The Value of Industrial Design in the Age of AI

Beyond Process Automation

When discussing AI in engineering, attention often focuses on its ability to generate content, automate tasks, or rapidly produce new solutions. The applications emerging in industrial environments reveal a different—and in some respects more interesting—picture, however.

The most tangible technical use cases are not about replacing the designer, but about supporting the stages where the volume of information, variables, and alternatives makes it difficult to identify the most effective path forward. In this context, artificial intelligence is proving particularly valuable as a tool for managing complexity.

A significant example comes from the field of advanced materials. In recent years, systems capable of identifying hundreds of thousands of theoretically stable materials through artificial intelligence techniques have been developed. Such a vast pool of candidates undoubtedly represents an opportunity, but it also introduces a new challenge: transforming a large number of theoretical possibilities into a focused set of options that are genuinely relevant for a specific industrial application.

It is precisely at this stage that AI-based tools are delivering some of their most concrete results. Their ability to analyze large volumes of data and identify meaningful correlations makes it possible to narrow down the number of alternatives requiring further investigation, allowing design and validation efforts to concentrate on solutions with the highest likelihood of success.

The Complexity of Industrial Design

This logic is particularly relevant in the day-to-day practice of industrial design. In most projects, the challenge is not a lack of possible solutions. More often than not, several configurations are developed before converging on a final one. Each alternative requires evaluation, comparison, verification, and simulation. Required performance, costs, component availability, assembly methods, maintenance requirements, future system developments, and many other factors come into play—factors that can rarely be captured through simple, universal rules.

The quality of a design solution depends on the ability to bring all these elements together and interpret them within the specific context in which the machine or system will operate. For this reason, artificial intelligence fits naturally into the decision-support process. It can help classify information, identify recurring configurations, highlight incompatibilities, and suggest avenues worth exploring. The final decision, however, still requires a contextual understanding that goes beyond simply processing available data.

The distinction becomes clear when examining a CAD model. Behind what appears to be a simple geometry lie considerations related to manufacturability, achievable tolerances, ease of assembly, potential future modifications, and real operating conditions. Every decision incorporates the experience, constraints, and objectives that have taken shape throughout the project. Industrial design, therefore, is not merely about defining a technically correct solution; it is about building a solution that remains effective once it encounters the realities of production.

Simulation, Digital Twins, and Technical Context

A similar reflection applies to the integration of AI in engineering with simulation and digital twins. The most advanced platforms are introducing these technologies across the entire product lifecycle, with the goal of improving analysis quality and accelerating development activities.

Even here, the most interesting contribution is not simply a matter of processing speed. Determining which scenarios to analyze, which parameters are truly relevant, and which results warrant further investigation requires a deep understanding of the system being studied.

Simulation creates value when it accurately represents the real behavior of a machine or process. Achieving this requires setting appropriate boundary conditions, correctly interpreting constraints, and assessing the quality of the input data. These activities demand specific expertise and a direct understanding of the application context.

Artificial intelligence is expanding the range of available possibilities, but we are still far from tools capable of independently grasping all the variables that characterize an industrial project. Specialized systems and models trained on specific technical domains are progressively narrowing the gap with general-purpose tools, opening up promising opportunities—particularly in the early stages of analysis and configuration.

Reducing Errors Before They Propagate

Perhaps the most significant aspect concerns the impact that an early decision can have on the entire development trajectory of a project. Many of the costs generated during design do not stem from the error itself, but from its propagation. An incomplete assessment in the preliminary stages can affect component sizing, drive selection, control system configuration, and procurement activities. When an inconsistency surfaces later in the process, correcting it often means revisiting a chain of decisions that has already become entrenched.

The ability to flag inconsistent configurations, overlooked constraints, or fragile alternatives at an early stage is therefore one of the most valuable contributions AI can offer to contemporary engineering. Its capacity to rapidly analyze large datasets and compare different scenarios makes it possible to accelerate many preliminary activities and support decision-making with information that would be difficult to process manually within the same timeframe.

It is precisely here, however, that the difference between an advanced tool and genuine design experience becomes apparent. A designer does not evaluate a solution solely on the basis of available data. They also draw on what that data does not explicitly reveal: issues encountered in previous projects, compromises that proved critical during machine commissioning, modifications requested after months of operation, choices that looked correct on paper but proved fragile once exposed to real production conditions.

This ability to connect technical information, direct experience, and contextual knowledge remains one of the most difficult capabilities to replicate. It is built through an accumulation of successes, failures, attempts, and corrections that accompany every professional career and gradually solidify into engineering judgement.

For this reason, artificial intelligence delivers its greatest value when it works alongside the designer rather than replacing them. It can help reduce complexity, accelerate the analysis of alternatives, and support preliminary assessments—but the responsibility for interpreting a problem in its entirety still rests on the human capacity to understand context, anticipate the consequences of decisions, and recognize nuances that are not always contained within the available data.

AI-based tools will undoubtedly continue to reshape the way products and systems are developed. What is becoming increasingly clear, however, is that their most significant contribution is not replacing engineering expertise, but enabling it to focus on the decisions that require the greatest degree of experience, judgement, and systems-level thinking.