For FedEx, deploying AI across Asia-Pacific means building systems that can adapt to different regulations, infrastructure, and operating conditions while supporting real-time logistics.
Speaking with Frontier Enterprise, FedEx’s APAC Vice President of IT Sandeep Shahi discussed the company’s approach to data governance, cybersecurity, AI deployment, and technology integration across the region.
How does APAC’s diversity affect the scaling of logistics systems and data flows?
Asia-Pacific is one of the most diverse operating environments globally, and that diversity defines how we scale. Markets range from highly digitised economies such as Singapore and Japan to fast-growing ones like Vietnam and Indonesia, each with different levels of infrastructure maturity, regulatory frameworks, data localisation requirements, and customer expectations.
Systems and data flows must be designed to operate within this variability. At FedEx, we use modular, API-driven platforms that can be configured to meet local customs requirements, tariff structures, and regulatory frameworks while maintaining consistency across the region.
Data sits at the centre of this. From tracking and customs clearance to forecasting and network planning, it must be managed and governed carefully across markets with different regulations. Regulatory expectations are also evolving, with increasing demand for product-level traceability, including origin and manufacturing data, particularly in complex supply chains such as semiconductors. These requirements are shaping how data is structured and used, while also supporting supply chain transparency.
The complexity extends beyond technology. Workforce readiness, adoption rates, and expectations for digital services vary across markets, influencing how systems are deployed. At the same time, shifting trade patterns and the continued growth of e-commerce are increasing demands for speed, flexibility, and resilience.
Where do data security challenges arise in cross-border logistics, and how does FedEx address them?
Data security faces the greatest pressure in cross-border environments where operations span multiple jurisdictions with different regulatory requirements and data policies. Data must move across these environments while remaining secure and compliant.
At FedEx, cybersecurity is built into the design and operation of our systems.
Security is built into the architecture and governance of our systems. Our infrastructure, platforms, and applications incorporate layered security controls, including identity management, encryption, access controls, and continuous monitoring. Governance frameworks help maintain consistent standards across regions while aligning with local requirements.
We use continuous monitoring, advanced analytics, and AI-based threat detection to identify anomalies in real time. Incident response protocols and business continuity plans are also in place to help maintain operations during disruptions.
Security is also an organisational responsibility. We provide ongoing training to help employees understand emerging risks, including those associated with AI, and apply security practices in their daily work.
This combination of architecture, monitoring, and employee training helps us maintain security and resilience across a complex cross-border network.
How are you using AI to improve real-time visibility across your operations?
One example is FedEx Surround, which combines sensor technology, advanced analytics, and AI to provide visibility across the network. Through SenseAware ID, a compact sensor transmits location data every two seconds via Bluetooth, enabling near real-time tracking. The data feeds into a dashboard that provides network-wide visibility and helps identify potential disruptions. The information is used to prioritise shipments, manage cold chain requirements, and intervene before disruptions escalate.
At the global network level, we handle more than 17 million shipments daily across 220 countries and territories. AI analyses this data to detect demand shifts, identify bottlenecks, and support dynamic routing adjustments.
In cross-border operations, AI improves speed and accuracy. Tools such as the Harmonized Tariff Schedule Code Lookup Feature and Customs AI simplify tariff classification and documentation, reducing errors and supporting faster customs clearance in complex regulatory environments.
Customer expectations continue to shape these capabilities. Customers increasingly expect visibility and timely information throughout the shipping process. AI helps provide earlier notifications and data-driven insights, giving customers greater visibility into their supply chains and supporting decision-making.
What makes deploying AI in logistics more difficult than in purely digital environments?
Integration is a key challenge. AI must work across legacy systems, partner platforms, government infrastructure, and physical assets, many of which were not built to support AI. Scaling beyond pilot projects also requires alignment across technology, operations, and external stakeholders.
Adoption is equally important. Operational teams need to understand and act on AI outputs. This requires alignment with existing workflows and targeted training. At FedEx, AI is treated as a cross-functional capability, supported by organisation-wide training to build AI literacy.
AI is also embedded in customer-facing and operational tools, increasing expectations for accuracy, reliability, and consistency. These tools directly affect both customer experience and operational performance.
What technical lessons from logistics are most relevant to enterprises operationalising AI?
There are several practical lessons from deploying AI in a global logistics network.
First, data foundations determine outcomes. The quality, consistency, and accessibility of data define what AI can deliver. Building that foundation requires sustained investment in architecture, governance, and integration.
Second, scaling requires moving beyond pilot projects in a structured way. Many organisations succeed with proofs of concept but struggle to operationalise them. At FedEx, technology investments are assessed against business outcomes and operational impact, with a focus on integrating them into existing platforms and workflows across multiple markets.
Third, systems must be built for real-world conditions. AI must be able to handle incomplete data, variability, and edge cases. Robustness and reliability are critical in operational environments.
Finally, AI adoption depends on organisational capability. Teams need to understand how to use AI outputs in practical contexts. This is why we invest in AI literacy across the organisation, as roles increasingly require data fluency, AI enablement, and new technical skills.
