After an initial assessment of AI readiness and opportunities a definition of AI vision and objectives can be developed. Accompanying this, AI governance frameworks can be drawn up followed by the creation of a roadmap for implementation.
Workshops can be set up to identify potential AI applications based on the potential business benefits and feasibility. This will be followed by Proof of Concept plans leading to the creation of business cases for implementation.
A process of data preparation and feature engineering including the selection and training of a machine learning model. These can then be tested and validated before being integrated into standard business processes.
We can examine the need for AI ethics guidelines and principles as well as developing bias detection and mitigation strategies. AI explainability techniques can be included as well as the setting up of review boards to examine AI ethics.
To ensure the value of AI model performance monitoring tools can be put in place to work alongside audit trails and logging methods. There can also be the development of AI model retraining processes and the creation of AI model versioning and management systems.
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