In April 2023, an engineer at Samsung pasted sensitive internal source code into ChatGPT. A few weeks later, staff in one of the company’s biggest divisions received a memo. Generative AI was now banned on company phones, computers and tablets, and over the internal network. Staff were also asked not to share company information with such tools on their personal devices, and the memo warned that breaking the rule could lead to disciplinary action up to and including dismissal.
It was, by every corporate standard, a responsible decision. A leak had happened. A risk had been named. A rule had been written. Samsung was not alone: within months, a string of banks and large companies had banned or restricted ChatGPT for similar reasons, and “we have blocked it” became a respectable answer in boardrooms everywhere.
Three years later, we can measure what those memos achieved. The answer is uncomfortable for anyone who signed one. Blanket bans did not stop people using AI. They stopped leaders seeing it. They converted a visible, governable behaviour into an invisible, ungovernable one, and they did it while giving the people at the top a pleasant feeling of control.
This edition is about that feeling, and about what it costs. My argument is simple: banning AI is not an AI policy. It is the absence of one, dressed up as decisiveness.
We have run this experiment before
On 17 January 1920, the United States made the manufacture, transport and sale of intoxicating liquor illegal. The intention was moral, the law was national and the enforcement was real. Agents raided warehouses, smashed barrels and poured whisky into gutters for the newspapers.
Americans kept drinking. What changed was where. In New York alone, contemporary estimates of the number of speakeasies ran from 30,000 to 100,000. Drinking moved from licensed saloons, where the owner had a name, an address and something to lose, into back rooms, basements and unmarked doors. The alcohol got worse, because nobody could inspect it. The economics got darker, because the only people willing to supply an illegal market were people already comfortable with illegality. By 1929, the city’s own police commissioner, Grover Whalen, was warning Mayor Jimmy Walker about who the trade had pulled in.
“The illegal nature of the business attracts to it the stickup thug, the gangster, the degenerate, the racketeer and the gunman.”
Grover Whalen, New York Police Commissioner, report to the mayor, 1929 (via the NYC Municipal Archives)
The point is not that drinking was harmless. The point is that prohibition did not reduce the harm. It removed the supervision. It took a behaviour that society could see, regulate and tax, and moved it somewhere nobody could inspect the bottles.
The workplace has its own, smaller versions of this history. In the late 2000s, plenty of IT departments refused to let staff connect personal smartphones to company email. Staff forwarded their work mail to personal accounts instead. Eventually the industry gave up and invented a name for the thing it could not stop: bring your own device. The policy that followed was not a ban. It was a set of rules for managing what was already happening.
Every one of these stories has the same shape. A new tool arrives that makes individual work easier. The institution sees the risk before it sees the value. It bans the tool. The tool survives the ban, and the institution loses the one thing it actually needed, which was the ability to see.
The mechanism: demand does not obey memos
To understand why AI bans fail, start with the demand, because the demand is not a fringe phenomenon.
Microsoft and LinkedIn’s 2024 Work Trend Index, based on a survey of 31,000 people in 31 countries, found that 75% of knowledge workers were already using AI at work. More importantly, 78% of those AI users were bringing their own tools, rather than waiting for their employer to provide them. Microsoft gave it a name, bring your own AI, and the name is telling. It describes something done to the organisation, not by it.
Then Software AG asked the question that every banning executive should have asked first. Its survey of 6,000 knowledge workers across the US, UK and Germany found that 46% would keep using their personal AI tools even if their organisation banned them completely. In Germany, the figure was 49%. Those workers cited time savings, easier jobs and higher productivity as their reasons. In other words, they were not rebels. They were people trying to do their jobs well, who had found a tool that helped.
The largest study of all makes the pattern unmistakable. The University of Melbourne and KPMG surveyed more than 48,000 people across 47 countries for their 2025 global study of trust in AI. 57% of employees said they hide their use of AI and present AI-generated work as their own. Almost half admitted to using AI in ways that contravene company policy, including uploading sensitive company information into free public tools.
And the trend has not reversed. In June 2026, PagerDuty published a survey by Wakefield Research of 1,250 office professionals at companies with at least $500 million in revenue. Two thirds said they had used AI tools at work despite believing they were not permitted under company policy. At organisations with 1,500 or more employees, the figure rose to 72%.
Put those findings together and the mechanism is clear. A ban does not change the incentive. The employee still has a report due on Thursday, a backlog of emails and a manager who expects faster turnaround than last year. AI still makes those things easier. The only thing the ban changes is the route the work takes. It goes from the company laptop to the personal phone, from the enterprise account to the free one, from the logged network to the home broadband.
I call this the speakeasy effect: when you prohibit a tool that people find genuinely useful, you do not remove the use, you remove the inspection. The behaviour continues, but every safeguard you could have applied to it disappears.
What the ban actually costs
For a while, the speakeasy effect was a theory. It now has a price tag.
IBM’s 2025 Cost of a Data Breach report was the first edition to look hard at shadow AI, meaning AI tools used without the knowledge or approval of the organisation. It found that one in five breaches in its study involved shadow AI. Organisations with high levels of shadow AI paid an average of $670,000 more per breach than those with low levels or none. Those breaches were also more likely to expose personal data and intellectual property. And among organisations that reported an AI-related breach, 97% lacked proper AI access controls.
Read those numbers alongside the Samsung memo. The original fear was that staff would paste sensitive data into a public tool. The ban’s answer was to forbid the tool. The result, across the economy, is that staff paste sensitive data into public tools on devices the company cannot monitor, and the company finds out during the breach investigation.
But the financial cost is the easiest one to see. The deeper costs are the ones that never appear in a report, because they are, by definition, invisible. I count three.
First, you lose the learning. When AI use is visible, an organisation can discover which uses work. A sales team finds a prompt that halves proposal time. A finance analyst works out which reconciliation tasks the model gets wrong. Those discoveries can be shared, tested and turned into standard practice. When AI use is hidden, every discovery stays private. Every employee runs their own experiment, alone, and the organisation learns nothing from any of them. The KPMG study found that only 47% of employees had received any AI training. In a banning culture, the other half are teaching themselves in secret.
Second, you lose the error signal. The same study found that 66% of employees rely on AI output without evaluating its accuracy, and 56% say they have made mistakes in their work because of AI. In an open culture, those mistakes surface. Someone says, “the model invented that clause”, and the team adds a check. In a hidden culture, nobody admits the source of the error, because admitting the source means admitting the breach of policy. The mistake gets fixed, perhaps, but the cause stays buried, ready to recur.
Third, you lose honesty itself. This is the cost I worry about most. A ban that most people break teaches everyone that the official rules and the real rules are different things. It trains people to present AI-assisted work as unassisted work. It turns ordinary productivity into a small, daily act of concealment. Once a workforce has learned that the safest move is to say nothing, that lesson does not stay neatly confined to AI.
The four ban archetypes
Not every ban looks the same. In the conversations I have had with leaders over the past year, I see four recurring patterns. Each one feels like governance. None of them is.
The Panic Ban. Something goes wrong, usually a leak or an embarrassing output, and the response arrives within days. The Panic Ban is the purest form of the speakeasy effect, because it is written in fear and enforced with threats. It names the tool, not the risk. It is often the right short-term call and the wrong long-term policy, and the trouble is that nobody ever comes back to replace it.
The Paper Ban. The policy exists, somewhere, in a document nobody reads. It says AI tools require approval. There is no approval process. There are no approved tools. Everyone knows the rule exists and everyone knows nobody enforces it. The Paper Ban is the most common archetype and the most corrosive, because it gives legal cover to leaders while giving permission to nobody. The KPMG study found that only 40% of employees said their workplace had a policy or guidance on generative AI use. I suspect many of the rest work under a Paper Ban they have never seen.
The Selective Ban. AI is banned for most staff, but quietly allowed for a favoured few: the leadership team, the innovation lab, the consultants. The Selective Ban teaches the rest of the organisation that AI is a privilege of status rather than a tool of the job. Survey work this year suggests that decision-makers are often more likely to use unsanctioned AI than the staff below them, which makes the Selective Ban not just unfair but faintly comic.
The Perpetual Pilot. This one does not call itself a ban. It calls itself a pilot. A small group gets access to a sanctioned tool “while we evaluate”. The evaluation never ends. Eighteen months later, the pilot group is still the only group with access, and everyone else has long since found their own tools. The Perpetual Pilot is a ban with a better press office.
What unites all four is that they answer the wrong question. They ask, “should our people use AI?” That question was settled by the people themselves, years ago, without asking permission. The only live question is, “how should they use it, and how will we know?”
The uncomfortable truths
A ban is the easiest policy a leader can write. It requires no understanding of the work, no budget for tools and no difficult conversations about which data matters. It produces a clean memo, a clear audit trail and a clear conscience. That comfort is precisely why it is so popular, and precisely why it should be treated with suspicion.
Your real adoption rate is higher than your licence count. If you have bought seats for 10% of your workforce and banned everything else, you do not have 10% adoption. You have 10% visible adoption and an unknown amount of invisible adoption. On the evidence above, the invisible part is probably the larger one.
The people breaking your ban are often your best people. The workers most likely to route around a ban are the ones under the most pressure to deliver and the most curious about better methods. A ban does not punish your least diligent staff. It punishes the ones trying hardest, or teaches them to hide.
Fear is not a control. Security teams know this better than anyone. A control is something you can test, monitor and audit. A threat of dismissal is none of those things. It only works on people who expect to be caught, and the whole point of shadow AI is that nobody expects to be caught.
The risk you can see is always smaller than the risk you cannot. A sanctioned tool with data controls, logging and a clear owner carries risk. An unsanctioned tool on a personal phone carries the same risk with none of those things. The choice is never between risk and no risk. It is between risk you can manage and risk you cannot even count.
What to do instead
Replacing a ban does not mean replacing it with a free-for-all. It means replacing a single prohibition with a small number of clear answers. A real AI policy answers four questions: which tools are sanctioned and why they beat the free alternative, which data may never leave the building, who owns and checks the output before it reaches a customer, and how people disclose their use without being punished for honesty. Here is what that means depending on where you sit.
If you are early in your career:
Do not hide your AI use. Hidden use is invisible skill, and invisible skill does not get promoted. The ability to use AI well, check its output and explain how you got to an answer is one of the most valuable things you can demonstrate right now, but only if someone can see it.
Learn your organisation’s data boundaries before you learn its prompts. Know which information is confidential, which is personal and which is regulated. The fastest way to end an early career is to be the person whose upload appears in the breach report.
If there is no sanctioned tool, ask for one, in writing, with a specific use case. You become the person who surfaced the problem rather than the person caught causing it. That is a much better reputation to have.
If you are hiring:
Ask candidates how they use AI, and reward honest answers. If your interview treats every mention of AI as cheating, you are selecting for people who conceal, and you will get a workforce of them.
Test judgement about data, not just fluency with tools. Give candidates a scenario: a deadline, a confidential document, a public chatbot. What would they do? The answer tells you more than any prompt-writing exercise.
Look for people who can describe a time AI got something wrong and how they caught it. That is the skill your policy depends on.
If you are leading:
Count before you rule. Before you write or rewrite any AI policy, find out what is already happening. Run an anonymous survey. Look at network traffic. Ask teams directly, and promise no consequences for the answer. You cannot govern a behaviour you have refused to measure.
Make the sanctioned path easier than the shadow one. People route around friction, not principles. If the approved tool requires a three-week access request and the free one requires a phone, you have already lost. Speed of access is a security control.
Write rules about data, not about tools. Tools change every quarter. The categories of information you must protect change far more slowly. A policy that says “never paste customer personal data into any tool that is not on this list” will outlive any list of banned websites.
Create an amnesty. Tell people that disclosing their current AI use will not be punished, and mean it. The information you gain about what your workforce actually needs is worth far more than the satisfaction of enforcing a rule nobody followed.
Replace “never” with “here is how”. Every blanket ban is a training budget you chose not to spend. Every hour you spend teaching people to use AI safely is an hour they do not spend teaching themselves in secret.
The memo you should have written
Go back to that Samsung memo in 2023. It was a reasonable reaction to a real incident, and I do not think the people who wrote it were foolish. They did what responsible institutions have always done when a new tool arrives faster than their understanding of it. They reached for the lever marked “stop”.
The trouble is that the lever is not connected to anything. Prohibition did not stop New York drinking. Blocking smartphones did not stop people reading work email in bed. Banning AI has not stopped people using AI. Every time, the lever only did one thing: it moved the behaviour to a place where the institution could no longer see it, shape it or learn from it.
The memo that would actually have reduced the risk would have been longer and harder to write. It would have said: here are the tools you may use, here is the data you must never share, here is who checks the output and here is how to tell us what you are doing without fear. It would have required investment, judgement and honesty about how work really gets done.
That memo is still available to any leader willing to write it. The alternative is to keep the ban, keep the clean conscience and keep the comfortable silence that comes with it.
Just remember what that silence means. You did not stop AI. You stopped hearing about it.