The logistics industry is currently navigating a tectonic shift. While the potential of Artificial Intelligence (AI) to optimize global supply chains is no longer a matter of debate, the industry has hit a wall of practical reality: moving from theoretical pilot programs to generating a measurable, tangible Return on Investment (ROI).
Dan Bailey, Co-Founder and CEO of Nexcade, argues that the current discourse surrounding AI in freight often misses the forest for the trees. While massive corporate maneuvers—such as C.H. Robinson’s $300 million synergy target following its acquisition of RXO—capture headlines, they underscore the volatility and complexity inherent in scaling AI-driven efficiency. For the average logistics provider, the struggle is not a lack of interest, but rather the difficulty of translating decades of "embedded operational knowledge" into data structures that modern large language models (LLMs) and predictive engines can actually interpret.
The Contextual Challenge: Why AI Struggles in Freight
At the heart of the logistics industry lies an immense volume of unstructured data. From the nuances of international freight brokerage to the specific idiosyncrasies of regional road lanes, logistics is a business defined by expertise and context.
"Context is the biggest challenge in our space," Bailey explained during a recent discussion. "Whether you’re talking about international freight or on the brokerage side, there is so much embedded knowledge built up through years of experience and through real market nuance. Turning that into insight that AI can use is an extremely, extremely tough challenge."
For many firms, the primary barrier is that this "institutional memory" resides in the minds of veteran brokers and within disconnected email threads rather than in structured databases. When AI is applied to this "dark data," it often lacks the necessary frame of reference to make accurate decisions, leading to hallucinations or, at best, mediocre performance.
Shadow AI: The Unspoken Reality of the Workforce
While corporate boards debate the merits of enterprise-wide AI rollouts, the workforce has already taken matters into its own hands. Bailey points to the prevalence of "shadow AI"—the unauthorized use of consumer-grade tools like ChatGPT and Claude by employees to handle routine tasks.
"If you’re not adopting it, I can guarantee you that your employees are in shadow capacity one way or another," Bailey noted. "There was a ton of shadow ChatGPT, shadow Claude being used across this industry and many others right now."
This trend highlights a critical disconnect: employees recognize the efficiency potential of these tools, but in the absence of corporate governance and secure, integrated platforms, they are resorting to workarounds that may pose security risks or lead to fragmented data silos.
Chronology of the AI Surge in Logistics
To understand the current state of the industry, one must look at the rapid evolution of technology over the last 18 to 24 months.
- Q1–Q2 2023: The Awareness Phase. Following the public release of generative AI tools, logistics firms began "experimenting" with simple chatbots and automated email summaries.
- Q3–Q4 2023: The Pilot Fatigue. Companies realized that generic AI models could not handle the complexity of freight documentation, leading to a period of re-evaluation and a focus on specialized, domain-specific AI training.
- Q1 2024–Present: The Integration Era. Firms are now moving toward "agentic" workflows—systems that do not just provide information, but take action. Nexcade, for instance, has moved into the space of autonomous agents that interact directly with customer inboxes to classify freight and execute procurement lookups.
Supporting Data: Efficiency at Scale
The tangible benefits of moving beyond basic automation are becoming clear. Nexcade’s data provides a glimpse into the operational shift. According to the company, roughly 90% of quote requests in the freight industry still arrive via email—a process that has historically been manual, slow, and error-prone.
By deploying agents capable of reading inboxes, classifying freight types, and checking against both contract rates and spot market portals, companies are seeing dramatic shifts in performance:
- Throughput: Teams using AI-augmented platforms have doubled their files-per-head throughput.
- Responsiveness: AI agents allow companies to provide quotes 24/7, even during off-hours, allowing them to capture business that would otherwise go to competitors who are offline.
- Volume Distribution: Approximately 75% of current AI-driven volume in this segment is focused on air and ocean freight, with the remaining 25% dedicated to road freight, including complex domestic and European lanes.
Implications for Corporate Strategy
The $300 million synergy target associated with the C.H. Robinson/RXO merger serves as a bellwether for the industry. While lean operating models can yield significant bottom-line results, Bailey warns that applying these models to new business units—especially those with limited customer overlap and different operational knowledge—carries "real execution risk."
For organizations looking to deploy AI successfully, Bailey suggests a dual-pronged strategy:
- Top-Down Strategic Governance: Leadership must identify high-impact workflows and commit to building the "data foundations" required to support AI. This involves cleaning legacy data and ensuring that internal systems are compatible with API-first architectures.
- Bottom-Up Empowerment: Companies should encourage managers and frontline teams to run small, low-risk pilots. These grassroots experiments allow firms to test hypotheses quickly without the massive overhead of a full-scale enterprise deployment.
"Companies often learn as much from those smaller experiments as from large projects," Bailey noted, adding that this approach allows for "three or four bites at the apple" before major initiatives reach the rollout phase.
Measuring Success: Beyond Time Savings
The most common mistake firms make is measuring AI ROI solely by time saved. While "time-to-quote" is a critical metric, it is only one piece of the puzzle. Bailey suggests that companies must also account for:
- Risk Mitigation: By automating exception handling and reconciliation, AI can significantly reduce costs associated with demurrage, detention, and unexpected billing errors—costs that often aggregate into millions of dollars over an 18-month period.
- Commercial Impact (Revenue Side): Companies should correlate speed-to-quote with win rates and margin analysis. By tracking how quickly a quote is returned and the resulting success rate, companies can quantify the direct revenue impact of their AI investment.
The Human Element: Change Management
Finally, the transition to an AI-driven environment is not purely a technical challenge; it is a human one. Drawing on his experience scaling the logistics software company Sedna—which grew from 20 to over 250 customers—Bailey emphasizes that change management remains the ultimate bottleneck.
When replacing legacy systems like Microsoft Outlook with specialized AI-integrated platforms, the resistance to change is often internal. Employees who have spent decades relying on manual processes may view AI as a threat rather than a tool. Successful organizations, therefore, are those that frame AI as an "augmenter" of human capability—a way to remove the "grunt work" of email processing and pricing lookups, allowing brokers to focus on high-value client relationship management and strategic decision-making.
As the industry moves toward the 2026 horizon, the distinction between "AI-enabled" firms and those still struggling with manual workflows will likely become the primary driver of market share. The winners will not necessarily be those with the most powerful algorithms, but those with the best strategy for integrating their unique, embedded institutional knowledge into the digital age.
