Advanced data-driven strategies to navigate a competitive automotive landscape

Emerging technologies are now seen as key to enabling car manufacturers and their suppliers to become more competitive. According to the latest Forvis Mazars C-suite barometer, artificial intelligence (AI) was the top investment priority for car manufacturers’ technology transformation plans, followed by data security, infrastructure, revenue growth and operational agility.

In particular, the ability to use AI effectively in the research and development (R&D) process is now seen as key to gaining a competitive advantage in the car industry.  As CEOs look to adapt, an advanced, data-driven strategy that leverages AI helps drive down costs by greatly reducing the time needed to develop and produce parts.

New development and production platforms emerge

The race to adapt and develop faster than before can also be applied across the production ecosystem. For example, introducing new AI-powered production planning tools can yield significant savings in time management and quality control. It also helps to maximise human resources by redeploying staff to more advanced data-driven roles as car manufacturers shift from manual to technology-led business models.

In addition, manufacturers are looking to use AI to align the development process with the production platform. AI-driven production platforms can seamlessly integrate with the development process, resulting in significant time and cost savings.

Managing the challenge of achieving cost savings and mitigating risks

In the past, building R&D centres in low-cost countries held attractions for the car industry. However, the increasing use of AI requires CEOs to consider the location of R&D centres and where AI expertise is based. Having AI developers sourced elsewhere creates complex regulatory, legal and operational risks.

Such an approach can trigger conflicting jurisdictional requirements. Questions about where human responsibility lies can significantly raise cybersecurity and IT risk levels, particularly when data must be stored locally or when access is restricted. Supply chain security and data ownership rules are further considerations. In addition, uneven AI regulations can also create compliance problems regarding standards, transparency and ethics. While cost savings are key, careful consideration of the right balance between the efficiencies gained and the risks involved is vital.

Update AI-critical implementation strategies

CEOs know that implementing AI requires significant investment, but any implementation decision is only as good as what it aims to achieve. Whether it’s for cost savings, production efficiency or R&D, each project should clearly reflect its goals and identify where AI can help ensure those goals are met.

It’s also important to build in plan corrections along the way.  AI implementation is non-linear: the speed of AI’s evolution alongside external economic and geopolitical factors requires a flexible approach to AI-critical strategies, investment and training.

Evaluate supply chain disruption

According to the latest Forvis Mazars C-suite barometer, automotive leaders’ main supply chain investment priorities include integrating new technologies and growth ambitions in current markets. In the long term, the integration of new technologies will help reduce the reliance on, for example, rare earth for electric car batteries. At the same time, there is a shift from global to regional production to reduce transportation costs and reliance on distant markets.

However, as deglobalisation of the car industry continues, any 'local-for-local' approach to reduce costs and source components and raw materials closer to home creates both opportunities and challenges along the supply chain. Not least, requiring manufacturers to reinvest in local expertise to maintain any competitive advantage.

In addition, geopolitical instability is raising questions on the reliability of supply chains, compounded by tariff-driven cost increases. The agility to understand and assess the impact and risks on a location-by-location basis is essential. It requires a data-driven strategy and skilled talent to source alternatives and protect margins in real time.

Diversify revenue streams to achieve future growth

In response to competition and the need to grow, OEMs and, increasingly, automotive suppliers are looking beyond core markets. Among the most challenging external trends are economic uncertainty, and increased competition. Diversifying resources is the key operational shift in reaction to global trade disruption for leaders in the sector, according to the Forvis Mazars C-suite barometer. By identifying and applying existing strengths to new market opportunities, CEOs can diversify revenue streams, optimise performance and maximise value.

Outside automotive, growth opportunities lie in the agriculture and defence sectors, where leveraging existing car fixture expertise can be applied. The high level of automation already present in the industry also helps to efficiently transition into these new markets. 

In terms of growth opportunities within the sector, data-driven diversification involves suppliers monetising proprietary insights, while product licensing extends revenue streams. Longer-term, shifting consumer mindsets toward a ‘feature-on-demand' model when purchasing cars offers potential for transformative growth.

To thrive amid automotive disruption, integrating emerging technology is essential. An advanced data-driven approach leveraging AI can drastically reduce production cycles, drive efficiencies and unlock new opportunities, allowing companies to foster growth and remain competitive.