Hybrid Intelligence in Business Analytics: Integrating Large Language Models with Traditional Predictive Analytics

Business analytics faces growing complexity due to heterogeneous data sources, dynamic decision-making requirements, and the need for scalable yet interpretable solutions. Traditional machine learning (ML) models, although effective for pattern recognition, often struggle with preprocessing unstructured data and contextual adaptability. Conversely, large language models (LLMs) excel at semantic understanding but lack domain-specific precision and reliability. This paper introduces Hybrid Intelligence, a novel framework that synergizes rule-based systems, LLMs, and ML to bridge these gaps. The proposed system automates end-to-end business analytics work-flows through three core integrations. First, rule-guided LLMs streamline data preparation by combining deterministic logic (e.g., handling missing values) with context-aware recommendations from Groq’s DeepSeek-R1, reducing manual effort by 60% compared to conventional methods. Second, dynamic insight generation leverages LLMs to interpret natural language queries, generate visualizations, and explain trends, enabling non-experts to interact with tabular data. Third, ML model orchestration uses LLM-inferred problem categorization to automate predictive tasks (e.g., XGBoost for regression, LSTM for forecasting) while embedding rule-based constraints for compliance. Validated on financial and retail datasets, the framework demonstrates a 35% improvement in forecasting accuracy over standalone ML models and a 50% reduction in data preparation time. Case studies in supply chain optimization and customer churn prediction highlight its adaptability. By integrating deterministic safeguards with generative AI, the framework mitigates LLM hallucination risks while preserving flexibility. This work advances hybrid AI systems in business contexts, offering a scalable blueprint for combining structured logic, generative models, and predictive analytics.