The automotive industry today is undergoing a major overhaul as consumer expectations shift away from utility to experience, and enterprises re-think their business models as software takes the center stage. It is entering a phase where the need for speed, complexity, and precision outpaces the current pace of product development.
Challenges galore for automotive enterprises
Software adoption has proven to be a daunting task for OEMs that have traditionally been focused on mechanical engineering. On top of that, the automotive industry faces frequent supply chain disruptions due to a highly volatile geopolitical environment and margin pressures stemming from a softening demand. Concurrently, concerns such as shrinking product timelines, siloed development practices, costs associated with physical prototyping and testing, and talent availability for embedded engineering continue to impact quality and profitability.
Augmenting product development with AI
At this pivotal stage, it becomes imperative for enterprises to adopt AI solutions across the product development lifecycle to bridge the gap between rising complexities and the limits of traditional process digitalization. AI will fuel the transition from resource intensive efforts to insights driven optimization, with potential to add value across the PDLC, as illustrated below:
Everest Group’s Key Priorities Survey ranks adoption of generative AI use cases as the topmost priority for enterprises, with nearly 54% of them actively looking for suppliers who can support in integration of generative AI use cases in the value chain. The automotive industry has been one of the leaders in adoption of AI use cases across processes and products. OEMs, for instance, have been able to achieve 30% more accuracy and nearly 80% reduction in ADAS validation by leveraging AI solutions.
Thefollowing example on Continental demonstrates how AI-powered solutions can be leveraged to reduce the time and effort required for traditionally tedious tasks.
Continental’s AI leap: A case study
Problem statement: Requirement gathering, traditionally, had been a manual process where experts spent more than 37,500 hours on a project manually processing hundreds of pages of requirements from customers on software or automotive systems. With cars becoming increasingly complex, Continental realized that they needed to make requirements management more efficient and accurate.
Solution: Continental partnered with NTT DATA to develop an ‘AI-based Requirements Engineering Tool’ based on Microsoft’s Azure AI Services that can read extensive specifications in an error-free manner and automatically assign tasks or projects to relevant Continental development centers.
Impact: The tool took less than an hour to train and has helped reduce the efforts required for such a manually intensive task by nearly 80 percent by accurately extracting, classifying and mapping an average of 30,000 requirements per RFQ project.
The future: multi-agent ecosystem
As the Continental example indicates, the industry today is at a stage where AI-enabled applications are gradually permeating into the automotive product development value chain. As these applications mature, they will pave the way for the next course of evolution – autonomous agents that can learn from data and make decisions with minimal to no human intervention.
An ecosystem of autonomous agents that can run independently, yet communicate and synchronize with each other will enable scaled automation, almost instantaneous decision-making, enhanced adaptability, and reduced silos for enterprises. As the solutions mature and AI agents fill in for rule-based automation and repetitive tasks, enterprises should also be mindful to:
- Prioritize the use cases based on business impact, ease of adoption, and criticality in case of failure
- Build a flexible and interoperable ecosystem of agents rather than a closed-tightly linked ecosystem resembling a monolithic AI system
- Build robust infrastructure, workflows, and governance policies before large-scale adoption of use cases
AI and autonomous agents will reshape automotive product development significantly through seamless automation, translating to enhanced speed, precision, and adaptability. Enterprises that are able to adopt and integrate AI agents into their processes will find themselves ahead of the curve in this hype-competitive industry.
If you found this blog interesting, check out our recent blog focusing on The Ultimate Guide To AI Agents In Cybersecurity: Innovations, Investments, And Future Trends | Blog – Everest Group, which delves deeper into a similar topic relating to AI Agents.
To discuss the latest trends in AI and their implications for the automative sector and service providers alike, feel free to reach out to Nishant Udupa ([email protected]).
