Decision Intelligence: Merging AI and Analytics for Strategic Gains

Abstract

This study aims to explore the role of decision intelligence, combining artificial intelligence (AI) and analytics, in enhancing strategic decision-making processes within organizations. A qualitative research approach was employed, utilizing semi-structured interviews to collect data from 22 participants recruited from online platforms. The study adopted a theoretical saturation approach, with interviews continuing until no new insights were observed. Data were analyzed using NVivo software, and open coding was employed to identify key themes, subcategories, and concepts within the dataset. The analysis revealed four main themes: 1) Strategic Benefits of Decision Intelligence, which emphasized the positive impacts on decision-making accuracy, cost efficiency, and competitive advantage; 2) Integration Challenges, identifying barriers such as data integration issues, algorithmic bias, system interoperability, and high implementation costs; 3) AI-Driven Analytics Techniques, which highlighted the importance of predictive models, real-time analytics, sentiment analysis, and data visualization in enhancing decision-making; and 4) Future Prospects and Innovations, which pointed to the potential for next-generation AI models and human-centric innovations in shaping future strategic decision-making. The findings suggest that while decision intelligence offers significant strategic advantages, including improved decision speed and accuracy, its successful implementation is hindered by several challenges. Addressing data integration issues, mitigating algorithmic bias, and managing the high costs of implementation are crucial for organizations to fully realize the potential of decision intelligence. Future research should focus on expanding the sample size and exploring the long-term effects of decision intelligence on organizational performance.