Project Management

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How can artificial intelligence be used in construction projects to reduce project risks?

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AHMED IBRAHIM samaa engineering consultancy copany Riyadh, 1, Saudi Arabia

Artificial intelligence can analyse:

Past schedules.

Team productivity.

Weather data.

Supply chains.

It then identifies the activities most prone to delays and suggests proactive measures such as reordering tasks or increasing resources.

Benefit: Reduced risk of delays and contractual penalties.
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Luis Branco CEO| Business Insight, Consultores de Gestão, Ldª Carcavelos, Lisboa, Portugal
An excellent example of how AI can support risk management in construction projects.

What is particularly interesting is that its potential extends far beyond identifying activities that are likely to be delayed.

AI can assist in identifying emerging risks by analysing historical projects, schedules, productivity trends, supply chains, weather patterns, quality data, and operational performance.
It can also support qualitative and quantitative risk analysis, help evaluate schedule and cost uncertainty through simulations, contribute to contingency reserve assessments, and assist teams in evaluating alternative response strategies.

However, perhaps its most significant contribution is enabling risk management to become more continuous and adaptive.

Traditionally, many risk reviews occur at predefined intervals.
By continuously analysing new information as it becomes available, AI can help project teams detect changes in risk exposure earlier, reassess assumptions more frequently, and focus attention on the areas that matter most.

Ultimately, AI does not eliminate uncertainty, nor does it replace human judgment. Its greatest value may be helping project teams make better-informed decisions before risks evolve into issues.
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Wissam Al Zahab Senior Consultant
One of the powerful Gen-AI assistance to improve construction projects' income and reduce risks is to analyze lessons learned from years of enterprise documentation. This is very helpful to avoid potentail risks associated to a new project, however, the Quality, Volume and variety of the data being processes are critical for ensuring good quality output.

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