# The Time Machine: Future Scenario Generation Through Generative AI Tools
## 1 Introduction
### The Potential of GAI in Future Scenario Generation  
Owing to its proven narrative capabilities, GAI is poised to assume a more prominent role in the visualisation and conceptualisation of scenarios. AI-driven scenario generation enables a forward-looking perspective on potential futures through a collaborative dialogue between humans and machines, given structured interaction [20]. While general-purpose AI services like OpenAI's ChatGPT and Anthropic's Claude offer flexibility and deliver valuable results for experts across various tasks, more specialised tools may be essential for users who are not experts in scenario planning or GAI applications. [PERSON] et al. illustrate this need by examining the use of AI for design fiction among students familiar with scenario planning or the design process, from which user experience and previous knowledge can be seen on effective interaction of GAI applied to design and scenario planning [21]. Similarly, [PERSON] et al. highlighted the limitation that AI scenario planning relies heavily on data, requiring prior knowledge of trends to supply GAI with appropriate datasets [22]. The authors created a custom GPT which follows certain instructions to generate scenarios based on multiple variables and conversational iterations with the user. Nonetheless, the reliance on the model itself can also introduce notable biases and hallucinations (i.e., in [PERSON] et al. 's tests, no additional data were provided to the GPT), behaviours that should be minimised or, at least, clearly identified. As a result, effective scenario planning is often limited to foresight experts or those with specially designed AI tools for this purpose. The gaps in the previous examples score on some challenges in human-AI collaboration, whether applied to scenario planning or other professional practices. The evolving landscape of human-AI collaboration is driving the creation of an augmented workforce, where the accessibility and suitability of AI tools can provide a significant competitive edge. GAI's capabilities offer an opportunity to those companies whose scenario planning is too resource-intensive and which could now benefit from including these approaches in their workflows. Seemingly, developing countries stand to benefit from AI technologies, provided they have the necessary infrastructure and conditions for effective access and utilisation [23]. However, merely having access is insufficient; the appropriateness of AI tools is equally important. GAI technologies offer speculative and generative capabilities that enhance human cognition, necessitating further investment in skill development to equip professionals for this transformation [13]. Unless these skills are democratically acquired, using GAI might only benefit those with a competitive advantage. Despite the aforementioned challenges and perils, many opportunities exist for the intersection of GAI and scenario planning. GAI can be used in the early stages of design to create narratives and speculative solutions, providing speed and adaptability in exploring future scenarios. Additionally, it can potentially support the conception and exploration of alternative futures from a critical perspective [24]. Generative AI can significantly enhance scenario planning in several aspects:  
* Scale Change: Generative AI allows for the rapid generation of future scenarios far beyond human capacity, efficiently processing large volumes of information and processes. Unlike traditional methods, AI can analyse massive datasets and develop adaptable narratives that facilitate the sharing and eventual acceptance of strategieswithin organisations [22]. GAI enables a parametric and iterative approach to exploring multiple scenarios, providing initial ideas that can be gradually refined [22]. * Personalisation: Generative AI allows for the adaptation of scenarios to each organisation's specific needs and preferences using particular data.