Managing water supply from dams down to municipal taps demands rapid forecasting, forcing South Korea’s state-run water agency to overhaul its infrastructure with artificial intelligence.
Severe flood damage downstream during 2020 pushed Korea Water Resources Corporation, known as K-water, to move past historical climate patterns and adopt digital twins alongside machine learning models.
Climate volatility rendered decades of historical operating data unreliable for predicting sudden extremes in rainfall and drought. The agency established an AI Division in late 2025 to centralize efforts previously scattered across IT, civil engineering, and environmental departments. This unit aims to unify dam management, purification plants, and smart water distribution networks into a single operational framework.
Redefining data points for algorithmic consumption
Data readiness remains a primary bottleneck for deploying enterprise-scale neural networks across municipal infrastructure. K-water accumulated decades of water levels, rainfall metrics, and quality indicators designed strictly for manual monitoring dashboards rather than algorithmic consumption. Feeding these numbers into large models required redefining what individual data points represent and mapping their physical relationships.
“In the past, the purpose of our data work was to collect and store data and display it so people could understand it at a glance,” Ha-Dong Jung, VP of K-water’s AI division, told cio.com.
“Today, that accumulated data is just a collection of numbers. To use it for AI, we need to define what each piece of data means and how the different data points relate to one another.”
Connecting upstream rainfall metrics to downstream treatment output required investing in an ontology and Retrieval-Augmented Generation systems. Heavy data extraction schedules initially caused legacy monitoring equipment to freeze under the unexpected load. Engineers responded by upgrading collection hardware and establishing redundant data pathways before planning a dedicated AI data center in Sejong by 2030.
Bridging proprietary tools with global generative models
Internal adoption required bridging the gap between proprietary enterprise tools and global generative models used daily by staff. K-water initially deployed an in-house assistant dubbed K-water GPT using Meta’s Llama architecture, continuously updating the underlying weights. Employees continually benchmarked the internal utility against external alternatives like ChatGPT, Gemini, and Claude.
“Employees already judge AI against global services such as ChatGPT, Gemini, and Claude,” Jung told cio.com. “It’s difficult to keep up with the pace of development in global AI models through in-house development alone, so we introduced external services that allow employees to choose among major AI models as needed in an environment with enhanced security.”
Isolating the internal work network from the public internet created friction when processing internal documents through external generative platforms. K-water engineered secure network bridges to let employees query proprietary files safely. Broad initial usage caps were eventually restructured into individual limits to manage escalating token and compute expenses.
Matching administrative timelines to model updates
Public enterprise procurement schedules clash directly with the rapid release cycles of foundational AI architectures. Traditional IT project pipelines span six months of preparation, a year of implementation, and a six-month closeout phase. Model capabilities shift multiple times during a single procurement window, prompting discussions around granting internal staff direct operational control.
“Things have changed by the time you finish preparing, and they change again while you’re working your way through implementation,” Jung told cio.com. “Over the longer term, I think we may need to create an environment where our own employees can respond directly, rather than continuing to rely entirely on outside contractors.”
Despite bureaucratic hurdles, OpenAI highlighted the agency as a notable case study in disaster response applications. Jung joined the organization in 1997 and witnessed its technological evolution from basic monitoring dashboards to predictive neural networks.
