For VPs of Supply Chain, heads of logistics, and eCommerce operations leaders responsible for on-time, in-full (OTIF), cost-to-serve, WISMO volume, and SLA performance, rising complexity makes it harder to hit delivery windows without increasing exception-handling costs.
They’ve responded by investing heavily in digital transformation, especially AI. But more doesn’t always mean better. PwC’s Digital Trends in Operations data shows that 92% of operations and supply chain leaders say their technology investments haven’t fully delivered the expected results.
Too often, these investments have added complexity, slowed decision-making, and widened the gap between what logistics and operations teams can see and how quickly they can act on it.
Teams have visibility into delays or disruptions but still struggle to respond in time, leading to missed delivery windows, increased customer inquiries about order status, higher support costs, and more reactive firefighting.
Closing that gap depends less on adding tools and more on reliable data movement between systems and the speed of operational follow-through — something that still separates proactive supply chains from those playing catch-up. The gap points to a few hard truths about how today’s digital supply chains actually operate.
Key takeaways
- A connected digital supply chain is defined by reliable data exchange between systems, not how many tools are in place.
- If your operation is reactive, you’ll spot disruptions after they’re already expensive, increasing missed delivery expectations and recovery costs.
- AI can scale bad data faster than teams can verify it, so data consistency determines whether outputs are usable.
1. The future of supply chain isn’t built on a single platform.
Many organizations have built their logistics technology stacks over time, adding systems as new challenges, partners, and priorities emerged. The problem isn’t the number of systems. It’s whether shipment, inventory, and exception data stays consistent as it moves between them.
The FedEx 2026 Future of Logistics Intelligence Report found that 66% of organizations use three or more systems to track and manage shipments, meaning critical data often lives across multiple environments rather than in a single, unified view.
The result is a fragmented digital environment, with data spread across multiple tools and teams forced to piece together what’s happening across the network. When a carrier scan conflicts with a warehouse timestamp, teams spend hours reconciling the ‘true’ status instead of addressing the exception before it hits the delivery window.
That fragmentation can make a single platform seem like the obvious solution. But most supply chains rely on a mix of internal systems, external partners, and specialized tools that one platform won’t fully replace. Even when teams try to consolidate, gaps still emerge across workflows, regions, or partner networks.
Instead of forcing everything into one system, the focus shifts to making sure data can move reliably across systems. That means data stays consistent, accurate, and continuously updated across tools and partners, giving teams near real-time visibility into what’s happening across the network.
With that foundation in place, teams can work from a more complete and dependable view of operations and spend less time reconciling conflicting or outdated information.
How to operate as a connected system
- Choose solutions that support flexible integrations, such as APIs and webhooks, so shipment status, exception codes, and ETA updates move between systems in near real-time without heavy customization.
- Make integration a requirement, not a nice-to-have, when evaluating new tools and systems, and require vendors to demonstrate integration with your TMS/WMS/OMS in a sandbox.
- Standardize how data like event names, timestamps, location codes, and exception reasons is captured, stored, and labeled to reduce discrepancies across systems.
2. If you’re reacting to disruptions, you’re already behind.
The industry assumes visibility solves disruptions. In reality, many tools surface issues after they’ve already driven SLA risk and recovery costs, because alerts arrive at the same time customers feel the impact.
Status updates, milestone scans, and partner inputs provide useful information, but they don’t surface issues early enough to change the outcome. When a late scan hits after cutoff, teams must choose between expedited shipping or a missed promise date. Either way cost-to-serve increases.
FedEx research found that only 59% of decision-makers are using data proactively to predict and prevent issues. That gap shows up in how teams operate: identifying risk after it materializes and focusing on recovery instead of prevention.
Closing the gap requires more than better data. It requires clear exception ownership, trigger-based workflows, and predefined customer communications so teams act before the service failure.
How to stay ahead of disruptions
- Track shipments continuously, not just at key status updates, using live data feeds where possible.
- Set automated alerts that enable proactive customer communication to improve the customer experience, reduce WISMO inquiries and protect support capacity during peak periods.
- Assign an owner by exception type (carrier delay, inventory short, address issue) with an SLA for response.
3. AI won’t fix your supply chain if your data foundation is broken.
Supply chain and operations teams have used AI and machine learning for tasks like invoice processing and route planning. Today, it’s playing a much bigger role in how teams plan and respond to what’s happening across the network.
In the rush to expand AI capabilities, some organizations have layered the technology onto systems where data isn’t consistent, structured, or readily accessible. When ETA inputs differ across the TMS, carrier portals, and the OMS, AI forecasts can amplify the mismatch — forcing teams into manual verification before they can act.
That weak foundation limits accuracy and makes outputs harder to trust. Teams revert to manual checks, cross-checking recommendations across systems and delaying action until they can verify what’s actually happening, which undermines the speed and efficiency AI is meant to improve. In reality, AI can scale bad data faster than teams can verify it, which delays decisions instead of accelerating them.
How to get real value from AI
- Ensure data is consistent and reliable across systems so AI can generate accurate forecasts and insights.
- Make operational data accessible across platforms through event-driven data pipelines that standardize timestamps and exception codes.
- Start with focused use cases, such as predicting delays to enable proactive shipment updates for customers, before scaling AI more broadly so teams can notify customers before WISMO spikes and recovery costs escalate.
The real measure of a modern digital supply chain
Many organizations have invested heavily in digital supply chains, but day-to-day execution still breaks down at the moment of action.
What matters is whether systems exchange data in a way teams can use, whether risks are surfaced early enough to act on, and whether the data behind each recommendation can be trusted. The advantage isn’t visibility alone. It’s how fast teams can make a trusted decision and execute it.
Connect with our team to see how FedEx helps teams deliver more reliable operations powered by accurate, near real-time data and shipment insights across the network.
