An enterprise technology readiness model for artificial intelligence

Nortje, Morne Andries (2020-03)

Thesis (MEng)--Stellenbosch University, 2020.

Thesis

ENGLISH ABSTRACT: In the continuously changing and developing landscape of business, rapid growth in technology forms a vital part in leveraging competitive advantage and generating new types of value. One of these emerging technologies is artificial intelligence. Businesses wishing to capitalize on the opportunities that this technology could provide, have unique challenges. One of these challenges is the strategic and organizational implementation and integration of artificial intelligence into the business. The need thus exists for a framework/model to assist businesses in determining their readiness for artificial intelligence to assist in solving these challenges. The aim/goal of the study is to develop a conceptual technology readiness model aimed at artificial intelligence. This model aims to provide two main outputs. These outputs encompass the numerical calculation of the business’ readiness. The second output focuses on providing the business with the ability to categorize and prioritize readiness dimension and elements from an overall, strategic, operational and tactical perspective. The readiness model foundation is developed through the incorporations of academically rooted methodologies and systematized literature reviews. This foundational and core readiness dimensions and elements encompass 7 readiness dimensions and 42 elements, these are further validated through the use of a developed validation process, which incorporates validation steps in various sections that form part of the completion of this study. Through the application of developed requirements, the appropriate, applicable and viable subject matter experts and case study were identified for the study. The readiness model developed was aimed towards use in large enterprises. After the readiness model was developed, improved and validated, it was applied to a large real-world insurance corporation. The readiness model identified that the business’s best performing dimension was the organizational governance and leadership with a score of 5.85 and the lowest dimension was Employee and culture with a score of 3.87. The use of the Importance-performance analysis prioritized the dimension that requires the most attention and resources in the short to-medium term, as the knowledge and information management dimension. The three elements within this dimension with the largest difference in performance and importance is identified as, Management information system and data processing, Enterprise resource planning in terms of databases and software and Technology knowledge management. Their respective readiness scores are 3.44, 4.375 and 3.875. The overall deduction is that the business requires more time, resources and effort as indicated in the results to consider artificial intelligence implementation. Through the conducted literature reviews, it was evident that there is a lack of academic papers, which assist businesses in the implementation and integration of AI into their business, as well as determining a business’ readiness. The process of developing the model is systematically developed, followed and presented. This allows for ease of developments and improvements to the model in the future to assist businesses with the implementation of this continuous changing and evolving technology.

AFRIKAANSE OPSOMMING: In ʼn voortdurende veranderende en ontwikkelende landskap van die besigheidswêreld, is die vinnige groei van tegnologie ʼn noodsaaklike faktor om mededingend te wees, asook om deel te vorm van waardeskepping. Een van hierdie ontwikkelende tegnologieë, is kunsmatige intelligensie. Besighede wat op hierdie tegnologie se geleenthede wil kapitaliseer, het unieke uitdagings. Een van hierdie uitdagings is die strategiese en organisatoriese implementering en integrasie van kunsmatige intelligensie in besighede. Die behoefte bestaan dus vir ‘n raamwerk/model om besighede te help om hulle gereedheid vir kunsmatige intelligensie te bepaal. Die doel van die studie is om ʼn tegnologiese gereedheidsmodel wat gemik is op kunsmatige intelligensie te ontwikkel. Die model poog om twee uitsette te lewer. Die een uitset behels ‘n numeriese berekening van die besigheid se gereedheid. Die tweede uitset verskaf die besigheid met die vermoë om gereedheidsdimensies en elemente van ʼn oorhoofse, strategiese, operasionele en taktiese perspektief te kategoriseer en prioritiseer. Die gereedheidsmodel se fondament is ontwikkel deur die insluiting van akademiese metodologieë en sistematiese literatuur resensies. Die fundamentele gereedheidsdimensies en elemente sluit in 7 dimensies en 42 elemente. Hierdie word verder geëvalueer deur die gebruik van ʼn valideringsproses, oor verskeie afdelings wat deel vorm van die voltooiing van hierdie studie. Deur die toepassing van die vereistes, is toepaslike en lewensvatbare vakkundiges en gevallestudies geïdentifiseer. Die gereedheidsmodel wat ontwikkel was, is op groot ondernemings gemik. Na die gereedheidsmodel ontwikkel, verbeter en gevalideer was, was dit by ʼn internasionale versekeringsmaatskappy toegepas. Die gereedsheidsmodel het bewys dat die beste presterende dimensie organisatoriese bestuur en leierskap was, met ʼn telling van 5.85. Die laagste dimensie was werknemer en kultuur met ʼn telling van 3.87. Die prestasie analise het die dimensie wat die meeste aandag en hulpbronne in die kort- na mediumtermyn benodig, geïdentifiseer as kennis en inligtingsbestuur. Die drie elemente in hierdie dimensie met die grootste verskil in prestasie en belangrikheid is bestuursinligtingstelsels en data verwerking, hulpbronbeplanning in terme van databasisse en sagteware asook bestuur van tegnologiese-kennis. Die onderskeie gereedheidstellings is 3.44, 4.38 en 3.88. Die algehele gevolgtrekking is dat die besigheid meer tyd, hulpbronne en moeite moet aanwend, om kunsmatige intelligensie te implementeer. Deur die literatuur oorsig is dit duidelik dat daar ʼn tekort van akademiesebronne is wat besighede met implementering en integrasie van kunsmatige intelligensie ondersteun. Die gereedheidsmodel se sistematiese ontwikkelings stappe maak dit eenvoudig en maklik vir toekomstige ontwikkeling en verbeterings. Die voortdurende verbeterings en ontwikkeling aan die gereedheidsmodel kan besighede ondersteun met die implementering van hierdie veranderende tegnologie in die toekoms.

Please refer to this item in SUNScholar by using the following persistent URL: http://hdl.handle.net/10019.1/108095
This item appears in the following collections: