Articles | Open Access | DOI: https://doi.org/10.37547/tajmei/Volume07Issue03-02

Algorithmizing B2B Sales: Can AI Create a Sales Framework That Guarantees Predictable Results?

Birchak Oleksii , B2B Sales Specialist at Uniqa New York, USA

Abstract

The article examines the impact of artificial intelligence on B2B sales processes, in particular, its role in increasing efficiency and predicting customer behavior. The article analyses the use of such technologies as natural language processing [NLP], machine learning [ML], computer vision, and chatbots. Particular attention is paid to the practical case of the American company Dell, which has achieved an increase in the conversion rate and optimization of the sales department with the help of Lattice Engines analytics.

The study demonstrates that AI integration allows branding agencies to create more accurate customer profiles, automate routine tasks, and increase the level of interaction personalization. It is concluded that further development of predictive models and integration of AI with customer relationship management [CRM] systems are essential for achieving predictable results and enhancing companies' competitiveness. The study's novelty highlights the practical benefits of integrating AI into the B2B sales process, particularly its role in improving sales efficiency, personalization, and lead generation.

However, the research is limited to analyzing existing AI applications and does not cover the potential risks associated with data privacy and ethical concerns. Future studies should address these challenges to ensure the responsible use of AI. The practical implications of this research include increased productivity, improved customer targeting, and enhanced decision-making processes. Social implications involve the transformation of the labor market, as AI automates routine tasks, necessitating workforce reskilling and adaptation to new roles. Thus, AI not only optimizes sales processes but also drives broader societal changes, highlighting the need for balanced technological adoption.

Keywords

B2B sales, machine learning, natural language processing, predictive analytics, branding agencies, automation

References

Alamäki A, Korpela P. Digital transformation and value-based selling activities: seller and buyer perspectives. Baltic Journal of Management. 2021, 16(2), 298–317.

Cotter T, Guan M, Mahdavian M, Razzaq S, Schneider JD. What the future science of B2B sales growth looks like [Internet]. McKinsey & Company; 2018 Jan [cited 2025 Feb 18]. Available from:

https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/what-the-fu ture-science-of-b2b-sales-growth-looks-like (accessed Feb. 20, 2025).

Davenport T, Guha A, Grewal D, Bressgott T. How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science. 2020, 48(1), 24–42. Enholm IM, Papagiannidis E, Mikalef P, Krogstie J. Artificial Intelligence and Business Value: a Literature Review. Information Systems Frontiers. 2021.

Fischer H, Seidenstricker S, Berger T, Holopainen T. Digital Sales in B2B: Status and Application. In: Markopoulos E, Goonetilleke RS, Ho AG, Luximon Y, editors. Advances in Creativity, Innovation, Entrepreneurship and Communication of Design. Cham: Springer International Publishing; 2021. p. 369–375.

Forsyth D, Ponce J. Computer vision: A modern approach. Upper Saddle River, NJ: Prentice Hall; 2011.

Jarek K, Mazurek G. Marketing and Artificial Intelligence. Central European Business Review. 2019; 8(2), 46–55.

Hunter GK. On conceptualizing, measuring, and managing augmented technology use in business-to-business sales contexts. Journal of Business Research. 2019, 105, 201–213. King R. How Dell predicts which customers are most likely to buy [Internet]. The Wall Street Journal; 2012 Dec 5 [cited 2025 Feb 19]. Available from:

https://blogs.wsj.com/cio/2012/12/05/how-dell-predicts-which-customers-are-most-likely-to -buy/ (accessed Feb. 18, 2025).

Mattila M, Yrjölä M, Hautamäki P. Digital transformation of business-to-business sales: what needs to be unlearned? Journal of Personal Selling & Sales Management. 2021, 41(2), 113–129. Mehta D, Senn-Kalb L. In-depth: Artificial Intelligence 2021: Statista Digital Market Outlook [Internet]. Statista; 2021 [cited 2025 Feb 19]. Available from:

https://de.statista.com/statistik/studie/id/50489/dokument/artificial-intelligence/ (accessed Feb. 19, 2025).

Paschen J, Kietzmann J, Kietzmann TC. Artificial intelligence (AI) and its implications for market knowledge in B2B marketing. Journal of Business & Industrial Marketing. 2019, 34(7), 1410–1419.

Syam N, Sharma A. Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice. Industrial Marketing Management. 2018, 69, 135–146.

The AI index report. 2024 [Internet]. Stanford University [cited 2025 Feb 18]. Available from: https://aiindex.stanford.edu/report (accessed Feb. 19, 2025).

Zhang D, Mishra S, Brynjolfsson E, Etchemendy J, Ganguli D, Grosz B, Niebles JC, Sellitto M, Shoham Y, Clark J, Raymond P. The AI Index 2021 Annual Report [Internet]. AI Index

Steering Committee, Human-Centered AI Institute, Stanford University; 2021 [cited 2025 Feb 18]. Available from:

https://aiindex.stanford.edu/wp-content/uploads/2021/11/2021-AI-IndexReport_Master.pdf (accessed Feb. 20, 2025).

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Oleksii, B. (2025). Algorithmizing B2B Sales: Can AI Create a Sales Framework That Guarantees Predictable Results?. The American Journal of Management and Economics Innovations, 7(03), 08–13. https://doi.org/10.37547/tajmei/Volume07Issue03-02