It all begins with design. How the future of product development will look like

It all begins with design. How the future of product development will look like


All start with a vision and then moves on to the design process, which is an integral part of creating objects that accompany us in our daily lives. In today’s times, thanks to generative algorithms, designers can significantly accelerate and improve the process of generating diverse solutions. These algorithms enable the creation, analysis, and optimization of designs in a way that is challenging to achieve using traditional design methods.

By considering multiple variables, criteria, and constraints, generative algorithms allow for the rapid exploration of different scenarios. Intelligent algorithms not only contribute to creating better products but also have the potential to revolutionize the product development process.

The modern industry heavily relies on advanced technologies, and one of them is generative design. Leveraging advanced machine learning algorithms and neural networks is the direction we are heading towards to automatically generate new solutions in various industrial fields.

There is no doubt that generative design is a hot topic that attracts the attention of many professionals. It is one of the most cutting-edge technologies that truly indicates the future use of computers, computational power, and engineering. Essentially, it involves the computer designing something for you – an engineer just needs to input parameters into the software, and after some time, we see the generated design. Such designs look very organic, resembling something created by nature, and it’s not by chance.

Generative algorithms currently serve as an incredibly versatile tool that extends beyond the realm of construction and design. Their application spans various engineering areas, including fluid flow optimization, electrical design, optics, and even acoustics. Architects and urban planners use generative techniques to optimize building layouts and urban spaces, enabling the efficient consideration of diverse design criteria.

These same algorithms are also applied to solve complex optimization problems that go beyond traditional product design boundaries. With the ability to generate diverse solutions and consider multiple criteria simultaneously, generative algorithms become an excellent tool in today’s engineering environment.

The future of product development

Most tasks related to product development are complex optimization problems. Project teams approach them iteratively, refining initial best ideas during the engineering analysis. However, each iteration requires time and money, and teams can only go through a few iterations within the development schedule. Since teams rarely have the opportunity to explore alternative solutions significantly deviating from their baseline assumptions, the final design is often suboptimal.

Today’s advanced technology offers rapid digital simulations and analyses. Algorithms have the ability to automatically adjust part geometries between successive simulations, eliminating the need for manual corrections. This means designers can dynamically experiment with different shapes and configurations, achieving optimal results in a much shorter time than with traditional design methods.

By leveraging artificial intelligence techniques, modern generative design systems have the ability to explore a much broader range of possible solutions. For some types of engineering problems, generative algorithms already surpass human engineering teams. With the ability to consider multiple variables simultaneously, they can generate solutions that may be challenging to discover using traditional design processes.

Moreover, generative algorithms often have the capability to create non-intuitive, innovative solutions, going beyond conventional approaches. This opens new design perspectives and allows exploration of alternative concepts that can bring significant benefits in terms of efficiency, performance, and sustainable development. In this way, generative algorithms become an indispensable tool in the engineering process, supporting and complementing human creativity and design skills.

Currently, the most common application of generative design algorithms is structural optimization. These algorithms are used to create parts with sufficient strength, stiffness, and fatigue resistance while minimizing material consumption. With such an approach, designers can optimize the geometry and structure of elements, reducing material consumption while meeting all requirements for strength and functionality.

Such applications are widespread wherever weight is significant, such as in designing internal components of hand tools (to improve ergonomics), sports equipment (to increase performance), vehicles and aircraft (to reduce fuel consumption or increase payload), or any product for which mass is a significant cost factor.

This approach is crucial in the context of sustainable development, contributing to the reduction of raw material consumption and environmental impact.

Generative algorithms have a significant impact on various industries, ranging from automotive and aviation to the production of sports equipment. In these fields, they can reduce part costs by 6 to 20%, decrease the weight of components by 10 to 50%, and shorten the development time by 30 to 50%.

One example of the application of this technology in the aviation industry is its implementation at Airbus. By leveraging generative design, the company created a new version of the aircraft cabin bulkhead. The new design turned out to be not only lighter but also more durable than the previous one.

Initial higher financial investments in production were not a concern, as it quickly became apparent that the return could occur in a very short time. After precise calculations, it was found that reducing the mass of a specific part significantly lowered the consumption of aviation fuel, contributing to the economic efficiency of the project.

One of the key advantages of generative design in the automotive industry is its ability to optimize components for weight reduction. In this industry, designing lightweight car components using this technology combined with additive manufacturing can yield remarkable results. During the development of new vehicles, increasing customer demands for comfort and high safety standards often lead to an increase in the mass of individual car elements. To meet these challenges and the subsequent increase in fuel consumption, the product development process requires the implementation of complex and lightweight structures. Generative component design is akin to a potent remedy in this context.

This technology can also revolutionize the way automotive components are manufactured. By optimizing material usage and minimizing waste, generative design can lead to significant savings in the manufacturing process. Additionally, generative design aligns well with advanced manufacturing technologies such as additive manufacturing (3D printing), enabling the production of complex geometries that would be difficult or impossible to achieve using traditional manufacturing methods. The combination of generative design and additive manufacturing allows car manufacturers to produce lightweight, high-performance components more efficiently and cost-effectively than ever before.

The role of generative design in comprehensive product development

Generative design techniques have already proven that they can significantly enhance efficiency in real-world applications. However, their full potential will not be realized until companies apply these concepts on a large scale, making them an integral part of product development processes.

Purchasing the right software is only part of the solution. Engineers must know how to effectively use these new tools, fully understanding both their capabilities and limitations. Generative design methods can generate creative, non-intuitive solutions, but engineers still need to verify the results through tests or analyses, ensuring that the design can be implemented according to the intended process. This human-machine interaction will continue to evolve as algorithms become increasingly intelligent.

The question every engineer who is already familiar with this technology should ask themselves today is: Is this the beginning of the end for engineering as a human-driven creative process?

Generative design is not intended to replace designers but rather to augment their capabilities. However, looking 20-30 years into the future, hypothetically, it could mark the end of the job of a human design engineer. An AI-based program could potentially completely replace the work of a design engineering professional, and this might be the ultimate goal of this technology. For now, we are far from that scenario. There are many aspects to engineering beyond shaping, such as material selection and its application in accordance with regulations.

Certainly, it is currently a tool that we can have in our toolkit to aid in creating and developing new parts. Designers still play a crucial role in interpreting the generated results and incorporating their creativity and industry knowledge into the final project.

The challenges of adopting new technology

For product development leaders, generative design technology makes the design process easier and more convenient. However, there are several challenges associated with adopting this new technology.

  • Knowledge of technology: Effectively using generative tools requires engineers to understand both the technology and algorithms. Proper training and skill development within the team are necessary.
  • Integration with existing software: Implementing generative design systems may require integration with existing software and tools used by the company. Ensuring smooth collaboration between different systems is crucial.
  • Control of the design process: Introducing generative design may challenge traditional design processes. Adjusting procedures to effectively incorporate generative approaches is necessary.
  • Verification and validation: Despite the powerful capabilities of generative algorithms, verification and validation of results are necessary to ensure they meet specified qualitative and functional criteria.
  • Understanding technology limitations: Engineers need to be aware of the limitations of generative algorithms, including technical constraints and ethical issues such as responsibility for AI-generated outcomes.
  • Cultural and organizational acceptance: Introducing new technology may face resistance from employees and management. It’s essential for company leadership to understand the benefits and effectively manage the cultural and organizational acceptance process.
  • Costs and return on investment: Implementing generative design may incur costs, both related to acquiring suitable tools and training employees. Companies must carefully assess costs and benefits to achieve satisfactory returns on investment.
  • Data security: Utilizing generative algorithms often involves processing large amounts of data. Ensuring data security and compliance with privacy regulations is crucial.

Implementing generative design is a process that requires careful analysis, planning, and organizational adaptation. In the coming years, generative algorithms will continue to evolve, becoming more powerful, widely used, and user-friendly. As computational power increases, the application of this approach can expand beyond part-level design to optimize team workflows, ultimately fostering a comprehensive approach to product creation.

Several leading companies are already moving beyond the pilot phase of generative design and implementing it within their organizations. It is crucial to be aware that this requires investment in tools, education, and cultural changes.


Currently, generative design technology is still in its early stages, and various types of computational models with completely different purposes are being explored.

Expectations are enormous, heading towards the belief that artificial intelligence will eventually provide unimaginable capabilities, especially in the realm of idea exploration. The application of generative design is continuously evolving, and progress in artificial intelligence and computational models consistently enhances its possibilities.

Considering the aggressive pace of development in this technology, it is entirely possible that in the coming years, artificial intelligence will be able to design components with complexity and efficiency levels far beyond human capabilities within a matter of seconds. Consequently, this could redefine the roles of engineers and designers in the world of design, leading to more innovative and optimized products across various fields.