Home Business NewsBusinessAutomotive Automotive sectors need to move to tech to survive as AI market to hit $8.9bn

Automotive sectors need to move to tech to survive as AI market to hit $8.9bn

29th Jul 20 12:41 pm

As the coronavirus crisis unfolded, businesses had to rely on the cloud and other disruptive technologies to continue their operations virtually and service customers. That trend isn’t going anywhere, and one of the outcomes of Covid-19 will be hugely accelerated digital transformation.

In the automotive space, this will act as a further catalyst for transformation in an industry that has already changed beyond recognition. Who could imagine five or ten years ago the kind of work that Waymo is doing with bringing autonomous vehicles into the mainstream?

The growth of AI, and its application through machine learning, is revolutionising the industry – and the global automotive AI market is predicted to reach $8.9 billion by 2025. So it’s imperative for all automotive  sub-sectors, not just the big names, to adopt advanced tech to compete and survive in a rapidly-changing, crowded marketplace.

The auto repair market, for one, is set for a radical shift. According to GiPA UK, the automotive and aftermarket industry has been hit hard by the pandemic, and by the end of May workshops saw a 74% drop in activity despite many re-opening as restrictions eased. The pandemic means that going contactless is here to stay, so service providers have to be mobile-enabled, perform fast diagnostics remotely and provide timely advice and virtual consultations. To flourish in this ‘new normal’, traditional local mechanics must reinvent themselves and become digitally-savvy now.

In its report on ‘The Future of Mobility’, the Government Office for Science highlights that technology has completely  transformed the way we think about transport, with “mobility-as-a-service” emerging from the rise of intelligent technologies and how they can interact with data.

Progress with IoT, too, has led to cars evolving into digital, fully connected and integrated vehicles. Through telematics, in-built software updates, dashcams, sensors, automated safety features and more, cars can monitor their environment, communicate with service providers, self-diagnose and alert the driver before any issues get worse.

The strength of AI and machine learning lies in the fact that it’s self-learning, more accurate with time, and constantly improving with minimal manual intervention. It can make sense of huge, unstructured data sets to create smart, intelligence-backed insights in real time, speeding up problem solving that would otherwise take many hours.

Machine learning algorithms can help companies detect anomalies or deviations from usual patterns. BMW, for example, is optimising production through an innovative AI tool that evaluates images and compares them to hundreds of others in a database to catch inconsistencies or potential problems early. This adds an extra layer of reliability and business efficiency.

For the car repairs sector, applying data analytics through machine learning can facilitate an automated engine that reflects more accurately the best value quote for the nature of the job. Digitalisation in this way can save time and costs for both businesses and consumers at a time when budgets are likely to be strained after lockdown.

Delving deeper into existing data pools, these solutions can amalgamate scattered elements, such as industry parts and labour pricing, to deliver a higher quality and more cost-effective offering to drivers, providing a wider range of services and building customer trust.

These days, consumers want speed, convenience and frictionless service, as well as greater engagement with providers through mobile channels. The gig economy and ‘uberisation’ are also transforming the way we live and work. Digitalisation hasn’t reached its full potential yet, and I see it becoming increasingly vital to the automotive market. It can revive activity across the sector and help it play its part in post-Covid economic revitalisation.

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