2026 Transportation Management Benchmark Survey<\/a><\/em> shows that, despite virtually universal AI engagement in some form, fewer than one in five companies have actually deployed AI at scale.<\/p>\nWhy are so many transportation organizations having difficulty moving AI from experimentation to scaled production? According to the report, the missing piece may be a strong digital foundation.<\/p>\n
The report signals that AI is a key focus for shippers and LSPs in 2026. Introduced in the survey for the first time this year, AI adoption and innovation debuts as the third most influential factor impacting transportation management in the next five years, and even ranks second specifically for LSPs (35%), highlighting just how fast AI has become a focal point of the industry.\u00a0<\/p>\n
The survey also finds that almost all shippers (97%) and LSPs (99%) are engaged with AI in some capacity. For example, respondents report using AI for data entry (60%), real-time visibility and tracking (59%), route and load optimization (57%), customer service (56%), fraud detection (55%), pricing optimization (52%), freight forecasting (51%),and load matching and capacity sourcing (48%).\u00a0<\/p>\n
AI adoption is also the top shared priority, cited by 29% of both shippers and LSPs, but the execution isn\u2019t keeping up with the ambition. While survey findings clearly reflect a growing embrace of AI, real-world usage is limited. Many respondents \u2014 29% of shippers and 38% of LSPs \u2014 are still in the exploration stage. About a quarter are in the pilot stage, and another quarter are partially deployed, but only 19% of shippers and 15% of LSPs report actually using AI at scale.<\/p>\n
With fewer than 20% of shippers and LSPs deploying AI at scale, what is impeding these companies from realizing their AI goals? The survey suggests the constraint is the digital foundation that supports AI. For example, the dominant barrier to scaling AI is data quality, cited by almost half of LSPs (45%) and a third of shippers (33%). The most useful AI is fueled by actual execution data, and AI\u2019s potential simply cannot be realized if transportation data is fragmented, outdated or incomplete.\u00a0<\/p>\n
Integration complexity is also a considerable impediment to AI scaling for both shippers (39%) and LSPs (38%). Together, these findings reveal that the underlying digital foundations necessary to enable AI to function effectively in full-scale production mode are not in place at many organizations.<\/p>\n
Conversely, lack of skills and change-management barriers were only cited by one fifth of less of respondents, suggesting organizational readiness is no longer a major limitation to achieving the AI agenda. Transportation teams seem to be functionally prepared to expand AI, but realizing its full value at scale will depend on building up the digital infrastructure needed to support it.<\/p>\n
Report findings note that AI\u2019s greatest potential could be in tightening what survey respondents identify as their widest operational gaps: visibility and automation.<\/p>\n
Real-time visibility remains the most significant operational gap for shippers (27%), reflecting the continuing challenge of tracking freight from origin to destination across increasingly complex transportation networks. Real-time visibility and tracking is already the number-one AI use case for LSPs (67%), and is used by more than half of shippers (51%), yet many shippers (35%) and LSPs (41%) still rely on spreadsheets or post-delivery emails, uncovering substantial opportunities to further improve shipment visibility with the technology.<\/p>\n
In addition, automation presents the top operational challenge for LSPs (26%), and second-ranked challenge for shippers (25%). While 59% of LSPs selected \u201cautomation and AI\u201d as the primary strategy for using technology to differentiate their services, only 13% of LSPs and 16% of shippers describe their transportation operations as fully automated with AI. This disparity presents a potential opportunity for AI in the area of automation as well.\u00a0<\/p>\n
Both shippers and LSPs are moving forward with AI, but this year\u2019s findings indicate that companies need a stronger digital foundation on which to build AI initiatives that can effectively transform transportation performance. With this objective in mind, the following four capabilities are key considerations for both shippers and LSPs:<\/p>\n
Connected transportation data.\u00a0<\/strong>The AI journey should start with connected, operationally ready transportation data. To deliver meaningful results, AI requires more than just a company\u2019s internal transportation management system and enterprise resource planning data. Transportation workflows are inherently inter-enterprise in nature, so shippers and LSPs should support AI with comprehensive, accurate and timely data collected across carriers, drivers, suppliers and other trading partners.<\/p>\nReal-time visibility.\u00a0<\/strong>One of the most valued advantages of AI is support for smarter decision-making, but this can only be achieved based on real-time visibility into carriers and shipments, such as available capacity, load location and condition, status changes, ETAs and exceptions. Visibility delivers the context that enables AI to fully comprehend what\u2019s happening now, and to rapidly recommend or initiate (with permission and oversight) the intelligent decisions and actions that will improve operations.<\/p>\nAutomated workflows.\u00a0<\/strong>Transportation workflows should be automated based on the insight afforded by AI. Knowledge about a shipment has limited value if a stakeholder has to manually identify and analyze the problem, collect information, contact the appropriate parties and update downstream systems. AI can automate many of these processes by continuously monitoring transportation events, identifying exceptions, initiating outreach, collecting relevant data and moving routine workflows forward while flagging situations that require human oversight.\u00a0<\/p>\nEmbedded AI.\u00a0<\/strong>AI delivers greater value when deployed within transportation operations, rather than as a standalone advisory tool. It\u2019s most productive when embedded directly into processes such as planning, routing, visibility, carrier communications, document collection, fraud detection and exception management, to automate workflows, accelerate decision-making and improve the quality of outcomes.\u00a0<\/p>\nConsider a freight broker managing hundreds of shipments a day. A truck is approaching its delivery appointment, but its location updates have stopped. The broker can\u2019t confidently determine whether the shipment is delayed, the driver has arrived, or tracking has simply disconnected. An AI workflow intended to identify late deliveries and notify customers now lacks the information needed to act reliably. When these gaps occur across many carriers and shipments, manual follow-up becomes a barrier to scaling automation.<\/p>\n
AI agents that resolve routine visibility exceptions like\u00a0a tracking gap or uncertain delivery arrival\u00a0help address this challenge. Agents can engage drivers to help restore tracking or confirm arrival and departure when location data is inconclusive. This helps close information gaps that would otherwise require an employee to investigate, improving the data available for subsequent decisions and reducing repetitive check calls.<\/p>\n
Descartes\u2019 findings demonstrate that shippers and LSPs are gaining benefits attributed to AI including freight cost reduction (43%), administrative and labor cost reduction (52%), productivity gains (49%), improved service (53%), risk reduction (42%), and even revenue growth (37%). These numbers prove that AI is delivering on the promise for both shippers and LSPs. However, the digital infrastructure supporting AI will determine how quickly and effectively organizations progress from experimentation to scale to achieve these results.<\/p>\n
As shippers and LSPs continue to adopt AI, they should not overlook the digital foundation: connected data, real-time visibility and automated workflows, with AI embedded directly into operations. These fundamental capabilities enable AI to make a tangible impact on the business and differentiate organizations in the AI-powered supply chain.<\/p>\n
Max Lloyd is director of marketing at Descartes<\/a>.<\/em><\/p>","protected":false},"excerpt":{"rendered":"Artificial intelligence is quickly becoming an essential technology for both shippers and logistics service providers…<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","enabled":false}}},"categories":[13],"tags":[],"class_list":["post-22317","post","type-post","status-publish","format-standard","hentry","category-education"],"jetpack_publicize_connections":[],"yoast_head":"\n
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