The AI Lifecycle refers to the iterative, end-to-end process of creating, deploying, and maintaining an artificial intelligence system, from the initial concept and problem definition to its eventual retirement or decommissioning.

Unlike traditional software development, which often follows a linear path (code → build → deploy), the AI lifecycle is cyclical and data-dependent. It requires continuous monitoring and retraining because AI models can degrade over time (model drift) as the real-world data they encounter diverges from the data they were trained on. Effective management of this lifecycle is the core function of MLOps (Machine Learning Operations) and is explicitly required by regulations like the EU AI Act and the NIST AI Risk Management Framework.

The AI Lifecycle is typically divided into the following key phases:

• 1. Planning & Design: Defining the business problem, determining if AI is the right solution, and establishing ethical guardrails and risk tolerance (e.g., "We will not use facial recognition").
• 2. Data Acquisition & Preparation: Collecting, cleaning, and labeling the data required to train the model. This stage is critical for preventing bias and ensuring data privacy compliance (GDPR).
• 3. Model Development & Training: Selecting the algorithm architecture, training the model on the prepared data, and fine-tuning its parameters to achieve desired performance metrics.
• 4. Evaluation & Validation: Rigorously testing the model against "holdout" data to ensure it generalizes well. This phase includes "Red Teaming" to check for security vulnerabilities and bias before release.
• 5. Deployment: Integrating the model into the production environment (e.g., an app or website) where it interacts with real users.
• 6. Operation & Monitoring: Continuously tracking the model's performance for accuracy, latency, and "drift." If performance drops, the cycle restarts at the Data or Training phase.
• 7. Decommissioning: Safely retiring the model when it is no longer useful or compliant, ensuring all data is deleted or archived according to policy.

Strategic Impact: Treating AI as a lifecycle rather than a one-time project is essential for long-term success. Organizations that neglect the "Operation & Monitoring" phase often experience "performance decay," where a once-accurate model becomes a liability. Furthermore, comprehensive lifecycle documentation is now a legal requirement for high-risk AI systems in many jurisdictions.