The aerospace and defense industry is entering 2026 under sustained pressure to deliver faster—without compromising quality or readiness. Nearly 75% of aerospace and defense executives expect artificial intelligence (AI)-driven automation to significantly improve operations in the next few years. That expectation is quickly becoming an execution requirement: in 2026, aerospace organizations are being measured on speed, quality, and readiness under sustained production and mission pressure. AI is no longer emerging on the margins—it is becoming the operational standard for teams that need to deliver faster, reduce rework, and improve decision-making at scale.
This shift is happening as the broader industry faces simultaneous growth and constraint. Deloitte’s 2026 Aerospace & Defense Outlook notes that the sector is entering a “new era of growth powered by AI, digital sustainment, and rising demand” across both commercial and defense domains, while also confronting significant operational constraints and supply chain volatility.
The sections below highlight where AI is already delivering measurable operational value across commercial aerospace, defense, and advanced air mobility (AAM)—and why scaling it responsibly will define industry leaders in 2026 and beyond.
AI’s Expanding Role Across the Aerospace & Defense (A&D) Industry
Commercial Aerospace
A report from Aerospace Industries Association (AIA) highlights that the commercial aerospace sector is facing a “defining crossroads,” with AI becoming essential to overcoming mounting pressures such as capacity constraints, aging information technology (IT) infrastructure, and a rapidly evolving workforce. According to AIA Vice President Tim White, strategic investment in enterprise AI is already improving efficiency, operations, and product quality—helping manufacturers meet rising production demand and stay competitive. The report also highlights AI’s role in modernizing legacy systems and preserving institutional knowledge as experience levels decline and job complexity increases.
AIA frames AI as a workforce multiplier, not a replacement. Today, the biggest gains are showing up in quality and inspection (catching defects earlier to reduce rework), manufacturing execution (improving scheduling, planning, and throughput during ramp-up), and workforce enablement (helping technicians and engineers solve problems faster in high-compliance environments). AI is also accelerating software development and systems integration by improving how teams manage requirements, traceability, and documentation—reducing friction between engineering, production, and sustainment workflows.
Regulatory and operational infrastructure is evolving alongside these use cases. The Federal Aviation Administration (FAA) recently published its Safety Framework for Aircraft Automation, helping establish clearer criteria and terminology for evaluating increasingly automated aircraft systems in safety-critical environments. In Europe, the European Union Aviation Safety Agency’s (EASA’s) Notice of Proposed Amendment (NPA) 2025-07 sets guidance for Level 1 AI assistance and Level 2 Human–AI teaming, covering AI assurance, human factors, ethics, and data-driven machine learning—with plans to expand the framework to more advanced AI methods over time.
Real-World Example: GE Aerospace monitors commercial engines 24/7 and uses digital insights to support predictive maintenance. According to GE Aerospace, advanced AI and machine learning models have enabled a 60% earlier lead time in identifying preventative maintenance recommendations, a 45% increase in detection rates, and cut false alerts in half over the past decade.
Defense
In 2026, the Pentagon is accelerating its shift toward an AI-first warfighting force. Aviation Week reports that the Department’s new AI Acceleration Strategy positions AI as a core capability across military functions—pushing faster adoption, deeper integration, and a stronger competitive edge against peer adversaries. This direction is reinforced by the Department’s updated AI strategy memorandum, which calls for removing bureaucratic barriers, scaling experimentation, and leveraging U.S. advantages in computing, frontier models, and operational data. To operationalize this shift, the Pentagon has launched seven Pace-Setting Projects (PSPs) across warfighting, intelligence, and enterprise missions—Swarm Forge, Agent Network, Ender’s Foundry, and GenAI.mil, along with three additional PSP efforts designed to embed AI agents into core workflows and compress delivery timelines. These initiatives mark a shift from experimentation to scaled, operational implementation.
Real-World Example: The U.S. Army’s Army Vantage platform is already operational as an enterprise AI/data capability used to support faster decision-making across the force. According to Palantir, Army Vantage supports over 100,000 users, connects 30,000 plus datasets and more than 180 systems, and provides a 360-degree view of operations, logistics, and personnel. With OpenAI integration and industry-standard APIs, it also enables teams to automate analysis and build next-generation workflows across the Army.
Autonomy & Advanced Air Mobility
AI is foundational to modern AAM platforms, enabling capabilities that go beyond what traditional avionics can support. In next-generation electric vertical take-off and landing (eVTOL) and autonomous aircraft, AI drives real-time perception and obstacle detection through sensor fusion, supports autonomous flight control for stable hover and route execution, and enables dynamic responses to changing conditions like weather, traffic, and no-fly zones. AI is also improving reliability and efficiency through predictive maintenance and energy optimization, helping extend battery life and maximize mission range.
Forbes analysts project the AAM sector could generate over $40 billion in aviation value by 2033, growing at a 38.1% CAGR from 2024 to 2033. Even as many applications remain early-stage, AI is already improving safety, lowering operational costs, and strengthening real-time decision-making across advanced mobility systems.
Real-World Example: In late 2025, Qatar’s Ministry of Transport conducted its first-ever urban flight of an uncrewed electric vertical take-off and landing (eVTOL) aircraft. The aircraft—operating between the Old Doha Port and the Katara Cultural Village—flew without direct human control, using a self-control system powered by AI and advanced air navigation technologies to manage airspace and optimize a safe operational trajectory.
The successful trial reflects a strategic commitment to AI-enabled urban mobility and is part of a phased program to evaluate technical, operational, and regulatory readiness for integrating autonomous air taxis into urban transportation networks.
The Risk of Falling Behind
In A&D, the risk is no longer whether AI is “promising”—it is whether organizations can scale execution fast enough to keep up with demand. Aerospace Testing International reports that defense contractors are facing significant backlogs and are actively looking for ways to increase throughput—doing more with the same workforce and facilities. At the same time, the sector’s documentation and compliance requirements continue to expand, adding friction to testing, certification, and program delivery. As programs become more complex, organizations that rely solely on traditional workflows will struggle to increase capacity without adding cost, extending schedules, or taking on additional risk. AI adoption is increasingly becoming the differentiator—enabling faster, more accurate, and more agile operations while maintaining the rigor required in safety-critical environments.
Performance Is an AI-First Company
Performance has moved beyond AI exploration and into operational execution. We are integrating AI-enabled engineering into active delivery—both on customer programs and internally—to increase consistency, accelerate cycle times, improve early issue detection, and strengthen knowledge retention across teams.
Performance engineers apply AI through structured delivery practices appropriate for regulated and safety-critical work, with governance mechanisms designed to maintain traceability, review discipline, and engineering accountability.
AI-enabled workflows are already established across multiple active efforts. In Q4 2025, Performance reported nine AI-first programs, representing approximately $10M in software and test development, demonstrating both scale and repeatability.
In one aircraft data loading verification effort, AI-enabled execution achieved measurable improvements—81% fewer engineering hours, 46% schedule reduction, 75% staffing reduction, and a 93% inspection quality rate—demonstrating outcomes that translate directly to customer value.
This progress is reinforced by an AI-first culture built for sustained, responsible adoption, with clear expectations for human oversight, disciplined review, and continuous improvement based on measurable outcomes.
Accelerate Your Program
The aerospace leaders of the future are being defined now. Organizations that embrace AI early will gain compounding advantages in cost, speed, innovation, and mission performance—while those that delay will face a widening gap they may not be able to close.
If you’re ready to explore how AI can shorten development cycles, reduce rework, and make faster decisions across your program lifecycle, we’re ready to help. Our goal is clear: stay ahead of the curve—and ensure our customers do too.
Let’s build the future of aerospace together. Contact us today or call +1 623-780-1517
