A Smart Playbook for Supply Chain AI Investment

Supply Chain AI Investment Is Growing, but Execution Remains the Biggest Challenge

Artificial intelligence is moving quickly from pilot programs into daily supply chain operations, but many companies are still struggling to turn their investments into measurable business results.

Research cited by FreightWaves shows that most supply chain organizations expect to deploy artificial intelligence or generative AI for decision support within the next two years. The larger divide is between companies experimenting with the technology and those that have integrated it into transportation, warehousing, procurement and planning workflows.

That distinction matters for trucking companies and professional drivers because supply chain AI increasingly influences the decisions that shape freight volumes, dispatching, routing, appointment scheduling and equipment utilization. Technology that remains separate from existing systems may produce useful insights without changing how freight actually moves.

ABI Research’s 2025 survey of 490 supply chain professionals found that 94% of companies plan to deploy AI or generative AI for decision support within two years. At the same time, fewer than one in four organizations have a formal strategy for doing so, according to the research cited.

The findings suggest that AI adoption is becoming common, while effective implementation remains less widespread. Companies that have deployed the technology across multiple functions are described as gaining advantages in profitability, cost efficiency and operational resilience.

Accenture’s definition of a mature, next-generation supply chain includes more than AI. It also covers digital twins, advanced automation and integrated planning. Even so, the research identifies AI adoption as a major dividing line between higher-performing organizations and the rest of the market. The cited analysis points to a 23% profitability gap between the groups.

Integration remains a problem

Other research shows that companies often express confidence in their digital progress while acknowledging that the technology has not been fully integrated.

PwC data cited in the material indicates that 57% of operations and supply chain leaders have already integrated AI. In the energy sector, 97% of respondents said they were implementing an enterprise-wide AI strategy, but only 30% said AI was fully embedded across business units.

Current uses include planning and forecasting, cited by 66% of respondents, and sourcing and procurement, cited by 64%. Those functions can affect carriers indirectly through changes in demand forecasts, purchasing schedules and shipment timing.

The gap between adoption and results also appears in a 2026 digital trends survey of 767 operations and supply chain leaders at U.S. companies. While 85% said their organizations were ahead of most competitors in digital transformation, 89% said their technology investments had not fully delivered the expected results.

Among technology and telecommunications companies, 94% of respondents said their businesses had implemented AI, including 40% that were scaling it across the enterprise. However, only 21% said the strategy was fully embedded across business units.

For trucking operations, incomplete integration can create practical problems. A planning tool may recommend a routing or scheduling change, but dispatchers and drivers may not receive the information through the systems they already use. If data from transportation management systems, maintenance records, customer appointments and driver workflows is inconsistent, the resulting recommendations may be difficult to apply.

Starting with focused applications

The material emphasizes a gradual approach rather than attempting to automate an entire supply chain at once. One example describes beginning with a single category and a narrowly defined renewal process instead of applying AI to a broad and unstructured data set.

A focused implementation gives a company a clearer way to measure whether the technology is improving a specific process. In transportation, similar applications could involve defined tasks such as forecasting demand, identifying appointment delays or organizing maintenance information. The supplied material does not claim that any one application guarantees a return, but it stresses the importance of starting with a manageable use case.

Measuring success also requires more than calculating immediate labor savings. The analysis recommends viewing AI as part of a broader portfolio of investments and tracking how early efficiency gains affect longer-term results, including cash management and operational performance.

That approach is particularly relevant to trucking, where a small improvement in planning may affect asset utilization, empty miles, detention exposure, fuel consumption and driver time. Those outcomes may not appear in a single technology metric, making it important to connect operational changes with financial and service results.

Autonomy is still in its early stages

The discussion also addresses the growing interest in agentic AI and autonomous supply chain operations. Current AI agents can take on limited autonomous tasks, but fully autonomous supply chains remain at an early stage.

Human accountability remains essential when physical operations are involved. Freight still has to be loaded, secured, moved, delivered and handled when something goes wrong. Algorithms can support decisions, but they do not eliminate responsibility for the results of those decisions.

For drivers, that means AI-generated recommendations should be treated as operational tools rather than replacements for judgment. Road conditions, facility delays, equipment issues and other real-world factors can change faster than a system’s data. A technology program that does not provide a clear way to review or challenge its recommendations can create additional risk.

Data quality and training are central

The research identifies clean data, standardized processes and disciplined governance as requirements for scaling AI, particularly in manufacturing and automotive supply chains. The same requirements apply to transportation networks that rely on accurate location, load, appointment and equipment information.

Employee training is another key factor. Planners, analysts, dispatchers and operators need the skills to work with AI tools and understand their limitations. For drivers, that may mean clearer explanations of how route, delivery or compliance-related recommendations are generated and how exceptions should be reported.

Trust is built through transparency, according to the findings. Leaders can improve acceptance by being open about the data being used, the results being achieved and the limits of the technology. Demonstrating measurable outcomes is more effective than presenting AI as a solution to every operational problem.

The central lesson for supply chain leaders is that the next AI investment should be judged by how well it fits into real work. Adoption alone will not close the readiness gap. Companies that connect focused applications to reliable data, employee training, human oversight and measurable operating results are better positioned to turn AI spending into practical improvements across the freight network.