How predictive AI will curb construction risk

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How predictive AI will curb construction risk

How predictive AI will curb construction risk

How predictive AI will curb construction risk and improve decision-making

Construction projects are highly complex and interconnected undertakings, and the potential for inefficiency and risk – which inevitably lead to project costs and delays – can grow exponentially along with the size of the project.

Many engineering and construction (E&C) organizations have accelerated their automation efforts as they feel the squeeze of growing risk, strained supply chains, and narrowing margins. Traditionally organizations have focused on gaining operational improvements by using technology to refine processes and procedures, but the data accrued from digitization can sometimes be an afterthought.

What if you could improve your chances of delivering a project on time and on budget by utilizing the growing volume of data that you previously only archived for future reference? Artificial intelligence (AI) holds enormous potential to help E&C organizations optimize their decision-making, and to drive project success by proactively unlocking new predictive insights from project data.

Turning data into insights
Vast amounts of data are being generated by the construction industry as digitization is embraced, and with it there is a significant opportunity for teams to learn from and use this data to create better estimates, plan smarter, and avoid – or at least mitigate – potential risks.

Historical data provides a critical starting point for organizations to provide deeper analysis of their business. By looking at existing project data, E&C professionals can answer questions such as:

  • What is the amount of time it takes to complete a process?
  • Which subcontractors are the best and worst performing?
  • Which activities have typically been delayed in the past?
  • Once you’ve been able to define baselines, benchmarks, standards and key performance indicators (KPIs) from your data, you can now apply these insights to present conditions. For example, in the case of estimating, you can discern:
  • Are our estimates accurate and have we accounted for historical delays?
  • How are we performing compared to historical benchmarks and organizational baselines?
  • Did we select the best partners for the job based on previous project performance?
  • Should we change the requirements or frequency of our reporting to ensure we avoid “surprises”?
  • The ability to gain insights from historical data and apply them to current projects is key to providing a basis to prevent mistakes from being repeated and ensure there is a focus on driving continuous improvements.

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