AskChief CEO: Shipping’s AI edge lies in deployment
The real advantage lies in how companies deploy AI, not the technology itself, says AskChief’s CEO.
The shipping industry is entering a new phase of artificial intelligence adoption, where success hinges not on acquiring technology but on how effectively companies integrate it into their operations. According to Michalis Gkontas, co-founder and CEO of AskChief, the real advantage lies in transforming widely available AI tools into specialized systems that enhance decision-making and workflows.
Modern AI systems can already perform valuable knowledge-based tasks, such as searching and interpreting large volumes of information, comparing documents, and assisting with complex technical work. However, simply providing employees with access to these tools is not enough. Without proper integration, AI-generated outputs, though polished, may lack critical context, such as the vessel’s specific circumstances, applicable procedures, or supporting evidence.
From generic tools to specialized systems: shipping ai deployment
A generic AI model does not inherently understand a company’s safety management system (SMS) versions, equipment-specific manuals, or approval processes. The key to effective deployment lies in building a tailored system around the technology, incorporating company knowledge, role-specific instructions, workflows, and review mechanisms. While the underlying AI may be general-purpose, the operational system must be customized to fit the organization’s needs.
Vague directives like “use AI more” often lead to inconsistent results, as employees are left to determine the technology’s purpose and reliability. A more effective approach is to start with a well-defined, recurring task, such as investigating a technical issue, preparing for an inspection, or assessing regulatory impacts on company procedures. This allows teams to see AI’s value in familiar workflows before scaling its use.
Strategic deployment and the 10-80-10 rule
Gkontas suggests the 10-80-10 rule as a framework for structuring AI-assisted work. The first 10% involves an expert defining the problem, providing context, and setting quality standards. The middle 80% is handled by AI, which gathers and structures information, compares records, and prepares drafts. The final 10% returns to human oversight, where professionals challenge results, make corrections, and approve actions. This balance shifts based on risk, safety-critical decisions demand more human review than routine tasks.
For AI to succeed in shipping, leaders must establish clear expectations, designate responsible owners, and create feedback loops. Teams should share successful use cases, identify weak outputs, and refine workflows. Trust in AI grows when users can verify underlying evidence and see that professional responsibility remains central. Ultimately, the industry’s AI leaders will be those who build robust systems around the technology, not just those who adopt it.
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