AI Policy and Guidelines - An Outline For Your Business.
As we navigate the integration of artificial intelligence into our operations, it is essential to establish a governance framework that balances innovation with responsibility. The Draft AI Guidelines below serve as a strategic starting point for your internal discussion, proposing nine core pillars that range from ensuring purposeful, value-aligned use to maintaining strict standards for privacy and data protection. However, transforming these principles into actionable policy requires leadership alignment on specific operational boundaries. The following discussion questions highlight critical decision points such as defining the necessary scope of human oversight, establishing your transparency protocols, and determining your tolerance for risk. This will ensure your final guidelines effectively support our organizational goals.
To further assist you, I have created a customGPT that will allow you to describe your business and your tolerance for risk, and draft guidelines will be created. These are based on a repository of model guidelines that I have found. Here is the link: https://bit.ly/LLguidelines
Draft AI Guidelines (Starting Point for Leadership Team Discussion)
1. Purposeful and Value-Aligned Use
AI should only be used when it clearly supports our organizational goals, improves efficiency, or enhances service quality.
Every AI use case should have a defined business purpose.
Human judgment remains accountable for important decisions.
Discussion question: What outcomes do we most want AI to improve and which decisions must always stay human-led?
2. Transparency and Explainability
Teams should understand the limitations of the AI they use, including potential bias and uncertainty in outputs.
AI-generated content should be identifiable when shared externally.
Users must be clear when they are interacting with AI, not a human.
Discussion question: How transparent do we need to be with customers, partners, or internal stakeholders about our AI use?
3. Privacy and Data Protection
AI must be used in ways that respect confidentiality, regulatory requirements, and data-minimization practices.
Do not enter sensitive, personal, or confidential data into tools unless explicitly approved.
Use the minimum amount of data necessary for a task.
Discussion question: What types of data in our organization need strict controls before AI can interact with them?
4. Security and Safe Deployment
AI tools must comply with IT security standards and be vetted before operational use.
Use only approved AI platforms.
Monitor outputs for security risks (e.g., hallucinated instructions, harmful artifacts).
Discussion question: Who approves new AI tools, and what’s the process?
5. Fairness and Responsible Outcomes
AI should avoid reinforcing bias, discrimination, or inequitable practices.
Review AI recommendations for fairness.
Document where risks of unintended harm could occur.
Discussion question: Where in our workflows could biased AI outputs have material consequences?
6. Human Oversight and Review
AI augments, not replaces, professional expertise.
Humans must validate AI outputs before use in decisions, customer interactions, or published content.
Staff remain accountable for outcomes.
Discussion question: Which roles are responsible for final review and sign-off when AI is involved?
7. Skill Development and Organizational Learning
We commit to continuously improving our AI literacy across roles.
Provide training on safe, effective AI use.
Share lessons learned from pilots or experiments.
Discussion question: What level of AI capability do we want each team or role to achieve?
8. Ethical Use and Social Responsibility
AI should be used in ways that reinforce trust, dignity, and societal well-being.
Avoid deceptive, manipulative, or harmful practices.
Consider long-term impacts on employees and customers.
Discussion question: How does AI support or potentially conflict with our organizational values?
9. Evaluation, Monitoring, and Continuous Improvement
AI tools and use cases should be monitored for accuracy, performance, and unintended consequences.
Establish measures of success and risk indicators.
Regularly review and refine guidelines.
Discussion question: How frequently should we review AI performance and update our governance?
