AI in Aerospace and Defense Engineering: From Grassroots Adoption to a Governed AI Strategy

To gain deeper insights into the key trends shaping aerospace and defense, Molex surveyed 1,021 engineering stakeholders. The survey results revealed a significant gap in AI adoption:  engineers are rapidly embracing AI tools at a grassroots level, outpacing formal corporate governance. While 98% of stakeholders believe AI will be beneficial, 79% of engineers at companies without approved tools are using AI despite lacking official endorsement. 

This disconnect represents a strategic challenge arising from two key priorities: engineers’ focus on productivity and leadership's responsibility to manage risk. Success in this new era depends on creating a unified strategy that bridges this gap and unlocks AI's full potential.

The adoption gap reflects two distinct professional priorities. While engineers use AI for immediate productivity gains, leaders focus on mitigating long-term strategic risks. This presents a significant challenge, highlighted by the 81% of stakeholders who express concerns about using AI.

The Engineer's View: A Quest for Productivity
The data shows that 54% of engineers already use AI tools frequently and indicate that they tailor their AI use based on the task. Publicly available tools accelerate routine work such as code development, while approved CAD software with integrated AI capabilities is used for more complex tasks like signal integrity analysis.

Leadership Perspective: Managing Risks in AI Adoption
From a leadership standpoint, the rapid and unregulated adoption of AI presents significant risks. Survey data supports this cautious approach, showing that hands-on experience with AI tools reveals their challenges. Notably, 43% of engineers who frequently use AI report having significant concerns, more than double the rate of occasional users. 

Industry experts highlight intellectual property (IP) control as a primary concern, emphasizing the need to prevent proprietary designs from being exposed to public AI models. Another key risk involves the accuracy of AI-generated results, which may fall short of requirements for mission-critical designs. Additionally, unresolved questions around IP ownership for AI-assisted designs pose important legal considerations. 

Addressing these challenges will be critical to unlocking AI’s full potential in mission-critical applications.

Creating a governed and trusted environment where engineers can innovate safely is essential to bridging the gap between engineers and leadership. The survey data illustrates a clear correlation between formal tool approval and higher usage rates. European companies are leading in formalizing AI tool approvals, with Germany at 89% and France at 87%, compared to 72% in the US. This structured approach proves effective: French engineers report the highest rate of frequent AI use at 66%, compared to 52% in the US. 

These findings suggest that a formal approval framework supports innovation by providing clear guidelines. The data also indicates that experience with AI increases awareness of potential risks: 43% of design engineers who use AI frequently report significant concerns, compared to 17% of occasional users. A successful governance strategy, therefore, combines approved tools with a reliable infrastructure that directly addresses design engineers’ expert-level concerns, enabling innovation while managing risk effectively.

The challenges of AI in aerospace and defense extend beyond design processes. While closing the adoption gap remains an important goal, the ultimate measure of success is creating powerful, AI-driven systems. This requires shifting focus from the tools engineers use to the significant physical demands that AI-enabled applications place on their underlying hardware.

A successful, governed AI strategy must be built on a trusted hardware foundation, a fact underscored by the survey data. Increasing power requirements (52%) and mounting security demands (48%) are now the top two challenges for engineers. Experts confirm AI intensifies these pressures by requiring more compute resources, which increases power consumption and creates thermal management challenges. Additionally, 44% of teams plan to invest in high-speed data transfer capabilities, making signal integrity a critical concern.

This increased system criticality often necessitates greater system redundancy, introducing new engineering constraints related to size and weight pressures, as cited by 30% of engineers. Using the “Swiss Cheese Model” analogy, mission-critical systems require multiple layers of defense. One vital layer is the interconnect itself, which must include built-in redundancy, such as multi-point contact designs.

Molex and AirBorn, a Molex company, provide the trusted hardware foundation for a successful enterprise AI strategy, offering a portfolio of solutions engineered to meet the specific demands of AI-driven systems. These solutions include components purpose-built to withstand high vibration, support intense bandwidth requirements and meet strict size and weight constraints.  

Solving for High Vibration and Redundancy
For mission-critical AI systems that must perform reliably in high-vibration environments, the SInergy family of connectors delivers a powerful combination of high data rates and redundant design features. Its proven multi-point-of-contact design maintains stable connections, preserving signal continuity even under intense shock and vibration.

Meeting High-Bandwidth Demands
To address the intense bandwidth demands of AI processing over long distances, Active Optical Cables (AOCs) provide superior signal integrity for error-free data transmission. By converting electrical signals to optical and back, AOCs reduce signal degradation and EMI, which can compromise high-speed data streams in dense systems.

Addressing Size and Weight Challenges
To meet size and weight constraints introduced by increased system redundancy, Flex Circuit Assemblies offer a proven, miniaturized alternative to traditional cable assemblies. Their three-dimensional routing capabilities allow engineers to optimize space and reduce system weight without sacrificing performance.

The survey data and expert insights point to a clear path forward for companies addressing the AI adoption gap. A key takeaway from the commercial sector is the rapid progress made in resolving core IP protection challenges, despite having different risk profiles.

Molex believes success in this new era depends on a dual focus: adopting powerful AI tools while grounding those systems in a foundation of trusted, reliable hardware.

To get the full data-driven picture and explore all the trends shaping the future of aerospace and defense, download the complete survey report, The State of Design Engineering in Aerospace and Defense.