Consumer Engagement through Predictive Intelligence and Automated Decision Systems: Examination of role of Artificial Intelligence in Modern Marketing
Year: 2026
Type: preprint
Abstract: This working paper attempts to examine how machine learning, natural language processing and generative models are being embedded into marketing decisions. The examination moves the discipline from periodic, campaign-led planning toward continuous, evidence-based optimization. The study reviews the technological foundations of AI-enabled marketing, describes the data architecture that supports it. The working also proposes to analyses six major application areas for evaluation. These domains are recommendation and personalization, predictive scoring, programmatic advertising and attribution, conversational interfaces, generative content production, and dynamic pricing. Illustrative implementations from streaming, retail, beverages, beauty and business-to-business (B2B) software are discussed to show how these tools operate in practice. The paper also evaluates the risks that accompany adoption, including privacy exposure, embedded bias, model opacity, uneven access to capital and talent, and the temptation to manipulate rather than serve consumers. It concludes that durable advantage will not come from the sophistication of algorithms alone. It will come from organizations that pair automated decision systems with human judgment, treat customer data as a trust obligation, and measure success by long-term customer value rather than short-term click behaviour.
Author: Shivesh Kumar
Language: English
Topic: AI in Service Interactions
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225144741