Dr. Lei Huang
Dr. Lei Huang discussed the role of AI across the sales cycle at the Society for Marketing Advances annual conference.
Papers evaluating AI presented by Professor of Business Administration Huang at the international conference, held Nov. 5 to 8 in Las Vegas, NV, were informed by the established phases of prospecting, the sales call and follow-up.
The topic is pedagogically practiced by Huang with students in two courses Huang teaches, BUAD 411: Strategic Marketing Management, BUAD 351/HONR 306: Digital Marketing.
“The students’ feedback in these courses gave me valuable information in polishing the relevant research focuses,” Huang noted.
In this research, Huang, of the Department of Business Administration, and his collaborators identify where artificial intelligence (AI) improves outcomes and where human judgment, empathy and rapport are indispensable. Despite its promise, AI is not a plug-and-play solution. Salespeople must be trained to interpret AI insights, question data sources and recognize algorithmic bias.
He also noted that there is also a risk of over-reliance, treating AI as a crutch instead of as a coach. Many customers, particularly in high-value business-to-business (B2B) contexts, resist automation and prefer relationship-based selling.
An additional challenge is ethical. AI-driven persuasion can cross into manipulation, undermining informed choice and autonomy. Ethical AI use must prioritize transparency, consent and long-term trust.
Huang’s research extends sales process models by explicitly integrating AI-human role delineation at each stage of the personal selling cycle. Customer perceptions shape AI’s effectiveness in sales interactions while cultural differences further complicate adoption.
For instance, in relationship-oriented markets (e.g., parts of Asia and Latin America), heavy reliance on AI for interpersonal stages can be perceived as impersonal or disrespectful. Compliance with emerging regulations such as General Data Protection Regulation (GDPR) and AI transparency laws adds another layer of complexity, requiring sales organizations to ensure that AI-driven personalization does not compromise privacy or informed consent.
Theoretically, Huang’s research contributes to adaptive selling literature by testing AI’s differential value across stages and contexts. Practically, this study provides sales educators and managers with empirically supported guidelines for integrating AI tools into sales training and operations, identifying when to automate, when to augment and when to preserve human-led interactions.
