AOM Implementation for Water Utilities A Strategic & Technical Guide

“Think Big” - AOM Vision for Unified Water Operations:   

“Start Small” - Head-Pin Use Cases with High-ROI:           

Mapping Dependencies & Synergies Across Use Cases            

Integration with Existing OT/IT Systems       

Change Management for Workforce Adoption        

“Scale Fast” - Roadmap for Expansion & Continuous Improvement: 

Business Outcomes & ESG Impact          

Introduction

Implementing Asset Operations Management (AOM) in a water utility, from treatment plants to distribution networks, requires a holistic, phased approach. AOM is an integrated strategy that unifies asset performance, process control, and operational efficiency under a composable digital twin and industrial AI framework. AOM goes beyond traditional Asset Performance Management (APM) by bridging maintenance, control, and operations domains – ensuring that asset health improvements translate into real operational gains, such as reliable water supply. This guide introduces the “Bowling Strike” methodology – “Think Big, Start Small, Scale Fast”, for water utilities. We will define an ambitious end-state vision, identify high-impact “head-pin” use cases to pilot, and lay out a roadmap to rapidly scale across the water supply and distribution value chain. Along the way, we integrate the 4C design principles - Composable, Contextual, Cognitive, Collaborative, enabled by composable digital twins and agentic AI, to drive modular, intelligent, and collaborative solutions. The approach addresses both executive priorities (ROI, ESG outcomes) and technical considerations (architecture, integration, data) to ensure a sustainable digital transformation for water utilities.

 “Think Big” - AOM Vision for Unified Water Operations:

Water utilities define a 3-5 year vision for an integrated, end-to-end AOM platform across water supply and distribution. The envisioned end state is a unified Water Operations Command Center where AI co-pilots/auto-pilots and digital twins span the entire water network, from treatment plants and pumping stations to storage reservoirs and distribution zones. All critical assets and processes are continuously monitored by digital twins that mirror real-time conditions, and industrial AI agents proactively optimize operations and maintenance in concert. Key elements of this vision include:

• Holistic Situational Awareness: A digital twin ecosystem provides real-time visibility of plant performance, pump statuses, reservoir levels, network pressures/flows, and water quality in one unified view. This breaks down silos between treatment operations, network distribution, and maintenance teams, enabling coordinated decisions, for example, aligning plant output with network demand and storage capacity. Operators see the “single source of truth” for system conditions, enhancing system-wide awareness.
• AI-Powered Decision Making: AI agents act as “industrial autopilots” for water operations. For example, they can recommend optimal pump schedules and speeds to balance supply with energy efficiency, adjust valve setpoints to maintain pressure and reduce leaks, or advise when to perform maintenance to minimize service impact. These agents employ both predictive models, e.g., forecast demand or equipment failure, and prescriptive intelligence, e.g., optimal control policies. Operators and engineers collaborate with the AI agents, intervening as needed, but otherwise benefit from automated optimizations.
• Seamless IT/OT/IoT Integration: Data flows seamlessly from Operational Technology (OT) systems like SCADA (plant control systems, pump station PLCs) and IoT sensors (pressure, flow, quality monitors) into the AOM platform, where it’s fused with Information Technology (IT) data like maintenance records (CMMS), GIS asset data, and customer service portals. This integration ensures a Common Operating Picture,  and enables AI agents to factor in both equipment condition and operational context when making decisions. For instance, an AI agent scheduling pump operations will consider real-time pump vibration trends and the maintenance history of that pump.
• Aligned with Business Outcomes: The AOM vision explicitly targets key business KPIs, O&M metrics, and ESG goals – ensuring technology serves tangible outcomes. For a water utility, this means maximizing reliability and service (e.g. minimal supply interruptions and water quality compliance), minimizing water losses and energy cost, and improving sustainability (energy efficiency, carbon footprint, water conservation). For example, the AI-assisted operations center aims to reduce non-revenue water and energy per million gallons delivered, improve asset uptime, and cut unplanned outages – directly contributing to financial performance and ESG targets (AOM Bowling Strike Approach.pdf). The vision of a digitally integrated, AI-optimized water system supports corporate goals like water supply resilience, operational efficiency, and carbon neutrality.

By articulating this ambitious end-state vision up front, leadership can secure buy-in and ensure chosen technologies (digital twin platform, AI/ML tools) are scalable to eventually cover the full water network. This “big picture” guides implementation: even as we start with small pilots, each step will build toward the unified command center concept.

“Start Small” - Head-Pin Use Cases with High-ROI:

With the long-term vision in mind, the next step is to identify and launch pilot use cases, the initial “bowling pins” to knock down, that demonstrate quick wins in a controlled scope. In a water utility context, we select head-pin use cases across the supply & distribution chain that have clear ROI, manageable scope (3-6 month implementation), and cross-functional synergies, bridging maintenance, operations, and planning. These pilots will serve as proofs-of-concept for AOM, showing how unified asset-and-operations management yields tangible benefits. Key head-pin candidates include:

• Pump Energy Optimization: Pumping often accounts for the majority of a water utility’s energy use. Inefficient pump operations lead to excessive energy cost and even risks like overflowing tanks or unmet pressure requirements. In this use case, AI-driven optimization, using machine learning or even reinforcement learning,  adjusts pump schedules and speeds to meet demand at minimal energy cost, while respecting constraints (tank levels, pressure). The pilot could focus on a major pumping station or treatment plant pumps. ROI: Energy savings directly reduce operating cost and carbon emissions – studies show optimized pump control can cut energy costs by ~30% and reduce CO2 emissions by tens of tons annually. Additionally, smoother pump operation reduces wear and unplanned outages, improving asset life.
• Leak Detection & Pressure Management: Reducing non-revenue water (NRW) loss is a high-impact goal. This pilot would deploy a combination of sensor analytics, hydraulic modeling, and AI to detect leaks early and optimize network pressure. For example, a “digital twin” of a distribution zone can continuously ingest pressure/flow data and use anomaly detection to flag probable leaks or bursts. AI agents can also recommend pressure setpoint adjustments, or control pressure reducing valves, to minimize excess pressure, which often causes leaks without compromising service. ROI: Water saved from early leak detection has direct financial and environmental benefits. Globally, ~30% of drinking water is lost in distribution; AI-based leak detection can reduce that loss by up to 50%. Early leak repair avoids major main breaks, preventing costly emergency repairs and service outages. Pressure optimization further lowers leakage rates, e.g., a 10% pressure reduction can cut leakage by ~20-30%. Notably, an AI-driven NRW reduction program in one utility achieved 75% reduction in water loss and 33% reduction in energy use for pumps, by eliminating wasted pumping of lost water, while pipeline breakage incidents dropped by 300%. This freed up water to serve new customers and improved network resilience. Such quick wins in water loss reduction make a compelling case to expand across the network.
• Water Quality Assurance: Ensuring safe, high-quality water is mission-critical for any utility. This use case leverages digital twins and AI to monitor and maintain water quality from plant to tap. For example, an AI agent might integrate data from online water quality sensors (chlorine, turbidity sensors, etc.), lab results (LIMS), and network models to detect water quality deviations or predict where issues might arise, e.g., risk of low chlorine residual or contamination events. The system could provide early warnings and prescriptive actions, e.g., advising operators to flush a particular pipe segment or adjust treatment dosing if water age is high in a zone. ROI: Though harder to quantify in direct dollars, the value is in risk reduction and compliance. Early detection of contamination or quality degradation protects public health and avoids regulatory fines or reputational damage. For instance, AI agents monitoring a river or reservoir can detect pollutant spikes and trigger containment actions. In distribution, maintaining optimal disinfectant levels while minimizing disinfection byproducts is a balancing act that AI agents can help optimize continuously. Successful pilots here ensure water quality compliance 24/7, building trust in AI agents for critical safety tasks.
• Intelligent Asset Maintenance: This pilot targets a critical asset, or set of assets, like high-lift pumps or valves, to demonstrate how predictive maintenance integrated with operations can prevent failures. Sensors (vibration, temperature) and ML models predict pending failures or condition decline; the AOM system then proactively schedules maintenance at an optimal time, coordinating with operations to minimize impact on supply. It may also automatically generate work orders in the CMMS and guide technicians with AR/AI support. ROI: Avoiding a single major pump failure or main break can save hundreds of thousands in reactive repair costs and prevented downtime. Predictive maintenance improves asset availability and extends equipment life, contributing to higher overall system uptime. Additionally, coordinating maintenance with operations, e.g., taking a pump out of service when demand is low, and redundancy is available, ensures reliability of water delivery is not compromised. This pilot would show how an AI agent can bridge maintenance and operations – a hallmark of AOM.

Each of these head-pin projects should be scoped to 3-6 months implementation in a contained area, one plant or one district zone, to prove value quickly. They also overlap in meaningful ways – for example, a leak detection pilot deploys pressure sensors and analytics that could be reused for pump optimization and vice versa. By selecting high-impact, achievable pilots, we secure quick wins: energy cost saved, water loss reduced, etc., that demonstrate AOM’s unified approach. These wins build stakeholder confidence and generate momentum and budget for scaling to additional use cases.

Mapping Dependencies and Synergies Across Use Cases

Just like a bowling strike, knocking down the head-pin use cases will help topple adjacent opportunities. It’s important to map how these pilots complement each other and create a foundation for broader rollout:

• Shared Data & Models: The pilots will generate data streams and models that can be composed into a larger digital twin. For example, the hydraulic model and pressure sensor network used for leak detection can also feed the pump optimization algorithms, to inform it of network conditions, and vice versa, the optimized pump operations will create stable pressures that reduce new leaks. Similarly, asset health data from the maintenance pilot can inform operational decisions, e.g., avoid stressing a pump known to be in poor condition. Designing pilots with open data integration ensures the resulting systems aren’t siloed. This is where the Composable principle comes in: each use case’s digital twin component, be it a pump station model or a network zone model, should adhere to standards that allow them to plug into a wider system-of-systems later.
• Logical Sequence: Some use cases naturally pave the way for others. For instance, one might start with pump optimization and basic pressure management to stabilize the system, which then makes advanced leak detection easier. Or begin with anomaly detection in leaks/quality to establish trust in AI recommendations, then progress to letting AI agents take direct control in optimization tasks. Mapping these dependencies helps in planning an efficient sequence of deployment, ensuring early pilots remove barriers for subsequent ones. The goal is to avoid tackling highly complex, interdependent problems at once; instead, solve one to make the next easier. In our example, reducing background leakage and pressure fluctuations (Pilot 1) can make the distribution network more predictable, which improves the accuracy of water quality predictions (Pilot 2) because hydraulic conditions are steadier.
• Technology Reuse & Modular Expansion: A composable digital twin architecture means that adding new assets or zones is faster after the first pilots. If the pilot in one district showed success, the same blueprint - sensor types, AI models, integration approach, can be replicated to adjacent districts, like setting up additional “pins”. For example, after one pump station is optimized, the AI scheduling agent can be duplicated and configured for the next pump station with minimal effort, since the underlying platform and data pipeline are already in place. This modular approach accelerates scaling, rather than treating each new deployment as a from-scratch project. It’s akin to developing a library of digital twin components and AI skills during pilots that can be reused enterprise-wide. This addresses the Contextual and Composable principles: solutions developed in context of one part of the system are packaged so they can be applied in another context, with appropriate configuration.
• Unified Visualization & Interaction: During pilots, each use case might have its own visualization or recommendation system. Part of mapping synergy is planning how these will converge. Ultimately, operators prefer a single unified view, such as a few role-based dashboards, rather than separate interfaces per use case. Early on, define how the insights from pump optimization, leak detection, and quality monitoring will feed into one Operations Center view. For example, a leak alarm and a pump efficiency alert should both surface in the command center with appropriate context, perhaps categorized by type. This way, when scaled up, the utility isn’t left with disjointed point solutions, but an integrated Collaborative environment where cross-domain teams share real-time situational awareness. Collaborative agentic workflows might include automatic notifications, e.g., the leak detection agent not only alerts control room operators but also notifies field maintenance crews with probable leak location and size, as determined by the AI. This cross-functional coordination, even at pilot stage, demonstrates the power of AOM to break silos.

By mapping these interdependencies up front, the water utility can ensure that each pilot builds toward the unified end-state rather than creating new silos. The pilots collectively establish the core AOM platform capabilities: data ingestion, models, user interfaces, AI agents, which subsequent deployments will leverage. Essentially, we are engineering a controlled domino effect: the momentum from one success accelerates the next, and the cumulative outcome is greater than the sum of individual projects. This systems perspective will help avoid the common pitfall of fragmented digital initiatives and instead drive a coherent program aligned to the big-picture vision.

Integration with Existing OT/IT Systems

A successful AOM implementation must integrate with the utility’s existing Operational Technology (OT), Information Technology (IT), and IoT infrastructure. Rather than rip-and-replace, the approach should be to overlay AOM capabilities on top of legacy systems, gradually enhancing them. Composable digital twins in AOM for water utilities integrate/combine physical systems through sensors, actuators, SCADA, data platforms, analytics platforms with data-driven ML and physics-based hydraulic models, and visualization/UX tools. Such composable architecture enables a secure, connected operation that delivers actionable results, optimized performance, and informed decisions.

Data Integration Layer: Water utilities typically have SCADA systems monitoring treatment plant processes and network operations (pressures, flows), as well as IoT deployments like smart meters (AMI/AMR) and remote sensors on critical points. All these real-time OT data streams need to be ingested into the AOM platform. In parallel, enterprise IT systems hold valuable data: asset registries and maintenance logs in CMMS/EAM, customer data and consumption patterns in CIS, hydraulic models from engineering software, GIS maps of the network, and perhaps ERP data on costs. AOM’s digital twin platform acts as the integration hub, pulling data from these sources into one unified model. The water network twin continuously absorbs sensor telemetry (pressure monitors, flow meters) and calibration data, keeping a high-fidelity living model of the system. This real-time data integration architecture should include connectors or APIs for SCADA (OPC UA, historian databases), GIS databases, IoT platforms, and so on.

Technology Stack: A composable approach allows using microservices or modular platforms for different functions. For instance, one might use a hydraulic simulation engine, such as EPANET, for physics-based modeling, a machine learning service for demand forecasting or anomaly detection, and a business intelligence tool for planning  – all orchestrated under the digital twin umbrella. The 4C principle of Composable means each component (simulation, ML model, etc.) is encapsulated and can be upgraded or replaced without overhauling the whole system. Cloud infrastructure or hybrid cloud can be leveraged to scale data processing and AI workloads, for example, deploying the leak detection twin on a private cloud to manage heavy analytics and ensure accessibility for users. Importantly, the integrated design should ensure real-time or near-real-time data flow for operational decisions, while also maintaining historical data for trending and training AI models.

Legacy System Coexistence: AOM should augment not disrupt. Operators will still use SCADA HMI for direct control, maintenance crews will still use CMMS for work orders – at least initially. The AOM platform can push recommendations or alerts into those existing tools. For example, if the AI detects a pump’s condition deteriorating, it could auto-create a maintenance work order in the CMMS and notify the maintenance planner. Or an AI-recommended pump schedule could be sent to the SCADA control system for execution (closed-loop control) with operator supervision. Integration may involve custom adapters, e.g., linking a digital twin’s recommendations to a SCADA control setpoint requires an OPC connection and ensuring any autonomous action has failsafes. On the IT side, linking with GIS means the digital twin can reference the network’s spatial model; linking with customer billing data (CIS) might allow the AI to correlate pressure zones with customer complaints or usage patterns. All integration points should be secured and tested for reliability, as they form the “digital nervous system” of the utility.

Cybersecurity & Data Governance: With greater connectivity comes risk, so the integrated architecture must follow strong security practices. This includes network segmentation between IT and OT (using DMZs for data transfer), encryption of sensor data streams, authentication for system access, and rigorous user permission controls, especially if AI agents can issue control commands. The digital twin platform should maintain audit logs of data and decisions – this not only aids cybersecurity but also helps engineers trust AI by reviewing its actions. Data governance policies should define data ownership, data quality responsibilities, and backup/recovery plans for this integrated environment. A secure, well-governed integration builds confidence for scaling up AOM, as stakeholders see that it enhances the existing systems without exposing the utility to undue risk.

In summary, integration is about making data fluid and usable across the organization. By interfacing AOM with SCADA, telemetry, GIS, CMMS, and more, the utility creates a cohesive digital thread. This allows the AI and digital twin to be contextually aware, for example, a pump’s control parameters are considered in context of its maintenance history and the network’s current state. When done right, this seamless integration ensures AOM becomes the “brain” orchestrating inputs from all “nerves” (sensors, and systems) to drive optimal decisions, while operators and engineers maintain oversight and control.

Change Management for Workforce Adoption

Technology alone cannot achieve the AOM vision – people and processes are equally critical. Change management ensures that the workforce, from control room operators and field technicians to engineers and managers, embraces the new AOM solutions and workflows. Key strategies include:

• Early Engagement & Training: Involve end-users from the pilot stage onwards. For example, have control room operators help design the unified dashboard – this gives them ownership and eases adoption later. Provide training that demystifies AI and digital twins: how they work, what outputs to expect, and how operators can interact with AI agents. Hands-on workshops where operators run scenarios with the digital twin, for example, simulate a main break and see AI recommendations, can build trust in the system. By the time the system goes live, users should feel comfortable and see it as an aid, not a threat.
• Clear Roles: AI as Collaborators, Human in Command: Emphasize that AI agents are for decision-support, decision-augmentation, or decision-automation when proven reliable and safe, and human expertise remains vital. Establish operating procedures for the human-AI collaboration. For instance, an AI may implement pump adjustments automatically within a certain range, but anything beyond that triggers a human confirmation. Communicate success stories where the AI agent helped avert a problem or saved effort, crediting the team that worked with it. This helps reposition staff from being doers of rote tasks to strategic overseers. The workforce will become a champion of AOM rather than a resistor when they see it augments their capabilities.
• Iterative Change & Feedback Loops: Use the phased nature of the Bowling Strike approach to gradually acclimate staff. After each pilot, gather feedback from users: What worked? What frustrated them? For example, maybe field crews find the leak detection AI alerts too frequent or not sufficiently vetted – this feedback can be used to adjust threshold settings or provide better context in the alert. By addressing concerns in early phases, you pave the way for smoother adoption when scaling up. Implement a change advisory group or “AOM champions” team that includes experienced operators and technicians who advise on the rollout and advocate among peers.
• Workflow & Organization Alignment: Adopting AOM may blur traditional departmental boundaries – maintenance and  operations need to work in tighter coordination. This might require process changes: e.g., instituting joint daily meetings between maintenance planners and operations to review AI recommendations, or cross-training staff on both asset management and operations. Leadership should adjust KPIs and incentives to encourage collaboration, for instance, operations and maintenance could share a common KPI like overall downtime or water loss reduction, reinforcing that they must work together with the AOM to achieve it. Change management should address any “not my job” sentiments by reinforcing a culture that values shared outcomes over siloed responsibilities.
• Communication & Executive Sponsorship: Continuous communication about the AOM program’s goals, progress, and wins is essential. Celebrate the quick wins from head-pin pilots, e.g., announce how the AI leak detection pinpointed a leak that would have gone unnoticed, saving X liters of water. Tie these stories to the broader mission, ensuring reliable water for the community, being environmental stewards, etc. Executive leaders should visibly champion the initiative, acknowledging employees’ efforts in adapting and highlighting how it makes the utility a technology leader, which can be a point of pride. Frontline staff seeing top-level commitment and hearing consistent messaging about “why we’re doing this” will be more likely to get on board.

In essence, change management for AOM is about fostering a Collaborative culture – collaboration between humans and AI, and across separate teams. When people understand that AOM will make their jobs safer (fewer emergency repairs), easier (automation of drudgery), and more meaningful (focus on higher-level problem solving), they will champion it. Adequate training, involvement, and alignment of goals ensure that the AOM implementation is not seen as an imposed IT project, but rather a jointly-owned transformation of how the utility operates. With the workforce engaged, the organization can fully leverage the technology’s potential, and the changes will stick long-term.

“Scale Fast” - Roadmap for Expansion and Continuous Improvement:

Once the initial pilots prove successful, the utility enters the Scale Fast phase – rapidly extending AOM across more assets, processes, and zones, while continuously improving the platform. A phased roadmap might look like:

• Phase 1 (Year 0-1: Foundation and Pilot Wins): Build up the core AOM platform with data integration layer, initial digital twin models, basic AI/analytics capability. Execute the head-pin pilot projects identified, e.g., optimize one pump station, implement leak detection in one district, etc. Validate ROI and capture lessons. By the end of this phase, have a handful of demonstrated wins: energy saved, leaks reduced, etc., and a baseline architecture in place.
• Phase 2 (Year 1-2: Extend to Adjacent “Pins”): Using the templates from pilots, roll out AOM to adjacent areas. For example, onboard additional pump stations to the optimization agent, expand leak detection coverage city-wide by equipping more zones with sensors or using existing smart meter data, and integrate water quality AI across all treatment plants and key network nodes. During this phase, interoperability is key. Also, start linking use cases: e.g., the pump optimization agent now works in tandem with the leak detection module, coordinating pressure and flow changes. Incremental scaling is accelerated by the composable design – new digital twins for assets or processes can be added as modules. By phase 2’s end, perhaps 50-70% of the network and plants are under some level of AOM supervision, and most operations staff have begun using the new system daily.
• Phase 3 (Year 2-3: Unified Operations & Advanced Automation): Achieve the full “strike” by covering the end-to-end water value chain. All treatment plants, pumping stations, and distribution zones are integrated into the AOM platform. The command center functions as a unified operations hub with real-time analytics and AI suggestions for all key activities. At this stage, closed-loop control can be gradually increased where it makes sense, e.g., AI agents automatically manage pump scheduling 24/7 with minimal human intervention, or an agent directly adjusts pressure valve settings in real time to respond to transients with oversight. The focus also turns to optimizing between domains: for instance, coordinating production at treatment plants with network storage and demand, balancing water production, energy tariffs, and distribution constraints, – a system-level optimization that yields additional efficiency. Additionally, the utility can integrate external data for even more context, for example, weather forecasts to anticipate demand spikes or heavy rainfall that could affect source water quality, etc., allowing truly Contextual operations. By end of Year 3, the AOM system is the “digital brain” of the water utility, orchestrating across formerly siloed domains.
• Phase 4 (Year 3+ Continuous Improvement & Innovation): After full deployment, the journey isn’t over. The utility should institutionalize continuous improvement of the AOM system. This involves retraining AI models with new data to improve accuracy, adding new use cases as technology or needs evolve. The utility can also explore AI agents for higher-level goals – for instance, an AI agent tasked with minimizing the utility’s carbon footprint could coordinate across water production, pumping, and treatment processes to schedule operations when grid electricity is greenest, or to shift loads in response to demand response signals. Throughout, keep measuring performance and comparing against the baseline pre-AOM metrics to identify gains and areas to tune. Also, feedback from staff should continuously be gathered to improve the system. The Collaborative aspect means user needs drive system tweaks.
• Governance & Scalability: As AOM scales, formalize governance. This could mean establishing a center of excellence for digital twin and AI within the utility, to maintain the models and integrate new ones. Also, ensure the IT infrastructure scales – more data streams, more analytics, possibly requiring cloud scaling or edge computing deployment for latency-sensitive control. Scalability also includes geographic or enterprise scaling: if the utility manages multiple water systems or jurisdictions, the AOM blueprint can be replicated to new areas. Standardize the rollout methodology so that future projects - new treatment plant, new pipeline, come with digital twin and AI agents from day one.

By following this roadmap, the utility can de-risk the journey and avoid stagnation after pilots. The quick wins from Phase 1 fund and motivate Phase 2. Early adopters become internal champions who help train others in Phase 2. By Phase 3, the integrated platform unlocks compound benefits that far exceed isolated improvements. At full scale, the utility achieves a “strike” – a step-change improvement in performance across the board. It’s important that throughout these phases, the alignment to business value is maintained by tracking KPI improvements, and communicated, to keep executive and stakeholder support strong. Each phase should have well-defined targets, for example KPI improvements and capability milestones, and a review before proceeding, ensuring the scale-up stays on track and delivers expected value.

Business Outcomes and ESG Impact

Every phase and use case in the AOM Bowling Strike approach must tie back to business outcomes, operational metrics, and sustainability goals. This ensures the program delivers not just tech for tech’s sake, but meaningful improvements in how the utility performs and the value it provides to stakeholders. Let’s summarize the key outcomes and how they map to use cases:

• Water Loss Reduction (Non-Revenue Water): One of the clearest outcomes from leak detection and pressure management use cases is a drop in NRW percentage. If a utility currently loses 30% of its water to leaks, cutting that in half (to 15%) through AOM means millions of gallons of water saved annually. This directly improves the bottom line (more billable water, or reduced production for the same delivered volume) and contributes to water conservation goals. ESG impact: Water conservation, improved service reliability (customers see fewer outages or low-pressure events due to main breaks), and better stewardship of a scarce resource.
• Energy Efficiency & Cost Savings: Pump optimization yields quantifiable reductions in kWh used per million gallons (MG) pumped. AOM allows a utility to track energy per volume in real-time and optimize it. Many utilities aim for a benchmark like ~3 kWh per cubic meter (or ~11 kWh per MG) or lower. AOM can help approach those targets by smoothing operations. Financially, reducing energy usage by 20-30% in pumping can save hundreds of thousands of dollars annually, depending on utility size. ESG impact: Lower energy consumption means reduced carbon emissions, helping meet climate action or carbon neutrality commitments. Pump efficiency improvements in one case led to 50+ tons CO2 cut per year; scaled up, the utility could reduce its carbon footprint significantly and perhaps even utilize more renewable energy through smart scheduling, aligning pumping to off-peak times when grid mix is greener.
• Operational Resilience & Reliability: With predictive maintenance and AI oversight, asset reliability improves. Fewer pump failures or main breaks mean higher uptime of the water supply system - a key KPI often measured as percentage of time water service is available or number of interruptions per 1000 connections. While each system is different, AOM should target a measurable reduction in unplanned outages and faster incident response. Outcome: More reliable service to customers, better compliance with regulatory standards for service continuity, and improved asset longevity, which defers capital expenditure on replacements. This also ties to public safety and customer satisfaction, as major failures that risk safety or cause large disruptions are minimized.
• Water Quality Compliance & Safety: Using AI and digital twins to monitor quality means the utility can maintain a high percentage of compliance with drinking water standards and reduce the occurrence of any violations or advisories. KPIs here include regulatory compliance rate (percentage of samples meeting standards), number of water quality incidents per year, and response time to contamination events. An AOM system might help achieve near 100% compliance and early mitigation of issues, for instance, if a contaminant is detected, an isolation and flush can be done faster, limiting any customer impact. ESG/Social impact: Safeguarding public health and trust, contributing to the Sustainable Development Goal of clean water. Additionally, if the system optimizes chemical usage, like chlorine dosing, by better understanding water quality needs, it can reduce chemical consumption and byproducts, which is an environmental and cost benefit.
• Cost Savings & ROI: Executives will look at the ROI of the AOM program. The benefits above – reduced energy cost, water loss, reactive repair costs – all translate to monetary savings. These can be estimated and tracked. On the flip side, the costs of AOM need to be justified. A phased approach “pays for itself” by using initial savings to fund later phases. For example, energy savings from pump optimization in year 1 might fund the expansion of sensors for leak detection in year 2. Over a 3-5 year horizon, the aim is a positive ROI with a strong business case. It’s helpful to develop a value framework: e.g., X dollars saved per year from energy, Y from NRW reduction, Z from avoided failures, against the cost. Often, improvements in efficiency and reliability also defer big capital expenses, if you extend asset life or avoid needing a new source because you saved water, that’s millions saved. These strategic financial outcomes should be communicated to stakeholders and regulators, possibly aiding in securing innovation grants or favorable rate treatment for the investments.
• Environmental, Social, and Governance (ESG): Beyond direct financial metrics, AOM aligns with many ESG objectives. Environmental: less water wastage, lower energy and chemical use, and data to support sustainable water resource management, for instance, better understanding of water balance and losses helps in planning conservation. Social: improved service reliability and water quality contribute to the community’s well-being; also workforce upskilling and safety - fewer midnight emergency repairs in dangerous conditions improves worker safety and job quality. Governance: the data-driven approach increases transparency and accountability – performance metrics are tracked rigorously, and decisions are logged. Investors or boards focused on ESG will see AOM enabling quantifiable progress on sustainability targets, e.g., carbon reduction or resiliency against climate impacts like droughts, since a more efficient system can better cope with shortages.

To ensure continuous alignment, each phase of the roadmap should have associated KPI targets, e.g., “Pilot will reduce energy per MG by 15% in Zone A” or “Year 2 target: cut NRW by 5%”. Regular reports should be produced showing the impact.

It’s worth noting the intangibles: AOM positions the utility as a leader in innovation. This can enhance the utility’s reputation, help attract talent - young engineers are keen to work with advanced tech, and potentially open up new business models. Some utilities might offer services to smaller utilities once they build expertise. Those broader benefits, while not as easily measured, are part of the strategic value of “Thinking Big” with AOM.

In summary, the Bowling Strike AOM approach, when executed, yields a water utility that is more efficient, reliable, and sustainable. It directly supports corporate strategies to improve service to customers, manage costs, and meet environmental responsibilities. By tracking and communicating these outcomes, utility executives ensure that the AOM initiative is recognized not just as a tech upgrade, but as a fundamental business transformation delivering high ROI and positive impact.

Conclusion

The AOM Bowling Strike Strategy for Water Utilities provides a structured, value-driven roadmap to transform water supply and distribution operations in a phased, low-risk manner. By “Thinking Big”, the utility establishes a unifying vision: a digitally integrated, AI-powered water network from source to tap, where every asset and process is intelligently monitored and optimized in real-time. By “Starting Small” with head-pin pilots, the organization secures quick wins, such as preventing downtime on a critical pump, cutting energy costs at one plant, or halving leaks in a pilot zone. These pilots validate the technology - digital twins, AI agents, smart sensors, in the utility’s specific context and build the confidence and skills needed for broader implementation. Then, leveraging the composability of the AOM framework, the utility “Scales Fast”, rapidly extending successful solutions across the network and tackling more ambitious optimizations. The result is a self-reinforcing improvement cycle – early wins fund and inform the next projects, and the momentum drives the organization toward the strike: full deployment of AOM across the enterprise.

This journey is not just about technology, but about integration and composable thinking: technologically, it interfaces with legacy systems and breaks down data silos, and organizationally, it brings together departments and aligns maintenance and operations toward common goals. In the end-state, the water utility operates with a kind of “digital nervous system” and AI “brain” augmenting human decision-makers. The entire system, from a single pump up to the entire network, is managed with foresight and coordination, guided by data-driven insight rather than reactive firefighting. The utility becomes capable of predictive, closed-loop operations: adjusting to disturbances adaptively, optimizing performance continuously, and learning and improving over time.

Through careful change management, the workforce is evolving into a high-performance team that trusts and leverages AI. Far from replacing humans, AOM empowers operators to focus on strategic oversight and innovation while AI handles routine optimization and first-line anomaly response. The culture shifts to one of proactive asset management and collaborative problem-solving.

By following this Bowling Strike approach, a water utility can methodically achieve a “strike”, i.e. a comprehensive AOM deployment yielding step-change improvements in efficiency, reliability, safety, and sustainability. The utility will be better equipped to meet its service obligations and future challenges: whether that’s expanding capacity for a growing population, dealing with the uncertainties of climate change (droughts, floods), or meeting aggressive carbon reduction targets. In essence, AOM provides the platform to turn a traditional water utility into a smart utility, where intelligent systems and human expertise work in harmony to deliver water services that are more resilient, cost-effective, and environmentally friendly. This positions the organization as a leader in the water industry’s digital era, the utility can now lead in demonstrating how Composable Digital Twins and Agentic AI revolutionize the management of one of our most vital resources: water.