实现增强工程：人工智能和数字连续性在航空航天和国防转型中的作用

The role of AI and digital continuity inaerospace and defense transformation Make it real.Make it real. Table of contents Who should readthis report and why? This report is intended for C-suite and seniorleaders at aerospace and defense (A&D)organizations, including OEMs, suppliers, andMRO providers. It offers practical insights to helpleaders move from fragmented digital continuityand AI initiatives to an integrated, scalableaugmented engineering operating model. Giventhe convergence of AI and lifecycle data acrossengineering workflows, the report is particularlyrelevant to CEOs, CTOs, CIOs, CDOs, and ChiefAI Officers/Heads of AI, as well as leaders drivingengineering, data, and transformation agendas. and system-level performance. In addition, programleaders and functional heads across engineering,manufacturing, and operations will find it usefulin understanding how AI-enabled workflows andconnected systems can improve time-to-market,quality, compliance, and lifecycle outcomes. This report is based on original findings from aglobal survey of 200 A&D organizations, with tworespondents from each organization representingdigital continuity and AI-enabled engineeringfunctions, conducted in April–May 2026. Respondentsspan multiple geographies and industry segments,including OEMs, suppliers, operators, and MROproviders. The research is complemented byinterviews with 10 industry experts, offeringadditional insights on AI adoption, engineeringtransformation, governance, and workforce readiness. It is equally valuable for AI, data, and digitalleaders seeking to scale AI beyond pilots andembed it into core engineering workflows, as wellas for engineering and digital engineering leadersresponsible for lifecycle integration, digital thread, Executive summary outputs, managing trade-offs, and exercisingaccountable decision-making. Productivitygains are increasingly being redirected towardR&D, advanced engineering, and innovation,positioning AI as a growth and competitivenesslever rather than solely a cost-reduction tool. Augmented engineering isemerging as a key competitivefocus for AI-driven transformation In response, augmented engineering is emergingas a new source of competitive advantage.Organizations increasingly recognize thatengineering transformation depends on twomutually reinforcing capabilities: digital continuityand AI-enabled engineering. Digital continuityprovides the trusted lifecycle context needed toconnect requirements, design, manufacturing,certification, suppliers, and sustainment, whileAI helps convert connected data into fasterand better engineering decisions. Together,they form the foundation of a more integratedengineering system. A&D engineering is entering a new competitiveera. Geopolitical uncertainty, supply-chainfragility, cybersecurity risks, talent shortages,and increasingly software-defined products areplacing unprecedented pressure on engineeringorganizations. The readiness gap is significant:81% of organizations cite talent and skillshortages as a major threat, yet only 46% feelwell prepared to address it. The consequencesare strategic as well as operational, with 65%of organizations concerned about losing futureprogram positions, workshare, or access to topengineering talent if they fail to adapt quickly. 46% The convergence of these capabilities is alsoreshaping engineering work. Engineers arespending less time on routine activities and moretime defining intent, validating AI-generated Percentage of organizations that arewell prepared to address talent andskills shortages. Executive summary Digital continuity remains thefoundational bottleneck to scalingAI-enabled engineering AI adoption is accelerating, but enterprise-scale transformation remains early engineering processes. Supplier ecosystems alsoremain heavily document-driven, limiting visibility,configuration control, and lifecycle learning. AI adoption is gaining momentum across A&D, butlarge-scale transformation remains at an early stage.While organizations are increasingly deploying AIacross engineering activities, only 17% report workfloworchestration or end-to-end integration. These shortcomings explain why manyorganizations can demonstrate digital and AIsuccesses within individual functions yet struggleto scale them across programs, platforms, andpartner networks. As organizations strengthenthese foundations, digital continuity isincreasingly becoming a prerequisite for scalingAI beyond isolated deployments. Despite its importance, digital continuityremains the industry’s most significant structuralconstraint. Only 6% of organizations haveachieved end-to-end digital continuity supportedby a connected enterprise-wide architecture and asingle source of truth. AI scales fastest in contained workflows where data isstructured, outcomes are measurable, and validationis straightforward, such as predictive maintenance,simulation interpretation, production planning, andfault diagnosis. More complex use case