Artificial intelligence (AI) hasn’t progressed to the point where all of our day-to-day functions and activities are fully automated and controlled by robots and technology. But our society has integrated recent developments in AI to optimize efficiency and enhance performance across a variety of industries. From fully integrated, AI-driven production lines that increase the supply chain output of Fortune 500 companies, to Amazon Alexa devices that help consumers conveniently order another gallon of milk to their front door, AI’s use in modern day society continues to grow, and the further potential for this technology is expanding exponentially.

One field that will be deeply impacted by AI in the coming years and decades is the automotive industry. With manufacturers rolling out new enhancements yearly for their already iconic brands, they will have to continue to implement AI-driven features, as well as discover new innovative methods of utilizing this technology, to stay ahead of their competitors while shaping the future of how consumers drive cars.

It’s questionable just how much or how quickly these manufacturers will be able to implement AI in their production and business activities, but their long-term effect will reshape the entire action and concept of driving. Here’s a dive into AI manufacturing disruptions in the automotive industry.

Past and current applications of AI in the automotive industry

Since the late 1800s, the automobile has remained a hallmark of design and innovation. Manufacturers dedicate multi-million-dollar budgets each year to improving aesthetic and performance components of an individual car, always yearning to incorporate the latest technological trends and disruptions into their production. There are two primary areas where AI can impact the automotive industry: design and operations.

The successes of AI in design have been clearly advertised across a variety of industries, but those achievements may not be as easily applicable to manufacturing processes in the automotive industry. Wireless communication between manufacturing entities over reliable networks, large scale automated data collection, storage and analysis, and relatively inexpensive access to AI computational power in the cloud are among the current surge of promising technology related to or driven by AI. But automotive manufacturing may more likely be a follower than a leader as the risk of turning over already reliable and efficient manufacturing design and manufacturing decision making to AI-driven technology grows.

The more probable disruptive applications of AI in the automotive industry will likely be seen in more intelligent driving-assistance modules ,like AI technology within a driver’s dashboard telling them if there’s a car in their blindspot or if there’s a slow passenger jaywalking across a street 50 yards ahead.  This type of AI-driven feature is clearly of the most beneficial offerings AI can present to the automotive industry, but its successful implementation relies on manufacturers, designers, and engineers being able to digitally replicate the years of experience and instinct human drivers have gained while on the road. That type of technology requires a hefty amount of reliable sensor development and computer processing power, which is another hurdle that manufacturers will need to overcome in their future design processes.

Ironically, technology has already affected the design and operation developments of cars without the implementation of AI. Through the power of the Internet of People (IoP), consumers are influencing, if not entirely changing, automotive design through product reviews and peer-to-peer communication. Enough consumer feedback about a manufacturer’s new advanced headlights or interior aesthetic can push an automotive manufacturer to continue or discontinue these features entirely. Where AI in the form of data analytics can make an impact in that realm is how that actual feedback data from consumers are compartmentalized, analyzed, and ultimately implemented to serve an auto manufacturer’s design objectives.

Disruption effects on consumers in the future

Right now, we’re already seeing implementation of new technologies in current vehicles through consumer/vehicle interfaces for electronic safety—items like dashboard lights indicating to drivers when preventive maintenance needs to be performed, or advanced security options that can help prevent or combat car theft. These items aren’t technically AI and are based on sensor and processor improvements but could become more AI-driven in the future. We’re already seeing this implemented now through vehicles being able to “phone home” to their manufacturers and provide feedback on their operating history and current operating condition and environment. This feedback will probably influence future design choices made by those manufacturers.

AI will likely change not just how an individual drives a car, but the experience of purchasing a vehicle as well. Manufacturers currently use these safety and maintenance innovations as revenue-producing options, which could contribute to increasing prices. If a manufacturer rolls out an AI-driven dashboard interface with an advanced amount of processing power, it is something that they could easily use to increase the sticker prices of their vehicles. But as these technologies become normalized, competitive pressures and volume are turning these options into standard equipment, consequently giving consumers a more intelligent vehicle at the same price.

If there is a breakthrough in autonomous driving through AI technology, it will enable the transformation of the vehicle interior into a living, working and entertainment space. This transformation isn’t so much things like enhanced streaming options so kids have a multitude of cartoons to watch in the backseat, but the potential erosion of a back or front seat entirely. The car interior could be used by businesspeople to make final revisions to a presentation they’re about to deliver, or families using that shared space to watch a movie while safely coasting on the freeway. We’re still a long way off from this reality, as highly publicized mishaps with self-driving features show that the technology, as well as human trust in self-driving technology, is still not at the level where it is wholly reliable or entirely safe. Still, the potential for AI in self-driving features is exponential.

Developments in AI could also spell out potential benefits, or pitfalls, for drivers when obtaining insurance. Similar to those tracking devices that send data back to automobile manufacturers themselves, that technology could be implemented to provide feedback to auto insurance providers, allowing them to offer customized coverage based on driving preferences and experiences. Depending on a driver’s habits, that could potentially help them save or spend more on premiums and other insurance payments. This also would require insurance providers to obtain permission and access that data from drivers and their vehicles in the first place. But just as car manufacturers could use AI-driven technology as a means of increasing sticker prices, insurance providers could offer a financial incentive to drivers willing to submit that data in the first place.

Emerging technology comes in waves that seem to dissolve in the short-run but slowly evolve into a truly remarkable change in the long-run. We don’t immediately notice the conveniences of online shopping until years after initial inception when consumers are purchasing every conceivable item online and marvel at how we’ve progressed to this point. The historical tech trends will hold true for AI and the auto industry. The benefits won’t be immediately visible or distinguishable in the short term, but the long-term impacts of AI on the automotive industry will be undeniable.