ESG risk management supported by artificial intelligence systems

• Autorzy: Płońska Monika , Kądzielawski Grzegorz

Identyfikatory

DOI

10.29119/1641-3466.2023.172.32

• Języki publikacji: EN

Abstrakty

EN

Purpose: ESG risk management and adapting to decarbonization requirements are among the key challenges European industrial enterprises will face in the upcoming decade. Addressing this challenge will involve the significant role of new technologies, particularly artificial intelligence. This article discusses research aimed at evaluating the effectiveness of a system utilizing artificial intelligence for risk management in the process of managing ESG goals. Design/methodology/approach: In order to achieve the intended goal, the following research questions were formulated: Does the implemented system support the realization of ESG objectives in the studied organization, and would these objectives be achieved without implementing an AI-supported ESG risk management system? The research was conducted in a petrochemical sector company using qualitative methods (systematic literature review, case study description, self-observation and participant observation, informal interviews with selected system users). Due to the qualitative nature of the research, according to the methodology, no research hypotheses were formulated. Both the purpose of the research and the content of the above-mentioned issues indicate that they fit into the functional-systemic paradigm. Findings: The analysis of research results indicates that the ESG risk management system based on artificial intelligence algorithms contributes significantly to the realization of ESG objectives in the studied organization. Additionally, managing the ESG risk in the organization is possible without implementing a system supporting this process, however, the effectiveness of such actions is limited significantly. Research limitations/implications: Limitations result from the adopted research method. The systematic literature review, despite following the procedure derived from management and quality sciences, may be incomplete. Cited studies were conducted in various organizations and cultures. The case study description does not apply to every organization. Furthermore, self-observation as a method may be burdened with subjectivity, resulting from, among other things, the researcher's experiences. Practical implications: Among technologies with the highest potential for managing risks in the ESG area, particularly in the context of decarbonization, artificial intelligence undoubtedly stands out. AI has the most significant impact on the digitalization of the economy, the implementation of the 2030 Agenda, the Green Deal, and the Paris Agreement. Arealization of climate goals - from monitoring trends, predicting weather events, to specific solutions reducing or completely eliminating greenhouse gas emissions. Originality/value: The results of the conducted research demonstrate the significant potential of using artificial intelligence in managing ESG goals, especially in the implementation of decarbonization objectives and the digitalization of production processes in industrial enterprises. Additional value is the possibility of ensuring economic (cost reduction of processes), practical and reliable, high-quality production, as well as accelerating data analytics in the pursuit of identifying risks and achieving ESG goals.

Słowa kluczowe

EN

artificial intelligence; management; ESG; sustainable development

PL

sztuczna inteligencja; kierownictwo; ESG; zrównoważony rozwój

• Wydawca: Wydawnictwo Politechniki Śląskiej
• Czasopismo: Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska
• Rocznik: 2023
• Tom: z. 172
• Strony: 527--536
• Opis fizyczny: Bibliogr. 39 poz.

Twórcy

autor

Płońska Monika

• WSB Academy in Dąbrowa Górnicza

• https://orcid.org/0009-0001-0222-9445

autor

Kądzielawski Grzegorz

• WSB Academy in Dąbrowa Górnicza

• https://orcid.org/0000-0003-0804-2232

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