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AI Applications in Aerospace Discussed at AIAA AVIATION Forum 2026

AI Applications in Aerospace Discussed at AIAA AVIATION Forum 2026
Evolving Perspectives on AI in Aerospace
At the recent AIAA AVIATION Forum 2026, Gregory Roth, the incoming chair of the American Institute of Aeronautics and Astronautics (AIAA) Artificial Intelligence in Aerospace Applications Working Group and an aerospace engineer at the Air Force Research Laboratory in Ohio, expressed a cautiously optimistic view on the evolving role of artificial intelligence in aerospace. Once regarded as a niche technology, AI has now become a central focus within the industry, with its definitions and applications continuing to develop. Roth highlighted that while AI terminology and methodologies are rapidly changing, many of the fundamental challenges and solutions remain consistent with those encountered in earlier decades. He remarked, “As old ideas and concepts get leveraged and interact with new perspectives and roles, there may also be new problems. The terms are new, but the fundamental challenges and solutions may not be new.”
The history of AI in aerospace dates back to the 1950s, and today the term encompasses a wide array of technologies, including neural networks and large language models (LLMs). However, the rapid expansion of AI tools has led to some conflation with digital engineering, complicating the discourse around its practical applications.
Divergent Attitudes and Practical Challenges
A prominent theme at the forum was the contrasting attitudes toward AI among aerospace engineers. Seasoned professionals often approach AI with skepticism, questioning whether it represents genuinely novel capabilities or merely a rebranding of existing techniques. Conversely, newer entrants to the field tend to exhibit greater enthusiasm and openness toward AI’s potential. Roth underscored the challenge posed by the sheer volume of AI tools and resources now available, noting that one of the most significant hurdles is guiding users toward appropriate technologies and providing sufficient training to foster confidence and competence.
Roth also praised Lockheed Martin’s Renee Pasman for her pragmatic approach during her plenary presentation, “AI: Hype, Reality and Readiness in Aerospace.” Pasman framed AI fundamentally as a form of software, emphasizing its deterministic nature despite the stochastic approximations employed by popular AI models such as LLMs. Roth elaborated, “Software is a recipe and instantiates a model. It is deterministic, not stochastic, even though stochastic processes are approximated. Popular AI use today, such as LLMs, lean on stochastic approximations, but in safety-critical environments, that won’t fly—literally.” This distinction is critical in aerospace, where risk assessment and reliability are paramount.
Balancing Innovation, Risk, and Cost
The forum also addressed broader industry challenges associated with AI implementation. The integration of AI in aerospace demands extensive data and substantial computational resources, which in turn drive up costs. Market responses have mirrored these complexities, with some sectors experiencing sell-offs amid concerns over AI-related expenditures and the pace of tangible adoption. While competitors are increasing investments in AI to maintain technological advantages, this escalation can lead to higher operational costs and uncertainty regarding long-term returns.
Roth cautioned against treating AI as a universal solution, emphasizing that not all decisions carry equal weight and that indiscriminate application of AI could introduce unacceptable risks. Clear articulation of the scale and stakes involved in each decision is essential to determine the appropriate role for AI technologies.
As the aerospace industry continues to integrate AI, the focus remains on balancing innovation with safety, cost-effectiveness, and practical implementation. The goal is to ensure that AI serves as a valuable tool that enhances aerospace capabilities without becoming a source of new risk.

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