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AI Inventory

1. Inventory of AI Technologies

Compile a comprehensive list of all AI technologies and platforms in use.

Categorize AI tools by function (e.g., machine learning, natural language processing, computer vision).

Assess the licensing and ownership of each AI technology.

Identify any redundant or overlapping AI tools within the organization.

2. Data Management Practices

Inventory data used for training AI models, including sources and formats.

Evaluate data pipelines and workflows for efficiency and effectiveness.

Assess the security measures in place for sensitive data used in AI.

Ensure data is regularly updated to maintain AI accuracy and relevance.

3. AI Development Lifecycle

Document the processes for AI model development, testing, and deployment.

Identify tools and frameworks used in the AI development lifecycle.

Review version control practices for AI models and associated code.

Establish clear guidelines for testing and validation of AI models.

4. Risk Assessment

Conduct a risk analysis for AI systems, identifying potential vulnerabilities.

Evaluate the impact of AI failures on business operations and reputation.

Develop a risk mitigation plan for identified AI-related risks.

Review disaster recovery plans to include AI systems.

5. Performance Evaluation Framework

Establish key performance indicators (KPIs) for AI effectiveness.

Implement regular performance audits of AI systems.

Gather user feedback on AI system performance and usability.

Create a feedback loop for continuous improvement of AI solutions.

6. Governance and Policy Framework

Define governance structures for AI initiatives, including roles and responsibilities.

Document policies regarding the ethical use of AI in the organization.

Ensure compliance with industry standards and best practices for AI.

Set up mechanisms for regular review and updates of AI policies.

7. Collaboration and Ecosystem Engagement

Identify partnerships with external organizations or vendors in the AI space.

Explore opportunities for collaboration on AI research and development.

Engage with industry forums and communities for knowledge sharing.

Promote interdisciplinary collaboration within the organization for AI innovation.

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