Boeing, Pelico test AI to speed C-17 heavy maintenance

Boeing Global Services and Pelico, an AI data optimization specialist, have signed a memorandum of understanding to explore modernizing C-17A Globemaster III heavy maintenance planning and execution through artificial intelligence. The collaboration aims to shorten depot maintenance cycles, improve fleet availability, and deliver more predictable aircraft turnaround times by introducing AI-enabled scheduling, supply chain coordination, and workflow automation.

Central to the evaluation is Pelico's Manufacturing Orchestration Platform, which consolidates supply, engineering, planning, and depot operations into a single digital workspace. The platform is designed to replace spreadsheet-based processes with real-time visibility into material shortages, maintenance priorities, and production constraints. Beyond basic scheduling, the companies will assess predictive shortage management, dynamic maintenance prioritization, and agentic AI workflows—automated systems capable of handling routine approvals and supplier communications without manual intervention.

Boeing's confidence in the approach stems from prior success: an earlier deployment of Pelico's platform in the company's commercial spare parts operations successfully demonstrated the concept and has since been extended. "This work is focused on delivering measurable operational improvements and creating a repeatable capability we can scale as we validate outcomes," said Travis Williams, Boeing vice-president of Mobility and Surveillance Aircraft Services.

The agreement establishes a phased evaluation framework with the potential to expand the technology across other Boeing heavy maintenance programmes if the trial proves successful, indicating a pathway for broader adoption once the C-17A pilot validates the promised improvements in turnaround times and operational efficiency.

Boeing Global Services and Pelico's collaboration reflects a broader industry shift toward digitizing maintenance operations, particularly for legacy military platforms like the C-17A that rely on complex, labor-intensive depot cycles. The memorandum of understanding signals Boeing's confidence that AI-driven workflow optimization—tested and validated in its commercial spare parts division—can deliver measurable operational improvements at scale. By consolidating supply, engineering, planning, and depot data into a single workspace, the platform aims to eliminate the bottleneck of fragmented spreadsheet-based scheduling and supplier coordination that typically extends maintenance timelines. The phased evaluation framework and conditional expansion pathway suggest Boeing is adopting a measured approach to validate outcomes before committing to wider rollout across other heavy maintenance programs.

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