The Evolution of Financial Analysis: From Manual Methods to AI and AI Agents

Abstract Purpose: This study examines the transformation of financial decision-making through the adoption of artificial intelligence, focusing on the shift from conventional AI systems to AI agents and agentic AI. It differentiates between automated analytical tools and autonomous, goal-oriented systems that increasingly assume decision-making authority within financial operations. Design/Methodology/Approach: Employing a qualitative multi-method approach—comprising semi-structured expert interviews, industry report synthesis, in-depth case studies, and a comparative performance evaluation—this research investigates AI agent implementation across SMEs, pharmaceutical analytics, and ERP-integrated corporate finance. Theoretically, it extends foundational models including the Efficient Market Hypothesis (EMH), Behavioral Finance, and the Adaptive Markets Hypothesis (AMH) by embedding the dynamic, learning-driven nature of AI agents into financial decision logic. Findings: The results indicate that AI agents introduce novel forms of informational asymmetry, enhance bias mitigation through adaptive modeling, and give rise to emergent decision structures via multi-agent interactions. These dynamics challenge core assumptions of market rationality and static efficiency. Practically, the study offers a structured framework for AI agent integration, emphasizing explainability, hybrid human-AI governance, and risk-specific safeguards to navigate ethical and regulatory constraints. The proposed conceptual taxonomy and cross-industry implementation roadmap reposition agentic AI as a strategic transformation—reshaping how financial institutions process data, execute judgments, and regulate algorithmic autonomy.