The future of defense aerospace is a connected battlespace , a unified system where sensors, aircraft, weapons, and commanders all 'see the same truth at the same time'. This vision is being powered by AI agents—software and autonomous systems—that can perceive, decide, and act across air, land, sea, space, and cyber as one.

We're already seeing impressive milestones, such as DARPA's ACE programme, which successfully conducted AI-controlled dogfights in real jets, proving that trusted air combat autonomy is possible at the tactical edge. Other programmes, like the USAF's Collaborative Combat Aircraft (CCA), are accelerating autonomy at scale by moving toward operational prototypes of teaming aircraft.

But despite this cutting-edge progress, there's a serious bottleneck that’s holding AI initiatives back.

• Rigid, Siloed Data: Traditional databases are often rigid, siloed, and optimized for structured data. Operational facts are split across multiple tools like PDM/PLM, flight ops, and maintenance logs. This makes it difficult to integrate and analyze the vast and disparate datasets—such as sensor data, flight records, and maintenance logs—that AI models need to succeed.

• A Lack of Scalability: Legacy systems often lack the scalability and interoperability needed for modern AI and cloud-era patterns, forcing them to run on outdated stacks with limited support for contemporary APIs.

• No Operational Context for Agents: AI agents need a single place to reason over relationships and commit actions. However, legacy databases are optimized for static records and cannot keep pace, meaning agents often lack live operational context and its history (memory and state).

• Fragmented Digital Threads: Achieving a true digital thread requires end-to-end traceability. It's nearly impossible to turn scattered data schemas into a consistent, queryable operational picture when data is locked in many silos.

This technical debt delays innovation, elevates cybersecurity risks, and prevents the industry from fully realizing AI's benefits in areas like flight optimization and predictive maintenance.

Unlocking AI's Full Potential with Smarter Data Foundations

The next leap for aerospace AI will not come from algorithms alone, but from building smarter, more adaptable data foundations. The aerospace sector needs to modernize its data layer to be more flexible, scalable, and AI-ready.

This is where the concept of a multi-model approach becomes essential. Aerospace operations involve a wide variety of data types, including:

• Telemetry (time-series)

• Maps and tracks (geospatial)

• Parts and maintenance (relational)

• Relationships between aircraft, subsystems, and missions (graph)

Modern multi-model databases are capable of keeping all of these data types together. This approach ensures that digital twins are live (not static files) and that digital threads are queryable end-to-end.

Key capabilities of this next-generation data foundation include:

• Auditable Memory: A multi-model store can persist a durable, versioned-over-time record of observations, decisions, and outcomes. This provides the explainable, auditable memory agents need for governance and adherence to strict rules like the US DoD Directive 3000.09.

• Edge-Readiness: In-the-field decision-making at ranges, forward bases, or contested comms demands local state with eventual reconciliation. New models support real-time queries and local operation on edge or air-gapped hardware, allowing units to act now and sync later. Centralized, cloud-only patterns simply create the fragility the industry is trying to remove.

• Real-Time Context: Multi-model databases offer real-time subscriptions so applications and agents see updates as they happen. They also provide change history and replay (CDC) to audit or sync downstream systems.

The Path Forward

Leadership in aerospace recognizes that reliable, transparent data is the foundation that enables AI, digital twins, and the next generation of intelligent operations. The ecosystem is ready to trust agents with operations like real-time MRO and connected supply chains.

The path forward isn't just about building smarter algorithms; it's about creating the right environment for AI to succeed. This requires a data layer that treats relationships, time, space, semantics, and change as first-class citizens.

By modernizing the data foundation, the aerospace industry can move beyond the bottlenecks of legacy infrastructure and fully realize the transformative potential of autonomy in the connected battlespace.