TITLE: 21st Century Enablers for Improving Cost Prediction Accuracy

SPEAKER: James Arrow – Project Risk Management Principal – BHP

With more than 20 years’ professional experience, James has played a key role in successfully delivering critical capital assets, in a variety of locations, around the world. Having had the opportunity to work with diverse teams across the globe, James is well-versed on project best practices and applies exceptional communication skills to lead multi-disciplinary teams. An effective hands-on team-player, James is also an acclaimed writer and speaker on topics concerning project risk management, data analytics, data science, including digital disruption in the engineering and construction sector. In recent years, on several occasions, James has been formally recognized by his peers for his contributions to the profession.


This decade, the fourth industrial revolution and, for engineering and construction in particular, a 5G enabled industrial internet of things (IIoT), in conjunction with advanced analytics, artificial intelligence or AI, promises rapid near-term gains in the field of PRM (Project Risk Management). However, AI is not a silver bullet. Its potential if often over-hyped or misunderstood. If our goal is improving cost prediction accuracy, and if we prefer a path of rapid progress, over one that is long or winding, we should pause to better understand the technology available to us today. From which position are we planning to advance? What incremental improvement might we realistically accomplish in the near-term? Not all organizations are positioning themselves to head down the right path. In this presentation, suited for all project professionals, we shall take a moment to consider the enablers organizations may employ to embrace a future of AI-enabled capital project decision-making. Ultimately, we shall highlight the proven tools and techniques that can deliver data-driven, fact based decision-making.

LOCATION: (Virtual via Go To Meeting)


Feb. 10th, 2022 @ 12:00 PM (PST)