As climate change, regulatory demands, and infrastructure stressors intensify, the need for agile, intelligent, and integrated water systems has never been more dire. Following our deep dive into hard tech, we now turn to the digital layer – the suite of software-driven and AI-enabled tools that are reshaping how water is managed, monitored, and safeguarded.
These “soft tech” solutions don’t replace pipes, plants, or pumps – they supercharge them. From real-time data streams and predictive analytics to autonomous infrastructure management and cybersecurity protections for mission-critical systems, digital water innovation is emerging as a cornerstone of long-term water security. Unlike many hard tech solutions, these tools often boast faster paths to market, lower upfront capital needs, and scalable business models that appeal to both investors and adopters.
In a world where utility and industry leaders face increasing pressure to manage aging infrastructure, rising operational costs, and intensifying workforce shortages, AI and software tools are becoming indispensable. For a utility manager, this means doing more with less – such as equipping field service teams to operate lean and reduce downtime, leveraging advanced data analytics to identify and manage anomalies in real time, adequately preparing for extreme weather events, or safely remediating the public water supply in the case of a contamination incident. Farmers can monitor soil moisture remotely and know when their crops have been watered sufficiently to prevent water waste and crop losses. Moreover, project developers are starting to use machine learning to automate the design of key infrastructure like wastewater treatment plants, delivering necessary services much faster to communities that need them. Digital transformation across all of these sectors is essential, not just for the sake of keeping up with trends, but to solve real, tough problems.
Of course, the effectiveness of AI-based and other software tools requires access to clean, usable proprietary data. The water sector has historically relied on analog, siloed systems that are in urgent need of overhaul. AI can certainly accelerate that process – by digitizing paper records, integrating disjointed platforms, and converting unstructured data into actionable insights. There is a massive opportunity to fundamentally transform how water data is collected, organized, and put to work.
Below, we explore three critical soft tech domains gaining traction across utilities, heavy industry, agriculture, and the built environment: digital infrastructure management, predictive analytics, and cybersecurity for critical systems.
Modern water systems are undergoing a quiet digital revolution. Whether it’s a utility installing smart meters across its systems or a food processing facility deploying autonomous drones to monitor its discharge pipelines, the theme is the same: visibility, automation, and control.
Digital management platforms integrate data from sensors, meters, and physical infrastructure to deliver actionable insights. Combining AI capabilities and software with increasingly advanced hardware solutions allows utilities, industry leaders, and real estate managers to operate much more efficiently. Solutions like rapid leak detection in aging pipes, remote water quality monitoring, and unmanned drones that inspect remote or inaccessible infrastructure empower users with early alerts, hands-free management, and critical data-based insights that not only reduce response times and operational risk, but also cut costs and minimize disruptions.
Just last year, after managing severe water-main breaks, Atlanta launched a pilot AI system for real-time detection of pipeline leaks and established a task force to modernize the city’s aging water infrastructure. As new regulations mandate better visibility and reporting on lead service lines, major urban centers are increasingly relying on AI-enabled, less-invasive, less labor-intensive solutions. For example, Detroit reportedly saved $380M by using BlueConduit’s AI mapping tool to detect lead pipes.
By transforming raw data into strategic decisions, digital management enables smarter investments, faster responses, and a more resilient water system – benefiting everyone from public water system managers to private homeowners.
If smart hardware is the body, data and analytics are the brain. AI and machine learning models harnessing both historical and real-time data are increasingly being embedded into water management systems to optimize flow, predict failures and critical events, and forecast demand – in ways that were unimaginable just a few years ago.
Demand prediction is a long-standing need for public and private utilities, many now leveraging AI to model consumption trends, seasonal variations, and even customer behavior. These insights drive better decisions around sourcing, storage, and pricing of water resources. Cloud-based platforms, digital twins, and scenario modeling enable virtual replicas of real-world water systems to simulate various interventions and help identify bottlenecks before they arise. In San Antonio, Arcadis (a global design and engineering consultancy) supported SAWS in deploying AI-based failure-risk models using asset metadata, allowing the utility to schedule preventive repairs where most needed.
Predictive analytics are also transforming water use. In agriculture applications, AI models can integrate data from soil sensors, weather forecasts, and crop types to deliver water exactly when and where it’s needed – reducing waste, improving yields, and mitigating risk.
For infrastructure systems, predictive maintenance can reduce emergency repairs and extend the life of critical assets. Flood / storm prediction tools protect both infrastructure and at-risk communities from the severe impacts of climate change and extreme weather events. Among other innovative startups, Floodbase (formerly Cloud to Street) leveraged Yale research to develop real-time flood mapping using satellite data, hydrology, meteorology, and machine learning – supporting insurers, governments, and utilities around the world. The company has mapped over 500,000 square miles during the 2024 hurricane season alone, providing key data for parametric flood insurance, FEMA and municipal emergency response teams, and local community stakeholders to respond faster than ever before.
Beyond just enabling efficiencies, AI and advanced predictive modeling tools can deliver continuous, hyper-local insights with a potentially transformative impact on communities.
As water systems digitize and become more connected, they also unfortunately become more vulnerable. Software innovations in cybersecurity – including real-time threat detection, network segmentation, and automated response – are crucial to protect operations from cyberattacks.
Advanced cybersecurity software is increasingly being integrated into water control systems to monitor for anomalies, isolate threats, and respond in real-time. These tools leverage AI to detect patterns missed by traditional firewalls, allowing for better threat detection and mitigation. The EPA also provides guidance, targeted grants and other financial assistance to support utilities in increasing cyber resilience.
As cybersecurity risks rise, insurers are increasingly requiring proof of cyber resilience – including for water utilities, creating both risk and opportunity for innovators in this space.
Digital tools are helping utilities, industry leaders, farmers, and developers to gain better control over their resources. These solutions can often be adopted incrementally – layered into existing infrastructure with minimal CapEx and clear ROI. Utilities can incrementally add onto their foundational, physical systems – first with sensor and control mechanisms, then data digitization, collection, management, display, and analytics solutions, before advancing to AI-driven insights and prioritization goals. The same is true for digital transformations in other sectors like energy, manufacturing, agriculture, or real estate. For investors too, digital solutions make a compelling case: lower capital needs, shorter validation cycles, scalable models, and a broad base of enterprise and municipal customers with clear needs and increasing willingness to adopt.
