The Aerospace and Defense (A&D) sector is entering a phase where operational readiness matters as much as platform innovation. Sustained geopolitical instability, expanding defense budgets, and longer platform life cycles are pushing fleets harder than at any point in the past decade. As a result, maintenance, repair, and overhaul (MRO) is no longer viewed simply as a downstream support function; it is increasingly recognized as a strategic lever that directly influences force readiness, fleet availability, and life cycle economics. This shift is driving A&D enterprises to reengineer maintenance operations through digital services, AI, and model-based approaches. However, unlike earlier automation waves, the current transformation is explicitly human-centric, designed to augment technician judgment rather than eliminate it.
Human-centric, AI-guided MRO is gaining traction as a direct response to a convergence of capacity, workforce, and regulatory pressures that are fundamentally reshaping sustainment operations. Production ramp-ups across the US, Europe, and Asia, driven by sovereign sourcing mandates and regional manufacturing expansion, are increasing the volume of assets entering sustainment pipelines, while simultaneously distributing maintenance footprints. At the same time, fleets and platforms are becoming more software-defined and data-rich, even as the maintenance workforce continues to age and deep platform expertise remains concentrated among a shrinking pool of specialists. Layered onto this is the reality of strict certification and safety requirements, which limit the feasibility of fully autonomous decision-making in maintenance environments. Together, these forces are pushing A&D enterprises toward human-in-the-loop AI models that can digitalize expert knowledge, guide less-experienced technicians in real time, and scale sustainment capacity without compromising safety or compliance.
These structural pressures on sustainment are also elevating MRO as a focal point within broader defense transformation agendas. As readiness, availability, and life cycle performance become explicit enterprise priorities, defense organizations are increasingly treating sustainment as a digital modernization domain rather than a purely operational function. This shift creates the conditions for AI-enabled maintenance to scale beyond localized improvements and become embedded within defense-wide transformation programs.
While these pressures are reshaping MRO from the inside, they are unfolding in parallel with a broader shift in how defense organizations are approaching digital transformation at the enterprise level. Modernization programs are increasingly extending beyond engineering and manufacturing into sustainment and lifecycle support, creating a foundation for AI-enabled maintenance to move from isolated initiatives to an integral part of defense operating models.
An important catalyst is defense-led digital modernization. Our Aerospace and Defense Digital Services 2025–2026 Market Insights™ report notes that digital twins, AI, and model-based engineering are moving from pilots to production across engineering, MRO, and sustainment. This transition is reinforced by policy. The US Department of Defense Instruction 5000.97 formalizes digital engineering adoption across acquisition and sustainment, pushing primes and suppliers to embed digital models throughout the life cycle.
This policy shift legitimizes AI-enabled maintenance as part of the official defense operating model rather than an experimental add-on. When digital artifacts are required as part of contracts, AI-guided inspection, planning, and sustainment become scalable rather than bespoke. As a result, A&D enterprises can move from isolated, program-specific AI deployments toward repeatable, enterprise-scale MRO capabilities that improve readiness while lowering sustainment complexity.
Another notable development is the adoption of commercial-style operating models in defense sustainment. In 2024, the top 100 arms-producing companies saw a 5.9% increase in their revenue, partly driven by more modular, productized offerings, according to our Aerospace and Defense Digital Services 2025–2026 Market Insights report. In MRO, this translates into reusable analytics modules, standardized inspection workflows, and configurable AI services that can be deployed across programs and regions.
As defense modernization programs push digital engineering, AI, and digital twins into production, MRO organizations are beginning to translate this direction into concrete, operational capabilities.
In practice, AI-guided MRO is evolving through layered capability development rather than big-bang transformation.
At the foundation is data infrastructure. Enterprises are standardizing sensor data, digitizing maintenance histories, and linking configuration data across engineering and sustainment. Without this groundwork, AI models cannot be trusted or certified.
On top of this, predictive analytics is becoming the first production use case. AI models analyze usage patterns and degradation signals to prioritize maintenance actions. The Aerospace and Defense Digital Services 2025–2026 Market Insights report points to Boeing advancing AI-enabled predictive maintenance and digital-twin-based fleet support within military MRO environments, demonstrating that these capabilities are now being used in live operations rather than controlled pilots.
Inspection is another area of rapid progress. Robotics and machine vision systems are increasingly used for repetitive visual inspections in hangars. These systems do not replace inspectors, instead, they flag anomalies and reduce inspection cycle times, allowing certified technicians to focus on validation and repair decisions. As a result, inspection throughput improves without increasing certification risk, enabling MRO organizations to process higher asset volumes while maintaining regulatory compliance and technician accountability.
Organizations across industries are increasingly turning to generative AI to reduce the time technicians and engineers spend searching across manuals, logs, and historical work orders, and to accelerate access to actionable knowledge at the point of work. In MRO environments, this need is amplified by high asset complexity, large volumes of unstructured documentation, and pressure to resolve issues faster without increasing headcount.
Generative AI is beginning to influence MRO workflows, though its role remains deliberately constrained. Current deployments focus on accelerating access to maintenance manuals and historical work orders, synthesizing probable troubleshooting paths, and drafting maintenance documentation for human review.
What distinguishes A&D from other industries is governance, because maintenance decisions directly affect safety, certification status, and mission readiness. Enterprises are embedding generative AI within closed, validated data environments rather than open-ended systems. This aligns with Avasant’s observation that buyers increasingly prefer platform-led, AI-first service models that embed intelligence directly into workflows instead of relying on labor-heavy augmentation.
A key enabler of human-centric AI-guided MRO is the shift toward platform-centric operating models. Instead of fragmented tools for diagnostics, planning, and execution, A&D enterprises are investing in integrated platforms spanning design, manufacturing, and sustainment.
This mirrors broader industry shifts. The Aerospace and Defense Digital Services 2025–2026 Market Insights report notes that there was a 38% increase in collaborative R&D proposals under the European Defence Fund in 2025, reflecting a move toward ecosystem-based delivery. In MRO, this means OEMs, operators, service providers, and technology vendors are sharing data environments and interoperable systems.
The defining feature of this transformation is not AI sophistication, but human trust. Maintenance decisions directly affect mission readiness and safety. AI systems must therefore be explainable, traceable, and auditable. Human-centric design ensures that technicians understand why an AI system is recommending a specific action and retain authority to override it.
From a workforce perspective, this model also reshapes roles. AI becomes a capability multiplier, enabling fewer experts to support larger fleets and accelerating skill development for junior technicians. Over time, this mitigates the impact of workforce attrition without compromising safety or compliance.
Resilience considerations are increasingly shaping MRO design. In 2025, the EU SAFE regulation earmarked up to EUR 150 billion in loans and guarantees to support urgent defense industrial investments, alongside growing regulatory pressure from the EU Critical Raw Materials Act. Together, these measures are pushing enterprises to localize sustainment capabilities, diversify supplier bases, and strengthen multitier supply visibility—areas where AI-guided MRO platforms play a critical role.
For leaders, the path forward is not about deploying AI everywhere, but about making deliberate, value-driven choices. This begins with investing in strong data foundations, as AI-guided MRO depends on high-quality sensor data, digitized maintenance records, and consistent configuration management—without which initiatives will stall. With this foundation in place, enterprises should prioritize narrow, high-impact use cases, such as predictive maintenance for critical subsystems or AI-assisted inspections within a single hangar, to demonstrate measurable value and build organizational confidence. At the same time, governance must be embedded from the start, with explainability, audit trails, and human-in-the-loop controls designed into systems rather than retrofitted later. Finally, as A&D enterprises increasingly adopt commercial-style, platform-led delivery models, MRO organizations must evolve roles, training, and incentives to align operating models with this reality.
Human-centric, AI-guided MRO represents a pragmatic evolution of maintenance operations in the A&D industry. It reflects the industry’s need to balance speed, scale, and safety in an era of heightened operational demand. The opportunity lies in moving decisively, but thoughtfully, toward AI-enabled sustainment models that keep humans at the center while unlocking measurable gains in readiness, resilience, and cost efficiency.
By Jyotika Jain, Lead Analyst, Avasant
Avasant’s research and other publications are based on information from the best available sources and Avasant’s independent assessment and analysis at the time of publication. Avasant takes no responsibility and assumes no liability for any error/omission or the accuracy of information contained in its research publications. Avasant does not endorse any provider, product or service described in its RadarView™ publications or any other research publications that it makes available to its users, and does not advise users to select only those providers recognized in these publications. Avasant disclaims all warranties, expressed or implied, including any warranties of merchantability or fitness for a particular purpose. None of the graphics, descriptions, research, excerpts, samples or any other content provided in the report(s) or any of its research publications may be reprinted, reproduced, redistributed or used for any external commercial purpose without prior permission from Avasant, LLC. All rights are reserved by Avasant, LLC.
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