The automotive industry is shifting gears, accelerating into the future as new technologies push the boundaries of efficiency, sustainability, and safety.

The next phase is already here—the integration of artificial intelligence (AI) and big data within every facet of the industry. From the factory floor to the driving experience itself, AI and big data are fuelling safer, more efficient, and more intelligent vehicles, as well as better manufacturing and maintenance processes.

Here are six exciting ways AI and big data are reshaping the automotive landscape.

• Predict the future

Every car owner understands the value of regular oil changes and brake inspections, in the hopes of staving off more costly maintenance down the road. Now, big data and AI are turbocharging proactive vehicle health.

Enter: predictive maintenance.

Predictive maintenance gives dealerships the ability to monitor vehicle performance data remotely and continuously. Integrated in-car sensors collect data on the health of any given car, and leverage AI and big data analytics to assess issues in real time, keeping dealerships and drivers updated on what’s going on under the hood. Dealers can be automatically alerted to potential or ongoing issues in any given vehicle and can reach out and offer maintenance proactively—before a car deteriorates or even breaks down.

A related practice is preventative maintenance, wherein computer vision systems powered by AI are used to detect issues that would go unnoticed by the human eye, preventing them from getting worse and damaging the vehicle further.

Fleet management will particularly benefit from such maintenance technologies. By proactively identifying issues like failing components, worn-out tires, or fluid leaks, companies can know what issues to look for across the entire fleet and schedule repairs in advance, saving considerable amounts of money and frustration along the way. Commercial fleets from taxis to delivery trucks to busses can minimize downtime and disruptions, ensuring that packages (and people) are delivered on time.

• Eco-friendly roads

Emissions alone are not the only environmental impact of cars. It can take up to 151 cubic meters of water to manufacture a single vehicle and 11 million cars annually are taken out of circulation in Europe alone, representing as many metric tons of waste.

Big data analytics allow automakers to optimize vehicle design and manufacturing with a focus on sustainability. Data analytics on the most efficient and environmentally friendly materials and assembly processes will allow manufacturers to ensure that every vehicle has been made with the smallest environmental footprint possible. Manufacturers can also anticipate demand fluctuations more accurately, reducing waste by minimizing excess inventory.

By streamlining logistics and maximizing efficiency in manufacturing, automakers can also ensure they’re getting the right parts from the right places. Eliminating surplus and reducing the number of scrapped parts will not only save time and money but will create a more sustainable automotive manufacturing process.

Beyond the assembly line, AI enables driving simulations for any given model or design, allowing manufacturers to engineer vehicles for improved fuel efficiency and reduced emissions.

AI can also track and analyze data throughout a vehicle’s lifecycle and facilitate sustainable management even after a car has run its course. AI algorithms designed to understand vehicle health, from production to end-of-life, can identify opportunities for component reuse and recycling, optimizing resources, maximizing the recovery of valuable materials, and reducing environmental impact. These insights can also be integrated into future models, enabling manufacturers to create vehicles designed for easier disassembly and recyclability.

• Safety in autonomous driving

The development of autonomous vehicles (AVs) is fuelled by AI and big data. These powerful tools enable AVs to view and navigate their surroundings unmanned, through sensor fusion and machine learning algorithms trained to make real-time decisions for navigating complex road environments safely.

Autonomous vehicles create as much as 1 terabyte of data hourly. Accordingly, improvements to data gathering, storage, and analysis will make autonomous driving safer. As a technology that can make real-time decisions, AI will play a critical role in teaching cars to learn the rules of the road and improve performance continuously.

In addition, as AVs continue to track more miles, the AI systems powering them are receiving more and more data on roadway environments, creating more granular maps and protocols. The more precise and accurate the maps, the better equipped AVs are for navigating those environments smoothly and safely.