Standalone article from the series “AI and You”.
There is a handful of phrases that repeat across almost every company, regardless of industry, that almost always mean the opposite of what they say: “Don’t reinvent the wheel,” “If it works, don’t touch it,” “We’ve always done it this way,” “Now is not the right time.” They sound like prudence, experience, and a cool head. And most of the time, they are just a polished wrapper for something else: nobody wants to take on the risk, cost, or effort of changing something that has been working halfway for years.
This article is not about any specific technology. It is about organizational inertia: the force that makes companies resist change even when change is obviously beneficial. And it is about how the arrival of AI has made those excuse-phrases harder to defend, to the point where some companies have fallen into a curious contradiction: they demand the results of someone who uses AI, without providing an official way to use it properly. If you work somewhere like that — and the numbers suggest you probably do — the final section of this article is for you.
It is worth dismantling them one by one, because each contains a grain of truth that makes it dangerous.
“Don’t reinvent the wheel.” The grain of truth: rebuilding from scratch something that already exists and works is usually a waste of time. The problem: the phrase is used to justify sticking with the wooden wheel from ten years ago while the rest of the world rides on pneumatic tires. There is an enormous difference between reinventing (rebuilding what already exists) and updating (using what exists in its modern form). The two are deliberately confused to avoid updating anything.
“If it works, don’t touch it.” The grain of truth: don’t break what puts food on the table without good reason. The problem: it turns every system into an untouchable black box that degrades in silence. And “works” usually means “hasn’t exploded yet.” In software, what is not updated doesn’t stay the same — it becomes insecure, because the rest of the world (attackers included) keeps moving.
“We’ve always done it this way.” The grain of truth: long-running processes can encapsulate lessons that are not obvious. The problem: they also encapsulate fossilized mistakes that nobody remembers the reason for anymore. “We’ve always done it this way” is, in most cases, a signature that nobody has reviewed that process since the person who set it up left.
“Now is not the right time” / “There’s no time.” The most honest-sounding and the most treacherous in practice. There is almost never a good moment to invest in improving something that already “works.” Improvement always competes against the urgent, and the urgent always wins — until the lack of improvement becomes the emergency.
The pattern the four share: all of them prioritize avoiding the visible risk of changing over the invisible risk of not changing. And the invisible risk is almost always greater, precisely because it stays hidden until it explodes.
Let’s start fairly: these phrases did not come from nowhere. For decades, change carried a very high cost. Updating a system meant weeks of manual work, a real risk of breaking things, and learning new tools without a safety net. In that world, “if it works, don’t touch it” was sometimes a rational decision: the expected cost of touching exceeded the expected benefit.
What has changed is not that change has become free, but that the cost of changing has fallen much faster than the cost of not changing. Two forces explain this:
When the two curves cross, excuses that were debatable before become indefensible. Not because change is easy, but because not changing has stopped being the safe option.
An important caveat, to avoid falling into the opposite extreme: this does not mean “change everything constantly.” The enthusiast who rebuilds their system every six months chasing the latest trend does as much damage as the immobilist. The point is not to change for change’s sake; it is that the balance has shifted, and many decisions that were previously settled with “better not touch it” now deserve, at minimum, to be reconsidered.
The term Shadow AI — the use of AI tools without formal approval — typically evokes a single image: the company that explicitly bans it while employees use it on the sly. That image is real and well-documented. But the data reveals at least four additional situations that produce exactly the same result: employees using AI outside official channels, without any organizational visibility.
WalkMe and Fortune survey cited above. Concealment scales with hierarchy and generation.
Five distinct situations that lead to the same result: AI use outside official channels, without organizational visibility.
The pattern shared by all five cases is the same: the company cannot see what is happening and cannot manage either the risk or the benefit. The most documented variant — and the costliest when it goes wrong — remains the explicit prohibition, with real data flowing through uncontrolled services.
Many companies have simultaneously adopted two incompatible stances: they officially ban AI (for data protection, fear, or caution) while at the same time demanding the delivery speed of someone who uses it. The employee lands in a trap: comply with the rule and miss the deadlines; meet the deadlines and break the rule. The problem with the hypocritical ban is not just the incoherence. It is that it worsens exactly the risk it claims to protect against: when you ban AI but demand AI-level results, people do not stop using it — they use it in secret, with free tools, pasting real company data into uncontrolled services. According to IBM’s 2025 report, organizations with high Shadow AI usage pay on average $670,000 more per breach than those with low or no usage. The “banned” policy does not eliminate use; it only eliminates visibility into use. Which is worse. This is just a summary — a dedicated article covers success and failure patterns along with a practical decision framework for AI adoption in companies (article in Spanish).
The prohibition policy without an alternative does not eliminate the risk: it conceals and worsens it.
The five variants of Shadow AI group into three root causes. When an organization fails on all three at once, Shadow AI becomes systemic.
The misalignment shown in these five scenarios has a measured cost: the EY Work Reimagined Survey (15,000 employees, 1,500 employers, 29 countries) calculated that organizations that implement AI without aligning it with talent strategy, leadership, and a learning culture waste up to 40% of their productive potential. As the study puts it: “When AI tools are implemented on fragile organizational foundations — weak culture, insufficient training, poorly designed incentives — the efficiency benefits stagnate.”
Corporate schizophrenia has a side effect that nobody will mention aloud and that is hard to admit: it pushes employees to hide productivity. Not out of bad faith, but out of basic logic.
If you finish a task in two hours instead of four thanks to AI, and saying so only results in another task being assigned immediately — or your manager revising downward the estimated time for all similar future tasks — what is the incentive to say anything? None that’s visible. The gain belongs to the employee if they stay quiet; the gain belongs to the company if they speak up. The result is more responsibility, more multitasking, and more mental load, without a proportional benefit to offset it: according to the EY Work Reimagined Survey 2025, 64% of employees experienced an increase in workload over the past twelve months despite using AI. If an employee who knows how to use AI produces, say, four times as much, where are the salaries multiplied by four? That is without counting the cases where the employee pays for AI out of their own pocket, or where access to AI tools is presented in the job interview as a perk — the way “cutting-edge technology” used to be touted — when in reality it is simply the tool with which the employee will generate value for the company. Nobody presents a laptop as a job benefit. This is not a criticism of companies. In fact, it is the opposite: if an organization invests in expensive AI models and employees hide the additional productivity, the total value generated falls and part of the investment is lost. It is the classic value balance between employee and employer, present since labor relations first existed. When that balance breaks, all parties lose.
Economists — Adam Smith among them — have spent decades studying this phenomenon under the name of the principal-agent problem**:** when the agent (the employee) holds information that the principal (the company) does not have, and when interests are not aligned, the agent rationalizes keeping that information. What is new is the scale at which AI has generated that information differential in just a few months.
When AI enters without a corresponding compensation adjustment, the value extracted by the company (green) diverges from the cost borne by the employee (red) and from what the employee receives (blue). At the first crossover, hidden productivity begins; at the second, the employee starts looking elsewhere. The curves are conceptual and illustrate documented trends, not data from any single study. Note: “slack” here does not mean slacking off or doing nothing — it refers to something far more important for the health of any workflow: the margin of maneuver, the buffer time, or the breathing room an employee has between tasks.
The macroeconomic data reflects this indirectly and in a somewhat puzzling way. The St. Louis Federal Reserve calculated in 2025 that the average AI time saving is 5.4% of working hours (roughly 2.2 hours per week for a worker who uses AI regularly, in a representative August 2024 survey). That should appear in labor productivity statistics as a visible jump. And it does — but only in a few places: sectors with the highest AI exposure drove 1.7 percentage points of US labor productivity growth in 2025 ( AEI and Morgan Stanley, 2025). The problem is that this gain is brutally concentrated: according to a global PwC study (2026) covering 1,217 executives across 25 sectors, 74% of the economic value generated by AI is captured by just 20% of organizations. For the remaining 80% — which is where most people work — the gain simply does not show up in their metrics. And they are right: in their case, it genuinely is not there. Part of this is explained by still-partial adoption. Another part — nobody knows exactly how much — is explained by the gain being absorbed in places no corporate metric captures:
The paradox also operates in workers’ own perception. The People at Work 2026 report from ADP Research (39,000 employees, 36 countries) found that regular AI users are
This is the 2026 version of the Solow paradox**:** in the 1980s, economist Robert Solow observed that computers were visible everywhere except in productivity statistics. Today, AI is used everywhere except in the numbers companies try to measure. In February 2026, the NBER published a study of nearly 6,000 senior executives from the US, UK, Germany, and Australia (Yotzov, Barrero, Bloom et al.): 90% reported no measurable impact of AI on productivity or employment in the previous three years, even though 69% were already using it. Fortune summarized it without irony:
The CEO of OpenAI himself offers, in retrospect, the most striking testimony. In 2015, Sam Altman summarized his relationship with technological progress without mincing words: “My job is to help destroy jobs.” A decade later, in May 2026, he declared being “happy to have been wrong”: “I thought the elimination of entry-level administrative positions would already have had a bigger impact than it actually has.” The same man who built much of the labor-apocalypse narrative admits that AI — his own — has not destroyed jobs at the pace he himself anticipated.
The organizational consequence is striking: the company that creates no incentives for employees to report the productivity gained builds exactly the system that later prevents it from justifying its AI investment. Management sees no ROI, concludes that “AI doesn’t work,” and tightens restrictive policies. The loop closes. The problem was not AI; it was the incentive structure.
The only way out of the loop is for the company to share the upside. Not necessarily in money: it can be autonomy, recognition, fewer meetings, or more interesting projects. But if extra productivity always converts into more work at the same price, the market for reporting productivity gains has a single equilibrium price: silence.
Let’s assume you are not the one setting policy. You are someone with sound judgment inside a resistant organization, and you want neither to burn yourself fighting windmills nor to resign yourself to doing things poorly. Here are some strategies to avoid falling into frustration:
1. Change the argument: from “it’s better” to “it’s cheaper / less risky.” Decision-makers are not moved by “this is more modern” or “this is more elegant.” They are moved by cost and risk. Do not ask for an update “to stay current”; ask for it to reduce a specific expense, close a specific security gap, or avoid losing the team that is getting bored. The same change, told in the language of whoever signs the check.
2. Let the data speak, not you. An opinion can be argued; a report gets attention. If you can show in numbers the cost of inertia — lost hours, accumulated risks, client complaints — your proposal stops being “what you think” and becomes “what the data says.” This neutralizes ego-driven debates.
3. Demonstrate on a small scale; don’t argue at scale. Don’t try to convince the organization to transform everything. Take a small, isolated, low-risk area, do it well the new way, and show the measured result. One example that works is worth more than ten presentations. And if it goes wrong, the damage is contained.
4. Protect your energy and your judgment. This is the most important one and the most often forgotten. In an organization that resists strongly, fighting every battle burns you out without changing anything. Choose where you invest your effort, document your recommendations (so it is on record that you made them, without needing to say “I told you so”), and do not confuse professional judgment with the need to be right. Sometimes the healthy answer is to do your part well, learn on your own what the company won’t let you learn on the clock, and save your energy for when you can actually move something.
An honest note on using AI on the sly: the answer to any of these five forms of Shadow AI is not to bypass data-protection rules. It is to push for the company to provide an official, safe channel (approved tools that actually work, models that don’t expose data), because covert use leaves you exposed if something goes wrong. The inconsistency is the company’s; the consequences, if you are caught using real data through uncontrolled services, can end up being yours.
The corporate excuse-phrases — “don’t reinvent the wheel,” “if it works, don’t touch it,” “we’ve always done it this way” — were born in a world where change was expensive and risky. That world has changed: the cost of improving has fallen and the cost of stagnating has risen, and that turns many of yesterday’s excuses into today’s negligence. It is not about changing for change’s sake, but about recognizing that the balance has shifted and that “better not touch it” is no longer the automatically safe answer it once was.
The most revealing contradiction of the moment is not just the company that bans AI while demanding its results: Shadow AI also arises when the company does not fund the tools, when the ones it provides don’t work, when it has no policy, or when employees hide their usage out of fear of judgment — five different routes to the same blind spot. If you find yourself in any of these situations, your best move is neither a crusade nor resignation: translate your proposals into the language of cost and risk, demonstrate on a small scale, let the data speak, and above all, protect your judgment and your energy for the battles you can actually win.
“We’ve always done it this way” describes the past. It has never been an argument about the future.
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Originally published at https://jarroba.com on July 17, 2026.
“If It Works, Don’t Touch It” and Other Excuses That No Longer Hold — Jarroba was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.