The logistics industry has a growing AI paradox. Investment is rising, use cases are multiplying, and belief in AI’s potential is near-universal. Yet for shippers, the promise has been slow to materialize. According to a January 2026 BCG and Alpega survey of more than 180 logistics executives, only one in ten logistics service providers have embedded AI into core operations at scale. The efficiency gains, better visibility, and cost improvements that AI should be delivering are, for most companies, still on the horizon.
The industry is busy experimenting, but not yet delivering. So, what is holding logistics AI back, and what should shippers be thinking about as a result?
The Real Barriers Are Not What You Think
Ask most logistics executives what prevents AI from scaling and they will likely point to cost or technical complexity. The data tells a different story. The same BCG and Alpega research found that roughly 40% of both logistics providers and shippers cite unclear return on investment and internal capability gaps as their primary obstacles, not the technology itself.
This matters because it changes where the focus needs to go. A few years ago, the question was whether AI was ready and affordable. Today, capable tools are widely accessible and deployment costs have dropped considerably. The challenge has shifted from technology access to execution. Getting AI to work at scale is fundamentally an organizational challenge, and that is harder to solve than finding the right technology.
Trust and Transparency: The Often-Overlooked Prerequisites
One barrier that rarely makes headlines deserves more attention: trust. AI systems in logistics, particularly in transport planning and dispatch, can only deliver value if the people using them understand and act on the recommendations they receive. A system that planners cannot interpret will be bypassed in daily operations, regardless of how technically capable it may be.
For shippers, this has a direct implication when evaluating logistics partners. A simple question worth asking is whether they can explain how their AI recommendations are made. If the answer is vague, the system may not be embedded in day-to-day decision making as deeply as claimed.
Trust also runs the other way. Shippers who share better data with their logistics partners, including cleaner forecasts, earlier demand signals, and more consistent booking patterns, directly improve the quality of the AI outputs generated on their behalf. The shipper-LSP relationship is increasingly a data partnership. What both sides put in shapes what both sides get out.
Where AI Is Gaining Traction Today
Transport planning and execution is where AI is creating the most tangible gains, leading adoption among logistics providers. This includes route optimization and network efficiency improvements that help reduce empty miles. Shipment visibility is a close second, with predictive ETAs and exception management helping reduce manual workload in dispatch and customer service.
Back-office automation is often where organizations start. Processing inbound communications, supporting quote generation, and handling routine customer interactions are use cases that tend to be faster to implement and show results relatively quickly, which helps build internal confidence for broader rollout.
Where AI continues to struggle is at the edges: unexpected disruptions, incomplete data, and decisions that genuinely require human judgment and experience. Strikes, infrastructure failures, and sudden capacity shortfalls: in these situations, AI can support the decision, but it does not replace the person making it.
Why Scaling Is Harder Than It Looks
Most organizations that struggle to scale AI beyond a pilot are not facing a technology problem. They are facing a structural one. What works well in a controlled environment often runs into inconsistent data formats, process misalignment, and unclear ownership across IT, operations, and management when applied more broadly.
Shippers face this too. The BCG and Alpega research shows that just 7% of shippers report measurable improvements in supply chain activities from AI. Many have prioritized AI in other areas of the business while logistics remains in early exploration. Given how directly logistics performance affects costs and customer experience, that gap is worth closing.
Some Questions Worth Asking
Rather than a checklist, it may be more useful to think about a few honest questions, both for your own organization and when working with logistics partners.
Is AI actually being used in daily operations, or is it simply running in the background while planners continue to work the way they always have? Are the outcomes being measured in concrete terms, like on-time rates, exception volumes, or cost per shipment? Is the data feeding these systems clean and consistent enough to produce reliable outputs? And when a pilot has worked well in one area, what would it actually take to expand it?
Getting honest answers to these questions, internally and with partners, is what separates organizations making real progress from those still finding their footing.
The Window Is Narrowing
The gap between logistics providers that have embedded AI into daily workflows and those still running pilots will likely continue to widen. For shippers, that creates a practical consideration: the partners investing seriously in AI today are building capabilities that will be difficult to match later.
The organizations that will lead the next phase are not necessarily those with the most ambitious plans. They will be the ones that connect AI to the operational challenges that matter most and build the trust, internally and with their partners, to actually use it.
Patrick van Denzel is Chief Revenue Officer at Alpega. He has spent his career at the intersection of transportation, logistics, and supply chain technology, with previous roles at FedEx and Transporeon.
Why Most Logistics AI Projects Never Scale Beyond the Pilot: What Shippers Should Do About It
The logistics industry has a growing AI paradox. Investment is rising, use cases are multiplying, and belief in AI’s potential is near-universal. Yet for shippers, the promise has been slow to materialize. According to a January 2026 BCG and Alpega survey of more than 180 logistics executives, only one in ten logistics service providers have embedded AI into core operations at scale. The efficiency gains, better visibility, and cost improvements that AI should be delivering are, for most companies, still on the horizon.
The industry is busy experimenting, but not yet delivering. So, what is holding logistics AI back, and what should shippers be thinking about as a result?
The Real Barriers Are Not What You Think
Ask most logistics executives what prevents AI from scaling and they will likely point to cost or technical complexity. The data tells a different story. The same BCG and Alpega research found that roughly 40% of both logistics providers and shippers cite unclear return on investment and internal capability gaps as their primary obstacles, not the technology itself.
This matters because it changes where the focus needs to go. A few years ago, the question was whether AI was ready and affordable. Today, capable tools are widely accessible and deployment costs have dropped considerably. The challenge has shifted from technology access to execution. Getting AI to work at scale is fundamentally an organizational challenge, and that is harder to solve than finding the right technology.
Trust and Transparency: The Often-Overlooked Prerequisites
One barrier that rarely makes headlines deserves more attention: trust. AI systems in logistics, particularly in transport planning and dispatch, can only deliver value if the people using them understand and act on the recommendations they receive. A system that planners cannot interpret will be bypassed in daily operations, regardless of how technically capable it may be.
For shippers, this has a direct implication when evaluating logistics partners. A simple question worth asking is whether they can explain how their AI recommendations are made. If the answer is vague, the system may not be embedded in day-to-day decision making as deeply as claimed.
Trust also runs the other way. Shippers who share better data with their logistics partners, including cleaner forecasts, earlier demand signals, and more consistent booking patterns, directly improve the quality of the AI outputs generated on their behalf. The shipper-LSP relationship is increasingly a data partnership. What both sides put in shapes what both sides get out.
Where AI Is Gaining Traction Today
Transport planning and execution is where AI is creating the most tangible gains, leading adoption among logistics providers. This includes route optimization and network efficiency improvements that help reduce empty miles. Shipment visibility is a close second, with predictive ETAs and exception management helping reduce manual workload in dispatch and customer service.
Back-office automation is often where organizations start. Processing inbound communications, supporting quote generation, and handling routine customer interactions are use cases that tend to be faster to implement and show results relatively quickly, which helps build internal confidence for broader rollout.
Where AI continues to struggle is at the edges: unexpected disruptions, incomplete data, and decisions that genuinely require human judgment and experience. Strikes, infrastructure failures, and sudden capacity shortfalls: in these situations, AI can support the decision, but it does not replace the person making it.
Why Scaling Is Harder Than It Looks
Most organizations that struggle to scale AI beyond a pilot are not facing a technology problem. They are facing a structural one. What works well in a controlled environment often runs into inconsistent data formats, process misalignment, and unclear ownership across IT, operations, and management when applied more broadly.
Shippers face this too. The BCG and Alpega research shows that just 7% of shippers report measurable improvements in supply chain activities from AI. Many have prioritized AI in other areas of the business while logistics remains in early exploration. Given how directly logistics performance affects costs and customer experience, that gap is worth closing.
Some Questions Worth Asking
Rather than a checklist, it may be more useful to think about a few honest questions, both for your own organization and when working with logistics partners.
Is AI actually being used in daily operations, or is it simply running in the background while planners continue to work the way they always have? Are the outcomes being measured in concrete terms, like on-time rates, exception volumes, or cost per shipment? Is the data feeding these systems clean and consistent enough to produce reliable outputs? And when a pilot has worked well in one area, what would it actually take to expand it?
Getting honest answers to these questions, internally and with partners, is what separates organizations making real progress from those still finding their footing.
The Window Is Narrowing
The gap between logistics providers that have embedded AI into daily workflows and those still running pilots will likely continue to widen. For shippers, that creates a practical consideration: the partners investing seriously in AI today are building capabilities that will be difficult to match later.
The organizations that will lead the next phase are not necessarily those with the most ambitious plans. They will be the ones that connect AI to the operational challenges that matter most and build the trust, internally and with their partners, to actually use it.
Patrick van Denzel is Chief Revenue Officer at Alpega. He has spent his career at the intersection of transportation, logistics, and supply chain technology, with previous roles at FedEx and Transporeon.
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