AEC AI IndexWater, wastewater, electric and gas utilities manage large networks of buried and overhead assets with long lives and limited budgets. AI tools for utilities mostly help with three things: finding problems earlier (leaks, failures, vegetation risk), planning where to spend capital, and turning imagery, sensor and inspection data into usable GIS and asset records. This guide groups the AI utilities design and planning tools listed in the AEC AI Index by the work they support. Tool names link to their directory pages, with category, pricing model and website.
Where AI fits in utility work
Utilities already collect a lot of data: SCADA and meter readings, CCTV inspection video, GIS records, work orders, aerial and satellite imagery. AI is most useful when it sorts that data faster than people can, for example flagging likely leaks, coding defects in inspection footage or ranking pipes by failure risk. It does not replace hydraulic modeling, engineering judgment or regulatory accountability. Treat AI output as a prioritized list for engineers and operators to check.
Water distribution: leaks, breaks and network operations
Non-revenue water and main breaks are where most water utility AI tools start. Fracta uses machine learning to estimate water main break likelihood and prioritize pipe replacement. ASTERRA and Rezatec use satellite data to point to likely underground leaks and pipeline risk, which narrows down where field crews search. FIDO Tech focuses on finding, sizing and prioritizing leaks, while TaKaDu applies machine learning to network data to flag leaks, bursts and meter faults and track each event to resolution.
For day-to-day operations, Qatium offers a browser-based digital twin to visualize the network and simulate closures and failures, and Xylem Vue combines analytics and digital twins for utilities. Timeseer.AI sits underneath these tools, validating sensor data and catching drift, flatlines and gaps before they reach billing, compliance reports or models.
Lead service lines and compliance
Lead service line inventories are a major workload for US water systems. BlueConduit uses predictive modeling to estimate which service lines are likely lead, and 120Water provides a data platform for inventories and sampling programs. For broader regulatory work, Klir centralizes sampling, permits, pretreatment and reporting for drinking water and wastewater utilities.
Wastewater, sewers and stormwater
Sewer CCTV inspection produces hours of video that must be coded to a standard before it is useful for planning. VAPAR and SewerAI automate defect coding of that footage, and RedZone Robotics pairs inspection robots with AI-assisted condition assessment. For live network monitoring, SmartCover Systems tracks manhole levels to help prevent overflows and find inflow and infiltration, and Kando monitors wastewater networks to trace pollution sources.
Flood and stormwater planning tools include Fathom (flood hazard maps and terrain data), 7Analytics (high-resolution pluvial flood risk) and Previsico (surface water flood forecasting with warnings up to 48 hours ahead).
Utility planning and design
AI is starting to help with capital planning and early design, not just operations. Optimatics optimizes water and wastewater capital plans and system design, Aquasight covers asset risk, capital planning and treatment optimization, and Transcend automates preliminary design of water and wastewater treatment plants. AquaTwin (Aquanuity) runs water and sewer network modeling inside ArcGIS Pro, with an AI agent that runs simulations and builds reports from plain-language requests. These sit alongside established, non-AI hydraulic models such as EPANET (US EPA) and Bentley OpenFlows, which remain the reference for detailed design.
Electric and gas utilities: vegetation, lines and assets
For electric utilities, vegetation management and line clearance are common AI use cases. AiDASH and Overstory use satellite and aerial imagery to prioritize trimming and reduce wildfire and outage risk. Neara and Sharper Shape build digital twins of transmission and distribution networks from LiDAR and imagery for clearance, resilience and asset management work.
Underground utilities, locating and GIS
Utility strikes during excavation are costly and dangerous. Urbint (Itron) risk-profiles 811 tickets to help prevent strikes, 4M Analytics builds subsurface utility maps from records, imagery and field data, and AiMaps (IDS GeoRadar) uses deep learning to detect buried utilities in GPR data. On the GIS side, Esri ArcGIS (GeoAI) provides GeoAI feature extraction and utility network analysis, and Bunting Labs (QGIS AI Vectorizer) helps digitize scanned maps into vector features, which is useful when old paper records are the only source.
How to evaluate AI tools for utilities
- Start with a known problem. Pick one measurable issue, such as water loss in a pressure zone or a CCTV coding backlog, and test against it.
- Pilot on data with known answers. Compare predictions to past breaks, confirmed leaks or coded inspections you already trust.
- Check integration. Results should flow into your GIS, asset management and work order systems with attributes intact.
- Ask about data ownership and security. Utility network data is often sensitive critical infrastructure information.
- Keep engineers in the loop. AI rankings guide inspection and spending; decisions and sign-off stay with qualified staff.
More resources
Browse the full lists in Water & Wastewater and GIS & Utilities, see non-AI hydraulic modeling, metering and GIS tools in Beyond AI, or read the companion guides on AI tools for civil engineers and reality capture for AEC.