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Many logistics companies have moved beyond AI pilots, but far fewer have embedded AI across core operations or can point to measurable value. For mid-market operators, the practical divide is less about access to AI tools than about whether data, systems, and teams can support them in everyday work. That makes infrastructure readiness a priority—but it does not mean every legacy system must be replaced before any AI can be useful.
What the AI adoption gap looks like in logistics
In a survey fielded in January 2026, BCG and Alpega heard from more than 180 logistics providers and shippers across Europe, North America, Asia Pacific, and the Middle East. About 40% of logistics service providers surveyed had moved beyond pilots, but only about one in ten had embedded AI into core operations at scale. Just 13% of respondents reported measurable value from AI. Unclear return on investment (ROI) and internal capability gaps were among the barriers cited most often. BCG’s March 2026 findings describe an execution challenge, not a lack of interest.
These figures should not be treated as a universal industry benchmark. A separate Statista survey of more than 500 global logistics decision-makers, conducted in Q4 2024 and reported by Maersk, found that 3% said AI was fully implemented in their company’s logistics. The studies differ in date, respondents, and definitions, so they cannot be combined into a single adoption rate. Maersk’s summary of the Statista survey provides a separate snapshot, not a direct comparison with BCG’s results.
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Why infrastructure affects whether AI can scale
Infrastructure for AI is more than servers or a cloud subscription. In logistics, it includes the operational systems that record orders, shipments, inventory, and transport; the data connections between those systems and partners; usable, accessible data; and the people and governance needed to maintain quality and put outputs into a workflow.
Broader operations research points to the same execution issues, although it does not isolate mid-market logistics companies. PwC’s 2025 Digital Trends in Operations survey found that 92% of respondents cited at least one reason technology investments had not fully delivered expected results. Integration complexity and data issues were the most common reasons. While 57% said they had already integrated AI partially or fully into operations, PwC also identified existing-system integration and data availability or quality as leading obstacles to scaling. These are operations and supply-chain findings, not a logistics-only adoption estimate. PwC’s survey overview gives the broader context.
One supply-chain-specific survey offers further evidence, with an important attribution caveat: Logility reported that its Supply Chain Horizons 2025 survey, conducted by Vanson Bourne in January and February 2025, found 57% of participants cited data quality as a barrier to AI adoption and 52% said on-premise platforms hindered progress. The participants came from varied industries and roles, so these rates should be understood as findings from that survey, not independently verified industry-wide figures. Logility’s report announcement describes the results.
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What the evidence says about mid-market readiness
RSM and Big Village surveyed 966 U.S. and Canadian middle-market decision-makers from February 21 to March 4, 2025. This was cross-industry research, not a logistics-company survey. Among middle-market respondents who had experienced AI implementation issues, 41% cited data quality. Among respondents who said they were unprepared for AI implementation, 39% cited a lack of in-house expertise as their top issue. Those subgroup findings suggest that data and skills deserve attention, but they do not establish logistics-specific rates. RSM’s 2025 Middle Market AI Survey explains its scope.
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Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →The practical implication is to assess readiness around a real operational problem rather than assume that a particular technology purchase will remove the barriers. A company may be able to run a limited AI use case with its current systems; scaling it across locations, workflows, or trading partners can require better data access, reliable integration, clearer ownership, and staff who can maintain the process.
How to choose an AI use case worth preparing for
Logistics examples in the cited sources include transport planning and execution, forecasting, visibility, inventory management, predictive maintenance, and route optimization. BCG respondents pointed to transport planning and execution, forecasting, and visibility as areas with potential value. Maersk also describes forecasting, inventory management, predictive maintenance, and route optimization as logistics applications. These are candidate use cases, not promises of results. BCG’s logistics survey and Maersk’s AI overview provide examples.
Compare candidate projects against the same practical questions before choosing one:
- Business goal: Which costly, time-sensitive, or service-sensitive problem should improve?
- Data readiness: What data does the use case need, who owns it, and is it accessible and sufficiently reliable?
- Integration effort: Which internal systems and external partners must exchange information for the result to be usable?
- Measurement: What is the current baseline, what outcome would count as success, and how will ROI be assessed?
- Workflow and skills: Who will act on the AI output, and can the team support the process in daily operations?
This is a decision framework derived from the barriers and opportunities reported in the sources, not a validated scoring model. It helps distinguish a promising demonstration from an operational project that can be measured and maintained.
A practical sequence for infrastructure and AI work
Use a focused readiness process tied to one business outcome. It does not require a blanket replacement of existing technology or a universal move to cloud.
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- Choose an operational problem. Start with a specific issue in transport, forecasting, visibility, inventory, maintenance, or routing that matters to the business.
- Map the workflow and systems. Identify where the relevant information originates, how it moves across teams and partners, and where decisions are made.
- Check data and ownership. Assess completeness, consistency, timeliness, access, and responsibility for correcting errors. Decide what quality is adequate for the intended use.
- Assess integration and operating needs. Determine how the AI capability would connect to current systems and how staff would use its output. Consider cloud or compute choices where they address a defined need; the cited evidence does not establish that every workload must migrate to cloud.
- Set a baseline and expected value. Record the current operational measure and define a realistic target and method for evaluating ROI before implementation.
- Pilot in the actual workflow and measure. Test with the people and systems involved in the work, then assess whether the result is useful, reliable, and supportable before expanding it.
This sequence synthesizes the sources’ reported barriers and recommendations; it is not a tested formula or guarantee of results. Roland Berger’s study of 50 executives from nine prominent logistics companies in the Gulf Cooperation Council region recommends beginning with business strategy needs and involving the relevant business units. It is regional evidence, rather than a globally representative finding. Roland Berger’s GCC logistics study provides that perspective.
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The evidence supports prioritizing usable data, integration, internal capability, and fit with daily operations. It does not show that every legacy system is inherently unsuitable, that all logistics workloads need to move to cloud, or that a company must complete a broad modernization program before it can try AI. A system should be changed when it blocks access, reliable data exchange, security, supportability, or the workflow needed for a specific business goal—not simply because it is old.
That distinction matters for a mid-market company with limited implementation capacity. A narrow project can reveal whether the data and integration work needed for a use case is manageable. If it exposes recurring data-quality problems or fragile system connections, address those specific constraints before expanding the project. If existing systems already provide dependable access and a workable integration path, wholesale replacement may add cost and complexity without solving the actual problem.
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