Artificial Intelligence and Strategic Governance: Enabling Real-Time Decisions in Complex Business Ecosystems
DOI:
https://doi.org/10.62486/agma2025174Keywords:
Decision-making, Artificial intelligence (AI), business ecosystems, market developmentsAbstract
Introduction: In today’s fast-paced and determined business environments, management operations, making strategic decisions, and responding to market trends have become increasingly more difficult. To overcome these difficulties, Artificial Intelligence (AI) has emerged as a strong implement that facilitates real-time decision-making across several business functions. By applying data-driven insights, strategic governance, and AI can improve decision-making in complicated ecosystem.
Objective: Study explores how AI can enhance strategic governance to help direct decision-making in complex business environment. It focuses on how AI can be used to assess dynamic data streams, forecast possible results, and offer useful insights to boost competitiveness and effective integrity.
Methods: Study looks at a number of AI approaches, such as deep learning (DL) and machine learning (ML), which are useful in business management settings like supply chain optimization, forecasting consumer behavior, and analyzing market trends. Several cases from different industries are analyzed to show how AI is basically utilized to facilitate quick decision-making.
Result: The outcomes of AI-enabled systems extensively enhance the speed and accuracy of decision-making, allow businesses to direct resources more efficiently, react proactively to unique opportunities, and react to disruption. Artificial intelligence (AI) models provide predictive insights into supply chains, customer behaviour prediction, market trends, ensuring that decisions are based on up-to-date and well organized data.
Conclusion: Businesses can respond to problems more efficiently, optimize processes, match decisions completed in real time with more general strategic objectives when AI is included with strategic governance.
References
Basole RC, Park H, Seuss CD. Complex business ecosystem intelligence using AI-powered visual analytics. Decision Support Systems. 2024 Mar 1;178:114133. https://doi.org/10.1016/j.dss.2023.114133 DOI: https://doi.org/10.1016/j.dss.2023.114133
Priyono A, Hidayat A. Fostering innovation through learning from digital business ecosystem: A dynamic capability perspective. Journal of Open Innovation: Technology, Market, and Complexity. 2024 Mar 1;10(1):100196. https://doi.org/10.1016/j.joitmc.2023.100196 DOI: https://doi.org/10.1016/j.joitmc.2023.100196
Zabel C, O'Brien D, Natzel J. Sensing the Metaverse: The microfoundations of complementor firms' dynamic sensing capabilities in emerging-technology ecosystems. Technological Forecasting and Social Change. 2023 Jul 1;192:122562. https://doi.org/10.1016/j.techfore.2023.122562 DOI: https://doi.org/10.1016/j.techfore.2023.122562
Snihur Y, Bocken N. A call for action: The impact of business model innovation on business ecosystems, society and planet. Long Range Planning. 2022 Dec 1;55(6):102182. https://doi.org/10.1016/j.lrp.2022.102182 DOI: https://doi.org/10.1016/j.lrp.2022.102182
Spaniol MJ, Rowland NJ. Business ecosystems and the view from the future: The use of corporate foresight by stakeholders of the Ro-Ro shipping ecosystem in the Baltic Sea Region. Technological Forecasting and Social Change. 2022 Nov 1;184:121966. https://doi.org/10.1016/j.techfore.2022.121966 DOI: https://doi.org/10.1016/j.techfore.2022.121966
Rzevski G, Skobelev P, Zhilyaev A. Emergent intelligence in smart ecosystems: conflicts resolution by reaching consensus in resource management. Mathematics. 2022 Jun 3;10(11):1923. https://doi.org/10.3390/math10111923 DOI: https://doi.org/10.3390/math10111923
Ghobakhloo M, Iranmanesh M, Mubarik MS, Mubarak MF, Amran A, Khanfar AA. Blockchain technology as an enabler for sustainable business ecosystems: A comprehensive roadmap for socioenvironmental and economic sustainability. Business Strategy & Development. 2024 Mar;7(1):e319. https://doi.org/10.1002/bsd2.319 DOI: https://doi.org/10.1002/bsd2.319
Mann G, Karanasios S, Breidbach CF. Orchestrating the digital transformation of a business ecosystem. The Journal of Strategic Information Systems. 2022 Sep 1;31(3):101733. https://doi.org/10.34218/IJAIRD_01_01_007 DOI: https://doi.org/10.1016/j.jsis.2022.101733
Xu Z, Ni W, Liu S, Wang F, Li J. Logistics supply chain network risk prediction model based on intelligent random Forest model. IEEE Transactions onEngineering Management. 2023 Oct 10;71:9813-25. https://doi.org/10.1109/TEM.2023.3317922 DOI: https://doi.org/10.1109/TEM.2023.3317922
Camur MC, Ravi SK, Saleh S. Enhancing supply chain resilience: A machine learning approach for predicting product availability dates under disruption. Expert Systems with Applications. 2024 Aug 1;247:123226. https://doi.org/10.1016/j.eswa.2024.123226 DOI: https://doi.org/10.1016/j.eswa.2024.123226
Li Y, Stasinakis C, Yeo WM. A hybrid XGBoost-MLP model for credit risk assessment on digital supply chain finance. Forecasting. 2022 Jan 29;4(1):184-207. https://doi.org/10.3390/forecast4010011 DOI: https://doi.org/10.3390/forecast4010011
Panda SK, Mohanty SN. Time series forecasting and modeling of food demand supply chain based on regressors analysis. IEEE Access. 2023 Apr 11;11:42679-700. https://doi.org/10.1109/ACCESS.2023.3266275 DOI: https://doi.org/10.1109/ACCESS.2023.3266275
Xiahou X, Harada Y. B2C E-commerce customer churn prediction based on K-means and SVM. Journal of Theoretical and Applied Electronic Commerce Research. 2022 Apr 6;17(2):458-75. https://doi.org/10.3390/jtaer17020024 DOI: https://doi.org/10.3390/jtaer17020024
Ebrahimi P, Salamzadeh A, Soleimani M, Khansari SM, Zarea H, Fekete-Farkas M. Startups and consumer purchase behavior: Application of support vector machine algorithm. Big Data and Cognitive Computing. 2022 Mar 25;6(2):34. https://doi.org/10.3390/bdcc6020034 DOI: https://doi.org/10.3390/bdcc6020034
Safara F. A computational model to predict consumer behaviour during COVID-19 pandemic. Computational Economics. 2022 Apr;59(4):1525-38. https://doi.org/10.1007/s10614-020-10069-3 DOI: https://doi.org/10.1007/s10614-020-10069-3
Wu S, Yau WC, Ong TS, Chong SC. Integrated churn prediction and customer segmentation framework for telco business. Ieee Access. 2021 Apr 16;9:62118-36. https://doi.org/10.1109/ACCESS.2021.3073776 DOI: https://doi.org/10.1109/ACCESS.2021.3073776
Yin L, Li B, Li P, Zhang R. Research on stock trend prediction method based on optimized random forest. CAAI Transactions on Intelligence Technology. 2023 Mar;8(1):274-84. https://doi.org/10.1049/cit2.12067 DOI: https://doi.org/10.1049/cit2.12067
Basher SA, Sadorsky P. Forecasting Bitcoin price direction with random forests: How important are interest rates, inflation, and market volatility?. Machine Learning with Applications. 2022 Sep 15;9:100355. https://doi.org/10.1016/j.mlwa.2022.100355 DOI: https://doi.org/10.1016/j.mlwa.2022.100355
Li C, Li L, Zheng J, Wang J, Yuan Y, Lv Z, Wei Y, Han Q, Gao J, Liu W. China’s public firms’ attitudes towards environmental protection based on sentiment analysis and random forest models. Sustainability. 2022 Apr 22;14(9):5046. https://doi.org/10.3390/su14095046 DOI: https://doi.org/10.3390/su14095046
Kiangala SK, Wang Z. An effective adaptive customization framework for small manufacturing plants using extreme gradient boosting-XGBoost and random forest ensemble learning algorithms in an Industry 4.0 environment. Machine Learning with Applications. 2021 Jun 15;4:100024. https://doi.org/10.1016/j.mlwa.2021.100024 DOI: https://doi.org/10.1016/j.mlwa.2021.100024
Dalal S, Lilhore UK, Simaiya S, Radulescu M, Belascu L. Improving efficiency and sustainability via supply chain optimization through CNNs and BiLSTM. Technological Forecasting and Social Change. 2024 Dec 1;209:123841. https://doi.org/10.1016/j.techfore.2024.123841 DOI: https://doi.org/10.1016/j.techfore.2024.123841
Bassiouni MM, Chakrabortty RK, Sallam KM, Hussain OK. Deep learning approaches to identify order status in a complex supply chain. Expert Systems with Applications. 2024 Sep 15;250:123947. https://doi.org/10.1016/j.eswa.2024.123947 DOI: https://doi.org/10.1016/j.eswa.2024.123947
Sharma R, Shishodia A, Gunasekaran A, Min H, Munim ZH. The role of artificial intelligence in supply chain management: mapping the territory. International Journal of Production Research. 2022 Dec 17;60(24):7527-50. https://doi.org/10.1080/00207543.2022.2029611 DOI: https://doi.org/10.1080/00207543.2022.2029611
Najjartabar Bisheh M, Nasiri GR, Esmaeili E, Davoudpour H, Chang SI. A new supply chain distribution network design for two classes of customers using transfer recurrent neural network. International Journal of System Assurance Engineering and Management. 2022 Oct;13(5):2604-18. https://doi.org/10.1007/s00521-021-06548-9 DOI: https://doi.org/10.1007/s13198-022-01670-w
Tariq MU, Babar M, Poulin M, Khattak AS. Distributed model for customer churn prediction using convolutional neural network. Journal of Modelling in Management. 2022 Aug 22;17(3):853-63. https://doi.org/10.1108/JM2-01-2021-0032 DOI: https://doi.org/10.1108/JM2-01-2021-0032
Chaudhuri N, Gupta G, Vamsi V, Bose I. On the platform but will they buy? Predicting customers' purchase behavior using deep learning. Decision Support Systems. 2021 Oct 1;149:113622. https://doi.org/10.1016/j.dss.2021.113622 DOI: https://doi.org/10.1016/j.dss.2021.113622
Sayyad J, Attarde K, Yilmaz B. Improving Machine Learning Predictive Capacity for Supply Chain Optimization through Domain Adversarial Neural Networks. Big Data and Cognitive Computing. 2024 Jul 28;8(8):81. https://doi.org/10.3390/bdcc8080081 DOI: https://doi.org/10.3390/bdcc8080081
Predić B, Ćirić M, Stoimenov L. Business purchase prediction based on XAI and LSTM neural networks. Electronics. 2023 Nov 2;12(21):4510. https://doi.org/10.3390/electronics12214510 DOI: https://doi.org/10.3390/electronics12214510
Cavalli S, Amoretti M. CNN-based multivariate data analysis for bitcoin trend prediction. Applied Soft Computing. 2021 Mar 1;101:107065. https://doi.org/10.1016/j.asoc.2020.107065 DOI: https://doi.org/10.1016/j.asoc.2020.107065
Huang K, Zhou Y, Yu X, Su X. Innovative entrepreneurial market trend prediction model based on deep learning: Case study and performance evaluation. Science Progress. 2024 Jul;107(3):00368504241272722. https://doi.org/10.1177/00368504241272722 DOI: https://doi.org/10.1177/00368504241272722
Saha D, Young TM, Thacker J. Predicting firm performance and size using machine learning with a Bayesian perspective. Machine Learning with Applications. 2023 Mar 15;11:100453. https://doi.org/10.1016/j.mlwa.2023.100453 DOI: https://doi.org/10.1016/j.mlwa.2023.100453
Alghamdi M, Al-shathry NI, Said Y, Beyari H, Sorour S, Sattar KN, Alallah FS, Alshareef A. Improved chaotic fruit fly optimization with elman neural network based data-driven approach on product reviews classification. Alexandria Engineering Journal. 2025 Aug 1;127:628-41. https://doi.org/10.36325/ghjec.v21i1.18766 DOI: https://doi.org/10.1016/j.aej.2025.05.042
Agarwal S, Kapoor R. Value creation tradeoff in business ecosystems: Leveraging complementarities while managing interdependencies. Organization Science. 2023 May;34(3):1216-42. https://doi.org/10.1287/orsc.2022.1615- DOI: https://doi.org/10.1287/orsc.2022.1615
Riquelme-Medina M, Stevenson M, Barrales-Molina V, Llorens-Montes FJ. Coopetition in business Ecosystems: The key role of absorptive capacity and supply chain agility. Journal of Business Research. 2022 Jul 1;146:464-76. https://doi.org/10.1016/j.jbusres.2022.03.071 DOI: https://doi.org/10.1016/j.jbusres.2022.03.071
Koepke R, Paetzold S. Capital flow data—A guide for empirical analysis and real‐time tracking. International Journal of Finance & Economics. 2024 Jan;29(1):311-31. https://doi.org/10.1002/ijfe.2687 DOI: https://doi.org/10.1002/ijfe.2687
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Copyright (c) 2025 Trupti VN, Sasanka Choudhury , Syed Rashid Anwar, Ayesha Taranum, Sethuraman R, Pragati Saxena, Kunal Meher (Author)
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