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Introducing Generative Artificial Intelligence in Tech Organizations: Developing and Evaluating a Proof of Concept for Data Management powered by a Retrieval Augmented Generation Model in a Large Language Model for Small and Medium-sized Enterprises in Tech
Linköping University, Department of Management and Engineering, Product Realisation.
Linköping University, Department of Management and Engineering, Product Realisation.
2024 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Introducering av Generativ Artificiell Intelligens i Tech Organisationer : Utveckling och utvärdering av ett Proof of Concept för datahantering förstärkt av en Retrieval Augmented Generation Model tillsammans med en Large Language Model för små och medelstora företag inom Tech (Swedish)
Abstract [en]

In recent years, generative AI has made significant strides, likely leaving an irreversible mark on contemporary society. The launch of OpenAI's ChatGPT 3.5 in 2022 manifested the greatness of the innovative technology, highlighting its performance and accessibility. This has led to a demand for implementation solutions across various industries and companies eager to leverage these new opportunities generative AI brings. This thesis explores the common operational challenges faced by a small-scale Tech Enterprise and, with these challenges identified, examines the opportunities that contemporary generative AI solutions may offer. Furthermore, the thesis investigates what type of generative technology is suitable for adoption and how it can be implemented responsibly and sustainably. The authors approach this topic through 14 interviews involving several AI researchers and the employees and executives of a small-scale Tech Enterprise, which served as a case company, combined with a literature review. 

The information was processed using multiple inductive thematic analyses to establish a solid foundation for the investigation, which led to the development of a Proof of Concept. The findings and conclusions of the authors emphasize the high relevance of having a clear purpose for the implementation of generative technology. Moreover, the authors predict that a sustainable and responsible implementation can create the conditions necessary for the specified small-scale company to grow. 

When the authors investigated potential operational challenges at the case company it was made clear that the most significant issue arose from unstructured and partially absent documentation. The conclusion reached by the authors is that a data management system powered by a Retrieval model in a LLM presents a potential path forward for significant value creation, as this solution enables data retrieval functionality from unstructured project data and also mitigates a major inherent issue with the technology, namely, hallucinations. Furthermore, in terms of implementation circumstances, both empirical and theoretical findings suggest that responsible use of generative technology requires training; hence, the authors have developed an educational framework named "KLART". 

Moving forward, the authors describe that sustainable implementation necessitates transparent systems, as this increases understanding, which in turn affects trust and secure use. The findings also indicate that sustainability is strongly linked to the user-friendliness of the AI service, leading the authors to emphasize the importance of HCD while developing and maintaining AI services. Finally, the authors argue for the value of automation, as it allows for continuous data and system updates that potentially can reduce maintenance. 

In summary, this thesis aims to contribute to an understanding of how small-scale Tech Enterprises can implement generative AI technology sustainably to enhance their competitive edge through innovation and data-driven decision-making. 

Place, publisher, year, edition, pages
2024. , p. 91
Keywords [en]
UX, HCD, HCI, AI, Artificial Intelligence, Generative AI, Implementing AI, LLM, GPT, RAG, Chatbot, Prompt engineering, Prompting framework, Hallucinations, Transparency, Interaction Technologies, Usability, Linguistics, Innovation, SME, Tech Enterprise, Qte, Growth factors, Documentation, Communication, Knowledge retention, Competence development, Education, Training, Retrieval Model, Data, Data Management, Database, Knowledge base, Maintenance, Data security, Data driven decision making, Data analytics, Social sustainability, Economical sustainability, Figma, Trello, Github, OpenAI, GTPs, VsCode, Azure, CoPilot, Google Bard, Zapier, GCS, API, Pipeline, Parsing data, Sorting data, Storing data
National Category
Human Computer Interaction Interaction Technologies Language Technology (Computational Linguistics)
Identifiers
URN: urn:nbn:se:liu:diva-201800ISRN: LIU-IEI-TEK-A--24/04772—SEOAI: oai:DiVA.org:liu-201800DiVA, id: diva2:1846505
External cooperation
Qte development
Subject / course
Product Development
Presentation
2024-02-16, A32, Campus Valla, Linköping, 10:00 (Swedish)
Supervisors
Examiners
Available from: 2024-04-08 Created: 2024-03-22 Last updated: 2024-04-08Bibliographically approved

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