The Integration of Generative AI and Advanced Analytics for Business Innovation

Abstract

The emergence of generative artificial intelligence (GenAI)—encompassing large language models (LLMs), diffusion-based image generators, and code synthesis systems—has fundamentally disrupted the landscape of business analytics, decision intelligence, and enterprise innovation. While traditional analytics focused on descriptive, diagnostic, and predictive capabilities using structured data, GenAI introduces generative and prescriptive intelligence that can produce novel content, automate complex reasoning, synthesize unstructured information, and augment human creativity at unprecedented scale. This paper presents a comprehensive review-based and case-study investigation of GenAI integration with advanced analytics for business innovation across four enterprise domains: marketing content personalization, financial report generation and analysis, supply chain demand sensing, and customer service automation. A systematic review of 110 peer-reviewed publications (2022–2026) was supplemented by three original enterprise case studies conducted at the Centre for AI-Driven Business Research of Begum Rokeya University, Rangpur, Bangladesh, in collaboration with three Bangladeshi SMEs. Case Study 1 (marketing): a retrieval-augmented generation (RAG) system using GPT-4-Turbo with company product catalogs achieved 84.6% customer preference rate for AI-generated personalized email content versus 62.4% for template-based campaigns—a 35.6% relative improvement. Case Study 2 (finance): an LLM-powered financial narrative generator produced quarterly earnings summaries from structured financial data with a ROUGE-L score of 0.724 and factual accuracy of 96.8% verified by chartered accountants. Case Study 3 (supply chain): a multimodal GenAI system integrating GPT-4 text reasoning with time-series foundation model (TimesFM) forecasts reduced demand forecast MAPE from 18.4% (traditional ARIMA) to 11.2%—a 39.1% improvement. The findings demonstrate that GenAI-augmented analytics delivers measurable business value across enterprise functions when deployed with appropriate retrieval grounding, human-in-the-loop verification, and domain-specific fine-tuning [1], [2].

KEYWORDS: Generative AI, Large Language Models, Business Analytics, Business Innovation, GPT-4, Retrieval-Augmented Generation, Marketing Automation, Financial Analytics, Supply Chain, Enterprise AI