As the engineering and construction industry continues down its journey of digitization, one area that is becoming top-of-mind for organizations is machine learning. The engineering industry has already begun to realize the benefits from integrating data across systems, but the full potential that predictive analytics can bring to the industry has yet to be recognized. As the adoption of machine learning continues to gain traction across the industry, it's hard not to get excited about the opportunity that it presents in reimagining every phase of the plan, build and operate stages of the asset life cycle. Looking at advancements in deep learning, and the commercialized use of unstructured data like images, videos and text, as well as the ability to process big data through distributed cloud computing, these benefits will make themselves more apparent in existing work practices. Any good machine learning strategy needs data to work.

The Covid-19 pandemic has raised lots of challenges for those working in project management, but the UK's GDP fell by 20.4% in the first two quarters of 2020, therefore potentially there are more difficulties on the horizon. How will this environment affect project delivery and can AI solutions help mitigate some of the challenges?

Technology is an invaluable contributor to business efficiency. And the kind you use actively today says a lot about your adaptability to change and market uncertainties. Tools are subjected to continuous technological updates, whose quality is superior to that of their predecessors. Movements in the Artificial Intelligence industry are being closely followed by experts and researchers alike in order to make sense of the impact it wields on work, workplaces and the workforce. Machine Learning has enabled project managers to run more, and better projects on the basis of the data processed.

When you are in cardiac arrest, every lost minute decreases your chance of survival by 10%, so any chance of saving those critical minutes in response to a possible cardiac arrest victim is extremely important. The AI in this case analyzes words and non-verbal sounds that indicate someone is in cardiac arrest. It is able to do that after it has trained itself to spot warning signs by analyzing a massive collection of emergency call recordings over a period of time. In one study, it was found that this startup's AI was able to detect cardiac arrest with a 95% accuracy, compared to 73% for Copenhagen's human dispatchers. If AI can save lives like this – apparently 22% more accurately than humans can – can it save or improve project delivery?