# Leveraging Large Language Models for Enhancing Safety in Maritime Operations
## 6 Challenges and Limitations
### Operational Challenges
#### 6.3.2 Cost and Resource Allocation  
The implementation of LLMs represents a significant financial investment for maritime organizations. Initial costs include the acquisition of hardware capable of supporting the computational demands of advanced AI models, procurement or development of the software itself, and expenses related to data collection, storage, and processing. Additionally, organizations must consider the costs associated with cybersecurity measures to protect sensitive information and comply with data protection regulations. Long-term maintenance considerations further impact resource allocation. LLMs require ongoing updates to maintain performance, address security vulnerabilities, and adapt to evolving operational needs. This includes software updates, model retraining with new data, and potential scalability enhancements. Organizations must allocate funds for technical support services, whether in-house or through external vendors, to ensure the sustained functionality of these systems [177; 178]. Financial investment extends beyond the technological infrastructure to encompass human resources. As previously discussed, training programs are essential and entail costs related to curriculum development, training materials, and potential downtime during which personnel are engaged in learning rather than operational duties. There may also be a need to hire specialized staff with expertise in AI and data science to oversee the integration and maintenance of LLMs [179]. To address these financial challenges, organizations should conduct comprehensive cost-benefit analyses to evaluate the return on investment associated with LLM implementation [180]. These analyses should consider not only the direct costs but also the potential gains in operational efficiency, risk reduction, and compliance adherence. Exploring funding options such as grants, partnerships, or phased implementation strategies can help distribute costs over time and reduce the immediate financial burden [181]. Resource allocation must be strategically planned to balance the organization's operational priorities with the investment required for LLM integration. This includes setting realistic budgets, timelines, and performance metrics to monitor progress and adjust strategies as needed. By carefully managing costs and resources, maritime organizations can successfully implement LLMs in a sustainable manner that supports long-term operational objectives. Despite their transformative potential, Large Language Models (LLMs) are not without limitations, and these challenges must be addressed to ensure their safe and effective integration into maritime operations. One critical limitation is the occurrence of hallucinations, where LLMs generate outputs that are factually inaccurate or inconsistent with the provided data. For example, in test scenarios involving maritime communication, LLMs have occasionally misinterpreted regulatory instructions, leading to potentially misleading recommendations. Such inaccuracies pose significant risks in safety-critical environments, where precise and reliable information is paramount. Additionally, LLMs often struggle with context-specific decision-making, particularly when faced with ambiguous or incomplete data. In the maritime industry, where decisions must frequently account for dynamic factors like weather changes, cargo conditions, and regulatory compliance, the reliance on LLM outputs without human oversight could result in suboptimal or even hazardous outcomes. Furthermore, the \"black-box\" nature of many LLMs complicates interpretability, making it difficult for operators to fully trust or understand the rationale behind certain recommendations. To mitigate these limitations, several strategies should be adopted. A human-in-the-loop (HITL) approach is essential, where human operators remain actively involved in evaluating and validating LLM outputs before implementation.