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The Adoption of Artificial Intelligence in Modern Business Strategies

Didcov, Gabriela (2020) The Adoption of Artificial Intelligence in Modern Business Strategies. Masters thesis, Dublin, National College of Ireland.

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The emergence of Artificial Intelligence (AI) now indicates that companies can quickly establish long term competitive edges. Despite the widespread evidence that AI is creating competitive edges for companies, there lacks evidence that companies are adopting the technology to a possible extent. The diffusion theory suggests that technology enters the market through properly defined phases. They include innovators (2.5%), early adopters (13.5%), early majority (34%), late majority (34%) and laggards (16%). For determining the extent to which companies adopt AI, this research presents a case study on five main entities- Dublin Airport, IBM, the public sector and AnalogFolk.
This research is a descriptive case study. Descriptive case studies require the observation of selected subjects. The underlying subjects entail the five companies chosen for case study analysis. Also, the research is qualitative as opposed to quantitative. Usually, qualitative research requires an in-depth theoretical analysis. On the other hand, quantitative research requires statistical calculations. The collection of data requires opening the home websites belonging to the companies under analysis. For instance, Dublin Airport, FedEx, and IBM. After collection, the main methods of data analysis include grounded theory, narrative, and content analysis.
The companies reviewed view AI systems as enhancements of service delivery. Dublin Airport and IBM view AI as an enabler of the delivery, both practical and efficient service delivery. Also, the Defense Advanced Research projects Agency (DARPA) views AI as an enhancement to the delivery of services. Although this approach to AI systems is necessary, it is not sufficient. The reason is that there are a variety of other ways that AI can assist companies such as automated decision making at the apex of managerial grids. Some challenges prevent the said companies from attaining a large-scale AI installation. For instance, legal gaps, the need for comprehensive training data sets, and the need for labeled training data sets.

Item Type: Thesis (Masters)
Subjects: H Social Sciences > HF Commerce
Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Divisions: School of Business > Master of Science in International Business
Depositing User: Mr Kevin Loughran
Date Deposited: 31 May 2021 09:55
Last Modified: 31 May 2021 09:55

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