Year: 2025
Type: dissertation
Abstract: Artificial Intelligence (AI) is rapidly advancing and has already reshaped sales practices in traditional markets, especially in business-to-business (B2B) markets.Even though strategic-level discussions around AI are numerous, the true operational impact of AI on sales managers-especially on those lower in the hierarchy who manage sales activities directly at the "ground level"seems to be a question left largely unexplored.This study hope to fill that gap by looking at the lower-tier sales managers and examining how they might benefit from AI-powered automation in managing the sales processes.This thesis takes a qualitative exploratory research approach and uses semi-structured interviews, expert podcasts, webinars, and industry case studies to collect rich, contextually relevant data.It then uses systematic thematic analysis to provide deep insights into the application and impact of AI across the traditional seven steps of the sales process. UEFResults demonstrate that AI significantly improves operational efficiency by automating basic tasks such as lead qualification, customer profiling, and data analysis.This allows sales managers to spend more of their time on the all-important job of building relationships and engaging with customers.It especially emphasizes that AI can foster interactions between companies and customers that are almost personalized greetings.But it warns that if they try to automate everything, they could wind up losing the customer relationships that they have worked so hard to build.Ultimately, the work focuses on specific training and infrastructure upgrades that will ignite the human-AI partnership and allow it to reach its full revolutionary potential.Again, this literature assists with both understanding AI and making it work in the B2B sales realm specifically at the lower level of management.Results from analysis .......
Author: Shuvo, MD Mahmudul
Institution: University of Eastern Finland
Language: English
Topic: AI in Service Interactions
Subfield: Marketing
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7215414224