How Freight Companies Can Maximize the Value of AI

Article written by: James Novino, Head of Engineering at Alvys

Over the past few years, AI has emerged as a buzzword in many industries — particularly in data-heavy sectors like supply chain management and freight. Despite representing about 5% of the U.S. economy, freight remains an under-served market for software solutions–a gap that forward-thinking investors like NEA view as a prime opportunity for AI-driven innovation. New technologies, including AI-assisted email and voice agents, promise to augment or replace manual and labor-intensive workflows that burden freight brokers and carriers. However, these emerging tools and agents are only scratching the surface of what a proper AI-enabled carrier and broker could be capable of doing in just a few years.  

By accelerating a shift to more integrated solutions, AI agents open the door for modern platforms that target “vertical slices” of this complex logistics landscape–everything real-time route planning to automated phone triage and load management. Tools such as genetic optimization and AI-powered simulations can intelligently adapt routes on the fly and sharpen demand forecasts. Meanwhile, automated anomaly detection exposes disruptions or fraudulent data before they ripple through a supply chain, helping all the players in the market have more reliable and trusted fulfillment models. 

As AI grows both more powerful and accessible, freight leaders who invest in robust data collection and adopt strategic, agentic workflows will be best positioned to capture the technology’s full potential. This disruptive capability could finally usher in a new era of efficiency, resilience, and real-time visibility in a sector historically dependent on manual processes. Companies prioritizing AI today will lay the groundwork for a far stronger competitive position tomorrow.

AI is transforming supply chains

AI adoption has soared in recent years. According to McKinsey, nearly two-thirds of organizations now report that they’re “regularly using gen AI,” almost double the proportion from ten months earlier. Three-quarters of respondents predict that generative AI will bring “significant or disruptive change in their industries in the years ahead” — a figure twice as high as it was the year before. 

Leading AI adopters tend to apply the technology across multiple business functions, with supply chain and inventory management among the top areas generating meaningful returns. This segment is the most likely to report a revenue boost of over 5% from AI deployment. Yes, despite widespread interest, freight and logistics organizations are often not capitalizing on AI’s full potential.  While Gartner reports that 95% of data-driven decisions will be at least partially automated this year, only 10%of CEOs say their organizations use AI strategically–highlighting a gap between enthusiasm and real-world use cases. 

Moreover, striking performance gaps between high and low performers in the supply chain sector in how they use AI for a wide range of tasks. Top performers were twice as likely to use AI for demand forecasting, over four times as likely to use it for order management and fulfillment, and more than three times as likely to use it for logistics and distribution. At a time when AI is a clear competitive differentiator, many supply chain leaders still aren’t using it strategically. This needs to change. 

Why AI is particularly valuable in the freight sector

Freight and logistics leaders cannot fully leverage AI without first understanding where it can add the most value. The complexity–multiple stakeholders, high-touch transactions, and razor-thin margins–makes it especially attractive for AI-driven efficiencies. At the same time, real-world challenges like volatile demand cycles, post-COVID market disruptions, and even debates over “transparency” underscore the urgent need for more data-driven, efficient platforms. 

Consider an AI-powered capability like time-series forecasting, which allows carriers to integrate multiple time-dependent variables–freight cycles, capacity constraints, and economic indicators–and generate highly accurate predictions. AI can also model various “what-if” scenarios to guide strategic decisions around fleet investments or tariff changes. These capabilities are particularly critical now, given that the freight sector's fortunes rose sharply during the  COVID boom followed by a long and crippling recession — as well as major transformations such as supply chain onshoring. These are reminders that freight cycles are becoming less predictable, which is why AI-enabled forecasting and scenario modeling will become increasingly important in the years to come. 

Yet adopting AI solutions in freight isn’t as simple as flipping a switch. As agentic AI tools become more readily available, businesses often manage a patchwork of specialized apps–route-optimization software here, contract management there–each with its interfaces and data requirements. Integrating these disparate tools into a cohesive workflow can be daunting, especially for smaller players without robust in-house IT or Engineering resources. Early adopters often find themselves compelled to invest heavily in data pipelines, API integrations, infrastructure, and user training, which can erode short-term ROI. Over time, these complexities can undermine even the most promising AI initiatives if companies lack a well-coordinated plan for orchestration and ongoing maintenance. 

AI’s potential for delivering operational insights and productivity gains is increasingly attractive in an environment still grappling with tariff uncertainty and a sluggish post-recession recovery. Whether by optimizing routes, projecting future demand, or clarifying cost structures, AI is poised to reshape freight networks that have historically relied on manual processes and opaque workflows. The key to realizing AI’s promise lies in balancing technological capability with practical deployment—ensuring that each new solution integrates seamlessly into the daily fabric of freight operations.

How AI can maximize freight efficiency

There’s a vast array of existing and new AI applications available in 2025. Take route optimization, for instance — AI can enable efficient and dynamic routing that saves fuel costs, improves on-time deliveries, and streamlines the entire supply chain. Techniques like ant colony optimization can restructure the layout of warehouses to make distribution more efficient, improve scheduling and resource allocation, and make networks more interconnected. At a time when 20 to 35 percent of trucking miles are empty, AI can cut down on waste by optimizing fleet management. 

Beyond routing, high-quality data is becoming increasingly vital for informed decision-making, which can analyze vast, diverse datasets for insights into shifting marketing dynamics, capacity constraints, and cost structures–ultimately boosting agility and productivity. AI is also vital for visibility, which is a core aspect of supply chain resilience. This doesn’t just apply to real-time analytics on driver behavior, route efficiency and delivery times, and warehouse operations — it also applies to visibility into datasets themselves. As noted above, AI-powered anomaly detection enables carriers to identify problems with datasets, such as inconsistencies, human errors, and malicious activity. 

As large language models (LLMs) and other AI tools become more powerful and accessible, it will be easier than ever for carriers of all sizes to leverage this revolutionary technology. A central focus for carriers in the coming years will be the amount and quality of the data they have access to, as well as the development of workflows and strategies that will help them make the most of AI.