Italia-Croatia

04/03/2026

Artificial intelligence is entering many sectors that have traditionally relied on experience, observation and seasonal knowledge, and fisheries are among them. While fishing will always be grounded in human skills and deep familiarity with the sea, AI and machine learning are increasingly being discussed as tools that can support a better understanding of marine resources, offering forecasts and insights that may help institutions, researchers and operators make more informed decisions.

At the heart of this transformation is a simple idea: the sea generates a vast amount of data, but it is complex and difficult to interpret. Ocean temperatures, currents, salinity, oxygen levels, chlorophyll concentration, weather patterns and many other variables interact to influence where species are found, how abundant they are, and how they move over time. Artificial intelligence, especially machine learning, can help process large datasets and detect patterns that would be hard to capture with traditional methods alone.

From weather forecasts to “resource forecasts”

A useful comparison is the way we already use forecasting in everyday life. Weather prediction models ingest huge volumes of data and, through mathematical and statistical methods, provide estimates of what may happen in the atmosphere. Similarly, AI models can be trained to predict the distribution and quantity of marine resources, offering “resource forecasts” that support planning and management.

This doesn’t mean that AI can “guarantee” results: marine ecosystems are dynamic, and uncertainty remains an inherent part of working at sea. But AI can strengthen the quality of available information, improving scenario analysis and helping stakeholders navigate complexity.

Why it matters: productivity, efficiency, and sustainability

Better knowledge of where resources are, and how they might change, can support multiple goals:

• Improving operational efficiency, by reducing unnecessary time at sea and helping plan routes and activities more strategically.

• Supporting fisheries management, by providing additional inputs that can complement scientific monitoring and policy decisions.

• Reducing environmental pressure, by enabling smarter approaches to resource use and promoting a more responsible interaction with ecosystems.

In short, AI can contribute to the long-term balance between economic viability and environmental stewardship, especially in regions where small-scale fisheries and coastal economies are tightly connected.

AI and climate change: anticipating shifts before they become crises

One of the most urgent reasons for applying predictive models to the marine environment is climate change. Rising sea temperatures, changing oxygenation levels and altered current patterns are influencing species behaviour and migration routes. As a result, species may shift towards colder waters or move further from shore, with direct implications for fishing practices, costs and safety.

AI models can help explore these scenarios by estimating how resources might react under different environmental conditions. This supports planning not only for the next season, but also for longer-term adaptation strategies, especially relevant for communities and sectors that need stability to survive.

A tool for decision-making, not a replacement for experience

It is important to be clear: AI is not a substitute for fishermen’s knowledge, nor does it remove the complexity of marine ecosystems. Instead, it can be understood as an additional layer of support: an analytical tool that can help interpret signals, build forecasts and improve decision-making processes.

The real value lies in the ability to bring together different types of knowledge: scientific research, environmental monitoring, local expertise and practical operational needs. When combined, these perspectives can help build a more resilient approach to fisheries, one that is capable of adapting to fast-changing conditions.

Looking ahead

Artificial intelligence is likely to play an increasing role in how societies understand and manage marine resources. The challenge will be ensuring that these tools are developed and used responsibly: with transparency, quality data, proper interpretation and a clear focus on sustainability.

For fisheries and coastal communities, the promise of AI is not about “high-tech for its own sake.” It is about better knowledge, better planning, and the possibility of reducing uncertainty in an era when the sea is changing faster than many sectors can adapt.

As the conversation on sustainability continues to grow, AI-based predictive models may become one of the practical instruments that help connect environmental priorities with the everyday reality of those who work at sea.