hnologies effectively. However, the process does present some common challenges. This article explores the five most frequent mistakes made during the integration of into your company’s non-technical teams. AI Lack of Clear Objectives Many companies rush into AI projects without taking the necessary time upfront to define clear goals. Establishing objectives is a critical first step. Carefully consider what problems they want AI to solve and what benefits it will deliver to the business.
Take time to audit your existing data sources. Look for inconsistencies, missing fields, duplicate records, and out-of-date information. This data clean-up process is crucial but often overlooked. Establish governance guidelines around data collection, storage, and access as well. Clearly defining roles and standards helps facilitate model training down the road. It’s also important to map your data to identify relationships between fields.