AI Implementation refers to the process of integrating artificial intelligence technologies into existing business processes, systems, and workflows to solve specific problems or enhance operational efficiency.

Key phases of AI implementation include:

• Problem Definition: Identifying specific use cases where AI can add value
• Data Preparation: Collecting, cleaning, and organizing relevant data
• Model Selection: Choosing appropriate AI/ML algorithms and approaches
• Development & Training: Building and training AI models
• Integration: Incorporating AI into existing systems and workflows
• Deployment: Rolling out AI solutions to production environments
• Monitoring & Maintenance: Ongoing performance tracking and model updates

Successful AI implementation requires cross-functional collaboration, change management, and consideration of ethical, legal, and technical constraints.