Emerging technologies have become one of the main concerns of societies of today. Governments and societies are well aware that their economic future and survival in today's changing world are tied to new technologies, especially artificial intelligence technology. This paper aims to help managers, practitioners, and policymakers in prioritizing the implementation of artificial intelligence technology in applications such as transportation, health, security, education, and research.
After a relatively complete follow-up in the field of artificial intelligence research, the criteria for prioritizing emerging technologies have been calculated and weighted by the Friedman method. Then, the application areas have been identified by the thematic analysis method. Finally, using the COPRAS multi-criteria decision-making technique, the application areas of artificial intelligence have been ranked.
This research resulted in a model and indicated that the three areas of economics, transportation, and governance were given more priority for the application of artificial intelligence technology, respectively.Research limitations/implications: Designing mechanisms to monitor advances in emergent technology and artificial intelligence to improve the proposed model and update the model were the most important suggestions. Another suggestion was to identify the gap between the theoretically developed model and practice.
This paper provides a reliable prioritization of the applied areas using scientific criteria appropriate to the country. This provides a scientific-operational package, and policy-making by creating a logical relationship between theory and practice. If such a document is presented in policy and legislative circles, this prioritization can be one of the most important tools for decision-making and rescuing from political and strategic dilemmas.
Improving the quality of human life by implementing artificial intelligence in various usages of daily life, and facilitating social availability to the important types of possibilities are the most important achievement of this research.
Since no model has been found to prioritize the implementation of artificial intelligence technology in applications, the proposed model closes such a gap. The paper provides appropriate tools both to practitioners and policymakers who intend to decide and deploy artificial intelligence.
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