Defining Opportunities to Leverage Artificial Intelligence, Machine Learning, and Data Analytics Applications for AWP

RT-391 Topic Summary
RT 391


Advanced Work Packaging (AWP) is a disciplined process for project planning and execution. Work by previous CII research teams (e.g., RT-272, RT-319, RT-365) and others (e.g., Construction Owners Association of Alberta) has shown that, used correctly, AWP can increase overall productivity and consequently decrease total installed cost. For these reasons, AWP promises to revolutionize the delivery of capital projects. However, the adoption of AWP has been slow, due in part to the complexity of correctly applying the AWP framework. The increasing prevalence of artificial intelligence (AI), machine learning (ML), and data analytics across many sectors, such as manufacturing, shows that these technologies can support the application of complex procedures to optimize outcomes.

RT-391 embarked on a project to identify, characterize, and rank opportunities for the application of AI, ML, and data analytics to the AWP workflow. In this work, the team took a broad and inclusive view of these technologies, accepting many forms of advanced automation as potentially representative of AI. It referred to the application of these technologies in AWP as intelligent AWP, or iAWP.

As it sought to identify iAWP opportunities, RT-391 took inspiration from the Design Thinking process. The team started by engaging with stakeholders and lead users of AWP in order to better understand their challenges and pain points. The researchers then translated these user data into a set of user stories that helped to crystal the main themes and needs. Through an iterative process, RT-391 identified the user stories that best balanced feasibility and potential impact, and eventually the team arrived at a set of seven core opportunity areas.

Next, RT-391 surveyed 169 respondents in 26 countries, representing over 1,000 years of collective industry experience. Through careful analysis of the survey data, the team identified the most important criteria for evaluating iAWP opportunities, determined which of these opportunities the survey respondents perceived as most important for the construction community, and traced out which job role held which specific perceptions of iAWP. The team used these findings to provide a handbook of iAWP opportunities that summarizes the pain points that drive the opportunity, existing technologies that can support implementation, and potential risks involved.

Key Findings and Implementation Tools

1 : Projects Live and Die by the Data

Throughout all interactions with stakeholders, RT-391 repeatedly encountered frustrations, challenges, and needs surrounding data. As data is a vital precursor for later stages of AI and ML development, there is an urgent need for the community to address this situation (FR-391, in press).

Reference: (FR-391)

2 : Identified Opportunities for Artificial Intelligence

By designing an expert survey and distributing it to more than 160 AWP practitioners across more than 26 countries (representing more than 1,000 years of experience), RT-391 assessed seven key opportunity areas and identified which ones the community perceived to be most important. Note: respondents perceived many of the opportunities as almost equally (and very) important (FR-391, in press).

Reference: (FR-391)

3 : Value Scenarios Rated by Role

The table outlines how different stakeholders rated specific functional needs. Again, even within roles, many of the opportunities were rated closely together. With that in mind, the team offers these findings primarily as examples of the potential risks and to indicate where mitigation strategies could be necessary in these roles (FR-391, in press).

Reference: (FR-391)

4 : Training and Upskilling Needs

Delivering on the potential and promise of AI will require focused investment in organizational readiness. This includes providing the IT infrastructure that is generally recognized as important, but it also extends to human resources investment, including upskilling and reskilling employees. Ultimately, the goal should be to increase organizational readiness to embrace data analytics, machine learning, and artificial intelligence.

Reference: (FR-391)

Key Performance Indicators

Safety, Productivity, Schedule, Quality, Predictability

Presentations from CII Events

Session - iAWP: Harder, Better, Faster, Stronger

Publication Date: 08/2023 Presenter: Number of Slides: 56 Event Code: AC23

Session - iAWP: Harder, Better, Faster, Stronger booklet

Publication Date: 08/2023 Presenter: Number of Slides: 11 Event Code: AC23