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AI Solution Development Life Cycle: 7 Stages Explained
This episode of the ISACA Advanced in AI Audit (AAIA) exam prep series walks through the full journey of building an AI system, from initial business concept to eventual retirement. You’ll see how each stage of the life cycle introduces its own controls, risks, and audit checkpoints, and why this work tends to run as an iterative loop rather than a single pass. The discussion gives auditors a map for stepping into any AI project and quickly locating where things are most likely to break.
What this episode covers
- Use case development — defining the business problem, the five common failure causes, and why business-technology partnership matters.
- Design and feature selection — data ingestion, feature picking, the underfitting/overfitting trade-off, and how deep learning shifts the auditor’s lens.
- Development, preprocessing, and training — the deep cleaning phase, splitting data into training, validation, and testing sets, and hyperparameter tuning.
- Evaluation — using precision, recall, and the F1 score, plus testing for toxic or biased outputs.
- Deployment — wrapping the model in a user interface and going live with inference.
- Monitoring and maintenance — detecting model drift, applying observability, and retraining with fresh information.
- Decommissioning — locating derivative tools and the difficulty of cleanly removing learned data.
Watch the full episode above for the worked examples and detailed explanations of each concept.
Frequently Asked Questions
What are the stages of the AI solution development life cycle?
The life cycle moves from developing the use case, to design, to development, to evaluation, to deployment, to monitoring and maintenance, and finally to decommissioning. It is a repetitive loop of planning, designing, cleaning data, training, testing, and monitoring rather than a one-time straight line.
What is the difference between overfitting and underfitting?
Underfitting happens when you pick too few features, so the system is too simple to see real patterns and cannot explain the true drivers of a problem. Overfitting happens when you pick too many features, so the system memorizes its exact training examples but fails on new, slightly different data while also driving up computing cost.
How is data split during AI model training?
Clean data is split into at least three groups to prevent bias. The training dataset is the largest at roughly sixty to eighty percent and acts as the textbook. The validation dataset takes ten to twenty percent and works like a practice quiz to tune settings. The testing dataset takes the final ten to twenty percent and is reserved strictly for the final exam.
What is model drift in AI systems?
Drift happens when the real world changes but your system stays exactly the same. A model trained to recommend winter coats from December habits will give completely wrong recommendations by July. Observability acts like a continuous x-ray that watches system health so teams can detect drift and retrain the model with fresh information.
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Reference: This article is based on concepts discussed in AI Solution Development Life Cycle: 7 Stages Explained.