Somewhere in your organization right now, someone could be pasting a client brief into an AI tool your IT team has never reviewed. They may not be trying to cause headaches. They may simply be trying to get their work done, and the tools they’ve been given aren’t keeping up.
But it’s still a big potential problem.
This is shadow AI: employees using AI tools that haven’t been vetted, approved, or disclosed to their employer. The pattern isn’t new. A decade ago, the same thing happened when teams signed up for file-sharing apps and project management platforms because their IT group took too long to approve them.
Marketing, creative, and technology teams are often the first to pick up new tools, because so much of their daily work can be supported by AI tools. For leaders of those teams, shadow AI usage creates real risk. It also shows you where your official tools, training, and policies could be falling short.
When the tools haven’t been approved for the work, shadow AI can look like ordinary tasks:
Use through personal accounts and browser tabs can bypass normal purchasing and approval processes, making it easier for managers to overlook.
When employees reach for unsanctioned tools, something at work may be pushing them there.
The approved options are slow, restrictive, or missing. If getting a new tool approved takes three months, or the sanctioned tool is locked down so tightly it can’t handle real work, employees will be tempted to find something that can. In many organizations, there’s simply no approved AI tool at all.
Expectations have outpaced support. Leaders are asking teams to move faster and use AI to do it, often without providing the necessary tools or guidance. Keeping up with fast-changing AI tools without training is already a common driver of employee burnout, and an unapproved tool can feel like the quickest way to close the gap.
The policy is unclear or doesn’t exist. When there’s no clear guidance, people guess. Some read silence as permission. Others read it as a ban and use unsanctioned AI tools anyway, just quietly.
People worry about how it looks. Some employees hide their AI use because they’re afraid it will make their work seem less valuable or make them seem easier to replace.
Ignoring shadow AI doesn’t stop it. And it can leave your organization without a clear picture of where company data is going.
Data security. Client information, unreleased product details, employee records, and financial data can end up in tools that store inputs or use them to train future models. Once that information enters an unapproved tool, your organization has limited control over how it is stored, used, or shared.
Compliance and legal exposure. Many client contracts include confidentiality terms that employees could violate by sharing information with unapproved tools. Regulated industries face even stricter rules. Commercial use of AI-generated work also requires attention to tool licensing terms, copyright considerations, and client requirements.
Inconsistent quality. AI output that skips review can introduce factual errors, off-brand messaging, or workslop into client deliverables. When every team member uses a different tool with different habits, quality becomes hard to predict and difficult to fix.
Lost visibility. When AI use is hidden, you can’t measure what’s working or share best practices across teams. A workflow that saves one designer five hours a week stays locked in that designer’s browser history.
Your team may already be experimenting with outside AI tools. Bringing that activity into the open gives you a chance to guide it before problems arise. The goal is to make responsible AI use easier than secret AI use. Here’s where to start.
Start by listening to employees describe which AI tools they use, what they use them for, what kind of information goes into them, and what frustrates them about the approved options. Focus on understanding their day-to-day needs and where existing tools fall short. Work with IT to use those insights when deciding which tools to approve, replace, or block.
A strong AI policy is short enough that people will read it and specific enough that they can follow it without guessing. One simple option is a three-tier system:
Assign a clear owner for the policy and plan to revisit it every quarter. AI tools change quickly, and a policy written a year ago is probably already out of date.
Give people a simple way to suggest tools, such as a short request form or a dedicated chat channel, and commit to a response within a set timeframe. When someone brings forward a tool that ends up helping the whole team, recognize it publicly so others can see that speaking up pays off.
Managers can reinforce this in regular one-on-ones by asking a simple question: “What tools are helping you get your work done right now?” Asking it routinely, the same way you’d ask about workload, makes AI use a normal topic of conversation.
If half the team is using the same unsanctioned tool, that’s a strong signal about what people need. Work with IT or security to assess whether the company could license an enterprise version with appropriate data protections. Sometimes the fix is simpler: upgrading a tool you already pay for to a plan that includes the features people keep leaving to find elsewhere.
Review your restrictions the same way. If a security setting blocks every AI browser extension, or the approved tool caps file uploads too low for designers to use it on projects, employees will likely want to work around it. For each restriction, ask IT or security to explain the risk it addresses and explore whether there’s a safer way to meet the team’s needs. Pilot any changes with a small team before rolling them out more broadly.
AI training should not focus only on how to get better output. Employees also need to know what information is safe to share, how to check AI output for accuracy, and when a human review is required before anything goes to a client. Build a review step into every client-facing deliverable and encourage people to draft key work themselves before turning to AI. That habit catches errors and helps prevent skill atrophy, where people gradually lose the ability to do or evaluate the work on their own.
When managers share how they use AI, including the tasks they keep AI out of and why, employees see that disclosure is expected. Walking through one real example in a team meeting does more to set that expectation than a policy memo.
24 Seven helps organizations find AI-fluent marketing, creative, and technology professionals who bring relevant skills and sound judgment to their work. Our experts can also help teams align talent, workflows, and technology to support responsible AI use and maintain quality standards. With the right expertise in place, teams are better equipped to turn AI experimentation into effective everyday practices.
Ready to hire AI-fluent talent? Contact 24 Seven today.