Artificial Intelligence-Enabled Business-to-Business Digital Selling: A Mechanism-Based Framework for Entrepreneurial Business Development
Abstract
Artificial intelligence (AI) is increasingly embedded in business-to-business (B2B) selling through automated lead scoring and routing, generative tools that draft and personalize outreach, and call-analytics systems that provide coaching. Yet many adoption narratives treat these tools as productivity aids, even though business development depends on relationship processes such as trust, perceived authenticity, and frontline agency. Drawing on an integrative review of recent research across marketing, sales management, information systems, and human-automation interaction (primarily 2016-2025), this paper develops a mechanism-based framework for AI-enabled digital selling in entrepreneurial and growth-oriented firms. The framework explains outcomes through three pathways: efficiency gains, autonomy reconfiguration as tasks are delegated and monitored, and authenticity shifts that reshape buyer interpretations and recalibrate trust across the salesperson, the firm, and the AI system. We identify boundary conditions, including task codifiability, relationship criticality, frontstage versus backstage use, disclosure, control rights, and incentive intensity, that help predict when AI complements selling and when it backfires. We conclude with propositions, a research agenda, and practical suggestions for scaling business development with AI while protecting relationship quality and reducing ethical risk.
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