Optimisation of the rational proportion of intelligent technologies application in service organisations

Ivaschenko Anton (), Diyazitdinova Alfiya R. () and Nikiforova Tatiyana ()
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Ivaschenko Anton: Samara State Technical University, Samara, Russia
Diyazitdinova Alfiya R.: Povolzhskiy State University of Telecommunications and Informatics, Samara, Russia
Nikiforova Tatiyana: Samara State Technical University, Samara, Russia

Organizacija, 2021, vol. 54, issue 2, 162-177

Abstract: Background and Purpose: The growing role and involvement of Artificial Intelligence in modern digital enterprises leads to a considerable reduction of personnel and reorientation of the remaining staff to new responsibilities. However, in many areas like services and support the total elimination of the employed human resources still remains impossible. It is proposed to study the organisational problem of finding the optimal proportion of computer agents and human actors in the mixed collaborative environment. Methods: Using the technology of semantic and statistical analysis, we developed an original model of computer agents’ and human actors’ cooperative interaction and an optimization method, which is novel in considering the focus of the executors while calculating the compliance indicators. Results: The problem was studied by an example of service desk automation. Considering the semantics of the problem domain in the form of ontology introduces the logic for better distribution and automation of tasks. Conclusion: In a modern digital enterprise there exists and can be estimated a rational balance between the computer agents and human actors, which becomes a significant indicator of its performance. In general, human actors are preferable for processing unpredictable events in real time, while agents are better at the modelling and simulation.

Keywords: Service organisations automation; Artificial intelligence; Human resources; Decision-making support (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:organi:v:54:y:2021:i:2:p:162-177:n:5

DOI: 10.2478/orga-2021-0011

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