Facilitating AI Implementation A Framework for Implementing Generative AI and AI Agents at Manufacturing Small and Medium-sized Enterprises in the Gothenburg Region
| dc.contributor.author | Andersson, Felicia | |
| dc.contributor.author | Palm, Elias | |
| dc.contributor.department | Chalmers tekniska högskola / Institutionen för matematiska vetenskaper | sv |
| dc.contributor.examiner | Gerlee, Philip | |
| dc.contributor.supervisor | Stöhr, Christian | |
| dc.date.accessioned | 2026-06-10T11:51:31Z | |
| dc.date.issued | 2026 | |
| dc.date.submitted | ||
| dc.description.abstract | The rapid advancement of generative AI (GenAI) and AI agents presents significant strategic opportunities within the industrial sector, yet small and medium-sized en- terprises (SMEs) frequently struggle to navigate the socio-technical complexities of technology implementation. This study aims to develop a conceptual framework tai- lored to management teams within manufacturing SMEs in the Gothenburg region, facilitating their transition from traditional workflows to AI-augmented operations. Utilising a qualitative multi-method research design, a systematic literature review of 15 academic papers was conducted to examine the drivers and barriers of SME technology adoption, complemented by local empirical insights gathered from eight semi-structured interviews involving 14 participants across various operational roles. Through an iterative development process, the theoretical and empirical findings were synthesised inductively through thematic analysis and subsequently deduc- tively mapped using the Technology-Organisation-Environment-Individual (TOEI) framework. The study identifies 15 distinct Influencing Factors shaping implementation, culmi- nating in the development of the AKSEA Framework. To facilitate local utilisation, the framework was created in Swedish and is structured around five core Priority Areas: Ansvar (Responsibility), Kompetens (Competence), Strategi (Strategy), En- gagemang (Engagement), and AI-policy (AI Policy). The empirical findings reveal that local SMEs exhibit significant variations in digital maturity, necessitating a non-sequential, flexible architecture for the AKSEA Framework. Rather than en- forcing a rigid roadmap, it functions as a dynamic decision-support tool. Ultimately, this study concludes that successful GenAI and AI agent implementation within manufacturing SMEs requires a shift from general models toward context-specific, adaptable guidance, thereby enabling management to formulate tailored strategies aligned with their unique organisational readiness. | |
| dc.identifier.coursecode | CLSX35 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12380/311179 | |
| dc.language.iso | eng | |
| dc.setspec.uppsok | PhysicsChemistryMaths | |
| dc.subject | Generative AI, AI Agents, Manufacturing SMEs, Technology Implemen- tation, Change Management, Organisational Learning, Framework, TOEI, Gothen- burg. | |
| dc.title | Facilitating AI Implementation A Framework for Implementing Generative AI and AI Agents at Manufacturing Small and Medium-sized Enterprises in the Gothenburg Region | |
| dc.type.degree | Examensarbete för masterexamen | sv |
| dc.type.degree | Master's Thesis | en |
| dc.type.uppsok | H | |
| local.programme | Learning and leadership (MPLOL), MSc |
Ladda ner
Original bundle
1 - 1 av 1
Hämtar...
- Namn:
- Master's Thesis - Facilitating AI Implementation - Felicia Andersson & Elias Palm.pdf
- Size:
- 2.92 MB
- Format:
- Adobe Portable Document Format
License bundle
1 - 1 av 1
Hämtar...
- Namn:
- license.txt
- Size:
- 2.35 KB
- Format:
- Item-specific license agreed upon to submission
- Description:
