AAM deployment enablers work together as a cycle: streamlining resources and compliance helps operations run smoothly, while strong collaboration and infrastructure give teams what they need to perform reliably. Data and feedback loops drive improvement, and the sharing of clear documentation about what works lets everyone, from start-ups to operators, learn and scale more quickly. That way, lessons from successful pilots become standard tools and checklists everyone can use, turning isolated wins into repeatable, trusted industry practices. The Saudi Data and Artificial Intelligence Authority’s (SDAIA) AI registry offers a model for how shared documentation and standards can support AAM. By maintaining centralized and trusted records, such as approved mission profiles and checklists, SDAIA’s approach could help ensure that best practices and compliance requirements are clearly defined and accessible. Linking the six enablers to such a system means that lessons, tools and standards from successful AAM operations are not isolated; they are brought together and reused across teams, promoting consistent, safe and scalable growth in the industry. The SDAIA’s AI registry is a national platform managed by the Saudi Data and Artificial Intelligence Authority to register AI systems developed, deployed or used in Saudi Arabia. It maintains an inventory of approved solutions, ensures compliance with national AI ethics and data protection requirements and provides a governance mechanism that encourages transparency and trust. While designed for AI, its registry model could be adapted for AAM to maintain a centralized record of, for example, approved mission profiles, checklists and operational templates. 2.3 Spearheading learning: AAM community-led collaboration model Lessons from peer-led collaborations across sectors BOX 2 The AAM network is not alone in facing fragmentation and scaling hurdles. Lessons from other industries highlight how peer-led coordination can accelerate maturity. Example 1: Drone industry – Global Unmanned Traffic Management Association (GUTMA) –Challenge: Competing manufacturers and service providers were building proprietary interfaces, threatening interoperability and slowing UTM adoption –Approach: Created a neutral consortium that facilitates working groups and task forces to co- develop standards and operational roadmaps –Impact: Enabled cross-provider data exchange, allowed small and medium-sized enterprises (SMEs) to integrate with national UTM trials and established a foundation for global standards Example 2: Offshore wind – Offshore Renewable Energy (ORE) Catapult –Challenge: Subsea cable failures were common and costly, but developers were reluctant to share data due to competitive sensitivities –Approach: Facilitated anonymized, aggregated data-sharing on failures through a trusted innovation centre –Impact: Provided sector-wide benchmarking, reduced repeat failures and accelerated learning curves for new entrants, lowering operational risk across the industryWhile deployment enablers play a crucial role, Saudi Arabia’s emerging AAM network faces several persistent challenges, including stakeholder fragmentation, underused capacity, limited visibility for smaller, innovative players and isolated operational insights in individual projects. These barriers delay network readiness and limit the potential for AAM to expand into new use cases and markets. One way to identify and address some of these challenges is by creating synergy among network players by sharing precompetitive learnings that can support the industry as a whole. Box 2 highlights some lessons from peer-led collaborations in other sectors. Overall benefits include: –Reduces fragmentation across stakeholders –Reduces duplication and unlocks underused resources –Increases trust, transparency and market access –Accelerates growth of the entire network Deployment Pathways for Advanced Air Mobility: Lessons from Early Implementation in Saudi Arabia 15

Deployment Pathways Advanced Air Mobility 2025