Logistics AI ROI: How to Maximize Your Investment

Logistics Companies Look to AI for Measurable Gains, Not Just Automation

Artificial intelligence is moving deeper into freight operations, but logistics companies face a practical question: Can the technology produce a measurable return on investment?

Dan Bailey, co-founder and CEO of Nexcade, said the answer depends less on simply deploying AI and more on whether companies can provide the technology with the operational context needed to make useful decisions. That context includes years of employee experience, customer preferences, market knowledge, pricing practices and the informal processes that often guide daily freight decisions.

“Context is the biggest challenge in our space,” Bailey said during an interview with FreightWaves. “There is so much embedded knowledge built up through years of experience and through real market nuance — and so turning that into insight that AI can use is an extremely, extremely tough challenge.”

The issue is particularly important as large logistics companies pursue broad efficiency programs. C.H. Robinson has promoted a “Lean AI” operating model that combines artificial intelligence with its logistics data and the expertise of its employees. The company has said the approach is designed to help its supply chain operations make faster, more informed and continuously improving decisions.

AI also is central to C.H. Robinson’s planned acquisition of RXO. The transaction carries a projected $300 million in synergies, according to the information discussed in the interview. Bailey said that target demonstrates the potential scale of AI-supported efficiency, while also highlighting the challenge of applying an established operating model to a different business.

RXO would bring its own customers, processes and operational knowledge. Bailey said that transferring C.H. Robinson’s lean model into that environment will require the company to absorb and organize a large amount of information in a relatively short period. The two businesses have limited customer overlap and different operating knowledge, creating execution challenges even if the underlying AI strategy has already produced bottom-line results at C.H. Robinson.

For freight operators, the technology is most visible in routine work such as quoting, shipment updates and exception management. Bailey said approximately 90% of quote requests still arrive by email. Nexcade’s AI agents are designed to read incoming messages, identify the type of freight, extract shipment details and perform related procurement research.

That research can include checking customer contracts, reviewing spot-market rates and accessing overseas agent portals through application programming interfaces or browser-based tools. The objective is to prepare quotes for an employee’s review rather than require the employee to complete every lookup manually.

Bailey said some teams using the technology have doubled their files-per-person throughput. He also described a customer receiving automated responses to overseas agent requests at 3 a.m., allowing that company to begin work on a quote before competitors are available to respond. About 75% of Nexcade’s current customer volume is in air and ocean freight, with the remaining 25% involving road freight, including U.S. domestic and European lanes.

Those applications have implications for trucking operations, although they do not eliminate the need for human judgment. Faster processing can help teams respond to customers, compare rates and identify shipment details earlier. For drivers and dispatch personnel, better-organized information may also reduce the time spent tracking down missing details or responding to repeated status requests.

Bailey said logistics companies should measure AI’s value through more than labor savings. Risk reduction is one area that can be overlooked. Tools that assist with reconciliation and exception handling may help reduce demurrage, detention and other unexpected charges. The financial effect of those improvements may become clearer over a period of 12 to 18 months rather than appearing immediately after implementation.

Revenue performance is another measure. Companies can track how response times affect win rates and compare margins by lane. That allows managers to determine whether faster quoting is producing additional business or simply allowing employees to process more requests without improving financial results.

Bailey recommended that companies use both an executive-led and employee-led approach to AI adoption. Senior leaders can identify strategic workflows and establish the necessary data systems, while individual managers and teams can test smaller projects without waiting for a companywide rollout.

Those smaller experiments can reveal whether a tool fits the way work is actually performed. They also can expose problems with data quality, system access or process design before a company commits to a larger deployment. Bailey said companies may learn from several smaller pilots before their largest AI initiatives reach implementation.

He also noted that employees are already experimenting with consumer AI tools, including ChatGPT and Claude, whether or not their employers have formally approved an enterprise program. That informal use can create security and consistency concerns, but it also shows that workers are looking for ways to reduce repetitive tasks.

Nexcade, which builds AI agents for freight forwarders, came out of stealth in October 2025 and later announced a $6 million seed round led by Project A Ventures. The company said it has moved from early design partnerships to production deployments with customers including XPO, Zencargo, Cardinal Global Logistics and CargoTrans in Europe and the United States.

The broader lesson for transportation companies is that AI adoption alone is not a business result. The technology must be connected to reliable information, measured against operating and financial goals, and adapted to the way employees actually handle freight. For carriers, brokers, forwarders and drivers, the value will depend on whether these systems reduce avoidable work and errors while preserving the human knowledge required to manage changing conditions on the road and throughout the supply chain.