Digital solutions

Picture a logistics operation where at-risk shipments are automatically flagged, delivery routes adjust in real time to avoid delays, and customers are informed of potential issues before they escalate. Inventory forecasts respond dynamically to changing conditions, and analytics tools surface anomalies without the need for manual oversight. 

For logistics and supply chain leaders, this scenario is no longer a distant vision. AI is making proactive performance the new standard. But many organizations still struggle to reach this level of agility.

The 2026 Future of Logistics Intelligence Report shows that while 97% of decision-makers say they have end-to-end visibility, only 18% can always intervene during disruptions, and just 59% use their data proactively to predict and prevent issues.

So, where's the disconnect? Fragmented information, disconnected systems, and manual workflows keep teams and AI tools from turning insights into action. Without a strong data foundation, even the most advanced technology leaves organizations stuck in reactive mode.

Today’s customers expect more than basic shipment tracking. They want accurate delivery windows, timely shipment updates, proactive delay notifications, and faster issue resolution. 

AI-powered logistics tools enable these capabilities, but only when they have access to accurate, connected, and up-to-date data from every part of your operation.

When data is fragmented, delayed, or buried in manual processes, both people and technology can only react to problems instead of preventing them. 

Here’s how these data and process gaps hold teams and AI back:


Messy or incomplete data

Only 22% of decision-makers say they have access to all the data types they need. Missing shipment updates, outdated delivery details, and conflicting inventory counts make it hard for teams to see what’s really happening across their networks. When AI tools rely on incomplete or inaccurate data, their predictions lose accuracy and their recommendations become less reliable.


Disconnected systems

Two-thirds (66%) of organizations juggle three or more platforms to manage shipments, while just 4% rely on a single system. As a result, information is often scattered across tools that don’t communicate, making even simple problem-solving slow and inefficient.

This fragmentation leads directly to limited visibility. According to the Future of Logistics Intelligence Report, only 43% of decision-makers say all relevant teams in their organizations can access and use the same logistics data in a timely manner.

When teams can’t easily access and share the same information, it’s much harder to coordinate responses and resolve issues. For AI solutions, disconnected systems mean missing context and broken connections, limiting their ability to spot risks and support proactive decisions across the operation.


Manual workflow

When systems don’t connect and data lacks consistency, teams end up filling gaps by hand. In fact, 31% of decision-makers cite inefficient manual workflows as a top pain point with their logistics systems. 
Every manual step, whether escalating an issue by email or updating inventory in spreadsheets, adds delays and increases the risk of errors. In these settings, AI can flag issues and suggest solutions, but it still depends on people to act. That limits how quickly teams can resolve issues.

Data challenges in logistics may feel daunting, but they aren’t permanent barriers. The key to unlocking AI’s value in logistics lies in building a strong data foundation that gives AI tools the accurate, connected information they need to identify problems sooner and drive timely actions and recommendations across your supply chain.
 

1. Standardize and cleanse your data: Make your data trustworthy and easy to use by standardizing formats and keeping information current. Prioritize cleaning up core datasets (e.g., inventory and shipment data) so AI tools have reliable information to work from.
   
2. Connect your systems: Integrate platforms such as order management, warehouse, and carrier systems to eliminate silos and keep everyone on the same page. When your systems work together, teams and AI tools can make more informed, coordinated decisions.
3. Automate repetitive, manual work: Automate routine manual updates like inventory reconciliation and delivery confirmation. Prioritize tasks that slow down daily workflows or require manual data entry. This will help you reduce errors, speed up responses, and free up your team for higher-value work.

Siloed data, scattered systems, and manual processes stand in the way of  progress, holding back both your teams and AI from taking decisive action when it matters most.

To translate AI investments into measurable logistics improvements, start with a solid data foundation. Unify your data, connect your systems, and automate routine tasks. With these fundamentals in place, you can move beyond simply reacting to disruptions and start proactively shaping the future of your logistics and supply chain operations.

Want to see how industry leaders are bridging the logistics data gap? Access the full 2026 Future of Logistics Intelligence Report for more insights.