Ai Pitfalls
7 Common mistakes in the implementation of Ai
2. Poor Data Quality & Management
1. Lack of Clear Business Objectives
3. Poor Integration with Existing Systems
4. Insufficient Change Management & User Adoption
5.Ignoring Model Monitoring & Maintenance
6. Security, Privacy & Ethical Oversights
7. Overcomplicating AI Solutions
✔ Start with a Clear Business Objective – Define what success looks like before implementing AI
✔ Plan for Model Maintenance – Set up monitoring, retraining, and performance checks.
✔ High-Quality Data – Establish data governance policies and continuously monitor input data.
✔ Engage Stakeholders Early – Train employees and integrate AI into existing workflows.
✔ Prioritize Security & Compliance – Follow best practices for data privacy, security, and ethical AI.