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Week 3 Assignment: enterprise rental car private for-profit business
Prompt
Write an APA 7 formatted paper that incorporates the following information in a cohesive integrated paper. Do NOT present a “Q/A list.”
Conduct factual research and select a real life currently operating private for-profit business OR NGO [article] that is using an AI application in its operations.
This can be a publicly-traded company, a private business, or NGO that you personally know or work for or one that you are interested in. It is important that you can research and obtain the information you need about the company to complete this assignment.
Content: Develop the following elements in your paper:
1. Name and describe the company; describe its industry and business. (Example: if you choose a grocery store chain business, the industry is retail. Name the company, and in describing the company, include its size, location(s), and so forth.)
2. Identify the AI application utilized by the company and its main subfield of AI. For example, is it NLP, deep learning, or another subfield of AI? (Likely more than one subfield will be involved. Identify the primary subfield. Refer to Week 2.) In non-technical terms, describe the AI application’s function.
3. Describe the AI’s operational role in your selected company.
4. Explain the benefits this AI application provides both the industry and your selected company in particular.
Format: Instructions:
1. Structure your paper according to APA Style 7th Edition guidelines. (Refer to APA Help materials in Classroom CONTENT/Overview.)
2. Use appropriate subheadings in organizing your paper. Follow the Word template attached to this Assignment. Also, The APA Sample Student Paper in APA Help shows you how to set up your subheadings.
3. Follow the Word template attached to this Assignment.
4. Length: 600–750 words (narrative text portion of paper). Do not include an abstract or other “extra” items—these will not count.
5. References: minimum of 3 credible sources.* All sources on the References list must also be cited in the paper as in text citations.
6. Be original, concise, and organized. Be sure to include all elements of the assignment.
Yu, D., Li, Z., Zhong, Q., Ai, Y., & Chen, W. (2020). Demand Management of Station-Based Car Sharing System Based on Deep Learning Forecasting. Journal of Advanced Transportation, 2020, 15. https://doi.org/10.1155/2020/8935857
Zhang, M., Xie, Y., Huang, L., & He, Z. (2014). Service quality evaluation of car rental industry in China. The International Journal of Quality & Reliability Management, 31(1), 82-102. https://doi.org/10.1108/IJQRM-11-2012-0146

 

Proposed APA Title:
Enterprise Rent‑A‑Car’s Use of Artificial Intelligence: Enhancing Operations in the Private For‑Profit Car Rental Industry

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Enterprise Rent‑A‑Car and AI in the Car Rental Industry

Introduction and Company Description
Enterprise Rent‑A‑Car is a major American private, for‑profit car rental business founded in 1957 and headquartered in Clayton, Missouri. It is the flagship brand of Enterprise Mobility (formerly Enterprise Holdings), which also owns National Car Rental and Alamo Rent a Car, and manages a diversified fleet of more than 2.3 million vehicles across over 9,500 global locations. Enterprise serves both individual consumers and corporate clients with services ranging from short‑term rentals to fleet management, carsharing, and vehicle subscriptions. Its revenue and operational scale reflect its position as one of the largest car rental providers worldwide, processing millions of transactions annually and emphasizing a customer‑centric approach supported by technological innovation (Enterprise Rent‑A‑Car Wikipedia; martini.ai Research).

AI Application Utilized and Subfield of AI
One notable technology trend in the car rental industry is the integration of artificial intelligence (AI) to support operational efficiency, customer experience, and data‑driven decision‑making. While Enterprise has not publicly documented the use of specific AI scanners for car inspections, the company leverages AI‑enabled telematics, predictive analytics, and connected vehicle data within its operations. These AI functions primarily draw on subfields such as machine learning (ML), predictive analytics, and data analytics to process real‑time vehicle data, forecast maintenance needs, and streamline operational tasks. In non‑technical terms, this AI system analyzes vast amounts of data generated by vehicle sensors and fleet usage to predict issues before they occur and optimize resource allocation, improving service reliability and customer satisfaction.

AI’s Operational Role at Enterprise
Within Enterprise’s operational framework, AI‑driven telematics and fleet analytics play a role in monitoring vehicle performance, tracking mileage, forecasting maintenance needs, and enhancing the overall rental process. Connected vehicle technology allows Enterprise to automatically collect data on sensor status, fuel levels, and diagnostic information from vehicles, transmitting it to centralized analytics for proactive maintenance planning and efficient fleet usage. These capabilities support rapid decision‑making about vehicle allocation, reduce downtime, and help minimize unexpected breakdowns.

Benefits of AI for the Industry and Enterprise
For the broader car rental industry, AI delivers significant improvements in predictive maintenance, fleet utilization, and customer experience. Research indicates that predictive analytics and machine learning models enhance resource utilization, reduce operational costs, and improve fleet performance, enabling companies to anticipate issues and schedule necessary services before failures arise (Roy, 2025). AI also strengthens dynamic pricing and personalization strategies—an increasingly important competitive advantage in the travel and mobility markets. For Enterprise specifically, these technologies offer operational benefits including increased vehicle uptime, reduced maintenance costs, and improved reliability of service offerings. Real‑time vehicle data helps optimize fleet distribution and responsiveness to customer demand.

AI’s influence on customer service is equally impactful. By integrating data analytics into booking platforms, rental firms can better understand customer preferences and tailor marketing or service offerings accordingly, leading to increased satisfaction and loyalty. In addition, back‑office automation through AI reduces manual workload, allowing staff to focus on higher‑value tasks and improving overall service quality (GitNux AI in Car Rental Industry Statistics).

Conclusion
Enterprise Rent‑A‑Car exemplifies how private for‑profit businesses in the transportation industry can leverage AI technologies—especially machine learning and predictive analytics—to transform fleet operations and customer service. By harnessing telematics and intelligent analytics, Enterprise enhances fleet reliability, streamlines maintenance, and responds more effectively to market demands, providing measurable benefits both to itself and to the wider car rental sector. These AI applications help the company maintain competitiveness in a rapidly evolving mobility landscape.

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References

Enterprise Rent‑A‑Car. (n.d.). Wikipedia. Retrieved from https://en.wikipedia.org/wiki/Enterprise_Rent-A-Car

Enterprise Rent‑A‑Car. (n.d.). martini.ai Research. Retrieved from https://martini.ai/pages/research/Enterprise%20Rent-A-Car%20Inc-a3f4288d1cc1ab95e3c76f2612f0a0b3

GitNux. (2025). AI in the Car Rental Industry Statistics. Retrieved from https://gitnux.org/ai-in-the-car-rental-industry-statistics/

Roy, P. (2025). AI‑Driven Fleet Analytics: Revolutionizing Modern Fleet Management. International Research Journal of Modernization in Engineering, Technology and Science.

Fleet telematics system. (n.d.). Wikipedia. Retrieved from https://en.wikipedia.org/wiki/Fleet_telematics_system

Predictive maintenance. (n.d.). Wikipedia. Retrieved from https://en.wikipedia.org/wiki/Predictive_maintenance

 

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