AI exposure in the U.S. logistics workforce

Our analysis begins with a detailed list of tasks performed by workers in logistics occupations, captured through the Occupational Information Network, or O*NET. O*NET comprehensively documents these tasks, including their relative importance to job performance, how frequently each task is performed, and whether each task is “core” or “supplemental.” Core tasks are central to an occupation’s primary responsibilities, while supplemental tasks play a supportive, yet less fundamental, role.

The distinction between automating core versus supplemental tasks is crucial, as research underscores that automation of core tasks typically has more significant implications for occupational stability and employment outcomes. Similarly, as various scholars highlight, task frequency is an important differentiator of AI exposure across occupational tasks. Task frequency directly influences the cumulative economic returns of automation by highlighting repetitive tasks that yield substantial productivity gains.

Explicitly integrating task frequency alongside task importance and the core-supplemental classification can therefore yield a more robust, economically meaningful assessment of AI exposure. Figure 1 below illustrates the relationship between task importance and task frequency within occupations, displaying that task frequency exhibits substantial independent variation—though also showing it positively correlates with importance, too. (See Figure 1.)

Figure 1

We utilize our frequency-weighted AI exposure metric to calculate AI exposure by weighting tasks according to the estimated number of times they are performed annually, based on frequency data reported in O*NET. This approach provides a more economically grounded and practically relevant measure, aligning closely with real-world automation incentives in U.S. supply chain and logistics occupations. This refined exposure metric can better identify the tasks and roles most susceptible to generative AI-driven changes, thus supporting proactive policy formulation and strategic workforce planning.

Logistics sector occupational impacts of generative AI

Understanding generative AI’s occupational impacts within the logistics sector requires a detailed analysis across specific roles and transportation modes. Within the logistics sector, there are specific occupational categories, including truck and water transportation, support activities for air transportation, and warehousing and storage, among others. For each of these categories, we calculated AI exposure scores, derived from our modified AI exposure methodology described above, and weighted them by task frequency. (See Table 3.)

Table 3

What clearly emerges from Table 3 is that different transportation modes within the U.S. logistics sector—for example, freight trucking, water transportation, and pipeline transportation—vary significantly in their exposure to generative AI disruptions. Freight trucking, characterized by dynamic routing, real-time scheduling, and frequent documentation requirements, shows particularly high potential for generative AI exposure, especially in administrative roles. By contrast, water and pipeline transportation, which involve more specialized manual tasks and fewer frequently repeated cognitive activities, exhibit relatively lower immediate susceptibility—though predictive maintenance and monitoring tasks remain promising AI applications.

Notably, rail, freight, and logistics services—and particularly freight transportation arrangement services and customs brokering services—exhibit the highest exposure of any logistics industry analyzed here. With more than 75 percent of tasks potentially decreasing in duration by 50 percent or more, this industry could see a reduction in total employment over the next decade while maintaining its productivity level and increasing its profitability. Although some occupations might maintain employment levels after adopting productivity-enhancing technological innovations, widespread disruption at the sector level inevitably leads to workforce displacement.

Consequences of AI exposure for logistics-sector workers

While exposure to AI offers significant benefits to firms in terms of reduced labor costs and improved productivity, the downside for workers—particularly those in vulnerable roles—includes potential job displacement and wage suppression. Figure 2below introduces this dimension by showing the variation in average annual earnings versus LLM exposure by industry. (See Figure 2.)

Figure 2

The blue bubbles in Figure 2 reveal a striking bifurcation within the trucking and ground-freight subsector. On one hand, truck drivers—by far the largest occupation, employing more than 1 million people—earn relatively modest wages and face relatively low AI exposure, implying limited technical potential or economic incentive for automation. On the other hand, a much smaller but substantially higher-earning group of logistics managers—such as operations, warehouse, and transportation managers, totaling more than 100,000 employees in this sector alone—sits at the very top of the LLM-exposure index, with more than 90 percent of their tasks susceptible to AI. This contrast underscores how, within the same subsector, firms may eschew automating low-wage, low-exposure roles yet aggressively target the high-wage, high-exposure managerial positions for AI-driven productivity gains—along with the attendant displacement of jobs or wage-pressure risks.

Much of our analysis so far has focused on the direct automation of workplace tasks, but indirect exposure to AI also merits consideration. Take, for example, administrative assistants who manage logistics and scheduling for warehouse operations. Even if warehouse loaders have limited direct AI exposure, administrative staff increasingly use AI-driven software to optimize inventory placement and shipping sequences. Predictive models can ensure that frequently ordered items are placed near loading docks, significantly reducing retrieval time for warehouse loaders. Thus, productivity in occupations without direct task automation can improve substantially due to spillovers introduced elsewhere.

This reveals two lenses through which to evaluate AI automation within an industry: one where workers’ tasks are automated to decrease labor hours demanded of that worker type (or to free up worker capacity), and one where the quality of task execution is improved by automation, allowing for efficiency spillovers.

Now that we have showcased the variation across logistics subsectors and occupations of AI exposure, we turn to how specific occupations may be vulnerable to automation and thus to AI adoption. Deeper dives into two occupations that are highly exposed to AI within the logistics sector help illuminate potential vulnerabilities.

Two highly exposed occupations that are heavily represented in logistics industries are customer service representatives and dispatchers (except police, fire, and ambulance). These two occupations each score the maximum value in our exposure index—100 percent—meaning that all their typical tasks are exposed to LLM-powered solutions. This makes these occupations highly vulnerable to job displacement or wage losses as a result of AI adoption.

Let’s turn first to the example of customer service representatives.

Logistics customer service representatives

The typical earnings of customer service representatives—an annual median of $39,680 in 2023—are low, compared to peer occupations requiring similar distributions of skill, knowledge, abilities, and work activities. If displaced, these workers may be relatively more likely to transition to jobs with wages comparable to those they are accustomed to earning. Yet, to the extent that a displacement shock from AI affects similar occupations, these transition options could simultaneously become restricted, placing significant pressure on wages and employment for disrupted customer service representatives. (See Figure 3.)

Figure 3

Job displacement in customer service seems likely, as core tasks such as “confer with customers by telephone or in person to provide information about products or services, take or enter orders, cancel accounts, or obtain details of complaints” and “keep records of customer interactions or transactions, recording details of inquiries, complaints, or comments, as well as actions taken”were already being automated for telephone and virtual support lines prior to the increased availability and accessibility of generative AI. Now, AI automation in customer service is simply a matter of paying for one of the many available services.

Unfortunately for these workers, their next potential occupation may not be a safe harbor from displacement as a result of future automation. When calculating the average exposure level (weighted by historical occupational transition shares, or the expected level of exposure across the set of likely alternative positions), we find that the statistical worker in fields similar to customer service across the U.S. economy has a 95.22 percent AI exposure. Though this is less than the 100 percent score we found for customer service representatives, it is not low enough that these other workers should feel insulated from further disruption. Even if these workers deviate from traditional career pathways, similar occupations tend to be more exposed than the economywide median. (See Figure 4.)

Figure 4

The widespread adoption of automation solutions may therefore lead to disruptions across this occupational cluster. If productivity gains do not lead to compensating demand for labor (fewer workers per task, but a greater volume of tasks to be performed), then we may expect downward pressure on wages, as workers compete for a limited number of job opportunities.

Logistics dispatchers

Our second example occupation—dispatchers (except police, fire, and ambulance)—earned a median wage of $46,860 in 2023. Dispatchers are paid comparable wages to workers in peer occupations requiring similar distributions of skill, knowledge, abilities, and work activities. (See Figure 5.)

Figure 5

We might expect workers transitioning involuntarily (due, for example, to a technological shock) from employment in one occupation to enter a new occupation at or below their current percentile of earnings. In other words, we expect a worker at the 90th percentile in one occupation to be offered a position with pay below the 90th percentile in their new occupation due to their comparatively lower experience in their new role. If so, then Figure 5 suggests that displaced workers at the top of the income distribution in this particular occupation may face significant challenges in maintaining their level of earnings after an occupational transition.

Despite typically earning more than customer service representatives, dispatchers are already subject to automation. This makes their replacement by AI just as technically feasible—and even more incentivized from an employer’s standpoint, as the potential wage savings are greater.

Additionally, the quality of dispatchers’ work has greater influence on the productivity of their colleagues within firms, compared to customer service representatives. This means that quality is an important metric to consider. If generative AI technologies produce worse results than a human, those effects will be magnified, causing a ripple effect throughout organizations. Likewise, if generative AI proves superior to a human dispatcher, then the potential savings may exceed the compensation of the displaced workers, as affiliated workers gain efficiency benefits from improved coordination.

Importantly, the tolerance for AI-committed errors will vary by setting, depending on the cost of the failure in relation to the nature of the error committed, the existing rate of human error, and the feasibility of identifying and perhaps correcting errors. This variance could be across industries—for instance, a low tolerance for error in piloting aircraft versus in warehousing—as well as by the position of the task in the value chain. Errors in delivery, for example, are potentially costly both from lost productivity and because they affect the customer experience, while errors in optimal storage might affect costs through productivity alone.

Like customer service representatives, dispatchers also face challenges in moving to jobs less exposed to AI, though dispatchers are slightly better off than customer service representatives. Our research indicates that similar jobs to dispatchers also are highly exposed to AI, though not as exposed as 100 percent, which is the score we found for dispatchers. (See Figure 6.)

Figure 6

If diverse industries adopt automation at varying rates, displaced workers may be displaced multiple times over the course of a few years. Each time, competition for similar jobs would become steeper, as a growing number of workers fight to the bottom of the earnings distribution. Much worse would be a scenario where many similar industries adopt automation technologies simultaneously.

A firm’s decision to adopt generative AI in logistics, as in any other industry, is ultimately driven by economic and operational incentives. Logistics operations are typically labor-intensive, involving substantial labor costs associated with moving and managing goods. Consequently, there are considerable economic incentives to deploy AI to automate high-frequency, routine tasks—such as documentation, tracking, and inventory management—particularly in sectors where labor constitutes a significant portion of operational expenses.

Labor costs can further shape AI adoption decisions. Higher-wage roles in logistics, such as transportation managers or supply chain analysts, often involve cognitive tasks highly suited to AI tools. Automating or augmenting these tasks can deliver substantial cost savings and productivity improvements for firms. Conversely, lower-wage roles typically offer fewer immediate incentives for AI adoption, not only because these positions often entail tasks less amenable to automation but also due to limited immediate economic returns. Furthermore, retaining employees in these lower-wage roles could enhance overall efficiency through productivity spillovers, reflecting traditional capital-labor complementarities.

A relevant quantitative example demonstrates the varied impacts of AI assistance on customer service agents’ handling times, depending on task complexity. The researchers found that AI significantly improved efficiency for moderately uncommon problems, suggesting substantial benefits through reduced labor costs per interaction. Conversely, AI had minimal impact on very routine or extremely rare problems, implying potential scenarios where AI implementation and maintenance costs might outweigh benefits for firms.

The return on investment for AI adoption in logistics, therefore, similarly depends on specific task characteristics and the corresponding efficiency improvements that AI might realistically achieve in the near term, considering uncertainty about the long-term technical potential of AI.

An important obstacle to AI adoption is resistance from inside or outside of firms. Internally, the resistance might come from decision-makers themselves, who could be put in a position to impact the scope and amount of work available to them by choosing whether to adopt AI. Similarly, lower-wage workers are more likely to be represented by a labor union that will advocate on their behalf against any possible adverse impact of AI adoption.

Policymakers face significant challenges in managing U.S. labor market disruptions arising from generative AI. Effective regulatory frameworks must balance the promotion of innovation and productivity gains with safeguarding employment and ensuring equitable outcomes.

One option policymakers might consider is establishing comprehensive worker-transition policies, including robust reskilling and upskilling initiatives. While the existing literature highlights the potential importance of targeted training programs to equip displaced workers with skills aligned with emerging labor market needs, recent analyses suggest caution in this area, noting mixed evidence regarding the effectiveness of traditional retraining efforts. Consequently, policymakers should ensure these retraining initiatives are thoughtfully designed, evidence-based, and specifically adapted to address the unique challenges posed by AI-driven displacement.

Additionally, transparency standards for reporting employment changes due to AI adoption are essential. Developing standardized frameworks can facilitate the systematic collection and dissemination of data on job losses, job creation, and shifts in occupational demand resulting from generative AI. Such transparency helps policymakers and stakeholders alike monitor the impacts of generative AI more accurately, enabling timely and informed interventions.

Relatedly, policymakers can also leverage existing U.S. Bureau of Labor Statistics data collection methods or develop new indicators to identify early warning signs of occupational disruptions from AI. Recent literature highlights the utility of analyzing job postings to detect shifts in skill demands and potential vulnerabilities related to AI exposure.

Additionally, drawing on the experience of existing programs—such as the Trade Adjustment Assistance Program, a federal initiative administered by the U.S. Department of Labor that provides training, reemployment services, and income support to workers displaced by increased importing of goods—can offer valuable insights for designing AI-specific programs. This approach would be particularly useful for creating incentives that encourage businesses to transparently share employment-impact data without inadvertently motivating executives or shareholders to accelerate AI adoption for short-term gains.

Moreover, policies incentivizing firms toward labor-complementing rather than labor-replacing AI technologies could help mitigate employment losses. Worker decision rights around technology adoption could help drive such incentives. Encouraging investment in AI technologies that enhance human productivity, such as predictive analytics and workflow automation tools, rather than entirely substituting human roles, can help maintain employment levels while driving productivity improvements.

Possible policy levers in this area range from tax credits focusing on specific types of technological investments, emphasizing labor-complementing productivity gains, to reskilling programs (for example, accelerating transitions into new occupations for at-risk workers or helping workers gain skills that can help widen their scope of potential transition options to increase their robustness to technological disruption), as well as specific tax credits contingent on payroll targets, such as linking capital investment to wage enhancement.

Regulatory policies must also consider the competitive dynamics introduced by generative AI. Data are increasingly recognized as a critical complementary asset for leveraging generative AI effectively. Policies promoting equitable access to relevant data, such as public data assets or interoperability standards, and those that mitigate monopolistic tendencies in data ownership and processing, such as facilitating market-based purchasing of computing resources, can ensure broader economic benefits and prevent entrenched advantages among incumbent firms. Ensuring a competitive landscape by AI adopters could enable downward pressure on prices, thus exerting an upward force on output and hence labor.

Finally, strategic regional and sector-specific economic planning, informed by detailed analyses of occupation-specific AI exposure, is essential. Policymakers should proactively identify vulnerable communities and occupations, facilitating targeted support through direct interventions, economic diversification initiatives, and stimulus for job creation in sectors less exposed to AI disruption. The geographic concentration of employment in logistics-related occupations makes regional strategies more urgent. For instance, BLS data show a much higher concentration of transportation, storage, and distribution managers in some states versus others, which could make those local labor markets more vulnerable to disruption in occupations with correlated technology exposure.

Integrating these policy considerations can help governments and stakeholders navigate the complex labor market dynamics posed by generative AI, fostering inclusive growth and workforce resilience in the U.S. logistics sector.

This essay provides an assessment of generative AI’s potential impacts within the U.S. logistics and supply chain sector, highlighting key quantitative findings and clarifying implications for policy and industry stakeholders.

Our analysis demonstrates that roles within the U.S. logistics sector exhibit starkly divergent AI-automation risk profiles. Among the more than 200,000 logistics managers—including operations managers, warehouse managers, transportation managers, and similar titles—more than 90 percent of tasks are susceptible to AI-driven automation, with virtually all of those classified as core activities, signaling a substantial displacement risk. In contrast, bus and truck mechanics—a workforce exceeding 70,000—face virtually 0 percent task exposure, underscoring the wide gulf in technical potential and economic incentive for automation across the logistics ecosystem.

These findings emphasize the necessity of nuanced, role-specific workforce interventions and strategic adoption of generative AI technologies. Importantly, individual sectors within the logistics industry also exhibit marked differences in AI exposure. Freight transportation, for example, demonstrates particularly high vulnerability, whereas warehousing and storage faces relatively lower susceptibility. Mobility within occupational clusters and across salary ranges further complicates worker transitions, necessitating targeted policy responses that address potential dislocation and ensure sustainable career pathways.

Policymakers must also consider measures to address long-term earnings losses for displaced workers. Comprehensive policies, including wage insurance, transitional income support, and targeted reskilling and upskilling initiatives, are crucial to mitigate economic hardships. Future research should continue tracking employment outcomes and productivity changes post-AI adoption to refine these policy strategies and improve their effectiveness.

Christophe Combemale is an assistant research professor at Carnegie Mellon University’s Department of Engineering and Public Policy, where his research focuses on technological impacts on workforce skills and organizational structures. His recent work explores labor implications of generative AI and industrial transitions across multiple sectors. He is also CEO and principal partner of Valdos Consulting, specializing in techno-economic modeling to support market, technology, and workforce strategy.

Laurence Ales is a professor of economics at Carnegie Mellon University’s Tepper School of Business. He received a Ph.D. in economics from the University of Minnesota in 2008 and a B.S. in physics from the University of Rome Tor Vergata. His research focuses on the design of tax policy and on the labor implications of technological change.

Dustin Ferrone is a senior engineer at Valdos Consulting, providing expertise in complex systems analysis, labor and service supply chains, and scalable analytical software solutions. With a background in systems engineering and extensive industry experience, Ferrone advises both public- and private-sector clients on managing workforce transitions and technological integration.

Andrew Barber is a senior economist at Valdos Consulting, specializing in economic modeling, labor market analysis, and the impact of emerging technologies, such as generative AI. His research evaluates public-sector initiatives, workforce preparedness, and incentive-compatible policy solutions, supporting government and industry efforts to navigate technological disruptions.