Search for a command to run...
As technological development gains more momentum and competition in the global economy intensifies, organizations have resorted to data-driven decision-making (DDDM) and artificial intelligence (AI) as a way of strengthening the strategy management and ensuring the sustainability of a business growth. The strategic management based on the data improves the capacity of the organizations to mince actionable information out of the large heterogeneous and live time data sources so that these organizations can make the better forecast and effective operation and competitive advantage. At the same time, the artificial intelligence technologies, including machine learning, predictive analytics, natural language processing and intelligent automation, are changing the customary frameworks of decision-making and enhancing the abilities of all businesses to think strategically, distributing resources more efficiently, and modifying their business models. This paper will present an argument about the combination of DDDM and AI in strategic management and the possibility of the two issues to cooperate to ensure higher sustainability, value creation in the long-term, minimized risks, and organizational resilience. It deals with a more contemporary literature, and covers the state-of-the-art technique of analysis and relies as well as offers a conceptual side of the ways AI-based decision-systems may assist in strategic planning, performance measurements, stakeholder management and environmental, social and governance (ESG) activities. Besides it, the paper discusses real-life use in the industries as well as outlines the barriers to the adoption and also offers recommendations on the further development of the data-centric and AI-enabled strategic ecosystems. The findings spur the notion that these types of organizations that are open to data intelligence and AI-driven decision architecture are more likely to expand sustainably, at scale, and be future-craft in an extremely dynamic digital landscape.
Published in: International Journal of Advances in Signal and Image Sciences
Volume 12, Issue 3s, pp. 1296-1304
DOI: 10.29284/ew51d963