One of the biggest challenges I’ve seen when applying AI to business is bridging the gap between technical capabilities and real-world needs. Many clients expect AI to be a magic fix, but they often underestimate the importance of clean, structured data. Another major hurdle is aligning AI models with existing workflows—people resist change, and integration takes time. There’s also the ethical aspect: making sure the AI is transparent and fair, especially when it influences decisions. Lastly, maintaining trust is critical. If an AI system fails even once, stakeholders lose confidence quickly. Balancing innovation with reliability is always a tightrope walk.