New research shows that there is a sharp disconnect between artificial intelligence (AI) interest and actual operational impact throughout the supply chain. The recent Supply Chain Execution Readiness Report from Infios shows that just 23% of organizations have implemented AI in select workflows and 41% report they are still in the proof-of-concept or pilot stage. When disruptions occur, only 6% say they use analytics or AI for automated, prescriptive responses. However, investment in AI and new technology is not the concern.
The primary constraints seem to be structural. The report shows data quality and integration complexity (69%), and legacy systems and technical debt (63%) far outweigh budget concerns as barriers to modernizing intelligent supply chain execution.
AI is no longer a futuristic concept in logistics – it’s a present-day game changer, and those that do not have a strategic plan in place to implement AI successfully and purposefully will fall behind if they haven’t already. Nearly six in 10 organizations (59%) plan to increase spending on supply chain execution solutions over the next 12 months. As global supply chains become more complex and customer expectations rise, AI-driven solutions are transforming how goods move from manufacturers to consumers, and intelligent supply chain execution, powered by AI, is enhancing efficiency, reducing costs and enabling unprecedented agility.
The evolution of supply chain execution
Traditional supply chain execution relied heavily on manual processes, static planning, and reactive problem-solving. This approach often led to inefficiencies, delays, and increased operational costs. The introduction of AI has shifted the paradigm toward proactive, intelligent, and data-driven decision-making. By embedding AI in workflows, issues are prevented – not just flagged – so you can adapt instantly, reduce costs and deliver without compromise.
So, what does this look like in practice? AI shouldn’t just be an add-on. It should be deeply woven into every part of the supply chain to create a truly connected, intelligent ecosystem.
Intelligent order and fulfillment management
Imagine automating the most complex and time-consuming aspects of order management. With generative AI agents embedded directly into operational systems, teams can use conversational AI to create new sales channels, update inventory thresholds or adjust fulfillment rules in minutes, simply by typing or speaking the request.
AI can proactively identify and resolve potential fulfillment issues before they affect customers, such as incomplete orders or shipment delays. When a stockout occurs, agents can automatically reroute orders to the optimal warehouse or store based on cost, proximity and delivery speed, enabling dynamic order rebalancing that ensures faster, more reliable and cost-effective fulfillment every time.
Taken together, these capabilities elevate order and fulfillment management from a reactive execution layer to a strategic control point for the entire supply chain. Instead of responding to disruptions after the fact, organizations can anticipate constraints, rebalance inventory and orders in real time, and continuously optimize service and margin outcomes. In this way, AI transforms the supply chain from a back-office function into a strategic powerhouse at the center of business performance and operational excellence, building an ecosystem that is as resilient as it is efficient.
AI-powered transportation management
Transportation management depends on a constant stream of data from internal systems, logistics partners and external platforms, and success hinges on how quickly and effectively that information can be turned into action. AI plays a critical role in making that possible. By serving as an intelligence layer above the data, AI can analyze enormous volumes of information at a speed and scale far beyond human capacity and do so efficiently. The insights produced through AI-driven analysis can then be operationalized through AI agents, enabling faster, smarter decisions that directly support business objectives.
For example, predictive AI can analyze real-time data – like traffic, weather, or port congestion – to anticipate delays and reroute shipments before problems arise. AI-powered dynamic load building means every shipment is optimized for space and cost, while automated carrier management ensures you’re always working with the best partners. AI agents can even support automated carrier management, handling tasks like checking in with drivers for ETAs or selecting the best carriers based on past performance.
Smart warehousing management
Warehouses have always played a central role in the supply chain, influencing everything from order accuracy to delivery timelines and customer satisfaction. Despite years of digital investment, many operations remain fragmented, with isolated systems, limited data understanding, and teams forced into reactive decision-making. That dynamic is beginning to change.
By bringing together AI, integrated data, and connected workflows, warehouses can gain the ability to detect disruptions early, respond dynamically, and maintain smooth execution. This shift moves operations beyond traditional efficiency toward true resilience, transforming the warehouse into a smart, adaptive environment that continuously learns, coordinates activity, and strengthens the entire supply chain.
For example, machine learning can predict the best placement for products, speeding up picking and packing. AI can monitor workloads and predict bottlenecks, suggesting staff reallocation before slowdowns happen.
AI can be embedded directly into warehouse workflows so operations become proactive, orchestrated, and adaptive. But this doesn’t need to stop at the four walls of the warehouse either. When warehouse intelligence connects with order and transportation systems, execution shifts from operating in isolation to delivering true end-to-end orchestration.
Strategic inventory protection
Many companies today are facing a new kind of demand crisis – one powered by data. Supply chain leaders managing inventory are inundated with information that most legacy systems were never designed to interpret at scale. The challenge is no longer access to data, but the ability to convert it into fast, accurate decisions about not just how much inventory to hold, but where it should be positioned across the network.
AI is helping decision-makers cut through this complexity and strike the right balance between overstocking and stockouts. Predictive analytics can anticipate demand swings based on historical patterns, seasonality, promotions and external signals. But true inventory optimization goes beyond forecasting volume. It also requires determining the optimal location for inventory – whether in a regional distribution center, a forward fulfillment hub, or a retail store – to align service levels with cost and profitability targets.
More data alone doesn’t guarantee better outcomes. In fact, without context, it can slow decisions. Inventory intelligence must be paired with real-time, network-wide visibility so leaders have a unified view across all channels and nodes. When organizations can see what they have, where it sits, and how quickly it can be moved, they can protect margins, meet customer expectations, and avoid the costly mistake of selling what they don’t actually have.
The future is connected
Looking ahead, logistics is entering a new era of precision and autonomy driven by AI, but with human oversight where needed. The focus will shift from questioning AI’s role to exploring how deeply it can be woven into daily operations and decisions. Humans will remain in the loop where their expertise adds value, creating a collaborative model that makes problem-solving faster, more accurate, and less reactive.
It’s critical to note that the future of logistics isn’t about isolated AI applications. It’s about building one connected, intelligent ecosystem. By integrating AI across order management, transportation, warehousing, and inventory, organizations can break down silos, respond to disruptions in real time, and continuously improve performance. The most successful companies will be those that not only adopt AI, but embed it purposefully throughout their supply chain, creating a resilient, adaptive, and truly intelligent operation. The question is no longer “What can AI do?” but “How can we harness AI to transform every link in the supply chain?”
