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    AI in Water and Wastewater – Why Is Progress So Slow?

    2026-05-18

    A new article examines why the adoption of AI and machine learning is proceeding relatively slowly in the water and wastewater sector, despite the technology’s great potential.

    Emmanuel Okwori, researcher at RISE and Mistra InfraMaint

    The Mistra InfraMain project Roadmap for Scaling Up the Use and Adoption of AI and Machine Learning in Swedish Water Utilities aims to facilitate and accelerate the adoption and use of artificial intelligence and machine learning tools in Swedish water and wastewater companies. Although the technology offers many benefits, most organizations have not made significant progress in its implementation.

    A new article from the project explores why the adoption of AI and machine learning in water infrastructure asset management remains slow and fragmented despite its high potential.
    – We moved beyond technical discussions to investigate the sociotechnical barriers—such as organizational culture, data governance, and “pilot fatigue”—that prevent utilities from turning digital experiments into long-term operational value, says Emmanuel Okwori, project manager and one of the article’s authors.

    What are your main conclusions?
    – The core conclusion is that successful AI implementation depends less on the technical sophistication of the model and far more on organizational alignment, data governance, and structured lifecycle planning. The study introduces the Readiness-Adoption-Impact (RAI) Loop, a framework designed to help utilities assess their maturity and close the gap between initial adoption and realized impact, ensuring that AI is treated as a strategic asset rather than an isolated project.
    Who should definitely take note of the results?
    – These results are essential for Municipalities and water utilities, regulators, and technology partners. Working with AI or planning to start working with AI.  Specifically, it provides a roadmap for decision-makers to bridge the underinvestment gap in Swedish water infrastructure by shifting from reactive, experience-based decisions to proactive, intelligence-augmented systems.

    Read the article here

    The project has also produced an easy-to-understand fact sheet summarizing the results; you can download it here

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