AI in business operations: driving urban growth and societal sustainability
Approximately 30% of smart city applications will use artificial intelligence (AI) by the end of 2025, thereby radically altering the urban sustainability landscape in the future (Yan et al., 2023). The advent of AI in reshaping traditional businesses into sustainable operations is evident. Whenever AI is brought to the forefront, it is considered a cornerstone in the business domain, enabling a transition towards more innovative and sustainable practices (Appio et al., 2024). Incorporating AI into business practices has many facets. According to Grand View Research (2023), the global AI market size was anticipated at USD 196.63 billion in 2023 and is expected to grow at a CAGR of 36.6% from 2024 to 2030. The recent fanfare surrounding AI has elevated it to a key enabler of sustainable development, prompting many companies to prioritize and integrate it into their business operations; hence, there is a stark difference between traditional and new practices. In tandem with this evolution, urban growth and societal dynamics are experiencing profound changes as AI-driven solutions come to the fore in various aspects of modern society (Shahidi Hamedani et al., 2024). AI applications in city government, transforming conventional cities into efficient ones (Ortega-Fernández et al., 2020), have significantly shifted from functional systems to more sustainable and intelligent ones. Furthermore, from another perspective, the role of AI in optimizing business processes has surpassed comparison with its implication for improving logistics operational capabilities and reducing environmental impacts (Jorzik et al., 2024a) till manufacturing reduces downtime, all of which contribute to the growth of urban economics. In the meantime, with the speedy pace of adoption of AI in business operations, it is also imperative to amalgamate with sustainable practices. Acting on this matter requires a thoughtful approach that aligns AI with social, economic, and environmental sustainability.The intersection of AI role and business operations has recently gained widespread attention. Some studies (Chen et al., 2024;Shahzadi et al., 2024)focused on AI's role in supply chain management, highlighting its role in minimizing inefficiencies and improving logistics by utilizing AI more often;supply chains become leaner and reduced carbon footprints, paving the path to sustainable operations. It is estimated that by 2026, 60% of businesses will adopt AI-powered warehouse solutions instead of just 10% in 2020 (MHI, 2024).In line with this shift, (Dilmegani & Ermut, 2025) note that businesses also invest heavily in warehouse robots to enhance their supply chain management through AI technology. Robots can manage operations more efficiently and accurately by automating picking, packing, sorting, and inventory management, thus saving labor costs and accelerating order processing. Amazon, for instance, has deployed more than 200,000 robots in its warehouses to optimize operations.AI can be used to optimize resource utilization, automate processes for improved efficiency, and enable real-time monitoring that aligns with sustainability goals (Waltersmann et al., 2021). As sustainable supply chain management focuses on reducing waste and enhancing traceability, AI-driven technologies such as machine learning and big data analytics have been pivotal in achieving these goals. (Tsolakis et al., 2023) Companies like eBay leverage AI for machine translation, enhancing decision-making and operational efficiency . Similarly, Vodafone employs AI-driven analytics to personalize services, exemplifying its transformative impact. (Jorzik et al., 2024a).These technologies help reduce forecasting errors, minimize excess inventory, and lower energy consumption. (Sharma et al., 2020) Likewise, Smart grid protection sensors can detect defects up to 80% more accurately than traditional sensors, reducing losses and improving the system's reliability by adjusting to grid conditions dynamically (Mahadik, Sheetal et al., 2025). These applications contribute to urban economic growth by fostering technological innovation. AI leverages advanced techniques like deep reinforcement learning (DRL) to optimize dynamic business operations (Shuford, 2024). DRL improves supply chain management through adaptive routing and inventory optimization, dynamically adjusting to real-time changes in demand and logistics; with the help of DRL, researchers can develop systems that can dynamically adapt to changes, optimize resource utilization, and facilitate multi-objective decision-making for instance, (Dehaybe et al., 2024).In addition, it enables businesses to prevent equipment failures and minimize downtime, thereby streamlining workflows significantly (Mohan et al., 2021). Moreover, in urban centers, these advancements catalyze economic growth and foster innovation. In other words, a key contribution of AI is to facilitate smart urban development and efficient resource allocation, thereby ensuring that cities are resilient and economically prosperous (Li et al., 2024). In developing smart cities, AI has a transformative impact on urbanization trends. Through the application of AI, urban infrastructure can be optimized by improving energy efficiency, streamlining transportation, and managing housing needs; AI makes it possible to reduce traffic congestion and advance mobility in transportation systems, such as prescriptive traffic management and autonomous vehicles (Regona et al., 2024).In cities like Singapore, AI manages real-time traffic and monitors energy consumption, setting urban efficiency benchmarks (Padhiary et al., 2025). On a similar note, Tennet TSO, a German transmission system operator, has been utilizing AI-based forecasting and IBM Watson's cognitive computing platform to anticipate renewable energy generation in real time, allowing real-time grid adjustments and maximizing clean energy use. (Mahadik, Sheetal et al., 2025) 3Nowadays,...