Manual processes bottlenecked 34 ports and 270 vessels. Now, employees deploy AI agents to balance containers and optimize fleet speeds.
A global logistics organization overseeing 34 ports and terminals and a fleet of 270 vessels that handle complex, data-heavy processes across international supply chains.
Managing this vast network relied on paper-heavy procurement, fragmented knowledge bases, and manual processes for complex tasks like balancing empty...
“AI is a major part of everything we do—it’s not separate from our core operations.”
Global trade, logistics, and industrial zone operator based in the UAE.
Enterprise software, cloud infrastructure, and consumer electronics platform.
AD Ports Group's Operations automation is part of this use case:
Related implementations across industries and use cases
Staff manually searched files to resolve vessel delays. Now, they use Copilot to organize past data into tables for rapid responses.
Deciphering messy emails and handwritten PDFs took employees 7 minutes per order. Now, AI parses the unstructured data to build orders.
Routine code and junior support bogged down leads. AI now lets juniors work independently, freeing seniors for complex architecture.
Scattered systems left a long tail of manual actions untouched. AI agents now operate UIs directly to run workflows, escalating exceptions.
Data scattered across 700 tables forced operators to perform 100 daily manual tasks. Now, AI agents autonomously execute these workflows.
Attendants couldn't memorize procedures for 340 equipment types. Now, an AI app surfaces exact fixes directly to frontline staff.
Complex logistics terms bottlenecked output. AI trained on definitions now drafts posts, helping new hires ramp in three weeks.
Pulling answers from a sprawling customer dataset once meant weeks of SQL; now anyone asks in plain language and explores it in seconds.
Manual underwriting and billing created operational bottlenecks. AI now automates submissions and policy checks, freeing brokers for advisory work.
Manual processes bottlenecked 34 ports and 270 vessels. Now, employees deploy AI agents to balance containers and optimize fleet speeds.
A global logistics organization overseeing 34 ports and terminals and a fleet of 270 vessels that handle complex, data-heavy processes across international supply chains.
Managing this vast network relied on paper-heavy procurement, fragmented knowledge bases, and manual processes for complex tasks like balancing empty...
“AI is a major part of everything we do—it’s not separate from our core operations.”
Global trade, logistics, and industrial zone operator based in the UAE.
Enterprise software, cloud infrastructure, and consumer electronics platform.
AD Ports Group's Operations automation is part of this use case:
Related implementations across industries and use cases
Staff manually searched files to resolve vessel delays. Now, they use Copilot to organize past data into tables for rapid responses.
Deciphering messy emails and handwritten PDFs took employees 7 minutes per order. Now, AI parses the unstructured data to build orders.
Routine code and junior support bogged down leads. AI now lets juniors work independently, freeing seniors for complex architecture.
Scattered systems left a long tail of manual actions untouched. AI agents now operate UIs directly to run workflows, escalating exceptions.
Data scattered across 700 tables forced operators to perform 100 daily manual tasks. Now, AI agents autonomously execute these workflows.
Attendants couldn't memorize procedures for 340 equipment types. Now, an AI app surfaces exact fixes directly to frontline staff.
Complex logistics terms bottlenecked output. AI trained on definitions now drafts posts, helping new hires ramp in three weeks.
Pulling answers from a sprawling customer dataset once meant weeks of SQL; now anyone asks in plain language and explores it in seconds.
Manual underwriting and billing created operational bottlenecks. AI now automates submissions and policy checks, freeing brokers for advisory work.