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Simon Rawlinson

Partner - Head of Strategic Research and Insight

We must be nimble and flexible if we are to keep up with accelerating AI
The pace of change in AI is breath-taking. Non-specialists like you and me need to take note, says Simon Rawlinson of Arcadis.

When I decided to focus a regular blog on Artificial Intelligence, my first idea was to focus on the Bletchley Park AI Summit, the global get-together hosted by UK Prime Minister Rishi Sunak focused on technology safety risks. Getting 25 countries and the EU to publish a declaration on the safety of AI is an important milestone in the dizzying rise of technology like ChatGPT, and I thought it was worthwhile highlighting.

And then I looked at the sector a little closer. Within the space of a week in November 2023, Microsoft had launched its integrated AI assistant for Office, Co-Pilot; Elon Musk had entered the fray with Grok, his ‘AI with a sense of humour’; and OpenAI had announced the opening of its GPT Appstore. That’s a lot of progress.

AI in the Architecture, Engineering and Construction (AEC) industry

GPT’s latest development is probably the most significant, given the way that the original AppStore turbocharged mobile technology development. This shift to a more varied AI market, using task-focused models trained on smaller subs-sets of data, could be a real challenge for the construction industry, because the sector often struggles to organise and share its data to good effect.

The AEC industry has of course been using process automation and AI for years. Many design challenges can’t be solved without the power of genetic algorithms that learn as they solve. Arcadis increasingly benefits from a growing cohort of citizen-developers using tools like Python and Knime as well as Large Language Models (LLMs) to exploit data more effectively. This enables us to create new insights and optimise decision-making for problems that our clients bring to us, like rail maintenance optimisation.

However, the industry’s AI experts are at the tip of much less capable iceberg. Non-specialist users like me have a different, arm’s length relationship to AI. We happily accept recommendations from streaming sites, we occasionally use ChatGPT and might be aware of the pace of change in the wider AI economy. But we use tools sparingly – partly because we haven’t yet developed the skills to prompt Large Language Models (LLMs), but mainly because we still rely on pre-AI processes to form an independent point of view based on conventionally sourced evidence, knowledge and experience.

Adopting AI

But the industry’s wider adoption of AI is now more a question of timing rather than one of choice. We have no option other than to plan, prepare and act. For people at any stage of their career, that’s quite a challenge.

The current speed of development means that, assuming that Microsoft and other providers get their pricing right, non-specialists like me will be using AI as a routine business support tool in the foreseeable future. Similarly, OpenAI’s Appstore initiative raises the tantalising prospect of a new generation of niche AI-driven Apps focused on the needs of specific sectors like commercial property.

What steps need to be taken to make sure that construction and property attracts AI investment and then has the capability to use it?

An underlying issue for all sectors is trust, and this was at the core of the Bletchley Park agenda. Existential risks around cyber security, misinformation and even biotechnology are so consequential that they must be addressed through global cooperation. Risk reports that accompanied the Summit highlight that investment incentives are focused on scaling AI’s predictive power rather than ‘conditioning’ AI applications to perform in appropriate and socially acceptable way. The declaration should help to reset that balance of investment towards greater safety.

However, there are further trust issues associated with AI being used to support professional advice. Whose advice is it? Will it account for the specific circumstances of the project? Even basic considerations about how AI-supported advice can be assured have yet to be addressed when the advice comes out of a black box.

A second and equally important consideration will be the AEC industry’s ability to scale its intelligence resource. LLMs are trained using the entire internet. Generative design models can access tens of thousands of optimisation iterations from cloud-based servers. Asset management systems can collect millions of data points. But in parts of the industry, data remains in silos, is transaction specific and is often protected by IP provisions. Access to large volumes of good quality, useable data is an age-old problem – particularly associated with commercial transactions and building performance. However, as data resources for solvable problems grow - contributing to better informed decision making and assurance – the problems that have less data to support decision making will become riskier and might attract less investment in AI as a result.

Collaborating for an AI powered future

Our sector needs to keep on the data-rich side of the equation, and to facilitate this, an industry-wide approach to anonymous data sharing might be needed to train the AI. That’s a big step, so we need to think about it sooner rather than later.

Trust and data are important issues but without capable people to pave the way, construction’s route to an AI-powered future will be slow and uncertain. Elon Musk may believe in a world without work, but today construction needs to create and retain a cohort of people with the capability and agency to develop the technology, processes, standards and culture to accelerate the adoption of AI as well as the leaders to take these solutions to market.

Some but not all of these people will be technology experts, but to create this capability an industry response also needs speed and scale – speed to respond to pace of change, and scaling because AI’s adoption will be needed in the industry’s SMEs as well as the sector giants that can afford to invest in training, policies and bespoke data sets.

I could not have written this blog a year ago, yet such is the pace of change that the launch of an OpenAI App Store is bringing the technology closer and closer to our work lives and everyday lives. Problems that can be solved with trainable data will be derisked, and as AI is adopted more widely in all walks of life, clients will increasingly expect AI-backed as well as professionally based advice. No industry will stand still, and those that rely on analogue processes will be exposed to greater risk and will attract less investment and less talent.

The only response to AI is to respond with scale and speed. Are construction and property professionals ready for this challenge?

A version of this blog was previously published by Building Magazine in November 2023.


Simon Rawlinson

Simon Rawlinson

Partner - Head of Strategic Research and Insight