What’s the Best Way to Bring AI Into Engineering?

The collaborative approach is key to keeping human decision-making central, to ensure safe and reliable outcomes.

AI as an Enabler, Not a Replacement

AI has the potential to significantly enhance the workforce by saving time, generating new concepts, and increasing the amount of information to inform decisions. However, AI can make mistakes. It is unrealistic and unwise to entirely remove the human element from the equation. Instead, companies should position AI as an enabler in the engineering process, empowering engineers rather than replacing them. We should recognize AI as a partner in product design that can help us unlock the full potential of product design and engineering without undermining human expertise.

Shifting Focus to Creative Tasks

One of the primary benefits of AI is its ability to handle repetitive, non-value-add tasks. This allows workers to focus on more creative and intellectually stimulating activities. This shift not only enhances job satisfaction but also drives innovation. As AI handles mundane tasks in the background, engineers can get their energy toward solving complex problems and developing new ideas.

Real-World Applications of AI in Engineering

Over the next few years, we’ll see more and more uses cases, but here are three examples we see today where organizations are applying AI to support a collaborative approach to AI in engineering:

1. AI-driven design

Today we see human-driven interaction assisted by human-prompted AI. If we arm AI with a comprehensive digital thread and ask well-formed questions, it can deliver reliable answers. Soon, we will start to see human-driven interaction guided by AI-prompted suggestions. In the background, AI will continuously monitor the digital thread and digital twins, looking for changes that impact design and offering suggestions to optimize engineering work.

Not too far off into the future, we will see AI-led interaction with systems guided by human-prompted suggestions. Engineers will use chatbots to perform complex tasks, analyze results, and select the best designs for refinement. While humans will still maintain control over the solution space, AI will handle the tedious work of exploring combinations and optimizing for factors like cost, sustainability, and reliability.

2. Workforce augmentation

In many organizations, numerous quality issues go unreported back to engineering. By integrating PLM and quality reporting, AI can parse natural language descriptions of problems to automatically direct issues to the appropriate development teams. When teams see that problem reports are being actively addressed, AI can be further leveraged to detect anomalies and recommend solutions.

3. Virtual assistant for change control

One transformative use case for AI is its role as a virtual assistant to support collaboration and assist engineers. AI can be trained as a virtual assistant to support meeting planning, task organization, and approval workflows, including updating customer-facing, manufacturing, and service documentation. The virtual assistant would detect patterns indicating 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 change, and generate recommended deliverables and tasks to address the issue.

Training and Transparency: Key to Successful AI Integration

As we see in these use cases, for AI to be a true partner in engineering, it requires human interaction to evaluate and verify decisions.  Comprehensive training is essential.  According to a Deloitte report, executives cite a lack of technical talent and skills as the single 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. This underscores the need for targeted training and upskilling.

Workers must understand the process, feel comfortable allowing AI to take over certain tasks, and be aware of AI's limitations, such as the potential for "AI hallucinations" where the system generates plausible but incorrect answers. Organizations must implement proper guardrails 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-infused engineering begins with data – new ways of tracking and visualizing thread-connected information will emerge to help us see problems from new angles. The more data, the better, including more integrated and connected data across the product lifecycle. The greater the quantity and quality of data AI has to work from, the better results will be created.

Embrace the idea that repetitive or non-value-add work can be done by a machine. Sometimes it is hard to conceptualize the future, especially when it involves complex ideas that only the experts in a particular discipline truly understand. While some may be fearful of AI, this may be because they only see the simple use cases that potentially create harm. These concerns are certainly valid and must be governed if AI is to succeed, but understanding how AI can effectively prevent disasters is also a valid conversation to be had.

Verify the data and assumptions AI is proposing. The work of experienced, knowledgeable engineers and other subject matter experts is vital to authenticate the findings. AI may only introduce partial solutions based on the models it is working from, but it takes human intellect to know how to interpret the information, direct AI’s additional analysis and conclude what is best for the situation. Engineers and their counterparts always need to be the decision makers in the process. We can’t just trust, we must still take extra steps to verify.

The journey of integrating AI into engineering is not just about adopting new technology but about redefining how we work. When AI is seen as an enabler rather than a replacement of 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.

However, this transformation can only occur if organizations commit to comprehensive training, complete transparency, and a commitment to honoring the role of human expertise. As we move forward, the most successful organizations will be those that delicately balance the relationship between AI and their workforce, so that both can thrive. With this approach, AI will not overshadow human intelligence but amplify it, leading to remarkable advancements and a more dynamic engineering landscape.