Textron deploys AI to help aircraft mechanics access decades of repair knowledge

Textron has successfully deployed a generative AI solution called TAMI (Textron Aviation Maintenance Intelligence) across its global service centers, helping aircraft mechanics access decades of maintenance knowledge through natural language queries. The initiative, led by global CIO Todd Kackley, demonstrates how aerospace manufacturers can bridge critical knowledge gaps as experienced technicians retire while reducing aircraft downtime and improving repair efficiency.

The big picture: Textron’s approach bypassed traditional corporate AI implementation hurdles by starting with a targeted use case and proving value before seeking major investments.

• Kackley launched the initiative without dedicated budget, team, or technology resources, relying instead on organizational trust and rapid experimentation.
• The company used a consumption-based, pay-as-you-go model for the proof of concept rather than building traditional ROI models or requesting upfront capital.
• Senior leadership, including CEO Scott Donnelly, personally tested early versions, creating unified executive support.

How it works: TAMI aggregates maintenance data, repair logs, service manuals, and YouTube tutorials using a RAG (Retrieval-Augmented Generation) model to provide contextual answers.

• Mechanics can query the system in natural language and receive precise answers, often with direct links to specific frames in instructional videos.
• The system successfully answered 19 out of 20 challenging questions from senior mechanics during initial testing.
• Early indicators show dramatic reductions in information search time and improvements in first-time fix rates.

In plain English: RAG technology works like having an extremely knowledgeable assistant who has read every manual, watched every training video, and remembers every repair case. When mechanics ask questions in everyday language, the system searches through all this information and provides specific answers with references to exactly where the information came from.

Key details: The solution addresses a critical workforce challenge facing the aerospace industry.

• The initiative targets the knowledge gap between junior mechanics and retiring veteran technicians with specialized expertise.
• Aircraft downtime carries significant costs, making faster, more accurate repairs economically crucial.
• Over 1,500 mechanics across global service centers now have access to the system.

What they’re saying: Leadership enthusiasm drove rapid adoption across the organization.

• “This is a game changer. We need to scale it globally,” said Ron Draper, president and CEO of Textron Aviation, during Kackley’s follow-up presentation.
• “CIOs often get stuck trying to explain ROI or navigate policy hang-ups,” Kackley explained. “We didn’t let policy stall innovation. We treated gen AI like any other tool, with appropriate use and evolving guardrails.”
• “Sometimes you get a window of opportunity to show the art of the possible,” he noted. “You have to take it, even if you don’t have the resources yet.”

Scaling strategy: Textron established a cross-functional council to ensure solutions could be replicated rather than reinvented across business units.

• A solution originally built for aircraft repair can be retooled for Textron’s industrial or defense businesses.
• Within weeks of the initial success, a gen AI solution from one defense business was cloned for another division.
• The reusable approach enables rapid deployment across Textron’s diverse portfolio of brands including Cessna, Beechcraft, and Bell.

Why this matters: The initiative demonstrates how established manufacturers can successfully integrate generative AI into frontline operations while managing the transition from experienced to newer workforce.

• Textron’s $13.7 billion industrial conglomerate proves that traditional companies can move quickly on emerging technologies when leadership provides clear support.
• The approach offers a blueprint for other organizations facing similar workforce knowledge transfer challenges.
• Results show mechanics spend less time interpreting manuals and more time on actual repairs, directly improving operational efficiency.