Proving that AI investments are paying off is one of the most persistent challenges in the field. The difficulty isn't just measurement - it's attribution. When an AI system improves a process that involves multiple steps and human decisions, isolating AI's specific contribution is genuinely hard. Did sales increase because of the AI-powered recommendation engine, or because of the new marketing campaign that launched the same month? Traditional ROI frameworks often struggle with AI because the benefits are frequently indirect, delayed, or distributed across multiple business areas. Effective measurement requires setting clear baselines before deployment, defining success metrics that connect to actual business outcomes rather than technical performance, and designing controlled experiments where possible. It also means being honest about what you can and can't measure. Some AI benefits - like faster decision-making or improved employee satisfaction - are real but difficult to quantify precisely. The organisations that handle this well use a mix of hard financial metrics and softer indicators, and they accept that perfect attribution isn't always possible while still maintaining rigorous standards for investment decisions.