Artificial Intelligence in the Automotive Dealership Operating Model: From Predictive Pricing to Sales Automation and Service Scheduling

Dmytro Puzynkin

Citation: Dmytro Puzynkin, "Artificial Intelligence in the Automotive Dealership Operating Model: From Predictive Pricing to Sales Automation and Service Scheduling", Universal Library of Business and Economics, Volume 03, Issue 03.

Copyright: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Automotive dealerships operate under persistent margin pressure from transparent online pricing and shifting service expectations. This paper examines how artificial intelligence (AI) methods, specifically machine learning-based predictive pricing, automated lead management, and natural language processing (NLP) appointment scheduling, can be embedded in a dealership’s core operational architecture. The study applies a systematic literature review of peer-reviewed publications indexed in Scopus and Web of Science, supplemented by case evidence from published industry analyses. Results show that gradient boosting and random forest models achieve R2 scores above 0.90 for used-vehicle price prediction, NLP-driven scheduling agents reduce appointment no-shows by approximately 30-40%, and AI-powered lead scoring increases conversion rates by 40-60% compared to rule-based CRM filtering. Based on these findings, the paper proposes an original four-stage AI Maturity Progression Model that maps a dealership’s transition from reactive, rule-based processes to adaptive, data-driven operations. The framework offers practitioners a structured roadmap for phased AI investment. These findings are relevant to dealership managers, automotive retail strategists, and researchers working at the intersection of operations management and applied machine learning.


Keywords: Automotive Dealership; Predictive Pricing; Machine Learning; Natural Language Processing; Appointment Scheduling; Lead Scoring; Inventory Optimization; AI Maturity Model; Random Forest; Sales Automation.

Download doi https://doi.org/10.70315/uloap.ulbec.2026.0303005