INTRODUCTION

Future scenarios design is a critical aspect of the design process that enables designers to anticipate and shape alternate futures. Scenarios are complex meta-information maps that provide both a strategic and technological direction, assisting designers in the design process [1]. They are considered business forecasting tools or strategic tools to inform, validate, and endorse design decisions [2]. Future scenarios play a critical role when used by designers to collaborate with business experts. It enables them to visualize and realize futures that are not only viable from an organizational perspective but also desirable by the users [3]. Although scenario generation has several utilities, it also comes with various complexities, involving extensive trend analysis, in-depth knowledge of design subject matters, and an understanding of different known and unknown stakeholders.

There are various methodologies for future scenario design, each with its own strengths and limitations. One of the most widely used methodologies for future scenario design is the intuitive logics approach [4]. This approach involves identifying the key drivers of change, such as political, economic, social, technological, environmental, and legal factors, and assessing their impact and uncertainty. Based on the combination of the most impactful and uncertain drivers, a set of scenarios are developed, each representing a plausible and coherent story of the future. The scenarios are then used to test the robustness and adaptability of the current or proposed strategies, policies, or actions. The intuitive logics approach is flexible and intuitive and can accommodate a wide range of perspectives and sources of information. However, it also has some drawbacks, such as the subjective and qualitative nature of the scenario development, the difficulty of validating and updating the scenarios, and the potential for cognitive biases and groupthink.

Another common methodology for future scenario design is the probabilistic modified trends approach [5]. This approach uses quantitative models and data to project the future trends of the critical drivers of change, such as population, GDP, energy consumption, and emissions. The projections are based on the assumptions of the continuation or modification of the current trends and are assigned probabilities. The scenarios are then derived from the combination of the most probable or extreme projections and are used to assess the implications and risks of future trends. The probabilistic modified trends approach is rigorous and quantitative and can provide more precise and consistent scenarios. However, it also has some limitations, such as the reliance on historical data and trends, the difficulty of capturing nonlinear and discontinuous changes, and the tendency to overlook the human and social aspects of the future[1][3].

A third methodology for future scenario design is the backcasting approach [6]. This approach starts with defining a desirable or undesirable future state or vision, such as a low-carbon society or a dystopian world. Then, the steps and actions required to achieve or avoid that future state are identified, working backwards from the future to the present. The scenarios are then constructed from the combination of the steps and actions to guide the decision-making and planning processes. This approach helps address complex and wicked problems like climate change and sustainability and stimulates creativity and innovation. However, it also has some challenges, such as difficulty defining and agreeing on the future state or vision, the uncertainty and feasibility of the steps and actions, and the possibility of neglecting alternative or unexpected futures.

This paper focuses on creating future visions using the intuitive logics approach with AI as a collaborator. This approach was chosen as it aligns with the intuitive and creative process of the design profession and has been successfully used in design and architectural domains [7]. AI has been identified as an effective tool to collect and analyze enormous amounts of information from various disciplines and sources, such as social media, news, scientific publications, and databases, and identify the key drivers and trends of change. This study explores AI’s proven capacity as a co-creator in design processes [8][9] and gauges its efficacy in generating future scenarios.

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