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Bridging the experience gap: strategic collaborations in the age of generative AI

Malik-Kozłowska Beata, Kozłowski Rafał

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska

2025

z. 217

301--318

Purpose: The primary aim of this study is to analyze the real-world adoption of generative AI within organizational contexts. This work aims to contribute to the broader understanding of how generative AI is reshaping business processes and driving innovation across industries. Design/Methodology/Approach: The study adopts a qualitative content analysis approach, grounded in a systematic review of peer-reviewed articles and grey literature published from 2022 to 2024. Articles were screened based on strict inclusion and exclusion criteria, emphasizing documented real-world applications of generative AI. Data extraction focused on identifying sector-specific use cases, challenges encountered during its integration, and the strategies employed by organizations to overcome these obstacles, as well as the organizational impacts of generative AI. Findings: The review identified six empirical studies documenting real-world implementations of generative AI across diverse sectors, including insurance, finance, creative industries, retail, and fact-checking. Generative AI is shown to enhance efficiency, streamline workflows, and support decision-making, while also fostering creative innovation. Challenges such as data reliability, legal ambiguities, and organizational readiness were commonly observed. Strategies to address these barriers include leveraging human-AI collaboration, ensuring regulatory compliance, and investing in cohesive organizational structures. Research Limitations/Implications: The research is constrained by the limited number of empirical studies available, reflecting the nascent stage of generative AI adoption. Additionally, the findings are subject to the rapid evolution of AI technologies, requiring ongoing updates through longitudinal research. Expanding future studies to include a broader geographic and sectoral scope is essential for generalizability. Practical Implications: The study provides actionable insights for organizations considering the adoption of generative AI. It highlights the importance of strategic planning, ethical data management, and fostering collaboration between human experts and AI systems. These findings are particularly relevant for small and medium-sized enterprises seeking to leverage AI to enhance efficiency and competitiveness. Social Implications: Generative AI democratizes access to advanced technology, enabling non-specialists to use AI tools. However, its rapid adoption demands robust regulation and stake-holder engagement from policymakers. Originality/Value: This study addresses a critical gap in the academic literature by focusing on the documented implementation of generative AI.