The Complete AI Project Lifecycle
An AI project is a managed lifecycle—not a model-training event. Strong projects connect a human need with data, design, evaluation, deployment, monitoring and governance.
Learning objectives
What you will be able to do
- Explain the complete ai project lifecycle accurately in your own words.
- Recognise how the concept appears in real AI products and professional work.
- Identify an important limitation, risk or evaluation requirement.
- Apply the concept through a practical activity and knowledge check.
Core explanation
Build the right mental model
Discovery defines the user, decision, expected value and unacceptable harm. Feasibility checks data, technical capability, legal constraints and workflow readiness. A small pilot should test the riskiest assumptions before major investment.
Development includes data preparation, model or vendor selection, user experience, security controls and evaluation. Deployment introduces ownership, documentation, incident response and change management.
Operation monitors quality, drift, cost and impact. Retirement is also part of the lifecycle: systems should be removed when they no longer provide value, cannot meet requirements or are replaced.
Discovery
Define the problem, user, value, constraints and risks.
Pilot
A limited experiment designed to test important assumptions.
Operation
Monitor, support, secure and improve the deployed system.
Retirement
Safely remove a system and manage retained data or dependencies.
University knowledge assistant
A pilot answers common policy questions using approved documents and citations.
Apply your learning
Practical activity
- Create a one-page lifecycle for an AI idea.
- At each stage name the owner, evidence required and go/no-go decision.
- Add a retirement trigger.
Write your answers in a learning journal. The value comes from connecting the concept to your own profession.
Knowledge check
Test your understanding
Q1Why begin with discovery rather than a model?
Answer: The team must first establish that the problem is valuable, appropriate, feasible and safe to address with AI.
Q2What happens after deployment?
Answer: Continuous monitoring, support, incident handling, updating, governance and eventual retirement.
If you cannot explain the answer without reading it, revisit the core explanation and example before continuing.
Continue with authoritative sources
Important industry resources
These links lead to official organisations, industry laboratories or established open-source learning projects.