A Business Guide to AI Policy

The Reality of Unstructured AI Use 

AI use is already happening in most organizations, whether it’s formally approved or not. Without clear expectations, employees make individual judgment calls about what data to use, how outputs are applied, and whether results are reviewed. 

This creates real challenges: 

• Data risk from sensitive information entered into public tools  

• Inconsistent outputs and client experience  

• Lack of accountability for outcomes  

• Duplicated effort across teams  

The result is increased risk, wasted time, and uneven return on AI investment. 

Unstructured AI use also creates opportunity cost. Teams work in isolation, repeat mistakes, and fail to scale what works across the organization. 

AI policy is a key step in moving from ad hoc experimentation to consistent, reliable use. 

Pillar 1: Make AI Use Visible 

Understanding Shadow AI 

Shadow AI refers to AI use that happens informally, without oversight or defined expectations. It often emerges because employees are trying to work more efficiently without clear guidance. 

This can compromise sensitive information or degrade the client experience. 

Common Risky Use Cases 

Examples include: 

• Publishing client-facing content generated by AI without proper review  

• Summarizing internal reports using public tools  

• Entering internal or client data into AI tools without understanding how that data is handled 

Creating Visibility into AI Usage 

The first step in governing AI is visibility. Leaders should acknowledge that AI is already part of daily work and take a simple inventory of where and how it is currently being used. 

This is often done through an internal survey. The goal is to surface existing use cases and identify where guidance is needed. 

Building Guidelines for Safe AI Use 

Organizations may choose to approve specific tools or define which use cases are appropriate in different environments. 

The goal is to allow teams to experiment safely while ensuring sensitive workflows are handled within secure tools. 

Encouraging Shared Learning and Transparency 

Creating a shared space for AI learnings helps teams build capability together. For example, a dedicated Teams channel can be used to share workflows, prompts, and lessons learned. 

This allows organizations to monitor usage patterns while helping teams learn from each other. 

Reducing Risk Through Transparency 

Shadow AI creates risk when usage is hidden. Encouraging transparency reduces that risk and helps organizations scale AI use more effectively. 

Pillar 2: Ownership and Accountability 

One of the biggest gaps in AI governance is ownership. When no one owns AI, decisions become fragmented and risks go unnoticed. 

Most organizations benefit from defining two roles: 

• AI Owner: Oversees usage patterns and ensures governance is addressed at a leadership level  

• AI Adoption Lead: Helps teams apply AI in real workflows and supports ongoing adoption  

These responsibilities can sit within existing roles and typically require only 1–2 hours per week. 

What matters is clear ownership and a defined path for support and escalation. 

Pillar 3: AI Principles 

Draw Principles from Your Values 

AI principles should be grounded in company values and focused on behavior, not technology. They should be simple, practical, and easy to apply. 

Examples of Practical AI Principles 

• AI-generated work is reviewed before being shared externally  

• AI supports decision-making but does not replace accountability  

• AI is used to improve efficiency without sacrificing quality  

• AI outputs are reviewed with the same scrutiny as a new employee’s work  

Guiding Decisions in New Situations 

AI policies cannot cover every scenario. Principles help teams make decisions in new or evolving situations while staying aligned with company expectations. 

Pillar 4: AI Policy Contents 

Positioning the Policy as a Practical Guide 

An AI policy should function as a practical guide, not a legal document. It should be clear, concise, and grounded in real workflows. 

Core Elements of an AI Policy 

At a minimum, an AI policy should include: 

• The purpose of AI within the organization  

• AI principles  

• Approved and prohibited uses  

• Expectations for review and oversight  

• Ownership and governance structure  

• How and when the policy will be updated 

Keeping Policies Usable and Relevant 

Policies that are too complex or disconnected from daily work will be ignored. Clarity and usability should be the priority. 

From Control to Capability 

AI is becoming a core business capability, similar to email or cloud platforms. Organizations that succeed do not rely on informal experimentation. They build light, intentional structure through visibility, ownership, shared principles, and practical policy. 

The goal of AI governance is confidence, not restriction. When expectations are clear, teams can use AI effectively, responsibly, and with less risk.