Why AI Supply Chains Are Moving Away From Data Sharing—and Toward Signal Sharing
One of the most persistent barriers to supply chain resilience is not a lack of analytics tools, but reluctance to share data.
During IIoT World Manufacturing & Supply Chain Day in December, speakers discussed how AI is being applied across demand forecasting, supplier risk, and logistics. While AI depends on data, several panelists emphasized that effective collaboration does not require full transparency across the supply chain.
Instead, they explained, it requires sharing signals.
Visibility declines beyond direct suppliers
In the session “Can AI Predict and Prevent the Next Supply Chain Disruption?”, Maria Araujo described how visibility weakens as supply chains extend beyond direct partners. Manufacturers, she noted, generally have strong awareness of Tier-1 suppliers, but that understanding drops significantly at Tier-2 and becomes minimal at Tier-3.
These deeper tiers are also where many disruptions originate. However, access to detailed operational data at those levels is limited by competitive concerns, security risks, and the absence of standardized data-sharing frameworks.
As a result, attempts to build full end-to-end visibility often stall before they reach the areas of greatest risk.
Signals provide early warning without exposing sensitive data
Rather than pushing for raw data access, Maria Araujo emphasized the value of sharing high-level indicators that reveal change without revealing proprietary information. She described this as sharing the signal rather than the data.
Examples discussed during the session included trends such as rising lead-time variability, declining on-time delivery, emerging logistics congestion, and external conditions like customs delays or weather-related risks. These indicators are sufficient to trigger mitigation actions even when the underlying data remains private.
Signal sharing allows organizations to coordinate responses while maintaining control over sensitive information.
AI changes timing, not ownership, of decisions
Srinivasan Narayanan explained how supplier risk evaluation was historically a periodic process, often conducted quarterly. By the time issues were identified, teams were already in a reactive position.
AI enables continuous monitoring of operational, financial, and external signals, shifting the timing of awareness earlier in the disruption cycle. The goal is not to replace suppliers or automate decisions, but to give organizations time to act—whether by dual sourcing, adjusting inventory, or renegotiating constraints.
Early signals create options. Late data removes them.
Governance determines whether sharing happens at all
Signal sharing depends on trust, and trust depends on governance. Jamie McIntyre Horstman described how her organization relies on privately governed AI platforms rather than open systems, supported by recurring training on secure data usage.
Clear role-based access, defined boundaries, and consistent education help ensure that AI tools do not become a backdoor for unintended data exposure. Srinivasan Narayanan added that modern ERP and AI systems increasingly include guardrails that restrict access even when advanced analytics are in use.
Across the discussion, speakers agreed that suppliers are more willing to share indicators when expectations and protections are explicit.
A shift in how resilience is built
The discussion highlighted a shift in how manufacturers are approaching supply chain resilience. Instead of attempting to aggregate all available data into centralized platforms, organizations are prioritizing early-warning indicators that can be shared safely and acted on quickly.
AI plays a critical role in detecting and correlating those signals, but the strategy succeeds only when companies recognize that collaboration does not require full transparency.
For manufacturers navigating ongoing volatility, the ability to detect change early—and respond before constraints harden—may depend less on how much data they collect, and more on which signals they choose to share.
This article is based on insights shared during the session “Can AI Predict and Prevent the Next Supply Chain Disruption?” at IIoT World Manufacturing & Supply Chain Day, organized by IIoT World in December 2025.