Editor’s Comment by Intent Communications CEO: Namrata Nadkarni. Image credit: generated using gemini

We’ve all seen the headlines. A maritime tech startup announces a new ‘AI-driven’ routing algorithm, or a legacy hardware provider claims their latest sensor package is ‘fully AI-enabled.’ Suddenly, valuations soar, boards celebrate, and the industry nods along in collective fascination.

It feels a lot like the broader market madness we’ve witnessed recently. Take the famous case of ‘NewBird AI’ (formerly All Birds), a company that sold off its merino wool shoe manufacturing business and saw its stock price skyrocket overnight simply by announcing that it was an AI company. Investors didn’t stop to ask what the AI was actually doing, they simply bought the hype.

It really reminds me of the story of  ‘the emperor’s new clothes’, where a vain emperor is tricked into wearing invisible ‘magic’ clothes that can only be seen by the wise, but is actually parading naked. He is not challenged by any of his subjects who pretend they can see the clothes, and it is only when a child asks why the emperor is naked that everyone realises that no one else can see the clothes either – and that they were all fools.

AI is being presented as an amazing solution to virtually every problem. But this is an extremely flawed approach. As we have pointed out before in the Demystifying Digitalisation and AI in Maritime project, AI is an umbrella term covering vastly different technologies, of which these are the most prevalent:

• Machine Learning (ML): Predictive models that find patterns in historical data (e.g., predicting engine failure or identifying the best times for people to make company announcements to generate media/market interest).
• Generative AI (LLMs): Large Language Models are tools that predict patterns and are thus able to create text and code for documentation including manuals, the creation of code to do specific outputs or even to answer questions via platforms such as ChatGPT or Perplexity.
• Agentic AI: A bot that is able to execute commands, whether this means answering questions, making bookings, organising data etc.

Right now, the maritime sector is suffering from a severe case of rebranding air into invisible clothes. Features that were called ‘automation,’ ‘data analytics,’ or ‘digital insights’ five years ago have suddenly been repackaged as artificial intelligence. Why? Because the market demands it. Companies feel compelled to slap an AI label on their products to justify their value and command a premium.

I recently spoke with a senior leader at a maritime organisation who told me that they were ‘AI enabled’ – and when I pushed for clarification, they said, “we have given Co-Pilot access to our SharePoint.” While this technically meets the qualification to be AI enabled, I definitely felt that the manner in which it was being presented was intended to mislead.

And I am seeing this trend everywhere, with an increasing number of (maritime and non-maritime) vendors claiming their solution offers ‘AI-driven optimisation’ or ‘enhanced performance,’  without details of the specifics. By hiding behind vague language, suppliers are making it impossible for ship owners and operators to properly assess what they are buying. What does ‘more efficient’ actually mean in terms of fuel burn, crew workload, or port turnaround times?

This lack of transparency introduces three massive risks for ship operators: it creates a ‘premium trap’ where companies pay inflated prices for rebranded automation they already use; it hides dangerous algorithmic flaws and safety vulnerabilities introduced by AI deployed without proper governance; and it suffocates genuine innovation by drowning out  groundbreaking applications in a sea of marketing noise.

For maritime to truly reap the benefits that AI offers, we need to demand specific details. When assessing a solution, we should be asking which type of AI it uses, what specific problem it solves, and how that enablement delivers measurable ROI. If the person is not able to fully flesh out specifics of how the product or solution works, then it’s time to show them the door.

Simply put, we should stop nodding along in the court of a naked emperor and be unafraid to ask the questions that will get to the heart of the matter: will the use of AI actually resolve a specific pain point.

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