Generative artificial intelligence (AI) has become one of the most talked-about topics in transportation and logistics. Shippers, brokers, and third-party logistics services providers (3PLs) are eager to harness it for efficiency and cost savings. Descartes’ 9th Annual Global Transportation Management Benchmark Survey found that 96% of companies surveyed are using generative AI in some parts of their operations, but only 17% say they are fully automated and optimized (see Figure 1).
Figure 1 (Source: Descartes)
So why should companies invest in generative AI and automation? The study found that companies that have reached a high level of automation are also better financial performers—over 50% of the companies with industry leading financial position are fully automated, versus only 8% of companies with below average financial position claiming full automation. Companies with industry leading financial position also use AI across multiple processes in transportation at a much higher rate than poorer performing companies.
The gap between the AI hype we see in news from many tech startups compared to how companies are actually using generative AI signals an important truth: real transformation won’t come from a standalone AI gadget or some new siloed app. It will come from strategically integrating AI into the transportation management and visibility systems and workflows you already rely on every day.
AI as an Extension, Not a Replacement
Most transportation operations are powered by a tech stack of transportation management systems (TMS), visibility platforms, back-office systems and analytics. The most productive approach is to layer AI into these systems and workflows rather than bolt on disconnected apps or rip out core infrastructure. That means using AI to tackle clear pain points—automating data entry, taking over phone and email-based tasks, optimizing load plans, managing capacity matching—inside the platforms your teams already know.
This approach reduces implementation friction and accelerates user adoption. Employees can continue working in familiar workflows while AI enhances efficiency in the background. Additionally, involving teams early in designing AI-driven processes also builds confidence in the outputs, reducing second-guessing and rework. The result is technology that complements human expertise instead of clashing with it.
Why Established Technology Partners Matter
AI startups are everywhere, but not all vendors are created equal—and when it comes to mission-critical operations like transportation, the pedigree and support structure of your tech partners matters enormously. Larger, established logistics tech providers offer significant advantages over startups in implementing, supporting, and scaling AI solutions:
Industry knowledge: Established providers understand the complexities of freight, having installed solutions for hundreds, or thousands, of customers across all industries and operating models. Their AI features are grounded in real operational needs supporting established technologies, not theory.
Integration and support: Mature vendors already connect to your back-end systems and carrier network, so their AI can be woven into existing workflows with less friction. They also bring large customer support and implementation teams.
Long-term viability: Logistics is mission-critical. Established providers offer financial stability, research and development investment into product roadmaps, and a proven record of continuous improvement. Leveraging existing partners for AI capabilities ensures that AI is road-mapped into your core tech with regular updates and support.
By contrast, new startups often face challenges: limited support, integration gaps, uncertain ROI, and the risk of disappearing after a funding downturn. For mission-critical operations, that’s a gamble most can’t afford.
Practical AI in Action
The benchmark study also revealed where companies are applying AI in transportation management today. The top use cases include data entry automation (41%), route and load optimization (39%), AI-driven freight forecasting (35%), capacity sourcing (35%), and customer service chatbots (34%). Other areas like dynamic pricing (27%), driver safety systems (26%), and disruption or dock scheduling (23%) are gaining traction as well. Only 4% of respondents said they are not using AI at all (see Figure 2).
Figure 2 (Source: Descartes)
This spread of adoption tells an important story: companies aren’t looking for abstract innovation; they’re turning to AI for practical, everyday pain points. Automating data entry reduces errors and frees staff from repetitive tasks. Optimization and forecasting improve planning accuracy. Capacity sourcing helps cover loads faster. Customer service chatbots lighten the load of status calls and emails.
What these use cases have in common is that they work best when embedded in core systems—your TMS, visibility platform, or other execution tools. When AI is layered into these workflows by established providers, companies can capture immediate value with minimal disruption.
Staying Strategic About AI
To capitalize on investments, companies should remain problem-focused, not technology-focused. Start with real operational bottlenecks, select AI solutions that directly address them, and implement them through the systems your teams already trust. Equally important is choosing partners with the scale, support, and longevity to deliver lasting value. Companies with a track record of delivering successful implementations of transportation applications and a proven technology roadmap are uniquely positioned to help shippers, brokers, and 3PLs apply AI effectively and for the long haul.
In Summary
AI in logistics isn’t about chasing the latest trend. It’s about strategic integration with your existing systems and workflows. Companies that focus on embedding AI into core transportation workflows are already seeing improvements in cost, productivity, fraud prevention, and customer service.
For shippers, brokers, and 3PLs, the path forward is clear: let AI run on the rails you’ve already built. With the right partners and strategy, AI becomes a natural extension of your logistics operation—and a real driver of competitive advantage.
Mike Hane is Director, Product Marketing, Transportation Management at Descartes.
Steering AI Adoption in Logistics: Why Building on Your Existing Tech Stack Is Key
Generative artificial intelligence (AI) has become one of the most talked-about topics in transportation and logistics. Shippers, brokers, and third-party logistics services providers (3PLs) are eager to harness it for efficiency and cost savings. Descartes’ 9th Annual Global Transportation Management Benchmark Survey found that 96% of companies surveyed are using generative AI in some parts of their operations, but only 17% say they are fully automated and optimized (see Figure 1).
So why should companies invest in generative AI and automation? The study found that companies that have reached a high level of automation are also better financial performers—over 50% of the companies with industry leading financial position are fully automated, versus only 8% of companies with below average financial position claiming full automation. Companies with industry leading financial position also use AI across multiple processes in transportation at a much higher rate than poorer performing companies.
The gap between the AI hype we see in news from many tech startups compared to how companies are actually using generative AI signals an important truth: real transformation won’t come from a standalone AI gadget or some new siloed app. It will come from strategically integrating AI into the transportation management and visibility systems and workflows you already rely on every day.
AI as an Extension, Not a Replacement
Most transportation operations are powered by a tech stack of transportation management systems (TMS), visibility platforms, back-office systems and analytics. The most productive approach is to layer AI into these systems and workflows rather than bolt on disconnected apps or rip out core infrastructure. That means using AI to tackle clear pain points—automating data entry, taking over phone and email-based tasks, optimizing load plans, managing capacity matching—inside the platforms your teams already know.
This approach reduces implementation friction and accelerates user adoption. Employees can continue working in familiar workflows while AI enhances efficiency in the background. Additionally, involving teams early in designing AI-driven processes also builds confidence in the outputs, reducing second-guessing and rework. The result is technology that complements human expertise instead of clashing with it.
Why Established Technology Partners Matter
AI startups are everywhere, but not all vendors are created equal—and when it comes to mission-critical operations like transportation, the pedigree and support structure of your tech partners matters enormously. Larger, established logistics tech providers offer significant advantages over startups in implementing, supporting, and scaling AI solutions:
By contrast, new startups often face challenges: limited support, integration gaps, uncertain ROI, and the risk of disappearing after a funding downturn. For mission-critical operations, that’s a gamble most can’t afford.
Practical AI in Action
The benchmark study also revealed where companies are applying AI in transportation management today. The top use cases include data entry automation (41%), route and load optimization (39%), AI-driven freight forecasting (35%), capacity sourcing (35%), and customer service chatbots (34%). Other areas like dynamic pricing (27%), driver safety systems (26%), and disruption or dock scheduling (23%) are gaining traction as well. Only 4% of respondents said they are not using AI at all (see Figure 2).
This spread of adoption tells an important story: companies aren’t looking for abstract innovation; they’re turning to AI for practical, everyday pain points. Automating data entry reduces errors and frees staff from repetitive tasks. Optimization and forecasting improve planning accuracy. Capacity sourcing helps cover loads faster. Customer service chatbots lighten the load of status calls and emails.
What these use cases have in common is that they work best when embedded in core systems—your TMS, visibility platform, or other execution tools. When AI is layered into these workflows by established providers, companies can capture immediate value with minimal disruption.
Staying Strategic About AI
To capitalize on investments, companies should remain problem-focused, not technology-focused. Start with real operational bottlenecks, select AI solutions that directly address them, and implement them through the systems your teams already trust. Equally important is choosing partners with the scale, support, and longevity to deliver lasting value. Companies with a track record of delivering successful implementations of transportation applications and a proven technology roadmap are uniquely positioned to help shippers, brokers, and 3PLs apply AI effectively and for the long haul.
In Summary
AI in logistics isn’t about chasing the latest trend. It’s about strategic integration with your existing systems and workflows. Companies that focus on embedding AI into core transportation workflows are already seeing improvements in cost, productivity, fraud prevention, and customer service.
For shippers, brokers, and 3PLs, the path forward is clear: let AI run on the rails you’ve already built. With the right partners and strategy, AI becomes a natural extension of your logistics operation—and a real driver of competitive advantage.
Mike Hane is Director, Product Marketing, Transportation Management at Descartes.
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