanexpansionofthe businesslogicelements–rules,information/objects andprocesses–toincludeintentsandevents.The businesslogicelements,oftenhiddeninside applications,mustbeexternalizedtosupportthe conversionofAIfindingstoautomatedBSS behavior.Thebusinessinformationmustbe structuredinanontologyandmadeavailabletoall businesssupportusersandapplications,AIsystems included,intoabusinessinformationlake. AI-nativeBSSmustsupportbothrun-timeand business-design-time.TraditionalBSSareprimarily Further reading ❭❭ Ericsson, The dawn of machine intelligence, available at: https://www.ericsson.com/en/news/2017/9/the- dawn-of-machine-intelligence ❭❭ Ericsson, Zero-touch could herald a new era in service provider customer interaction, available at: https:// www.ericsson.com/en/press-releases/2018/5/ericsson-zero-touch-could-herald-a-new-era-in-service-provider- customer-interaction builtwithfewconfigurationoptionsforrun-time, resultinginlessagility[5].WhileitistruethatAIcan helpabusinessevolveinrunningBSS(forexample incontinuousdevelopmentandoperationsmode), thereisnoavoidingthefactthatthisrequiresaBSS architecturethatisatleastpartiallynew. Conclusion Itiswidelyrecognizedthat5GandtheIoTrepresent themaingrowthopportunitiesforcommunication serviceproviders(CSPs)inthecomingdecade.To supportemergingusecasesintheseareas,CSPs requirebusinesssupportsystems(BSS)thatcan handlecomplexbusinesssituationsandoptimize outcomeswithminimalmanualintervention. Artificialintelligence(AI)istheobviousanswer,but introducingitintoexistingBSSisproblematicfora numberofreasons.Instead,Ericssonrecommends anarchitecturalchangetotraditionalBSStocreate AI-nativeBSS.Mostsignificantly,thisevolution requirestheinclusionofanenterprise’sstrategic, tacticalandoperationallevelsintheBSS,together withtheintroductionoftwonewbusinesslogic elements(intentsandevents).Oneofthekey differencesbetweentraditionalBSSandAI-native BSSisthefactthatAI-nativeBSSenablethevarious applicationswithintheBSStosharebusiness informationwitheachotherinanefficientand securemanner–acriticalcapabilityinthe emerging5G-IoTworld.
  11. 11. AI-NATIVE BUSINESS SUPPORT SYSTEMS ✱ JANUARY 30, 2019 ✱ ERICSSON TECHNOLOGY REVIEW 11 References 1. TM Forum, Open Digital Architecture, 2018, available at: https://www.tmforum.org/resources/whitepapers/ open-digital-architecture/ 2. Ericsson, Unlocking 5G’s revenue potential: a roadmap for operators (press release), February 26, 2018, available at: https://www.ericsson.com/en/press-releases/2018/2/unlocking-5gs-revenue-potential-a- roadmap-for-operators 3. Forbes, How Artificial Intelligence Is Revolutionizing Business In 2017, September 10, 2017, Louis Columbus, available at: https://www.forbes.com/sites/louiscolumbus/2017/09/10/how-artificial-intelligence- is-revolutionizing-business-in-2017/#58ebeab25463 4. Harvard Business Review, Artificial Intelligence for the Real World, January-February 2018, Thomas H. Davenport and Rajeev Ronanki, available at: https://hbr.org/2018/01/artificial-intelligence-for-the-real-world 5. McKinsey, Smarter analytics for banks, September 2018, Carlos Fernandez Naviera et al., available at: https://www.mckinsey.com/industries/financial-services/our-insights/smarter-analytics-for-banks?cid=other- eml-alt-mip-mck-oth-1810&hlkid=d3be9327efb84eccb44a1d8d391d0d8f&hctky=2669978&hdpid=ddc7fd1c- 3815-4675-ae1a-f53e67d88452 6. OODA loop definition available at: https://en.wikipedia.org/wiki/OODA_loop 7. CRISP-DM definition available at: https://en.wikipedia.org/wiki/Cross-industry_standard_process_for_data_ mining theauthors Lars Angelin ◆ is an expert in BSS within Business Area Digital Services at Ericsson. He has more than 30 years of experience in the areas of concept development, architecture and strategies within the telco and education industries. Angelin joined Ericsson in 1996 as a research engineer, and in 2003 he moved to a position as concept developer in the M2M and OSS/BSS areas. Since 2006 he has focused on BSS – specifically business support, enterprise architectures and the software architectures to implement BSS systems. He holds an M.Sc. in engineering physics, a Tech. Licentiate in tele-traffic theory from Lund Institute of Technology in Sweden, and an honorary Ph.D. from Blekinge Institute of Technology in Sweden. Johan Silvander ◆ is a senior specialist in information management who has worked at Ericsson for more than 20 years. Over the years, his work has focused on the areas of OSS and BSS in a variety of different roles, including serving as a member of core architecture teams, working as a designer, taking technical responsibility for integration and installation projects, and being a test leader. He holds a Tech. Licentiate in computer science from Blekinge Institute of Technology, where he is currently pursuing a Ph.D. Theauthorswould liketothank JörgNiemöllerfor hiscontributions tothisarticle.
  12. 12. ISSN 0014-0171 284 23-3325 | Uen © Ericsson AB 2019 Ericsson SE-164 83 Stockholm, Sweden Phone: +46 10 719 0000

×