While artificial intelligence (AI) is often celebrated for its role in advancing aircraft systems and operations, one of its most transformative applications lies in the design process itself. From conceptualization to production, AI is reshaping how aerospace components are created, enabling faster development cycles, improved part performance, and unprecedented innovation.
The Crust Cycle: Aerospace’s Growing Problem
The aerospace industry faces a fundamental challenge that extends far beyond Boeing’s well-documented recent troubles. This deeper issue can be characterized as the “Crust Cycle”—a self-reinforcing pattern that traps the industry in increasingly bureaucratic processes that stifle innovation and efficiency.
When new industries emerge, they brim with innovation and optimism. However, as things inevitably go wrong, stakeholders demand rules and regulations, making the system more bureaucratic. This shift causes companies to pivot from innovation to compliance, which slows development. Over-engineering sets in, causing costs to spiral. Projects decelerate, and fewer reach completion. As experienced professionals leave the industry, the loss of expertise leads to more failures, which predictably triggers even more regulation.
The statistics paint a stark picture. McKinsey estimates over $1 trillion in annual project waste globally, with 65% of complex projects failing to meet schedule, budget, or quality targets according to the Project Management Institute. The aerospace industry represents a significant portion of this inefficiency.
This cycle creates a compounding problem through several interconnected stages. Outdated systems, legacy technology, manual processes, and siloed data create inefficiencies and errors. Project underperformance in the form of delays, cost overruns, and quality issues triggers reactive measures. Well-intentioned attempts to mitigate risks through patching systems and implementing new regulations lead to increased complexity.
The resulting compliance burdens, overlapping regulations, and bureaucratic hurdles reduce investment as risk-averse investors withdraw support, starving R&D budgets and diminishing innovation capacity. This ultimately reinforces the outdated systems that started the cycle.
The Competitive Threat
This would merely be an internal industry problem if everyone operated under the same constraints. However, new countries are entering the aerospace arena without the legacy burden, radically cutting development timeframes. China’s recent unveiling of their sixth-generation fighter exemplifies this challenge—while Western nations remain largely in the conceptual sketching phase, competitors are advancing rapidly with functioning prototypes.
The West must figure out how to accelerate development timeframes while still working within established cultural and regulatory processes. This represents both the challenge and the opportunity for AI implementation in aerospace design.
AI as Assistive Intelligence
The key to leveraging AI effectively lies in reframing it as “assistive intelligence” rather than “artificial intelligence.” This perspective shift is crucial for understanding how AI can be applied to aircraft development processes. When organizations examine what AI excels at, they can begin to see its applications in aerospace development workflows.
The power emerges from understanding complementary strengths. People excel at intuition and creativity, bringing common sense and contextual understanding to complex problems. AI, conversely, excels at grappling with massive amounts of information and tirelessly asking systematic questions that humans might miss or find tedious. When you combine the strengths of assistive intelligence with human capabilities, something magical happens—you amplify the human mind, enabling people to see further and accomplish more than they could achieve alone.
Beyond Traditional Software Solutions
Aerospace companies have long been consumers of Product Lifecycle Management (PLM) and Requirements Management software. However, these systems essentially function as glorified filing cabinets. While useful for organization and information storage, they remain passive participants in the development process. They don’t accelerate progress—they simply manage it.
To truly accelerate development, automation is essential. AI provides this capability by enabling computers to apply knowledge for the first time. This knowledge application means organizations can leverage their previous experiences, using AI to automate tasks that previously required significant time investment, such as requirements design and planning documentation.
Transforming Institutional Knowledge into Competitive Advantage
The tribal knowledge and institutional knowledge that organizations have accumulated over decades can now be transformed into a significant advantage. AI can tirelessly apply this knowledge to projects, ensuring they stay on track through automated processes that would be impossible to maintain manually.
The efficiency gains are remarkable. AI can run 1,000 checks in just a few minutes for approximately $65, compared to the $5,000 cost and two-week timeline required for human completion. This represents a two-order-of-magnitude improvement that can dramatically enhance individual company performance in product development.
AI capabilities extend beyond basic automation. It can critique individual elements and suggest improvements, check coverage, and identify areas where compliance is suboptimal. This comprehensive analysis capability provides continuous quality assurance that would be impossible to maintain through human resources alone.
The Swiss Cheese Approach to AI Integration
Effective AI implementation requires designing systems that work collaboratively with stakeholders. This approach yields two significant benefits. First, AI accelerates and improves the quality of work that most engineers find less exciting, such as documentation and compliance tasks. The key is maintaining interactive processes where stakeholders provide feedback while AI handles the repetitive analysis.
When stakeholders make changes, AI can quickly rerun all analyses from the beginning to ensure self-consistency—something humans find difficult to maintain consistently. AI can also continuously run checks and critiques without fatigue or burnout, maintaining intensity levels that would crush human productivity.
The second benefit is that building processes to interact with humans in the loop reduces error probability. This aids compliance and certification efforts through what can be called the “Swiss cheese approach” to AI implementation. Rather than replacing people, AI adds another expert tool with detailed knowledge of aerospace and company-specific expertise. This creates an additional layer of oversight, reducing the chance that mistakes pass through the development process.
Practical Implementation Strategy
The framework of AI as assistive intelligence provides an effective approach for bringing AI into development processes. The goal is not to discard current practices but to accelerate them and improve their effectiveness. Experience shows that people, especially engineers, quickly warm to these systems. Writing plans and determining system requirements can be tedious and challenging to execute well. Most engineers prefer focusing on the actual engineering work—making and building things. AI makes the entire process more engaging while improving quality and saving significant time and resources.
Organizations often struggle with AI implementation decisions. The recommended approach is to start small. Begin by identifying the business case you want to solve, then determine how AI can assist with that specific challenge. Consider how stakeholder interaction will function and whether the approach reduces risk. Notably, extensive data cleaning isn’t a prerequisite for getting started.
Too many organizations spend excessive time fretting about data quality. While good data is necessary, it’s more efficient to clean data as you go and curate datasets specifically for AI models. When you define your use case first, it’s much more efficient to clean only the data needed for your specific task rather than attempting comprehensive data cleaning upfront.
The Future of Aerospace Innovation
Traditional software couldn’t apply knowledge effectively. Now, AI enables the use of industry and enterprise-specific expertise to solve problems, delivering speed improvements and quality enhancements that will allow Western aerospace companies to shift focus from compliance back toward innovation.
This transformation represents more than technological advancement—it’s a fundamental shift in how the aerospace industry approaches design and development. By embracing AI as assistive intelligence, companies can break free from the Crust Cycle that has trapped the industry in inefficient processes.
The integration of AI into aerospace design processes offers unprecedented opportunities to accelerate innovation, improve quality, and reduce costs. As global competition intensifies and new players enter the market with fewer legacy constraints, the aerospace industry’s ability to leverage AI effectively will determine its competitive future.
AI’s role in aerospace extends far beyond the aircraft itself—it’s rewriting the rules of how we design and build. For engineers, executives, and industry enthusiasts, this represents a pivotal moment where technology can restore the innovation and efficiency that originally drove the aerospace industry’s greatest achievements. The question isn’t whether AI will transform aerospace design, but how quickly and effectively organizations will embrace this transformation to maintain their competitive edge in an increasingly dynamic global market.
