Companies have spent the past few years encouraging employees to experiment with AI. A surprisingly large share of those employees would rather not talk about how they use it.
AI adoption at work has a measurement problem.
Companies can count Copilot licences, approved ChatGPT accounts and employees who attend AI training. What they cannot see nearly as easily is the work happening outside those systems: the proposal rewritten in a personal AI account, the spreadsheet checked with Claude, the research summarized before a meeting or the presentation drafted with a tool nobody formally approved.
The gap is becoming difficult to ignore.
A 2025 global study of more than 48,000 people across 47 countries and jurisdictions, led by the University of Melbourne in collaboration with KPMG, found that 57% of employees said they hid their use of AI and presented AI-generated work as their own.
At first, that might have looked like a temporary side effect of companies adopting a new technology faster than they could write policies around it. More recent research suggests the issue is becoming more established.
A 2026 workforce study found that 55% of full-time employees were using “shadow AI”, meaning AI tools used without formal organizational approval or guidance. Separately, a survey of 1,250 office professionals across the US, UK, Australia and Japan found that 66% had used unauthorized AI tools for work.
The studies measure different behaviors, so their percentages should not be treated as directly comparable. Taken together, however, they point to the same problem: workplace AI use is developing faster than the norms and systems meant to govern it.
Employees are not hiding AI simply because it is banned
The easiest explanation is that workers are breaking company rules and trying not to get caught.
The evidence suggests something more complicated.
A 2024 survey of more than 17,000 desk workers across six countries found that 48% would feel uncomfortable admitting to their manager that they had used AI for at least one common workplace task.
Among workers who felt uncomfortable disclosing AI use, 47% worried it would look like cheating, 46% feared appearing less competent and another 46% worried they would look lazy. Only 21% pointed to company rules that discouraged or prohibited AI.
A separate survey of 31,000 people across 31 countries found a similar tension. Among people already using AI at work, 52% were reluctant to admit using it for their most important tasks, while 53% worried that doing so could make them appear replaceable.
Those concerns are not entirely imagined.
Researchers tested the social consequences of AI use in four preregistered experiments involving 4,439 participants. Employees who used AI were judged as less competent, less diligent and lazier than people receiving other forms of technological assistance. Participants also anticipated some of that penalty before disclosing their AI use.
Companies can therefore send employees two messages at the same time. They can encourage people to become more productive with AI while maintaining a culture in which relying on AI is interpreted as evidence that the employee contributed less.
That contradiction gives workers a reason to use the technology and a reason not to talk about it.
The real question is not simply whether someone used AI
“Did you use AI?” sounds like a useful disclosure question until the range of possible use becomes clear.
One employee may use AI to fix the grammar in an email. Another may ask it to produce most of a client proposal. A developer may use an assistant to explain an error message and then write the solution independently, while someone else may accept hundreds of lines of generated code with minimal review.
Calling all of those situations “AI use” tells a manager or client very little.
This is where workplace rules have struggled to keep pace with the technology. Many organizations have focused on whether AI is permitted when the more useful question is how much AI contributed to a particular piece of work.
The distinction matters because undisclosed AI use is not automatically misconduct.
There is little practical value in requiring an employee to declare every AI-assisted sentence, spellcheck, transcription or search. Disclosure becomes more important when AI materially shapes a recommendation, analysis, design, financial model, piece of code or client deliverable.
In those situations, transparency is not really about giving AI credit. It helps the person relying on the work understand how it was produced, where additional review may be necessary and who is responsible for the result.
The same global study behind the 57% headline found that 66% of employees had relied on AI output without evaluating its accuracy, while 56% said AI had contributed to mistakes in their work.
Once those risks are involved, visibility matters for a practical reason. If nobody knows where AI entered the process, it becomes harder to know what should have been checked when something goes wrong.
Shadow AI is partly a governance problem
Unauthorized AI use is often framed as an employee behavior problem. That framing makes it easy to ask why workers are ignoring approved tools and company rules.
It is also worth asking why so many workers feel the need to go outside those systems.
The 2026 survey of office professionals using unauthorized AI found that 72% believed they understood how to use AI for their job better than the team responsible for managing AI at their company. Workers who concealed AI use cited factors including managerial scrutiny, restrictive policies, peer judgment and uncertainty over whether the tools they were using were actually allowed.
The workforce research showing 55% shadow AI use found another part of the problem. Sixty-eight percent of full-time employees wanted more support from leadership to experiment with AI, while 78% looked outside their organization when they could not get enough AI learning opportunities internally.
Employees are not necessarily waiting for an AI strategy before trying to solve the work in front of them. If an outside tool gets the job done faster than the approved system, some will use it first and worry about the policy later.
That leaves organizations in a difficult position. Employees can discover useful tools before internal processes are ready for them, while security, legal and compliance teams inherit risks they may not know exist.
The result is not only an AI adoption gap. It is an AI visibility gap. Management may know which tools the company has approved without knowing which tools employees actually use.
Freelancers face a different version of the disclosure problem
For freelancers, AI disclosure is less about an employer’s internal policy and more about the expectations between a professional and a client.
That difference matters because trust has direct commercial value in independent work. Repeat assignments, referrals and reputation can all influence how reliably the next project arrives. Flexable’s research into inconsistent freelance work shows how fragile continuity can already be for independent professionals.
A disagreement over AI use is therefore not simply an internal workplace issue. A client who believes a freelancer crossed a line can decide not to send another project.
New academic research suggests that freelancers and clients do not always begin with the same assumptions.
An IBM Research study of AI disclosure in freelance work used worker interviews and separate surveys of freelancers and clients. The researchers found that freelancers often took a passive approach to disclosure, mentioning AI when asked because they assumed clients could already recognize AI-assisted work.
Clients were much less confident in their ability to identify AI involvement and generally preferred freelancers to disclose it proactively. The researchers also found that unclear or nonexistent client policies repeatedly left freelancers guessing about what was expected.
That makes “Should freelancers use AI?” a less useful question than it first appears.
AI is already part of modern freelancing. What matters commercially is whether the client understands what role it played in the work they are buying.
A client commissioning a logo may care deeply about whether generative AI produced the underlying artwork. Someone hiring a developer may actively encourage AI-assisted coding as long as the freelancer reviews and tests the result. A company paying for original research may have a much stricter view of AI-generated analysis.
There is no universal disclosure rule because different kinds of professional work carry different expectations and risks. What needs to become clearer is the agreement between the people involved.
Better disclosure needs more information than “AI was used”
One reason AI disclosure feels awkward is that it is usually treated as a binary choice.
Either AI was used or it was not.
That leaves out most of the information a manager or client actually needs.
A 2026 study examining how workers respond to different forms of AI disclosure found that people were more comfortable with disclosures that explained how AI contributed, whether its use complied with company policy and whether a human had reviewed the result.
A generic AI disclosure did much less to resolve the underlying concern.
In practice, a useful disclosure may need to answer only three things: what AI did, whether the output was checked and whether that use was permitted for the work.
Those details tell a manager or client much more than a label saying something was “created with AI.”
They also move the conversation away from whether using AI is somehow questionable in itself. The more useful issue is whether the way it was used was appropriate for the work.
Disclosure can still carry a career penalty
Companies often approach transparency as if employees simply need to become more willing to admit when they use AI.
The evidence increasingly suggests that the response to disclosure matters just as much.
A 2026 controlled experiment involving nearly 1,000 US knowledge workers asked participants to evaluate identical work while varying whether AI use was disclosed. People evaluating a colleague who disclosed AI use were 24 percentage points less likely to recommend that person for a high-visibility project.
That result fits closely with the earlier experimental evidence showing that AI users can be judged more negatively on competence and motivation.
This creates a problem that a written AI policy cannot solve on its own.
If employees see people rewarded for AI-assisted productivity but penalized for admitting how they achieved it, hiding that use becomes predictable. A company may formally ask for transparency while its culture creates incentives for silence.
For disclosure to work, employees need to believe that appropriate AI use will be judged on the quality of the process and result, not treated automatically as evidence that the worker did less.
The next useful AI metric is visibility
For the past few years, AI adoption has been the headline number.
Companies have wanted to know how many employees use AI, how frequently they use it, which models they use and how much time the technology saves.
Those questions made sense when AI use itself was novel. They become less informative as AI moves into everyday work.
The harder questions now sit beneath adoption.
Companies need to know how much important work is being completed with AI that managers or clients never see, how often AI materially influences an output without anyone knowing, what information is entering unapproved tools and whether important AI-generated results receive meaningful human review.
They also need to know whether they can reconstruct the process after something goes wrong.
That is increasingly the difference between having employees who use AI and having an organization that actually understands how AI is being used.
For independent workers, the same issue becomes part of the trust that holds a client relationship together. Payment reliability and continuity of work already create significant uncertainty, as Flexable’s research on freelancer payment problems and the growth of the global freelance economy show. Unclear expectations around AI add another layer.
The 57% figure matters because it gives the visibility problem scale.
Employees are not waiting for companies to finish deciding how AI should fit into work. Many have already incorporated it into the job.
A significant share would simply prefer not to say how.
About the research
The headline statistic comes from Trust, Attitudes and Use of Artificial Intelligence: A Global Study 2025, led by Professor Nicole Gillespie and Dr Steve Lockey at the University of Melbourne in collaboration with KPMG.
The global AI trust and adoption study surveyed 48,340 people across 47 countries and jurisdictions using representative research panels, with data collected between November 2024 and mid-January 2025. The University of Melbourne research team led the research design, data collection, analysis and reporting.
The 57% figure refers specifically to employees reporting that they hide their use of AI and present AI-generated work as their own. It should not be interpreted as meaning that 57% of workers violate company AI policies or that every undisclosed use of AI is inappropriate.
The other studies cited in this report measure related but different behaviors, including unauthorized AI use, discomfort disclosing AI use and the career consequences of disclosure. Their percentages therefore should not be combined into a single estimate of hidden AI use.
The stronger conclusion is narrower: across several large studies and experiments, employees repeatedly show reasons to use AI privately, while many organizations still lack a clear view of where and how that use is happening.