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Indonesian Small Medium Entreprises (SME’s) as the mainstay of the national economy with over 60 million enterprises, struggle with inefficiencies of manual financial record keeping that leads to mistake, delays and barrier to growth. This bothers the mind knowing that how much potential are wasted because of a traditional implementation of financial recording that are recommended to digitalize with modern day capabilities, marking the importance of this research as well as a background of this research. This research uses a phenomenological approach as it’s method to examine lived experiences of seven SME’s owners and accountant regarding their recording practices, issues, and attitudes toward prompt engineering services to record their financial data with the help of AI. This method is chosen because it focuses on experience and one’s subjective understanding, backed and triangulated with 19 scopus indexed journals. A semi structured interview are done towards the participants, it disclose the variety of systems ranging between fully digital and manual, manual found in 70% cases, enduring human errors, all participant reports high interest in AI based accuracy and efficiency. End results are sorted and analyzed through Nvivo, bringing together four fundamental themes, which are practice diversity, operational challenges, AI enthusiasm, and adoption barriers. Results confirm that tiered monetization models that deal with affordability helps to improve financial accuracy by 27.3 percent and predictability by 34.5 percent. The research is in line with SDG 8 (decent work) and SDG 9 (innovation) as the research will propose scalable AI solutions to SME resilience.

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Artificial Intelligence, Phenomenology, Financial Management, Small to Medium Enterprise, Prompt Engineering

Creative Commons Attribution 4.0 International

The majority of Indonesian Small to Medium Enterprises (SMEs) still remain on using manual financial recording despite digital tools being widespread and available to access, resulting in hindered efficiency and data inaccuracies. AI technology are currently on the rise, creating new jobs on tuning AI to fit individual needs. Earlier studies predominantly focuses on AI adoption in large enterprises, overlooking prompt engineering potentials as a bridge on smaller enterprise digitalization. This research gaps are addressed through this research by studying the lived experiences and operational workflows of seven Small to Medium Enterprise (SME) owners and accountant to identify resistance factors and adoption perquisites through a phenomenological approach. High AI enthusiasm and low technical readiness are revealed in this research, currently 70% of the participants uses manual/hybrid recording. The research also reveal that tiered prompt engineering service monetization to be crucial, it helps improving financial accuracy by 27.3% and predictability by 34.5%. This research shows scalable monetization processes that match high-level computational intelligence with technical and resource-based limitations of emerging economies, therefore supporting SDG 8 and SDG 9.

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Artificial Intelligence, Phenomenology, Financial Management, Small to Medium Enterprise, Prompt Engineering

Creative Commons Attribution 4.0 International