Construction projects are complicated, interconnected operations with copious potential for inefficiency and risk in an industry traditionally plagued by overruns of time and cost.
Typical issues include lack of co-ordination between the different parties, unaligned scope expectations, gaps in competency of one or more parties, under-costing or under-resourcing and fitting inappropriately skilled personnel to certain tasks who may happen to have availability but not the requisite competencies.
Inevitably, the bigger and more complex the project, the bigger the risk, so the use of AI and other advanced technologies are being increasingly adopted to improve productivity all round.
Usually however, the focus has been on using tech and data collected from digitisation to refine current procedures and processes rather than to inform decisions – looking back rather than ahead. Instead of collecting project data simply for reference then, engineering and construction companies could use predictive insights revealed by AI from the data to make the optimum decisions and drive the success of the project. As clients are increasingly looking for actual data to inform costs and productivity, those developers adopting digitisation are likely to be at an advantage in terms of productivity and profit. This can assist in the selection of a service provider where traditional procurement processes have been heavily weighted towards initial cost savings rather than selecting a low-risk supplier with the necessary credentials to deliver the project on time, in full. Any additional initial cost would likely pay itself back several times over – delivering a successful conclusion and a positive impact on team moral, and enhanced credibility and career prospects for individual team members. Analysing the data intelligently means they can plan more efficiently, mitigate risk, estimate more accurately with regard to completion timings the delay factors that might cause the project to overrun (by how long and at what cost), subcontractor performance, or health and safety risks. A company that uses historical data from its completed projects to define KPIs, key standards and baseline measurements, can also use the data to estimate performance for new projects. This means that as well as being informed of how they should be performing, they can predict what is likely to challenge and cause friction in the new project, refine their system, and mitigate the problem ahead of time. For example, the preferred subcontractors for a new project are unlikely to be the same ones who performed poorly on a previous project. Using historical data to predict future outcomes prevents repeated mistakes and drives improvement as an ongoing process. The AI technologies learn from their re-trainable machine learning models, which means that accuracy improves, which in turns ‘makes’ the re-trainable AI smarter, which means that accuracy improves again, and the cycle of improvement and progress continues. AI is set to play a growing role with how active intelligence can improve quality of work in the construction industry and add value to nearly every aspect of project management.
