How construction firms can unlock the value of AI

By Rob Smith of Creative ITC03 August 2022

Artificial intelligence has proved its value in the construction industry over recent years, improving site safety, efficiency and planning. However, although many firms are achieving successful results from AI projects, scaling up to enterprise-wide often remains elusive.

With the effects of the Covid-19 pandemic still lingering, it’s no surprise that construction industry productivity remains consistently below average. Labour and materials shortages continue to impact construction projects across Europe, forcing financial and budget overruns and damaging businesses’ bottom lines.

Rob Smith, CTO with Creative ITC

The potential of new technologies such as AI to boost productivity now puts springboard advances within reach – enabling AEC firms to improve decision making, automate calculations, test designs against real-world constraints, and lower risk to fast-track client projects.

The newfound ability – to quickly learn from data, identify patterns and make informed decisions with minimal human intervention – is a huge game changer for a sector that has long battled with low margins.

With increased use of drones and growing adoption of advanced cameras by site workers AI is ideal for sewing together captured images and videos into usable 3D-reality models, floor plans and site maps.

AI can be successfully used to compare those images and models to create design versus reality visualisations and rapidly identify deviations.

Advanced analytics accelerate and improve decision making, with ability to run millions of permutations of how a project schedule and costs will be affected to ensure the right course of action is taken and minimise risks and delays.

As well as boosting productivity through workflow automation, there’s also a role for AI in making busy construction sites safer. For example, cranes and hazardous machinery can be located in optimal positions, based on up-to-the-minute work schedules and footfall data, and robots and autonomous vehicles can be deployed to reduce risk to site workers.

Achilles heel

However, this doesn’t mean AI solutions are a silver bullet for the industry. Recognising the potential of AI to transform the construction sector and ability to harness its wider benefits across complex supply chain networks are two different things.

For many firms, scaling up from successful AI projects to an enterprise-wide level remains elusive and all too often exposes underlying problems.

Legacy IT infrastructure is frequently the most severe limitation revealed. Huge AI processing requirements can easily overload data centre and network capacity, causing latency issues or even outages.

Many IT leaders find themselves struggling to derive the timely analysis and deliver the actionable insights their organisations need.

Failing to overcome these obstacles can lead to poor user experiences and prevent efficient collaboration.

Attempts to share actionable insights with stakeholders across the supply chain, can further expose weak points in legacy infrastructures, which haven’t been designed to share such valuable assets and huge datasets securely at speed and scale.

IT budgets in many industries are being stretched to accommodate new technologies. Of course, investment in AI doesn’t end with acquiring the solution itself – total cost of ownership also includes implementing and maintaining the right IT infrastructure, integration systems and resources to support AI deployment in the long-term.

Many construction businesses don’t have the luxury of employing extensive multi-skilled IT teams, with the specialist skillsets required to optimise AI workloads and enable their company to realise the full business benefits.

Greater agility

Increasing cloud use is one route enabling businesses to escape the burdens of expensive IT infrastructure upgrades and access the latest technologies. Many companies use a combination of cloud and on-premises platforms to give them the agility and scalability they need for both their overall IT and AI workloads.

Interestingly, research shows that the companies enjoying the biggest gains from AI are taking more advantage of cloud infrastructure than their peers.

The best performing businesses deploy two thirds (64%) of their AI workloads in public or hybrid cloud, compared with 44% at other companies.

Many leading players in the construction sector are turning to Infrastructure-as-a-Service (IaaS) to gain on-demand access to cloud-based systems and specialist skills for successful AI deployment. This gives them newfound agility, while offloading the burden of hardware costs and upgrade burdens to a managed service provider (MSP).

A specialist MSP will boost in-house resources, taking away the headache of designing, implementing, managing and optimising IT infrastructure and AI systems. The MSP route quickly pays back with savings on data centre space, infrastructure, licensing, support, training and headcount, providing a fully-managed service in a predictable, monthly OpEx model.

Stacking the AI odds in your favour

When transitioning to the cloud to boost AI capabilities, remember not all clouds or cloud services providers are the same. Read the fine print carefully and make sure you’ll benefit from access to the latest technologies and regular updates, rather than having to invest in expensive upgrades during the contract.

As AI deployment in the construction industry grows, demand for extra infrastructure capacity is increasing and cloud adoption rates continue to rise. The leading construction firms are spearheading a shift towards as-a-Service IT models across the sector in order to derive greater return on their investment in these new technologies.

IaaS is empowering ever more effective handling of complex and shifting AI workloads, with simple, stress-free management. The result is invaluable flexibility and speed on a realistic budget and time frame, enabling scalable and reliable AI-driven problem-solving to help construction firms stay ahead of the competition.

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