AI for Sustainable Management: Integrating Innovation, Corporate Responsibility and Business Performance
DOI:
https://doi.org/10.69996/fmep.2026015Keywords:
Artificial Intelligence, Sustainable Management, Innovation, Corporate Responsibility, ESG, Business Performance, Sustainability, Digital Transformation.Abstract
Sustainable management asks firms to create value that is economically sound, socially responsible, and environmentally responsible at once, a balance that has long been difficult to strike. Artificial intelligence (AI) is emerging as a general-purpose enabler that can help organisations pursue these goals simultaneously rather than in tension. This conceptual paper examines how AI links three dimensions that are often managed separately: innovation, corporate responsibility, and business performance. It argues that AI’s core capabilities, data analytics, prediction, automation, and optimisation, allow firms to innovate more responsibly, to measure and improve their environmental, social, and governance (ESG) performance, and to raise operational and financial performance, and that the greatest value arises when these dimensions are integrated rather than optimised in isolation. The paper develops an integrated conceptual framework in which AI operates as a central hub connecting the three dimensions in the service of sustainable management, and it maps representative AI capabilities to each dimension along with their mechanisms and intended outcomes. A conceptual trajectory illustrates how the combined value of these dimensions may grow super-additively as a firm’s AI-enabled sustainability maturity advances. The paper also addresses the risks that accompany AI adoption, including energy and resource costs, bias, opacity, data governance, and the danger of “greenwashing” through selective reporting, and it offers managerial recommendations for responsible integration. As a conceptual contribution, the paper advances propositions intended to guide practice and to invite empirical testing.
References
[1] S. Goyal, K.K.Gattupalli, B. Rallabandi and M.S. Medarametla, “Integrating terrestrial and nonterrestrial networks for reliable rural coverage in agriculture and smart grids,” In 2025 1st IEEE Uttar Pradesh Section Women in Engineering International Conference on Electrical Electronics and Computer Engineering (UPWIECON), pp. 846–850, 2025.
[2] N. Pachauri, V. Suresh, M. V. V. Prasad Kantipudi, R. Alkanhel, and H. A. Abdallah, “MultiDrug Scheduling for Chemotherapy Using Fractional Order Internal Model Controller,” Mathematics, vol. 11, no. 8, 2023.
[3] M. V. V. Prasad Kantipudi, S. Vemuri, N. S. Pradeep Kumar, S. Sreenath Kashyap, and S. Eslamian, “Proposing Model for Water Quality Analysis Based on Hyperspectral Remote Sensor Data,” in Handbook of Hydroinformatics, Elsevier, pp. 317–324, 2023.
[4] M. Rana, S. Srinivas, L.K. Jamili, I.A. Jaiswal, S. Nakka and S. Kasetti, “Real-Time Monitoring and Prediction of Blood Sugar Levels in Diabetic Patients with Functional Models,” In 2025 International Conference on Engineering, Technology & Management (ICETM), pp. 1-6, 2025.
[5] I. A. Jaiswal, “Intelligent Cybersecurity Framework for Large-Scale RESTful Service Architectures,” International Journal of Research Radicals in Multidisciplinary Fields, vol.2, no.1, pp.178–184, 2023.
[6] I. A. Jaiswal, “Multilingual and culturally adaptive AI models for global education platforms” International Journal for Research in Education (IJRE), vol.12, no.9, pp.17–27, 2023.
[7] M. S. Prashanth, R. Aluvalu, and M. V. V. Kantipudi, “Enhancing Health Product Traceability on the Blockchain: A Novel Approach for Supply Chain Management Inspection to AI,” EAI Endorsed Transactions on Pervasive Health & Technology, vol. 10, no. 1, 2024.
[8] H. K. Jani, M. V. V. Prasad Kantipudi, G. Nagababu, D. Prajapati and S. S. Kachhwaha, “Simultaneity of Wind and Solar Energy: A Spatio-Temporal Analysis to Delineate the Plausible Regions to Harness,” Sustainable Energy Technologies and Assessments, vol. 53, no. 102665, 2022.
[9] I.A. Jaiswal, “Natural language processing for security policy and log analysis,” International Journal of Research in All Subjects in Multi Languages (IJRSML), vol.10, no.4, pp.57–67, 2022.
[10] Swara Snehit Patil, “Artificial Intelligence: A Way to Promote Innovation,” JSIHS, vol. 2, no. 1, pp. 1–5, 2024.
[11] S. Choudhary, C. H. Kandikattu, S. Kumar, M. V. V. P. Kantipudi, and M. Kumar, “Enhancing cybersecurity through combined convolutional neural network-gated recurrent unit approach for distributed denial of service attack detection,” in Proceedings of the 2024 1st International Conference on Innovative Engineering Sciences and Technological Research (ICIESTR), pp. 1–6, 2024.
[12] S. Rani, D. Ghai and S. Kumar, “Reconstruction of wire frame model of complex images using syntactic pattern recognition,” in IET Conference Proceedings CP791, vol. 2021, no. 11, pp. 8–13, 2021.
[13] Swathi, S. Kumar, S. Rani, A. Jain and R. K. M. V. N. M., “Emotion classification using feature extraction of facial expression,” in Proceedings of the 2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS), pp. 283–288, 2022.
[14] S. Choudhary, S. Kumar, S. Gowroju, M. Gulhane, and R. Sri Lakshmi, Eds., “Genomics at the Nexus of AI, Computer Vision, and Machine Learning,” Hoboken, NJ, USA: John Wiley & Sons, 2024.
[15] S. Kumar, S. Choudhary, S. Gowroju and A. Bhola, “Convolutional neural network approach for multimodal biometric recognition system for banking sector on fusion of face and finger,” in Multimodal Biometric and Machine Learning Technologies: Applications for Computer Vision, pp. 251–267, 2023.
[16] S. Choudhary, S. Kumar, M. Gulhane, and M. Kumar, “Secured automated certificate creation based on multimodal biometric verification,” in Multimodal Biometric and Machine Learning Technologies: Applications for Computer Vision, pp. 269–281, 2023.
[17] I.A. Jaiswal, “AI-powered observability and incident prediction in distributed enterprise platforms,” Scientific Journal of Artificial Intelligence and Blockchain Technologies, vol.1, no.1, pp.1–14, 2024.
[18] S. Choudhary, C. H. Kandikattu, S. Kumar, M. V. V. P. Kantipudi and M. Kumar, “Enhancing cybersecurity through combined convolutional neural network-gated recurrent unit approach for distributed denial of service attack detection,” in Proceedings of the 2024 1st International Conference on Innovative Engineering Sciences and Technological Research (ICIESTR), pp. 1–6, 2024.
[19] I.A. Jaiswal, “High-Performance AI-Augmented Content Management Systems for Distributed Clouds,” International Journal of Multidisciplinary Innovation and Research Methodology, vol. 2, no.2, pp.90–97, 2023.
[20] Segun KehindeSegun Kehinde, “AI in Everything, and Everything in AI: A Review of the Ubiquitous Role of Artificial Intelligence in Shaping the Next Technological Epoch,” JCAI, vol. 3, no. 5, pp. 17–53, 2025.
[21] I. A. Jaiswal, “Self-healing REST services using artificial intelligence in multi-cloud environments,” Scientific Journal of Artificial Intelligence and Blockchain Technologies, vol.1, no.3, pp.1–7, 2024.
[22] V. Saini, A. Jain, Anurag, and M. V. V. Prasad Kantipudi, “An Efficient Approach for Improving the Performance of Autonomous Vehicle Using Advanced Computer Vision,” in International Conference on Soft Computing and Pattern Recognition, pp. 164–174, 2023.
[23] S. Khan, “The role of privacy-preserving techniques in database security: Secure multi-party computation, differential privacy, and homomorphic encryption,” The Research Journal, vol.3, no.4, pp.29–35, 2017.
[24] S. Khan, “A study of data provenance and integrity in database security: Ensuring authenticity, non-repudiation, and accountability in data lifecycle management,” International Journal of Research in Electronics and Computer Engineering, vol.4, no.3, pp.180–186, 2016.
[25] K. Cherladine, B.R. Rallabandi, A. Goel, K. Kaushik, A. Dhillon and M. Soni, “Generative adversarial defense models for simulated cyberattack scenarios in virtual networks,” In 2025 International Conference on Digital Innovations for Sustainable Solutions (ICDISS), pp. 1–6, 2025
[26] B.R. Rallabandi, “MEC-native 5G systems: Orchestration algorithms for ultra-low latency cloudedge integration,” International Journal of Intelligent Systems and Applications in Engineering, vol.8, no.4, pp.398–408, 2020.
[27] B.R. Rallabandi, “Joint deployment and operational energy optimization in heterogeneous cellular networks under traffic variability,” International Journal of Communication Networks and Information Security, vol.10, no.3, pp.638–647, 2024.
[28] C. H. Neetha and M. P. Kantipudi, “Adaptive Edge-Based Bilinear Interpolation for Smart Healthcare,” in IET Conference Proceedings CP824, vol. 2022, no. 26, pp. 7–13, 2022.
[29] R. Aluvalu, R. Venkatachalam, V. Uma Maheswari, R. J. Anandhi, M. V. V. Kantipudi and S. Prashanth Mallellu, “IWHO-Based Cluster Head Selection for Vehicle-to-Vehicle Communication in Intelligent Transport System,” International Journal of Transport Development and Integration, vol. 8, no. 2, pp. 154–166, 2024
[30] S. Velamuri, S. K. Sudabattula, M. V. V. Prasad Kantipudi, and N. Prabaharan, “Q-Learning Based Commercial Electric Vehicles Scheduling in a Renewable Energy Dominant Distribution Systems,” Electric Power Components and Systems, pp. 1–14, 2023
[31] M. Singh, B. Rallabandi, K.K. Gattupalli and M. Sai Medarametla, “Autonomous private 5G field area networks: Achieving secure, scalable, and self-healing smart grid communications,” In 2025 2nd International Conference on Recent Trends in Electrical, Electronics and Computing Technologies (ICRTEECT), pp. 1–6, 2025.
[32] A.B. Desai, B. Rallabandi, K.K. Gattupalli and M.S. Medarametla, “Agentic AI for rural connectivity and spectrum-aware networks to power smart agriculture ecosystems,” In 2025 2nd International Conference on Recent Trends in Electrical, Electronics and Computing Technologies (ICRTEECT), pp. 1–6, 2025.
[33] S. Goyal, B. Rallabandi, K.K. Gattupalli and M.S. Medarametla, “Digital twin-enabled contextaware networks: Enhancing smart grid resilience through predictive intelligence,” In 2025 2nd International Conference on Recent Trends in Electrical, Electronics and Computing Technologies, pp. 1–6, 2025.
[34] N. Tripathi, K.K. Gattupalli, B. Rallabandi and M.S. Medarametla, “AI-native Cloud-RAN orchestration for enterprise private 5G using digital twin models,” In 2025 1st IEEE Uttar Pradesh Section Women in Engineering International Conference on Electrical Electronics and Computer Engineering (UPWIECON), pp. 851, 2025.
