Businesses are increasingly integrating AI to enhance efficiency and remain competitive; it is anticipated that AI will automate up to 50% of jobs by 2033, streamlining activities within marketing and sales teams by enhancing productivity and reducing costs (Frey & Osborne, 2017). However, the application of generative AI in the B2B sector remains underexplored, despite its potential to transform complex, interaction-heavy marketing and sales processes where salespeople are crucial in building trust and long-term relationships through demanding informational exchanges. This research investigates the application of generative AI within B2B marketing and sales operations and how it can enhance firms’ competitive advantage. Utilizing the Situated AI Framework, the study empirically evaluates the relationship between specific AI-driven activities and competitive advantage in B2B settings. Through a qualitative research approach involving three detailed case studies, this research examines the strategic utilization of generative AI, focusing on grounding, bounding, and recasting activities as defined by the framework. Findings indicate that generative AI contributes to competitive advantage by enhancing operational efficiency, improving customer engagement, and enabling strategic data-driven decision-making. Each activity within the framework appears to play a significant role in adapting generative AI technologies to align with specific firm objectives. This adaption potentially enhances more than just task automation, but also possibly contributes to a strategic enhancement of the firms’ competitive advantage. In conclusion, this study encourages further investigation to validate and expand upon the strategic applications of generative AI in B2B marketing and sales operations. Adopting a quantitative approach in future studies could provide more comprehensive insights into how grounding, bounding and recasting activities enhance competitive advantages.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-532417 |
| Date | January 2024 |
| Creators | Forsell, Matilda |
| Publisher | Uppsala universitet, Institutionen för informatik och media |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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