Enhancing SME Financial Accuracy through Prompt Engineering Services to Address Manual Recording Challenges (Phenomenological Study)
Description
Artificial Intelligence has been rapidly developed and adopted by the public audience since ChatGPT released it’s first model. People of various backgrounds have been known to integrate AI to solve their problems, be it in the form of content creation, a partner to talk to, insight gatherings, as well as assisting one’s business. This paper aims to examine how small and medium enterprises (SMEs) require immediate engineering services in financial data recording with an emphasis on eliminating inefficiencies in manual processes that are likely to be affected by human errors and irresponsible reporting as well as creating good monetization strategies for engineers to implement on this market. The study presents the experiences of SME owners/certified professional and their financial management problems through a phenomenological study as it’s method and relies on primary data collected in the form of in depth interviews and secondary data in the form of relevant journals. The results reveal the possibility of prompt engineering in creating tailored AI systems that will automate the process of data categorization, data recording, and monthly reporting on the basis of evidence provided, including receipts and explanations. This service will help SMEs to input raw data and the AI will do the sorting into categories such as income, expenses, amounts, and proofs, and the perfect monthly ledgers will be downloadable. The study also creates a monetization strategy of immediate engineers to this market, such as subscription and tier pricing and alliances. The study helps to fill the gap between AI development and SME implementation, which will encourage efficiency, accuracy, and financial viability in new markets.
Files
Institutions
- BINUS Malang
- Bina Nusantara University