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The Time Machine: Future Scenario Generation Through Generative AI Tools

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  • Jan Ferrer i Picó

    (Innex Labs, Institut pel Futur, Carrer de Lluís Millet 8, 17190 Salt, Catalonia, Spain
    Departament d’Enginyeria Gràfica i Disseny, Escola Tècnica Superior d’Enginyeria Industrial de Barcelona (ETSEIB), Universitat Politècnica de Catalunya, Avinguda Diagonal 647, 08028 Barcelona, Catalonia, Spain
    These authors contributed equally to this work.)

  • Michelle Catta-Preta

    (Innex Labs, Institut pel Futur, Carrer de Lluís Millet 8, 17190 Salt, Catalonia, Spain
    These authors contributed equally to this work.)

  • Alex Trejo Omeñaca

    (Innex Labs, Institut pel Futur, Carrer de Lluís Millet 8, 17190 Salt, Catalonia, Spain
    Departament d’Enginyeria Gràfica i Disseny, Escola Politècnica Superior d’Enginyeria de Vilanova i la Geltrú (EPSEVG), Universitat Politècnica de Catalunya, Avinguda de Víctor Balaguer 1, 08800 Vilanova i la Geltrú, Catalonia, Spain
    These authors contributed equally to this work.)

  • Marc Vidal

    (Innex Labs, Institut pel Futur, Carrer de Lluís Millet 8, 17190 Salt, Catalonia, Spain)

  • Josep Maria Monguet i Fierro

    (Innex Labs, Institut pel Futur, Carrer de Lluís Millet 8, 17190 Salt, Catalonia, Spain
    Departament d’Enginyeria Gràfica i Disseny, Escola Tècnica Superior d’Enginyeria Industrial de Barcelona (ETSEIB), Universitat Politècnica de Catalunya, Avinguda Diagonal 647, 08028 Barcelona, Catalonia, Spain
    These authors contributed equally to this work.)

Abstract

Contemporary society faces unprecedented challenges—from rapid technological evolution to climate change and demographic tensions—compelling organisations to anticipate the future for informed decision-making. This case study aimed to design a digital system for end-users called the Time Machine, which enables a generative artificial intelligence (GAI) system to produce prospective future scenarios based on the input information automatically, proposing hypotheses and prioritising trends to streamline and make the formulation of future scenarios more accessible. The system’s design, development, and testing progressed through three versions of prompts for the OpenAI GPT-4 LLM, with six trials conducted involving 222 participants. This iterative approach allowed for gradual adjustment of instructions given to the machine and encouraged refinement. Results from the six trials demonstrated that the Time Machine is an effective tool for generating future scenarios that promote debate and stimulate new ideas in multidisciplinary teams. Our trials proved that GAI-generated scenarios could foster discussions on +70% of generated scenarios with appropriate prompting, and more than half included new ideas. In conclusion, large language models (LLMs) of GAI, with suitable prompt engineering and architecture, have the potential to generate useful future scenarios for organisations, transforming future intelligence into a more accessible and operational resource. However, critical use of these scenarios is essential.

Suggested Citation

  • Jan Ferrer i Picó & Michelle Catta-Preta & Alex Trejo Omeñaca & Marc Vidal & Josep Maria Monguet i Fierro, 2025. "The Time Machine: Future Scenario Generation Through Generative AI Tools," Future Internet, MDPI, vol. 17(1), pages 1-15, January.
    • Handle: RePEc:gam:jftint:v:17:y:2025:i:1:p:48-:d:1571332
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    References listed on IDEAS

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    1. Farrow, Elissa, 2022. "Determining the human to AI workforce ratio – Exploring future organisational scenarios and the implications for anticipatory workforce planning," Technology in Society, Elsevier, vol. 68(C).
    2. Christian Mühlroth & Michael Grottke, 2018. "A systematic literature review of mining weak signals and trends for corporate foresight," Journal of Business Economics, Springer, vol. 88(5), pages 643-687, July.
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