International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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AI-enabled Circular Economy Strategies: Transforming Start-up Ecosystems for Sustainable Business and Technological Progress

Author(s) Hidaytunnisha Ansari, Pragati Shukla, Dr. Mohammad Anees
Country India
Abstract The convergence of artificial intelligence (AI) and circular economy (CE) principles offers a transformative pathway for start-up ecosystems seeking sustainable business models. In the face of resource depletion and environmental challenges, AI-powered circular strategies enable start-ups to design regenerative products, optimize resource use, and create innovative closed-loop business models. This chapter adopts a theoretical literature-synthesis approach to explore how AI can catalyze circular economy adoption in start-up contexts, driving both sustainable development and technological progress. We examine the roles AI plays in enhancing circular strategies – from intelligent product design and predictive maintenance to waste reduction and recycling – and how these innovations reshape entrepreneurial ecosystems. Key strategic enablers are identified, including digital infrastructure, data-driven business model innovation, supportive networks, and policy frameworks that facilitate AI integration. We also discuss the potential benefits of AI-enabled circular models (such as improved resource efficiency, new value creation, and competitive advantages) alongside the key barriers that start-ups face (like high implementation costs, skill gaps, and market hurdles). The findings highlight that, with the right enablers in place, AI-driven circular economy initiatives can significantly enhance the sustainability and resilience of start-up ecosystems.
Keywords Circular Economy; Artificial Intelligence; Sustainable Business Models; Start-up Ecosystems; Innovation
Field Business Administration
Published In Volume 7, Issue 4, July-August 2025
Published On 2025-08-10
DOI https://doi.org/10.36948/ijfmr.2025.v07i04.53351
Short DOI https://doi.org/g9w5c5
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