Latest news with #FactoryOperationsAgent
Forbes
10-04-2025
- Automotive
- Forbes
Agentic AI In Connected Vehicles: Data-Driven Design And Analytics
Shakir Syed explores the role of Agentic AI in shaping the future of connected vehicles. The car industry is evolving with the integration of agentic artificial intelligence (AI) in intelligent vehicles, revolutionizing the car manufacturing process through data-driven design and analytics. Unlike rule-based automation, agentic AI learns, adapts and refines processes in real time, emerging as the key enabler of next-generation mobility solutions. Agentic AI is a self-operating AI that autonomously makes decisions to optimize efficiency, safety and innovation in car production. Deploying agentic AI in auto manufacturing enables the shift away from linear assembly-line manufacturing towards dynamic, self-optimizing manufacturing systems. AI agents monitor and adjust assembly operations in real-time to maximize efficiency and reduce defects. Predictive analytics allow manufacturers to forecast mechanical failure, optimize production scheduling and reduce waste. For example, Schaeffler's Hamburg plant uses Microsoft's Factory Operations Agent to spot defects and inefficiency and automate once-tedious manual processes. AI predictive quality control saves costly rework, and vehicles pass through safety and performance testing before departing the assembly line. Also, customer demand, sensor data and performance targets are blended by agentic AI and used to schedule cars according to market demand. Automobile companies using modular solutions will be able to use AI technology to customize cars in line with individualistic target segments by modifying aerodynamics, fuel and user interface to specific real-world performance targets. With incoming emerging autonomous technology, agentic AI creates moment-to-moment interaction between cars, infrastructure and cloud services to enhance safety, traffic flow and user experience. Navigation systems based on AI decide the best route by considering current traffic, weather and road conditions. Researchers in Michigan use networked vehicle data to map out hotspots of high-crash risk locations. AI-based safety features track the driver's behavior, road conditions and sensor inputs to predict and prevent accidents beforehand. Hyper-personalization also allows personal seat adjustment, climate control and infotainment according to the driver's choice. However, AI-based in-car infotainment for automobile advertising has riders potentially distracted and the security of the driver's personal data, therefore necessitating cautionary regulation. Predictive analytics driven by agentic AI redefines auto supply chains and fleet operations, optimizing resource utilization, preventing part failure and reducing downtime. Supply chain automation through AI helps producers better handle demand uncertainty, maintain lean inventories and avoid manufacturing bottlenecks. Virtual twin testing makes cars more reliable through driving condition simulation. Engineers can replicate design flaws by subjecting simulated stress tests to vehicles virtually. Octo Telematics applies AI-driven fleet management software, enhancing working efficiency, preventing engine failure, optimizing fuel consumption and prolonging car life. While there is enormous potential, visions of agentic AI are currently too good or too visionary. For example, widespread deployment of vehicle-to-everything (V2X) communication networks is unlikely to be enabled unless pervasively rolled-out 5G coverage and significant investments in city-scale infrastructure in cities, still unequally and expensively spread, become a reality. Transparency and interpretability of AI are still issues. Autonomous AI decision-making raises concerns over moral responsibility, regulation and accountability. Ensuring fairness and avoiding algorithmic bias in AI systems require a lot more ongoing research and constant observation, and many stakeholders are concerned with surrendering crucial decisions to autonomous systems. Aside from that, there are legitimate barriers to the deployment of end-to-end connected vehicle ecosystems through cybersecurity. Connected vehicles generate vast volumes of data that are vulnerable to unauthorized access, which requires strong, constantly developing cybersecurity protection. Lack of clarity on data ownership and AI liability in law only makes deployment more complicated. Besides, switching typical automobile assembly lines to AI-dependent automated machines is a cash-rich venture and calls for staff to be retasked. Not all will be able or will have the financial capacity to carry out such conversions, particularly smaller players or newcomers in the nascent markets. Finally, concerns regarding loss of employment and socioeconomic consequences of having humans but machines for jobs would drive social resistance and political hindrances. The second significant limitation is the level of digital twin technology and predictive analytics powered by artificial intelligence, neither of which has been fully tried and validated to be capable of consistently simulating all real situations. Facilitating assured data capture, data quality and complex AI modeling requires enormous resources and skills, which are stillbeing developedg in most of the automotive industry. What I am most looking forward to personally is the agentic AI potential to exponentially transform vehicle safety and operating efficiency, i.e., predictive maintenance and personalized driver experience. Plenty more needs to be accomplished on regulatory frameworks, ethical AI management, data privacy and infrastructure readiness. So while agentic AI is hugely promising, a balanced approach recognizes the complexity and pragmatic hurdles before transforming automotive manufacturing and mobility. Agentic AI promises revolutionary transformation but requires level-headed consideration of probable constraints and forward-looking action on ethical, infrastructural and regulatory challenges. A balanced but optimistic vision will best prepare motor industry stakeholders to use agentic AI responsibly, bringing enduring and valuable innovation. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
WIRED
24-02-2025
- Automotive
- WIRED
AI Assistants Join the Factory Floor
Feb 24, 2025 6:00 AM Manufacturers already have the data. LLM-powered tools could help them make use of it. A factory worker operates a CNC machine in an industrial manufacturing plant. Photograph: Getty Images The basic machine for grinding a steel ball bearing has been the same since around 1900, but manufacturers have been steadily automating everything around it. Today, the process is driven by a conveyor belt, and, for the most part, it's automatic. The most urgent task for humans is to figure out when things are going wrong—and even that could soon be handed over to AI. The Schaeffler factory in Hamburg starts with steel wire that is cut and pressed into rough balls. Those balls are hardened in a series of furnaces, and then put through three increasingly precise grinders until they are spherical to within a tenth of a micron. The result is one of the most versatile components in modern industry, enabling low-friction joints in everything from lathes to car engines. That level of precision requires constant testing—but when defects do turn up, tracking them down can present a puzzle. Testing might show a defect occurring at some point on the assembly line, but the cause may not be obvious. Perhaps the torque on a screwing tool is off, or a newly replaced grinding wheel is impacting quality. Tracking down the problem means comparing data across multiple pieces of industrial equipment, none of which were designed with this in mind. This too may soon be a job for machines. Last year, Schaeffler became one of the first users of Microsoft's Factory Operations Agent, a new product powered by large language models and designed specifically for manufacturers. The chatbot-style tool can help track down the causes of defects, downtime, or excess energy consumption. The result is something like ChatGPT for factories, with OpenAI's models being used on the backend thanks to the company's partnership with Microsoft's Azure. Kathleen Mitford, Microsoft's corporate vice president for global industry marketing, describes the project as 'a reasoning agent that operates on top of manufacturing data.' As a result, Mitford says, 'the agent is capable of understanding questions and translating them with precision and accuracy against standardized data models.' So a factory worker might ask a question like 'What is causing a higher than usual level of defects?' and the model would be able to answer with data from across the manufacturing process. The agent is deeply integrated into Microsoft's existing enterprise products, particularly Microsoft Fabric, its data analytics system. This means that Schaeffler, which runs hundreds of plants on Microsoft's system, is able to train its agent on data from all over the world. Stefan Soutschek, Schaeffler's vice president in charge of IT, says the scope of data analysis is the real power of the system. 'The major benefit is not the chatbot itself, although it helps,' he says. 'It's the combination of this OT [operational technology] data platform in the backend, and the chatbot relying on that data.' Despite the name, this isn't agentic AI: It doesn't have goals, and its powers are limited to answering whatever questions the user asks. You can set up the agent to execute basic commands through Microsoft's Copilot studio, but the goal isn't to have the agent making its own decisions. This is primarily AI as a data access tool. That's particularly valuable in manufacturing, where tracking down a set of errors might mean comparing data across quality assurance systems, HR software, and industrial control systems like kilns and precision drills. Within the industry, this is known as the IT/OT gap: the disconnect between information tech like spreadsheets and the operational tech that's used in a factory. AI companies believe large language models like the Factory Operations Agent will be able to work across that gap, allowing it to answer basic troubleshooting questions in a conversational way. The Factory Operations Agent is due to leave public preview later this year, making it broadly available to Azure AI users. But there will be plenty of competing systems hoping to play a role on the factory floor. As tech companies look for ways to make money from recent breakthroughs in LLMs, manufacturing has proven to be a tempting target. Last September, Google rolled out an update to its Manufacturing Data Engine specifically aimed at unlocking data held on industrial devices, and both Microsoft and Google maintain platforms where independent developers can test out systems with different fine-tuning strategies and different tolerances for risk. That competition is good for the field, but the increasing use of industrial AI also raises the stakes for safety—particularly on the factory floor, where malfunctions can be a matter of life or death. Crucially, the Factory Operations Agent only manipulates data rather than directly controlling machinery, but there are still concerns. Speaking in his personal capacity, Duncan Eddy, executive director of the Stanford Center for AI Safety, says the biggest concern for AI models like the Factory Operations Agent is simply that users won't recognize when the system is starting to fail, or won't know how to intervene once they do. 'These systems can fail in new and surprising and unpredictable ways,' he says.
