Shadow AI is Already in Your Business – What Next?

Published 21/05/25 under:

Let’s get one thing out of the way: if you think Shadow AI isn’t happening in your business, you’re almost certainly wrong.

It’s already here. Maybe it’s a junior using ChatGPT to summarise a 40-page contract, a sales rep crafting pitch emails, or a CTO running their blog through Copilot to ensure it’s grammatically correct. None of this is malicious or even necessarily reckless. It’s just people trying to work smarter. But it does present a new kind of challenge.

As CTO, I don’t see this as a doomsday scenario. I see it as a huge opportunity if we handle it the right way.

What is Shadow AI?

We’ve been dealing with “Shadow IT” for years. People spinning up SaaS tools without IT sign-off, sharing data on Dropbox, or setting up Trello boards that never get archived. Shadow AI is the next evolution of that same problem. Only now, it’s harder to spot, moves faster, and carries a different type of risk.

It’s any AI-powered tool or interaction happening outside your approved tech stack or governance frameworks. It might be a tool that isn’t enterprise-ready, a use case that introduces data leakage risk, or simply a process that no one in IT even knows is happening.

Why it’s already happening

The genie’s out of the bottle. Large language models and AI agents are now so accessible and useful that they’ve become part of how people think about getting work done. We’re seeing a shift in mindset: “Why would I spend an hour on this if I can ask AI to draft it for me?”

This isn’t something IT triggered. It’s user-led, just like mobile working or BYOD once were.

The reality is your team isn’t waiting for a policy or a framework. They’re already experimenting.

The ‘possible’ risks to consider

I’m not one for scare tactics. But let’s be practical:

• Data exposure – Staff could unknowingly input sensitive info into public tools with unclear data policies
• Inconsistent outputs – Using AI without context or training can lead to flawed, biased, or just plain wrong results
• Fragmented tooling – When different teams use different AI tools, there’s no consistency, audit trail, or insight into what’s working

But the biggest risk? Saying no, and driving it further underground.

So, what do we do? Here’s how I think about it

• Acknowledge it openly
  • Start by being honest with your exec team and your employees. This is happening, and it’s not inherently bad. Make it safe for people to talk about how they’re using AI, even if it’s not yet approved.
• Create guardrails, Not roadblocks
  • Not every AI tool is enterprise-ready, and not every use case is appropriate. But the answer can’t just be “don’t use it.” We need to define sensible, practical guardrails. What data can’t be shared with third-party AI tools? What tasks are safe to experiment with?
• Offer better tools
  • If you don’t want employees turning to ChatGPT, give them access to Copilot in Microsoft 365 or Azure OpenAI under your control. If people are building GPTs or building agents, help them do it securely within your own tenant. The best way to displace Shadow AI is to offer a safer, supported alternative.
• Educate and empower
  • People are hungry to use AI. Let’s equip them to do it well. Run internal sessions. Share guidance. Train your teams on prompt writing, the limitations of AI, and where it fits (or doesn’t) in your workflows. Make AI literacy part of your culture. If you’re looking to understand the power of AI and where it can best help your business, my team are running regular AI Art of the Possible workshops for customers.

The bigger picture

Shadow AI isn’t a tech problem. It’s a signal. It tells you your people are ready to work differently. Faster, smarter, more creatively. The role of IT and leadership isn’t to shut it down. It’s to channel that momentum safely and securely, in a way that benefits the whole business. We don’t need to fear Shadow AI. We just need to bring it into the light.