The Time Machine: Future Scenario Generation Through Generative AI Tools
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, wh...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/17/1/48 |
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| _version_ | 1832588421185404928 |
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| author | Jan Ferrer i Picó Michelle Catta-Preta Alex Trejo Omeñaca Marc Vidal Josep Maria Monguet i Fierro |
| author_facet | Jan Ferrer i Picó Michelle Catta-Preta Alex Trejo Omeñaca Marc Vidal Josep Maria Monguet i Fierro |
| author_sort | Jan Ferrer i Picó |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7931c182a81f430d98e76d2311eba01f |
| institution | Kabale University |
| issn | 1999-5903 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj-art-7931c182a81f430d98e76d2311eba01f2025-01-24T13:33:41ZengMDPI AGFuture Internet1999-59032025-01-011714810.3390/fi17010048The Time Machine: Future Scenario Generation Through Generative AI ToolsJan Ferrer i Picó0Michelle Catta-Preta1Alex Trejo Omeñaca2Marc Vidal3Josep Maria Monguet i Fierro4Innex Labs, Institut pel Futur, Carrer de Lluís Millet 8, 17190 Salt, Catalonia, SpainInnex Labs, Institut pel Futur, Carrer de Lluís Millet 8, 17190 Salt, Catalonia, SpainInnex Labs, Institut pel Futur, Carrer de Lluís Millet 8, 17190 Salt, Catalonia, SpainInnex Labs, Institut pel Futur, Carrer de Lluís Millet 8, 17190 Salt, Catalonia, SpainInnex Labs, Institut pel Futur, Carrer de Lluís Millet 8, 17190 Salt, Catalonia, SpainContemporary 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.https://www.mdpi.com/1999-5903/17/1/48scenariosfuturesgenerative AIlarge language models (LLMs)prompt engineering |
| spellingShingle | Jan Ferrer i Picó Michelle Catta-Preta Alex Trejo Omeñaca Marc Vidal Josep Maria Monguet i Fierro The Time Machine: Future Scenario Generation Through Generative AI Tools Future Internet scenarios futures generative AI large language models (LLMs) prompt engineering |
| title | The Time Machine: Future Scenario Generation Through Generative AI Tools |
| title_full | The Time Machine: Future Scenario Generation Through Generative AI Tools |
| title_fullStr | The Time Machine: Future Scenario Generation Through Generative AI Tools |
| title_full_unstemmed | The Time Machine: Future Scenario Generation Through Generative AI Tools |
| title_short | The Time Machine: Future Scenario Generation Through Generative AI Tools |
| title_sort | time machine future scenario generation through generative ai tools |
| topic | scenarios futures generative AI large language models (LLMs) prompt engineering |
| url | https://www.mdpi.com/1999-5903/17/1/48 |
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