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Artificial Intelligence Impacts and the Importance of Advanced Digital Construction Management Systems

May 28, 2024

Many organizations continue to work toward data driven decision-making and leveraging artificial intelligence (AI). Organizations are discovering the importance of data governance, but also a modern technology portfolio and a high quality, connected data environment. Once the foundational aspects for data governance are in place, ensuring modern systems and technology are the next step to ensure success. Those foundational aspects include data quality and proper methods of collection, identification, and classification. Many Departments of Transportation (DOTs) are currently assessing their technology portfolio and making decisions to modernize various systems to meet these demands and one key system at the core of all DOT’s is their construction management solution, or Advanced Digital Construction Management Systems (ACDMS).

ACDMS are transforming the construction industry by integrating cutting-edge technology into every phase of the project. These systems are central to DOT’s ability to deliver their respective construction programs. A primary key benefit is these systems enhance project efficiency by assuring quality of the project, streamlining project workflows, enabling real-time collaboration and communication between all project stakeholders, managing and auditing claims and payments to contractors to name a few. By enhancing project efficiencies and leveraging interconnected construction project data, stakeholders have increased transparency, accountability and scalability of project success.

By leveraging advanced data analytics, these systems, with the right set-up, have the capability to provide analytics that support the foundational elements of organizational data management as well as predictive analysis, performance optimization, and quality controls. To effectively support analytics, these systems must also be open and able to integrate with other key systems to leverage a connected data environment. For example, data from an ACDMS system and an Asset Management system can provide predictive analytics to stakeholders with real time operation data (condition, performance) combined with installation data (timing, materials) to help prioritize maintenance, and lifespan decisions. Imagine how quickly AI could look across data to provide a forensic analysis of material used in the past that is failing currently.

As organizations pursue artificial intelligence, the quality of the data in those systems is a crucial component. When data quality meets the standard for its intended use, it ensures that the data used for reporting, decision-making, and analysis is trustworthy and reliable. High data quality ensures that information is suitable for analysis, decision-making, risk management, reporting, and other data-driven activities.

The opportunity for artificial intelligence to improve projects within these systems will allow project owners to better understand how to manage risks better, provide better safety measures, and increase sustainability and compliance for projects. It's critically important that project owners begin to assess the interoperability of these various systems. As most organizations have learned, their analytics program will require data from multiple, disparate systems and ensuring those systems have a modern technology platform and reside in an open environment is of paramount importance. Using an open environment to support your operations is the most efficient way to scale as your needs grow and change. This open technology will provide for the flexibility and capability to integrate with other systems. without this, the mature analytics capabilities will be diminished.

Did you know that Infotech provides technical support for the AASHTOWare Project solution used by 80% of state DOTs? As we collectively pursue AI as the next technology advancement, working to keep this application current and modern is our goal. This will ensure DOT’s can leverage AI and other analytical capabilities as they become available to be as efficient and effective as possible in managing the transportation infrastructure.


Jim Ferguson
Associate VP of AASHTOWare Products Analysis and Support
Jim is a firm believer in collaborative and mindful communication between co-workers, clients, and customers to create quality deliverables. With over 35 years in both public and private sectors, Jim has experience in requirements analysis, software development, resource management, strategic implementation, and company collaboration efforts. Jim holds a Bachelor of Science in Agriculture from the University of Nebraska. While this degree may not seem to fit where Jim is today, it serves as a reminder that hard work and passion for that work determines your path, not a piece of paper.
Randy Lawton
Principal Architect: Data Science and Analytics
As Principal Architect for Data Science and Analytics, Randy Lawton works closely with Infotech’s data science team and AASHTOWare data analytics team to organize and explore a wide range of data, focusing on uncovering insights and tailoring results for specific business needs. Using his thirty years of experience providing data services, primarily related to highway construction, he establishes strong collaborative relationships with analysts, end-users, and engineering teams to turn data analysis into data solutions.