The strain on our water resources is reaching alarming levels: the gap between global water supply and demand could reach nearly 40% by 2030. Demographic growth, global warming, variations linked to hydrological cycles and extreme weather events all increase the vulnerability of regions that are already subject to water stress and threaten areas that have been spared until now.
In response to these challenges, the international community is rallying around Sustainable Development Goal 6 (SDG 6), which aims to ensure access to water and sanitation for all, as well as to ensure sustainable management of water resources. To achieve this goal, the use of technology becomes a strategic lever. The widespread deployment of solutions based on data and artificial intelligence (AI) is a priority transformation pillar for many countries. However, many data-related challenges remain, limiting the large-scale adoption of AI.
Leveraging water resources in the age of digital transformation
The massive development of digital data sources and processing, especially Big Data, is paving the way for the deployment of artificial intelligence (AI) for more efficient management of water resources. AI facilitates the cross-referencing of real-time data with paleoclimatological data dating back to the last millennium. It thus contributes to the understanding of water flows and the deployment of early-warning systems, facilitating decision-making for governments and water operators.
That way, research projects such as INRIA’s ARCHES (“AI Research for Climate Change and Environmental Sustainability”) aim to develop effective solutions to predict variations in natural resources and support decision-makers in the face of climate change. Major digital players such as Google Research is also investing in AI for the forecasting of floods, droughts, and water levels, with the aim of facilitating the implementation of emergency plans by governments.
In addition, researchers are looking into the advantages of technology in the distribution of and access to drinking water. Its use is increasingly popular for predictive maintenance of infrastructures. In some countries, undetected leaks can be responsible for the loss of more than 60% of drinking water. Anticipating network failures would therefore ensure considerable cost savings for operators. At the same time, real-time data processing paves the way to the development of digital twins in water management. This technology, which allows virtual modelling of infrastructures, streamlines controls and compliance with regulatory constraints at all stages of drinking water and sanitation systems.
Structural limitations to overcome
Despite new opportunities for the sector, the widespread adoption of new technologies for water seems to be compromised. In some regions, the effectiveness of models faces a major limitation: the quality, accessibility, and representativeness of data. The World Meteorological Organization also reports that less than 60% of countries collect and process data related to water resources. As a result, algorithms are often developed from global, non-contextualized data, which limits their ability to respond to local hydrological and socio-economic specificities.
A recent article on the use of AI for surface water management illustrates this challenge. In uninstrumented basins or data-poor regions, AI models risk overfitting or producing physically inconsistent results, limiting their ability to generalize learning (Gacu et al., 2023).
However, there is no shortage of data collection initiatives. For example, the international TRISHNA program, led by France and India, uses satellite data to refine the measurement of soil surface temperatures and anticipate the risks of water stress at the agricultural plot level. Despite these efforts, operational barriers remain. On the one hand, recent field studies have shown that the absence or lack of maintenance of sensors in remote areas limits the accuracy of models and hinders the deployment of AI in vulnerable regions. On the other hand, poor connectivity in some regions limits the optimal transmission of captured data to water resource management systems (Eze et al., 2025).
Added to these difficulties are technical deficiencies all along the water treatment process. An article published in the journal Water in 2023 shows that the deployment of water treatment systems in developing countries are challenged by uneven data collection and loss of information during the water treatment cycle (Bulti & Yutura, 2023). According to the researchers, this decline in information reflects the weaknesses of infrastructures in terms of data management. Thus, systematic and rigorous documentation of infrastructure and its operation remains essential prerequisite for the successful integration of AI into water resource management operations.
Infrastructure 4.0 at the service of our water resources
To achieve the Sustainable Development Goals, infrastructure 4.0 is emerging as a strategic response. This concept refers to smart infrastructures that rely on technology and information to deliver high-quality environmental, economic and social outcomes.
For development experts, this transformation is, first and foremost, systemic. A report from The Inter-American Development Bank notes that digital transformation involves creating or modifying existing business processes, cultures, and customer experiences to meet changing business and market demands (Féry, 2022).
In order to optimize this transition, members of the World Economic Forum Infrastructure 4.0 project community first suggests promoting access to and use of data. In particular, open data initiatives in the water sector represents significant progress but remains insufficient in the face of current challenges and growing risks related to data protection. It is therefore essential to structure systems around robust data policies.
This increased use of data also requires a transformation of skills within organizations. This requires the ongoing training of teams, the acquisition of solid skills in statistics, mathematics and data science, as well as capacity building for operators’ IT units. AI algorithms will not replace human knowledge, but rather enrich it. Their relevance depends above all on the ability of water sector professionals to interpret the results generated in order to derive real operational value.
To align financing strategies with these new challenges, members of the World Economic Forum Infrastructure 4.0 project community also recommends, in investment frameworks, to take into account the cybersecurity risks (particularly critical in developing countries) or the ecological footprint of AI.
The development of infrastructure 4.0 thus paves the way for technological solutions capable of meeting the needs and capacities of countries in the management of water resources. As such, frugal machine learning solutions appear to be a promising avenue to explore in contexts where digital infrastructures are still underdeveloped.
Finally, indicators, such as the artificial intelligence investment potential index (AIIPI) developed by the French Development Agency (AFD), allow professionals, researchers and decision-makers to identify the conditions conducive to the large-scale deployment of AI. Last, like the round table organized by the AFD titled “AI and water resource management – Opportunities and Challenges“, international synergies between water sector experts and development actors encourage the alliance towards technological progress and the achievement of SDG 6.