Optimization of Sales Forecasting and Market Growth Strategies for Biohort in the French B2B Market

Abstract

Purpose This thesis explores how small and medium-sized enterprises (SMEs) can effectively implement Artificial Intelligence (AI) in sales forecasting processes by addressing three key dimensions: change management, organizational culture, and data quality. Using Biohort as a case study, the research focuses on understanding the internal and external factors that influence the adoption of AI tools in the French B2B market. Methodology A qualitative case study approach was adopted. Data were collected through six semi-structured interviews, a focus group with sales and service staff, and direct observation. Thematic analysis was conducted using MAXQDA software, guided by predefined analytical dimensions: (1) Culture & Change Management, (2) Data Quality & Technical Readiness, and (3) Strategic Alignment of AI. Findings The study reveals that structured communication, early involvement of employees, and clear role definitions reduce resistance and foster collaboration. Cultural adaptability and leadership support enable openness to AI-driven change. However, poor data integration, low consistency, and lack of standardized processes severely hinder the reliability of AIbased forecasting models. Despite these barriers, SMEs can achieve meaningful progress through targeted training, internal champions, and phased implementation. Value This thesis presents a practical roadmap for AI adoption tailored to the realities of SMEs. It translates theoretical insights into actionable steps that can be adapted to various business contexts, helping organizations align cultural, organizational, and technical readiness. The roadmap and managerial implications offer scalable, low-risk strategies for SMEs to leverage AI in a sustainable and strategically aligned manner.

Date of Award	2025
Original language	English
Supervisor	Karin Palmetshofer-Hörschinger (Supervisor)