From productivity to purpose: Adopting AI strategically
11.03.2026
Written by Megan Greig
Associate
The structural engineering profession risks accelerating business-as-usual with its current focus on AI productivity gains.
Instead, a strategic approach is needed to prioritise AI adoption in the pursuit of sustainability goals.
We have a choice
Efficiency without purpose
Since 2020, the IStructE has listed climate action as one of its guiding principles, elevating sustainability to the same level of importance as safety1. This shift is evident through its endorsement of Structural Engineers Declare and support for net-zero carbon, ecological impacts, circular economy principles and regenerative design.
However, the discussion around artificial intelligence seems disconnected from these commitments. Without a strategic vision guiding technological adoption, we risk becoming very efficient at doing more of the same.
As predicted material extraction rates keep rising rapidly, we face a choice: will AI become an opportunity to disrupt the status quo or a tool to accelerate consumption?
Image: Circle Economy
CC BY-SA 4.0
www.circularity-gap.world/2022
From productivity to purpose: Adopting AI strategically
Purpose-driven approach
Instead of thinking about AI as a way to make us more productive, we could ask, ‘how can AI help us transition to a more sustainable industry?’
The current pattern for many AI tools is clear: make it faster, and if material efficiency happens to be a secondary benefit, that’s a bonus. We could flip this mindset. The IStructE Hierarchy of net-zero design provides a framework to evaluate use cases with an environmental lens.
Reducing virgin material use, enabling circular economies, exploring new bio-based materials and understanding planetary impacts should be our guiding principles. These can be used to determine which applications of AI align with these goals and allow us to prioritise those which align most strongly.
Through small steps, we’re testing this approach at Elliott Wood. We have used AI to review hundreds of planning applications local to project sites. The descriptions are often vague and non-standard. AI can flag which sites are most likely to include substantial demolition so we can take a closer look to see if there are any circular economy opportunities.
We’re also using it to help us develop automation. Large language models assist us to automate tedious design processes, and we use machine learning algorithms for optimisation where appropriate. In the past, much of our automation and AI-based work was based around speed and efficiency. However, now we are prioritising use cases which enable repurposing of existing structures or reduce material use.
Quote
The question isn’t whether to adopt AI. It’s whether we’ll direct it meaningfully or simply accelerate existing problems.
Transforming industry
The examples above demonstrate the strategic approach at the project level. But what happens when applying the same sustainability-first lens to industry-wide challenges?
Could image recognition create estimated stock lists for salvage yards? In the USA, the car-wrecking industry already uses AI to categorise scrap parts3. Applied to construction salvage, this could make it easier to connect designers to reclaimed materials, especially for small yards where manual cataloguing isn’t viable.
AI is identifying materials that could help us rethink the meaning of ‘build efficiently’. Machine learning algorithms have been used to design nanomaterials with the strength of carbon steel and the lightness of styrofoam4. How do we advocate for policies and frameworks that let us adopt these innovations quickly and safely?
These applications could shift the way we approach the design and construction of buildings.
The choice we face
AI will continue to develop, with new tools and capabilities continually coming forward. The question isn’t whether to adopt AI. It’s whether we’ll direct it meaningfully or simply accelerate existing problems.
Before implementing your next AI tool, pause and ask: ‘does this help us build a better world’. If the answer is simply: ‘it makes us faster’, then consider: speed towards what end? Efficiency in service of what goal?
We can lead this technology towards outcomes that align with our professional responsibility to future generations, or we can let market forces and competitive pressures decide for us.
1) Institution of Structural Engineers (2025) Five Years of Action: Climate Action End of Year Report 2024 [Online] Available at: www.istructe.org/resources/report/climate-action-end-ofyear-report-2024/ (Accessed: September 2025)
2) Gowler P. et al. (2023) Circular economy and reuse: guidance for designers, London: IStructE Ltd
3) Dawson J. (2023) ‘AI and automation in the car wrecking industry’, Medium [Online]
Available at: https://medium. com/@carwreckers20/aiand-automation-in-the-carwrecking-industry-272d95354166 (Accessed: September 2025)
4) Serles P., Yeo J., Haché M. et al. (2025) Ultrahigh specific strength by Bayesian optimization of carbon nanolattices’, Adv. Mater., 37, 2410651; https://doi.org/10.1002/adma.202410651
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