The 2026 Gartner® Market Guide for MES states “The potential of AI in MES is high in how it can remove barriers to MES adoption such as complex configurations and poor operator experience, but the direction and ROI are unclear.” That caution, paired with limited proven use cases, has created an adoption barrier that is proving difficult to overcome. Even so, Aerospace and Defense (A&D) manufacturers are forging forward to adopt AI for faster, more efficient output.
While every industry operates under its own unique parameters for privacy and discretion, Aerospace and Defense (A&D) manufacturers have to navigate heightened mandates for strict regulatory compliance and impenetrable data security because a single mistake can escalate into serious consequences like a quality escape or security breach.
For any Manufacturing Execution System (MES) software to serve A&D, data sovereignty and validation are mandatory capabilities to avoid critical lapses in operational security. Guardrails like in-environment processing and operational visibility cannot be treated as simply optional functionalities.
Why A&D Raises the Stakes
As AI adoption becomes more commonplace, it’s critical for A&D manufacturers to recognize that GenAI alone cannot address the stringent regulatory, security, and operational requirements that define their industry.
While AI can accelerate decision-making and improve productivity, it depends on accurate, governed, and context-rich manufacturing data. That foundation comes from the MES. However, not all MES platforms are designed to meet the unique demands of highly regulated industries. Many are built as horizontal solutions intended to serve a broad range of manufacturers, while others are purpose-built for specific industries and use cases.
“Gartner predicts over 50% of GenAI models used by enterprises will be industry or function specific by 2027. Larger MES vendors will distinguish themselves in the AI race by offering better insights with their own manufacturing-specific models.”
For A&D manufacturers, an appropriately configured MES should provide capabilities such as data sovereignty, configuration control, and end-to-end manufacturing traceability—creating the trusted operational foundation that enables both regulatory compliance and the effective use of AI to improve agility and production outcomes. A horizontal model trained for broad business use isn’t qualified to manage the nuances of complex manufacturing or the auditing requirements of a certified process. When A&D manufacturers rush in without accounting for that gap, their initiatives stall before delivering value — and in an industry where profitability tracks directly to operational efficiency, an undertrained, misconfigured model becomes a costly mistake.
The Market Is Moving Toward Purpose-Built
Hesitation to deploy AI in A&D doesn’t stem from an inherent distrust of the technology. It’s an indication of the tremendous value AI needs to offer to belong in a regulated environment — and the broader market is already moving in that direction. This signals a decisive shift away from general-purpose AI toward models trained on relevant data and tuned to the workflows of a particular domain.
The demands that make general-purpose AI unworkable in A&D — engineering-grade traceability, configuration control, the rhythms of complex discrete production — are precisely the conditions that purpose-built, vertical AI is designed to meet. A model architected around PLM, ERP, and MES processes does not treat compliance and context as obstacles to work around; it treats them as the conditions of the operating environment.
AI delivers value only when it’s trained on a strong, domain-specific system of record; layered over weak or disconnected workflows; it just amplifies the flaws faster. The execution platform sets the ceiling for what AI can contribute. For A&D, then, purpose-built design isn’t an advantage to evaluate down the road — it’s the precondition that dictates the durability of everything built on top of it.
The Three Pillars of Trustworthy MES AI
If purpose-built design determines whether an AI model belongs on an A&D shop floor, governance determines whether it can be trusted once it’s there.
Data integrity- AI is only as reliable as the data it’s structured upon. A model reasoning over disconnected or unvalidated documentation can hallucinate, cause leaks, or fall out of compliance. By anchoring AI to a validated, authoritative system of record, A&D manufacturers safeguard both their data and their processes.
Secure, flexible deployment– AI tooling should be able to run inside the customer’s own environment rather than depending on public cloud or shared hosting, through private-tenant or in-environment options. As internal AI strategies mature, that flexibility extends to bring-your-own-model approaches and customer-accessible integration layers, so the AI conforms to the organization’s security protocols instead of forcing a deviation.
Observability & explainability — Without transparency around AI output, the people held accountable for production — operators, inspectors, engineers — have no reason to trust its work, and adoption stalls on the shop floor. Clear avenues for monitoring, workflow transparency, and configurable guardrails turn AI from a black box into an auditable participant in a certified process. Human-in-the-loop validation is essential, and it depends entirely on the system being explainable.
Trust Is the Foundation of Execution Excellence
If you are just starting your modernization journey, chasing AI before fixing operations will backfire. Without the right foundation initiatives stall, underdeliver, or demand so much human intervention they stop being AI at all.
“While new customers can immediately leverage AI-enabled features, those operating on legacy platforms must recognize that upgrading is a necessary step to even evaluate or implement AI capabilities. These capabilities must align with the client’s enterprise AI strategy. In this context, AI can serve as a catalyst for building a stronger business case for modernization, even if its full value remains to be demonstrated,” concludes Gartner.
For A&D manufacturers contemplating AI deployment, the goal isn’t to choose between human expertise and machine speed—it’s finding a model that is capable of adhering to the strict regulations that govern their production. The manufacturers who understand this differentiation will be the ones who treat their execution platform as the thing that makes AI possible, not the thing AI replaces — anchoring every insight, every recommendation, every automated step to a system of record that already knows the rules.
That is the model Solumina AI was built around: intelligence that lives inside the authoritative platform rather than beside it. The result is AI that earns its place on the shop floor — auditable, accountable, and accurate enough to act on.
In aerospace and defense, the companies that lead the next decade won’t be the ones that adopted AI fastest. They’ll be the ones that adopted it without ever loosening their grip on security, traceability, and trust.
See what governed AI looks like in your operation. Schedule a call with the iBase-t team.
Gartner®, Market Guide for Manufacturing Execution Systems, 2026, by Jake Cunningham, Christian Hestermann, 23 March 2026
Gartner does not endorse any vendor, product or service depicted in its research publications and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose. Gartner is a trademark of Gartner, Inc. and/or its affiliates.
