AI in water management: Breakthrough tool or overstated innovation?

Utilities across the globe are investing in machine learning models to detect leaks, forecast consumption, optimize chemical dosing and automate control decisions. However, as the technology spreads, a key question persists: are these AI systems delivering measurable sustainability gains, or are they primarily improving model performance without transforming real-world outcomes?

The study AI Solutions for Improving Sustainability in Water Resource Management, published in Sustainability, offers a systematic, evidence-based examination of AI applications across the water sector, shifting the focus from algorithmic novelty to verified environmental, economic and governance impact.

Measuring sustainability, not just model accuracy

The study adopts a PRISMA 2020-compliant systematic review methodology to ensure transparency and reproducibility. An initial database search returned 920 records. After removing duplicates and applying eligibility criteria, 41 peer-reviewed studies were included in qualitative synthesis. Seven additional grey literature sources were incorporated to provide governance and sectoral context.

The author reframes the evaluation around sustainability outcomes. Environmental metrics include reductions in non-revenue water, lower energy consumption per cubic meter treated, optimized chemical dosing and decreased emissions. Economic and operational indicators include cost efficiency, reduced downtime and improved reliability. Governance considerations extend to cybersecurity, privacy, human oversight and accountability in critical infrastructure.

Many AI studies report high predictive accuracy, improved anomaly detection rates or lower forecasting errors. However, these metrics do not automatically translate into measurable reductions in water loss, energy use or operational costs.

To address this, the study implicitly differentiates between maturity levels of deployment. Applications that have been implemented at utility scale and evaluated against sustainability outcomes carry greater evidentiary weight than simulation-only studies. A significant portion of the literature remains confined to laboratory validation or retrospective analysis, revealing a persistent gap between technical feasibility and field-proven sustainability gains.

The review also observes that research activity accelerated sharply after 2020, reflecting growing industry and academic interest. Distribution networks and wastewater operations emerge as particularly active domains, often influenced by a limited number of technology vendors and innovation hubs.

Where AI delivers verified gains

Despite the translation gap, the review identifies several application areas where AI demonstrates strong near-term promise.

• Leak detection and burst identification in distribution systems are among the most operationally mature use cases. Machine learning models trained on flow, pressure and acoustic data can flag anomalies that signal pipe failures or hidden leaks. When these detection systems are integrated into maintenance workflows and supported by field verification, utilities can reduce non-revenue water and limit infrastructure damage.
• Short-term demand forecasting is another high-readiness application. Accurate predictions allow utilities to optimize pump scheduling, balance supply and demand and reduce energy intensity. The sustainability benefit emerges not from the forecasting model alone but from its integration into operational decision-making that adjusts production planning and system pressures.
• Anomaly detection in smart metering also offers tangible gains. By identifying unusual consumption patterns, utilities can detect leaks at the household level or prevent billing irregularities. However, The author emphasizes that privacy safeguards are critical in this context, as granular consumption data can reveal behavioral patterns.
• In wastewater and drinking water treatment plants, AI-based process optimization has shown potential to reduce aeration energy, fine-tune chemical dosing and improve compliance margins. These gains are especially relevant given that energy use in treatment facilities represents a major operational cost and environmental footprint. Yet regulatory environments require interpretable and constraint-aware systems, not black-box models that deliver outputs without causal transparency.

Across these applications, the paper highlights a key principle: sustainability impact arises when AI models are embedded within full operational loops that include monitoring, decision adjustment, verification and feedback. Without this integration, improvements in model metrics remain isolated achievements.

Digital twins, reinforcement learning and governance risks

The study focuses ondigital twins and reinforcement learning, two areas frequently portrayed as transformative.

Digital twins integrate sensor data, predictive models and control systems into a unified representation of a water network or facility. In theory, they enable real-time optimization and scenario testing. In practice, The author finds that definitions and implementation practices vary widely. Many digital twin projects remain in pilot stages or lack rigorous validation against sustainability outcomes.

For digital twins to deliver credible impact, they must meet system-level requirements. These include reliable data synchronization, calibration protocols, uncertainty management and clear decision integration pathways. Without these elements, digital twins risk becoming visualization tools rather than operational engines of sustainability.

Reinforcement learning presents another frontier. RL systems can, in principle, learn optimal control policies for reservoir releases, pump operations or treatment adjustments under uncertainty. However, the review notes that most reinforcement learning studies remain simulation-based. Field deployment introduces safety constraints, regulatory approvals and real-world variability that laboratory environments cannot fully replicate.

The governance dimension emerges as a recurring theme. Water systems are critical infrastructure. AI interventions that improve efficiency but introduce cybersecurity vulnerabilities, privacy risks or opaque decision-making processes may undermine public trust and system resilience.

The author notes that relatively few studies explicitly address cybersecurity safeguards or operational technology risk management. As utilities digitize control systems and expand remote monitoring, these risks become central to sustainable deployment.

The study also stresses the importance of measurement-and-verification frameworks. Claims of energy savings or water loss reduction must be backed by before-and-after comparisons or controlled evaluations. Procurement processes should align AI deployment with measurable sustainability indicators, interoperability standards and documentation of failure modes.