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As artificial intelligence (AI) becomes deeply embedded in modern business — powering everything from customer service interactions to strategic decision-making — the need for clear, actionable policy is urgent. Defining AI’s scope and application is now a foundational element of enterprise risk management, requiring attention to legal exposure, operational impact and ethics. 

Recent surveys show in at least one business function — up from the 55% reported two years ago. Yet despite AI’s rapid proliferation, effective governance remains elusive. Very few firms have implemented internal guidelines and policies to govern their use. That disconnect introduces real risk — not only in data security and compliance, but also to trust, performance and long-term innovation. 

Developing a strong AI policy is not simply about checking boxes or issuing static usage guidelines. It demands a proactive, principle-based framework that aligns with your organization’s risk appetite, evolves alongside your enterprise technology and scales across tools, vendors and geographies. It should be a flexible, strategic approach that moves AI policy from principle to practice, through three essential stages: intent, enablement and execution. 

Design governance that lets AI function 

If AI is positively reshaping workflows, as intended, then governance is simply the operating system behind it — invisible when working well but essential to everything running smoothly. 

And yet, governance remains one of the most misunderstood aspects of AI use. Too often, governance is viewed as a set of rules intended to minimize risk exposure by hindering AI’s effectiveness. That mindset doesn’t reduce risk, it just shuffles it around. Overly rigid policies create confusion, drive shadow AI use and stall education and adoption where they’re needed most. 

Done right, governance reframes AI policy as a strategic risk tool. It becomes a living framework that aligns innovation with enterprise risk appetite, operational context and ethical boundaries. It guides decision-making across the business and ensures consistency as AI becomes embedded in everything from agentic use cases to vendor ecosystems. 

Scalability is essential — not just across geographies, but across roles and technologies. The risks introduced by a generative AI content tool differ from those posed by predictive analytics in finance or compliance. Your policy should reflect that nuance, flexing where needed while maintaining core principles. 

And while such principles — transparency, accountability, fairness — should remain steady, your policy must be built for movement. AI evolves rapidly, as do the legal and operational risks accompanying it. Governance must include mechanisms for iteration, feedback and change so that your policy is resilient over time. 

Equip people with awareness, and they will use AI responsibly 

Even the most thoughtful AI policy will fail if the people it’s meant to guide don’t know they’re using AI in the first place. Today, AI is baked into everyday tools — sales enablement platforms, writing assistants, chatbots, CRM plug-ins — but many employees don’t recognize it. That lack of awareness is more than a training issue. It’s a risk exposure. 

Unintentional misuse remains one of the most significant threats to AI adoption. Here’s why: Employees may inadvertently upload sensitive data into generative tools, rely on AI outputs without human oversight or unknowingly violate privacy laws. But even when employees are aware they’re using AI, the risks don’t go away. More than , often out of fear of violating unclear guidelines or facing disciplinary action, creating gaps leadership can’t see or address.  

That’s why enablement is not a side task of policy deployment; it’s the backbone of responsible implementation. AI literacy must become a core workforce competency, and training must reach beyond technical teams to include sales, marketing, operations, HR and customer-facing staff. If AI touches every function, then every function needs a clear understanding of the rules, risks and responsibilities. 

Start by meeting people where they are: 

• Define the basics. Before asking employees to follow policy, ensure they understand what AI is, where it shows up in their work, and why responsible use matters. 
• Make training consumable. Long-form manuals and dense e-learning rarely stick. Drip campaigns, microlearning, gamified incentives and peer-led sessions are far more effective. 
• Test for clarity, not just completion. Ensure that staff aren’t breezing through these exercises to check a box. Ask people: Do you understand this policy? What doesn’t make sense? Don’t underestimate the cultural side of enablement. Employees are far more likely to engage with AI policy when it feels like an organizational priority, not just a legal requirement. 
• Executive buy-in matters. Not just in name but also in visibility. When senior leaders champion responsible AI use and tie it to business outcomes, policy becomes something people want to follow, not just have to. 

Operationalize oversight before it becomes urgent 

If governance is the blueprint and enablement is the buy-in, oversight is the engine. AI policy compliance doesn’t look like traditional compliance. It’s not just about whether a tool is approved. It’s about whether people know how to use AI responsibly, whether third-party risks are being accounted for and whether there’s visibility into how these tools evolve. 

A mature oversight strategy turns policy into an active feedback loop. The systems in place must be able to answer key questions in real time: 

• Who’s using AI, and for what purpose? 
• Are employees following approval workflows before adopting new tools? 
• How many vendors are using AI in ways that affect your data, billing models or liability? 

Metrics matter. Completion rates for training, policy engagement levels, the number of AI-related help desk requests and the volume of new tool approvals all signal whether your AI policy is functioning or being bypassed. But so does qualitative input: Confusion, resistance or silence around AI usage are red flags that the system isn’t working as intended. 

Oversight also means preparing for what hasn’t happened yet. As AI-related litigation increases and global regulations, such as the , take effect, organizations must be ready to adapt. Contracts will need to evolve. Internal inventories must be kept up to date. Incident response plans should include AI-specific scenarios. This isn’t about building an AI compliance fortress but creating a system that can identify blind spots early, track patterns over time and evolve. 

The cost of getting it wrong 

AI isn’t just a technology reshuffling. It is arguably a strategic ground shift for many organizations, introducing a new layer of risk exposure. When policies lag adoption or rely too heavily on restrictions, the result is vulnerability to data breaches, regulatory penalties and reputational damage. 

Just as cyber insurance forced a rethink of digital security, AI demands its own level of scrutiny. The question isn’t whether AI brings risk; it’s whether your future-proofed organization is ready to manage it. 

This article is published as part of the Foundry Expert Contributor Network.
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