Deploy AI agents to coordinate shipments, process documents, manage exceptions, support customers and keep work moving across your logistics ecosystem.
From freight forwarding and warehousing to transportation and customer service, agentic AI helps logistics teams move beyond fragmented workflows and manual coordination towards intelligent, connected operations.
Augment your workforce. Accelerate operations. Deliver greater visibility and service at scale.
The logistics industry has long invested in technologies that improve the physical movement of goods, from automated warehouses and smart inventory systems to route optimisation and real-time shipment tracking.
The next transformation is happening in the knowledge and coordination work surrounding those physical operations.
Traditional automation works well when processes are predictable and rules are clearly defined. Yet many logistics workflows involve unstructured information, changing conditions, multiple parties and decisions that require context.
Agentic AI extends automation into this more complex layer of work. AI agents can understand requests, retrieve information from multiple systems, reason within defined business rules, coordinate multi-step workflows, communicate with stakeholders and execute authorised actions, with human oversight where judgement or approval is required.
Teams still spend significant time reading and processing emails, orders, invoices and shipping documents.
Work spans customers, suppliers, carriers, freight forwarders, portals, enterprise applications and communication channels.
Shipment milestones, delays, exceptions and customer commitments require ongoing attention and follow-up.
Operational teams weigh cost, capacity, timelines, service requirements and business rules before acting.
Customers increasingly expect faster responses, proactive updates and greater visibility across the shipment lifecycle.
| Area of work | Traditional operations | Digitally automated operations | Agentic operations |
|---|---|---|---|
| Information gathering | Employees manually gather information across systems. | Rules automate selected process steps. | AI agents interpret information and coordinate work across systems. |
| Operational monitoring | Teams continuously check shipment status. | Dashboards provide operational visibility. | Agents monitor events and proactively initiate appropriate actions. |
| Documentation | Employees prepare and review documents manually. | Templates and workflow tools reduce data entry. | Agents extract, validate, prepare and route documents. |
| Exception handling | Every exception requires manual investigation. | Exceptions are routed to employees. | Agents investigate, recommend actions and escalate when required. |
| Customer communication | Updates depend on staff availability. | Automated notifications provide standard updates. | Agents deliver contextual updates and respond across channels. |
| Knowledge application | Knowledge is distributed across employees. | Procedures are documented digitally. | Agents apply approved knowledge, policies and operating rules consistently. |
As AI agents take on more transactional and coordination-intensive activities, logistics employees can spend more time on judgement, relationships, operational decisions and innovation.
Understand what AI agents can do, how to work with them and when human intervention is required.
Interpret operational data, evaluate AI-generated insights and use information to support decisions.
Understand how work flows across customers, carriers, warehouses, applications and functions.
Investigate unusual situations, assess trade-offs and act when standard workflows are insufficient.
Delegate appropriate work, review outcomes and provide feedback to improve performance.
Validate recommendations, recognise operational risks and apply business context.
Build trust, negotiate outcomes and manage complex stakeholder relationships.
Respond to evolving technologies, customer expectations and operating models.
AI agents can support individual activities or collaborate across an end-to-end logistics workflow, from order intake and freight planning to documentation, monitoring, customer communication and reconciliation.
Provide fast, contextual support across email, chat, messaging applications and other channels.
Coordinate customer requirements, rates, pricing rules and approvals to accelerate quotation turnaround.
Coordinate the activities required to move shipments from planning through delivery.
Move from reactive tracking to proactive operational intervention.
Reduce manual effort in preparing, reviewing and managing logistics documentation.
Support customs workflows while keeping regulatory judgement and approvals with qualified employees.
Convert unstructured customer requests into validated orders and downstream actions.
Help planners and warehouse teams identify risks and make faster inventory decisions.
Continuously assess demand signals to support responsive planning decisions.
Automate invoice processing while routing discrepancies and complex exceptions to employees.
Automate the collection and preparation of logistics-related sustainability information.
Every logistics operation is different. Build an AI agent around your workflows, systems, business rules and operating requirements.
Each function above maps to a dedicated solution page with implementation detail, system integrations and typical deployment scope. Deploy a focused AI agent for a high-value process, establish measurable value and then scale across connected workflows.
Coordinate shipment planning, freight-rate sourcing, bookings, shipping documentation and shipment tracking.
Explore Freight Management →Automate document processing, customs-related workflows, compliance checks and process tracking.
Explore Customs Declaration →Analyse demand signals, improve forecast responsiveness and support better inventory decisions.
Explore Demand Forecasting →Interpret customer orders, validate information and initiate fulfilment workflows across your systems.
Explore Sales Order Intake →Extract, validate and reconcile invoices while routing discrepancies for human review.
Explore Invoice Reconciliation →Provide contextual, round-the-clock support across customer communication channels.
Explore Customer Service →Extract emissions information from logistics documents and automate reporting workflows.
Explore Sustainability Reporting →Design an AI agent around your workflows, systems, business rules and operating requirements.
Discuss Your Use Case →Start with a focused business problem. Establish value, controls and employee confidence. Then expand towards connected workflows and collaborative AI agents.
Assess logistics workflows to identify high-value, feasible and governable opportunities.
Introduce AI as a digital copilot without automatically executing critical actions.
Enable AI agents to execute selected processes within approved rules, controls and human approval points.
Connect specialised agents across an end-to-end logistics workflow.
Redesign work around human-AI collaboration rather than simply automating existing steps.
The objective is not autonomy at all costs. It is the right level of autonomy for each workflow, decision and business risk.
Build an intelligent workforce of AI agents that can understand information, coordinate workflows, support employees and execute work across your existing systems.
Start with one high-value process. Prove the impact. Scale across your logistics operation.
Deploy AI agents to coordinate shipments, process documents, manage exceptions, support customers and keep work moving across your logistics ecosystem.
From freight forwarding and warehousing to transportation and customer service, agentic AI helps logistics teams move beyond fragmented workflows and manual coordination towards intelligent, connected operations.
Augment your workforce. Accelerate operations. Deliver greater visibility and service at scale.
The logistics industry has long invested in technologies that improve the physical movement of goods, from automated warehouses and smart inventory systems to route optimisation and real-time shipment tracking.
The next transformation is happening in the knowledge and coordination work surrounding those physical operations.
Traditional automation works well when processes are predictable and rules are clearly defined. Yet many logistics workflows involve unstructured information, changing conditions, multiple parties and decisions that require context.
Agentic AI extends automation into this more complex layer of work. AI agents can understand requests, retrieve information from multiple systems, reason within defined business rules, coordinate multi-step workflows, communicate with stakeholders and execute authorised actions, with human oversight where judgement or approval is required.
Teams still spend significant time reading and processing emails, orders, invoices and shipping documents.
Work spans customers, suppliers, carriers, freight forwarders, portals, enterprise applications and communication channels.
Shipment milestones, delays, exceptions and customer commitments require ongoing attention and follow-up.
Operational teams weigh cost, capacity, timelines, service requirements and business rules before acting.
Customers increasingly expect faster responses, proactive updates and greater visibility across the shipment lifecycle.
| Area of work | Traditional operations | Digitally automated operations | Agentic operations |
|---|---|---|---|
| Information gathering | Employees manually gather information across systems. | Rules automate selected process steps. | AI agents interpret information and coordinate work across systems. |
| Operational monitoring | Teams continuously check shipment status. | Dashboards provide operational visibility. | Agents monitor events and proactively initiate appropriate actions. |
| Documentation | Employees prepare and review documents manually. | Templates and workflow tools reduce data entry. | Agents extract, validate, prepare and route documents. |
| Exception handling | Every exception requires manual investigation. | Exceptions are routed to employees. | Agents investigate, recommend actions and escalate when required. |
| Customer communication | Updates depend on staff availability. | Automated notifications provide standard updates. | Agents deliver contextual updates and respond across channels. |
| Knowledge application | Knowledge is distributed across employees. | Procedures are documented digitally. | Agents apply approved knowledge, policies and operating rules consistently. |
As AI agents take on more transactional and coordination-intensive activities, logistics employees can spend more time on judgement, relationships, operational decisions and innovation.
Understand what AI agents can do, how to work with them and when human intervention is required.
Interpret operational data, evaluate AI-generated insights and use information to support decisions.
Understand how work flows across customers, carriers, warehouses, applications and functions.
Investigate unusual situations, assess trade-offs and act when standard workflows are insufficient.
Delegate appropriate work, review outcomes and provide feedback to improve performance.
Validate recommendations, recognise operational risks and apply business context.
Build trust, negotiate outcomes and manage complex stakeholder relationships.
Respond to evolving technologies, customer expectations and operating models.
AI agents can support individual activities or collaborate across an end-to-end logistics workflow, from order intake and freight planning to documentation, monitoring, customer communication and reconciliation.
Provide fast, contextual support across email, chat, messaging applications and other channels.
Coordinate customer requirements, rates, pricing rules and approvals to accelerate quotation turnaround.
Coordinate the activities required to move shipments from planning through delivery.
Move from reactive tracking to proactive operational intervention.
Reduce manual effort in preparing, reviewing and managing logistics documentation.
Support customs workflows while keeping regulatory judgement and approvals with qualified employees.
Convert unstructured customer requests into validated orders and downstream actions.
Help planners and warehouse teams identify risks and make faster inventory decisions.
Continuously assess demand signals to support responsive planning decisions.
Automate invoice processing while routing discrepancies and complex exceptions to employees.
Automate the collection and preparation of logistics-related sustainability information.
Every logistics operation is different. Build an AI agent around your workflows, systems, business rules and operating requirements.
Each function above maps to a dedicated solution page with implementation detail, system integrations and typical deployment scope. Deploy a focused AI agent for a high-value process, establish measurable value and then scale across connected workflows.
Coordinate shipment planning, freight-rate sourcing, bookings, shipping documentation and shipment tracking.
Explore Freight Management →Automate document processing, customs-related workflows, compliance checks and process tracking.
Explore Customs Declaration →Analyse demand signals, improve forecast responsiveness and support better inventory decisions.
Explore Demand Forecasting →Interpret customer orders, validate information and initiate fulfilment workflows across your systems.
Explore Sales Order Intake →Extract, validate and reconcile invoices while routing discrepancies for human review.
Explore Invoice Reconciliation →Provide contextual, round-the-clock support across customer communication channels.
Explore Customer Service →Extract emissions information from logistics documents and automate reporting workflows.
Explore Sustainability Reporting →Design an AI agent around your workflows, systems, business rules and operating requirements.
Discuss Your Use Case →Start with a focused business problem. Establish value, controls and employee confidence. Then expand towards connected workflows and collaborative AI agents.
Assess logistics workflows to identify high-value, feasible and governable opportunities.
Introduce AI as a digital copilot without automatically executing critical actions.
Enable AI agents to execute selected processes within approved rules, controls and human approval points.
Connect specialised agents across an end-to-end logistics workflow.
Redesign work around human-AI collaboration rather than simply automating existing steps.
The objective is not autonomy at all costs. It is the right level of autonomy for each workflow, decision and business risk.
Build an intelligent workforce of AI agents that can understand information, coordinate workflows, support employees and execute work across your existing systems.
Start with one high-value process. Prove the impact. Scale across your logistics operation.