Logistics and supply chain management embraced automation long before the current AI wave hit the shores of other industries, with predictive analysis and machine learning becoming  cornerstones of the way the supply chain industry operates. For example, big freight companies use algorithmic carrier pricing to design optimal routes for truck drivers so that there are fewer empty miles.

However, agentic AI and other developments in technology could help the industry move beyond task-specific automation, opening up a wealth of new possibilities. Rather than purely focusing on cost reduction and improved efficiency, it could lead to a completely new way of managing compliance and customer satisfaction.

Evolving Beyond Automation Limits

Most AI solutions in logistics at the moment focus on making repetitive tasks — such as inventory management or route optimization — more efficient. But sometimes these systems can operate in silos where they rely on human intervention to bridge the gaps between processes. This is where agentic AI can offer agents that can interact across systems, and make autonomous decisions.

Think of an inventory management system in which orders are triggered when a certain threshold is reached. AI agents could monitor various external factors related to market trends, supplier lead times, and even weather forecasts, in order to change reorder triggers at a moment’s notice. Similarly, when delays are forecast, most systems will now alert logistics managers to intervene, whereas agentic AI could automatically reroute shipments, adjust schedules and notify customers.

Decision-Making and Customer Support

AI is already capable of helping generate reports for regulatory compliance, but its capabilities can be extended to autonomously update systems and prepare filings to comply with changes and adjustments in regulations immediately. It’s not beyond the realms of possibility that agents could monitor global changes, such as new tariffs being introduced or environmental circumstances, and then subsequently recommend certain actions to help mitigate risks in the decision-making process.

Perhaps most notably, agentic AI can address a really crucial aspect of logistics — customer satisfaction. At the moment, AI can support basic tools to respond to queries, but agentic AI can preempt customer needs. For example, it can proactively inform customers about shipment status and delays, but also it can use historical data to tailor customer communications more precisely.

An Interface that Incorporates Many Systems

Logistics management systems can often be fragmented, deploying CRMs, ERPs, and warehouse/transport management systems. Agentic AI can help to ensure these systems are unified into one conversational interface, so that commands are not being sent across multiple systems.

For example, a warehouse manager can instruct an AI to prepare inventory or sustainability reports on emissions, or notify suppliers of any shortages, but it’s possible that this might not translate across various platforms, hence one interface that incorporates several systems would be an enormous benefit.

What Does the Future Hold?

Logistics companies are under more pressure than ever to reduce costs and meet sustainability targets. McKinsey estimates that the demand for greener logistics could reach roughly $350 billion, making up 15% of the total global logistics spend, not even taking into account the potential premium prices on green shipping.

The scalability that AI agents can give without requiring an enormous influx of resources, alongside the possibility to optimize every step along the supply chain is a big plus, and could ensure that supply chains stay operational when met with global disruptions.

It’s also worth remembering that AI, and AI agents in particular, should not replace the logistics workforce, because nuanced judgment is still needed, no matter how advanced the capabilities of AI agents. Tasks such as negotiating supplier relationships or addressing individual customer concerns are unique to humans. Many thought leaders in the industry have pointed to this, highlighting the importance of real-world experience to solve supply chain management challenges.

Given that traditional AI solutions have already shown their ability to reduce costs and streamline processes, but are potentially constrained by requiring predefined workflows and room for human error in interpretation, there is room for agentic AI to remove those constraints and deliver better autonomy by taking the role of collaborator a step further.

Ruban Phukan is co-founder and CEO of GoodGist Inc.