Despite all the hype, our industry has been relatively slow in apprehending AI. Adoption remains low, use cases vague, and legacy methods stubbornly resilient. For our industry to harness this potential, we must begin working together more effectively to identify opportunities and minimise risks.
Imagine: it’s 2010. The rollout of 4G, improving smartphones, and e-commerce is converging in Uber’s ride-hailing gig-economy breakthrough. Except, rather than Uber, the technology is in the hands of the construction industry.
Firms build different apps for individual locations. Tribal digital teams in each region compete on whose Uber app is best. Customers and drivers are left frustrated by the number of apps they must download. The scaling necessary for the concept is completely missed. We are left with long queues, pricey cabs, and the quintessential struggle for exact change at the end of a journey.
It’s a crude analogy, but reflects an important truth: the construction sector is fragmented. This obstructs productivity, hinders progress, and embeds unnecessary inefficiency. The emergence of AI and cluster of machine learning technologies is further exposing our industry’s lack of integration. Harnessing AI is harder in a divided, fragmented, and siloed industry. It’s riskier, it’s more expensive, and the benefits are less certain.
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If construction were a fringe industry, with only a trivial relationship to the global economy, this might be an acceptable quirk. But construction is vital to almost every major challenge of the 21st century, from meeting the needs of growing populations to decarbonisation and climate change resilience.
It isn’t due to a lack of enthusiasm. Many firms now have a compelling story to tell about how they have deployed AI. At AtkinsRéalis, we have used AI to analyse complex datasets to detect and resolve design clashes at a UK nuclear power station, and used machine learning to predict maintenance, reduce downtime and prevent failures on a mass transit system in Canada.
Yet, because every company is largely going it alone, the sheer volume of rework is sapping momentum and wasting precious capacity. If every company strives to design and deploy its own version, a crowded market lacking interoperability ends up suiting no-one. Comparative advantage is lost, economies of scale recede, and valuable data remains locked inside walled gardens. And all the while, our whole sector loses a once-in-a-generation opportunity to leap forward, at a time when the world needs it most.
Robots and rigidity
In construction, a risk-averse mindset upholds standards of care and responsibility. Unfortunately, it can also discourage breakthroughs. While I never want to cross a chasm on a wobbly bridge, we all use technology at pace to drive convenience and outcome, tolerating the odd glitch on the way. In the context of climate change, traditionalism may well be the higher risk option. Business as usual cannot deliver the efficient collaboration-at-speed necessary to win the race against a rapidly changing climate.
By 2030, it’s estimated that around $130tn will be invested globally in improving capital infrastructure and moving towards renewables. New schemes are conceptually challenging; permitting and consent is lengthy and labour-intensive; shifts in weather patterns, population, and regulations are challenging major projects to rethink their ways of working, from design to deployment and decommissioning. But even a sum like $130tn fails to guarantee momentous progress towards the infrastructure and energy systems we urgently need in the very near future.
As well as efficiency, AI is indispensable for the staffing crisis in construction. We are struggling to fill vacancies across a range of critical roles, and the problem tends to be more acute in burgeoning fields like data science. If we don’t use AI to tackle human capital deficits, we may not have enough people to transform our industry. Overheating, resiliency, decarbonisation: the inefficiency of legacy methods, combined with a lack of human resources, is seriously undermining our capacity to protect our existing physical infrastructure.
Impossible to inevitable
The good news is that change is happening. From identifying how best to make assets more resilient to damage, to where best to allocate capital investment, AI is accelerating our learning and decision-making across a variety of complex areas.
Our priority must be to focus on the most convincing use cases. Optimising and protecting critical national infrastructure amid increasing floods, droughts, and storms is as uncontroversial as they come. For example, machine learning enabled the Federal Emergency Management Agency (FEMA) to rapidly assess over 146,000 structures, identifying over 30,000 for inspection - accelerating resilience interventions and saving FEMA millions of dollars. Successful applications can serve as proof of value for broader needs to scale across the industry at large. As well as mitigating catastrophic effects, the broad penetration of AI in construction will lay the foundations for the next generation of innovations.
Engineers love to create, but can’t match the countless billions big tech has already poured into generative AI. Thankfully, we don’t need to. By working together, pooling our efforts, and making use of what’s already available for us, we can avoid reinventing the wheel - or indeed the ride-hailing app - and focus on what we do best: engineering a better future.
Darren Martin, AtkinsRéalis chief digital officer
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