Introduction
Artificial intelligence (AI), machine learning (ML), and deep learning (DL) are some of the most effective methodologies that experts use to improve business processes and outcomes. Some companies utilize these approaches to automate their operations, while others primarily use them to gather data. Regardless of the application, it is evident that AI technologies are continually becoming more and more relevant in the current business realities. The present outline presents a business proposal focusing on AI parking, which is an interdisciplinary process associated with the automotive industry and urban planning.
Business Proposal for AI Application
The incentive for the proposal is the current imbalance between parking lots and the number of vehicles in the United States. Namely, according to the latest research, there are approximately 256 million cars and two billion parking slots in the United States (Haponik, 2022). Regardless, vehicle owners struggle daily to find available spots, leading to financial loss and time waste. The proposal focuses on the AI real-time algorithms that help drivers find nearby open parking lots via a mobile application.
AI Parking System
The AI parking proposal is highly beneficial for drivers, parking lot managers, urban planners, and the whole automotive industry. The app searches for available spots in real-time based on ground sensors and provides an overview of nearby slots to the driver. The implementation of the methodology requires collaboration between parking lot managers and the business team to create a network of facilities covered by the mobile app.
Research Findings
The research generally supports the idea of AI parking and demonstrates evident trends toward automation and digitalization in the automotive industry. Namely, experts predict a drastic increase in autonomous parking both on the street and in parking lots by 2025 (Heineke et al., 2021). The same trend is noticeable in nearly every industry – people have realized the utmost impact of AI technologies in business start-ups and optimization projects (Weber et al., 2022). Additionally, the McKinsey Institute predicts that approximately 15% of all vehicles on the market in 2030 will be autonomous (Dilmegani, 2019). Considering the mentioned research findings, it is plausible that the present AI business proposal is an effective solution to the problems of traffic congestion.
Issues with Proposal
Nevertheless, there are several issues with the project that must be addressed. The most significant obstacle is the necessity of the collaboration and implementation of ground sensors or visual analysis via cameras in parking lots (Haponik, 2022). Although the technology is not exceedingly expensive, it still takes effort from parking managers and software teams to install the necessary devices. Moreover, it is essential to implement the system in many parking lots for optimal coverage, ensuring that drivers are provided with the most appropriate option (Haponik, 2022). The second relevant challenge is a generic problem in AI research and application – some people are reluctant to change, preferring less effective but tested solutions (Dwivedi et al., 2019). Lastly, John et al. (2021) indicate that implementing AI solutions is not sufficient in most cases. The application should have an appropriate end-user interface to foster provider-client communication and make the process more convenient. The mentioned issues are relevant but non-critical problems that can be avoided via collaboration efforts and intelligent planning/marketing.
Benefits with Proposal
Consequently, there are multiple benefits associated with the business proposal. Namely, the stakeholders that profit from this system are the automotive industry, urban planners, parking businesses, and drivers. The former benefits due to the integration of innovative technologies in vehicles, which might further stimulate the demand for autonomous parking, cars, and other processes in the industry (Heineke et al., 2021). Consequently, the proposal is helpful for city planning because it can mitigate traffic congestion, allowing for more effective transportation (Haponik, 2022). Moreover, it prevents resource wastage and might help establish a more appropriate balance between the number of parking slots and vehicles. Parking businesses benefit since they acquire a competitive advantage compared to those parking lots that have not installed AI solutions.
Lastly, the proposal benefits drivers the most and provides multiple advantages. Its optimization algorithms allow vehicle owners to find the closest available spot, estimate the number of cars in the parking lots, and choose the most suitable solution (Haponik, 2022). Since AI generates these options in real time, the accuracy of the provided solutions is high. It allows vehicle owners to save time, nerves, and money on fuel, making driving a more pleasing experience. In summary, the examined proposal benefits four primary stakeholders, with particular emphasis on the automotive industry and urban management.
Conclusion
AI technologies and digitalization are becoming continuously more relevant in most industries, including the automotive and urban sectors. The proposed solution implies real-time analysis of available parking slots near the driver and provides the most appropriate options to the vehicle owner via a mobile application. It is a highly beneficial incentive for multiple stakeholders, and the primary challenges include the necessity of collaboration, installation costs, and the public reluctance to change. Ultimately, the proposed AI application can mitigate the problem of traffic congestion and help drivers save more time and money during their search for available parking lots.
References
Dilmegani, C. (2019). In-depth guide to future of AI in 2023, according to top experts. AI Multiple. Web.
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T.,… & Williams, M. D. (2019). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57. Web.
Haponik, A. (2022). How can AI help optimize smart parking? Addepto. Web.
Heineke, K., Heuss, R., Kelkar, A., & Kellner, M. (2021). What’s next for autonomous vehicles? McKinsey & Company. Web.
John, M. M., Olsson, H. H., & Bosch, J. (2022). Towards an AI‐driven business development framework: A multi‐case study. Journal of Software: Evolution and Process. Web.
Weber, M., Beutter, M., Weking, J., Böhm, M., & Krcmar, H. (2022). AI Startup Business Models: Key Characteristics and Directions for Entrepreneurship Research. Business & Information Systems Engineering, 64(1), 91-109. Web.