Every TMS vendor in the freight market now claims to be AI-powered. The slide decks are full of it. The demos look impressive. The language has shifted overnight from “automation” to “agentic AI.” And Gartner just dropped a number that should make every buyer pause: over 40% of agentic AI projects will be canceled by the end of 2027. Escalating costs. Unclear business value. Inadequate risk controls. Projects launched on hype, killed by reality.
Gartner also coined a term for what is happening across the vendor landscape: “agent washing”—i.e., vendors rebranding existing chatbots, RPA tools, and basic automation as agentic AI without delivering genuine autonomous capabilities. Of the thousands of vendors now claiming agentic solutions, Gartner estimates only about 130 offer the real thing.
That is the environment freight companies are buying into right now. And the consequences of choosing wrong are not abstract. They are real money, real time, and real operational disruption.
This piece is not a product pitch. It is a buyer’s guide to asking the right questions before you commit.
Why AI Agents Fail in Freight
The failure modes are predictable. And they almost always trace back to the same root cause: the company building the AI does not understand the business it is automating.
A 2025 MIT study found that 95% of enterprise AI pilots deliver zero measurable return. Not because the models do not work. Because the infrastructure around them does not. The data is dirty. The workflows are not mapped at the level of detail the AI needs. The edge cases (the ones that define freight) are not accounted for. And the teams building the agents do not have the domain knowledge to know what they do not know.
In freight, that domain gap is where AI agents go to die.
Consider what it actually takes to automate a trucking operation. It is not one business model. It is dozens. The variables include driver type: company driver, owner-operator, lease-purchase, or team. Equipment type: dry van, flatbed, reefer, tanker, intermodal chassis. Freight type: and if it is hazmat, which hazmat? Sulphuric acid has different handling, routing, cleaning, and regulatory requirements than flammable liquids or corrosives. The tanks have to be washed between loads. The routes have to avoid restricted corridors. The driver must carry the required endorsements. The insurance has to cover the specific commodity.
Then layer in the operational reality. Detention at the dock. Appointment scheduling. Service-level management with real-time telematics. Lumper fees. Accessorial charges. Rate confirmation workflows that differ by customer, by lane, by season. Settlement processes vary depending on the driver’s pay structure. And every freight partner, every rule, every success factor is different from the last one.
A technology company that does not understand this complexity at the workflow level will build AI agents that look good in a demo and break in production. They will automate the 60% that is straightforward and fail on the 40% that actually defines whether the operation runs or does not. And by the time the freight company discovers this, they have spent six months and a significant budget on a system that cannot handle the realities of their business.
The Real Cost of AI That Does Not Understand the Business
The cost equation is not just the subscription or the implementation fee. It is the total cost of getting it wrong.
When an AI agent suggests rebooking a late shipment without knowing the carrier is at capacity, that the receiving warehouse has limited dock doors during peak hours, or that the customer’s schedule will not accommodate the change, that is not automation. That is expensive chaos. The operations team spends more time cleaning up the AI’s decisions than they would have spent making the decisions themselves.
And the development cost comparison matters. A third-party technology company entering freight has to learn the business before it can automate it. That learning curve (understanding every workflow, every exception, every edge case across carriers, brokers, 3PLs, and shippers) takes years. A company that already understands the business can build and ship AI agents more quickly because domain knowledge is already in the room. The development is faster, the iterations are tighter, and the agents are purpose-built for the business models they serve, not generic frameworks hoping to learn on the job.
The vendors who lack that depth will spend their customers’ money figuring out what they should have known before they started building. Their agents will fail not because the AI is bad, but because it was never designed for the specific realities of different freight transactions. A dry van brokerage load, a hazmat tanker haul, a JIT automotive delivery, and a refrigerated LTL consolidation are not the same business. They require different logic, different rules, different risk models. One-size-fits-all agents do not survive contact with that complexity.
Your Data Is Not Theirs to Train On
There is another dimension to the AI buyer decision that gets less attention than it should: data security and privacy.
When you deploy AI tools that route your operational data through third-party models, you are making a decision about who sees your freight, your rates, your customers, and your carrier relationships. A 2026 survey found that 77% of employees have pasted company information into AI tools, and 77% of enterprises have no AI-specific security policy to prevent it. Public LLMs can use submitted data for model training. Shadow AI (unsanctioned tools spreading department by department) is now the most common entry point for data leakage in the enterprise.
An AI infrastructure company that takes security seriously selects only tools and models that do not share customer data, are not used for training, and are not routed through third-party environments without explicit controls. The question is not just whether the AI works. It is whether the AI protects what it touches.
This is especially critical in freight, where rate data, lane economics, carrier performance scores, and customer relationships are competitive assets. If your AI vendor is sending that data through public models or third-party inference layers you cannot audit, you are not just risking a compliance issue. You are handing your competitive intelligence to an environment you do not control.
Garbage In, Garbage Out…at AI Speed
Even the best AI agent, running on the right architecture with genuine domain knowledge, will fail if the data underlying it is poor. And in freight, data health is one of the most under-discussed prerequisites for AI success.
Duplicate records. Inconsistent naming conventions across customers and carriers. Rate confirmations that do not match the actual billing. Driver records that have not been updated in months. Settlement data that lives in a spreadsheet on someone’s desktop. This is the reality of most mid-market freight operations, and it is the foundation on which AI agents are expected to run.
The difference between an AI infrastructure company and an AI feature vendor is that the infrastructure company takes responsibility for improving data health as part of the platform. Automated Workflows that standardize how data enters the system. WAMS that flag data inconsistencies as they happen, not after they have compounded into billing errors or compliance gaps. Document processing that validates extracted data against shipment records before it is entered into the system of record. The AI does not just consume data. It cleans, validates, and improves data as operations proceed.
If the platform you are evaluating does not address data health, the AI agents it promises will produce confident-sounding output built on unreliable foundations. That is not intelligence. That is garbage in, garbage out—running at machine speed.
Third-party API agents rely heavily on third-party application programming interfaces (APIs) to connect to customer systems to automate shipping, logistics, and financial workflows. However, third-party API connectivity exponentially expands the attack surface, creating massive data breach and network infiltration risks.
Six Questions to Ask Before You Buy
If you are evaluating AI vendors for your freight operation, these are the questions that separate real infrastructure from marketing:
1. How long have you operated in freight, and at what level of workflow detail? A vendor that cannot describe the difference between a carrier settlement and a broker settlement, or explain how accessorial charge logic differs by customer, does not have the domain depth to build agents that survive production. Ask for specifics. The answer should not be a pitch deck.
2. Show me a live agent completing a multistep task across two or more real systems. This is Gartner’s own recommended test for agent washing. If the demo is a chatbot answering questions or a single-step automation, it is not agentic. It is a feature with new branding.
3. Where does my data go, and who can see it? If operational data—rates, lanes, carrier performance, customer information—passes through public LLMs or third-party inference layers that the vendor cannot fully audit, that is a risk you need to price into the decision. Ask whether customer data is used for model training. Ask where inference happens. Ask for the security architecture in writing.
4. What is your answer for data health? If the platform assumes your data is clean and ready for AI, it has never worked with a real freight company. Ask how the system handles dirty data, inconsistent records, and exception management at the data layer.
5. What percentage of workflows are AI-operated today versus on the roadmap? A vendor with 11 agents shipped or shipping in a 12-month window is in a fundamentally different position than a vendor with one agent in beta and 10 on a roadmap. Ask what is live, what is in production, and what customers are running on it today.
6. What is your formal vendor governance policy to minimize external network risks?
Confirm AI vendor adherence to baseline security frameworks – implement continuous, automated platform monitoring to track external vendor threat changes in real time. Finally, Companies using AI agents must reserve a contractual right to audit vendor systems, ensuring clear, enforceable remediation timelines during an incident.
Why the Company That Knows the Business Builds the AI That Works
The freight industry is one of the most operationally complex business environments in the economy. The for-hire trucking business alone involves over 20 distinct data variables per load: lane, shipper, equipment, hazmat classification, stop count, mileage, commitment realization, driver qualifications, and more. Each variable interacts with the others. Each customer relationship carries its own rules. Each freight type carries its own risk profile.
Building AI agents that operate inside that complexity is not a technology problem. It is a business knowledge problem that technology enables. The critical success factors are understanding the business at the strategic and tactical level, knowing which technology applies to each workflow, and having the judgment to prioritize what to automate first and what to make autonomous next.
EKA Omni-TMS™ was built on that premise. The AI agents EKA is shipping in 2026 (across documents, load management, on-time delivery, detention, fuel optimization, carrier matching, and natural language search) are not generic frameworks applied to freight. They are purpose-built by a team that has operated inside trucking, brokerage, freight payment, risk management, and insurance for decades. The agents work because the people who designed them have already done the work the agents automate.
That is not a sales claim. It is the only model that consistently produces AI agents that survive contact with the real business.
The Bottom Line
The freight industry is being flooded with AI claims. Most of them will not survive the next 18 months. Gartner has already told you the number: 40% canceled. Ninety-five percent of pilots deliver zero measurable return. Only 130 vendors out of thousands are offering genuine agentic capabilities.
The vendors whose agents will fail are those that do not understand the business they are automating, do not protect the data they process, and do not take responsibility for the health of the information their AI runs on. Their agents will break on the edge cases that define freight (the hazmat routing, the JIT penalties, the carrier settlement exceptions, the dock scheduling conflicts) because nobody in the building has ever lived those problems.
The vendors whose agents will work are those that know the business at the workflow level, build purpose-built agents for specific freight transaction types, protect customer data as a first principle, and improve data quality as a function of operations. If that is the kind of AI infrastructure you are looking for, talk to EKA Solutions. Because in a market full of agent washing, the only thing that separates real AI from a rebrand is whether the company behind it has done the work.