Can AI Fix Aviation's Parts Procurement Problem?

Can AI Fix Aviation's Parts Procurement Problem?

By running AI in shadow mode, airlines are testing autonomous procurement tools against real human decisions to unlock massive efficiencies.

May 26, 2026 15:29 Difficulty: Intermediate Played
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Airvoyant was launched in April 2026 to streamline parts procurement in aviation using agentic AI.

Jon Baker Airvoyant product launch

Airvoyant's technology is designed to streamline parts purchases for airlines and MROs by 20% to 30%.

Lee Ann Shay Airvoyant product specifications

Procurement and aftermarket teams generally have not recovered to their pre-COVID staffing levels.

Jon Baker Industry labor observations

TL;DR

Aviation aftermarket operations face severe supply chain bottlenecks, but agentic AI offers a path forward. This episode explores how AAR's newly launched unit, Airvoyant, uses autonomous agents to streamline parts procurement by 20% to 30% [1] — Lee Ann Shay "20% to 30% reduction: AAR's newly launched unit Airvoyant targets a massive chunk of supply chain friction through automated procurement." 00:31 . By running AI models in shadow mode, major airlines like JetBlue and Virgin Atlantic are comparing machine recommendations against historical human choices to safely train systems [3] — Jon Baker "Before letting AI run wild, systems should operate in shadow mode to compare AI recommendations against actual choices made by human procur…" 05:41 . Transitioning from basic workflows to automated decision-making allows lean procurement teams to focus on high-value, strategic efforts [2] — Jon Baker "True aftermarket transformation requires moving past simple data connections to intelligent, automated decision-making. The goal is to buil…" 00:49 .

#Aviation Aftermarket #Supply Chain Automation #Agentic AI #MRO Technology #aviation #procurement #artificial #intelligence #aftermarket #mro #aerospace #supply #chain #automation #efficiency #airlines #software #airvoyant #aar

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Jon Baker, president and general manager of AAR's Airvoyant, joins James Pozzi and Lee Ann Shay to discuss the rollout of agentic AI in aviation parts procurement, exploring early benefits, shadow mode testing, and how lean aftermarket teams leverage automation.

Chapter list

  • James Pozzi and Lee Ann Shay welcome Jon Baker of Airvoyant to discuss using agentic AI to streamline parts procurement, outlining the unit's core goal of automating workflows and decision-making end-to-end [1] — Lee Ann Shay "20% to 30% reduction: AAR's newly launched unit Airvoyant targets a massive chunk of supply chain friction through automated procurement." 00:31 [2] — Jon Baker "End-to-end automation: The platform represents a shift from disconnected systems to a unified, autonomous agentic workforce that connects c…" 00:53 .

  • Jon Baker explains how AI delivers value in airline aftermarket operations through automated, intelligent decision-making, emphasizing the transitional role of human-in-the-loop systems that adapt based on confidence scores [1] — Jon Baker "Shadow mode benchmarking: By comparing what the system recommends with what human buyers actually chose, carriers can refine algorithm scor…" 06:07 [2] — Jon Baker "Hours of research per week: The velocity of modern AI development requires constant monitoring of new services, research papers, and softwa…" 09:08 [3] — Jon Baker "One of the things that you learn very quickly when you build these solutions is you can't build AI solutions in a vacuum." 05:44 .

  • The discussion focuses on how agents analyze data to make recommendations with clear confidence ratings, helping human teams take action while building trust toward higher levels of direct automation [1] — Jon Baker "Depressed staffing vs record demand: With teams remaining smaller and aircraft utilization running high, operators are highly receptive to …" 13:15 .

  • Jon Baker details how built-up confidence scores enable systems to automatically execute straightforward recommendations while reserving more complex choices for human review.

  • Jon Baker discusses collaborative efforts with major launch carriers, describing how historical data is utilized in shadow mode to analyze and tune the agentic workforce against historical choices.

  • The conversation covers custom-tuning AI tools to match individual carrier needs and leveraging early feedback to explore upstream functionality like requisition demand optimization.

  • Jon Baker addresses the high velocity of modern AI development, data ownership, security, and the industry's adoption drive fueled by reduced post-COVID staffing and ongoing demand pressures.

Agentic AI
A class of artificial intelligence systems capable of autonomous planning, decision-making, and executing multi-step workflows to achieve specific goals.
Human in the Loop
A design pattern in automation where an artificial intelligence system provides analysis and recommendations, but a human must review and authorize the final action.
Shadow Mode
A testing methodology where a new software system or AI model runs in parallel with active operations, receiving real production data to generate mock recommendations without affecting live workflows.
Data Lake
An environment or repository where vast amounts of raw data are stored in their native format until needed for analytics or machine learning applications.
Digital Twin
A virtual or digital representation of a physical object, process, or system used to simulate behavior and analyze performance under varying conditions.