AI in Logistics: 17 Real-World Examples, Company Use Cases & ROI Data 2026
The Catalyst Brands partnership marks a pivotal step for Figure in expanding from automotive manufacturing into retail logistics — and reflects the accelerating commercialization momentum across the broader humanoid robotics industry. Figure AI, founded by serial entrepreneur Brett Adcock, is currently one of the best-capitalized pure-play humanoid robotics companies in the world, having raised over $1.9 billion in total funding at a valuation of $39 billion. Its core team includes veterans from Boston Dynamics, Tesla, and Google DeepMind. In October 2025, the company unveiled Figure 03 — its third-generation humanoid engineered from the ground up for high-volume manufacturing and home environments. Powering its robots is Helix, Figure’s proprietary Vision-Language-Action (VLA) model that unifies perception, language understanding, and learned control for real-world deployment. Prior to this deal, Figure had already secured a commercial agreement with BMW, deploying robots at the automaker’s Spartanburg, South Carolina manufacturing plant.
How is artificial intelligence used in logistics?
AI-driven transportation management adjusts delivery routes in real time, optimizing fuel efficiency and reducing transit times. AI-powered quality control detects defects earlier in the production cycle, minimizing waste and rework costs. Digital twins allow companies to simulate different supply chain scenarios before making operational adjustments. AI-driven chatbots handle supplier negotiations, freeing procurement teams to focus on strategic planning.
AI Providing Resilience to the Supply Chain
This difficulty largely results from the sector’s predominantly fixed cost structure in most addressed processes. Even when AI reduces variable costs, the overall effect on operating expenses is capped, limiting the visible impact on profit and loss. Logistics companies are making progress in applying artificial intelligence to streamline back-office functions and enhance physical operations. AI is no longer a distant possibility but a current necessity in the logistics landscape.
Warehouse automation and robotics
Conventional forecasting techniques struggle with shifting consumer behavior and unstable markets. To https://uofa.ru/en/rol-logistiki-snabzheniya-v-deyatelnosti-kompanii-osnovnye-napravleniya/ more accurately forecast demand, AI algorithms examine past sales, seasonal patterns, meteorological information, and market trends. Businesses that use AI to forecast demand report 8-10% accuracy gains over traditional techniques.
- Sensors are used to monitor current performance data, including engine performance, tire pressure, fluid levels, and brake wear.
- Multiple chokepoints, including the Red Sea, Strait of Hormuz, Panama Canal and Black Sea, have provided short-term rate support while limiting alternatives.
- Traditional AI systems focused on automating individual tasks such as demand forecasting or inventory alerts.
- As if ripped from the pages of science fiction novels, Artificial Intelligence (AI) has evolved as rapidly as its integration into the world of business.
Explore how AI is augmenting capabilities, from network intelligence and planning to security, compliance, and resilience. On a smaller scale, businesses typically use operations research and human problem-solving to minimize the time, cost, and distance of trucking and freight. On a larger scale, with more complicated routes and additional factors, operations research and human problem-solving no longer suffice.
AI monitors risk factors in real time and automatically suggests alternative routes, suppliers, or transportation modes when disruptions occur. The EU AI Act is a European regulation that introduces mandatory compliance requirements for AI systems based on risk level. Logistics operations using AI for route decisions, driver monitoring, customs processing, or customer scoring may fall under regulated categories. Teams operating in Europe need to audit their AI deployments and establish documentation, human oversight, and control systems before the deadline. A January 2026 BCG survey of more than 180 logistics providers and shippers found that over 40% of shippers now factor AI capabilities into logistics provider selection. But only about one in ten logistics service providers has scaled AI across core operations, with most pilots still sitting disconnected from daily workflows.
That distinction makes this partnership particularly significant for the industry. When it comes to yard operations, De Muynck said that updating data-collection processes is where most companies’ technology journeys should start. He added that using AI on bad data will only amplify bad suggestions and actions. AI has the potential to help those running yard operations see that data, allowing them to shift from reactive to predictive decision-making, said De Muynck.
Companies Mentioned
De Muynck, who has no association with Lazer Logistics, is a supply chain strategic advisor and independent consultant with over 35 years of supply chain experience, including roles with Gartner, project44, and YMX Logistics. ITS has grown its trailer network from a few hundred vehicles a year ago to about 3,000 today, and AI has helped it manage its growing fleet. More specifically, the company uses AI models to pull data from shipper orders to determine where trailers should be located.
Generative AI in logistics: Benefits, use cases, and tools
He writes frequently about conflict, focusing heavily on the Middle East and Ukraine, and interviews with military and intelligence officials and industry leaders from around the globe. And then we just send drones there in hunting mode, and I cannot disclose how this works from the standpoint of intelligence, but the overall principle is that the intelligence just gives us the priorities for the targets. For example, they say that you need to target this type of vehicle with these types of cargo.
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