Shadow AI Starts Where the Approved Path Gets Hard

An approved AI tool only stops shadow AI when it is the easier way to get the work done.

Shadow AI is any AI tool or feature employees use for work without the company knowing about it or approving it. A midsize company handles it by bringing that use into the open and building an approved path that is easier to use than the shadow one, because an approved tool that is harder to use tends to lose.

A private medical practice I have been talking with shows how quickly that plays out. The practice wants to pull information from its electronic health record, its billing system, and its accounting software, and have AI summarize it in minutes. The opportunity is large. Much of that work is repetitive and low in value, and much of it sits close to patient information.

HIPAA turned the opportunity into a dilemma, and the practice was left with two choices: prohibit AI anywhere near patient data, or redact the information by hand first. Manual redaction consumed the time AI was supposed to save, and it opened the door to missing something.

Patient data happens to be regulated, but the dilemma is not unique to health care. Any company whose most useful work sits next to its proprietary information, its intellectual property, or its customers' confidential records faces the same choice.

What counts as shadow AI?

Shadow AI is AI used for company work outside anything the company has approved or can see. It usually takes one of three forms: a personal account on a public chatbot, an AI feature switched on inside approved software before anyone reviewed it, or a tool or agent someone set up without telling IT.

It is the newest version of shadow IT, with one difference. These tools take company information in and send finished work back out, in customer emails, analyses, and code.

How common is shadow AI?

Common enough that a midsize company should assume some of it is already happening. In Microsoft and LinkedIn's 2024 Work Trend Index, a survey of 31,000 knowledge workers in 31 markets, 78 percent of people who use AI at work said they bring their own AI tools, and the share rose to 80 percent at small and medium-sized companies.¹

Prohibition has not stopped it. In a 2025 Gartner survey of 302 cybersecurity leaders, 69 percent said their organizations suspect or have evidence that employees use public generative AI tools the company has prohibited.²

Should a company ban unapproved AI tools?

A ban on its own tends to move AI use out of sight, where it is harder to manage. In a 2025 global study by the University of Melbourne and KPMG, 57 percent of employees said they hide their use of AI and present AI-generated work as their own. Only 47 percent said they had received AI training, and only 40 percent said their workplace had a policy or guidance on generative AI.³

Gartner made the same point about AI agents this spring. Blocking them is "not a long-term solution," because employees who cannot work in the sanctioned tools will likely go around the organization's controls.⁴ Use that goes underground is use the company cannot steer, and its data goes wherever the tool's terms allow, which I covered in a piece on what AI vendors do with what your team types in.

Why isn't an approved AI tool enough?

An approved tool only works if people can use it for the work in front of them, and that test gets harder the more valuable the information is.

For the medical practice, the sensitive information is patient data. For a manufacturer, it is formulas and process know-how. For a distributor, it is customer pricing and margins. For a professional services firm, it is client files. Some of that information is regulated and most of it is simply proprietary, but the pattern repeats: the work people most want help with sits close to the information the company can least afford to lose.

The practice's experience shows what happens next. The AI plans that offered the protection it needed covered some of its work and left other parts exposed, and the closest fits were enterprise plans that, in my experience, are not built for a company its size. That left the two fallbacks a company reaches for when the approved path does not fit the work: prohibit AI near sensitive information, or scrub that information by hand before anyone uses a tool. Scrubbing by hand consumed the time AI was supposed to save, and each pass carried the risk of a miss. A prohibition with no usable alternative is the condition that sends people looking for a shadow route.

The approach under discussion with the practice is a platform that gives staff secure chat and API access to those systems, on terms built to keep sensitive information protected. It gives up some capability compared with the newest frontier models. It is one company's answer, and the reasoning applies to any business with something worth protecting.

A sanctioned tool is not enough. It has to be easy to use in practice, or people will either stop using it or find a shadow route.

How should a midsize company handle shadow AI?

Handle it in four moves: find it, sort it, make the approved path the easy path, and teach it.

Find it by asking before you audit. Since more than half of the employees in the Melbourne and KPMG study said they hide their AI use, a first inventory only works if telling the truth is safe. Start with a short, blame-free survey, then add what IT can already see.

Sort it by the information each use touches. Routine work can stay on an approved business-tier tool, while anything proprietary, confidential, or critical belongs under your control, the test in my guide to which AI work to keep under your own control.

Make the approved path the easy path. It has to reach the systems where the work lives, cover the tasks people actually bring to it, require no scrubbing by hand, and answer a request for a new tool within days. For everyday work, I would take a slightly less capable model that is approved, connected, and easy to reach over a frontier model wrapped in workarounds.

Teach it. AI fluency built together compounds faster than fluency left to each person, which is why I argued that AI enablement cannot be self-directed. Name one owner for decisions on new tools, and repeat the inventory every quarter.

What does shadow AI tell you about where to start?

Shadow AI shows you where your people have already found value, which makes the inventory the best first draft of an AI roadmap. Each shadow use is an unfunded pilot and an unpriced risk. The ones with the most value and the most exposure are the first candidates for an approved version that is easier than the workaround.

For the medical practice, the starting list is already clear: the repetitive, low-value tasks near patient information that staff want help with. In most companies the equivalent list sits near the pricing file, the customer records, or the engineering drawings.

Proxigee Services is an AI advisory firm in the Pittsburgh area that helps small and midsize companies find the AI their people already use, decide what belongs under their own control, and build an approved path people will choose.

Your people have already shown you where AI belongs. The work now is to make the safe choice the easy one.