Home / Current Issue / Paper 1713977
AI-Powered Sales Intelligence as a Strategic Asset: Redefining Managerial Decision Authority in Large Commercial Organizations
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence
DOI: https://doi.org/10.64388/IREV8I2-1713977
Abstract
In large commercial organizations, managerial decision authority has traditionally been shaped by hierarchical structures, experiential judgment, and periodic performance reporting. While advances in sales analytics have improved visibility and insight, decision power has remained largely human-centered, constrained by information asymmetries, organizational scale, and delayed response cycles. As commercial environments become increasingly data-intensive and complex, these limitations have exposed the structural inadequacy of traditional decision authority models. This paper examines the emergence of AI-powered sales intelligence as a strategic managerial asset that fundamentally reshapes decision authority in large commercial organizations. Moving beyond the view of sales intelligence as a reporting or analytical function, the study conceptualizes AI-powered sales intelligence as an institutionalized decision capability embedded within organizational systems. From a business management perspective, the paper analyzes how artificial intelligence transforms the allocation, execution, and governance of managerial decision authority across sales organizations. The study argues that AI-enabled sales intelligence shifts decision authority from individual discretion toward system-based architectures operating under managerial design and oversight. Rather than diminishing managerial control, this shift redefines managerial responsibility toward strategic intent, governance, and accountability. The paper further explores the implications of algorithmic authority for centralization, organizational power dynamics, and sales leadership roles. By framing AI-powered sales intelligence as a strategic asset rather than a technical tool, this study contributes to management theory by clarifying its role in redefining decision authority at scale. For practitioners, it provides a conceptual foundation for governing AI-enabled sales intelligence systems in a manner that enhances performance while preserving strategic control. The findings underscore that sustainable competitive advantage arises not from analytics adoption alone, but from the deliberate managerial orchestration of decision authority in AI-enabled commercial environments.
Keywords
AI-Powered Sales Intelligence, Managerial Decision Authority, Sales Management Strategy, Algorithmic Decision Systems, AI in Large Commercial Organizations
References
[1] Davenport, T. H., & Harris, J. G. (2007). Competing on Analytics: The New Science of Winning. Harvard Business School Press.
[2] Davenport, T. H. (2014). Big Data at Work: Dispelling the Myths, Uncovering the Opportunities. Harvard Business Review Press.
[3] Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116.
[4] Simon, H. A. (1997). Administrative Behavior: A Study of Decision-Making Processes in Administrative Organizations (4th ed.). Free Press.
[5] Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux.
[6] Shrestha, Y. R., Ben-Menahem, S. M., & von Krogh, G. (2019). Organizational decision-making structures in the age of artificial intelligence. California Management Review, 61(4), 66–83.
[7] Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192–210.
[8] Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human–AI symbiosis in organizational decision making. Business Horizons, 61(4), 577–586.
[9] von Krogh, G. (2018). Artificial intelligence in organizations: New opportunities for phenomenon-based theorizing. Academy of Management Discoveries, 4(4), 404–409.
[10] Power, D. J. (2002). Decision Support Systems: Concepts and Resources for Managers. Quorum Books.
[11] Provost, F., & Fawcett, T. (2013). Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking. O’Reilly Media.
[12] Brynjolfsson, E., Rock, D., & Syverson, C. (2021). The productivity J-curve: How intangibles complement general purpose technologies. American Economic Journal: Macroeconomics, 13(1), 333–372.
[13] Guszcza, J., Rahwan, I., Bible, W., & Cebrian, M. (2018). Why we need to audit algorithms. MIT Sloan Management Review, 60(1), 1–4.
[14] Makridakis, S. (2017). The forthcoming artificial intelligence (AI) revolution: Its impact on society and firms. Futures, 90, 46–60.
[15] Wierenga, B., Van Bruggen, G. H., & Staelin, R. (1999). The success of marketing management support systems. Marketing Science, 18(3), 196–207.
[16] Wedel, M., & Kannan, P. K. (2016). Marketing analytics for data-rich environments. Journal of Marketing, 80(6), 97–121.
[17] Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120.
How to cite this paper
@article{1713977,
author = {Ufuk Elevli},
title = {AI-Powered Sales Intelligence as a Strategic Asset: Redefining Managerial Decision Authority in Large Commercial Organizations},
journal = {Iconic Research And Engineering Journals},
year = {2024},
volume = {8},
number = {2},
pages = {1215-1225},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1713977.pdf},
abstract = {In large commercial organizations, managerial decision authority has traditionally been shaped by hierarchical structures, experiential judgment, and periodic performance reporting. While advances in sales analytics have improved visibility and insight, decision power has remained largely human-centered, constrained by information asymmetries, organizational scale, and delayed response cycles. As commercial environments become increasingly data-intensive and complex, these limitations have exposed the structural inadequacy of traditional decision authority models. This paper examines the emergence of AI-powered sales intelligence as a strategic managerial asset that fundamentally reshapes decision authority in large commercial organizations. Moving beyond the view of sales intelligence as a reporting or analytical function, the study conceptualizes AI-powered sales intelligence as an institutionalized decision capability embedded within organizational systems. From a business management perspective, the paper analyzes how artificial intelligence transforms the allocation, execution, and governance of managerial decision authority across sales organizations. The study argues that AI-enabled sales intelligence shifts decision authority from individual discretion toward system-based architectures operating under managerial design and oversight. Rather than diminishing managerial control, this shift redefines managerial responsibility toward strategic intent, governance, and accountability. The paper further explores the implications of algorithmic authority for centralization, organizational power dynamics, and sales leadership roles. By framing AI-powered sales intelligence as a strategic asset rather than a technical tool, this study contributes to management theory by clarifying its role in redefining decision authority at scale. For practitioners, it provides a conceptual foundation for governing AI-enabled sales intelligence systems in a manner that enhances performance while preserving strategic control. The findings underscore that sustainable competitive advantage arises not from analytics adoption alone, but from the deliberate managerial orchestration of decision authority in AI-enabled commercial environments.},
keywords = {AI-Powered Sales Intelligence, Managerial Decision Authority, Sales Management Strategy, Algorithmic Decision Systems, AI in Large Commercial Organizations},
month = {August},
doi = {https://doi.org/10.64388/IREV8I2-1713977}
}