Artificial Intelligence for Non-IT Retail Store Managers - Bridging the Gap for Technical Error Resolution

Sammanfattning

The retail sector plays a vital role in global economies but faces increasing challenges such as evolving customer expectations, complex inventory systems, and constant pressure to remain profitable. In this context, retail store managers—particularly those without IT expertise—must navigate operational complexities while addressing technical failures that can disrupt store performance. These issues often increase stress and divert attention from strategic, revenue-generating activities. To address this gap, the thesis investigates a central research question: How can AI solutions bridge the gap in technical error resolution for non-IT retail store management? The study adopts a mixed-methods approach, combining qualitative and quantitative data. Surveys were conducted with 50 non-IT-skilled retail store managers to gather insights on the nature and frequency of technical issues, current troubleshooting practices, and attitudes toward AI tools. In-depth interviews with 10 retail managers provided contextual understanding of their real-world experiences. A comprehensive literature review supported the primary research by examining existing AI applications in retail—such as predictive maintenance, real-time troubleshooting tools, and virtual assistants. The study also explored key barriers to AI adoption, including trust, usability, and training requirements. The results show that AI-driven solutions can significantly reduce store downtime, decrease reliance on external IT support, and improve the overall efficiency of retail operations. Managers reported that predictive maintenance and automated diagnostics were particularly useful, enabling them to manage technical disruptions independently. Additionally, user-friendly AI interfaces enhanced the customer experience by ensuring smoother store performance. The findings underscore the importance of tailored AI training and intuitive design for successful adoption among non-technical users. Moreover, the integration of AI tools fosters a more agile and responsive retail environment, enabling managers to make data-driven decisions in real time. Based on these insights, the thesis proposes a practical framework for integrating AI tools into non-IT retail settings, offering a pathway toward digitally empowered, stress-resilient retail management. The framework includes a phased implementation strategy, emphasizing change management and cross-functional collaboration to ensure long-term success. It also highlights the need for continuous feedback loops to adapt AI systems to store-specific contexts and evolving business needs. This research strengthens the link between technical challenges and managerial capability, positioning AI as a highly informative and empowering resource for the future of retail. It contributes to the broader discourse on digital transformation by focusing on the role of front-line managers in shaping AI adoption and success.