Strategic Management of Emerging Technologies: A Computational Perspective
DOI:
https://doi.org/10.15662/IJEETR.2026.0801002Keywords:
Emerging technologies, strategic management, computational modeling, digital transformation, decision support systems, innovation strategy, AI-driven analytics, technology foresight, simulation, technology adoption, Algorithmic GovernanceAbstract
The strategic management of emerging technologies has become a pivotal concern for modern enterprises operating in an increasingly complex and dynamic environment. As digital transformation accelerates across industries, organizations must develop agile and forward-looking strategies to harness the potential of new technologies such as artificial intelligence, blockchain, quantum computing, and the Internet of Things. This paper explores the strategic management of emerging technologies through a computational perspective, integrating analytical models, data-driven decision-making, and simulation-based frameworks to support effective technology planning, adoption, and integration.
The research begins by examining the nature of emerging technologies, focusing on their disruptive potential, lifecycle stages, and the uncertainties they introduce into strategic decision-making. Traditional strategic models often fall short in capturing the non-linear, fast-evolving nature of technological change. Therefore, this paper advocates for the incorporation of computational approaches—such as system dynamics, agent-based modeling, machine learning, and decision support systems—into the strategic management process.
A computational perspective allows organizations to model complex interactions among market forces, technological trends, and organizational capabilities. By simulating different scenarios and outcomes, decision-makers can evaluate risks, forecast adoption trajectories, and identify optimal resource allocation strategies. The study proposes a hybrid framework that combines foresight analysis, innovation diffusion theory, and real-time data analytics to guide strategic choices.
Empirical evidence is drawn from case studies across high-tech sectors where computational tools have enhanced strategic agility and technological foresight. These include AI-driven portfolio analysis in R&D management, predictive modeling for technology adoption in healthcare, and digital twin simulations in manufacturing strategy. The results reveal that computational models not only improve the accuracy and speed of strategic decision-making but also foster a proactive innovation culture.
The paper concludes by highlighting key enablers for successfully implementing computational approaches in strategic management, such as data infrastructure, cross-functional collaboration, and leadership commitment. Challenges related to model complexity, interpretability, and organizational resistance are also discussed. Ultimately, this research underscores the transformative potential of computational thinking in managing emerging technologies and provides a roadmap for future-ready organizations seeking to navigate technological turbulence with resilience and strategic clarity.
References
1. Mahajan, R. A., Shaikh, N. K., Tikhe, A. B., Vyas, R., & Chavan, S. M. (2022). Hybrid Sea Lion Crow Search Algorithm-based stacked autoencoder for drug sensitivity prediction from cancer cell lines. International Journal of Swarm Intelligence Research, 13(1), 21. https://doi.org/10.4018/IJSIR.304723
2. Rathod, S. B., Ponnusamy, S., Mahajan, R. A., & Khan, R. A. H. (n.d.). Echoes of tomorrow: Navigating business realities with AI and digital twins. In Harnessing AI and digital twin technologies in businesses (Chapter 12). https://doi.org/10.4018/979-8-3693-3234-4.ch012
3. Rathod, S. B., Khandizod, A. G., & Mahajan, R. A. (n.d.). Cybersecurity beyond the screen: Tackling online harassment and cyberbullying. In AI tools and applications for women’s safety (Chapter 4). https://doi.org/10.4018/979-8-3693-1435-7.ch004
4. Devan, Karthigayan. "ENHANCING CONCOURSE CI/CD PIPELINES WITH REAL-TIME WEBHOOK TRIGGERS: A SCALABLE SOLUTION FOR GITHUB RESOURCE MANAGEMENT."
5. Devan, K. (2025). Leveraging the AWS cloud platform for CI/CD and infrastructure automation in software development. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.5049844
6. evan K, Driving Digital Transformation: LeveragingSite Reliability Engineering and Platform Engineeringfor Scalable and Resilient Systems. Appl. Sci. Eng. J.Adv. Res.. 2025;4(1):21-29.
7. Karthigayan Devan. (2025). Api Key-Driven Automation for Granular Billing Insights: An Sre and Finops Approach to Google Maps Platform Optimization. International Journal of Communication Networks and Information Security (IJCNIS), 17(1), 58–65. Retrieved from https://ijcnis.org/index.php/ijcnis/article/view/7939
8. Rajeshwari, J., Karibasappa, K., Gopalakrishna, M.T. (2016). Three Phase Security System for Vehicles Using Face Recognition on Distributed Systems. In: Satapathy, S., Mandal, J., Udgata, S., Bhateja, V. (eds) Information Systems Design and Intelligent Applications. Advances in Intelligent Systems and Computing, vol 435. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2757-1_55
9. S. K. Musali, R. Janthakal, and N. Rajasekhar, “Holdout based blending approaches for improved satellite image classification,” Int. J. Electr. Comput. Eng. (IJECE), vol. 14, no. 3, pp. 3127–3136, Jun. 2024, doi: 10.11591/ijece.v14i3.pp3127-3136.
10. Sunitha and R. Janthakal, "Designing and development of a new consumption model from big data to form Data-as-a-Product (DaaP)," 2017 International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), Bengaluru, India, 2017, pp. 633-636, doi: 10.1109/ICIMIA.2017.7975538.
11. P. H. C and R. J, "A Comprehensive IoT Security Framework Empowered by Machine Learning," 2024 3rd Edition of IEEE Delhi Section Flagship Conference (DELCON), New Delhi, India, 2024, pp. 1-8, doi: 10.1109/DELCON64804.2024.10866748.
12. P. Bavadiya, P. Upadhyaya, A. C. Bhosle, S. Gupta, and N. Gupta, “AI-driven Data Analytics for Cyber Threat Intelligence and Anomaly Detection,” in 2025 3rd International Conference on Advancement in Computation & Computer Technologies (InCACCT), 2025, pp. 677–681. doi: 10.1109/InCACCT65424.2025.11011329.
13. Pathik Bavadiya. (2021). A Framework for Resilient Devops Automation in Multi-Cloud KubernetesEcosystems. Journal of Informatics Education and Research, 1(3), 61–66. https://jier.org/index.php/journal/article/view/3584
14. Bathani, R. (2025). Designing an ML-Driven framework for automatic generation of rollback statements for database commands. Journal of Information Systems Engineering & Management, 10(16s), 106–112. https://doi.org/10.52783/jisem.v10i16s.2574
15. Patel, K. A., Pandey, E. C., Misra, I., & Surve, D. (2025, April). Agentic AI for Cloud Troubleshooting: A Review of Multi Agent System for Automated Cloud Support. In 2025 International Conference on Inventive Computation Technologies (ICICT) (pp. 422-428). IEEE.
16. Dash, P., Javaid, S., & Hussain, M. A. (2025). Empowering Digital Business Innovation: AI, Blockchain, Marketing, and Entrepreneurship for Dynamic Growth. In Perspectives on Digital Transformation in Contemporary Business (pp. 439-464). IGI Global Scientific Publishing.
17. Hussain, M. A., Hussain, A., Rahman, M. A. U., Irfan, M., & Hussain, S. D. (2025). The effect of AI in fostering customer loyalty through efficiency and satisfaction. Advances in Consumer Research, 2, 331-340.
18. Das, A., Shobha, N., Natesh, M., & Tiwary, G. (2024). An Enhanced Hybrid Deep Learning Model to Enhance Network Intrusion Detection Capabilities for Cybersecurity. Journal of Machine and Computing, 4(2), 472.
19. Gowda, S. K., Murthy, S. N., Hiremath, J. S., Subramanya, S. L. B., Hiremath, S. S., & Hiremath, M. S. (2023). Activity recognition based on spatio-temporal features with transfer learning. Int J Artif Intell ISSN, 2252(8938), 2103.
20. Shanthala, K., Chandrakala, B. M., & Shobha, N. (2023, November). Automated Diagnosis of brain tumor classification and segmentation of MRI Images. In 2023 International Conference on the Confluence of Advancements in Robotics, Vision and Interdisciplinary Technology Management (IC-RVITM) (pp. 1-7). IEEE.
21. Karthik, S. A., Naga, S. B. V., Satish, G., Shobha, N., Bhargav, H. K., & Chandrakala, B. M. (2025). Ai and iot-infused urban connectivity for smart cities. In Future of Digital Technology and AI in Social Sectors (pp. 367-394). IGI Global.
22. Suman, M., Shobha, N., & Ashoka, S. B. (2026). Biometric Fingerprint Verification with Siamese Neural Network & Transfer Learning.
23. Godi, R. K., P, S. R., N, S., Bhoothpur, B. V., & Das, A. (2025). A highly secure and stable energy aware multi-objective constraints-based hybrid optimization algorithms for effective optimal cluster head selection and routing in wireless sensor networks. Peer-to-Peer Networking and Applications, 18(2), 97.
24. Shobha, N., & Asha, T. (2023). Using of Meteorological Data to Estimate the Multilevel Clustering for Rainfall Forecasting. Research Highlights in Science and Technology Vol. 1, 1, 115-129.
25. Jagadishwari, V., & Shobha, N. (2023, December). Deep learning models for Covid 19 diagnosis. In AIP Conference Proceedings (Vol. 2901, No. 1, p. 060005). AIP Publishing LLC.
26. Shanthala, K., Chandrakala, B. M., & Shobha, N. (2023, November). Automated Diagnosis of brain tumor classification and segmentation of MRI Images. In 2023 International Conference on the Confluence of Advancements in Robotics, Vision and Interdisciplinary Technology Management (IC-RVITM) (pp. 1-7). IEEE.
27. Jagadishwari, V., Lakshmi Narayan, N., & Shobha, N. (2023, December). Empirical analysis of machine learning models for detecting credit card fraud. In AIP Conference Proceedings (Vol. 2901, No. 1, p. 060013). AIP Publishing LLC.
28. Jagadishwari, V., & Shobha, N. (2023, January). Comparative study of Deep Learning Models for Covid 19 Diagnosis. In 2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) (pp. 1-5). IEEE
29. Jagadishwari, V., & Shobha, N. (2022, February). Sentiment analysis of COVID 19 vaccines using Twitter data. In 2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS) (pp. 1121-1125). IEEE.
30. Shobha, N., & Asha, T. (2019). Mean Squared Error Applied in Back Propagation for Non Linear Rainfall Prediction. Compusoft, 8(9), 3431-3439.
31. Ravi, C. S., Bonam, V. S. M., & chitta, S. (2024, December). Hybrid Machine Learning Approaches for Enhanced Insurance Fraud Detection. In International Conference on Recent Trends in AI Enabled Technologies (pp. 93-104). Cham: Springer Nature Switzerland.
32. Madunuri, R., Chitta, S., Bonam, V. S. M., Vangoor, V. K. R., Yellepeddi, S. M., & Ravi, C. S. (2024, September). IoT-Driven Smart Healthcare Systems for Remote Patient Monitoring and Management. In 2024 Asian Conference on Intelligent Technologies (ACOIT) (pp. 1-7). IEEE.
33. Madunuri, R., Ravi, C. S., Chitta, S., Bonam, V. S. M., Vangoor, V. K. R., & Yellepeddi, S. M. (2024, September). Machine Learning-Based Anomaly Detection for Enhancing Cybersecurity in Financial Institutions. In 2024 Asian Conference on Intelligent Technologies (ACOIT) (pp. 1-8). IEEE.
34. Madunuri, R., Yellepeddi, S. M., Ravi, C. S., Chitta, S., Bonam, V. S. M., & Vangoor, V. K. R. (2024, September). AI-Enhanced Drug Discovery Accelerating the Identification of Potential Therapeutic Compounds. In 2024 Asian Conference on Intelligent Technologies (ACOIT) (pp. 1-8). IEEE.
35. Whig, P., Balantrapu, S. S., Whig, A., Alam, N., Shinde, R. S., & Dutta, P. K. (2024, December). AI-driven energy optimization: integrating smart meters, controllers, and cloud analytics for efficient urban infrastructure management. In 8th IET Smart Cities Symposium (SCS 2024) (Vol. 2024, pp. 238-243). IET.
36. Polamarasetti, S., Kakarala, M. R. K., kumar Prajapati, S., Butani, J. B., & Rongali, S. K. (2025, May). Exploring Advanced API Strategies with MuleSoft for Seamless Salesforce Integration in Multi-Cloud Environments. In 2025 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC) (pp. 1-9). IEEE.
37. Polamarasetti, S., Kakarala, M. R. K., Gadam, H., Butani, J. B., Rongali, S. K., & Prajapati, S. K. (2025, May). Enhancing Strategic Business Decisions with AI-Powered Forecasting Models in Salesforce CRMT. In 2025 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC) (pp. 1-10). IEEE.
38. Polamarasetti, S., Kakarala, M. R. K., Goyal, M. K., Butani, J. B., Rongali, S. K., & kumar Prajapati, S. (2025, May). Designing Industry-Specific Modular Solutions Using Salesforce OmniStudio for Accelerated Digital Transformation. In 2025 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC) (pp. 1-13). IEEE.
39. Yadav, S. S., Gupta, S. K., Yadav, M. S., & Shinde, R. (2026). Development of smart and automated solid waste management systems. In Sustainable Solutions for Environmental Pollution (pp. 295-314). Elsevier.
40. Sivasamy, S., Whig, A., Parisa, S. K., & Shinde, R. (2026). Sustainable and economic waste management. In Sustainable Solutions for Environmental Pollution (pp. 463-485). Elsevier.
41. Israr, M., Alemran, A., Parisa, S. K., & Shinde, R. (2026). Sustainable disposal solutions: challenges and strategies for mitigation. In Sustainable Solutions for Environmental Pollution (pp. 443-462). Elsevier.
42. Sharma, S., Achanta, P. R. D., Gupta, H., Shinde, R., & Sharma, A. (2026). Planning for sustainable waste management. In Sustainable Solutions for Environmental Pollution (pp. 267-294). Elsevier.
43. Mishra, M. V., Sivasamy, S., Whig, A., & Shinde, R. (2026). Waste management and future implications. In Sustainable Solutions for Environmental Pollution (pp. 535-563). Elsevier.
44. Gummadi, V. P. K. (2025). MuleSoft Architectural Paradigms and Sustainability: A Comprehensive Technical Analysis. Journal of Computer Science and Technology Studies, 7(12), 534-540.
