Enhance manufacturing efficiency with AI

The automotive industry predominantly operates on single-digit margins, making manufacturing efficiency a top strategic imperative for companies.

However, today manufacturing itself faces significant challenges. New and disruptive products are introducing complexity as well as diversity; in addition, they often necessitate accelerated time to market. This challenge is compounded by others, including talent shortages and uncertainty around tariffs.

Challenges like these could transform the manufacturing footprint, along with the fundamentals of manufacturing efficiency and the management of product diversity.

To overcome the challenges, companies need to take manufacturing efficiency and agility to the next level, so that managing product diversity is no longer a challenge but a source of competitive advantage.

AI-powered automation is already underway

Automation and AI, including agentic AI, can enhance manufacturing efficiency and productivity in a wide range of ways, improving every aspect of production and helping companies hit their “right first time” quality targets, among other benefits. Adding AI plus sensors to robots can elevate them to “cobots” that are able to respond intelligently to their environment.

Already, AI-enabled smart factories are becoming the norm, with predictive maintenance and robotics playing a central role. For example, at Capgemini we have leveraged AI to help a major European OEM design and implement a data-driven manufacturing organization. This work established the operating model and capabilities needed to realize value from data and analytics at scale.

For the future, AI agents show special promise because of their ability to initiate actions autonomously, without prompting by humans.

Fully autonomous agents will take time to arrive

Introducing collaborative and trusted agents to a business requires a transformational journey. This journey begins with the identification of tasks and activities – both repetitive and non-repetitive – that could be undertaken by AI agents. The company must also identify appropriate types of agents (autonomous or not) for each of these activities and tasks.

The most important part of the journey, however, is the alignment of AI agents with human ones, so that their interactions add up to a flawless agentic approach, flawlessly executed.

This journey is likely to proceed stepwise. Initially, AI agents will just take over repetitive and lower-value tasks.

As the model matures, however, agents will be used to perform more advanced tasks, but still with human monitoring.

Eventually, AI agents will be able to anticipate human needs, providing companies with the ability to tackle a wide range of automotive manufacturing pain points.

For example, AI agents will be able to accelerate time to market in response to changing consumer demands. They will enhance automotive sustainability by supporting better traceability, smarter energy use, and more efficient waste management. Everything from productivity to material flows can be improved using these techniques.

Leveraging the collaborative power of AI agents will be essential

Much of AI agents’ strength lies in their ability to collaborate. Adding AI agents to a team can mitigate skills shortages and provide new ways to get the best out of the existing workforce. That is because working with agents empowers people, enhancing their efficiency and effectiveness.

As well as working in teams alongside humans, AI agents could in the future collaborate extensively with one another, each agent being responsible for a specific task. This approach could create immensely flexible and scalable solutions. Ultimately, a team of agents could take over the day-to-day running of a major manufacturing process or even an entire plant, freeing human experts to deal with more demanding work.

Capgemini is actively exploring the potential of agentic AI for automotive manufacturing

Applications of agentic technology that Capgemini has pioneered include a compliance assistant for lighting and signaling devices. This makes processes around 200 times faster and 1,000 times cheaper, as well as facilitating regulatory compliance.

We are currently working on a range of copilots (AI-powered conversational assistants advising and supporting employees) for the entire manufacturing lifecycle. Our line design copilot, for example, can de-risk shopfloor layout configuration and accelerate decision-making, improving areas such as crash detection, ergonomics, and maintainability, and generally enhancing manufacturing efficiency.

Another example from this range is the manufacturing execution system (MES) copilot, which accelerates requirements definition, configuration, and setup, and addresses regulatory topics and documentation creation. There are benefits in terms of both lead times and costs.

As a final example, we are working with agentic AI in the context of Identity & Access Management (IAM). AI can make “role recommendations” that optimize organizational structures from the point of view of both security and efficiency. In addition, it can also be an integral part of identity as a service (IDaaS) solutions.

Implementation of agentic AI in manufacturing is not straightforward

AI-based automation can be a challenge in its own right for several reasons. Some areas of operation may lack the real-time data needed to support AI-enabled decision-making. The diversity of hardware in the ecosystem can cause complexity, as can the need to rethink the operating model.

Before implementation starts, the lack of live use cases may make it difficult to obtain the return on investment stats needed to build a business case. And once a pilot exists, it may prove hard to scale and onerous to deploy enterprise-wide, or even factory-wide.

So how can automotive companies overcome the barriers and succeed with AI in manufacturing?

Four elements provide a foundation for implementing AI in your automotive manufacturing operation

To overcome these barriers, we suggest putting four basic elements in place.

1. Strong data & infrastructure foundation. Connect machines, assets, and control systems securely on the shop floor, and use industrial edge computing to unlock real-time data.
2. Integrated operational systems. Integrate and orchestrate MES, SCADA, and so on into a unified edge-to-cloud ecosystem, increasing data consistency, transparency, and control.
3. Support for intelligent & autonomous systems. This can include industrial AI, machine vision, predictive maintenance, and low-code platforms.
4. Advanced industrial simulation. Use digital twins to enable virtual commissioning, validation, and optimization of industrial systems, applying simulation to everything from IT-OT validation to warehousing.

As well as putting all these elements in place, companies must adopt a strategic approach to this major transformation of their businesses. This includes establishing the governance structures and mechanisms needed to keep a check on autonomous AI-powered systems.

Successful AI adoption in automotive manufacturing depends on specific capabilities

To develop and implement their AI strategy, automakers will require a range of technical and management expertise. As well as hands-on experience with all branches of AI, they need excellent abilities in organizational transformation management, since AI will not work unless it is fully embedded in the manufacturing operation and accepted by the workforce.

In addition, implementers should be conversant with Lean manufacturing principles. That is because to realize its full potential in manufacturing, AI, particularly agentic AI, must interact with Lean. For example, the elimination of waste required by Lean could be achieved more effectively with the help of an AI engineer.

Finally, companies must make workforce empowerment the primary focus of their AI initiatives. When AI enables people to realize their full potential, value can be unlocked from one end of the manufacturing operation to the other.

Of course, Capgemini would love to help you with this journey. Please get in touch to find out more. And, if you’ll be at IAA Mobility 2025, please come and meet our team at booth B22 in hall B1. They’ll be happy to discuss how advanced AI could enhance the efficiency of your own manufacturing operation.