Freight forwarders are putting agentic AI to work in specific parts of their operations where coordination takes up a lot of time. Instead of giving one AI system control over the entire forwarding process, companies are using individual agents for tasks such as RFQ intake and quotation, shipment booking, data enrichment, document processing, carrier follow-ups, milestone tracking, exception handling, appointment coordination, invoicing, and customer communication.
It is easy to mistake agentic AI for a more advanced chatbot. That misses the bigger change taking place in freight operations. AI agents can now handle a piece of work over several steps and across different systems. For example, an agent can receive an inquiry, identify missing details, collect the information it needs, check connected systems, wait for a carrier response, update the shipment record, create a follow-up task, and escalate the issue when it falls outside an approved policy. The practical benefit comes from keeping these activities moving without requiring someone to manage every handoff manually.
This does not mean freight forwarding is becoming fully autonomous. In the more practical deployments, routine tasks are handled by AI while decisions with significant commercial, customs, compliance, safety, or customer implications remain with people. A freight forwarder might let an agent prepare a quotation, open a booking record, follow up on a missing milestone, or request a required document automatically. However, decisions involving margin exceptions, carrier selection, customs classification, costly recovery actions, or contractual commitments can still require human approval.
For years, logistics companies have used AI for tasks such as forecasting, optimization, OCR, and customer support. These applications are still useful and are not going away. The bigger shift in 2026 is that AI is moving beyond individual tasks and becoming part of the wider workflow, allowing connected systems to take action instead of simply providing information.
DHL Global Forwarding offers a clear public example of this shift. In its May 2026 investor presentation, DHL described an RFQ-to-follow-up process handling 1.6 million quotes each year. The workflow uses email intent classification to extract RFQ details, structured inputs to support pricing, and AI agents to run scheduled follow-up cycles. DHL reported that the time required to prepare a quote fell from roughly 30 minutes to just seconds, while the hit rate improved by at least 10%.
The same DHL presentation covers a second workflow focused on air and ocean freight booking and data enrichment, handling 2.5 million requests a year. The system reads information from emails, documents, and attachments, converts it into structured data, and sends it into CargoWise One. Human-in-the-loop validation remains part of the process, so the AI does not operate without oversight. DHL reports that more than 70 manual fields are now automated, resulting in a 40% productivity gain. See DHL Group’s May 2026 investor presentation.
C.H. Robinson is following a similar path, using AI agents across a much wider range of operational activities. The company says its agents support pricing, planning, order management, appointments, freight matching, capacity securing, consolidation, tracking, ETA prediction, document processing, and invoicing. In its Q2 2026 update, C.H. Robinson also said its Global Forwarding business was standardizing workflows while developing AI-powered capabilities to reduce manual work and improve data quality.
C.H. Robinson’s wider agent program is particularly relevant because it extends beyond a single use case. The company describes AI agents across the quote-to-cash lifecycle, showing how these systems can be applied to several connected stages of forwarding operations rather than being limited to one isolated task. See C.H. Robinson’s 2026 Lean AI update.
The technology supporting freight forwarders is changing alongside the workflows themselves. In July 2026, Project 44 introduced LSP44 for 3PLs, freight forwarders, and brokers. The platform positions AI-agent capabilities and carrier API infrastructure as components that logistics providers can build into their own operating systems rather than treating AI as a separate tool. According to the company, nine of the world’s ten largest logistics service providers already run on its infrastructure.
WiseTech Global is taking a similar approach within CargoWise Next. The company is developing agentic AI workflows and what it calls “AI personas” to handle connected processes such as document ingestion, data entry, compliance, customs processing, invoicing, and exception management.
These developments do not mean every freight forwarder needs to automate its operations in the same way. They do, however, show how the conversation has moved beyond using AI simply to summarize emails or documents. The more practical questions now are much more specific: Which part of the forwarding process should the agent handle? What information should it rely on? And where should its authority end?
Freight forwarding has always depended on coordination. Forwarders do not manufacture the goods, operate every vessel, own every aircraft, control customs authorities, or manage every consignee. Their role is to bring all these moving parts together—people, documents, available capacity, regulations, timelines, and the decisions that keep a shipment on track.
This operating model is a strong fit for the kind of work agents can handle. Information does not always arrive at the same time, and the next step can change based on what comes back. Many inputs are unstructured, and the workflow can move between email, a Transportation Management System (TMS), carrier portals, APIs, spreadsheets, WhatsApp, CRM systems, customs platforms, accounting systems, and document repositories. Much of this work is repetitive, but it does not always follow the same set of steps, so it cannot be handled effectively with fixed rules alone.
Consider a typical ocean freight enquiry. A customer may email the origin, destination, cargo details, and preferred departure date but leave out the dimensions and Incoterm (International Commercial Term). The forwarder then has to request the missing details, check contract rates, local charges, sailing schedules, free-time terms, and customer pricing rules before preparing two options. If the customer later changes the cargo volume, the quote needs to be recalculated. Once the customer approves it, the forwarder books the carrier, creates the shipment, collects the required documents, tracks key milestones, and eventually raises the invoice.
A standard automation script can take care of individual tasks, but it usually follows a fixed sequence. Agentic AI becomes more useful when the software can keep track of the shipment context, understand what has already happened, and determine what needs to happen next.
Quotation is one of the clearest areas where agentic workflows can make a difference, mainly because response time can influence whether a forwarder wins the shipment. The process starts with the customer’s actual inquiry, which may come through email, WhatsApp, a customer portal, or an attached document.
The agent first works out whether the message is a new RFQ and pulls out the key shipment details. It checks if anything is missing and, when necessary, goes back to the customer for the required information. Once the request is complete, it can look up approved carrier or partner rates, bring different charges into a consistent format, apply the customer’s pricing rules, calculate the final commercial figures, and prepare the quotation.
The bigger challenge often starts after the quote has been prepared. A forwarder may lose an opportunity not because the pricing took too long, but because the follow-up was late or missed altogether. An agent can set a reminder for the next contact, check whether the customer has replied, record why the quote was lost, and keep the opportunity active without making the salesperson remember every quotation that is still open.
The commercial limits should be clearly defined. The agent can apply a pre-approved markup or stay within the minimum margin requirement. It should not waive a surcharge, accept a loss-making shipment, or make a strategic pricing exception unless the business has specifically authorized it to do so.
Once the customer accepts the quote, the work does not stop there. The commercial instruction still has to become a live shipment record. Someone needs to select the right service, create or confirm the booking, copy the required references, attach the documents, set up milestones, and make sure all customer-specific details are in place.
DHL’s 2026 booking and enrichment example shows why this part of the process matters. Its published workflow takes information from unstructured emails and documents, organizes the data, sends it for human validation when needed, and then feeds it into CargoWise One. The real benefit is not just extracting information from a message. It is turning that customer communication into a usable forwarding job without making staff enter the same information repeatedly.
A booking agent can also perform checks before moving the shipment forward. It can confirm that the selected sailing or flight is still available, verify that the quoted rate remains valid, check whether space needs to be confirmed, and flag shipments that need closer attention. Dangerous goods, temperature-controlled cargo, special equipment, unusual routing, and other exceptions are good examples of situations where human review may still be necessary.
In practice, the best approach is usually selective rather than fully automated. Routine shipments with established routes and approved carriers can move through the workflow quickly. Cargo with regulatory concerns, unusual requirements, or commercial exceptions should be held for review instead of allowing the agent to make assumptions.
Forwarders deal with a constant flow of documents, but simply being able to read those documents is not where an AI agent adds the most value. OCR and document extraction tools can already capture text and fields. The bigger opportunity is using that information to keep the shipment moving.
An agent can recognize documents such as bills of lading, commercial invoices, packing lists, certificates of origin, hazardous-goods declarations, delivery orders, proof of delivery, and booking confirmations. It can associate each document with the correct shipment, pull out the fields needed for the next step, compare information across documents, and check whether everything required for a milestone is available.
The agent can also act when something is missing or does not line up. If a certificate of origin has not been received, it can request the document. If the quantity on an invoice differs from the packing list, it can flag the mismatch for someone to check. If a carrier sends an updated booking confirmation, the agent can update the relevant shipment details while keeping the original document and its source information for reference.
Getting the shipment identity right is essential. Before a document can trigger any update, it first needs to be matched to the correct shipment or job. That match may involve several references, including booking numbers, container numbers, master or house bills, purchase order (PO) numbers, customer references, invoice numbers, and internal job IDs. In some cases, the agent may need to compare more than one of these details before it can confidently decide where the document belongs.
One of the biggest hidden workloads in freight forwarding is chasing updates from other parties. Has the shipment been picked up? Did the container get gated in? Has the flight left? Has the cargo reached its destination? Is the truck running late? Has the proof of delivery been received?
If a reliable API or EDI event already provides the answer, there is no reason to send an AI-generated message just because the system can. The useful role for an agent starts when a milestone is missing, out of date, or does not match the information already on the file.
The agent can work out who is responsible for the missing update, contact the right party through an approved channel, understand the response, and update the shipment timeline. If the response is unclear or cannot be verified, the case can be sent to a human for review.
Project44’s June 2026 release notes describe autonomous agents being used for this kind of ocean and air milestone follow-up. The agents can contact carriers and freight forwarders to collect missing information such as origin and destination status, ETA, and proof of delivery.
This is a strong use case because the communication is repetitive and the agent can operate within a clear boundary. It is collecting and confirming shipment information, not negotiating rates, changing terms, or making commercial commitments.
Shipment visibility is useful, but visibility alone does not fix an exception. The forwarder still needs to understand what the latest event means for the customer and work out what should happen next.
An exception agent can keep track of planned and actual milestones, spot a missed pickup or a change in ETA, pull the latest information from the carrier, and compare it with the delivery commitment already given to the customer. It can then gather possible recovery options and prepare the next operational response.
Some exceptions cannot be resolved immediately. If a carrier says it will have a revised schedule in two hours, the agent can keep the case open rather than starting the process again later. It can check back at the expected time, collect the new information, update the TMS, and determine whether the issue is resolved or needs to be handed over to a person.
The agent’s authority needs even more attention at this stage. Sending a factual status update is generally low risk. Rebooking a container, moving cargo from sea to air, approving storage charges, or promising the customer a new delivery date can affect costs and commercial commitments. Those actions should only happen when the business has clearly defined what the agent is allowed to approve and when human intervention is required.
Customs is a key part of freight forwarding, but it is also a clear example of why agentic AI does not mean giving software unlimited decision-making authority.
An agent can take care of much of the groundwork. It can collect the required documents, extract information from invoices, compare invoices against packing lists, check for missing fields or permits, look up product history, prepare a draft customs declaration, organize supporting evidence, and track the progress of a customs process.
WiseTech’s published agentic workflow examples in CargoWise reflect this approach. Document ingestion, commercial invoice processing, customs data evaluation, classification support, verification by a responsible person, invoicing, and release can be coordinated using specialized AI personas and workflow orchestration.
The boundary becomes important when a customs decision has legal or regulatory implications. Classification, valuation, origin, sanctions, and licensing can all affect whether and how a shipment is cleared. If the evidence is incomplete or the classification is genuinely ambiguous, the agent should not fill the gap with a guess. Its job is to bring the relevant documents and findings together, highlight the uncertainty, and involve a customs expert who can make the final call.
For forwarders managing road transport, pre-carriage, on-carriage, or brokered capacity, agents can take care of much of the coordination involved in dispatch. A shipment needs a suitable vehicle or carrier, so the system can review eligibility, availability, service history, rates, current location, customer restrictions, and route requirements before ranking the available options.
The agent can then contact eligible drivers or transport partners, keep track of who accepts or declines the job, and move to the next option if there is no response within the agreed time. It can also prepare the assignment once a suitable carrier is found. One important safeguard is to keep fixed requirements separate from the factors used to rank carriers. If a carrier does not have the required credentials, for example, it should be ruled out completely rather than simply receiving a lower AI score.
Carrier onboarding and award decisions require greater scrutiny when external providers are involved. Issues such as fraud, insurance coverage, identity verification, and commercial risk can all affect the decision. The agent can collect the required evidence and perform approved checks, but any unknown, incomplete, or suspicious capacity should be sent to a person for review rather than approved automatically.
Customer service automation is moving beyond simply answering questions in a chat window. When an agent has access to the underlying operational workflow, it can use current shipment information to keep customers updated without waiting for them to ask.
For example, if a confirmed carrier update changes the ETA, the agent can check whether the change affects the customer, prepare the appropriate message, attach any relevant document, and send it when the update falls within the company’s approved communication rules. If the shipment status has not been confirmed, the agent should say that it is being investigated. It should not fill the gap with an assumption or present an uncertain status as a fact.
Forwarders also need a clear line between reporting information and making a commercial commitment. “The carrier’s latest ETA is 18 October” reports what a known source has provided. “We guarantee delivery on 18 October” makes a promise to the customer. The first type of message can often be automated. The second may need to be reviewed and approved by someone who has the authority to make that commitment.
Freight forwarding does not stop once a shipment is delivered. Carrier invoices, local charges, accessorial fees, duty disbursements, destination charges, customer billing, and supplier payments all create follow-up work, much of which involves checking and reconciling the same information across different records.
An agent can match an invoice with the correct shipment, compare the billed amount with the agreed rate, flag duplicate charges, check supporting documents for accessorials, calculate any difference, and send exceptions to the appropriate team in operations, procurement, or finance. Where the transaction matches the agreed rules, it can be prepared for ERP posting or customer billing.
This is also a workflow where fixed rules need to stay in control. Tax calculations, approval limits, accounting mappings, bank-detail changes, and tolerance levels should be clearly defined and easy to test. The agent can handle the checks and coordination around those rules, but it should not override or replace them.
One of the less obvious but useful roles for agents is keeping day-to-day operations moving. Freight forwarding systems can build up thousands of open tasks: documents that have not arrived, milestones waiting to be confirmed, quotes that need follow-up, jobs missing billing details, invoices sitting in exception, customs files waiting for information, and shipments whose status has not been updated.
An agent can monitor these queues in the background, figure out what is keeping each item open, and handle the next low-risk action when it is within its authority. If a case needs human judgment, it can send it to an operator instead. This may have more practical value than a flashy standalone assistant because it takes care of the small coordination tasks that can consume a significant amount of an experienced operator’s time.
WiseTech’s direction with CargoWise AI management and workflow agents reflects this approach. The idea is to have agents working within the operating system and its workflows, rather than placing a separate chatbot alongside the system.
The easiest way to understand the change is to follow one shipment through the day. At 08:12, a customer sends an email asking for an air freight quote. The agent identifies it as an RFQ and pulls out the origin, destination, cargo details, requested service, and customer reference. The dimensions are missing, so it sends a request for them. Fifteen minutes later, the customer replies. The agent links the response back to the open inquiry instead of treating it as a new conversation.
It checks the approved rate options, applies the account’s commercial rules, and prepares two service options. One falls below the margin floor, so it cannot be released automatically. The compliant option is prepared for automatic release, while the lower-margin option is flagged for commercial approval. A salesperson reviews the exception, approves it, and the quotation is sent.
The customer accepts the quote. The agent then creates the shipment record, confirms the booking details, attaches the original inquiry and quotation, and prepares the document checklist. Later, a commercial invoice arrives. The document agent recognizes that it belongs to the same shipment, links it to the existing job, and extracts the relevant information.
The expected origin pickup update does not come through on time. The visibility system spots the missing milestone, and the agent follows up with the transport provider handling the pickup. The provider confirms that the shipment is delayed by two hours. The system then checks the flight cutoff and finds that the shipment can still make it as planned. It updates the internal milestone and does not escalate the issue to the customer because there is no immediate impact.
At the destination, the delivery milestone comes through, but the Proof of Delivery (POD) is still missing. The agent follows up, gets the document back, attaches it to the correct shipment, marks the file complete, and releases the billing task. Later, a supplier invoice arrives with a charge higher than the agreed rate. Instead of posting it automatically, the system sends the exception to finance for review.
None of these actions is unusual on its own. The real benefit is that the shipment context stays connected from the initial inquiry through to billing. Routine follow-ups, documents, and exceptions can be handled without staff having to rely on memory or manually piece together what happened at each stage.
Agentic AI does not replace the technologies freight forwarders already depend on. In a real production environment, it usually works alongside them, with each system continuing to handle the tasks it was built for.
The TMS, or forwarding platform, remains the system of record. Carrier APIs and EDI bring in structured shipment events. OCR and document intelligence handle information from unstructured files. Predictive models help estimate ETA and identify potential risks. Optimization engines compare routes and available capacity. Business rules continue to control margins, compliance, eligibility, and approvals. RPA can also handle a legacy portal when there is no API available. The agent connects these pieces and determines when a particular tool or system is needed.
This is why integration and operational context are becoming so important. CargoWise, LSP44, Project44, Microsoft, and major logistics providers are focusing heavily on how systems and data work together. The large language model is only one part of the overall setup. The bigger challenge is giving the agent a reliable shipment identity, the current workflow state, the right permissions, applicable rules, and access to the tools it needs to complete the work safely.
Having an AI agent does not mean every forwarding decision should be handled autonomously. The greater the impact of a decision, the stronger the need for human review and approval.
· Unresolved customs classification, origin, valuation, or sanctions decisions
· Unknown or suspicious carrier onboarding
· High-value or dangerous-goods movements outside standard policy
· Material margin exceptions or strategic pricing decisions
· Open-ended carrier negotiation outside approved commercial bands
· Major disruption recovery involving significant incremental cost
· Claims settlement or acceptance of liability
· Contractual changes or unusual customer commitments
· Supplier or carrier bank account changes
· Any action where source data remains contradictory, or identity is uncertain
This also reflects the broader direction of current enterprise research. Gartner’s 2026 guidance recommends starting with well-defined, lower-risk use cases, supported by reliable data and strong governance. It also favors a gradual expansion of AI capabilities instead of moving directly to full autonomy.
The practical impact is less about removing forwarding roles and more about changing how people spend their time. Operators who once copied data, followed up on routine milestones, checked shared inboxes, and moved information between systems can focus more on commercial decisions, exceptions that need judgment, customer relationships, complex customs issues, network planning, and carrier strategy.
The experience of the operator also becomes more valuable in an important way. Someone still needs to decide what the agent should do and when it should stop and ask for help. That means turning operational knowledge into clear rules, approval thresholds, trusted sources of information, escalation paths, and definitions of what counts as an exception.
This is why freight-forwarding AI cannot be treated as an IT project alone. Operations, commercial, customs, finance, and technology teams all have a role in defining the decision model. The technology can handle the workflow, but the business needs to decide how that workflow should work.
Large providers may be able to build several AI agents at once, but a mid-sized forwarder does not need to follow that path from the beginning. Start with one focused workflow and use it to test the approach in real operations. A small, well-chosen use case can provide enough evidence to decide what should come next.
Start with the processes where manual coordination takes up the most time. This could be turning around quotations, following up with carriers, chasing missing documents, handling shipment exceptions, or creating jobs from customer emails. Before introducing any new technology, measure how the process works today. Track the volume, time spent on each task, response times, touches per shipment, errors, rework, escalations, and the number of systems involved. That baseline makes it easier to see where the real bottlenecks are and whether the new workflow delivers a measurable improvement.
Begin by operating the first version in observation or recommendation mode. Give it access to the same work the team handles and see what decisions it would make. Then compare those recommendations with the choices made by the operators. Once the workflow has proved reliable, the agent can start preparing actions and gradually carry out lower-risk tasks within clearly defined guardrails.
A practical approach is to increase autonomy step by step. Start by monitoring and extracting information, then move to recommendations that require approval. As the workflow becomes more reliable, allow the agent to take controlled actions. For tasks where the risk is low and the supporting evidence is strong, selective autonomous execution can be introduced.
The right KPI depends on the workflow being improved. A broad measure such as “AI accuracy” rarely tells you enough. Instead, freight forwarders should track the operational results the agent is expected to improve.
· RFQ-to-first-response time
· Quote turnaround time
· Quote hit rate and margin compliance
· Manual minutes per booking
· Percentage of jobs created without re-keying
· Document completeness before cutoff
· Missing milestones per 100 shipments
· Manual carrier contacts per shipment
· Mean time to detect and resolve exceptions
· Percentage of exceptions resolved without human intervention
· Customer status enquiries per shipment
· POD completion time
· Invoice or freight-audit exception rate
· Delivery-to-billing cycle time
· Human override rate
· Agent retry/error rate
· Duplicate-action incidents
· Throughput per operations employee
The final two are particularly important. An agent may appear productive while quietly causing repeat work, duplicate messages, or additional corrections. Operational reliability should therefore be measured across the entire workflow, from start to finish.
A straightforward way to distinguish a production system from a demo is to ask how the agent responds when the expected workflow breaks down.
· Which forwarding workflows are already live in production rather than only demonstrated?
· What systems does the agent connect to, and which one remains the source of truth?
· How does the system identify the correct shipment when references are incomplete or inconsistent?
· What happens when a carrier API, portal, or TMS integration fails?
· How are retries handled without creating duplicate bookings, emails, or updates?
· Which rules are deterministic rather than left to an LLM?
· Can we define approval limits by customer, shipment type, value, margin, mode, or risk?
· How does the agent treat conflicting information from email, TMS, carrier feeds, and predictive models?
· Can an operator see why the agent chose an action?
· How is every external action logged?
· What is the safe-mode or rollback process?
· How do you evaluate the reliability of the complete workflow rather than only model accuracy?
Yes. Freight forwarders are already using AI agents for a range of real-world tasks, including preparing quotes, handling bookings, enriching shipment data, tracking shipments, communicating with carriers, scheduling appointments, processing documents, managing capacity and freight procurement, handling exceptions, and supporting finance workflows. However, adoption is still at different stages across the industry. For higher-risk decisions, many companies continue to keep a human in the approval loop.
RFQs and quotations, document processing, booking and job creation, routine carrier follow-ups, and milestone updates are some of the more mature AI-agent workflows in freight forwarding. These processes tend to involve high volumes of repetitive work, with clear inputs and decisions that can be kept within defined limits. Exception management is also developing quickly, particularly for situations where an agent can identify a problem, gather the relevant information, and recommend the next step for a human to approve.
WiseTech Global is building agentic AI workflows and AI personas for CargoWise Next. Its published materials describe AI-driven workflows for logistics tasks such as document handling, customs processing, bookings, compliance, invoicing, management activities, and exception resolution. Human review can still be included when a decision requires additional oversight.
Usually, no. The TMS remains the core system of record for freight operations. The agentic layer works alongside it, reading and updating TMS data while coordinating tasks across email, carrier systems, documents, APIs, CRM, accounting platforms, and other business tools. In practice, the AI agent acts as a layer that connects these systems and helps move work from one step to the next.
Yes, provided the right information is available. An AI agent can prepare freight quotes when it has access to reliable rate sources, charge rules, customer-specific pricing, and margin guidelines. Routine quotations can often be prepared automatically and, in some cases, released without manual intervention. However, strategic accounts, low-margin quotes, incomplete requests, and unusual shipments should still go through commercial review.
Yes, for well-defined situations. An AI agent can handle carrier bookings when the forwarder has clear rules for carrier eligibility, rate validity, cargo requirements, customer restrictions, and approval limits. Shipments involving hazardous cargo, special equipment, or non-standard commercial terms may still need human review before the booking is confirmed.
AI agents can handle routine customs tasks such as preparing documents, organizing data, checking for missing information, reconciling records, and following up on pending work. However, cases involving unclear tariff classification, country of origin, valuation, licensing, or other regulatory decisions should remain with qualified customs specialists.
Yes. In most practical setups, the AI agent works across the systems the freight forwarder already uses. It can connect through APIs, workflow integrations, document-processing services, and, where necessary, controlled RPA to carry out tasks across those systems. This means a forwarder can introduce agentic AI without having to replace its entire existing technology stack.
Usually not. For most freight forwarders, the real advantage comes from how the AI is connected to the business. Workflow design, proprietary data, customer and carrier information, system integrations, business rules, and operational knowledge often matter more than building a language model from scratch. The underlying AI model is simply one part of the larger system.
In the near term, agentic AI is more likely to change how freight-forwarding teams work than replace them completely. AI agents can take care of repetitive monitoring, data entry and movement, follow-ups, document handling, and routine actions. People are still needed for commercial decisions, complex exceptions, compliance, customer relationships, negotiations, and overall accountability.
A smaller forwarder does not need dozens of AI agents to compete. One well-integrated workflow that noticeably improves response times or frees up operator capacity can make a real difference. A good place to start is with the part of the operation where customers regularly face delays or where staff spend hours every day repeating the same coordination tasks.
The biggest operational risk is giving an AI agent the authority to act before the basics are in place. The company needs clear data ownership, reliable source-of-truth rules, appropriate permissions, well-defined exception handling, and proper audit trails. A wrong answer may cause a delay or extra work, but a wrong booking, incorrect quote, customs action, or system update can lead to a real financial or operational loss.
The key change in 2026 is not that freight forwarding has suddenly become autonomous. It is that AI agents are starting to take responsibility for specific operational workflows that once required people to move information between different systems.
Large freight forwarders and logistics providers are already using this approach across areas such as quotation, booking, shipment creation, capacity planning, tracking, document handling, customs support, appointments, exception management, invoicing, and customer communication. CargoWise and other logistics technology providers are also adding agentic capabilities to the systems that forwarders already rely on.
The competitive question is therefore less about whether a forwarder has ‘AI’ and more about how much of the routine work from enquiry to payment can move forward reliably without someone having to spot and handle every next task.
Forwarders that take a measured approach to this transition are likely to be better positioned. They can keep the systems, rules, customer relationships, and human judgment that are essential to the business while letting AI agents handle the repetitive operational work that often slows teams down.
aTeam Soft Solutions builds custom agentic AI and enterprise automation solutions for freight forwarders, 3PLs, transport operators, trading companies, and supply chain teams. Our logistics solutions cover freight quotation, shipment exception handling, document processing, customs workflows, supplier ETD tracking, dispatch, fleet operations, and freight and supplier invoice processing. We also integrate these solutions with TMS and ERP platforms, CRMs, carrier APIs, email, WhatsApp, and legacy systems.
Our approach starts with a real operational workflow. We identify the source systems and business rules, set clear boundaries for human approval, establish a baseline for measuring results, and then gradually give the agent more responsibility as the workflow proves reliable in production.
