5 Ways AI Is Revolutionizing the Car Rental Industry

By 2030, the concept of private vehicle ownership will be nearly a thing of the past, according to predictions of The World Economic Forum. Experts are calling it the “rentership society” which is driven by Millennials and Generation Zers who are less inclined to own things and would much rather take part in shared economies and pay for services instead.

Hello, Shared Economy

In the midst of the global shared economy, stands the car rental industry. As new options emerge, the fleet management expertise of car rental companies has become more relevant than ever. Decades of fleet management experience, a loyal customer base, ownership of vehicles, and the knowledge of how to maintain them gives today’s car rental industry a competitive edge in the emerging smart mobility space.

But something is missing.

With today’s advanced technology fleets can achieve greater efficiency, lower operating costs, and greater customer satisfaction. Those who wish to remain relevant and lead the smart transport revolution, are not only looking to fit their businesses to the new ways that people are getting around, but are also looking for ways to leverage new technologies in the market and operate their existing business in a way that is more efficient and that provides a better experience for their customers.

One of the areas that has progressed dramatically in the past few years is the availability of multiple data sources due to connectivity, and machine learning and artificial intelligence (AI) technologies that provide valuable insights out of it.

To lay out exactly how technology is affecting car rentals today, here's a breakdown of five ways in which AI is revolutionizing the car rental industry.

1. Fleet Utilization

Utilization is a game of demand and supply. In the past few years, data, which is considered the new oil for many industries, has created huge opportunities to better deal with this crucial challenge. By using AI-based technology, demand for services can be predicted and provide real-time and long term insights that can help rental companies become more proactive when planning and optimizing fleet utilization.

Most data sources that can affect demand are already available today such as demographics, weather, traffic, airport schedules, social events, hotel occupancy, and many others. Correlations with different data sources can vary throughout different locations or countries according to people’s behaviors, and AI-based demand prediction models can adjust themselves according to each specific scenario.

2. Maintenance

AI-based prediction models are not only crucial for predicting demand but are also useful for predicting and managing maintenance efficiently across the entire fleet. There are multiple parameters that can affect when a vehicle will require maintenance. Some of them have to do with fixed thresholds such as mileage or time, while others can be more difficult to analyze and address in an efficient, cost-effective manner. These include real-time fleet telematics data, vehicle data that is collected during periodic checks and external data such as weather conditions and customer driving patterns.

Dealing with various data sources and predicting the outcome is possible due to AI technologies, and can help fleet operators implement predictive maintenance. When maintenance is synchronized with the overall demand for service, car rental companies can maximize utilization and ensure efficient operations.

For example, with the assistance of AI, car rentals can leverage one-way rentals to move vehicles towards far away maintenance spots, while eliminating gas and driver hours that would otherwise be required.

3. Customer Satisfaction

Just as real-time data analysis can provide driving advice, it can also help match the right car to each customer. As a result, each customer can receive a vehicle that has been specifically assigned to their needs while taking into account various different parameters such as pick-up time, car type, destination, and even specific requirements such as child car seats, racks for carrying sports equipment and even snow tires.   

Car rental companies are in a transformative stage, and are looking to provide an experience that will place their customers first and maintain a long-term relationship with them. AI can help create this experience by predicting customer preferences and offering recommendations based on them.

4. Revenue Management

Models generated by AI can help predict vehicle depreciation and report exactly when the best time would be to de-fleet and sell assets. Additionally, AI engines can constantly comb through the entire fleet data to make sure that rent prices meet the demand for services.

Before AI, one of the aspects of a revenue manager’s job was to run around, shop rates, and key in new prices. By performing like a skilled analyst, AI tools have the power to enhance pricing management and optimize car rental rates according to pricing goals automatically, as well as keep close track of depreciation. By utilizing AI tools, revenue managers can review useful insights such as ongoing forecasting and optimization, and free their time to manage higher-level tasks.

5. Autonomous

Rent is here to stay, but the autonomous vehicle era may provide new rental use-cases that companies can already prepare for today. For example, autonomous vehicles will soon be synchronized to customers’ daily schedules and arrive exactly when they are needed. They will take care of our deliveries, getting us to our destination on time whether it be work or a far vacation spot, and even pick up or drop off our children.

One can debate how far these use cases can go, but it’s clear that there’s a big learning curve. Companies that will start using the technologies that enable personalization, usage prediction, and automation which rely heavily on AI will gain a huge advantage in learning the friction points early on and being prepared to lead the market when autonomous vehicles become part of our daily routine.

Israel Duanis, co-founder and CEO of Fleetonomy, a provider of AI-based fleet management solutions.