AI Agent Operational Lift for Grand Strand Water & Sewer Authority in Conway, South Carolina

Why AI matters at this scale

Grand Strand Water & Sewer Authority (GSWSA) serves a rapidly growing coastal region, managing water treatment, distribution, and wastewater collection for communities across Horry County, South Carolina. With 201–500 employees and an estimated $75 million in annual revenue, the authority operates at a scale where infrastructure complexity and customer expectations are rising, but resources for large-scale digital transformation remain limited. AI offers a pragmatic path to do more with existing assets—reducing costs, improving service reliability, and meeting regulatory requirements without massive capital outlays.

Three concrete AI opportunities with clear ROI

1. Predictive maintenance for critical assets
Water mains, pumps, and lift stations are expensive to repair after failure. By applying machine learning to SCADA sensor data (vibration, pressure, flow), GSWSA can predict equipment degradation weeks in advance. This shifts maintenance from reactive to planned, cutting emergency repair costs by up to 30% and extending asset life. The ROI is immediate: fewer overtime hours, lower contractor fees, and reduced water loss from main breaks.

2. Leak detection and non-revenue water reduction
Non-revenue water—lost through leaks or meter inaccuracies—can exceed 15% in aging systems. AI models trained on flow and acoustic data can pinpoint leaks with high accuracy, minimizing excavation and guesswork. A 5% reduction in water loss could save hundreds of thousands of dollars annually in treatment and pumping costs, paying for the AI system within the first year.

3. Customer service automation
GSWSA fields thousands of billing, outage, and service inquiries monthly. An AI-powered chatbot integrated with the customer information system can handle routine questions 24/7, deflecting up to 40% of calls. This frees staff to address complex issues and improves customer satisfaction with instant responses, all while containing support costs.

Deployment risks specific to this size band

Mid-sized utilities face unique hurdles. Legacy SCADA and GIS systems may lack modern APIs, requiring middleware to extract data. Staff may resist AI due to fear of job displacement, so change management and upskilling are critical. Data quality is often inconsistent—sensors may be uncalibrated or records incomplete—leading to unreliable models if not addressed upfront. Cybersecurity is paramount; connecting operational technology to AI platforms expands the attack surface. Finally, limited in-house data science expertise means GSWSA should consider partnering with a specialized vendor or leveraging cloud-based AI services with pre-built models for water utilities. Starting small with a single high-impact use case, proving value, and then scaling is the safest path to AI adoption.