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Guide de l'IA

The arrival of Artificial Intelligence in the consumer market has truly shaken the our understanding of what was previously possible with technology. Interestingly, these changes have already been brewing within the seafood industry for some time.

Read our AI Guide to learn more about how these innovations are contributing to traceability efforts – and your ROI.

Raw Material Costing - Dynamic Yields

Guide de l'IA

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A Deep Dive into AI and Seafood Cost Accounting

The only thing as unpredictable as the weather in the seafood industry is cost accounting.
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Navigating Uncharted Waters: Data Ownership, Security and Risk in the AI-Driven Seafood Industry The seafood industry is undergoing a digital transformation. From computer vision on fishing vessels to machine learning in processing plants and Generative AI (GenAI) in corporate offices, artificial intelligence is no longer a futuristic concept—it is an operational reality. However, as the industry scales these technologies, it sails into uncharted legal waters. Understanding the intersection of data ownership, security, and risk is essential for any seafood enterprise looking to innovate without sinking under the weight of regulatory or legal challenges. Defining the Tools: Beyond the GenAI Hype To understand the risks, one must first distinguish between the types of AI being deployed in your business. While many remain fixated on GenAI—such as ChatGPT, Claude, Grok and Gemini—the seafood industry relies heavily on Machine Learning (ML) for predictive analytics and Computer Vision (CV) for quality control and fisheries electronic monitoring. A critical risk distinction lies in whether an AI model is deterministic or probabilistic. Many machine learning models used in logistics or production are deterministic: given the same input, they repeatedly produce the same output. The AI algorithm is deterministic, not unlike 1 + 1 = 2. Conversely, neural networks used in computer vision and GenAI are probabilistic: they make data-driven predictions based on patterns in the data. Because these AI models "guess" the most likely outcome, they introduce a margin of error that has significant implications for liability and compliance. For example, an AI-enabled smart camera might detect a defect, such as a bruise, on a salmon fillet with a confidence level of 72 percent. A smaller or lighter bruise might have a confidence level of only 30 percent. Liability and the "Hallucination" Trap Perhaps the most daunting legal frontier is liability for AI errors, or "hallucinations." A landmark case involving Air Canada saw the airline held liable for a chatbot that hallucinated a non-existent bereavement policy. For the seafood industry, the stakes are even higher. If a probabilistic AI incorrectly clears a batch of fish that violates food safety regulations or customer specifications, who is financially liable? To mitigate this risk, companies can set up guardrails to limit AI’s ability to hallucinate using a method called Retrieval-Augmented Generation (RAG). Instead of letting a GenAI browse the entire web for answers to a food safety question, for example, a RAG may restrict the AI to a specific, trusted dataset—such as a company’s internal SOPs, quality management plan or FDA documentation. This creates "guardrails" that force the AI to cite only authoritative sources and provide links for human verification. Another strategy involves "calibrating for caution." In food safety, a "false negative" (letting a defect pass) is far more dangerous than a "false positive" (flagging a good product as defective). By lowering the "confidence threshold" of an AI-enabled smart camera, for example, a plant might reject three percent of products to ensure that a one-percent actual defect rate is caught. A human would then inspect the three percent of rejected products, returning any false positives back to the production line. In this case, AI automates 97 percent of inspection with humans only inspecting three percent. There is also a lot of research demonstrating that computer are better at image recognition than humans and so the risk of using AI in visual inspection should be lower than using human inspectors. Data Ownership: Universal vs. Proprietary Models Data is the fuel for AI, but who owns the "engine" once the fuel is burned? Data is so critical because it trains AI models to be smart. Seafood companies must navigate three primary AI model ownership use cases: 1. Universal Models: These AI models are trained on non-confidential, non-proprietary data. For example, an AI model that identifies species on a fishing vessel or detects defects on fish fillets use training images that typically have no confidential information in them. Seafood companies should work together to build robust universal models that can help standardize and improve processes across the industry. 2. Proprietary Models: These AI models involve highly sensitive operational data used to forecast demand or optimize supply chains or production processes. In these cases, the trained model itself becomes a trade secret. Companies with superior data can build superior models, creating a significant competitive advantage. Quality data is hard to come by in the seafood industry and so companies that possess it will have a competitive advantage. 3. Mixed Models: This is the "middle ground" where a universal AI model may be customized or augmented with proprietary data. A seafood company, for example, might take a standard AI model for visual inspection of seafood and fine tune it to their specific, subjective grading standards by adding additional images to the model, reannotating existing images and calibrating its confidence level. In this case, you are customizing a universal AI model. When signing software licensing agreements with tech providers, seafood companies must be vigilant about the different types of AI models and whether the data they are sharing is proprietary should be kept confidential as a competitive advantage. In some cases, seafood companies may want to contribute data to develop a robust universal AI model to improve and standardize the entire industry; in other cases, companies should protectively guard their data. What’s next is a cautionary tale. Leaking Proprietary Data through "Shadow AI" Last year, MIT published The GenAI Divide: The State of AI in Business 2025 (Download PDF). in which they survey 153 business leaders. Astonishingly, while only 40 percent of companies say they purchased an official GenAI tool subscription such as ChatGPT, 90 percent of employees reported regular use of personal AI tools for work tasks. They called this “Shadow AI”. “This shadow economy demonstrates that individuals can successfully cross the GenAI Divide when given access to flexible, responsive tools,” the researchers wrote. “The organizations that recognize this pattern and build on it represent the future of enterprise AI adoption.” The risk is that employees may be unintentionally leaking confidential commercial data to AI companies like OpenAI, Google and Meta since the free versions of their AI tools have a default privacy setting allowing them to use AI chatbot conversations to train their models. If an employee uploads confidential customer lists or proprietary processing formulas to a public GenAI tool, that data may become part of the tool's training set, effectively destroying the company’s claim to confidentiality. The most prudent strategy is for seafood companies to officially sanction the use of paid AI tools and double check the privacy settings on the terms of use for their software subscription. Conclusion: The Human Benchmark Ultimately, the risks of using AI are low and manageable, and the rewards can be significant across your business: improving employee productivity and satisfaction, strengthening quality inspection, reliably forecasting demand, optimizing production processes and supply chains. AI does not need to be perfect to be commercially viable; it simply needs to be as good as, or better than and cheaper than, a human performing the same task. By implementing RAG guardrails, maintaining clear data-ownership contracts, and eliminating "shadow AI," seafood leaders can harness the power of artificial intelligence while managing the risks and reaping the rewards.

Guide de l'IA

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Navigating Uncharted Waters: Data Ownership, Security and Risk in the AI-Driven Seafood Industry

Learn about navigating the risks and reaping the rewards of AI in your seafood business.
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Fish n' Chips Index

Guide de l'IA

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5 AI Trends to Watch in 2026 for Seafood

Beyond all the AI hype, here are 5 trends that you should watch for in 2026 as AI sweeps into seafood.
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A worker collects and annotates images to train an AI model in a tuna cannery.

Guide de l'IA, Nouvelles

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Revolutionizing Seafood Visual Inspection with AI-enabled Smart Cameras

AI-enabled smart cameras can inspect quality and food safety in seafood manufacturing with accuracy and efficiency not possible with human inspectors.
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Guide de l'IA

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Les marchés en ligne diversifient le commerce de gros et favorisent la collaboration au sein de la chaîne d'approvisionnement

La pandémie a perturbé les chaînes d'approvisionnement et a poussé de nombreuses entreprises de produits de la mer à se connecter en ligne pour la première fois afin de vendre directement aux consommateurs.
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Guide de l'IA

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  • 7
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La vérification des données et l'analyse avancée stimulent la concurrence entre les fournisseurs de technologies de traçabilité

Les récents problèmes de la chaîne d'approvisionnement et, bien sûr, la pandémie ont poussé à la mise en place de meilleurs systèmes de traçabilité.
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Guide de l'IA

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  • 7
  • temps de lecture min

La surveillance électronique des pêches à l'échelle mondiale grâce à l'IA

Les pêcheries ont de plus en plus recours à des routines de surveillance électronique alimentées par l'IA.
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Guide de l'IA

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  • temps de lecture min

Suivre la croissance explosive de l'IA dans l'aquaculture

L'aquaculture a connu une explosion remarquable de l'innovation en peu de temps.
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TallyBot / Intelligence artificielle

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TallyTag / Surveillance de la chaîne du froid

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