Transport leaders today are under increasing pressure to deliver more — faster, smarter, and at lower costs. As the logistics landscape grows more complex, many are turning to artificial intelligence (AI) to help make better decisions across supply chain planning and execution.
However, latest research from Gartner shows that 30% of supply chains do not have a formal AI strategy at their core. The gap between companies with formal AI supply chain strategies and those that don’t begs the question, “what can hold them back?”
Despite some organizations having formal AI strategies for their supply chain in place, most companies still struggle in embedding AI into everyday decision-making.
At Lingaro, we believe that AI does have incredible potential to transform how transportation in supply chains operates. But it’s always the human touch that makes the difference — the people behind the AI-powered systems who can turn the insights into action. Transport leaders should be able to combine data with experience, so they can take advantage of AI and shift from reactive firefighting to proactive, strategic planning.
One of the many ways Lingaro experts help transport leaders strategically utilize AI in their supply chain is optimizing the distribution network. With the use of AI and machine learning (ML), we support enterprises in bolstering the three key pillars in their transport network: forecasting, capacity planning, and shipment execution — while keeping human insight at the center.
Demand in transportation can shift in days or even hours. This leaves fleets underused and opportunities lost. The first step to optimizing is clarity: knowing what needs to move, when, and where.
Predictive machine learning makes this possible. It can forecast shipment volumes, truck types or sizes, and delivery timing so transport leaders can align demand and supply with precision. ML-powered forecasting anticipates volumes across lanes and time periods. It also determines the exact type, size, and number of trucks needed — whether FTL, LTL, or groupage service — and factors in seasonality, promotions, and historical trends.
These forecasts support both direct deliveries to final customers and indirect flows through distribution centers and cross-docking platforms. With this kind of insight, transport managers can stay ahead of demand, align resources proactively, and avoid last-minute scrambles or costly overcapacity.
Transport leaders face challenges such as limited transport capacity, port congestion, equipment shortages, geographical blockers, and climate-related risks. So once demand is forecasted, the next step is to check whether the network can handle it.
AI and ML enable supply chains to simulate different capacity scenarios, helping transport leaders spot bottlenecks, rebalance flows, and respond to disruptions with greater agility. These tools enable supply chains to assess the availability of transport assets, including both own fleet and third-party logistics (3PL). They can also check inventory levels across various locations, evaluate handling capacities at manufacturing plants and distribution centers, analyze warehouse throughput, and review loading and unloading constraints — all while accounting for regional variations in cost structures and service levels.
By running these simulations, transport leaders can uncover bottlenecks, underutilized assets, and opportunities to rebalance flows before they turn into operational issues.
Rising rates, labor shortages, inefficient fleet utilization, idle trucks, and empty miles push operational costs higher. The final piece of the puzzle is executing shipments efficiently and intelligently — turning plans into action without waste.
With AI-powered execution, transport leaders can automatically generate shipment plans that maximize vehicle fill rates (VFR), minimize empty miles, and choose the most cost-effective transport model. Shipments are grouped smartly by destination, volume, and timing, while transport costs from waiting time, demurrage, and penalties are reduced.
The result is a distribution network where every truck is used to its full potential, cutting cost per unit and lowering environmental impact.
The idea that AI alone can transform transportation is more myth than reality. In the hands of skilled transport leaders, however, it becomes a powerful enabler of real and measurable change.
When paired with human insight, AI can turn forecasting, capacity planning, and execution into a connected, proactive process that reduces waste, cuts costs, and supports sustainability goals. This blend of predictive intelligence and expert judgment empowers smarter decisions, faster responses, and transport networks that consistently perform at their best.
For transport and distribution leaders, the real opportunity is to turn complexity into a competitive advantage. Those who use AI to amplify human decision-making and adaptability will be best equipped to navigate disruptions, seize opportunities, and deliver lasting value to customers.