Artificial intelligence, or AI, has entered the sewer market in a big way. Not only is it expediting sewer inspection workflows, it’s providing an avenue for data to be organized, analyzed and updated automatically. The rippling effects of AI-powered defect coding alone are quickly changing the way municipalities approach sewer maintenance and asset management. And with ROI often top of mind for most municipal sewer teams, WinCan’s Sewermatics is already delivering positive results for AI defect coding projects.
Focus on Improving Specific Areas of Your Workflow
When shopping for the sewer inspection AI that best suits your team, think about which areas of your workflow AI will need to support. The current state of AI has the potential to positively impact efficiency in seemingly unrelated ways. For instance, AI defect coding may not directly incorporate machine learning into a GIS map, but the data that is processed by the AI ensures GIS maps can be updated in real-time, providing an accurate, reliable and complete database to tap. Such effects may seem small, but they are numerous, and each enhances remote collaboration between inspection teams as well as everyday field operations. Approach AI with an idea in mind of how you hope it will improve different parts of your team’s workflow.
Be Mindful of Integration Capabilities
From collection to analysis and reporting, sewer inspection data needs to travel between multiple locations and export into deliverables that are comprehensive and accurate. AI may not be directly involved in every step of the inspection process, but your team needs to be able to get process data into your asset management and GIS databases. WinCan’s cloud ecosystem includes everything from project creation to editing and reporting, and the data created and stored in the cloud can quickly be transferred to any one of WinCan’s integration partners, such as Esri, CentralSquare, Cityworks and Cartegraph. To get the full value of your processed data, ensure the AI you look at has the ability to move data to and from your asset and GIS database.
Calculate Return on Investment
While AI is still an emerging technology, municipal sewer inspection teams have already begun to invest in wastewater AI as a solution to backlogged coding and other data processing and management issues. Not only are teams seeing a strong return on investment in resource cost as teams get work done more quickly, they’re also saving valuable time that can be spent on other projects, compounding ROI. This is sustainable efficiency for operators and engineers alike, and at WinCan, we’re already seeing it among our customers.
Sewermatics AI Case Study
Problem: A southern city of 65,000 people with 55,000 manholes and 3,000 miles of city-maintained sewer lines experienced a staffing shortage which caused a backlog of 2,000+ manhole coding tasks. Additionally, manholes were being coded to level 1 due to bandwidth constraints, which means the data provided was less thorough and provided fewer insights.
Solution: Utilizing Sewermatics’s AI defect coding, the city was able to offload more than half of the backlog, saving time and improving the quality of its manhole inspections to a level 2.
Metrics:
- On its own, the city can complete inspections for 70-100 manholes/week, requiring a minimum of 5 months to work through the backlog if no additional bandwidth is added.
- With Sewermatics, municipal teams are inspecting 350-450 manholes/week, requiring only 1 month to work through the entire backlog.
- That’s an 80% time savings, with the AI completing the job in one-fifth of the time it takes the city to do it alone.
Summary: Time savings of 80% and overall quality improvement of inspections have yielded better insight on infrastructure.
Schedule a consultation to learn more about how Sewermatics’ AI-power data services are helping municipalities around the world:
