The growth of US cities is so phenomenal that this has put pressure on city resource management and to enhance utilization of artificial intelligence and data analytics. Indeed, this article examines the AI implementation level and its efficiency in maintaining critical urban wealth-generating elements, including energy, transportation, waste management and public service within the US cities. Employing an online survey of 300 participants comprising city planners, AI solution providers, urban infrastructure managers and government officials, this study aims at establishing AI implementation status, how frequently it is used and main perceived barriers. Results show that 52% of respondents confirmed AI implementation in their cities, with larger cities (population > 500,000) leading in AI adoption at 62%, compared to 33.7% in small cities (population < 100,000). AI was most effective in optimizing public services (mean = 3.10) and waste management (mean = 3.09), whereas energy management (mean = 2.87) saw the least effectiveness. The study also revealed some major hindrances to adoption such as data privacy issues (27. 3%), inadequate funds (26. 0%) and lack of skilled workers (18. 7%), especially in the medium and few large cities. Applying ANOVA test, it was established that resource sectors did not significantly affect AI effectiveness indicating uniform use in different domains. Regression analysis demonstrated a negative and marginal significance between AI effectiveness in water management and its effects on transportation management; B = -0. 113; p = 0. 048.

The study concludes that even as more organizations embrace AI adoption there are key hurdles that need to be overcome to enhance the effective implementation of AI especially in the smaller city locations. This means there is need to develop targeted policies that address the challenge of funding, work towards improving data privacy governance and also train candidates with the technical skills required. Further studies should be conducted on the effects of continued use of AI on sustainability of cities and the possibility of utilizing AI with developing technologies of IoT and blockchain in enhancing urban resource management.

AI adoption, data analytics, resource optimization

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