1. Introduction
As artificial intelligence (AI) continues to advance, its integration into design processes has become a growing area of interest in both academia and industry. Design, traditionally a complex and creative discipline, is increasingly supported by AI-based applications aimed at enhancing efficiency, creativity and precision. AI can assist designers in a range of activities, from generating innovative concepts to optimizing existing designs, offering solutions that were previously infeasible (Reference Cooper and BremCooper, 2024; Lee et al., 2024). Despite the named advantages, AI technologies are still rarely integrated in the daily life in companies developing new products (Cooper and Reference Cooper and BremBrem, 2024).
This paper aims to explore AI’s role in product development by addressing two key research questions: (1) What AI-based applications exist that specifically support designers throughout various stages of the design process? (2) Which specific design activities are facilitated by these AI applications? By examining these questions, the study seeks to provide a comprehensive overview of AI-based tools and their impact on product development, contributing to a clearer understanding of how AI can be effectively leveraged to support and enhance the work of designers across disciplines. This research also aims to identify gaps and opportunities within the current AI-support landscape, providing a foundation for future research and development in AI-driven innovation.
2. State of the art
Artificial intelligence (AI) is defined in various ways across disciplines (Reference Aphirakmethawong, Yang and MehnenAphirakmethawong et al., 2022; Reference ObermaierObermaier, 2019). One prominent definition highlights AI’s role in performing tasks traditionally associated with human intelligence. AI systems excel in analysing vast datasets, recognizing patterns, and generating new outputs, making them adaptable to numerous applications (Reference KreutzerKreutzer, 2023). At its core, AI refers to the ability of systems to autonomously perform tasks that would generally require human cognition, such as problem-solving and learning (Reference GethmannGethmann et al., 2022).
In product development, AI technologies are increasingly used to enhance design, automate processes and optimize outcomes. Generative AI (GenAI) tools are widely adopted, particularly for initial stages like ideation, where tools can generate design options, assist with drafting, or create visuals based on user prompts. Analytical AI, on the other hand, is prominent in evaluating designs, monitoring performance metrics and predicting outcomes in later development phases, such as prototype testing (Reference SinglaSingla et al., 2024).
Approaches to categorize AI vary across research fields. To name a few examples, Haenlein and Kaplan (Reference Haenlein and Kaplan2019) present a taxonomy distinguishing between 'Analytical AI', 'Human-Inspired AI' and 'Humanized AI'. Reference MauryaMaurya et al. (2023) also classified AI technologies in a taxonomic manner and opposed classical and modern AI, narrow and general AI, weak and strong AI as well as symbolic and sub-symbolic AI. In order to classify AI within the product development process according to Pahl & Beitz (Bender and Reference Bender and GerickeGericke, 2021), a process model designed for designers and their competencies, this study applies the landscape model by Gethmann et al. (Reference Gethmann2022) because it organise the main branches of AI and their alignment according to human cognition such human abilities (Figure 1).
Figure 1. Landscape of AI (own representation based on HUMM (2020), cited according to GETHMANN et al. (Reference Gethmann2022))
Figure 1 shows this AI landscape, including branches like Machine Learning (ML) and Knowledge-Based AI. Machine learning, driven by training data, enables machines to make probability-based predictions without specific programming for each outcome. This ability for autonomous prediction and learning often results in “black-box” models, where the decision-making process remains opaque (Reference GethmannGethmann et al., 2022; Reference Paaß and HeckerPaaß and Hecker, 2020).
In contrast, knowledge-based AI draws on explicit expert knowledge, utilizing tools such as knowledge graphs and ontologies. Expert systems, another subset, apply this knowledge to specific fields, providing tailored recommendations based on structured data. These systems effectively function as domain-specific “experts” (Reference GethmannGethmann et al., 2022).
Despite the wide variety of available technology, studies show that the potential is not being exploited in current product development practices (Reference Cooper and BremCooper and Brem, 2024). Integrating AI into product development still faces challenges, especially in later stages that require high precision and domain-specific adaptation. Companies find that, while GenAI is advancing rapidly, its capacity for complex, iterative design adjustments remains limited. Meanwhile, managing AI-driven outputs and ensuring data accuracy are emerging as priority concerns across industries, with many organizations implementing governance frameworks to handle these complexities (Reference Cooper and BremCooper and Brem, 2024; Reference SinglaSingla et al., 2024).
This growing body of knowledge underscores an incomplete understanding of AI's full potential in product development, particularly concerning long-term benefits and limitations in physical testing and iterative refinement stages. As companies and researchers explore these applications further, it is expected that AI-driven methodologies will better accommodate the nuances of physical product cycles, allowing for more robust integration across the product lifecycle.
3. Study design
To address the research questions, a structured search strategy was developed. The process began with an exploratory literature search using Google Scholar and Scopus, identifying eight highly relevant publications. These sources guided the selection of key terms (Table 1), which were categorized and combined using Boolean operators to maximize relevant results. The search criteria also included language and date restrictions (2015-2024), focusing on current studies. Searches were limited to journal articles and conference proceedings within relevant fields, such as engineering and social sciences, to ensure topic-specific results. For further precision, the PRISMA methodology was applied to screen titles, abstracts and full texts, filtering down from an initial 2894 publications to 144, with a final selection of 87 for analysis (Figure 2).
Table 1. Keywords used in the literature search
Figure 2. Flow chart for literature selection using the PRISMA method
4. Findings
Based on the 87 publications, the formal relationships are described in an overview. The identified literature is then analysed and structured with regard to the three research questions formulated, and the results are summarised.
4.1. AI-based applications in the PDP
This section examines the current integration of AI-based applications into the product development process (PDP) and their role in supporting various stages. The analysis is structured around the main phases of PDP as defined by Pahl & Beitz, using a systematic review of literature identified in this study (Bender and Reference Bender and GerickeGericke, 2021). Figure 3 illustrates the distribution of AI-focused publications across PDP phases by year of publication, highlighting where AI applications are most utilized and researched.
As seen in Figure 3, the majority of AI-related literature (60 publications) addresses support for conceptual design, suggesting that AI has a significant role in ideation and early design solutions. Additionally, 23 publications focus on task clarification, 27 on embodiment design, and 10 on detailed design activities, indicating AI’s diverse applications across PDP. Notably, 25 publications cover cross-phase applications, indicating AI's flexibility in supporting multiple stages simultaneously. Cross-phase applications often involve data-driven decision support or predictive analytics, reflecting AI’s capacity to assist in integrated or iterative PDP models. In a few publications difficulties arose in phase-specific categorization of AI technologies, because they often discuss applications in general terms rather than linking them to specific PDP phases as outlined by Pahl & Beitz. In this case, the described examples of applications were analysed in terms of the activities and linked to the Pahl & Beitz activity model (Bender and Reference Bender and GerickeGericke, 2021).
Figure 3. Chronological overview of publications according to PDP phases
4.2. Task clarification
AI technologies hold significant potential within the conceptual design phase of product development by enabling large-scale data analysis and pattern recognition, which can inform critical design decisions based on customer behaviour and market trends (Reference Brem, Giones and WerleBrem et al., 2023; Reference CooperCooper, 2024a, Reference Cooper2024b; Reference Wang, Guo and ChenWang et al., 2023; Reference Zhang, Zhang and SongZhang et al., 2021a). AI aids in deepening designers' understanding of customer needs (Aphirakmethawong et al., 2022; Reference Zhu, Liang and WangZhu, 2023), with machine learning (ML) being particularly effective for tasks that require pattern recognition and the analysis of extensive datasets (YüReference Yükselksel et al., 2023). Referencing historical data is essential for a comprehensive understanding of design requirements. Methods by Reference ArnarssonArnarsson et al.(2019) & Reference DehnDehn et al. (2023) highlight approaches for aligning AI with product requirements. Furthermore, requirements extraction from user-generated content, such as customer reviews and audio-visual data, offers insight into customer preferences. (Reference Wang, Mo and TsengWang et al., 2018) proposed a deep-learning approach linking customer ratings to design parameters, allowing customers to express needs in simple terms, while relevant keywords are automatically extracted for precise task specification (Reference Wang, Mo and TsengWang et al., 2018; Reference Brem, Giones and WerleBrem et al., 2023). Deep learning is also noted for its efficacy in identifying market-fit issues (Reference Brem, Giones and WerleBrem et al., 2023). Another application is Reference ZhangZhang et al. (2021b)’s ML tool, which predicts customer-preferred product specifications based on online sales data. NLP, a subset of ML, plays a vital role in translating unstructured, natural language data into structured insights about customer needs (Reference LaiLai et al., 2023; Reference Aphirakmethawong, Yang and MehnenAphirakmethawong et al., 2022). NLP allows extraction of product attributes and sentiment from text sources like social media and customer reviews, clarifying product requirements and design direction (Reference QuanQuan et al., 2023). In addition to customer data, NLP can expedite the search and analysis of technical documentation, streamlining the information-gathering process (Reference ArnarssonArnarsson et al., 2019). Reference CooperCooper (2024a) further emphasizes that NLP is valuable for analysing market data and usage patterns, providing a fuller picture of user needs and trends.
4.3. Conceptual design
The diversity of AI-based applications for the conceptual design phase is reflected in the number of publications. In this context, AI offers numerous possibilities for decision support, for example in group decision-making processes (Osman and Reference Osman and PericPeric, 2023). Furthermore, different AI technologies are used to support idea generation and creativity or to evaluate the developed concepts (Reference JingJing et al., 2022; Reference WendrichWendrich, 2020).
Machine learning is one of the main technologies used in this phase, for example to predict design performance or error probabilities (Reference GerschützGerschütz et al., 2023; Reference Pepper, Montomoli and SharmaPepper et al., 2022). In particular, generative AIs, such as generative adversarial networks (GANs), play a central role, for example, by being used to generate alternative design solutions (Reference Brem, Giones and WerleBrem et al., 2023), create realistic 3D renderings or suggest design modifications and new product ideas (Reference CooperCooper, 2024a, Reference Cooper2024b). Furthermore, they can be used to discover or optimise new, innovative design ideas, thus promoting creativity (Reference Brem, Giones and WerleBrem et al., 2023; Reference DemirelDemirel et al., 2024), to use e.g. various input data, such as images or audio files, can be used to generate alternative design solutions (Reference Brem, Giones and WerleBrem et al., 2023). Other AI technologies that can be used to predict 3D models include 3D convolutional neural networks, neural networks or Bayesian networks (Reference Aphirakmethawong, Yang and MehnenAphirakmethawong et al., 2022). Reference Li, Xie and ShaLi et al. (2022) presented an approach based on generative AI that can generate and predict complex 3D shapes from two-dimensional silhouettes. This enables developers to quickly and efficiently visualise, analyse and evaluate product ideas. Likewise, Reference Nobari, Rashad and AhmedNobari et al. (2021) developed a GAN-based tool designed to identify unique features in products and in turn integrate them into new designs. This allows novel and innovative features to be applied to multiple design alternatives. A similar model is discussed by Reference GuoGuo (2024) or Reference KraheKrahe et al.(2020), combining feature extraction and design generation. Machine learning is also used in the concept phase for pattern recognition and prediction from an extensive database (Reference GerschützGerschütz et al., 2023; Reference KraheKrahe et al., 2020). This pattern recognition can provide additional knowledge that can be important for the development of solution concepts and their evaluation, for example with regard to their creativity (Reference Song, Miller and AhmedSong et al., 2023). Furthermore, pattern recognition enables the identification of potential innovation opportunities (Arvind and Sindhu Reference Arvind and Sindhu xMadhuriMadhuri, 2023). AI-based predictions can be relevant for decision-making in the development of new products (Reference SouzaSouza et al., 2024) and can estimate the probabilities of success (Reference Relich, Nemec and ZapletalRelich, 2015). Neural networks are particularly well suited to identifying complex relationships and can use RNN-based pattern recognition from CAD models to predict subsequent design steps (Reference KraheKrahe et al., 2021). Reference HuangHuang et al. (2023) showed that deep learning can be used to extract knowledge from extensive unstructured data and to create a modular knowledge graph tailored to the conceptual product design. Machine learning also offers the possibility of classifying CAD models and analysing their similarities. Furthermore, neural networks enable the environmental impact of the future product to be assessed, which can support developers in selecting environmentally friendly development parameters (Singh and Reference Singh and SarkarSarkar, 2023; Reference WisthoffWisthoff et al., 2016). In addition, Reference HoornaertHoornaert et al. (2017) present an approach that uses machine learning algorithms, among other things, to process crowdsourcing ideas and predict whether they will be implemented in the future. The use of pattern recognition in combination with NLP to process textual data such as requirements, for example from social media or multiple databases, is also discussed in order to stimulate the creative thinking of developers or to integrate existing knowledge (Reference HanHan et al., 2020; Reference LiLi et al., 2024). Similar to the previous chapter, this technology can help to better align product features with user needs or to identify key concepts in the solution finding process, and enable developers to access a wider range of sources of inspiration (Reference Aphirakmethawong, Yang and MehnenAphirakmethawong et al., 2022; Reference LiLi et al., 2024; Reference LiuLiu et al., 2020). Semantic networks can also be used to represent knowledge or provide information and, by highlighting potential knowledge connections, can trigger new ideas that may have been overlooked by developers (Reference HanHan et al., 2021). Reference ChenChen et al. (2019) extended this with a machine learning based component so that the approach presented offers both semantic and visual inspiration for idea generation. Furthermore, the combination of machine learning with Kansei engineering is used to capture users' emotional responses (Reference LiLi et al., 2021). Combined approaches that use fuzzy logic to identify customer opinions and emotions from social media data related to the product can also provide valuable information for this phase (Reference GiannakisGiannakis et al., 2022). Grech et al. (Reference Grech, Mehnen and Wodehouse2023) propose combining AI technologies with a virtual reality environment to support creative problem solving and overcome limiting factors. It is emphasised that Large Language Models (LLM) are becoming increasingly important in conceptual product design (Reference TianTian et al., 2024). The application of case base reasoning (CBR) in combination with machine learning to use past experiences for future problem solving is also highlighted (Reference HuoHuo et al., 2022).
4.4. Embodiment design
Many AI technologies used in the concept phase also play a crucial role in the detailed design phase, particularly for generating 3D models and prototypes, which can be costly and error-prone when created manually (Reference Wang and ChenWang and Chen, 2024). Automated prototyping is significantly enhanced by AI, enabling quicker translations of designs into physical models and speeding up the overall design process (Cooper and Reference Cooper and BremBrem, 2024). AI also facilitates automated design evaluations based on pre-defined criteria, adding efficiency and precision to design verification (Reference Zhang, Zhang and SongZhang et al., 2021a). In simulations, AI assists with result prediction, optimization, and decision-making, reducing iteration time and resource costs (Reference ShaoShao et al., 2018; Xiao and Reference Xiao and NiNi, 2024). Machine learning also plays a crucial role in this phase. Recurrent neural network (RNN)-based algorithms can help to extract relevant knowledge from extensive databases (Reference Jiang, Hu and LuoJiang et al., 2021; Reference JiangJiang et al., 2022) and recommend further development steps (Reference KraheKrahe et al., 2021). In particular, generative AI is as essential as in the concept phase. Generative AI is able to create several preliminary designs based on specific requirements and with the help of different input data (Reference Brem, Giones and WerleBrem et al., 2023; Reference CooperCooper, 2024a; Reference RegenwetterRegenwetter et al., 2023). Furthermore, different types of generative AI technologies are used for the topology optimisation of design models (Reference Regenwetter, Nobari and AhmedRegenwetter et al., 2022). A machine learning-based optimisation of the design process can also be carried out. This is based on the prediction of possible potential errors in the design by using artificial neural networks that have been trained using data sets of frequently used CAD models (Reference Sabbella, Singh and MaheswariSabbella et al., 2020). Furthermore, deep learning-based (CNN) algorithms can also be used to extract features from 3D models and in turn compare similarities with existing components (Reference Bickel, Schleich and WartzackBickel et al., 2023). In this way, for example, preliminary designs could be supplemented by partial solutions from other variants. Reference Kobayashi and KumeKobayashi and Kume (2024) present an application in which deep learning prioritises the integration of customer requirements based on image generation. This method is particularly suitable for creating customised products. Reference Wang and ChenWang and Chen (2024) present an approach based on deep learning that takes on 3D modelling tasks and creates more accurate 3D models of products than conventional approaches, thus reducing the workload on designers. Due to the often time-consuming and resource-intensive design process, Reference ShaoShao et al. (2020) present an approach that combines dynamic simulation and design optimisation. This can enable an automated search for possible design solutions.
4.5. Detail design
Generative AI technologies are also instrumental in refining the final design, particularly when selecting materials, which is a critical aspect of this stage (Reference Bender and GerickeBender and Gericke, 2021). Aphirakmethawong et al. (Reference Aphirakmethawong, Yang and Mehnen2022) outline the various AI technologies employed in material selection, primarily machine learning and deep learning approaches like decision trees, nearest neighbor algorithms, and convolutional neural networks (CNNs). These AI methods minimize the reliance on designer experience alone, instead facilitating data-driven decision-making to identify the optimal material based on classification and performance metrics. Additionally, Reference Khanolkar, Vrolijk and OlechowskiKhanolkar et al. (2023) discuss AI applications that enhance material selection in tandem with topology optimization, helping to ensure that materials meet both design and functional requirements. A comprehensive framework developed by Reference Abadi, Manssouri and AbadiAbadi et al. (2022) further supports product development for complex products by integrating material selection, manufacturing process decisions, and final assembly. This framework leverages ontologies along with Case-Based Reasoning (CBR) and Rule-Based Reasoning (RBR) to enhance decision accuracy and streamline the development process, especially for complex products. These advancements allow for a robust, systematic approach to finalizing design choices, thus enabling an optimal balance between material performance, cost, and manufacturability.
5. Summary and discussion
In summary, AI applications have permeated all phases of the PDP, with a notable focus on the conceptual and embodiment design stages, as evidenced by a higher volume of relevant literature. This emphasis aligns with findings from Reference Abadi, Manssouri and AbadiKhanolkar et al. (2023), who highlight similar trends in AI adoption across design phases. Fewer applications were found in the later elaboration phase than in the early-stage activities, where AI’s role is primarily impactful. This suggests that the data models in later phases are not yet fully AI-compatible or that on-premise application/data protection has not yet been clarified. During the task clarification stage, ML and NLP technologies are prevalent, particularly for extracting user requirements from content. In the conceptual design phase, generative AI models, like generative adversarial networks, facilitate the generation of innovative solutions, often incorporating unique product attributes or assessing environmental impact. This enables exploration of multiple design alternatives, enriching the creative process.
In the elaboration phase, AI supports optimization, prototyping, and material selection, often automating traditionally manual, time-intensive tasks. These applications enhance process efficiency and have potential to reduce time-to-market. This integration of AI not only adds technical capabilities but also transforms the PDP, making it more adaptive to evolving product requirements.
While existing literature documents various AI applications, it generally focuses on individual activities within specific phases rather than end-to-end integration across the PDP. Figure 3 shows minimal cross-phase AI application, although Figure 4 indicates that technologies like machine learning and deep learning are broadly used. Cross-phase integration appears feasible technologically, yet inconsistencies remain as not all activities are supported uniformly, affecting continuity between stages.
Figure 4. Literature landscape - AI technologies vs. PDP phases
Several limitations affect these findings. The literature search was confined to Scopus and Web of Science, potentially limiting the scope. Additionally, given the rapid evolution of AI in PDP, newer advancements may not be represented.
Currently, AI applications focus on narrow, specialized tasks within specific activities, indicating that today’s AI remains at the level of Artificial Narrow Intelligence (ANI). According to Obermaier (Reference Obermaier2019) achieving full PDP automation would require Artificial General Intelligence (AGI) to handle the wide-ranging complexities of product design. Transitioning from ANI to AGI is anticipated around 2040 (Reference ObermaierObermaier, 2019; Reference Paaß and HeckerPaaß and Hecker, 2020), but whether the PDP itself will need reconfiguration to fully integrate AI is uncertain. Future research should explore how an AI-integrated PDP might be structured, ensuring it meets the needs of designers, method developers and AI tool creators in an AI-dominant ecosystem.
Open access
