Why Freight Logistics Urgently Needs AI Agents to Operate Portals Like Humans

What is AI Agents Operating Portals Like Humans?

AI agents operating portals like humans refer to advanced technologies designed to automate and streamline repetitive tasks in freight logistics by interacting with digital interfaces as a human would. These agents use machine learning and natural language processing to navigate complex systems, fill out forms, and extract data, effectively mimicking the actions of human operators. The primary benefit of employing AI agents in logistics is the dramatic increase in efficiency and reduction in human error. For instance, InTek Logistics has successfully leveraged Ventus AI to process 150 invoices in just 3 minutes, a task that previously took over 10 hours. This rapid processing translates to improved turnaround times and significant cost savings, as businesses can reallocate human resources to more strategic activities. By reducing manual data entry errors, AI agents also enhance accuracy, ultimately boosting the reliability of logistics operations.

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.

Comparison Table: Manual vs. Automated Approaches

January 2026 Update

Two regulatory shifts that matured through late 2025 are pushing even more work into carrier and government portals—and they’re not optional. First, the U.S. Federal Maritime Commission’s final demurrage and detention (D&D) billing rule, effective since May 2024, is now being actively enforced by shippers and NVOs who must validate clock-stops, availability notices, and dispute timelines with precise evidence pulled from multiple portals. Second, Canada’s CBSA CARM Release 2 rollout has made portal-based importer declarations, financial security management, and broker delegation table stakes; importers that fail to maintain CARM credentials and authorizations risk account holds within hours. In the EU, ICS2 Release 3 for maritime continues its phased enforcement into 2026, requiring earlier, richer Entry Summary Declaration data and more pre-loading risk checks. Layer on the EU ETS for shipping reaching 100% scope coverage in 2026, and invoice line items tied to emissions are becoming common—auditors expect an auditable trail of every rate, surcharge, and timestamp. The net effect: compliance-critical tasks increasingly live behind web forms, MFA prompts, and document upload flows rather than APIs.

Portals are also hardening. Carriers and customs systems have accelerated MFA rollouts, introduced rotating device fingerprints, and deployed more aggressive bot detection and visual CAPTCHAs. Scripted RPA breaks under these changes; teams are standardizing on browser-native AI agents that operate like a user, handle MFA/CAPTCHAs, and escalate edge cases via Slack or Teams with full click-level audit logs. Updated best practice: centralize credentials and per-portal MFA policies, run agents in ephemeral browsers with network allowlists, and route exceptions to a human-in-the-loop queue that can approve filings or edits in seconds. Start by targeting high-friction workflows—D&D dispute submission, appointment booking, POD retrieval, invoice/ETS surcharge verification, and ICS2 pre-loading checks—several of which we outline in the Top 7 Ways Freight Brokers and Carriers Can Automate Their Daily Workflow with AI. For mid-size firms, the fastest wins come from replacing “swivel-chair” work where sessions time out in minutes and OTPs rotate frequently; modern agents can even place or receive calls to confirm appointments or resolve terminal holds when portals stall.

Operational volatility remains the wildcard. Disruptions—whether weather, labor actions, or incidents like those discussed in Port of Long Beach Cargo Incident: Lessons in Supply Chain Resilience and Automation—trigger sudden surges in portal traffic for rebooking, free-time extensions, and accessorial disputes. Plan for “surge mode” by pre-authorizing agent access across your top 10 portals, setting rate limits to respect site policies, and defining escalation paths by lane or customer. Budget-wise, revisit your 2026 automation stack using the ROI levers in Most Cost-Effective AI for Mid-Size Freight Companies in 2025, but add explicit line items for regulatory workflows (CARM, ICS2) and invoice audit (ETS/D&D). A practical next step: run a 30-day pilot targeting two portals each for (1) compliance filings, (2) invoice validation, and (3) scheduling, with success metrics around cycle time, first-pass yield, and audit completeness. With browser-native agents that can deploy in under a week and communicate status via Slack, Teams, or email, you can meet the 2026 bar for compliance and speed without waiting on integrations that may never arrive.

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.

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.

Customer Case Study

By implementing Ventus AI, InTek Logistics achieved remarkable efficiency improvements. Before adopting AI agents, processing 150 invoices took more than 10 hours of manual work. Now, with AI handling these tasks, the same volume is processed in just 3 minutes. This drastic reduction in processing time illustrates the transformative potential of AI in logistics operations—enabling companies to reallocate human resources to more strategic tasks and significantly reduce operational costs.

Frequently Asked Questions

How do AI agents work in freight logistics?

AI agents operate by interacting with digital interfaces the same way a human would. They use technologies like machine learning and natural language processing to navigate systems, fill out forms, and extract data. This allows them to automate repetitive tasks without needing deep system integrations.

What is the cost and ROI of implementing AI agents?

The cost of implementing AI agents can vary based on the complexity of the tasks and the volume of work. However, the return on investment is often realized through significant cost savings, reduced processing times, and decreased human error. For instance, InTek Logistics reduced invoice processing time from 10 hours to 3 minutes using AI.

How long does it take to implement AI agents?

Implementing AI agents can be relatively quick. Many solutions, such as Ventus, offer the ability to deploy agents in under a week. This rapid deployment enables businesses to start seeing the benefits of automation almost immediately.

Are AI agents compliant with industry regulations?

Yes, AI agents are designed to comply with industry regulations. They offer features like full audit logs of their actions, ensuring that all operations can be tracked and verified, which is crucial for meeting compliance requirements in logistics.

What kind of results can businesses expect from AI agents?

Businesses can expect improved efficiency, reduced error rates, and faster processing times. For example, AI agents can handle invoice processing, customs submissions, and order entries more effectively than traditional manual methods, leading to significant operational savings.

How do AI agents handle changes in portal interfaces?

AI agents are equipped with modern computer vision and language understanding capabilities, enabling them to adapt to changes in portal interfaces. They can recognize new fields and interfaces, reducing downtime associated with interface changes.

Can AI agents be customized for specific logistics workflows?

Yes, AI agents can be tailored to meet the specific needs of different logistics workflows. They learn from human demonstrations and can be adjusted to handle various tasks such as shipment tracking, customs submissions, or invoice reconciliation.

What is the impact of AI agents on human jobs in logistics?

While AI agents take over repetitive and mundane tasks, they allow human workers to focus on more strategic activities such as exception management and customer service. This shift can lead to a more engaged and productive workforce while increasing operational efficiency.

Internal Links for Further Reading

• Explore the benefits of AI in logistics at our Logistics Page.
• Schedule a personalized demo with Ventus AI.
• Discover insights from our case study articles.
• Read about freight invoice auditing and how automation can improve this process.