The collaborative approach is key to keeping human decision-making at the center, ensuring safe and reliable results.
AI as an enabler, not a replacement
AI has the potential to significantly improve work performance by saving time, generating new concepts, and increasing the amount of information needed to make decisions. However, AI can also make mistakes. It is unrealistic and unwise to remove the human factor completely from the equation. Companies should instead position AI as an enabler in the development process, supporting engineers rather than replacing them. We should recognize AI as a partner in product design that can help us realize the full potential of product design and development without undermining human expertise.
Focus on creative tasks
One of the main benefits of AI is its ability to take on repetitive, non-value-added tasks, freeing employees to focus on more creative and mentally stimulating work. This change not only increases job satisfaction, but also encourages innovation. With AI taking over mundane tasks in the background, engineers can focus their energy on solving complex problems and developing new ideas.
Practical applications of AI in engineering
We’ll see more and more use cases over the next few years, but here are three examples we’re seeing today where organizations are using AI to support a collaborative approach to AI in engineering:
1. AI-driven design
Today we see human-driven interactions supported by human-driven AI. If we equip AI with a rich digital thread and ask well-formulated questions, it can provide reliable answers. Soon we will see human-driven interactions guided by AI-driven suggestions. In the background, AI will continuously monitor the digital thread and digital twins, looking for changes that impact the design and making suggestions to optimize engineering work.
In the not-too-distant future, we will see AI-driven interactions with systems guided by human suggestions. Engineers will use chatbots to perform complex tasks, analyze results, and select the best designs for improvement. While humans will still maintain control of the solution space, AI will take on the tedious work of exploring combinations and optimizing factors such as cost, sustainability, and reliability.
2. Increasing the workforce
In many organizations, many quality issues are not reported back to development. By integrating PLM and quality reporting, AI can analyze natural language problem descriptions and automatically route issues to the appropriate development teams. When teams see that problem reports are being actively addressed, AI can be further leveraged to identify anomalies and recommend solutions.
3. Virtual Assistant for Change Control
A transformative use case for AI is its role as a virtual assistant to support collaboration and support engineers. AI can be trained as a virtual assistant to support meeting scheduling, task organization, and approval workflows, including updating customer, manufacturing, and service documentation. The virtual assistant would identify patterns that indicate larger issues and then use generative AI to schedule meetings with the appropriate agenda. It can invite relevant team members, create impact reports that document the impact of changes, and generate recommended deliverables and tasks to resolve the issue.
Training and transparency: The key to successful AI integration
As we see in these use cases, for AI to be a true partner in engineering, human interaction is required to evaluate and verify decisions. Comprehensive training is essential. According to a Deloitte reportExecutives cite a lack of technical talent and skills as the biggest barrier to Gen AI adoption. Only 22% of respondents believe their organizations are “highly” or “very highly” prepared to address talent-related issues related to Gen AI adoption, highlighting the need for targeted training and education.
Employees need to understand the process, feel comfortable letting AI perform certain tasks, and be aware of AI’s limitations, such as the potential for “AI hallucinations,” where the system generates plausible but incorrect answers. Companies need to implement appropriate safeguards to manage these risks: verification methods, human oversight, continuous monitoring, ethical guidelines, regular training, and feedback loops. This will ensure transparency and trust in AI systems.
Practical tips for successful AI integration
Start with the data. The idea of AI-powered engineering starts with data – it will create new ways to track and visualize interconnected information that will help us look at problems from new angles. The more data, the better, including more integrated and connected data across the entire product lifecycle. The more and the better the data AI can work with, the better the results.
Accept the idea that repetitive or non-value-added work can be done by a machine. Sometimes it’s hard to imagine the future, especially when it comes to complex ideas that only experts in a particular discipline really understand. Some may be afraid of AI, but that may be because they only see the simple use cases that have the potential to cause harm. These concerns are certainly valid and need to be addressed if AI is to be successful. But how AI can effectively prevent disasters also needs to be discussed.
Check the data and assumptions suggested by the AI. The work of experienced, knowledgeable engineers and other subject matter experts is critical to authenticate the results. AI can only introduce partial solutions based on the models it works with, but it needs the human mind to interpret the information, guide the AI’s additional analysis, and conclude what is best in the given situation. Engineers and their colleagues must always be the decision makers in this process. We cannot simply trust, we still need to take additional steps to verify.
Integrating AI into engineering is not just about introducing new technologies, it’s about redefining the way we work. When AI is viewed as an enabler rather than a replacement for human capital, it can unlock new levels of creativity and efficiency. By managing repetitive tasks, AI allows engineers to focus on solving complex problems and driving innovation.
But this shift can only happen if companies commit to comprehensive training, full transparency, and valuing human expertise. In the future, the most successful companies will be those that balance the relationship between AI and their workforce in a way that allows both to thrive. With this approach, AI will not overshadow human intelligence but instead augment it, leading to remarkable advances and a more dynamic engineering landscape.
