ProfitPilot: Autonomous AI System for Profit Management

Abstract

Artificial Intelligence (AI) is becoming increasingly useful in business analysis and decision-making. Recent developments in Large Language Models (LLMs), agentic AI, and multi-agent systems allow AI to perform a series of connected tasks such as collecting information, analyzing data, using tools, and generating insights. However, many existing studies focus on individual tasks such as data analysis, forecasting, visualization, or workflow automation. There is still a need to understand how these capabilities can be combined to support enterprise profit management. This article presents a literature survey of 12 recent studies covering autonomous data science, multi-agent systems, enterprise analytics, domain-specific decision support, and agentic AI. The studies are reviewed and compared based on their objectives, approaches, architectures, data settings, evaluation methods, findings, and limitations. The review shows that agentic AI has made progress in autonomous analysis, multi-agent collaboration, knowledge retrieval, tool-based reasoning, visualization, and decision support. At the same time, challenges remain in areas such as domain knowledge, reliability, agent coordination, evaluation, explainability, and governance. Based on the findings of the reviewed studies, this article presents ProfitPilot as a proposed framework for applying agentic AI to enterprise profit management. The framework combines business-data processing, financial analysis, predictive analytics, domain knowledge, multi-agent reasoning, and explainable recommendations. ProfitPilot is presented as a proposed research framework rather than a proven system for increasing profitability. The article identifies the key challenges that need to be addressed in future work to develop reliable, explainable, and human-supervised AI systems for enterprise decision support.