AI Won’t Fix a Broken Business Process

Why enterprise AI projects should start with workflow design, not model selection An enterprise AI project often starts with the same question: Which model should we use? Should it be GPT, Claude, Gemini, an open-source model, or a smaller model running privately? That question matters, but it is rarely the best starting point. A more useful question is: Which business process are we trying to improve? Many enterprise processes are already difficult before AI enters the picture. Data is incomplete. Responsibilities are unclear. Different systems contain conflicting information. Business rules exist only in people’s memories. Exceptions are handled manually. Nobody can clearly explain where a process begins, where it ends, or how success is measured. Adding AI to such a process does not automatically solve these problems. It may simply make the process faster, larger, and more difficult to understand. 1. The AI Project Usually Starts in the Wrong Place Imagine a company building an AI assistant for its sales team. The assistant should recommend the next action for each customer. It might suggest a follow-up call, identify an inactive opportunity, or remind a salesperson about an unanswered request. At first, this sounds like a straightforward AI feature. But several questions appear immediately: What does “next action” actually mean? Who is responsible for defining it? Which data should the assistant trust? What happens when the CRM contains duplicate customer records? How should it treat an opportunity with no recent activity? What if the customer has already contacted support about the same issue? How do we measure whether the recommendation was useful? These are not primarily model-selection questions. They are questions about process definition, data ownership, business rules, permissions, and workflow design. A team can spend weeks improving prompts while the real problem remains unresolved. The model may become better at producing recommendations, but the recom