A warning from an IBM training manual in 1979 stated, “A computer can never be held accountable, therefore a computer must never make a management decision.” As artificial intelligence (AI) becomes more integrated into enterprise decision-making by 2026, this caution is increasingly relevant. AI is no longer just a tool for analysis; it is shaping decisions in areas like forecasting and risk assessment. However, the rapid adoption of AI is outpacing the development of governance frameworks, leading to significant accountability challenges. Organizations are now faced with the dilemma of determining responsibility when AI-driven recommendations result in errors or losses. This governance gap raises concerns about who is accountable—the executives who approved AI use, the engineers who built it, or the organization as a whole. Despite AI's capabilities, it still lacks ethical and contextual judgment, necessitating human oversight in decision-making processes. As companies shift focus from AI adoption to governance, new frameworks are being established to ensure transparency and accountability in AI systems, including real-time monitoring of AI behavior.