Introduction

For most of modern product development, the bill of materials has served as the backbone of how teams describe a product. It lists what the product is—its components, subassemblies, materials, quantities, and relationships. The BOM is the universal language between engineering, manufacturing, procurement, and service teams. But as reliable as it is, the BOM has always missed something important. It tells you what exists, but it rarely captures how it behaves. It doesn’t explain why certain structures matter, how features work together, or how variations affect assembly and performance. It doesn’t remember the lessons learned across generations of products.

AI is now pushing the industry toward something more dynamic. Instead of thinking purely in terms of structures and hierarchies, organizations are beginning to think in terms of behaviors—how a mechanism responds to variation, how assemblies adapt under load, how constraints propagate, and how real-world data shapes the product over time. The shift is slow, but unmistakable. Product structures in the AI era will be less about lists and more about living systems.

The Limits of Traditional BOM Thinking

A BOM works beautifully when products are stable, predictable, and built through well-understood processes. But as soon as variability enters the picture—whether through material changes, supplier shifts, or complex mechanisms—the BOM quickly reaches its limits. It cannot describe how a latch deforms under heat, how two components bind under tolerance drift, or why a small clearance dimension is more important than it looks.

This is the same structural blind spot that predictive CAD tries to address. A BOM cannot represent intent, behavior, or risk. It cannot tell a new engineer why a particular dimension is driving a mechanism. It cannot help a supplier understand why a seemingly minor feature exists. And because it cannot evolve with real-world feedback, organizations often treat the BOM as a static artifact rather than a source of insight.

AI Reframes Product Structure as a Behavioral System

AI changes the equation by learning from patterns that don’t live inside the BOM. It can observe which assemblies tend to fail, which features influence variation, which materials behave inconsistently across suppliers, and which constraint networks keep designs resilient.

Instead of storing this knowledge in people’s heads, AI can embed it directly into the product structure. A gear train is no longer just a list of parts—it becomes a behavior: how torque transfers, how backlash accumulates, how wear evolves. A hinge is no longer two components plus hardware—it becomes a motion profile shaped by friction, tolerance stack-ups, and environmental effects.

This is where behavioral modeling becomes relevant. AI isn’t replacing BOMs; it’s layering behavior on top of them, turning product structures into systems that understand themselves.

Product Structures Will Evolve Through Continuous Feedback

In a world driven by digital twins and cloud-native CAD, product structures stop being snapshots. They become evolving entities informed by production data. Every manufacturing batch, every assembly deviation, every warranty claim becomes part of the product’s extended memory.

When AI processes this feedback, product structures begin to shift. A tolerance that once looked safe becomes flagged because the factory shows rising variation. A material that used to perform well gets deprioritized because field data shows fatigue under new usage conditions. A fastening sequence becomes updated because real production teams discovered a more reliable order.

It is the same rhythm we see in organizational memory systems: products are no longer fixed definitions. They’re organisms learning from their environment.

Behavior-Centric Structures Improve Cross-Discipline Communication

One of the biggest challenges in product development is that every department interprets the product differently. Engineering sees constraints and features. Manufacturing sees processes and costs. Supply chain sees risk and availability. Service teams see failure modes.

A behavior-informed product structure gives everyone the same lens. It shows not only what the product is, but how it behaves when real-world forces act upon it. Engineers understand how assembly challenges influence design revisions. Manufacturing teams see why certain tolerances cannot be loosened. Suppliers understand which constraints protect functionality rather than aesthetics.

It mirrors the rise of collaborative intelligence—multiple disciplines aligning around a shared understanding, not around separate interpretations.

AI Will Treat Design Intent as Part of the Product Itself

Product intent has always been fragile. It lives in conversations, half-documented decisions, comments in CAD, and the memories of senior engineers. BOMs never captured it. Traditional CAD recorded hints of it, but not enough for future teams to navigate confidently.

AI introduces the ability to extract that intent. It can recognize which dimensions drive function, which features are sensitive, and which constraints exist to protect downstream behavior. Over time, this intent becomes part of the product structure—alongside components, materials, and operations.

This changes how teams evolve products. Instead of rediscovering intent, they inherit it.

Zixel Insight

At Zixel, we believe the future of product structures lies in merging physical hierarchy with behavioral intelligence. The BOM will continue to matter—it is still the backbone of production—but it will no longer be enough on its own. Products need representation that understands variation, intent, assembly logic, and real-world performance.

Our goal is to build CAD environments where these behaviors are not afterthoughts. We want product structures that teach teams how their designs function under uncertainty. Systems that adapt based on feedback. Models that carry memory across generations. When behavior becomes part of the product’s definition, teams design with more foresight, fewer surprises, and far more alignment.

Why This Evolution Matters for the Next Generation of Products

As AI becomes entrenched in engineering workflows, products will no longer be defined solely by their components. They will be defined by how they act, how they adapt, and how they improve through data. Teams that design with behavioral understanding will build more resilient mechanisms, more manufacturable assemblies, and more predictable production pipelines.

This shift isn’t just about smarter software—it’s about a deeper understanding of what a product truly is.