How AI Assistants can help you understand your business and drastically accelerate Scenario Planning

How AI Assistants can help you understand your business and drastically accelerate Scenario Planning

Traditional CPG forecasting is slow, manual, and often relies on intuition rather than data-driven insights. Our AI-powered solution automates scenario planning, cutting weeks of effort down to hours. Using machine learning, it identifies key factors—like competitor pricing and economic trends—while ChatCPG’s AI agents generate scenarios and provide clear, actionable insights. This streamlined approach enables faster, smarter decision-making, helping businesses confidently navigate market shifts.

AI-powered Learning Assistants: Bridging the Gap Between CPG Expertise and Data Analytics

Current data analysis is complex and inaccessible for most CPG decision-makers. This post proposes an AI powered learning assistant that bridges the gap. It combines large language models with CPG expertise to answer user questions and automate complex analyses. The assistant learns from user interactions, building a “collective intelligence” that empowers the entire organization. This technology has the potential to transform how CPG companies leverage data for better decision-making.

SmartForecast for CPGs: Revolutionizing Decision-Making with AI

This article explores how Generative AI (genAI) can enhance traditional Machine Learning (ML) in solving complex problems faced by CPGs and Retailers. By providing common sense summaries of the patterns uncovered by ML as well as providing a way to dialogue with ML models, genAI can build your (human) team’s intuition about root causes, deepen trust between CPGs and retailers around forecasts, and increase alignment on complex pricing and promotional strategies.

Creating Synthetic Data to Train Your Own LLM

Large language models (LLMs) have shown great promise in being able to simplify business analytics workflows. Powerful third-party foundational models are being equipped to perform analytic tasks such as data cleansing, model selection and optimization, chart generation, etc. However, there are barriers that must be overcome before there is a large scale enterprise level adoption.