This article originally appeared in the Environmental Business Journal, Volume 38 Numbers 11/12: Q4 2025, The Environmental Consulting & Engineering Industry in 2025
Emerald Technology Ventures is a globally recognized venture capital firm, founded in 2000, that manages and advises assets of over €1 billion from its offices in Zurich, Toronto and Singapore. Backed by a network of more than 50 leading corporate limited partners, Emerald invests in start-ups tackling the world’s biggest challenges in climate change and sustainability. The firm currently manages four funds, has completed hundreds of venture transactions, and oversees five third-party investment mandates, including loan guarantees to over 100 start-ups.
Clayton MacDougald, Investment Director, has been instrumental in deploying the firm’s first global water fund. He brings deep industry expertise to his board roles with FREDsense, Waterly, and StormHarvester, where he helps scale innovative solutions tackling critical water challenges. Before joining Emerald, Mr. MacDougald built his career at GE Water & Process Technologies and Xylem, leading global product strategies, driving digital transformation across the sector.
EBJ: How do you see AI reshaping the strategic landscape of water and waste[1]water investments over the next 5–10 years?
MacDougald: The water industry is undergoing a digital transformation, with AI poised to be one of the most influential drivers over the next decade. Its applications are rapidly expanding to include enhancing core utility operations, strengthening climate resilience and risk management, and advancing water and wastewater treatment processes. As adoption accelerates, AI has the potential to shift the industry from a reactive, fragmented approach to one that is predictive, integrated, and highly optimized. At the same time, the global rise of digitization and AI have significantly increased water demand. Major technology companies have reported year-over-year water consumption growth of more than 35%, driven largely by the rapid expansion of AI workloads. This trend is reshaping the investment landscape, creating opportunities to manage rising consumption in data centers through innovative cooling technologies, advanced treatment solutions, and expanded water management strategies.
EBJ: Where do you believe AI is generating the greatest return on resilience in the water sector today?
MacDougald: As extreme weather events grow more frequent and severe, they place added strain on aging and poorly maintained infrastructure. AI has proven especially valuable in extending infrastructure lifespans and enhancing system performance. Several of Emerald’s portfolio which utilize AI are proving out significant value for customers across the globe. For example, Fido Tech helps utilities to identify, size, and prioritize leaks in water pipelines, reducing Non-Revenue Water (NRW) and advancing water conservation efforts. In asset management, MentorAPM enables faster condition assessments, failure prediction, and optimized maintenance schedules—delivering significant cost savings compared to traditional approaches. Additionally, StormHarvester is integrating continuous sewer network data with weather forecasts, which is helping to prevent flooding and pollution events while improving transparency for both utilities and the public worldwide. Lastly, by using SewerAI’s AI-based solutions, utilities, contractors and engineering firms are understanding condition assessment of their sewer networks faster and more economically than ever before, thus improving overall budget management for critical infrastructure.
EBJ: How has your investment thesis evolved in response to the accelerating convergence of AI and water tech?
MacDougald: Several reports estimate that delivering effective water management worldwide will require investments exceeding USD 1 trillion per year. While the scale of this figure is daunting, breaking it down highlights the breadth of the need across the entire water value chain. This includes infrastructure, hardware, deeptech solutions such as advanced membranes and treatment processes, as well as IoT and software. Emerald’s investment thesis continues to target innovation across these areas for both municipal and industrial-scale water improvements. At the same time, the role of AI in driving higher returns, greater efficiency, and improved management is undeniable, making it a central pillar of Emerald’s investment strategy going forward.
EBJ: Where do you see the most promising use cases for AI in managing industrial wastewater?
MacDougald: AI enables industrial operators to shift from reactive facility management to proactive, data-driven optimization. The greatest benefits include reduced operational costs, enhanced regulatory compliance, and support for water reuse and circular economy initiatives. Key use cases include: Real-time monitoring and anomaly detection: AI continuously analyzes sensor data (e.g., pH, COD, TSS, heavy metals, temperature, flow rates) to identify abnormal patterns or discharge events. Instead of relying on static thresholds, it predicts when processes are trending toward noncompliance—allowing operators to take corrective action before costly or reputational issues arise. Automated compliance assurance: AI-driven visualization provides transparent, auditable evidence of continuous compliance, while automated reporting tools dramatically reduce the time required for manual submissions to regulators. Process optimization: Machine learning models can optimize chemical dosing (e.g., coagulants, flocculants) based on historical treatment data, minimizing overdosing and reducing costs. Similarly, AI can dynamically adjust aeration intensity in biological wastewater systems to balance oxygen demand with energy efficiency. Water reuse enablement: In real time, AI can evaluate effluent quality to determine its suitability for reuse in industrial processes such as cooling or equipment cleaning.
EBJ: In terms of economic value, which areas do you believe AI delivers the strongest cost-to-benefit ratio: operations, regulatory compliance, resource efficiency, or something else entirely?
MacDougald: The cost-benefit ratio of applying AI in water systems varies significantly depending on factors such as data maturity, operational efficiency, and regulatory requirements. When assessing where AI can deliver the greatest return, key considerations include the speed and certainty of value realization, implementation complexity, organizational readiness, risk tolerance, the balance between recurring and one-time gains, and the extent of existing inefficiencies. In most municipal and industrial water systems, the primary sources of waste stem from high operating costs, particularly excessive energy and chemical use, but also from asset losses caused by running equipment to failure or pipeline leaks that lead to non-revenue water. Energy alone typically represents 25–40% of total operating costs, driven largely by pumping in water systems and aeration in wastewater treatment. Both are often overused: leaks result in unnecessary pumping of water that never reaches end users, while operators tend to over-aerate biological treatment systems to ensure regulatory compliance. AI is already delivering measurable improvements in these areas. Leak detection models—such as those developed by Fido Tech—help utilities prioritize the most critical leaks for repair, reducing both pumping demand and associated energy consumption. Likewise, AI-driven aeration optimization enables precise control of biological treatment, lowering energy use while maintaining effluent quality and compliance.
EBJ: Has AI opened new opportunities for recurring revenue models in water that wouldn’t have been possible before?
MacDougald: AI has helped validate and accelerate several business models in the water sector that previously struggled to achieve widespread adoption. Before the rise of AI, many advanced analytics and monitoring technologies faced prohibitive costs, uncertain reliability, and limited differentiation from a customer perspective. By improving reliability, reducing costs, and providing measurable outcomes, AI has lowered these barriers—enabling recurring revenue models to gain traction across both municipal and industrial markets. “As-a-service” models such as Data as-a-Service or Monitoring-as-a-Service are becoming increasingly common. Rather than selling hardware or one-time analytics, providers offer continuous monitoring, alerts, dashboards, predictive insights, and maintenance recommendations under subscription or service-based pricing—capabilities that would have been prohibitively expensive to perform manually. AI has also driven the growth of pay for-performance contracts, where payments are tied to measurable outcomes. Since AI systems can continuously track and validate performance, both vendors and clients can share risk and reward more equitably. For example, a provider might be compensated based on the percentage reduction of water leaks achieved within a distribution network. These models strengthen alignment between vendors and customers while lowering the overall risk profile for both parties.
EBJ: Are there any underfunded or overlooked areas in the AI-for-water space that you believe are ripe for investment?
MacDougald: There are two primary areas that, over time, are expected to become highly attractive for investment within the AI-for-water space. While not entirely overlooked, both remain in the early stages of technical maturity, regulatory acceptance, and market adoption. The first area is AI for trace and emerging contaminant detection and prediction. The growing prevalence of contaminants such as PFAS, pharmaceuticals, and microplastics is creating significant regulatory and reputational risk for both utilities and industrial operators. AI-driven sensing and predictive modeling have the potential to shift contaminant management from reactive to proactive, helping organizations stay ahead of tightening global standards. The main challenge today lies in data scarcity where few labeled datasets exist for trace contaminants. However, with the emergence of field-based sensing technologies such as those developed by FREDsense, this limitation is expected to diminish. Combined with strong regulatory tailwinds and rising public pressure, this segment is likely to attract substantial investment in the coming years. The second area is AI for water system resilience and extreme event forecasting. Climate change–driven shocks are increasingly exposing the fragility of global water infrastructure. Current AI models struggle to extrapolate beyond their training data, and because extreme events remain relatively rare, limited data sets make accurate modeling difficult. Integrating these systems into operational decision-making also presents complexity for asset owners. Despite these challenges—and the likelihood of a longer investment horizon—AI systems capable of anticipating, simulating, and adapting to extreme weather events could fundamentally redefine how utilities and industries approach resilience planning.
EBJ: Are you seeing any risks of AI bias, cyber vulnerability, or over-automation in critical water infrastructure? And how do you evaluate or mitigate these during due diligence?
MacDougald: A key challenge with the growing adoption of AI is the gap between how companies market their AI capabilities and how they actually use the technology. Many organizations now promote their sophistication and expertise in AI to attract investors, customers, and partners. This trend creates a greater need for due diligence to verify such claims—ensuring companies not only understand their use cases, but also have the knowledge, governance, and risk management practices in place to responsibly deploy AI.
EBJ: Has AI allowed you to fund more capex-light, rapidly scalable water businesses than would’ve been feasible a decade ago?
MacDougald: Emerald’s water investment thesis has long encompassed a diverse portfolio of companies spanning infrastructure, hardware, deep tech solutions such as advanced membranes and treatment technologies, as well as IoT and software platforms. The emergence of AI has expanded this landscape, enabling a new generation of capex-light, scalable start[1]ups that lower barriers to entry through data-driven business models, enhanced transparency, and measurable operational improvements for utilities and industrial customers. Many of these companies would have struggled to gain traction in the industry prior to AI’s rise. That said, AI is not a universal remedy for the challenges facing the water sector. Substantial investment is needed in physical technologies including advanced treatment systems, membranes, and broader sensor deployment across water networks. Because water systems are highly local in terms of terrain, climate, regulation, and contamination profiles, each requires significant customization and fine-tuning to operate effectively. Consequently, a “one-size-fits-all” approach is not feasible, and diverse solution sets and investment themes, both within and beyond AI, will continue to be essential to drive meaningful impact across the sector.
Recent water investments and news at Emerald:
Emerald Announces €60 Million First Close of Global Water Fund II
Emerald backs FREDsense in $7M Series A to speed up PFAS field testing