How to simplify the process of building production-ready AI services and reduce the time for resource management in the automotive industry?

 Just a few years ago, artificial intelligence stirred our imagination via the voice of Arnold Schwarzenegger from "Terminator" or agent Smith from "The Matrix". It wasn't long before the rebellious robots' film dialogue replaced the actual chats we have with Siri or Alexa over our morning cup of coffee. Nowadays, artificial intelligence is more and more boldly entering new areas of our lives. The automotive industry is one of those that are predicted to speed up in the coming years. By 2030, 95-98% of new vehicles are likely to use this technology.

    What will you learn from this article?  

•     How to use AI in the production process  
•     How AI helps drivers to drive safely and comfortably  
•     How to use AI in vehicle servicing  
•     What companies from the AI ​​industry should pay attention to if they want to introduce such innovations  
•     You will learn about interesting use cases of the major brands  

Looking at the application of AI in various industries, we can name five stages of implementation of such solutions. Today, companies from the Communication Technology (ICT) and Financial Services ("Matured Industries") sectors are taking the lead. Healthcare, Retail, Life Science ("Aspirational Industries") are following closely behind. Food & Beverages and Agriculture ("Strugglers") and companies from the Chemicals and Oil and Gas sectors ("Beginners") are bringing up the rear. The middle of the bunch is the domain of  Automotive and, partly related to it, Industrial Machinery.

Although these days we choose a car mainly for its engine or design, it is estimated that over the next ten years, its software will be an equally significant factor that will impact our purchasing decision.

AI will not only change the way we use our vehicles, but also how we select, design, and manufacture them. Even now, leading brands avail of this type of technology at every stage of the product life cycle - from production through use, to maintenance and aftermarket.

Let's have a closer look at  the benefits a vehicle manufacturing company can get when implementing AI in its operations.

Manufacturing - how AI improves production

1. You will be able to work out complex operations and streamline supply chains

An average passenger car consists of around 30,000 separate parts, which interestingly enough, are usually ordered from various manufacturers in different regions of the world. If, on top of that,  we add a complicated manufacturing process, increasingly difficult access to skilled workers and market dependencies, it becomes clear that potential delays or problems in the supply chain result in companies losing millions. Artificial intelligence can predict these complex interactions, automate processes, and prevent possible failures and mishaps

•  Artificial intelligence complements     Audi's    supply chain monitoring. When awarding contracts, it is verified that the partners meet the requirements set out in the company's internal quality code. In 2020, over 13,000 suppliers provided the Volkswagen Group with a self-assessment of their own sustainability performance. Audi only works with companies that successfully pass this audit.

2. More efficient production due to intelligent co-robots working with people

For years, companies from the automotive industry have been trying to find ways to enhance work on the production line and increase efficiency in areas where people would get tired easily or be exposed to danger. Industrial robots have been present in car factories for a long time, but only artificial intelligence has allowed us to introduce a new generation of devices and their work in direct contact with people. AI-controlled co-bots move materials, perform tests, and package products making production much more effective.

•     Hyundai Vest Exoskeleton (H-VEX)    became a part of Kia Motors’ manufacturing process in 2018. It provides wearable robots for assembly lines. AI in this example helps in the overall production while sensing the work of human employees and adjusting their motions to help them avoid injuries.

•     AVGs (Automated Guided Vehicles)    can move materials around plants by themselves. They can identify objects in their path and adjust their route. In 2018, an OTTO Motors device carried a load of 750 kilograms in this way!

3. Quality control acquires a completely new quality

The power of artificial intelligence lies not only in analyzing huge amounts of data but also in the ability to learn and draw conclusions. This fact can be used by finding weak points in production, controlling the quality of car bodies, metal or painted surfaces, and also by monitoring machine overload and predicting possible failures. In this way, companies can prevent defective products from leaving the factories and avoid possible production downtime.

•     Audi    uses computer vision to find small cracks in the sheet metal in the vehicles. Thus, even at the production stage, it reduces the risk of damaged parts leaving the factory.
•     Porsche    has developed "Sounce", a digital assistant,  using deep learning methods. AI is capable of reliably and accurately detecting noise, for example during endurance tests. This solution, in particular, takes the burden off development engineers who so far had to be present during such tests.  Acoustic testing based on Artificial Intelligence (AI) increases quality and reduces production costs.

4. AI will configure your dream vehicle

In a competitive and excessively abundant market, selling vehicles is very difficult. Brands are constantly competing in services and technologies that are to provide buyers with new experiences and facilitate the purchasing process. Manufacturers use artificial intelligence services not only at the stage of prototyping and modeling vehicles, but also at the end of the manufacturing process, when the vehicle is eventually sold. A well-designed configurator based on AI algorithms is often the final argument, by which the customer is convinced to buy their dream vehicle. Especially when we are talking about luxury cars.

•     The Porsche Car Configurator    is nothing more than a recommendation engine powered by artificial intelligence. The luxury car manufacturer created it to allow customers to choose a vehicle from billions of possible options. The configurator works using several million data and over 270 machine learning modules. Effect? The customer chooses the vehicle of their dreams based on customised recommendations.

Transportation - how AI facilitates driving vehicles

5. Artificial intelligence will provide assistance in an emergency

A dangerous situation on the road, vehicle in the blind spot, power steering on a slippery surface. All those situations can be supported by artificial intelligence, which will calculate the appropriate driving parameters or correct the way the driver behaves on the road. Instead of making automatic decisions - which are often emotion-imbued or lack experience - brands increasingly hand them over to machines, thus reducing the number of accidents and protecting people's lives.

•     Verizon Connect    solutions for fleet management allow you to send speed prompts to your drivers as soon as your vehicle's wipers are turned on. This lets the driver know that they have to slow down due to adverse road conditions such as rain or snow. And the intelligent video recorder will help you understand the context of the accident - for instance, by informing you that the driver accelerated rapidly before the collision.

6. Driver monitoring and risk assessment increase driving safety and comfort

Car journeys may be exhausting. But not for artificial intelligence. The biggest brands are increasingly equipping vehicles with solutions aimed at monitoring fatigue and driver reaction time. By combining intelligent software with appropriate sensors, the manufacturer can fit the car with features that will significantly reduce the number of accidents on the road and discomfort from driving in difficult conditions.

•     Tesla    monitors the driver's eyes, thus checking the driver's level of fatigue and preventing them from falling asleep behind the wheel. It’s mainly used for the Autopilot system to prevent driver from taking short nap during travel.
•     The BMW 3 Series    is equipped with a personal assistant, the purpose of which is to improve driving safety and comfort. Are you tired of the journey? Ask for the "the vitalization program" that will brighten the interior, lower the temperature or select the right music. Are you cold? All you have to do is say the phrase "I'm cold" and the seats will be heated to the optimal temperature.

Maintenance - how AI helps you take care of your car

7. Predictive Maintenance prevents malfunctions before they even appear

Cars that we are driving today are already pretty smart. They can alert you whenever something needs your attention and they can pretty precisely say what they actually need – oil, checking the engine, lights etc. The Connected Car era however equipped with the possibilities given by AI brings a whole lot more – predictive maintenance. In this case AI monitors all the sensors within the car and is set to detect any potential problems even before they occur.

AI can easily spot any changes, which may indicate failure, long before it could affect the vehicle’s performance. To go even further with this idea, thanks to the Over-The-Air Update feature, after finding a bug that can be easily fixed by a system patch, such solution can be sent to the car Over-The-Air directly by the manufacturer without the need for the customer to visit the dealership.

•     Predi    (an AI software company from California) has created an intelligent platform that uses the service order history and data from the Internet of Things to prevent breakdowns and deal with new possible ones faster.

8. Insure your car directly from the cockpit

Driving a car is not only about operating costs and repairs, but also insurance that each of us is required to purchase. In this respect, AI can be useful not only for insurance companies (  see how AI can improve the claims handling process ), but also for drivers themselves. Thanks to the appropriate software, we will remember about expiring insurance or even buy it directly from the comfort of our car, without having to visit the insurer's website or a stationary point.

•  The German company     ACTINEO,    specialising in personal injury insurance, processes and digitises 120,000. claims annually. Their ACTINEO Cockpit service is a digital manager that allows for the comprehensive management of this type of cases, control of billing costs, etc.
•  In collaboration with     Ford, Arity    provides insurers - with the driver's consent, of course - data on the driving style of the vehicle owner. In return for sharing this information, the driver is offered personalised insurance that matches his driving style. The platform’s calculations are based on "more than 440 billion miles of historical driving data from more than 23 million active telematics connections and more than eight years of data directly from cars (source: Green Car Congress).

When will AI take over the automotive industry?

In 2015, it is estimated that only 5-10% of cars had some form of AI installed. The last five years have brought the dissemination of solutions such as parking assistance, driver assistance and cruise control. However, the real boom is likely to occur within the next 8-10 years.

From now on, artificial intelligence in the automotive industry will no longer be a novelty or wealthy buyers’ whims. The spread of the Internet of Things, consumer preferences and finding ways of saving money in the manufacturing process will simply force manufacturers to do this - not only in the vehicle cockpits, but also on the production and service lines.

To this end, they will be made to cooperate with manufacturers of sensors and ultrasonic solutions (cooperation between BMW and Mobileye, Daimler from Bosch or VW and Ford with Aurora) and IT companies providing software for AI. A dependable partner who understands the potential of AI and knows how to use its power to create the  car of the future is the key to success for companies in this industry.

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Generative AI is quickly emerging as one of the key drivers of automotive innovation. This shift is not just a future possibility; it's already happening. In 2023, the generative AI market in the automotive sector was valued at approximately  USD 387.54 million . Looking ahead, it's projected to surge to about  USD 2,691.92 million by 2032 , demonstrating a robust Compound Annual Growth Rate (CAGR) of 24.03% from 2023 to 2032. Major Original Equipment Manufacturers (OEMs) are integrating sophisticated AI algorithms into various aspects of the industry, from vehicle design to enhancing customer interactions.

The impact of generative AI in the automotive sector is already evident. For instance, NVIDIA's generative AI models empower designers to swiftly  transform 2D sketches into intricate 3D models, significantly speeding up the design process and opening up new avenues for creativity and efficiency​​. Meanwhile, automotive manufacturing companies are exploring collaborations with tech giants to integrate advanced AI language models into their vehicles, enhancing the driving experience​​.

This article will explore how leading automotive players are leveraging generative AI to not only keep pace with the evolving demands of the market but also redefine mobility and automotive excellence.

Software-defined vehicles and generative AI

The introduction of software-defined vehicles (SDVs) represents a significant shift in the automotive industry, moving beyond traditional performance metrics such as horsepower and chassis design to focus on software and digital capabilities. By 2025, it is estimated that vehicles could require as much as  650 million lines of code each. These vehicles heavily rely on software for critical operations such as driving assistance, navigation, and in-car entertainment systems.

The integration of generative AI in this domain further amplifies these capabilities. Generative AI is known for its ability to create and optimize designs and solutions, which is beneficial in improving both the software and hardware aspects of SDVs. It helps in generating efficient algorithms for vehicle control systems, contributes to the development of more effective and adaptive software solutions, and even assists in designing vehicle components for better performance and efficiency.

However, bringing generative AI into this landscape presents both unique opportunities and significant challenges.

The opportunities and challenges

•     Challenges  

Integrating advanced AI systems into modern vehicles is a complex and multifaceted task that demands  technical expertise and careful attention to data security and privacy , particularly with the increasing reliance on data-driven functionalities in vehicles.

The automotive industry is facing a complex regulatory environment. With the growing importance of AI and data in-vehicle systems, it has become crucial to  comply with various international regulations and standards , covering areas such as data protection, safety, and environmental impact.

One significant challenge for OEMs is the  lack of standardization within the software-defined vehicles industry, which can complicate the development and integration of new technologies as there are no universal norms or protocols to guide these processes.

 Internal transformation is also a critical aspect of this integration. OEMs may need to revamp their internal capabilities, processes, and technological infrastructure to use generative AI effectively.

•     Opportunities  

Integrating generative AI technology allows for more  creative and efficient vehicle design , resulting in  quicker prototypes and more innovative models .

It also allows for creating  personalized vehicles that cater to individual user preferences like never before. In manufacturing, generative AI promotes more efficient and  streamlined production processes , which optimizes resources and reduces waste.

Let's explore how automotive manufacturers already use gen AI to boost their operations.

Generative AI applications in the automotive industry

Generative AI's integration into the automotive industry revolutionizes multiple facets of vehicle design, manufacturing, and user experience. Let's explore these areas:

Design and conceptualization

•     Vehicle Design Enhancement    : Artificial Intelligence is revolutionizing the vehicle design process by speeding up the initial phase of the design cycle. Generative design algorithms use parameters such as material properties, cost constraints, and performance requirements to generate optimal design solutions. For example, in vehicle body design, AI can propose multiple design options that optimize for aerodynamics and strength while minimizing weight. This enables quick visualization and modification of ideas.

 Toyota Research Institute has introduced a generative AI technique to optimize the vehicle design process to produce more efficient and innovative vehicles. This approach allows designers to explore a wider range of design possibilities, including aerodynamic shapes and new material compositions.

•     Digital Prototyping    : The use of Generative AI technology makes it possible to create digital prototypes, which can be tested and refined extensively without the need for physical models. This approach is highly beneficial, as it enables designers to detect and correct potential design flaws early in the process.

 BMW's use of NVIDIA Omniverse is a significant step in design improvement. The company uses this platform to create digital twins of their manufacturing facilities, integrating generative AI to enhance production efficiency and design processes.

Manufacturing and production

•     Streamlining Manufacturing Processes    : Generative AI significantly enhances the efficiency of manufacturing processes. Unlike traditional AI or machine learning models, generative AI goes beyond identifying inefficiencies; it actively generates novel manufacturing strategies and solutions. By inputting parameters such as production timelines, material constraints, and cost factors, generative AI algorithms can propose a range of optimized manufacturing workflows and processes.

 BMW has implemented generative AI in a unique way to improve the scheduling of their manufacturing plant. In partnership with Zapata AI, BMW utilized a quantum-inspired generative model to optimize their plant scheduling, resulting in more efficient production. This process, known as Generator-Enhanced Optimization (GEO), has significantly improved BMW's production planning, demonstrating the potential of generative AI in industrial applications.

•     Supply Chain Resilience    : In the context of supply chain management, particularly during challenges like the automotive microchip shortage, generative AI plays a crucial role. Unlike conventional AI, gen AI can do more than just analyze existing supply chain networks; it can creatively generate alternative supply chain models and strategies. The algorithms can propose diverse and robust supplier networks by leveraging data about supplier capabilities, logistics constraints, and market demands.

•     Customized Production    : With generative AI, it is now possible to create personalized vehicles on a large scale, meeting the growing demand for customization in the automotive industry.

Predictive maintenance and modelling

Traditionally, predictive maintenance relies on historical data to forecast equipment failures, but generative AI enhances this process by creating detailed, simulated data environments. This technology generates realistic yet hypothetical scenarios, encompassing a vast array of potential machine failures or system inefficiencies that might not be present in existing data sets.

The generative aspect of this AI technology is particularly valuable in situations where real-world failure data is limited or non-existent. By synthesizing new data points, generative AI models can extrapolate from known conditions to predict how machinery will behave under various untested scenarios.

In modeling, generative AI goes a step further. It not only predicts when and how equipment might fail but also suggests optimal maintenance schedules, anticipates the impact of different environmental conditions, and proposes design improvements.

Customer experience and marketing

One of the challenges in using generative AI, particularly in customer interaction, is the accuracy of AI-generated responses. An example of this was an error by ChatGPT, where it was tricked into suggestion of  buying a Chevy for a dollar . This incident underlines the potential risks of misinformation in AI-driven communication, emphasizing the need for regular updates, accuracy checks, and human oversight in AI systems. Nevertheless, this technology offers many opportunities for improving the customer experience:

•     Personalized User Experiences and Enhanced Interaction    : AI's capability to adapt to individual preferences not only enhances the driving experience but also improves the functionality of vehicle features.

For instance, in collaboration with Microsoft,  General Motors is exploring the use of AI-powered virtual assistants that offer drivers a more interactive and informative experience. These assistants can potentially provide detailed insights into vehicle features and performance metrics and offer personalized recommendations based on driving patterns.

Also,  Mercedes-Benz is exploring the integration of generative AI through voice-activated functionalities in collaboration with Microsoft. This includes leveraging the OpenAI Service plugin ecosystem, which could allow for a range of in-car services like restaurant reservations and movie ticket bookings through natural speech commands.

Example applications

•     Simplified Manuals    : AI technology, enabled by natural language processing, has simplified the interaction between drivers and their vehicles. Beyond just responding to voice commands with pre-existing information, a generative AI system can even create personalized guides or tutorials based on the driver's specific queries and past interactions.

    Grape Up has developed an innovative          voice-driven car manual         that allows drivers to interact with their vehicle manual through voice commands, making it more accessible and user-friendly. With this technology, drivers no longer have to navigate through a traditional manual. Still, they can easily ask questions and receive instant verbal responses, streamlining the process of finding information about their vehicle.  

•     Roadside Assistance:    In this scenario, generative AI can go beyond analyzing situations and suggesting solutions by creating new, context-specific guidance for unique problems. For instance, if a driver is stranded in a rare or complex situation, the AI could generate a step-by-step solution, drawing from a vast database of mechanical knowledge, previous incidents, and environmental factors.

•     Map Generation    : Here, generative AI can be used to not only update maps with real-time data but also to predict and visualize future road conditions or propose optimal routes that don't yet exist. For example, it could generate a route that balances time, fuel efficiency, and scenic value based on the driver's preferences and driving history.

•     Marketing and Sales Innovation    : Generative AI-enabled content engine is transforming the creation of digital advertising for the automotive industry. This content is tailored to meet the unique requirements of automotive brands and their consumers, thereby revolutionizing traditional marketing strategies.

Safety and compliance

•     Enhancing Vehicle Safety    : Generative AI in vehicles goes beyond traditional AI systems by not only assisting drivers but also by creating predictive models that enhance safety features. It processes and interprets data from cameras and sensors to foresee potential road hazards, often employing advanced generative models that simulate and predict various driving scenarios.

•     Regulatory Compliance    : Similarly, gen AI helps automakers comply with safety standards and navigate complex regulation changes by monitoring performance data and comparing it against regulatory benchmarks. This allows automakers to stay ahead of the compliance curve and avoid potential legal and financial repercussions.

Autonomous vehicle development

•     Simulation and Testing    : Generative AI is crucial for developing autonomous vehicle systems. It generates realistic simulations, including edge-case scenarios, to test and improve vehicle safety and performance.

•     Enhancing ADAS Capabilities    : AI technology can improve essential Advanced Driver Assistance Systems (ADAS) features such as adaptive cruise control, lane departure warnings, and automatic emergency braking by analyzing data from various sensors and cameras. Generative AI's strength in this context lies in its ability to not only process existing data but also to generate new data models, which can predict and simulate different driving scenarios. This leads to more advanced, reliable, and safer ADAS functionalities, significantly contributing to the evolution of autonomous and semi-autonomous driving technologies.

Conclusion

As the automotive industry accelerates towards a more AI-integrated future, the role of expert partners like Grape Up becomes increasingly crucial. Our expertise in navigating the intricacies of AI implementation can help automotive companies unlock the full potential of this technology. If you want to stay ahead in this dynamic landscape, now is the time to embrace the power of generative AI. For more information or to collaborate with Grape Up,  contact our experts today.

 The car insurance industry is experiencing a real revolution today. Insurers are more and more carefully targeting their offers using AI and machine learning features. Such innovations significantly enhance business efficiency, eliminate the risk of accidents and their consequences, and enable adaptation to modern realities.

Changes are needed today

Approximately $25 billion is "frozen" with insurers annually due to problems such as fraud, claims adjustment, delays in service garages, etc. However, customers are not always happy with the insurance amounts they receive and the fact that they often have to accept undervalued rates. The reason for this is that due to limited data, it is difficult to accurately identify the culprit of the incident. It is also often the case that compensation is based on rates lower than the actual value of the damage.

 Insurers today need to be aware of the ecosystem in which they operate . Clients are becoming more demanding and, according to an IBM Institute for Business Value (IBV) study, 50 percent of them prefer tailor-made products based on individual quotes. The very model of cooperation between businesses is also changing, as relations between insurance providers and car manufacturers are growing tighter. All of this is linked to the fact that  cars are becoming increasingly autonomous, allowing them to more closely monitor traffic incidents and driver behavior as well as manage risk. Estimates suggest there will be as many as one trillion connected devices by 2025, and by 2030 there will be an increasing percentage of vehicles with automated features (ADAS).

No wonder there's an increasing buzz about changes in the car insurance industry. And these are changes based on technology. The use of  artificial intelligence , machine learning, and  advanced data analytics in the cloud will allow for seamless adaptation to market expectations.

 CASE STUDY

 SARA Assicurazioni and Automobile Club Italia are already encouraging drivers to install ADAS systems in exchange for a 20% discount on their insurance premiums. Indeed, it has been demonstrated that such systems can slash the rate of liability claims for personal injury by 4-25% and by 7-22% for property damage.

Why is this so important for insurers who want to face the reality?

Artificial intelligence-based pricing models provide a significant reduction in the time needed to introduce new offerings and to make optimal decisions. The risk of being mispriced is also lowered, as is the time it takes to launch insurance products.

The new  AI-based insurance reality is happening as we speak. The digital-first companies like Lemonade, with their high flexibility in responding to market changes, are showing customers what solutions are feasible.  In doing so, they put pressure on those companies that still hesitate to test new models.

Areas of change in car insurance due to AI

Artificial intelligence and related technologies are having a huge impact on many aspects of  the insurance industry : quoting, underwriting, distribution, risk and claims management, and more.

Changes in insurance distribution

Artificial intelligence algorithms smoothly create risk profiles so that the time required to purchase a policy is reduced to minutes. Smart contracts based on blockchain instantly authenticate payments from an online account. At the same time, contract processing and payment verification is also vastly streamlined, reducing insurers' client acquisition cost.

Advanced risk assessment and reliable pricing

Traditionally, insurance premiums are determined using the "cost-plus" method. This includes an actuarial assessment of the risk premium, a component for direct and indirect costs, and a margin. Yet it has quite a few drawbacks.

One of them is the inability to easily account for non-technical price determinants, as well as the inability to react quickly to shifting market conditions.

How is risk calculated? For car insurance companies, the assessment refers to accidents, road crashes, breakdowns, theft, and fatalities.

These days, all these aspects can be controlled by leveraging AI, coupled with IoT data that provides real-time insights. Customized pricing of policies, for instance,  can take into account GPS device dataon a vehicle’s location, speed, and distance traveled. This way, you can see whether the vehicle spends most of its time in the driveway or if, conversely, it frequently travels on highways, particularly at excessive speeds.

In addition, insurance companies can use a host of other  sensor and camera data, as well as reports and documents from previous claims. Having all this information gathered, algorithms are able to reliably determine risk profiles.

 CASE STUDY

 Ant Financial, a Chinese company that offers an ecosystem of merged digital products and services, specializes in creating highly detailed customer profiles. Their technology is based on artificial intelligence algorithms that assign car insurance points to each customer, similarly to credit scoring. They take into account such detailed factors as lifestyle and habits. Based on this, the app shows an individual score, assigning a product that matches the specific policyholder.

An in-depth analysis of claims

The cooperation between an insurance company and its client is based on the premise that both parties are pursuing to avoid potential losses. Unfortunately, sometimes accidents, breakdowns or thefts occur and a claims process must be implemented. Artificial intelligence, integrated IoT data, and  telematics come in handy irrespective of the type of claims we are handling.

•  These technologies are suitable for, among other things, automatically generating not only damage information but also repair cost estimates.
•  Machine learning techniques can estimate the average cost of claims for various client segments.
•  Sending real-time alerts, in turn, enables the implementation of predictive maintenance.
•  Once an image has been uploaded, an extensive database of parts and prices can be created.

The drivers themselves gain control as they can carry out the process of registering the damage from A to Z:  take a photo, upload it to the insurer's platform and get an instant quote for the repair costs. From now on, they are no longer reliant on workshop quotes, which were often highly overestimated in line with the principle: "the insurer will pay anyway".

Fraud prevention

29 billion dollars in annual losses These are losses to auto insurers that occur due to fraud. Fraudsters want to scam a company out of insurance money based on illegally orchestrated events. How to prevent this? The answer is AI.

 Analyzed data retrieved from cameras and sensors can reconstruct the details of a car accident with high precision. So, having an accident timeline generated by artificial intelligence facilitates accident investigation and claims management.

 CASE STUDY

 An advanced AI-based incident reconstruction has been tested lately on 200,000 vehicles as part of a collaboration between Israel's Project Nexar and a Japanese insurance company.

Assistance in the event of accidents

According to data from the OECD, car accident fatalities could be reduced by 44 percent if emergency medical services had access to real-time information about the injuries of involved parties.
Still, real-time assistance has great potential not only for public services but also in the context of auto insurance.

By leveraging AI to perform this,     insurers can provide drivers with quick and semi-automated responses during collisions and accidents    . For example, a chatbot can instruct the driver on how to behave, how to call for help, or how to help fellow passengers. All this is essential in the context of saving lives. At the same time, it is a way of reducing the consequences of an accident.

Transparent decision making (client perspective)

New technologies offer solutions to many problems not only for insurers but also for clients. The latter often complain about discrimination and unfair, from their point of view, calculations of policies and compensation.

"Smart automated gatekeepers" are superior in multiple ways to the imperfect solutions of traditional models. This is because, based on a number of reliable parameters, they facilitate the creation of more authoritative and personalized pricing policies. Data-rich and automated risk and damage assessments pay off for consumers because they have decision-making power based on how their actions affect insurance coverage.

The opportunities and future of AI in car insurance

McKinsey's analysis says that across functions and use cases AI investments are worth $1.1 trillion in potential annual value for the insurance industry.

The direction of changes is outlined in two ways: first by increasingly connected and  software-equipped vehicles with more sensors. Second, by the changing analytical skills of insurers. Data-driven vehicles will certainly affect more reliable and real-time consistent repair costs and, consequently, claims payments. And when it comes to planning offers and understanding the client, AI is an enabler of change for personalized, real-time service (24/7 virtual assistance) and for creating flexible policies. All signs indicate that such "abstract" parameters as education or earnings will cease to play a major role in this regard.

As can be inferred from the diagram above, the greater the  impact of a given technology on an insurance company's business , the longer the time required for its implementation. Therefore, it is vital to consider the future on a macro scale, by planning the strategy not for 2 years, but for 10.

 The decisions you make today have a bearing on improving operational efficiency, minimizing costs, and opening up to individual client needs, which are becoming more and more coupled with digital technologies.