Yet it remains underused in sustainability efforts, often confined to pilots and small-scale projects rather than being embedded in the systems where it can deliver the greatest impact.

This is especially clear in marine industries. Oceans play a crucial role in regulating the climate and supporting global trade, but are increasingly under strain from pollution, habitat loss and industrial expansion. AI can help address these challenges. It is already being used to monitor water quality, track biodiversity and support smarter design of infrastructure. These tools are not theoretical; they’re in use today. But the process of adoption is slow and disconnected. So, what’s holding things back?

Environmental data must be treated as infrastructure

One of the core challenges lies in the data. AI systems need consistent, high-quality data to function effectively. But in many marine and coastal settings, environmental data is often patchy, siloed or hard to access.

For example, river flow data is critical to understanding flood risk, pollution and overall water quality. However, it is often missing or incomplete, particularly in smaller or remote catchments. Traditional monitoring methods rely on expensive equipment and are limited in coverage, but recent developments in AI are helping to bridge these gaps. For example, a new open-source deep learning model, developed by Cognizant, estimates daily river flows by combining historic weather data with catchment characteristics without the need for physical sensors. Trained on decades of open datasets from hundreds of UK basins, it can generate rapid insights even in remote or previously unmonitored areas. This makes large-scale, cost-effective environmental assessments more accessible for those managing flood risk, water quality and ecosystem health.

The same principles apply in coastal and marine contexts. If we treat environmental data as critical infrastructure – investing in shared standards, open models and interoperable platforms – AI can become much more scalable. FAIR data principles (Findable, Accessible, Interoperable, Reusable) provide a solid starting point, but broader alignment is still needed to encourage widespread real-world adoption.

Moving from pilots to platforms

AI is already in use across different parts of the marine sector. Current models can detect harmful algal blooms, track biodiversity using environmental DNA and predict the impact of infrastructure on local ecosystems. Deep learning systems are forecasting water quality and tracing pollution to its sources. In engineering, generative AI is helping design marine structures that support biodiversity. For example, the European Space Agency’s (ESA) Sentinel satellites, combined with AI, are being used to map cyanobacteria blooms and marine litter, enabling large-scale coastal water quality monitoring that was previously unfeasible.

These examples show what’s possible, but most applications remain isolated. When insights stay locked within individual projects or departments, the broader benefits are lost. To realise the full value of AI, sectors need to work together. The same models used to monitor coastal waters, for example, could also inform port operations, infrastructure planning and conservation efforts.

This will require investment in shared infrastructure and collaboration between policymakers, industry and environmental experts. But the technology is already mature enough to support it.

Policy must support action

Policy frameworks have a major role to play in shaping the uptake of environmental AI. Many governments have set out strong sustainability targets, but support for deployment is often limited. Without clear standards or guidelines for how AI can be used, organisations hesitate to adopt it at scale.

What is needed is practical alignment. This includes regulatory standards for environmental AI tools, guidelines on data governance and incentives that encourage cross-sector collaboration. In marine contexts, policies that support the repurposing of existing assets, such as decommissioned energy platforms, can help align sustainability and economic priorities.

For instance, AI is already being used to assess the viability of converting oil and gas rigs into offshore wind bases, carbon capture hubs or aquaculture farms. Rather than removing ageing assets, digital twins and predictive models can help safely extend their life for green use cases, which benefits both the climate and the economy. With clearer frameworks and strategic funding, successful pilots can scale into long-term systems.

It’s time to act

Some commentators believe environmental AI is not yet ready, that we need more data, more consensus and better tools. But the solutions already exist, and many are affordable and scalable today. Waiting carries its own risks, especially as marine ecosystems face growing pressure.

AI is well suited to adaptive learning. Deploying systems now, even if imperfect, allows them to evolve and improve through real-world use. The more data they gather, the more accurate and useful they become.

Reaching environmental goals and maintaining the ocean’s essential functions depends on timely, data-led and predictive decision-making. AI can support that, if it is treated as a strategic priority rather than an experiment. The case for action is clear. Now is the time to scale it.

About the Author

Stig Martin Fiska is Global Head at Cognizant Ocean. Cognizant engineers modern businesses. We help our clients modernize technology, reimagine processes and transform experiences so they can stay ahead in our fast-changing world. Together, we’re improving everyday life.