What makes GenAI different
Conventional automation thrives only when the data are already structured. GenAI changes the rules: it reads, summarises, and decides in plain language. When you wrap a large-language model in a few deterministic guard-rails—think “voyage-ID must match XXNNN pattern”—the system can both understand messy inputs and act on them safely. The result is straight-through processing where nobody imagined it possible a year ago.
Two uses you can deploy right now
1. Intermodal order processing – inbox to TOS in seconds
The pain today: Even with EDI in place, many shippers still book rail and barge moves by e-mail; someone on your team retypes every line into ERP/TOS screens.
How GenAI fixes it:An agent reads the mail, pulls container numbers and dates from attachments, and pushes a validated order straight into the TOS. If capacity looks tight, it flags you; otherwise it auto-confirms and replies to the customer—all in under half a minute.
Why it matters: planners focus on true conflicts instead of data entry, customers get instant confirmations, and you scale rail volumes without hiring another clerk.
2. Voyage-number assignment – zero-latency vessel calls
The pain today: Arrival notices reach your IDM desk as free-text mails. The team searches the schedule, creates the voyage in ERP, and only then can berth planning start.
How GenAI fixes it: The agent recognises the vessel name or IMO, assigns the correct voyage number, updates the ERP, and triggers follow-up workflows. Berth planners see the call minutes after the line presses “send”, typos disappear, and billing disputes drop.
Why this is feasible today – not five years from now
Hybrid guard-rails now keep large-language models honest: every output is subjected to deterministic checks before anything is written back to your ERP. Cloud connectors do the heavy lifting in the background, with both pilots running on Azure and integrating through the standard APIs your TOS already exposes. Just as important, today’s commercial and open-source models handle container jargon with better than 90 percent extraction accuracy after only a few hundred in-domain examples, so you no longer need a million-record training set to achieve production-grade performance.
How your teams will work differently
Once the agents go live, routine data entry fades into the background and operations shift to true exception handling. Shift leads spend their time reviewing the occasional edge case instead of clearing backlogs, and each exception they resolve becomes a fresh training example, letting the agent improve continuously without the need for a formal second-phase project.
This is more than tactical efficiency. As the deck that inspired these pilots notes, “Artificial intelligence – especially generative AI – will fundamentally transform communication and decision-making across the logistics value chain.”
Your next possible steps
- Map the messy inboxes: where do free-text messages still enter your workflows?
- Start small: pilot with one customer or a single feeder line—aim for 70 % auto-processing, not perfection.
- Co-design guard-rails with ops and IT so everyone trusts the agent’s commits.
- Measure obsessively: handling time, exceptions, error rates. The numbers will sell the project internally.
If at any stage you’d like a sparring partner - whether to sanity-check a proof-of-concept or to tackle a thorny integration - HPC’s AI specialists are only a call or e-mail away and happy to share what we’ve learned.
Closing thought
Generative AI won’t replace the quay crane or the yard truck - but it’s already replacing the midnight copy-and-paste shift. Ports that adopt agent-based workflows now will clear vessels and trains faster, turn data into action instantly, and free their people to run the operation, not the keyboards. The technology is here; the competitive edge belongs to those who move first.
This article was written by Daniel Beck - for terminal and port operators ready to move from demos to production.
