AI/Computer Vision for Sewer Workforce Development

The January 2022 issue of APWA Reporter features an in-depth article by Gregory Baird, MPA, M.aff ASCE, AWAM, President of the Water Finance Research Foundation, examining how artificial intelligence and computer vision technology can address some of the most pressing workforce challenges facing sewer departments today.

The Workforce Crisis in Sewer Operations

Artificial intelligence can sound like a lot of hype—even "fake"—until it delivers practical benefits for the public works director. But as Baird argues, AI is now offering real, measurable assistance in workforce development: from training and retention to labor savings on the repetitive, high-stakes tasks where accuracy directly prevents infrastructure failures and saves money.

The sewer department faces a unique convergence of challenges. IT teams complain about the lack of data storage for endless CCTV video footage. Staff raise concerns about the quality of video submitted by CCTV contractors, yet lack the time to conduct proper audits. And looming over all of it is a generational workforce transition: veteran staff are announcing retirement plans while younger candidates are jumping ship to neighboring tech-savvy utilities.

The NASSCO PACP Challenge

One of the most significant technical hurdles in sewer inspection is the NASSCO Pipeline Assessment Certification Program (PACP) coding system. CCTV operators are required to memorize and correctly apply 226 distinct defect codes when reviewing pipe inspection footage. The learning curve is steep, and the consequences of errors are serious.

Research cited in the article points to a 20% error rate among CCTV operators who miss sewer and lateral pipe defects during manual review. When an entire sewer collection system's rehabilitation program depends on correct defect labeling to prioritize repairs and allocate budgets, a one-in-five miss rate represents a significant risk—both to infrastructure integrity and to ratepayer dollars.

All of this is compounded by affordability concerns that suppress needed rate increases, leaving utilities to do more with less at precisely the moment when experienced staff are walking out the door.

How SewerAI Addresses These Challenges

SewerAI's AI and computer vision platform is purpose-built to tackle these workforce and data quality challenges head-on. By automating the detection and coding of pipe defects from CCTV inspection video, SewerAI reduces the cognitive burden on operators, improves coding consistency, and enables utilities to process more footage with fewer staff.

Key ways SewerAI's technology addresses the workforce development problem include:

• Automated defect detection: AI-powered computer vision identifies and flags pipe defects in CCTV footage, reducing reliance on operators memorizing all 226 NASSCO PACP codes.
• Improved data quality: Automated review catches defects that manual operators miss, directly addressing the documented 20% error rate in CCTV inspections.
• Contractor audit support: AI-assisted review enables utilities to efficiently audit CCTV contractor submissions, ensuring quality without requiring dedicated staff hours for every video.
• Training acceleration: New staff can be onboarded more quickly when AI assists with defect identification, shortening the learning curve associated with PACP coding.
• Labor savings: By automating repetitive, time-intensive video review tasks, utilities can redeploy staff to higher-value work—a critical advantage as experienced personnel retire.

A Practical Path Forward for Public Works

Baird's article makes the case that AI in the sewer department is no longer a futuristic concept—it is a practical tool available today. For public works directors navigating budget constraints, workforce shortages, and aging infrastructure, computer vision technology offers a way to maintain and even improve service levels without proportionally increasing headcount.

The rehabilitation of a sewer collection system is only as good as the data driving it. When defect coding is inaccurate, repair priorities are misaligned, budgets are wasted, and failures go undetected. AI-assisted inspection review directly strengthens the foundation of any capital improvement program.

Read the full article in the APWA Reporter, January 2022 issue.