At iBase-t’s Excelerate conference in March, I posed a question to the audience that I think gets to the heart of where aerospace and defense (A&D) manufacturing is headed: What does it actually mean to be AI-native? Not AI-enabled or assisted. AI native. Whether A&D manufacturers recognize this fundamental difference and act on it will determine whether they lead the next era of operations or spend the next several years catching up.
AI Adoption: What We Can Learn from Cloud Technology
When cloud technology arrived in the 2010s, most organizations made one of two choices. They either treated it as a better place to host their servers, or they recognized that the cloud wasn’t just a different location for the same thinking and adopted it as a new operating model that changed how software was created, maintained, and operated. In other words, the shift wasn’t about where software ran. It was about how it was built, deployed, and continuously improved.
That shift in thinking led to the creation of Development Operations (DevOps) teams—a model in which developers could push changes, see immediate impact on end users, receive real-time feedback, and iterate in hours rather than months. Pre-cloud, developers needed months to deliver a software update to an end user. Post-cloud, it happened in minutes. Customers stopped asking, “Can the system do this?” and started asking, “Why can’t it do it instantly?”
AI is now at the same inflection point. The difference is that AI adoption is accelerating much faster than cloud adoption did. Organizations are no longer satisfied with AI-enabled systems. And increasingly, they don’t just want answers from AI. They expect systems to act, coordinate, and adapt in real time. They expect and need their systems to be AI-native, even if they don’t always know what that means.
Bolted On vs. AI-Native Manufacturing: What’s Actually Different
Most AI in manufacturing today is bolted on and limited to chat-based queries, document summarization, and anomaly flagging. These capabilities make existing workflows faster and more intelligent, but they remain fundamentally transactional—you ask AI a question, and it answers. That’s just using AI as a software feature.
Being AI-native means AI is not a feature—it is part of the operational system itself. It operates within the same workflows, permissions, and governance model as the system of record. It doesn’t just answer questions but participates in execution, decision support, and coordination, all while maintaining full traceability and control.
There’s a simple litmus test to distinguish AI-enabled and AI-native systems: “Does the AI enforce the same permissions as the underlying system of record? If it can surface data the user isn’t authorized to see in the base system, it’s bolted on. If it can’t, it’s embedded and operates within the same governance model as the system.
This requires a new capability: governed reasoning. AI must not only understand data, but reason over it within the constraints of manufacturing workflows, permissions, and compliance requirements. Without this, AI remains an external tool – powerful but disconnected from the system that actually runs the operation.
What AI-Native A&D Manufacturing Looks Like in Practice
One comment I often hear from customers is that they don’t want their technicians spending time on the Solumina screen. They want them out on the shop floor, assembling and inspecting. They also don’t want to lose manufacturing time while waiting for a technician with the right certification or for a supplier to confirm a component shipment. AI-native operations reduce that idle time by providing a holistic view of manufacturing operations.
In an AI-native environment, the application comes to the user where they already are, through systems they already use, such as Microsoft Teams or Slack, via voice commands or text messages. The user doesn’t have to navigate screens or learn new interfaces. They simply have a conversation with the AI, which orchestrates behind the scenes. AI can act on behalf of the user, coordinating tasks, flagging risks, and cross-referencing data across systems, but only with human approval. Let’s look at an example of what that might look like in a typical A&D manufacturing scenario.
A supervisor arrives at 7 a.m. They don’t open a dispatch board or run a report. The system has been monitoring production schedules, work order status, and supply chain updates overnight. It already knows which work orders are at risk and why. AI proactively sent a morning alert to the supervisor’s Teams channel. When the supervisor asks why a particular order is behind schedule, AI explains that a certification gap, due to a documentation issue, is the cause and notes that a similar problem several weeks ago caused a three-day delay.
When the supervisor asks the AI who can address it, the system cross-references technicians’ availabilities, skills, and certifications and returns two names. The supervisor no longer spends hours navigating screens to find that data. Instead, he or she has a conversation with an AI-native system acting as an operational assistant. This level of analytics enables informed decision-making, helping the supervisor address the issue more quickly.
The Mindset Shift Manufacturers Can’t Afford to Skip
The manufacturers who got the most out of the cloud didn’t just move their servers. They changed how they thought about software, operations, and the relationship between systems and users. AI demands the same shift, but even faster.
AI shouldn’t be viewed as a software feature. Rather, it’s an operational companion that shapes how manufacturers make decisions, how teams get work done, and how quickly it all happens. We designed Solumina AI with this in mind—not as a layer added on top, but as intelligence embedded in the governance, traceability, and operational workflow required by A&D manufacturing.
The shift to AI-native manufacturing is not optional – it is the next operating model for the industry. The question isn’t whether AI will be part of your operations, but whether it will be embedded within them – or remain outside, limited in impact.
Learn more about iBase-t’s Solumina AI and our approach to AI-native manufacturing.
