A Roadmap towards a Successful Implementation of AI methods in B2B Sales Forecasting Processes

Abstract

The integration of Artificial Intelligence (AI) into Business-to-Business (B2B) sales forecasting processes holds transformative potential, offering significant opportunities to enhance efficiency and precision in sales predictions. Especially during volatile times, AI-driven sales forecasting methods provide a strong basis for informed decision-making, ensuring resilience and adaptability in a highly competitive environment. This study aims to develop a comprehensive roadmap for the successful implementation of AI-driven sales forecasting methods. The research explores a wide range of AI methodologies to improve sales forecasting. It investigates strategies employed by companies to integrate AI into sales and marketing processes. Additionally, it identifies the primary challenges faced during implementation and examines the Critical Success Factors (CSFs) necessary for effective AI integration in B2B sales forecasting. A holistic case study methodology was selected due to its ability to provide a wide-ranging understanding of the complex interactions between AI technologies and organizational processes. This approach allows for the synthesis of an extensive literature review and qualitative findings from expert interviews with ten professionals from innovative companies in Upper Austria, capturing the complexities of real-world applications. Various forecasting techniques, ranging from time series analysis to advanced machine learning and hybrid methods, are examined in detail. Major challenges identified include data quality issues, integration complexity with existing organizational structures, and stakeholder resistance. Recommendations are provided for mitigating these obstacles. Key findings imply that successful implementation hinges on a range of critical success factors, including a clearly defined vision, effective data management, continuous improvement and feedback loops. The study concludes by presenting an extensive 7-step implementation roadmap, offering a strategic and systematic approach for companies to achieve significant improvements in forecasting quality and operational efficiency. This roadmap – the centerpiece of this study – outlines steps such as establishing clear objectives, selecting appropriate AI technologies, and fostering a culture of continuous learning. This is crucial as it enables organizations to realize enormous benefits, driving better decision-making and competitive advantage. These insights provide valuable and practical guidance, establishing a solid foundation for further research in the promising field of AI-driven sales forecasting methodologies.

Date of Award	2024
Original language	English
Supervisor	Piotr Kwiatek (Supervisor)