In this episode, Nick Heim is joined by John-Michael Wong, Dave Martin, and Ayush Singhania to discuss how AI in structural engineering is transforming workflows, reshaping engineering leadership, and redefining value across the AEC industry. From automation and data sharing to human judgment and strategic planning, the conversation dives into how firms can begin adopting AI tools for engineers, what leadership needs to consider, and how entry-level engineers and project managers can prepare for the future of digital transformation in construction.

Engineering Quotes:











Here Are Some of the Questions I Ask John-Michael,Dave Martin,  and Ayush Singhania:



How is AI in structural engineering reshaping workflows and decision-making across the AEC industry?

What misconceptions do engineers and firm leaders have about AI tools for construction and design?

What should engineering leadership consider when adopting AI-driven solutions in their firms?

How do build vs. buy decisions impact the scalability of AI in engineering workflows?

What skills should entry-level engineers and project managers develop to stay competitive in digital transformation for construction?

How can firms balance automation with human judgment in structural engineering processes?

What role does coding play in the future of engineering careers within AI-powered environments?

How can firms begin integrating AI in structural engineering without disrupting existing systems?



Here Are Some Key Points Discussed in This Episode About How AI in Structural Engineering Is Transforming Workflows and Leadership in the AEC Industry:





AI in structural engineering does not change engineers’ responsibility to design safe, resilient structures. It transforms how work is delivered across the AEC industry. Digital-first engineering workflows shorten timelines and allow professionals to focus on complex design challenges. Automation accelerates routine tasks, while human judgment ensures performance, resilience, and collaboration. Engineers gain more time to prioritize sustainability and high-quality outcomes. Codes, standards, and reference materials—when made computer-readable—unlock their full potential as data sources for AI-powered workflows.

Many engineers and firm leaders mistakenly view AI as a passing trend or limit its scope to chatbots. In reality, AI tools for construction and design include computer vision, automation, translation, and other technologies that enhance engineering workflows. Misconceptions often stem from limited exposure to practical applications and a narrow understanding of how AI integrates into daily engineering tasks.

Successful AI adoption in engineering firms begins with clear policies and strong leadership. Secure access, active encouragement, and alignment with business goals turn AI from an experiment into a strategic advantage. Long-term success comes from staying human while scaling with technology. Relationships, engineering judgment, and client value remain central as AI becomes part of daily practice.

Build-versus-buy decisions directly affect the scalability of AI in engineering workflows. Smaller firms typically benefit from off-the-shelf AI tools, while larger firms may justify investing in custom development. A genuinely useful AI solution solves a clear problem from start to finish. Live demos with developers, rather than sales presentations, reveal whether a product delivers real engineering value.

Expectations rise at every career stage. Entry-level engineers are expected to use AI and coding tools to accelerate learning, while project managers must adapt workflows and lead innovation. Beyond technical adaptability, continuous learning in engineering stands out as the most critical skill.