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

Author:

Ivaschenko Anton1,Diyazitdinova Alfiya R.2,Nikiforova Tatiyana1

Affiliation:

1. Samara State Technical University , Samara , Russia ,

2. Povolzhskiy State University of Telecommunications and Informatics , Samara , Russia

Abstract

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.

Publisher

Walter de Gruyter GmbH

Subject

Marketing,Organizational Behavior and Human Resource Management,Strategy and Management,Tourism, Leisure and Hospitality Management,Business and International Management,Management Information Systems

Reference36 articles.

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3. Barman, A., Ahmed, H. (2015). Big Data in human resource management – developing research context. http://doi.org/10.13140/RG.2.1.3113.6166

4. Bentley, P.J., Brundage, M., Haggstrom, O., Metzinger, T., Gutenberg, J. (2018). Should we fear artificial intelligence? STOA - Science and Technology Options Assessment, 40. http://dx.doi.org/10.2861/412165

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