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The Natural Evolution of Gen AI for the Maritime Ecosystem 

Generative AI has moved quickly from novelty to necessity. In maritime trade the conversation has shifted from “what is it?” to “how do we use it?” Yet real adoption is still uneven. 

The biggest barrier is not enthusiasm, but whether organizations have the right data foundations to make Gen AI practical and valuable.

Separating Signal from Noise

Generative AI has become the most overused term in business and technology. Every sector claims to be “powered by Gen AI,” and the maritime industry is no exception. 

The volume of hype often drowns out the real signal. 

Too many solutions are marketed as breakthroughs when, in reality, only a fraction of organizations have actually embedded generative AI into workflows that deliver measurable outcomes.

The challenge for leaders is to distinguish genuine value from marketing spin. And the evidence shows that when properly deployed, Gen AI is delivering tangible returns.

The Business Impact Beyond the Buzz

  • Google Cloud found that 74% of enterprises already see ROI from Gen AI, with nearly half reporting that employee productivity has at least doubled.
  • Bain reports that 95% of U.S. firms are now using Gen AI. Production-level use cases have doubled between October 2023 and December 2024, and 60% of deployments meet or exceed expectations, generating real business gains.

Why This Matters for Maritime Trade

For industries defined by complexity, regulation, and thin margins, the opportunity is not in chasing every new AI tool. It lies in applying generative AI to specific pain points. Whether automating documentation and validation, detecting compliance risks, or improving forecasting. The winners will be those who move past the noise and deploy AI where it sharpens judgment, reduces costs, and accelerates decisions.

Curation is the Cure for Data Issues 

Data is the foundation and the differentiator when it comes to successful Gen AI adoption. Applications may look impressive on the surface, but without the right data beneath them, they risk becoming ineffective.

Historically, many have relied heavily on purchasing data from third-party providers. Gen AI flips that dynamic: the real question is no longer, “What data can I buy?” but “What problem are we solving, and how do we use the sea of data already available to address it?”

Curated data is the cure. Raw, unstructured data on its own often leads to blind spots, inconsistencies, and unreliable outputs. The best practice for Gen AI is to enrich data with domain expertise, structured labeling, and rigorous quality assurance. This process transforms raw information into AI-ready datasets that can train vertical or domain-specific models. The result is more accurate, reliable, and context-aware insights, essential for industries where complexity and interdependence leave little room for error.

Structured vs. Unstructured Data

For industry decision-makers, trust in AI outputs remains a central concern. Reliable, transparent systems (backed by experienced vendors) are critical. At the core lies data: cleansed, harmonized, and continuously maintained. Without this solid foundation, even the most advanced AI will deliver inaccurate, or misleading results, eroding both confidence and value.

Effective technology must be able to handle and process structured and unstructured data. 

Structured data – such as vessel schedules, AIS data, and freight rates in the ocean logistics ecosystem is organized in predefined formats and typically stored in databases. This organization facilitates systematic access, querying, and analysis, and supports the development of AI models.

Unstructured data – including text documents and contracts, such as digital bills of lading (BoL) and port contracts between freight companies and carriers, and between freight companies and their customers – requires more sophisticated handling. 

This often involves using vector databases and embedding models to filter and extract relevant information. Managing unstructured data is a daunting task because of the convoluted processes involved. Despite the enormous potential for business growth, many companies are deterred by the time-consuming nature of these processes.

Integrating unstructured data into vector databases using embedding models helps filter and extract valuable information from text documents. Unstructured data can improve Gen AI’s performance, as it provides more context and data for training the models. 

Which Problems Are We Trying to Solve? 

Maintaining consistent workflows is particularly challenging when it involves repetitive tasks. This difficulty is amplified in expertise-specific domains, such as the shipping industry, where accurate decisions can take time, and time equals significant sums of money. This problem, together with the complexity and vastness of possible risks in the maritime ecosystem, raises three main challenges for stakeholders: 

Decreased productivity: evolving risk management requirements have significantly increased interactions between stakeholders across the supply chain. With a shortage of experts to manage these communications and increased due diligence complexity, relevant teams face productivity challenges with vessel risk screening and communication that can be extremely time consuming. 

Lack of human resources expertise: according to Gartner, one of the key challenges in the industry is hiring and training team members. Maritime is a specialized area, where the need for sanctions and risk experts far exceeds the supply. Organizations are struggling to hire, train and onboard experts – both in terms of speed and depth of domain understanding.

Counterparty communications: increased interactions between stakeholders and their counterparties has raised a standardization challenge. To increase efficiency, and maintain alignment, organizations must standardize their approach and queries to counterparties.

Once organizations recognize their specific challenges, advanced technologies, such as AI, can speed onboarding and training, and raise productivity among current employees. 

A Gen AI Agent Built for Maritime Challenges

To address these challenges, Windward has expanded our Maritime AI™ portfolio to introduce Windward Gen AI Agent – MAI Expert™. The industry’s first Gen AI agent is a virtual maritime subject matter expert that leverages Windward’s proprietary AI models and human expertise, using innovative Gen AI engines.

Designed for precision and efficiency, MAI Expert™ empowers your decision-making with comprehensive risk assessments and insight summaries. It seamlessly integrates a reliable maritime and risk expert into your daily workflows and automates repetitive tasks to offer a strategic edge, and reliability.

MAI Expert™ provides a tangible ROI per user that starts from 5 times for small organizations and up to 88 times for large ones with a significant screening volume.

MAI Expert™ in Action 

A Gen AI agent is only as good as its foundational data and the expert guidance on what to extract. We have paired our proprietary, AI-driven knowledge base with prompts created by maritime experts. These prompts instruct MAI Expert™ to consider:

Originally launched with vessel screening, MAI Expert™ now supports nine dedicated prompts, each designed to compress analysis time and increase confidence in decision-making:

  • Vessel Screening (Sanctions & Organization Defined Risk) – generates a structured vessel risk assessment, highlighting suspicious ownership changes, missing insurance, deceptive practices, or other red flags.
Risk Assessment 1
  • Adverse Media Screening – scans public sources for negative coverage, providing crucial context for sanctions and compliance reviews.
Adverse Media Screening 1024x568

  • Shipowner Query Email Generator – drafts professional inquiry emails (with selectable tone and templates) to counterparties, insurers, or clients, ensuring consistency in communications.
Email Inquiry Drafts
  • Vessel Security Analysis – identifies potential security risks tied to vessel behavior, enabling proactive monitoring.
  • Visual Link Analysis – untangles complex ownership structures, flags anomalies such as multiple recent changes, and suggests new investigative leads.
  • Shipment Summary – produces concise shipment overviews to accelerate trade documentation review and reduce manual processing.
  • Tracked Shipment Email Generator – automates customer communications, keeping stakeholders informed of shipment status with standardized updates.
  • D&D Invoice Explanation – translates complex detention and demurrage invoices into clear, structured explanations, reducing disputes and administrative workload.
  • Early Detection Anomaly Context – contextualizes anomalies with OSINT enrichment, validated for time, location, and causality, transforming anomaly detection into decision support.

Organization Defined MAI Expert™ (ODM)

For organizations with unique requirements, ODM allows MAI Expert™ to be customized. Existing workflows can be enriched with private or third-party data — such as contracts, BOLs, or API feeds — creating contextualized agents that reflect each organization’s policies and risk appetite.

Toward Workflow Automation

The next phase is workflow automation: connecting multiple MAI Expert™ agents to handle entire processes end-to-end. From anomaly detection to document verification and automated counterpart communications, this modular approach compresses time-to-decision and standardizes outputs across teams.

The Impact

By combining deep maritime expertise, proprietary data, and explainable AI, MAI Expert™ reduces vessel screening and investigation times by 20 minutes on average. With expanded prompts, ODM customization, and workflow automation on the horizon, stakeholders can increase throughput, ensure transparent decision-making, and scale growth — without scaling headcount.

The Gradual Approach & Precise Use Cases

Adopting Generative AI is not about speed, but about fit. Rushing to scale without the right data and processes risks wasted effort. The organizations making the most progress are those that start with specific, high-value use cases and build from there.

In maritime and global trade, that means using Gen AI where it can deliver real leverage, turning vast amounts of unstructured data into practical intelligence, streamlining compliance reviews, or accelerating decision-making across the supply chain.

As adoption matures, the conversation will shift from experiments to measurable outcomes. The real question for leaders is no longer if Gen AI can be applied, but where it will make the biggest difference first.

Make Gen AI Work for You