Automotive Artificial Intelligence (AI) Market: Unearthing Revenue Generating Opportunities 2037

The rising popularity of AI in the field of automation offers numerous lucrative opportunities for growth to the market players. Research Nester predicts that the automotive AI market size will reach USD 4 billion by the end of 2024. Furthermore, by 2037, the market is anticipated to garner USD 80 billion. In this blog, we will explore some of the latest trends in the market and other prospects.
Automotive Artificial Intelligence (AI) Market: Unearthing Revenue Generating Opportunities 2037

It has been estimated that almost 81.5% of automotive enterprises surveyed have actively explored or deployed Artificial Intelligence. Also, 73.5% of automotive enterprises surveyed have accelerated Artificial Intelligence in the past two years.


The rising popularity of AI in the field of automation offers numerous lucrative opportunities for growth to the market players. Research Nester predicts that the automotive AI market size will reach USD 4 billion by the end of 2024. Furthermore, by 2037, the market is anticipated to garner USD 80 billion. In this blog, we will explore some of the latest trends in the market and other prospects.

What are todayโ€™s consumers expecting in their cars?

Predictive maintenance: Almost, 47.3% of global manufacturing has implemented predictive technologies to eradicate operational costs. Practically, predictive maintenance has enhanced downtime by 85.4% and maintenance staff productivity by 55.4%. Some of the benefits of predictive maintenance are:

  • Prolonged vehicle lifespan
  • Less downtime and costs
  • Increased safety
  • Data-driven decisions

Behavior prediction: Behavior prediction is a prominent component of autonomous vehicle systems, enabling safe navigation in a dynamic environment. It also helps in anticipating the behavior of other traffic agents.

AI-enabled Voice recognition: According to an estimation, about 125 million people in the United States, drivers use voice control technology. Car manufacturers are turning to voice Artificial Technology to improve the safety, vehicle performance, and driving experience.

Smart energy management system: Artificial Intelligence powered systems in electric cars help use cars efficiently. The introduction of AI is revolutionizing the way users are managing fuel consumption. The AI features can identify cost-saving opportunities and manage fuel judiciously. Some of the benefits of the are as follows:

  • Streamlines the fuel purchasing process with the help of predictive analytics
  • Ensures optimization of fuel efficacy
  • Robusting fuel fraud detection
  • Enhance fuel quality monitoring
  • Vigilant about environmental compliance

Artificial Intelligence is crucial in developing autonomous vehicles, potentially reducing GHG or greenhouse gases by up to 34.3% by 2050.

Some of the practical use cases where AI has been deployed by leading companies are as follows:

  • Tesla

With the deployment of an Artificial Intelligence algorithm, Teslaโ€™s autopilot system can analyze data in real-time, allowing the vehicle to make informed decisions based on changing road conditions.

  • BMW

BMW has embraced Artificial Intelligence in the entire value chain, rendering enhanced value for customers, employees, products, and processes. According to the companyโ€™s website, BMW Group currently utilizes artificial intelligence in more than 400 applications and many relevant areas of the company.

  • Bosch

Bosch has adopted Artificial Intelligence technology most efficiently. Over the next decade, the company aims to utilize Artificial Intelligence in various services and products. A car with an onboard AI computer will accumulate new data about factors such as road conditions, surrounding traffic, congestion, etc.

Apart from this, automotive AI companies are giving astounding technological services to consumers. These companies are turning cars into a supercomputer on wheels. Some of these are listed below:

  • MaplessAI

The company has developed AI-driven solutions for remote car operations and enables autonomous vehicle control from a centralized operation. For instance, Mapless technology enables full operational remote control of existing fleet vehicles from hundreds of miles away.

The technology also renders low-latency connectivity between fleet vehicles and operators. The company has a fleet of over 4,100 vehicles and annual revenues exceeding USD 60 million.

  • LoopX

The companyโ€™s Operation Awareness System utilizes machine vision with the capability of data mining to find people, vehicles such as trucks and loaders, and buildings. Also, an advanced driver assistance system offers detection of situations such as crossover of pedestrians.

  • Ambro

The technology is capable of detecting vehicle damage and giving precise cost estimations filed directly with the insurance company. Some of the features of the technology are as follows:

  • Parts and damage detection
  • Text scanning
  • Fraud detection
  • Image recognition directly through the application
  • Claim cost estimation
  • Real-time claim assistance
  • Strada routing

The company is building AI systems to automate load bundling and bidding for truck shipments. The technology can cut down on waiting time and ensure drivers are paid well on time. Also, it lowers the rate for shippers while making sure carriers’ fleets are used to complete capacity. The work is done mainly in 4 steps: Understanding the needs, analyzing the environment, bundling optimal routes, and returning the directions.

Wrapping up,

It is quite evident that the market holds a vast pool of opportunities and there are multitudinous aspects to dive in. However, new as well as old market players are required to understand the market intricacies to excel in the cutthroat competition.

Availing an exhaustive market research report is an efficacious tool for getting information about constantly changing market dynamics. Factors such as regional growth analysis, market growth drivers, etc. help make judicious decisions.

Source: https://www.researchnester.com/reports/artificial-intelligence-in-automotive-market/1970


Recent Content

This article reflects on the misconceptions we have about AI, and discusses the fallacy of understanding AI’s underlying mechanisms, as it can demonstrate intelligent behavior despite our understanding. AI is developing its own form, capable of analyzing vast datasets, identifying patterns, and making connections that humans might take years to discover. And highlighting the power of partnership in AI projects, where both human and machine intelligence contribute their unique strengths. By combining human strengths with AI’s, we can create something greater than the sum of its parts.
AI is playing a key role in telecom security by strengthening threat detection, fraud prevention, and regulatory compliance. As 5G, IoT, and edge computing expand, telecom networks face cyber threats such as AI-specific attacks, network intrusions, and data breaches. AI-powered security solutions provide automated threat response, anomaly detection, and AI lifecycle protection, helping telecom providers maintain a secure and resilient network infrastructure.
Broadband leaders and utility companies, including CTA, NCTA, and PG&E, have extended the Voluntary Agreement for Small Network Equipment through 2028. The initiative has already improved home internet device energy efficiency by 89% since 2015, and new targets aim for an additional 10% reduction by 2026. With compliance from major ISPs and device manufacturers, this industry-led effort is making home broadband more sustainable while enhancing performance.
AI is transforming the relationship between telcos and hyperscalers like AWS, Google Cloud, and Microsoft Azure. With AI-driven automation, cloud-native networks, and edge computing, telecom operators are optimizing efficiency, reducing costs, and unlocking new revenue streams. As AI-powered innovations reshape 5G, cybersecurity, and digital services, these strategic partnerships are set to redefine the future of telecom.
Recent advancements in artificial intelligence training methodologies are challenging traditional assumptions about computational requirements and efficiency. Researchers have discovered an “Occam’s Razor” characteristic in neural network training, where models favor simpler solutions over complex ones, leading to superior generalization capabilities. This trend towards efficient training is expected to democratize AI development, reduce environmental impact, and lead to market restructuring, with a shift from hardware to software focus. The emergence of efficient training patterns and distributed training approaches is likely to have significant implications for companies like NVIDIA, which could face valuation adjustments despite strong fundamentals.

Download Magazine

With Subscription

Currently, no free downloads are available for related categories. Search similar content to download:

  • Reset

It seems we can't find what you're looking for.

Subscribe To Our Newsletter

Scroll to Top