AI is now firmly embedded across many areas of supply chain management. As adoption matures, logistics teams are gaining a clearer understanding of where it delivers the greatest operational impact.
In this article, we explore where the strongest value is emerging and what enables successful adoption.
Key AI use cases in supply chain management
Forecasting and planning
Accurate demand forecasting is essential for maintaining product availability while avoiding excess inventory.
AI helps companies analyze larger volumes of historical and real-time data, improving forecast accuracy and supporting more informed inventory decisions. As a result, organizations can reduce inventory costs and improve operational efficiency in volatile environments.
Visibility and operational monitoring
AI supports shipment visibility, ETA prediction, and disruption monitoring across complex supply chains. More accurate delivery forecasts and earlier identification of delays help logistics teams respond more quickly and improve operational planning. This provides customers with greater confidence in shipment status and delivery expectations.
In an environment shaped by disruption and ongoing market volatility, this improved visibility helps companies respond more effectively and strengthen operational decision-making.
Process automation and workflow support
Administrative processes remain one of the most manual and time-consuming areas of logistics, with large volumes of information often needing to be reviewed, validated, and transferred between systems.
AI is increasingly being used to automate repetitive tasks, including document processing, customs documentation, data extraction, and shipment booking.
Technologies such as Intelligent Document Processing (IDP) can quickly extract, classify, and validate information from emails, PDFs, and shipping documents, reducing manual effort, improving efficiency, and freeing teams to focus on higher-value activities.
Where AI is delivering the greatest operational value
These same areas of forecasting, visibility, and workflow automation are also where organizations are seeing some of the strongest value. It’s here that AI can help process large volumes of data, support rapid decision-making, and automate repetitive administrative work.
| Area | Typical operational benefits | Example outcomes |
| Forecasting & planning | Improved demand forecasting and inventory management | Forecast accuracy improvements of 25–50% and inventory reductions of 20–35% have been reported in some implementations |
| Visibility & operational monitoring | Earlier disruption identification and more accurate ETAs | AI-enabled logistics operations can reduce transportation costs by 5–10% and improve delivery reliability by up to 20% |
| Process automation & workflow support | Reduced manual effort and faster processing | Strong adoption in document-intensive workflows such as order processing, shipment booking, customs documentation, and data extraction |
Sources: Transport Distribution Europe (Deposco/Fulfillment IQ research), Isometrik AI Use Cases, and Isometrik Supply Chain Automation.
Successful AI adoption
While AI is delivering value across a growing range of supply chain use cases, successful adoption depends on several factors working together. The results organizations achieve are linked closely to the quality of their data, systems, and operational processes.
Reliable data
The effectiveness of AI is underpinned by the quality of the data it uses. Whether supporting demand forecasting, ETA prediction, shipment visibility, or process automation, AI systems rely on accurate and up-to-date information to generate reliable outputs.
This is particularly important in supply chain management, where logistics environments are often complex and information is distributed across multiple systems, partners, and transport providers.
Maintaining consistent and reliable data helps ensure AI systems can deliver meaningful operational value.
Integrated systems and information flows
High-quality data alone is not always sufficient. Supply chain information is often spread across multiple systems, organizations, and geographies, making it difficult to build a complete operational picture.
Connecting these information flows improves visibility and helps companies develop a more accurate and real-time view of their operations. This leads to more informed decision-making and stronger coordination across the supply chain.
Operational integration
The greatest value is often achieved when AI capabilities are embedded within existing operational workflows, such as planning, monitoring, and execution processes, rather than used as standalone tools or add-ons.
Research by BCG found that 56% of logistics providers plan to implement AI across existing systems over the next one to two years, making it the most common AI investment priority among those surveyed.
Many organizations begin with targeted operational use cases before expanding AI into additional workflows and processes.
AI in practice: the Forto approach
At Forto, we adopted a tactical and incremental approach to AI implementation.
Rather than attempting to automate the entire shipment lifecycle at once, we first analyzed where manual effort was highest and identified the operational workflows with the greatest potential impact.
This led us to focus initially on high-volume processes such as booking and document handling, where information must be interpreted, validated, and shared across multiple systems.
By starting with a clearly defined workflow, we were able to establish a strong foundation for broader AI adoption across our operations.
Flash by Forto
These capabilities are supported by Flash by Forto, our AI-powered transport management agent designed to assist Operations Managers across multiple stages of the shipment lifecycle.
Flash supports workflows such as booking, vessel selection, document handling, and communication, helping teams process information more efficiently and respond more quickly to operational requirements.
Rather than replacing human expertise, Flash is designed to reduce repetitive administrative work and support decision-making. This allows Operations Managers to focus more on exception handling, customer issues, and other higher-value activities where human judgment remains essential.
“With our AI agent Flash, we’re already seeing impressive results, with 65% of requests handled with minimal human touch and accuracy above 95%. Flash empowers our operations teams, freeing up their time and augmenting their expertise so they can deliver a level of service quality that truly stands out.”
Francesco Foschetti
Director Product Management – Forto
Creating value at scale
The clearest value from AI in supply chain management is emerging in areas of forecasting, visibility, and workflow automation.
Successful adoption depends on the quality of the data, systems, and processes involved. It also relies on the expertise and collaboration of the teams managing these operations.
Forto’s experience reflects a common approach to AI adoption. Rather than attempting large-scale implementation from the outset, organizations often achieve the best results when AI is deployed tactically and integrated into existing operational workflows.
Ultimately, the greatest long-term value from AI is likely to come when it is embedded across an organization’s entire supply chain operations, supported by the right technology, data, and human expertise.
Learn more about how Forto is leveraging AI to deliver unmatched freight forwarding service excellence.
Are you a freight forwarder or customs broker and looking for AI-powered solutions to automate manual work? Discover FortoLabs, Forto’s dedicated AI-native SaaS platform.
