AI and Engineering: Keeping Human Judgment at the Center

When it comes to technical subjects, AI, with a few questions, provides a lot of information, solutions, and suggestions for further investigation; sound technical knowledge, an understanding of interactions between systems, and an inquisitive mind remain vital for every engineer.

It is the responsibility of every organization and manager to support their resources with this insight by showing both the benefits and the pitfalls of AI tools.

I believe this can best be achieved by a mix of methods, such as formal training, Awareness moments in meetings, and short communication on the company's intranet.

This continuity will encourage discussion among colleagues and ensure that AI can be used as a valuable tool throughout all phases of a career, thereby stimulating development and performance.

Institute's Reflection: Strong Foundations Make Better AI Partners

This perspective reminds us that AI changes how people access knowledge, but not the need to understand it. Engineers and other professionals increasingly have instant access to explanations, recommendations, and potential solutions. Yet using these effectively still depends on domain expertise, systems thinking, and the ability to judge whether AI's suggestions are appropriate for a particular context.

Research supports this view. In a large field experiment with management consultants, AI improved performance on many knowledge-intensive tasks, but participants were significantly less likely to reach the correct solution when using AI for tasks beyond its capability frontier (Dell'Acqua et al., 2023). The findings suggest that expertise remains essential because people need to recognize when AI's outputs require further scrutiny.

The contributor also highlights the importance of creating continuous opportunities to learn about AI rather than relying on one-off training. Short discussions in meetings, practical examples, and regular communication can help normalize responsible AI use and encourage colleagues to learn from one another. Our own Sense-Making Labs reached similar conclusions. Participants consistently emphasized that organizations should create ongoing opportunities to share experiences, discuss both successful and unsuccessful AI use, and reflect collectively on how AI influences decisions and performance.

References:

• Dell'Acqua, F., McFowland, E., Mollick, E., Lifshitz-Assaf, H., Kellogg, K., Rajendran, S., Krayer, L., Candelon, F., & Lakhani, K. R. (2023). Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality. Harvard Business School Working Paper No. 24-013.