Artificial Intelligence Aboard

Artificial intelligence (AI) is seemingly everywhere, taking over headlines and our daily lives – but what can it really do for boaters?

Artificial intelligence is the indisputable buzzword of the year. Suddenly everything seems to be “powered by AI” or “AI-integrated,” and now there are even AI agents to manage your AI agents. Although getting out on the water is a way for many of us to escape the relentless march of technology, it didn’t take long for AI to weasel its way into boating, too.

Various AI-forward companies are promising improvements in collision avoidance, weather forecasting, troubleshooting, and more. But what exactly does all that mean? We investigated some of the most interesting AI-powered initiatives that are touching boating so far, how they work and don’t, and how they might change boating in years to come.

Is It AI?

Given how much of a buzzword it’s become, it’s worth attempting to draw some boundaries about what artificial intelligence actually is. The definition of AI is broad and varies slightly expert to expert. According to Merriam Webster’s definition, any computational system with the ability to imitate intelligent human behavior could count as AI. Machine learning, a subset of AI, involves analyzing historical data to improve over time without explicit human programming. Generative AI, one of the most rapidly growing subsets, involves using existing human-created knowledge to generate new, “original” content, like written text, imagery, or programming code. Chatbots like ChatGPT and Claude use both. Unlike a traditional search engine that simply identifies and pulls up an existing item from the internet, chatbots create a unique response to your specific question (generative AI) and theoretically customize responses over time based on what they learn about you (machine learning). The chatbots I use have learned that I’m a boat owner, and sometimes offer responses to completely non-boat-related questions with boating analogies – for better or for worse.

Despite the feeling that AI barreled into all our lives just in the past 18 months or so, in reality it’s been creeping in for a while. Social media algorithms, in-document grammar checkers, facial recognition software, and voice assistants like Siri, Google, and Alexa have already relied on AI technology for years. The current AI boom is rather an explosion in the types, uses, and advancement of AI – including some new applications on the water.

Still, it’s important to keep in mind that many AI companies working in the marine space are in their early days of development. As with most burgeoning tech, some of it has not always performed as expected. For boaters who come to rely on it with false confidence, this could prove disastrous.

Keeping Watch

In specific applications, however, AI can be useful. Take the Vendée Globe, which is one of the toughest competitions in sailing: a singlehanded, nonstop, unassisted race around the world, starting and ending in Les Sables d’Olonne, France. Usually, around 40% of competing boats don’t finish. Some retire due to technical failures or medical emergencies, but many are knocked out by collisions. Even with advanced radar and AIS systems, there are plenty of objects that are almost impossible to spot in the middle of the ocean, at night, while racing at 20-plus knots.

To help combat this issue, 18 of the 33 competitors in the 2020–21 race installed SEA.AI systems as a backup, and 25 of 40 did so in the 2024–25 edition. SEA.AI systems include one or several cameras and an AI-integrated “brain” programmed to classify what the cameras are picking up. According to the manufacturer, the SEA.AI Brain works by accessing its image library of millions of previously identified objects at sea, from fishing boats and unlit small craft to semisubmerged containers and marine mammals, in different light levels, weather conditions, and sea states.

According to SEA.AI, when the camera detects something, the Brain can compare it to the library, attempt to identify it, trigger an alarm, and flag it on a multifunction display, so the skipper is alerted, can assess it, and make decisions. It also tracks the target and calculates real-time distance and bearing.

SEA.AI isn’t the only brand in this space. LOOKOUT, according to the manufacturer, produces a similar system with a camera plus an AI-powered brain, and also integrates AIS data, Aids to Navigation (AtoNs), and “point of interest” pins for marinas, fuel docks, and landmarks. The result is that the skipper can see an augmented reality displayed on an MFD, with hazards identified and highlighted, recommendations on which side to pass AtoNs indicated with arrows, and distant landmarks labeled with digital flags. From there, the skipper can make evasive decisions.

The idea of relying on an AI-powered camera for watchkeeping and navigational guidance can and should make boaters uneasy. To be clear, only a human can meet the requirement for a proper lookout at all times. But this can be useful supporting technology when used as a tool – and this is key – rather than a replacement for human decision-making.

If you’ve ever stood a long watch in low-visibility conditions, you know that human vision is far from perfect, and fatigue a real danger. In theory, systems like these should often be able to help spot and then alert a human watchkeeper of collision risks that might otherwise go undetected, like a vessel not broadcasting AIS or a whale’s blow in the distance.

This technology’s use in boating is new and ever-changing, so there still are many possible points of failure – perhaps, for example, if something isn’t properly labeled in the existing catalog of imagery or if the camera can’t definitively identify a new object. What we do know is that in the most recent Vendée Globe race, skipper Boris Herrmann collided with an unknown object about 900 nautical miles off the coast of Brazil, and his boat suffered severe damage to the port foil – even though Boris was using a SEA.AI system. Still, some Vendée sailors say the technology was worthwhile to have. Skipper Samantha Davies reported that SEA.AI had “alerted me to several very small boats that I could hardly see under the spinnaker, behind the waves, one of which was right in our path … the worst was avoided.” Many Vendée sailors have also worked directly with SEA.AI to provide training data and feedback, which could help improve future iterations of the system.

“As these awareness systems mature and new ones join, I look forward to increased accuracy and better indications of confidence,” said marine electronics expert Ben Stein, who considers himself pragmatic about the state of these systems as they are now, and what they could be in the future. “If an electronic assistant in watchstanding can alert you to an object, tell you what it ‘thinks’ it is, and how confident it is about the classification, then it has made your watch better and safer.”

A SEA.AI Watchkeeper camera currently starts at $4,620 (depending on features), and the Brain adds $4,940. A LOOKOUT Camera + Brain Pro system retails at $13,990, although it’s likely this tech will become cheaper over time. AI-integrated cameras may also have future implications for maritime national security and search-and-rescue so, even if you don’t bring AI-powered cameras aboard your own boat, they may well be aboard other boats with which you share the water. Keep your eyes open out there.

Seeing The Future

Every experienced boater knows that the first thing to check before hitting the water is the weather forecast. But what are you really looking at? Whether you’re checking the National Oceanic and Atmospheric Administration’s (NOAA) official marine forecast or a private weather app, the data you’re seeing has historically been the output of a physics-based weather model. Physics-based models use data gathered from weather stations, buoys, aircraft, and more, as well as historical data, to create a facsimile of the atmosphere – a literal model. Then, a supercomputer solves millions, sometimes billions, of equations to move that artificial atmosphere forward in time according to the laws of physics, generating the weather forecast. But there’s a problem: Current physics-based weather forecasting supercomputers can take hours to run one model. Enter AI, which can spit out a forecast in minutes.

Although chatbots get most of the public attention, many of biggest players in AI – including Google, Microsoft, and Nvidia – have been quietly working on weather forecasting in the background. One of the primary examples is WeatherNext, a group of AI products developed by Google DeepMind and Google Research. Developers say that AI weather forecasts like WeatherNext operate entirely differently from traditional models, and are referred to as “data-driven” rather than physics-based. Instead of solving millions of preprogrammed equations to simulate a hypothetical atmosphere, data-driven forecasts work on pattern recognition. AI models are trained on millions of historical data points, until they can identify patterns and deduce how the atmosphere will likely change without needing to solve millions of human-programmed equations every time. And not only can a data-driven forecast be generated in minutes rather than hours, sometimes they predict certain weather phenomena better than traditional physics-based forecasts. After all, we humans still don’t know everything about atmospheric physics; the patterns are in the data.

So far, one of the biggest strengths of AI weather forecasts is that they seem to be better at predicting some complex components of extreme weather, such as the rapid intensification of hurricanes. Rapid intensification – when a hurricane’s wind speed increases by at least 35 miles per hour in under 24 hours – is becoming more common due to a warming climate but can catch coastal communities off guard with little time to prepare because traditional models can struggle to forecast it.

Last fall, Hurricane Melissa made landfall in Jamaica as a Category 5 hurricane after undergoing rapid intensification, claiming 95 lives and causing over $8 billion in damage. During Melissa’s early development, traditional models wavered, with landfall predictions ranging from Haiti to Jamaica and significant uncertainty about if or how much the storm might intensify. But five days out, when Melissa was still a tropical storm, AI-powered WeatherNext claimed 80% confidence that the storm would strike Jamaica as a Category 5 hurricane. Three days out, when Melissa was still only a Category 1, WeatherNext showed 100% confidence. In Melissa’s case, the AI-powered forecast was more accurate and more confident sooner than the traditional forecasts.

It’s not just the tech giants. NOAA has officially partnered with Google’s WeatherNext, and late last year the agency deployed its own suite of AI-powered weather models that it’s using alongside its traditional models. The European Centre for Medium-Range Weather Forecasts (ECMWF), the multinational research institute behind the “Euro” weather model, has also developed its own Artificial Intelligence Forecasting System (AIFS). Even if you’re only checking basic weather apps, AI is likely there, too. WeatherNext outputs are already integrated into Google’s Search, Gemini, and Pixel Weather apps, so you may have come across these AI forecasts without realizing it.

The upsides of faster forecasts with far less computing power are very real (some AI weather models use less than 1% of the computing power of traditional physics-based models after initial training). Coastal communities may be able to receive extra days of advanced warning ahead of tropical cyclones, and those extra days could save lives.

Still, there are downsides. While physics-based models produce probabilistic forecasts, AI data-driven models are deterministic, meaning there’s no “X% chance” that comes with the output. Currently, the best way to calculate probability using AI weather forecasting is to run the model hundreds or thousands of times to create a probability spread. Additionally, sometimes the atmosphere behaves in rare ways, creating patterns that AI doesn’t recognize from its prior training. One 2026 study published in Science found that AI data-driven forecasts are currently worse than traditional physics-based forecasts at predicting record-breaking heat, cold, and wind. Because these events are rare and sometimes entirely unprecedented in our historical data, AI can’t always see the patterns that may lead to those phenomena.

Currently, most major meteorological agencies – including NOAA – believe that the future is hybrid. While AI can help us make faster (and sometimes more accurate) forecasts, traditional methods and trained meteorologists are still required when patterns can’t replace physics. As National Hurricane Center science operation officer Wallace Hogsett put it after being asked whether AI could replace human forecasters, “The answer is a resounding ‘no.’” Rather, “none of the models are perfect, and they never will be. Now more than ever, we need trusted experts in the loop to observe, synthesize, and make sense of the vast amounts of information.”

Finding Answers Faster

Boat problems are complex, often highly specific to the boat, and sometimes high-stakes. Could AI really have the specificity, depth of knowledge, and understanding of nuance that a real human troubleshooter has? The answer, unequivocally, is no. An AI chatbot cannot know why your specific engine won’t start, because it’s not standing there with tools, investigating hands-on, and exercising judgment. But it might be an invaluable tool in helping to narrow things down.

In my troubleshooting, one sometimes-infuriating task that can take the longest is tracking down obscure parts. My boat is 26 years old, many parts aren’t made anymore, and there’s no easy, searchable database. But when I gave ChatGPT a photo of my boat’s original windlass that lacks any clear branding, it was able to identify the make and model, pull up a manual, and translate that manual from French, all in under a minute. From there I was able to quickly figure out what kind of oil the manufacturer recommended and what current load the new control buttons needed to be rated to. I was still the brains of the operation, but the fact that AI could identify the exact model in a few seconds saved me significant time on a traditional search engine.

Of course, the biggest issue with AI chatbots is that they’re trained on the slop of the internet. If I ask a chatbot a boat-related question, sometimes it will instantaneously pull up an incredibly helpful answer that I may never have found otherwise, like my ancient windlass’s exact manual. Other times, it will offer someone’s useless armchair opinion from a random forum. It’s key to remember that you’re still the troubleshooter, and AI just an assistant with suggestions. Clear, specific prompts can help. Push back against answers you know to be wrong, ask for manufacturer references and peer-reviewed opinions, and require chatbots to double-check their work. Set prerequisites, like “Do not use X, Y, or Z forums as a source,” to help filter out some of the most incorrect responses.

Some marine brands are trying to combat the misinformation problem and make it easier to generate only helpful answers by creating closed AI ecosystems to answer boat-specific questions. Rather than a general chatbot with access to all the good, bad, and strange info on the internet, they create an AI ecosystem only fed human-approved material. BoatClick (boat-click.com) is one of the first marine companies in this space, and one of its first successes is an AI-powered owners’ app built for Leopard Catamarans. According to BoatClick, the app’s foundation is its “Living Digital Twin” concept. BoatClick worked with Leopard to create human-labeled and human-catalogued 3-D renderings of Leopard’s models. From there, BoatClick says each individual hull’s Digital Twin is customized to reflect specific nuances, such as upgrades or rearrangements of systems. An owner (or service provider) can also edit the boat’s Digital Twin if systems are changed or items moved.

Although 3-D renderings are nothing new, BoatClick’s primary innovation is using this Digital Twin – alongside additional human-curated information, such as manuals and guidance provided by Leopard and parts manufacturers – as the basis for the app’s Marine Chat AI. According to BoatClick, the Marine Chat AI can then generate boat-specific responses to a user’s questions. If an owner asks something like, “How do I change the impeller on my water pump?” the Marine Chat AI will generate a customized response based on the specific boat and water pump that should, in theory, be significantly more accurate than a response from ChatGPT.

Of course, the accuracy of a system like this relies on an owner committed to reliably updating their boat’s Digital Twin as soon as anything aboard is changed. A closed ecosystem also means that, if a human developer didn’t pre-upload accurate content relevant to your specific question, the AI chat may not have an answer, although that could be a good thing. Ori Gal, BoatClick’s CEO and co-founder, demonstrated to me how the Marine Chat AI would admit when it didn’t know an answer and direct someone to a human technician, rather than hallucinate a fake answer.

Although BoatClick’s AI-powered apps are currently only available for select models, it’s likely that similar concepts will spread throughout other boatbuilders and app developers. As to whether this kind of tech could replace the tried-and-true method of poring over a manual and reading vetted insight from our experts here at BoatU.S. Magazine, well, the jury’s out.

Artificial intelligence is already on our boats and in our daily lives, for better or worse. Used carelessly, it can lull us into complacency and dull our hard-earned instincts and expertise on and off the water. But used wisely, AI could help tackle some pressing (and annoying) problems in boating, offering a second set of eyes, a faster weather forecast, and even a 26-year-old windlass manual translated from French. Stay tuned.