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Whole of AI lifecycle statements apply across multiple AI product lifecycle stages, for ease of use and to minimise content duplication.

Transactional services lead to a change in government-held records, typically involving an exchange of information, money, licences or goods.

Examples of transactional services include:

• logging in to a portal or platform
• submitting a claim
• registering a business
• updating contact details
• lodging a tax return
• subscribing to newsletters
• grant applications
• public consultation submissions.

The design statements includes concept development, requirements engineering, and solution design.

The data statements cover the establishment of the processes and responsibilities for managing data across the AI lifecycle. This stage includes data used in experimenting, training, testing, and operating AI systems.

The train statements covers the creation and selection of models and algorithms. The key activities in this stage include modelling, pre and post-processing, model refinements, and fine-tuning. It also considers the use of pre-trained models and associated fine-tuning for the operational context.

The evaluate statements' cover testing, verification, and validation of the whole AI system. It is assumed that agencies have existing capability on test management and on testing traditional software and systems. 

The integrate statements focuses on implementing and testing an AI system within an agency’s internal organisational environment, including with its systems and data.

The deploy statements caters for introducing all the AI technical components, datasets, and related code into a production environment where it can start processing live data.

The monitor statements provides for the operationa and maintenance of the AI system. Monitoring is critical to ensuring the reliability, availability, performance, security, safety, and compliance of an AI system after it is deployed.