Introduction
Demurrage has always lived in the margins—between clauses and assumptions, between what happened and what someone is willing to accept. But as chartering becomes more digital, AI in demurrage management is moving from theory to practical leverage. At the Demurrage Innovation Forum 2025 in Houston, a panel of legal, technical, and operational minds came together to explore how artificial intelligence is reshaping the way demurrage is negotiated, documented, and ultimately understood.
What emerged wasn’t a tech sales pitch. It was a sober view of a market in flux—where human judgment still matters, but must now coexist with systems that never sleep, never forget, and never bluff.
“Today, AI is not going to solve every problem,” said Utsav Mathur, Partner at Norton Rose Fulbright. “But if you use it to triage small claims, guide new hires, and surface smart arguments—it becomes more than automation. It becomes leverage.”
1. AI as Mentor, Not Just Machine: Fixing the Talent Gap in Demurrage
One of the Forum’s most human moments had nothing to do with machine learning. It was about people—or the lack of them.
“Seasoned demurrage managers are few and far between,” said Voyager CEO Matthew Costello. “The people who can spot a bad clause or write a sharp counterclaim are retiring faster than they’re being replaced.”
It’s a familiar challenge. In-house legal teams don’t have time for routine claim reviews. Ops teams are overstretched. And new demurrage hires often arrive with little industry context—making preventable errors early and taking months to become productive.
The panel suggested AI has a role not just as a calculator, but as a mentor.
“Imagine a system that says, ‘You’ve calculated this claim. Your colleague handled a similar one last week—did you notice they applied a different deduction?’” said Costello. “That’s not automation. That’s coaching.”
The implication? AI can serve as an institutional memory—surfacing similar cases, flagging deviations, and accelerating the development curve for new analysts.
2. Triage, Not Trial: How AI Redefines Risk Thresholds in Claims
Every legal team knows that not every demurrage dispute deserves escalation. But in reality, many small or mid-size claims clog workflows, generate delays, or languish in inboxes. AI can change that equation—if used with care.
“You’re not hiring me to argue about a $33,000 invoice,” said Mathur. “You’re hiring me when a comma could cost millions.”
By classifying claims by value, risk profile, or likelihood of contestation, AI tools can help teams focus where it matters. Low-value claims can be auto-processed within predefined parameters. Medium claims can be tagged for internal review. High-value disputes still get legal attention—but with cleaner records and better logic upfront.
It’s not about cutting corners. It’s about recognizing that legal and ops capacity is finite—and aligning it with exposure.
“Start small, stay small, grow big,” Mathur advised. “Use automation where the downside is negligible. Build confidence. Then scale.”
3. Clause Logic vs Clause Culture: What AI Still Can’t Learn Alone
Technology doesn’t operate in a vacuum—and shipping contracts don’t always reflect what parties actually enforce. The panel dove into this cultural friction, where contractual text and commercial reality part ways.
“Some contracts say a NOR isn’t valid until accepted,” said Mathur. “Almost nobody enforces that. But will your AI know that?”
It’s a fair challenge. A model trained only on legal text may interpret a clause literally. But a seasoned charterer knows what the industry does in practice—and when to let something slide for the sake of long-term commercial relationships.
Chuck Hollis, Head of AI at Norton Rose Fulbright, warned of the risks: “If the model starts bluffing with unfamiliar clauses or hallucinated precedents, it loses credibility—and so does the analyst using it.”
The key isn’t to let AI decide what matters. It’s to teach it the boundaries—what’s black letter, what’s grey area, and what’s simply never been enforced.
“Don’t compare AI to perfection,” said Mathur. “Compare it to a 22-year-old on day eight writing clauses at 4 a.m. It’s probably still an improvement.”
4. Consistency as Strategy: Institutionalizing the Best Claim Tactics
At the operational level, one of the most overlooked sources of value is consistency. With global teams interpreting the same contract differently, outcomes vary—not just in language, but in financial results.
“We see the same COA handled three different ways—Singapore, London, Houston,” said Costello. “Why aren’t we aligning around the best version?”
The answer may lie in how organizations share learnings—or fail to. AI can help surface which interpretations yield the best outcomes, then nudge other analysts toward them.
“You don't even need to train the model on everything,” said Voyager CTO Matthew Eric Bassett. “Just optimize for your best result—and apply it systematically.”
Legal strategy often works the same way. As Mathur noted, LNG and commodities firms sometimes choose one ideal case to fight—just to create a precedent they can use on future invoices. Technology, in that sense, can replicate the same playbook internally.
“Instead of hoping 100 analysts remember to coordinate,” said Mathur, “why not have one system that reminds them all?”
Closing
The future of AI in demurrage management isn’t about replacing people—it’s about elevating judgment, reinforcing decisions with precedent, and freeing teams from the drudgery that masks real risk.
At the Forum, the message was clear: AI isn’t coming for charterers. But it is coming for inefficiency, inconsistency, and institutional amnesia.
The question isn’t whether it can replace your best analyst. The question is:
What if it could help everyone else perform like your best analyst—on day one?
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