The graveyard of AI initiatives is full of successful pilots that never made it to production. This gap - sometimes called the "pilot purgatory" - is one of the most common and frustrating challenges organisations face. A proof of concept works beautifully in a controlled environment with clean data and enthusiastic users, but falls apart when confronted with real-world data quality issues, integration requirements, security constraints, and the need for ongoing monitoring and maintenance. Scaling from pilot to production requires planning for a different set of concerns than building the initial model. You need reliable data pipelines, model monitoring to detect performance degradation, retraining workflows, integration with existing systems, user training, change management, and clear ownership of the system once the project team moves on. The technical challenges are real but rarely the primary obstacle. More often, the barriers are organisational: unclear ownership, insufficient investment in the unglamorous infrastructure work, resistance from teams whose workflows are being changed, and leadership attention that has already moved on to the next exciting initiative. Successful scaling requires treating the pilot as the beginning of the journey, not the end, and budgeting accordingly - both in money and in sustained leadership commitment.