A new report by Thetius, commissioned by Lloyd’s Register, explores the current state of operational AI in the shipping industry and examines the latest trends and developments in AI applications.
According to the “Beyond the horizon: Opportunities and obstacles in the maritime AI boom” report, AI solutions for shipping continue to grow. In the last year, the industry has seen a wave of new AI technologies launched to improve energy efficiency, cut emissions and boost safety. The market is now worth a staggering US $4.13 billion, according to Thetius data, but in order to make the most of this growing market, shipping stakeholders must understand when, where, and why to in vest in AI technologies. Thus, the report suggests the following:
#1 Start with a specific problem and implement in iterations
When deploying AI technologies, it makes sense to do so in iterations to ensure the best risk management. For example, start by using AI and ML to automate repetitive, easy processes, giving people the opportunity to focus on more complex tasks. An easy example is email organization. Streamline time-consuming processes, then move on to bigger tasks before tackling the seriously complex ones. Implementing AI solutions incrementally allows for testing and optimization at each stage.
This approach helps to identify potential issues early and make necessary adjustments without disrupting operations. It is important to recognize that AI is a suite of tools and not one specific tool in a toolbox. AI should not be deployed in isolation; it should be combined with other technologies to create the most value.
#2 Don’t be afraid to embrace AI
While it is essential to manage the risks and remain cautious of AI’s capabilities, it is equally important to take the initiative. Do not wait for larger players to act first or delay until more advanced technology becomes available. Embrace the opportunity to be a first mover. Even if a more advanced solution emerges later, you will already have a foundation in place and can continue to build upon it.
#3 Consider AI as a stakeholder to foster trust and transparency
One of the challenges with the further growth and adoption of AI is the lack of transparency and trust around its decision-making process. This can generate feelings of unease and limit trust in the technology. One train of thought echoed by AI academics and researchers is that by engaging with AI like a human stakeholder, barriers to transparency and trust in the technology can be minimized. This two-way interaction fosters a culture of collaboration and trust, pushing users to embrace the technology rather than disengage with it out of fear. Systems such as AI will only be effective in the long run if users are confident and willing to engage with them.
Organizations can further enhance trust in AI by focusing on change management. Sharing knowledge and being transparent about successes and challenges can enhance the value of applying technologies like AI, especially in areas where caution and uncertainty remain.
#4 Ensure your AI can rapidly troubleshoot problems
AI must be able to rapidly troubleshoot problems to prevent minor issues from turning into significant failures. This is particularly important in remote operations where rapid detection of machinery issues is necessary to avoid costly vessel downtime. High-quality data is crucial for accurate detection and diagnosis, ensuring that repairs are targeted and effective.
#5 Deploy benefit tracking to understand the value of AI
AI solutions should incorporate benefit tracking to help users clearly identify the gains from deployment. Often, the advantages of implementing a particular solution aren’t immediately obvious. Benefit tracking highlights progress and helps assess how and when previous losses occurred in the absence of AI.
#6 Consider revenue creation, not just cost
AI is often seen as a tool for cost reduction, but Daniel Jacobsen, Vice President of Artificial Intelligence at Lloyd’s Register OneOcean, suggests it should chiefly be viewed as a means to generate revenue – something with no upper limit. AI can drive revenue growth in various ways, such as offering more accurate insurance models or very accurate predictive maintenance solutions.
