PIL Maps How Its Own Company Actually Works — Before Pointing AI At It

Pacific International Lines has partnered with Celonis and WNS to expand a global Process Intelligence Centre of Excellence, and the reasoning behind it is the most useful thing any carrier said this week: you cannot get value from enterprise AI until the AI knows how your company actually operates. The scale: PIL ranks among the world's top 12 container carriers, the largest home-grown carrier in Southeast Asia, running more than 100 container vessels serving customers at over 500 locations in more than 90 countries. WNS, now part of Capgemini, brings the shipping and logistics domain expertise to embed the tooling; Celonis supplies the process-intelligence platform. What it actually builds: a real-time digital twin of PIL's business processes — not of a vessel or a terminal, but of the workflows themselves. Where a booking stalls. Which exception path a shipment took and why. Where the same task is done three different ways in three regions. The stated outputs are end-to-end visibility from customer-facing operations through to back office, faster identification of inefficiency through process standardisation across trade operations, and explicitly, a foundation for AI-driven automation. The quote that carries it is from PIL Chief Commercial Officer Lionel Chatelet, who described dealing with dozens of different systems across the globe and not being able to see where things are actually slowing down. Celonis APAC VP Pascal Coubard put the AI logic directly: giving enterprise AI a holistic, living model of business operations creates the conditions for real outcomes and rapid return. Why this matters for ship managers: this is the least glamorous story in the digest and possibly the most transferable. Most maritime AI disappointment traces back to the same root — the model was pointed at a process nobody had mapped, in data nobody had standardised, inside an organisation where the official workflow and the real workflow diverged years ago. Process mining is how you find that gap before you automate it. Automating a broken process just produces broken outcomes faster and with less human sight of them. What to do now: before your next AI procurement, pick one workflow you believe is standard across your offices — port disbursement approval, crew change planning, defect reporting — and actually trace how it ran on the last twenty occurrences. If the variation surprises you, that variation is what your AI will learn. The honest caveat: this is an announcement, not an outcome. No cycle-time reduction, cost saving, timeline or contract value was published, and both Celonis and WNS are vendors quoted describing the value of their own products. Process-mining programmes have a well-documented tendency to produce beautiful dashboards and no operational change unless someone senior owns the fixes. [Container News · AJOT]

This story is part of the Maritime AI Digest of 9 August 2026.