Imagine a shipment getting stuck at a congested port. Traditionally, the system would send an alert to a logistics manager, who would then coordinate with transporters, warehouses, and customers to manage the disruption. But what if the system itself could identify an alternative route, reassign capacity, notify stakeholders, and execute those changes in real time?
That is the promise of agentic AI (artificial intelligence), a technology shift that logistics industry executives say is beginning to move supply chains from passive monitoring systems to autonomous execution engines.
Unlike traditional automation, which works on fixed rules and predefined workflows, agentic AI systems are designed to reason, decide, and work on solutions in real time.
“Predictive analytics tells you what will happen. Traditional automation operates on ‘if-then’ logic. Agentic AI does both and then acts,” says Gaurav Srivastava, Co-Founder of FarEye, a last-mile delivery platform. He says these systems don’t just flag a disruption or generate a report; they reason through the problem, query multiple systems simultaneously, and execute a response without waiting for a human to pull the trigger.”
The broader theme running across industry circles is the shift from visibility to action in supply chains.
“Supply chains today largely operate as systems of record. They can show what is happening, but responding to disruptions still depends on human teams to interpret, coordinate, and act,” says Soham Chokshi, Co-Founder & CEO at AI-native logistics platform Shipsy. He believes the industry now needs a shift toward “systems of action”.
Chokshi says AI systems are increasingly being designed as “AI co-workers” that monitor operations and intervene during disruptions. “AI co-workers don’t just flag the issue. They coordinate stakeholders, reroute shipments, notify customers, and drive resolution workflows within predefined guardrails,” he says.
Industry observers say the key difference between traditional logistics systems and the next generation of AI-led operations lies in how these systems function.
Rahul Sanghvi, Managing Director and Partner at consulting firm BCG, says most logistics tools deployed today are still “task specific” and predictive in nature. “The output is an insight on the screen which a human reads and decides what to do,” he says.
Agentic AI is being positioned as the next layer of operational intelligence where systems act, not just advise. “Agents take it further. They can integrate across multiple tasks and, more importantly, execute decisions,” Sanghvi says. According to him, an AI agent tracking shipments at risk of breaching service-level agreements (SLAs) could independently reassign riders, send new ETA (estimated time of arrival) windows to customers, and escalate only low-confidence or ambiguous cases to humans.
That shift, experts say, also requires a fundamentally different technology architecture.
Arindam Roy, Client Partner at data and AI solutions company Straive, says agentic AI marks a departure from traditional automation systems. “Traditional automation does exactly what you program it to do,” Roy says. “The moment conditions shift outside what was anticipated, they either fail silently or wait for a human to intervene.”
As Roy sees it, the architecture of agentic AI is fundamentally different. “You give it an objective, not a script,” he says, adding that the system then reasons across data sources, selects the right tools, takes action, and continuously adjusts.
Despite growing interest, most industry players agree that large-scale deployment of agentic AI is still at an early stage. “The honest answer is that most deployments are still in the ‘human-in-the-loop’ stage, where AI agents handle routine decisions autonomously but escalate edge cases to planners or dispatchers,” Srivastava says.
However, industry executives believe adoption of agentic AI could accelerate sharply over the next two years, even though enterprise deployment is still at an early stage. Citing a January 2026 Ortec survey of 400 logistics executives, Srivastava says 42% of companies are yet to explore agentic AI, while only a small minority had active pilots or deployments by the end of 2025. Even so, Gartner predicts 40% of enterprise applications will feature task-specific AI agents by the end of 2026, up from less than 5% in 2025, signalling what he calls “an inflection point right now, and not in the future”.
Sanghvi echoes a similar assessment. “Most companies are doing experiments, but full-scale rollout requires much more than AI,” he says. In Sanghvi’s assessment, such deployments also require governance structures, such as exception ladders, audit trails, decision rights, and adequate pre-launch testing to create confidence among stakeholders.
Roy argues that the real bottleneck in deploying large-scale autonomous AI systems or Agentic AI is often not the AI model itself but the quality of enterprise data infrastructure. Roy's assessment is that getting clean, real-time data flowing between systems is the “harder problem”. He points out that most enterprise supply chains still run on fragmented legacy systems that were “never designed to talk to each other at machine speed.”
Roy believes companies that are ahead in autonomous operations tackled the data integration challenge early in their transformation journeys. Industry trends show that early adoption of agentic AI is currently concentrated in areas where business outcomes are easier to measure. “Route optimisation and last-mile execution are leading the adoption curve,” Srivastava says. “The reason is simple: it has the clearest ROI, the fastest proof point, and the most real-time data to work with.”
Sanghvi’s assessment is that companies are also seeing growing adoption in customer service, invoice management & reconciliation, dynamic order allocation & dispatch, and control tower exception management.
Roy believes that demand forecasting currently represents the most mature area of production deployment because historical enterprise data is already available at scale. “Warehouse management is the second area seeing serious deployment,” he says, citing the availability of dense sensor and telemetry data within warehouse environments.
Even as the adoption of agentic AI remains gradual, experts say the broader direction of the logistics industry is becoming increasingly clear. “The organisations, instead of running separate projects, are building a shared intelligence layer where forecasting feeds inventory decisions, inventory feeds warehouse operations, and warehouse data feeds transportation planning,” Roy says.
