Many logistics leaders see generative and agentic AI as more than a tech curiosity. Where the former translates vast data into clear, conversational insights, the latter takes the next step—acting on those insights to make decisions and drive operations forward.
BCG reports that in 2025 over a third of global logistics executives recognize generative AI’s capabilities to address rising customer expectations, supply chain disruptions and aging, inefficient systems. However, only 10% of logistics companies have fully adopted this tool.
Data fragmentation and quality issues, integration with legacy systems and trust gaps suggest that only a small fraction of the industry has the data maturity for agentic AI. But early adopters are showing promise with small pilots and use cases that can be scaled over time.
Workflow Automation Accelerates Speed to Action
IBM reports that 62% of logistics executives expect agentic AI to make proactive recommendations and boost efficiency through workflow automation by 2026.
Workflows such as customer communication have already been implemented. The same report identified that 70% of CSCOs found that generative AI has enhanced their responsiveness and communications with customers.
Logistics firms like DHL and Flexport use these AI agents to automate routine tasks like rate negotiation and appointment booking, enhancing efficiency and reducing manual workloads. AI chatbots connected to verified internal systems, such as tracking databases and billing records, can also automate structured logistics inquiries like shipment status and invoice details. By escalating complex queries to human agents, these systems ensure accuracy and consistency while mitigating the risk of generating incorrect information.
Such use cases work well because they involve structured, low-risk communications, keep humans in review loops and use AI for supporting efficiency rather than making critical operational decisions.
After running two experiments in 2025, BCG also found that the most significant improvement was automating complex documentation, including requests for proposals (RFPs), customs paperwork and contractual agreements. These tasks require parsing multiple data sources and formatting documents correctly, which is time-consuming. However, they are repetitive, which means they are easy to automate with AI.
Agentic Digital Twins Take Simulations to the Road
Logistics companies constantly face unexpected bottlenecks since they are tightly interconnected in long and complex supply chains, where a single disruption can cascade globally. The Suez Canal blockage in 2021 and the Panama Canal’s low water levels in 2024 are examples of shipping route disruptions where leaders had to think on their feet to find workarounds to get cargo to its destination.
In these scenarios, simulating alternative routes in virtual replicas of end-to-end supply chains allows leaders to foresee how decisions impact operations before acting. Add to these digital twins an agentic layer, and planning teams can take the next step from simulating scenarios to selecting the optimal route and coordinating the plan to relevant stakeholders. Urban logistics has already seen proof-of-concept systems using agentic digital twins that plan and act autonomously, coordinating simulations, optimization solvers and domain engines to improve multimodal freight operations.
According to Market.US, the global supply chain digital twin market is expected to grow from $2.8 billion in 2023 to $8.7 billion by 2033, representing a 12% CAGR. Logistics use cases include transport route, mode and schedule management, helping ensure the right cargo is in the appropriate truck at the optimal time to maximize capacity and meet SLAs.
Risks, Barriers and Reality Checks
A Gartner projection suggests over 40% of agentic AI projects may be scrapped by 2027 due to unclear business value, inadequate risk controls and mislabeling of simpler tools as “agentic.”
The relabeling of existing tools, like AI assistants, robotic process automation (RPA) and chatbots as “agentic” without true autonomous functionality creates a gap between expectations and performance. Gartner estimates only about 130 of the thousands of agentic AI vendors have the maturity and autonomy to achieve complex business objectives or follow nuanced instructions over time.
Lack of domain talent is a bottleneck. According to Mordor Intelligence, fewer than 15% of AI practitioners have both deep logistics or supply chain expertise alongside the skills in building robust agentic systems. The report notes that although upskilling programs proliferate, near-term capacity shortfalls limit how aggressively enterprises can scale autonomous agents beyond core planning stacks. The skilled talent shortage is a fundamental barrier to deploying agentic AI in supply chain and logistics.
Even with the right people in place, it takes time to sort out the data. KPMG found that less than half (43%) of organizations have limited to no visibility of tier one supplier performance. Data quality, integration with legacy systems and transparency remain serious challenges. Putting solid, structured data pipelines and clear governance in place will enable logistics to leverage AI agents further down the line.
In logistics, AI is moving beyond predicting outcomes to actively making decisions and coordinating operations in real time. Successfully adopting these tools requires cutting through hype and addressing data quality and accessibility challenges to ensure reliable, actionable insights. By automating key areas like route planning and multimodal freight coordination, agentic AI can scale human expertise across the supply chain.
