Business Intelligence and Artificial Intelligence for Sustainable Business Operations
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In the modern business landscape, sustainability has become a fundamental goal for organizations, driven by growing environmental concerns, social responsibility, and the need for long-term profitability (Bocken et al., 2014). Companies are under increasing pressure to reduce their environmental footprint, optimize resources, and improve operational efficiency, all while maintaining competitiveness. Business Intelligence (BI) and Artificial Intelligence (AI) have emerged as key technologies in this transition, offering organizations the ability to make data-driven decisions that promote sustainability (Chen et al., 2020). BI encompasses tools and techniques that convert raw data into actionable insights, helping businesses optimize operations and minimize waste (Shollo & Galliers, 2016). On the other hand, AI, particularly machine learning and predictive analytics, enhances decision-making by forecasting trends, automating processes, and providing deeper insights into complex datasets (Jeble et al., 2020).
This article explores the integration of BI and AI in driving sustainable business operations. It examines their individual contributions and the synergistic benefits they bring when combined. Key applications discussed include energy management, where BI helps track energy consumption patterns, and AI optimizes resource allocation to minimize waste (Kemp et al., 2021). In supply chain optimization, BI analyzes supplier performance and inventory levels, while AI forecasts demand and automates processes to reduce carbon footprints (Saghafian et al., 2020). Waste reduction efforts are enhanced through predictive analytics, which help anticipate production needs and reduce excess output (Karim et al., 2021). Environmental monitoring, powered by AI and IoT sensors, allows for real-time analysis of environmental conditions, ensuring compliance with sustainability standards (Khan et al., 2021).
However, the implementation of these technologies also presents challenges. Data integration remains a significant barrier, as companies often face difficulties in harmonizing large datasets from disparate sources (Laudon & Laudon, 2019). The initial investment in BI and AI technologies can be high, making it difficult for small and medium-sized enterprises (SMEs) to adopt these solutions (Zhang et al., 2020). Additionally, a shortage of skilled professionals in data science and AI poses another challenge, limiting the effective use of these technologies (Brynjolfsson & McAfee, 2014). Despite these challenges, the potential of BI and AI to foster sustainable business operations is substantial, and overcoming these barriers will be key to unlocking their full potential. The article concludes by discussing strategies for successful implementation and the future outlook for BI and AI in sustainable business practices.
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