AI Governance in the Enterprise: Beyond the Hype

AI is advancing faster than most enterprises can absorb it. Every quarter brings new models, new capabilities, and new promises. But while leadership teams debate strategy in boardrooms, their employees have already moved on - they're using AI right now, whether the organisation knows it or not.

The Shadow AI Problem

Shadow AI is the new Shadow IT, except it moves faster and the risks are less visible.

Employees are pasting customer data into ChatGPT. Sales teams are running proposals through AI writing tools. Developers are feeding proprietary code into copilot assistants. None of this is malicious - people are just trying to work faster. But without visibility and guardrails, every unsanctioned AI interaction is a potential data leak.

This isn't hypothetical. If you manage endpoints, you can see it happening. Browser extensions, desktop apps, API calls to model providers - the fingerprints of Shadow AI are already in your telemetry if you know where to look.

Governance Starts with Visibility

The first step isn't policy - it's visibility. You need to know what AI tools are in use across your organisation before you can govern them.

This is where existing enterprise tooling earns its keep. Unified Endpoint Management (UEM) platforms, CASB solutions, and endpoint security tools can identify AI application usage, classify data flows, and enforce acceptable use policies - the same way they handle any other SaaS sprawl.

Practical governance means:

• Discovery - use your UEM and security stack to identify which AI tools employees are actually using
• Classification - determine which use cases involve sensitive data and which don't
• Policy enforcement - block high-risk AI tools at the endpoint or network level, while explicitly approving sanctioned alternatives
• Data loss prevention - extend your existing DLP policies to cover AI-specific data flows, including clipboard and API-level monitoring

None of this requires new frameworks or committees. It requires applying the security disciplines you already have to a new category of tools.

The Bigger Problem: AI Without a Strategy

But here's the uncomfortable truth - governance alone doesn't get you anywhere. You can lock down every unsanctioned AI tool in your organisation and still have zero return on your AI investment.

Most enterprises have failed to deploy AI with real, measurable ROI. The pattern is depressingly familiar: a flurry of pilots, a few impressive demos, a lot of executive enthusiasm, and then... nothing scales. The pilots stall. The business case evaporates. The team moves on to the next shiny thing.

The problem isn't the technology. It's the absence of strategic thinking about where AI actually creates value.

Asking the Right Questions

Before spending another pound on AI infrastructure, technology leaders need to step back and answer some fundamental questions:

What are the right use cases? Not every process benefits from AI. The highest-value use cases are typically those involving repetitive knowledge work, pattern recognition at scale, or decision support where speed matters. Be ruthless about prioritisation - five well-chosen use cases will outperform fifty scattered experiments.

What's the realistic benefit? Quantify expected outcomes before you build. Will this reduce manual processing time? By how much? Will it improve decision quality? How will you measure that? If you can't articulate the benefit in concrete terms, the use case isn't ready.

What AI technology fits? Not every problem needs a large language model. Some use cases are better served by traditional ML, rules engines, or simple automation. Choosing the right technology for the problem - rather than forcing the trendiest model into every gap - is where technical leaders add real value.

What will it actually cost? AI cost models are notoriously opaque. Cloud-hosted models charge per token; local inference requires GPU hardware and operational overhead. The difference between running a model via API and hosting it locally can be an order of magnitude in either direction, depending on volume. Leaders need a clear picture of both options before committing.

From Governance to Strategy

The organisations that succeed with AI are the ones that treat it as a portfolio decision, not a technology experiment. They map use cases to business outcomes. They model costs against realistic adoption curves. They make informed bets about where to invest - and where to wait.

This is the shift from AI governance to AI strategy. Governance keeps you safe. Strategy makes you competitive.

What's Next

I'm building a tool to help with exactly this - the Enterprise AI Planner. It's designed to help technology leaders model their AI use cases, compare deployment strategies, understand true costs (local vs. cloud), and build a clear picture of where AI investment will deliver the most value.

Not governance. Not compliance. Just a practical way to answer the question every CTO is asking: where should we spend our AI budget?