In the past year, drastic advancements have been made in the Artificial Intelligence (AI) sector. Generative AI models are not only challenging stable and formidable business sectors in the world but are also at the center stage of every economic, societal, and educational debate today. There are questions and fears about how AI could impact many jobs, replace humans in industry, and ultimately change how our society operates. US-Brazilian researcher Ben Goertzel recently suggested that AI could replace up to 80% of jobs in the next few years. [6]. However, such events are not isolated in human history. This has happened every time a general-purpose technology was discovered and implemented in the past and will happen again in the future. Technologies like the steam engine, the computer, the internet, etc., revolutionized our society. As much as they improved our ways of life, they hold a track record of eliminating a swathe of human jobs in their wake. Those who adopted such technologies faster and better, are the ones who survived and thrived. The rise of Ford Motor Company during the era of production at scale and Apple during the computer era are great examples of this. An overview of generative AI Generative AI is a category of AI algorithms that generate new outputs based on the data they have been trained on and feedback from their previous outputs. Unlike traditional AI systems, designed to recognize patterns and make predictions, generative AI creates new content in images, text, audio, and more. [1] The internet is filled with AI-generated art, even fake images, software codes, content, etc. ChatGPT has by far stood out as the most talked about model for Generative AI. Microsoft has even extended its partnership with its maker OpenAI. It is working to integrate ChatGPT to Bing, Microsoft’s search engine, and compete with the titan of the search-engine industry, Google. Google is not far behind and has recently released its answer to ChatGPT called Bard AI. [5] Several other AI-based systems fall under the Generative AI umbrella, like Whisper, an automatic speech recognition system that enables translation and transcription in multiple languages. It can even comprehend multiple accents and technical lingo and can sift through background noise. [4] DALL-E is an AI system that can create realistic images and art from a description in natural language. [7] Many more models and tools are being released nowadays, with the generative AI sector poised to be the next big sector of industry to watch out for. Generative AI in the automotive industry Several enterprises, organizations, and businesses are exploring and leveraging the potential of Generative AI. Viewed as a future differentiator, it presents an exciting way the world would function in knowledge collection and presentation. The impact of generative AI on the auto industry is already being felt. According to McKinsey, by using generative AI, development timelines for auto parts could reduce by 10-20%. The application of generative AI in the automotive industry is vast and far-reaching, with the potential to significantly enhance efficiency, innovation, customization, and customer experience. Here is a look at some of the potential use cases [3]: Supply chain and production process optimization: Generative AI can analyse data on suppliers, production processes, inventory, and logistics to optimize the production and supply chain. With a conversational experience to process planning, engineers can design these processes using AI based models that could help provide strategies to reduce lead times, improve product quality, and ensure efficient inventory management. Visualization of new and possible process models can also be iteratively created using generative AI models. Design optimization: It could be used to create multiple design options much faster based on input criteria provided by the designer. Manufacturers can also reduce the number of parts needed and improve design efficiency. Personalized customer experience: Generative AI can provide a more personalized customer experience by analysing preferences, patterns, and behaviour data. It can improve the buying experience and increase customer loyalty by suggesting vehicles and options tailored to each customer’s needs and preferences. In-car virtual assistant: These generative AI-based virtual assistant models can seamlessly integrate with vehicle infotainment systems to offer a variety of voice-activated functionalities. The virtual assistant may enable drivers to ask the car what to do when there is a fault and can guide them through the process in emergencies. What is happening so far in the automotive industry? General Motors is considering using ChatGPT for its vehicles as part of its collaboration with Microsoft. The AI language models behind ChatGPT may soon be powering virtual assistants in General Motors’ vehicles, where it may help drivers offer information about their vehicle’s features. [8] How can LTIMindtree help to adopt generative AI today? We have established, so far, that Generative AI is the next big revolution, the differentiator that will help the early adopters stand out. Here are some accelerators that LTIMindtree has built to kickstart your adoption of generative AI: Generative AI-enabled issue management tool: When a customer calls the help desk to report an issue through their preferred channel (call, WhatsApp, chat, etc.), the typical support workflow we observe would have timelines ranging from a few hours to a few days to resolve.What if we could have “someone” listening in to these conversations 24×7, helping customers with their specific needs, automatically resolving the issue and without any waiting time?That is what LTIMindtree generative AI-enabled Issue Management Accelerator does. It understands customers’ specific needs and triggers actions automatically. Depending on how the model is trained, it would either automatically resolve the issue, maintain proper audit logs in ITSM, or identify and assign the issue to the right SME. It can also be trained to understand the urgency and sentiment of the customer and set the priority accordingly. Automated Campaign Generator: Any e-mail campaign starts with a template. The content for the campaign needs to be written based on various parameters like topic and target segment. Finally, appropriate images and design artifacts are generated. LTIMindtree’s Generative AI Campaign Generator does all this with a few natural language inputs. It can be explicitly trained to adhere to brand guidelines and other specific requirements and generate content, banner images, and the campaign structure. It is an intelligent digital assistant that can instantly create sample templates. Risks and concerns The use cases above only scratch the surface of the capabilities of generative AI. And while there are several benefits of ai in the automotive industry, the risks associated with this technology must be addressed first to realize them. Algorithmic bias: It is difficult for generative AI models to be trained on what is good and bad. Most information sources today, especially from media enterprises, have biases associated with them which lead to AI models providing biased responses as well. Developers must consider a human-in-loop system to identify outlier prompts and responses requiring human intervention. With generative AI in its nascent stages, rigorous real-world testing and tuning of the model is required. Intellectual property: Companies will have to understand and find out how they can protect their IPs by using generative AI models. However, copyright or IP breaches from third parties will be a bigger cause of concern as generative AI models can source information from a vast pool of data over the internet. Generative AI models will have to have checks in place to prevent copyright infringements from external sources. Enterprises must develop mechanisms to help avoid IP-based liabilities before AI-based products are taken to market. Data security: GDPR and the upcoming EU AI Act could impact how data is handled, processed, protected, secured, and used. Enterprises must thoroughly analyse the impact of such regulations on their AI-based tools or products. Trust: There are concerns over the authenticity and accuracy of the responses provided, especially on chat-based generative AI models. This strongly affects the level of user confidence on the generative AI-based tools and products. Other concerns are user identity verification and the potential for misuse of such products. Human-in-loop systems coupled with rigorous real-world testing are possible mitigative steps to address these concerns. Conclusion The automotive industry has always created, pushed for, and led several technological revolutions and will continue to do so. But the need to adopt general-purpose technologies, as significant as generative AI, is now. Generative AI models will soon become ubiquitous, with the potential benefits of incorporating generative AI far outweighing perceived threats. Companies embracing this technology faster will likely gain significant competitive advantages and stay at the forefront of technological innovation. The future of the automotive industry is here, and generative AI can help you take advantage of it much faster. So, let’s get to the future, faster. Together!
