<abstract xmlns="http://www.w3.org/1999/xhtml"><p> Artificial Intelligence (AI) systems are rapidly evolving and becoming more common in management. Managers in business institutions are faced with the decision taking challenges and large amounts of data to be processed combining and harmonizing rational data with creative human experience in decision making. The aim of the study is to reveal the main obstacles of the harmonization of creative and rational decisions making in quality management using AI technologies in the Quality Management System (QMS). The first section presents a literature review of approaches and trends related to AI technology usage in organisations for data processing and creative-rational decision making, rational and creative quality management decision making and paradigms in decision harmonization. The Main Results section presents practical analysis and testing experience of automated AI Quality Management System developed at a higher education institution. During the analysis, an interview method was applied to find out specific system implementation issues. In the last section, the main analysis results and further development possibilities are discussed. The main findings and conclusions disclose two main problematic areas which may be defined as obstacles for rational and creative management decisions in quality management, related with clear responsibility distribution and assignment between data inputters and experience interpreters and duplicated qualitative data which AI system is not capable of rationalizing at the present development stage, speech and language processing techniques used when data processing algorithms cannot cope with the dual data processing technique, because in practice the system interprets and rationalizes only one category of data either quantitative - based on rational defined indicators, or qualitative, based on language recognition and speech related data interpretation. Managers’ experience in harmonizing creative human experience in organisation’s quality management was evaluated as positive. Data processed by tested AI system allows for rationalization of creative experience with ready quantitative data output from QMS system and final harmonized strategic quality management decisions.</p></abstract>

Artificial Intelligence (AI) systems are rapidly evolving and becoming more common in management. Managers in business institutions are faced with the decision taking challenges and large amounts of data to be processed combining and harmonizing rational data with creative human experience in decision making. The aim of the study is to reveal the main obstacles of the harmonization of creative and rational decisions making in quality management using AI technologies in the Quality Management System (QMS). The first section presents a literature review of approaches and trends related to AI technology usage in organisations for data processing and creative-rational decision making, rational and creative quality management decision making and paradigms in decision harmonization. The Main Results section presents practical analysis and testing experience of automated AI Quality Management System developed at a higher education institution. During the analysis, an interview method was applied to find out specific system implementation issues. In the last section, the main analysis results and further development possibilities are discussed. The main findings and conclusions disclose two main problematic areas which may be defined as obstacles for rational and creative management decisions in quality management, related with clear responsibility distribution and assignment between data inputters and experience interpreters and duplicated qualitative data which AI system is not capable of rationalizing at the present development stage, speech and language processing techniques used when data processing algorithms cannot cope with the dual data processing technique, because in practice the system interprets and rationalizes only one category of data either quantitative - based on rational defined indicators, or qualitative, based on language recognition and speech related data interpretation. Managers’ experience in harmonizing creative human experience in organisation’s quality management was evaluated as positive. Data processed by tested AI system allows for rationalization of creative experience with ready quantitative data output from QMS system and final harmonized strategic quality management decisions.

Harmonization of rational and creative decisions in quality management using AI technologies

Economics and Business

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Artificial Intelligence (AI) systems are rapidly evolving and becoming more common in management. Managers in business institutions are faced with the decision...

Harmonization of rational and creative decisions in quality management using AI technologies

Online veröffentlicht: 20. Nov. 2018

Seitenbereich: 195 - 208

DOI: https://doi.org/10.2478/eb-2018-0016

Schlüsselwörter
Artificial Intelligence (AI), decision making, total quality management (TQM), quality management system (QMS)

© 2018 Vytautas Paliukas, Asta Savanevičienė, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Harmonization of rational and creative decisions in quality management using AI technologies

Online veröffentlicht: 20. Nov. 2018

Seitenbereich: 195 - 208

DOI: https://doi.org/10.2478/eb-2018-0016

SchlüsselwörterArtificial Intelligence (AI), decision making, total quality management (TQM), quality management system (QMS)

© 2018 Vytautas Paliukas, Asta Savanevičienė, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
Artificial Intelligence (AI), decision making, total quality management (TQM), quality management system (QMS)