European automakers are turning to agentic AI to automate engineering work and narrow a development gap with Chinese rivals, which bring vehicles to market in roughly half the time.

The technology compresses tasks that traditionally take weeks into minutes, said Synera CEO Moritz Maier, whose company has created an agentic AI platform for engineering, manufacturing and R&D teams. Synera’s customers include BMW, Volkswagen Group and other leading automakers and suppliers.

Task times are slashed because autonomous AI agents eliminate the need for manual handoffs between hundreds of software tools. For example, Synera’s agentic AI platform enables engineers to evaluate more than 100 design variants or simulation checks per hour through orchestrated multi-agent setups.

The objective isn’t simply efficiency: faster iteration means automakers can potentially improve products, reduce costs or get to market sooner.

“We’re using agentic AI to automate, to make the decisions, to prepare the simulations, to decide whether this is the best material or design,” Maier said.

According to a September 2025 McKinsey report on agentic AI, the technology could potentially increase automakers’ total revenue by 5 to 10 percent while cutting operational costs by 30 to 50 percent through the dramatic compression of development timelines and product-to-market cycles.

European automakers lag Chinese rivals in development speed

Agentic AI offers European automakers an opportunity to narrow the time-to-market gap with Chinese rivals. For instance, EV startups such as Nio and Xpeng develop vehicles in roughly 24 months from concept to production, compared with 48 to 60 months for traditional European automakers.

“The biggest pain that European automakers have at the moment is the time to market. Agentic AI is definitely a great opportunity for them to catch up,” Maier said.

What’s more, legacy European automakers possess something Chinese newcomers lack: decades of engineering data, designs and accumulated expertise from crash tests to durability records to supplier quality metrics. “There’s a huge data pool and data treasure that established carmakers are sitting on,” Maier said.

But the same legacy makes European automakers harder to transform. They can’t simply start from scratch, Maier said.

There is also a knowledge retention problem as experienced engineers leave the industry. “Keeping the knowledge on how to develop a car inside of the company is a huge problem,” he said.

AI will change jobs, not end them

AI will change job roles rather than eliminate them, Maier said. Humans will still be needed to create and maintain agents and, crucially, to take responsibility for their output, he said.

One issue is who owns the intelligence embedded in these automated engineering workflows, he said. If automakers outsource engineering and suppliers begin building AI agents that encode how BMW or VW designs products, manufacturers risk giving away intellectual property.

The question reflects a broader tension facing European automakers: the same AI tools that could help them match Chinese development speed may also require sharing proprietary engineering intelligence with suppliers or investing heavily to build internal AI capabilities. How automakers navigate that trade-off will shape competitive dynamics in an industry where speed increasingly determines success.