Generative Models as a Catalyst for Rapid Digital Prototyping

Rapid prototyping is essential for validating new ideas and marketing campaigns. However, many professionals are currently not able to create digital prototypes without designers and developers. Rapid digital prototyping using low-code and no-code tools now empowers them to develop and test prototypes on their own in the day-to-day business environment. These tools are now massively accelerated and extended by generative AI such as ChatGPT or Midjourney: Ideas, texts and images can be generated quickly and automatically. In this, rapid prototyping is a win-win situation for all parties involved; managers and employees and customers: Managers benefit from a scalable and efficient method of brand development, (potential) employees are able to independently and directly test product and service ideas in-house and users receive target group specific offers.
Von   Alexander Hahn   |  Professor   |  Technische Hochschule Nürnberg Georg Simon Ohm
  Prof. Dr. Katharina Klug   |  Professorin   |  Hochschule Ansbach
3. Mai 2023

Rapid Prototyping as Essential Tool for Marketer 

Design thinking and lean startup as agile innovation techniques are standard in digital companies and are also spreading rapidly and worldwide in traditional industries. A central mechanism for the successful application of both approaches is the rapid, simple, and iterative development of prototypes. Rapid prototyping is suitable for all business phases: For testing a problem-solving strategy (problem-solution fit), for testing product-brand fit (product-market fit), and during scaling and optimizing business models (e.g., through A/B testing). Furthermore, rapid prototyping bases entrepreneurial action on the scientific principle: It enables hypothesis-based decisions and thus acts empirically and data-driven.

Applied knowledge in prototyping and testing is essential for brand and marketing and innovation managers and will become increasingly important. This is especially true for digital business processes based on adaptivity, adaptability and flexibility. However, rapid prototyping can be challenging for small and medium-sized companies. Since these companies usually cannot maintain their own digital departments or enable cost-intensive support from professional agencies. This makes it more important to equip their own employees with the appropriate know-how and to make targeted use of the possibilities offered by innovative technologies. Marketers are increasingly asking themselves how rapid prototyping with digital low code and no code tools can be implemented in an uncomplicated manner and accelerated automatically using generative artificial intelligence [AI] such as ChatGPT.

Rapid Prototyping in an Agile Business Environment 

In the past, creating digital prototypes for applications or web sites was costly and time-consuming. This has changed dramatically for two reasons: First, intuitive low-code and no-code tools allows for the quick and easy development of functional and digital prototypes. Hahn (2022) provides a free E-Book with empirically user-tested best of breed digital prototyping tools for use cases such as ChatBots, Landing Pages, Click-Dummies, Lead Generation Forms, AR Apps surveys etc based. Therefore, users do not need programming or design knowledge or skills to create prototypes. This changing environment enables user-centric and data-driven testing and integrative optimization of new products and services.

Second, due to the release and rapid improvement of generative AI for text or image generation like ChatGPT or Midjourney, the prototypes can be filled with content much faster. Previously, despite the fast and intuitive user experience of low code and no code tools, several hours still had to be invested in a prototype especially for copywriting, searching and incorporating visual content, etc. In the meantime, such tasks can be automated within a few minutes – and often with fewer errors and more context than was previously more important in rapid digital prototyping due to the usual 80:20 approach. Thus, the prototypes are created with higher quality but sometimes faster by a factor of 10.

One example is the generation of a chatbot: For instance, until now it was possible to use prototyping tools to develop a decision tree-based chatbot as a stand-alone landing page or integrated into a website via a drag-and-drop editor within 30 to 45 minutes. Now the various service providers offer a version that reads existing websites and automatically generates e.g. a FAQ-like chatbot in just under 30 seconds. Alternatively, the copywriting of the chatbot could be done by e.g., ChatGPT.

Another example would be the generation of a landing page or website (e.g., via a template-based website builder). While the no-code and low-code tools already provide well-designed and highly responsive templates for the user interface, the process of creating copy and adjust this to the target group remained a manual and sometimes cumbersome issue. With the arrival of generative AI this task can be automated to create highly targeted and personalized content. For instance, ChatGPT can be prompted with a task such as: “Please write a value proposition for a sustainable”.

Rapid Digital Prototyping Tools in Brand Management

A test of over 200 prototyping tools (Hahn 2022) in the context of theses and design thinking case studies at ten business schools in Austria, Belgium, France, Germany, Morocco, and Monaco shows that numerous freemium tools can be used very well for the rapid and flexible testing of digital product and service ideas. The reader can find detailed examples and evaluations of particularly relevant and efficient prototyping tools in a free E-Book linked in the references (Hahn, 2022).

Stakeholder Benefits of Rapid Digital Prototyping 

To get a holistic view of rapid prototyping with generative AI, it is helpful to look at different perspectives of a brand: those of the brand managers, the (future) employees, and the (potential) customers. For all stakeholders, rapid prototyping involving generative AI seems to make sense. However, it should be noted that generative AI does have factual errors in the results, so-called hallucinations: Wrong answers are passed off as correct. Thus, human factual and contextual knowledge is essential to make the prototypes valid.

The simple, fast and iterative development of prototypes is particularly appealing from the point of view of brand managers, as digital tools and generative AI can be used to achieve fast and scalable insights for brand development and new ideas. There is less discussion and more trial and error. Costly undesirable developments are stopped at an early stage.

Rapid prototyping with generative AI enables employees and project teams to evaluate their own ideas flexibly and independently of external service providers. In addition, positive learning effects result from and for other rapid prototyping projects. The participants recognize weaknesses in customer development, current problem solving and optimize the fit of product and solution in useful iteration steps. Last but not least, the method’s perceived self-efficacy enhances social skills such as creativity, convergent thinking, resilience and teamwork.

Current and potential users also benefit from rapid prototyping using generative AI in brand companies. The result of successful rapid prototyping processes are target group-specific and demand-oriented products and services that offer innovative solutions and reflect the brand identity well.

Prototyping is helpful in all business phases. However, many stakeholders are not yet able to develop and systematically test digital prototypes without the support and experience of designers, developers, and engineers. Likewise, they are not familiar with the precise use of generative AI – just as one once had to learn to use Google Search correctly, the right „prompt engineering“ for generative AI is already an important digital skill: What questions do I ask ChatGPT exactly? What context do I need to provide? How can I improve the results by means of targeted follow-up questions? How do I assess the validity of the content? Digital rapid prototyping using generative AI is an approach to independently develop and test prototypes using intuitive digital tools.

TOP Key Learnings

  • Rapid prototyping as an agile innovation technique is essential for (digital) managers/marketers (?)
  • Rapid prototyping with digital intuitive low code and no code tools enables flexible and fast testing of digital prototypes
  • Generative AI can accelerate rapid prototyping
  • Managers, employees and (potential) customers benefit from successful rapid prototyping


Hahn, A.; Klug, K. (2021): Vernetzung digitaler und analoger Lehre: Digital Prototyping Tools in der akademischen Marketingausbildung, in Naskrent, J.; Stumpf, M.; Westphal, J. (Hrsg.) Digitalität – die Vernetzung von Digital und Analog, SpringerGabler: Wiesbaden, 281-298.

Hahn, A. (2022): Digital Prototyping Tools, E-Book, abgerufen am 23.3.2023 von [].

Alexander Hahn ist Professor an der Technischen Hochschule Nürnberg. Seine Forschung fokussiert sich auf Digital Empathy, Digital UX und Affective Computing. Er lehrt in Belgien, Frankreich, der Schweiz und Marokko. Er hat in weltweit führenden Marketing- und Innovations-Journals veröffentlicht und berät Startups und Corporates zu UX Research.

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