In many organizations, leaders believe they are “managing” artificial intelligence (AI) by issuing a carefully worded policy. A directive goes out: Employees must only use company-approved AI tools. Training follows. The intranet is updated. Boxes are ticked. In many cases, AI can write a better AI policy than a human.

In my experience, employees at every level are using AI through personal accounts on their own devices. Some do it to save time or because they find the approved tool less effective. Others simply prefer their own way of working. But the reality is that in the modern workplace, convenience and speed often win over compliance. Once a tool as accessible and powerful as AI becomes part of the cultural and operational fabric of how people work, no policy, no matter how well-crafted, can put it back in the bottle.

Why Policies Alone Fail

Some leaders cling to the belief that employees will simply follow the rules if the rules are clear enough. But ignoring the reality of personal AI use creates significant risks, including the potential for data leakage (employees may inadvertently share proprietary information with unsecured AI platforms) and compliance violations (AI-generated outputs could infringe on copyright, breach data privacy laws or run afoul of industry regulations). There's also the potential for reputational damage. A single high-profile misuse of AI or lack of transparency can erode trust with customers, investors and the public.

This is why well-drafted policies are still essential. They set expectations, create boundaries and protect organizations legally. But in the case of AI, policies alone are insufficient for three key reasons:

1. Ease Of Access

Anyone with a smartphone can spin up an account on a free or low-cost AI platform in minutes. IT gatekeeping is virtually impossible without draconian surveillance.

2. Lack Of Immediate Consequences

Violating an AI use policy rarely leads to visible harm in the short term. Without a clear “cause-and-effect” link, compliance feels optional to many employees.

3. Cultural Resistance

A policy that conflicts with how employees believe work should be done will be ignored in practice, even if acknowledged in theory.

This is where leaders must shift their mindset from control through rules to management through process and engagement.

Governance As A Living Process

AI governance is not a static set of documents; it’s a living framework that requires ongoing human oversight. Organizations need to operationalize governance through processes that account for human behavior, not just technological risk. That means:

Integration, Not Isolation

AI should be built into workflows in ways that feel natural and useful to employees. If the company’s approved AI tools are overly restricted or guardrails impede efficiency, employees will likely find workarounds.

Human Gatekeeping

Establish cross-functional role-based checkpoints where managers or designated “AI champions” review how AI is being used in specific functions.

Scenario-Based Training

Move beyond generic “do’s and don’ts.” Use real-life examples and case studies to show how improper AI use can create legal, financial or reputational risks.

Feedback Loops

Governance should include channels for employees to suggest improvements to AI tools and policies, making them partners in the process rather than passive recipients.

Checks And Balances

Does this process belong to the human resources, legal or IT department? The answer is a little bit of each. The complexity of processes around AI mean checks and balances are needed. Every department protects itself and its budget so no one department should be responsible for results or KPIs may be skewed.

Building A Culture Of Responsible AI

The path forward is cultural and behavioral as much as it is procedural. Organizations must foster a workplace where responsible AI use is the norm, not because employees fear punishment, but because they understand the stakes and see value in compliance.

This cultural shift involves:

• Transparency from leadership: Leaders should share how they themselves use AI, modeling appropriate practices.

• Empowerment within boundaries: Give employees freedom to innovate with AI, but within clearly defined and well-communicated guardrails.

• Shared ownership: Position AI governance as a collective responsibility, not just an IT or legal mandate.

The challenge for leaders is not how to stop unauthorized AI use, but how to channel it into safe, productive and ethical use. Governance and oversight of AI and a deeper understanding of organizational behavior is where humans will still be needed in the future. This requires moving beyond a policy mindset to an operational governance mindset, one that recognizes human behavior as the key variable. Technology can be monitored, policies can be enforced but without embedding AI governance into the day-to-day rhythms of work, compliance will remain aspirational rather than actual.