The Port Authority of New York & New Jersey (PANYNJ) Enterprise Asset Management (EAM) Program is focused on ensuring that every asset is digitally ready for operations and maintenance immediately upon project turnover. However, asset data handover from capital projects has historically been a challenging, labor-intensive process. Coordinating submissions across numerous stakeholders with domain expertise outside of data preparation often resulted in inconsistent, incomplete, and invalid datasets.

To address this challenge, PANYNJ partnered with TwinKnowledge via the Transit Tech Lab to explore how artificial intelligence could identify equipment from design drawings and extract tabular asset data. By piloting TwinKnowledge’s AI-powered drawing analysis capabilities, the Port Authority was able to dramatically reduce the manual effort required to identify individual assets resulting from project work, in addition to improving data quality and compliance with PANYNJ standards.

Preliminary testing from the pilot demonstrated that the TwinKnowledge solution identified asset records from drawings approximately 80-100 times faster than manual processes, demonstrating a scalable and cost-effective alternative to traditional asset data turnover.

Join us to learn more about this project, the outcomes and the Port Authority plans to continue evaluating and refining AI-enabled asset data turnover as part of its broader Enterprise Asset Management strategy.

Agenda:

• Organization Overview
• The Challenge
• The Solution
• Making AI Work
• Key Take Aways

How to Join

Microsoft Teams meeting

Join: https://teams.microsoft.com/meet/323333362538799?p=Bu0CxSccb2Up3TR5fH

Meeting ID: 323 333 362 538 799

Passcode: zj3Yn6DN