Why Freight Logistics Urgently Needs AI Agents to Operate Portals Like Humans
- Peter Qian
- Apr 1
- 8 min read
Integration Promises vs. Manual Reality in Freight
For decades, the freight transportation sector – spanning trucking, ocean shipping, and intermodal logistics across North America and the EU – has chased the holy grail of seamless digital integration. Electronic Data Interchange (EDI) and later APIs were supposed to link up carriers, shippers, freight forwarders, and warehouses in one digital fabric. Yet the reality on the ground is very different: many companies still find themselves copying and pasting data between disparate systems every day. In a recent industry survey, over 41% of businesses have no EDI capability at all, and 21% rely solely on web portals for data exchange.
In other words, a huge chunk of the supply chain isn’t connected by system-to-system pipelines – it’s held together by people re-entering information. Another survey of freight forwarders in 2023 found that 24% of respondents still run completely manual operations, without even an ERP or freight software in place. Despite all the buzz about “digital transformation,” logistics teams remain bogged down by fragmented systems and labor-intensive processes.
Why have APIs and traditional integrations fallen short? A major factor is the sheer fragmentation of the logistics tech landscape. Large shippers might use SAP or Oracle, mid-size forwarders use their own TMS (Transportation Management System) or spreadsheets, carriers have proprietary tracking portals, and countless niche platforms handle everything from customs to warehousing. These systems often don’t talk to each other. Implementing a new API integration for every partner and platform is costly and slow – if an API even exists. EDI, while useful for standard transactions, has seen surprisingly low adoption – over 41% of surveyed companies haven’t adopted EDI, often due to cost and complexity.
Smaller carriers and suppliers frequently fall back on emails, PDFs, and website forms as their “integration” method. As Shipping Australia put it bluntly when examining their own supply chain: “lack of adoption of relevant standards” means the freight community wastes enormous time manually entering and re-entering data into outdated and disparate systems. This isn’t just an Australian issue – it’s a global reality that’s persisted in freight despite industry efforts.
The Hidden Cost of Manual Work in Freight
Reliance on human data entry isn’t just a minor inconvenience – it’s a massive drain on efficiency and a source of errors. Logistics companies often employ whole teams of staff whose day-to-day work is essentially acting as the “glue” between unconnected systems. Consider some of the routine (but critical) workflows that still depend on manual effort in a typical freight operation:
Order Entry: Taking order details from an e-commerce or ERP system (e.g. Shopify, SAP) and re-keying them into a Transportation Management System or Warehouse Management System.
Load Board Posting & Tendering: Manually entering shipment details into load boards or carrier portals because automated tendering isn’t available for all lanes or partners.
Rate Quotes: Copying rates from emails or Excel spreadsheets into a quoting tool or TMS. Many forwarders still update buying and selling rates by hand, lane by lane.
Status Updates: Logging into multiple carrier tracking portals to update a shipper’s system with the latest status, or vice versa, because there’s no unified visibility feed.
Customs and Compliance: Preparing customs entry forms or other trade compliance documents on government or carrier websites, typing in shipment details that already exist in another internal system.
Freight Invoice Reconciliation: Downloading invoices from a carrier’s billing portal and cross-verifying each line item against the shipper’s ERP or accounting system, often by manually comparing spreadsheets.
If you work in logistics, none of the above is news – these are the mundane miracles that ops teams perform daily to keep freight moving. But it’s worth understanding just how much this manual busywork costs us. Surveys show that 73% of freight procurement teams still rely on spreadsheets or fragmented systems for managing rates and capacity. A staggering 46% of supply chain professionals continue to use Excel as a primary tool for operations. All of that copy-paste labor adds up. One freight industry report estimated that inefficient handovers and interactions (like the back-and-forth at pickup and delivery, often handled via emails or calls) account for up to 19% of total logistics costs – about $95 billion in losses annually in the U.S. alone. Even within a single company, the waste is evident: employees spending hours on data entry means slower turnarounds and higher labor bills. For example, freight rate management often involves “many phone calls, emails, [and] transfer of data from some media and formats to others,” resulting in a lack of consistency and lots of extra work. It’s no surprise that manual processes can eat up 40–80% of a forwarder’s potential labor capacity (in other words, the opportunity cost of tasks that could be automated) according to one study.
Besides cost, think about errors. Humans make mistakes, especially when tired or rushing. If you have to key in a 10-digit container number or a freight class code over and over, eventually you’ll mistype one. The typical error rate for manual data entry is around 1% per field. That might sound low, but consider a single international shipment: dozens of fields from addresses to product codes to dates, across booking, customs, delivery, and billing. A 1% error rate per field virtually guarantees something will go wrong in a high-volume operation. Indeed, a PwC study found that manual data handling contributes up to $450 in added “cost of trade” per shipment, costs that ultimately get passed along to customers. Multiply small data mistakes across thousands of shipments, and you get big consequences – delays, missed pickups, overbilling, or compliance fines. In the U.S. and UK, human error across businesses is estimated to cost firms around $18 billion annually (about $435 per employee). In freight, where margins are thin, these inefficiencies and errors are undermining competitiveness. As an example, Australia’s logistics sector saw its global ranking in cross-border efficiency plummet in part due to slow tech adoption. The bottom line: the status quo of swivel-chair data entry is unsustainable and costly.
Manual data handling imposes a significant overhead on each shipment. A PwC analysis of trade processes found that the extra admin work and workarounds add up to $450 per container in cost – over $1.1 billion per year in aggregate – which ultimately gets passed to customers. The diagram above illustrates how fragmented systems and manual handling drive up the “Cost of Trade.”
Why “Screen-Level” AI Agents Are a Game Changer
If the industry has known about these problems for years, why haven’t we fixed it? We have tried – through software. Traditional automation efforts like RPA (Robotic Process Automation) attempted to script repetitive tasks. But older RPA technology was brittle and required a lot of maintenance. Essentially, an RPA bot is like a recipe: it clicks here, types there, exactly as programmed. If anything unexpected happens (say, a website layout changes or a pop-up appears), the bot gets confused and often stops. As one VC firm noted, last-generation RPA could “mimic the exact keystrokes and clicks” of a user and provided value for very rigid processes, but it “stumbled if the process was not clearly defined or when it underwent changes,” and implementations required expensive consultants.
This meant only the largest companies could afford to automate via RPA, and even then the automation was limited in scope. Meanwhile, many freight tech vendors pushed EDI and API integrations as the “proper” long-term solution. In practice, however, a huge swath of operations work lacks APIs or direct system integrations, meaning tons of work is still done via “phone calls, spreadsheets, fax lines, and paper forms,” even in 2024. The promise of a fully digital supply chain remained out of reach.
Enter the new paradigm: Agentic AI. What’s different about this approach? Instead of waiting for every system in the chain to speak the same language, we teach AI agents to operate on the same interfaces that humans use. In simple terms, an AI agent can look at a website or application screen, understand what it sees, and interact with it just like a person would – clicking buttons, typing in fields, downloading reports, etc. This means the AI doesn’t require an official API or deep integration to perform a task. If a human can do it through a browser or software UI, an AI agent can be trained to do the same. This is a profound shift. It bridges the integration gaps by leveraging the existing user interfaces as the integration surface. For example, instead of building a custom EDI feed from a small trucking carrier (who might not even have the IT resources for it), an AI worker could simply log in to the carrier’s portal, retrieve PODs (Proof of Delivery documents) or update statuses, and input the data into your system automatically.
Two technological advances make these AI agents far more robust than the screen-scraping macros of the past.
First, modern computer vision and OCR (optical character recognition) allow an agent to reliably “read” text on a screen or document. It’s not hard-coded to x,y coordinates; it actually recognizes labels, values, and even dynamic content. Second, transformer-based AI models (the kind behind GPT-style language models) give the agent a form of understanding and reasoning. Rather than just following a fixed script, the agent can handle some level of variability. It can be taught goals and rules: “Here’s how you create an order in System X. If you see an error message, take a screenshot and alert a human. If data looks incomplete, flag it,” and so on. This flexibility and context awareness is why we call it agentic. The AI isn’t simply a hard-coded bot; it has the autonomy to make decisions within bounds, much like a human would when navigating a new scenario. Crucially, these agents can also adapt as they learn – improving over time as they process more shipments or invoices, and handling edge cases more gracefully.
The impact of AI agents in freight operations can be transformative. Imagine a “digital ops team” that works 24/7, never gets tired, and can log into any portal instantly. All those workflows we listed above – order entry, load posting, tracking updates, invoice checks – can be handled by AI co-workers. A leading investor recently noted that we’re finally seeing a world where **AI agents fulfill the original promise of RPA, “turning what used to be operations headcount into intelligent automation”. In plain terms, that means your human team members are no longer stuck doing copy-paste work; they can focus on exception management, customer service, and other high-value tasks, while the agents handle the busywork in the background. The throughput of your operations can scale dramatically without equivalent headcount growth. And because the agents can operate on any software via its UI, you don’t have to wait for trading partners to modernize or spend months negotiating data sharing – the AI bridges the gap today, using what’s already there.
Importantly, this is real automation, not just marketing fluff. Agentic AI differs from simplistic automation because of its flexibility and resilience. If a form adds a new field tomorrow, a well-designed agent can figure out where to input the data (or at worst, ask for guidance) instead of crashing. If an email comes in a slightly different format, an AI with language understanding can still parse the intent. We’re not claiming these agents are infallible or magically “thinking” like a human, but the step-change in capability from earlier automation tech is huge. It’s the difference between a player piano and a self-driving car – one follows a fixed tune, the other can navigate dynamic conditions. For the freight industry, this means automation is no longer limited to the small subset of cases with clean data and predictable steps. AI agents can tackle the long tail of messy, exception-ridden workflows that previously defied automation. That’s a breakthrough for an industry as complex and fragmented as logistics.
Ventus: AI Agents Built for Logistics Workflows
Ventus delivers AI agents designed specifically for freight logistics, automating tedious manual tasks across disconnected systems and portals—without requiring costly integrations. Unlike rigid automation tools, Ventus agents adapt autonomously, learning from human demonstrations to seamlessly handle logistics workflows such as order entry, shipment tracking, customs submissions, and invoice reconciliation. The result is significantly increased efficiency, reduced errors, shorter cycle times, and the flexibility to rapidly scale operations, finally making the vision of true logistics digitization a practical reality.
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