If you walk the floor of an aviation trade show today, you will hear the same word repeated in nearly every conversation.

AI. And day at PBExpo was no different

It appears in panel discussions, vendor demonstrations, and hallway conversations between colleagues catching up over coffee. The word carries an unusual mixture of excitement and uncertainty. Some people talk about it as if the future has already arrived, while others approach it more cautiously, trying to understand whether it represents opportunity, risk, or something somewhere in between.

What quickly becomes clear, however, is that many of these conversations are not actually about the same thing.

For some, AI means automation. For others, it means predictive analytics or machine learning models that can recognize operational patterns. For many professionals encountering these tools for the first time, it simply refers to systems like ChatGPT that suddenly made advanced language models accessible to anyone with a browser.

Each of these interpretations reflects a different slice of the same technological shift, and together they reveal the moment aviation now finds itself navigating.

We are entering a period where technological capability is evolving faster than the industry is accustomed to absorbing it.

Historically, aviation has adopted new technology deliberately.

The industry has always prioritized safety, compliance, and reliability, which naturally encourages careful implementation rather than rapid experimentation. From digital inventory management to cloud-based logistics platforms, most technological transitions in aviation have unfolded over years rather than months, giving organizations time to adapt their workflows and train their teams.

Artificial intelligence is arriving under very different circumstances.

The capabilities are evolving quickly, sometimes improving dramatically from one quarter to the next. What began as specialized tools used primarily by software engineers and data scientists has now entered everyday professional workflows. Distribution companies, MROs, and manufacturers are suddenly able to interact directly with systems capable of analyzing documents, summarizing complex information, and generating insights from large datasets.

This shift has created a fascinating moment where curiosity and caution coexist.

Many aviation professionals are eager to explore what these tools can do, while others remain understandably careful about introducing unfamiliar technology into environments governed by strict regulatory frameworks.

Both instincts are reasonable.

The challenge lies in bridging the gap between experimentation and understanding.

One of the most revealing examples of this gap appears when companies begin experimenting with public AI tools using internal operational data.

It is increasingly common to hear stories of professionals uploading financial reports, maintenance records, or operational documents into generative AI platforms to obtain summaries or analytical insights. The motivation behind these experiments is entirely logical; people are simply trying to discover how modern tools might help them better understand their businesses.

However, in industries governed by regulatory frameworks such as NIST or CMMC, those experiments can quickly become problematic.

The distinction between open AI environments and closed-loop AI systems suddenly becomes critically important.

Open systems may store or process submitted information outside the organization’s direct control, potentially exposing proprietary or regulated data. Closed-loop AI environments, by contrast, operate strictly within the organization’s own infrastructure, ensuring that sensitive information remains protected and compliant with regulatory standards.

For companies working in defense supply chains or highly regulated aerospace operations, this distinction is not merely technical.

It determines whether experimentation with new technology remains responsible or unintentionally creates compliance risks.

What this situation highlights is not a failure of technology, but a gap in education.

The aviation industry contains an extraordinary amount of operational expertise, yet artificial intelligence introduces an entirely new vocabulary that many professionals have not yet had the opportunity to learn.

As the conversation around AI continues to expand, one principle is becoming increasingly clear.

Artificial intelligence cannot operate effectively without a strong architectural foundation.

AI does not exist in isolation. It sits on top of the systems that generate and organize operational data. If those underlying systems consist of fragmented spreadsheets, disconnected databases, and workflows dependent on email chains, then even the most advanced AI models will struggle to produce reliable insights.

In contrast, when an organization operates on structured transactional systems—where quoting, sourcing, inventory management, compliance documentation, and financial activity are integrated within a unified architecture—the possibilities become significantly more powerful.

In those environments, AI becomes a layer of intelligence applied to an already coherent operational structure.

The system can analyze patterns across transactions, identify anomalies in real time, and assist decision-makers in ways that extend far beyond simple automation. Instead of merely recording historical activity, the system begins to guide operational strategy.

But that transformation only occurs when the architecture underneath it is properly designed.

Without that foundation, AI risks amplifying chaos rather than delivering clarity.

This reality is quietly shaping the future of aviation technology adoption.

Companies that treat artificial intelligence as a feature to bolt onto legacy systems may discover that the results fall short of expectations. The models themselves may be impressive, but without structured data and integrated workflows, their outputs remain limited.

Meanwhile, organizations that have invested in coherent operational architectures—systems where transactions flow naturally between departments and records are consistently structured—are discovering that AI can enhance nearly every layer of decision-making.

The difference between these two scenarios has little to do with the sophistication of the models themselves.

It has everything to do with the structure of the systems beneath them.

Architecture, not algorithms, will determine which organizations benefit most from artificial intelligence in the years ahead.

Artificial intelligence is likely to remain the headline topic at conferences and industry events for the foreseeable future. The curiosity surrounding these tools is genuine, and the potential for improving operational efficiency across aviation supply chains is substantial.

Yet the most productive conversation may not be about which AI model an organization chooses to deploy.

The more important question is whether the systems underneath that model are capable of supporting intelligent analysis in the first place.

Aviation has always evolved through technological transformation, and the current moment represents another chapter in that long history. Each wave of innovation has required organizations to rethink how their operations are structured, and artificial intelligence will be no exception.

The companies that approach this transition thoughtfully—focusing first on the architecture that organizes their workflows and data—will likely find themselves best positioned as intelligent systems continue to mature.

AI may capture the headlines today.

But in the long run, the organizations that succeed will not necessarily be those with the most advanced models.

They will be the ones whose systems were designed to support intelligence from the very beginning.

PS. Thank you again to PartsBase Inc., PBExpo, Robert Hammond for the vision, and the many people that made this day happen including Rebecca C. Longo, and my fellow panelists @Brandon Watson, Lonni Kieffer, Libby Holder, and Jary Carter, all for their role in shaping the future and their insights from earlier today.