The End of the "Wild West" Phase<\/h2>\n
For many logistics companies, 2025 was a year of expensive trial and error. Encouraged by the rapid democratization of generative AI models, leadership teams rushed to deploy tools without the necessary technological foundation or change-management protocols.<\/p>\n
"I\u2019m seeing that the experimentation phase is mostly over," Quijano noted. "Companies that didn\u2019t have robust technology teams found themselves burning capital on AI projects that lacked clear ROI or operational integration. They tried it, they went into it without experience, and now they are dealing with the fallout."<\/p>\n
The "fallout" often involves brittle, unstable software that looked impressive in a sandbox environment but proved disastrous in production. Quijano highlights the rise and subsequent collapse of "vibe coding"\u2014the practice of using AI to generate software applications with minimal human developer oversight. Recent industry analysis suggests that as many as 99% of these "vibe-coded" applications have been abandoned or pulled from platforms entirely, proving that the speed of creation is not a substitute for business value.<\/p>\n
Chronology of a Hype Cycle<\/h2>\n
To understand the current state of the industry, one must trace the arc of AI adoption over the last eighteen months:<\/p>\n
- \n
- Early 2025: The Narrative Peak.<\/strong> Frontier AI labs and tech evangelists promised that "agentic workflows"\u2014AI models capable of performing complex tasks autonomously\u2014would revolutionize the brokerage floor, from load matching to billing.<\/li>\n
- Mid-2025: The Deployment Scramble.<\/strong> Logistics firms, fearing they would be left behind, began purchasing seat licenses and deploying "AI-first" tools. Many of these projects were launched as standalone "add-ons" rather than integrated into existing workflows.<\/li>\n
- Late 2025: The Reality Gap.<\/strong> As the initial novelty faded, companies realized that these tools required high-quality data, rigorous oversight, and defined standard operating procedures (SOPs). The "brittleness" of these systems began to cause operational delays rather than efficiencies.<\/li>\n
- 2026: The Correction.<\/strong> We are currently in the correction phase. Companies are moving away from "AI-first" branding and are instead focusing on "automation-first" strategies where AI serves as a surgical, specialized component rather than a catch-all solution.<\/li>\n<\/ul>\n
Supporting Data: The Cost of Disconnected Innovation<\/h2>\n
The struggle for modern logistics firms isn’t just about software\u2014it is about the fundamental management of technology. Quijano emphasizes that AI has not eliminated the need for traditional business pillars: project management, change management, and cost control.<\/p>\n
A significant point of concern for CTOs is "runaway token spend." With hundreds of thousands of AI agents now deployed across the global supply chain, companies are finding their cloud and API costs ballooning at an unsustainable rate. <\/p>\n
Furthermore, the "diminishing returns" of current frontier models are a growing point of contention. Quijano notes that the newest iterations of top-tier models offer only marginal performance improvements\u2014perhaps 6% to 7% better than their predecessors\u2014while coming at double the cost. This has led to a strategic pivot among sophisticated firms: looking toward open-source models and internal fine-tuning rather than blindly relying on expensive, general-purpose frontier models.<\/p>\n
Official Perspective: The "Employee" Model<\/h2>\n
Perhaps the most significant shift in perspective shared by Quijano is the conceptual framing of AI. He argues that organizations should stop treating AI as a "tool" to be configured and start treating it as a "junior employee" to be onboarded.<\/p>\n
"I don\u2019t think it\u2019s a tool. I think it\u2019s an employee," Quijano said. "What do you do with a junior employee that joins your company? You train them. You ensure that you give them a very defined job description so that they know exactly what they need to do."<\/p>\n
![\"The](\"https:\/\/www.freightwaves.com\/wp-content\/uploads\/2026\/07\/17\/Screenshot-2026-07-17-at-2.59.28-PM.png\")
<\/figure>\nThis "employee" framework necessitates:<\/p>\n
- \n
- Strict SOPs:<\/strong> AI, like a new hire, cannot operate in a vacuum. It requires documented, repeatable processes.<\/li>\n
- Exception Handling:<\/strong> If the AI encounters a scenario outside its SOP, there must be a "human-in-the-loop" to resolve it, rather than allowing the AI to hallucinate a solution.<\/li>\n
- Continuous Training:<\/strong> The "closed-loop" model ensures that feedback from human operators flows back into the system to improve performance over time.<\/li>\n<\/ol>\n
Implications for the Logistics Workforce<\/h2>\n
The transition from "AI-first" to "human-plus-AI" has profound implications for the talent market. The demand for pure software developers is being augmented by a new need for "bridge roles"\u2014professionals who sit at the intersection of logistics operations, business strategy, and technology implementation.<\/p>\n
Lean Solutions Group has attempted to codify this approach with the expansion of its LeanTek<\/em> platform. By integrating AI governance, workforce intelligence, and cost-visibility tools, the platform aims to provide the oversight necessary to manage AI like a department rather than a science experiment.<\/p>\n
The Human Element<\/h3>\n
Rather than replacing the workforce, the successful adoption of AI is forcing the industry to elevate it. The roles of the future will be "tech-powered humans"\u2014individuals who are not only capable of using the software but who understand how to optimize it. <\/p>\n
"We talk about elevating the role of the human to have more responsibilities," Quijano explained. "New responsibilities involve training and the upskilling of these roles so that they can be more tech-focused. They can have the ability to not only use the software, but be a part of it, part of the solution."<\/p>\n
Future Outlook: The Return to Basics<\/h2>\n
The message from industry leaders is clear: the era of "AI branding" is over. The companies that will thrive in the coming years are those that focus on the unglamorous work of data hygiene, process mapping, and disciplined ROI analysis.<\/p>\n
As the industry converges toward a more sustainable, human-centric model of automation, the focus will shift from the sheer power of the AI model to the efficacy of the infrastructure surrounding it. For those firms that spent 2025 "experimenting," the bill has arrived, and the lessons learned are likely to dictate the competitive landscape for the rest of the decade.<\/p>\n
The path forward, according to Quijano, is not to chase the latest, most expensive AI trend, but to integrate the right technology in the right place\u2014invisibly, reliably, and with clear accountability. In a world where AI is becoming a commodity, the true competitive advantage will be found in the quality of the "manager" overseeing the machine.<\/p>\n
\nJoin the Discussion at F3<\/h3>\n
For those looking to navigate the complexities of technology, compliance, and industry transformation, FreightWaves invites you to the Future of Freight Festival (F3)<\/strong>, held October 27\u201328, 2026, at The Signal in Chattanooga, Tennessee. <\/p>\n
Preceding the festival, the Brokerage Compliance Symposium<\/strong> on October 26 will offer critical insights into the regulatory and operational hurdles facing logistics firms today, including insurance gaps, fraud exposure, and the evolving legal landscape of AI in logistics. To secure your spot among 300 industry leaders, register at live.freightwaves.com<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"
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- Exception Handling:<\/strong> If the AI encounters a scenario outside its SOP, there must be a "human-in-the-loop" to resolve it, rather than allowing the AI to hallucinate a solution.<\/li>\n
- Mid-2025: The Deployment Scramble.<\/strong> Logistics firms, fearing they would be left behind, began purchasing seat licenses and deploying "AI-first" tools. Many of these projects were launched as standalone "add-ons" rather than integrated into existing workflows.<\/li>\n